From 4b8fc768c53949b0030100ea03c883769461baee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Thu, 27 Apr 2023 12:09:49 +0200 Subject: [PATCH 01/34] Update README.md --- README.md | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 29b2490..6a3ff50 100644 --- a/README.md +++ b/README.md @@ -40,17 +40,14 @@ model.score(X_unlabel, true_label) Citing --- ```bibtex -@software{jose_luis_garrido_labrador_2023_7565222, - author = {José Luis Garrido-Labrador and - César García-Osorio and - Juan J. Rodríguez and - Jesus Maudes}, - title = {jlgarridol/sslearn: V1.0.2}, - month = feb, +@software{jose_luis_garrido_labrador_2023_7781117, + author = {José Luis Garrido-Labrador}, + title = {jlgarridol/sslearn: v1.0.3.1}, + month = mar, year = 2023, publisher = {Zenodo}, - version = {1.0.2}, - doi = {10.5281/zenodo.7650049}, - url = {https://doi.org/10.5281/zenodo.7650049} + version = {1.0.3.1}, + doi = {10.5281/zenodo.7781117}, + url = {https://doi.org/10.5281/zenodo.7781117} } ``` From ff4d59e45947103aacae2fa1bc07f9272344287e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Tue, 6 Feb 2024 12:08:50 +0100 Subject: [PATCH 02/34] Update README.md --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 6a3ff50..f97a6c5 100644 --- a/README.md +++ b/README.md @@ -9,12 +9,12 @@ Installation --- ### Dependencies -* scikit_learn = 1.2.0 -* joblib = 1.2.0 -* numpy = 1.23.3 -* pandas = 1.4.3 -* scipy = 1.9.3 -* statsmodels = 0.13.2 +* joblib >= 1.2.0 +* numpy >= 1.23.3 +* pandas >= 1.4.3 +* scikit_learn >= 1.2.0 +* scipy >= 1.10.1 +* statsmodels >= 0.13.2 * pytest = 7.2.0 (only for testing) ### `pip` installation From c62fe6491e3e36866ce3ecbef2c4c2f397ef3a8f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Tue, 6 Feb 2024 12:09:34 +0100 Subject: [PATCH 03/34] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f97a6c5..8e71412 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ Semi-Supervised Learning Library (sslearn) === -![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) +![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn) The `sslearn` library is a Python package for machine learning over Semi-supervised datasets. It is an extension of [scikit-learn](https://github.com/scikit-learn/scikit-learn). From 882d855a7786d600d676bf6a3e51b81c5f4cc8ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Tue, 6 Feb 2024 12:17:14 +0100 Subject: [PATCH 04/34] Update README.md --- README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 8e71412..011cfa8 100644 --- a/README.md +++ b/README.md @@ -40,14 +40,14 @@ model.score(X_unlabel, true_label) Citing --- ```bibtex -@software{jose_luis_garrido_labrador_2023_7781117, +@software{jose_luis_garrido_labrador_2024_10623889, author = {José Luis Garrido-Labrador}, - title = {jlgarridol/sslearn: v1.0.3.1}, - month = mar, - year = 2023, + title = {jlgarridol/sslearn: v1.0.4}, + month = feb, + year = 2024, publisher = {Zenodo}, - version = {1.0.3.1}, - doi = {10.5281/zenodo.7781117}, - url = {https://doi.org/10.5281/zenodo.7781117} + version = {1.0.4}, + doi = {10.5281/zenodo.10623889}, + url = {https://doi.org/10.5281/zenodo.10623889} } ``` From b79adf21dbd9bd9cc1e6871fcdff9d027bb9b2a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Tue, 6 Feb 2024 13:34:07 +0100 Subject: [PATCH 05/34] Update setup.py --- setup.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/setup.py b/setup.py index 6182b0b..8af2b50 100644 --- a/setup.py +++ b/setup.py @@ -25,12 +25,12 @@ def get_version(): url='https://github.com/jlgarridol/sslearn', license='new BSD', download_url=url, - install_requires=["joblib==1.2.0", - "numpy==1.23.3", - "pandas==1.4.3", - "scikit_learn==1.2.0", - "scipy==1.9.3", - "statsmodels==0.13.2"], + install_requires=["joblib>=1.2.0", + "numpy>=1.23.3", + "pandas>=1.4.3", + "scikit_learn>=1.2.0", + "scipy>=1.10.1", + "statsmodels>=0.13.2"], packages=setuptools.find_packages(exclude=("tests", "experiments")), include_package_data=True, classifiers=[ From a3d86dead9c4222c3c55d8ff576a3b95830a5579 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Tue, 6 Feb 2024 13:35:53 +0100 Subject: [PATCH 06/34] Update __init__.py --- sslearn/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/sslearn/__init__.py b/sslearn/__init__.py index 6b0785a..7218536 100644 --- a/sslearn/__init__.py +++ b/sslearn/__init__.py @@ -1,4 +1,4 @@ -__version__='1.0.4' +__version__='1.0.4.1' __AUTHOR__="José Luis Garrido-Labrador" # Author of the package __AUTHOR_EMAIL__="jlgarrido@ubu.es" # Author's email __URL__="https://pypi.org/project/sslearn/" From 8b14ebd84b4fbe8e3b44374111b61f24d0ff796e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 12:46:13 +0200 Subject: [PATCH 07/34] Create .readthedocs.yaml --- .readthedocs.yaml | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 .readthedocs.yaml diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..f89fc90 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,32 @@ +# .readthedocs.yaml +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + # You can also specify other tool versions: + # nodejs: "19" + # rust: "1.64" + # golang: "1.19" + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Optionally build your docs in additional formats such as PDF and ePub +# formats: +# - pdf +# - epub + +# Optional but recommended, declare the Python requirements required +# to build your documentation +# See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html +# python: +# install: +# - requirements: docs/requirements.txt From 1b620badbe34f17d7e01a0c631c67b672c4f270c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 12:57:45 +0200 Subject: [PATCH 08/34] Add docs folder --- docs/conf.py | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 docs/conf.py diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..15626bb --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,34 @@ +# Configuration file for the Sphinx documentation builder. + +# -- Project information + +project = 'Semi-Supervised Learning Library' +copyright = '2024, J.L. Garrido-Labrador' +author = 'José Luis Garrido-Labrador' + +release = '1.0.4.1' + +# -- General configuration + +extensions = [ + 'sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', +] + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3/', None), + 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), +} +intersphinx_disabled_domains = ['std'] + +templates_path = ['_templates'] + +# -- Options for HTML output + +html_theme = 'sphinx_rtd_theme' + +# -- Options for EPUB output +epub_show_urls = 'footnote' \ No newline at end of file From 1ca7f9aaf298e9308d1030c9aecb7b9b0ff00293 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 12:59:36 +0200 Subject: [PATCH 09/34] Add docs folder --- docs/conf.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 15626bb..81c757a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,18 +11,8 @@ # -- General configuration extensions = [ - 'sphinx.ext.duration', - 'sphinx.ext.doctest', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.intersphinx', ] -intersphinx_mapping = { - 'python': ('https://docs.python.org/3/', None), - 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), -} -intersphinx_disabled_domains = ['std'] templates_path = ['_templates'] From 7d1c253b2e2b483914dce5768961954b84b2781f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 13:00:43 +0200 Subject: [PATCH 10/34] Add docs folder --- docs/conf.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 81c757a..f4b527e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,9 +16,5 @@ templates_path = ['_templates'] -# -- Options for HTML output - -html_theme = 'sphinx_rtd_theme' - # -- Options for EPUB output epub_show_urls = 'footnote' \ No newline at end of file From d877c60ff0403acbbbf27e1704ffda7b2fe89e81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 13:00:54 +0200 Subject: [PATCH 11/34] Add docs folder --- docs/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index f4b527e..03f2749 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -17,4 +17,3 @@ templates_path = ['_templates'] # -- Options for EPUB output -epub_show_urls = 'footnote' \ No newline at end of file From b4e9175d08178a56f2d1b50589c49a37b391af47 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 13:02:57 +0200 Subject: [PATCH 12/34] Add docs folder --- docs/conf.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/conf.py b/docs/conf.py index 03f2749..42d324a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -11,8 +11,19 @@ # -- General configuration extensions = [ + 'sphinx.ext.duration', + 'sphinx.ext.doctest', + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', ] +intersphinx_mapping = { + 'python': ('https://docs.python.org/3/', None), + 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), +} +intersphinx_disabled_domains = ['std'] + templates_path = ['_templates'] From 6d46dd2ba58d090c0b8de05096df12713b47607b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 13:15:30 +0200 Subject: [PATCH 13/34] Add docs folder --- docs/index.rst | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/index.rst diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..e69de29 From c64c0bf5f7fc180554e1defa5c26c2aca1a5cce5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 16:11:59 +0200 Subject: [PATCH 14/34] Add logos --- README.md | 3 +++ docs/conf.py | 30 ------------------------------ docs/index.rst | 0 source/conf.py | 28 ++++++++++++++++++++++++++++ source/index.rst | 20 ++++++++++++++++++++ 5 files changed, 51 insertions(+), 30 deletions(-) delete mode 100644 docs/conf.py delete mode 100644 docs/index.rst create mode 100644 source/conf.py create mode 100644 source/index.rst diff --git a/README.md b/README.md index 011cfa8..cd0f45d 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ Semi-Supervised Learning Library (sslearn) === + + + ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn) The `sslearn` library is a Python package for machine learning over Semi-supervised datasets. It is an extension of [scikit-learn](https://github.com/scikit-learn/scikit-learn). diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 42d324a..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,30 +0,0 @@ -# Configuration file for the Sphinx documentation builder. - -# -- Project information - -project = 'Semi-Supervised Learning Library' -copyright = '2024, J.L. Garrido-Labrador' -author = 'José Luis Garrido-Labrador' - -release = '1.0.4.1' - -# -- General configuration - -extensions = [ - 'sphinx.ext.duration', - 'sphinx.ext.doctest', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.intersphinx', -] - -intersphinx_mapping = { - 'python': ('https://docs.python.org/3/', None), - 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), -} -intersphinx_disabled_domains = ['std'] - - -templates_path = ['_templates'] - -# -- Options for EPUB output diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index e69de29..0000000 diff --git a/source/conf.py b/source/conf.py new file mode 100644 index 0000000..e52128e --- /dev/null +++ b/source/conf.py @@ -0,0 +1,28 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'sslearn' +copyright = '2024, José Luis Garrido-Labrador' +author = 'José Luis Garrido-Labrador' +release = '1.0.4.1' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [] + +templates_path = ['_templates'] +exclude_patterns = [] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'alabaster' +html_static_path = ['_static'] diff --git a/source/index.rst b/source/index.rst new file mode 100644 index 0000000..607ac1c --- /dev/null +++ b/source/index.rst @@ -0,0 +1,20 @@ +.. sslearn documentation master file, created by + sphinx-quickstart on Wed Apr 24 13:21:38 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to sslearn's documentation! +=================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` From fe3b473d1fac8a38fdafacfaea7b7ad607a9256f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 16:12:19 +0200 Subject: [PATCH 15/34] Remove all doc --- source/conf.py | 28 ---------------------------- source/index.rst | 20 -------------------- 2 files changed, 48 deletions(-) delete mode 100644 source/conf.py delete mode 100644 source/index.rst diff --git a/source/conf.py b/source/conf.py deleted file mode 100644 index e52128e..0000000 --- a/source/conf.py +++ /dev/null @@ -1,28 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# For the full list of built-in configuration values, see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Project information ----------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information - -project = 'sslearn' -copyright = '2024, José Luis Garrido-Labrador' -author = 'José Luis Garrido-Labrador' -release = '1.0.4.1' - -# -- General configuration --------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration - -extensions = [] - -templates_path = ['_templates'] -exclude_patterns = [] - - - -# -- Options for HTML output ------------------------------------------------- -# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output - -html_theme = 'alabaster' -html_static_path = ['_static'] diff --git a/source/index.rst b/source/index.rst deleted file mode 100644 index 607ac1c..0000000 --- a/source/index.rst +++ /dev/null @@ -1,20 +0,0 @@ -.. sslearn documentation master file, created by - sphinx-quickstart on Wed Apr 24 13:21:38 2024. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to sslearn's documentation! -=================================== - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` From 6d1ad94397dc5e6378c7904465135b8f89a66f2d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:40:57 +0200 Subject: [PATCH 16/34] Document all repository using pdoc --- .github/workflows/python-package.yml | 2 +- docs/index.html | 7 + docs/make.py | 59 + docs/search.js | 46 + docs/sslearn.html | 346 ++ docs/sslearn.svg | 355 ++ docs/sslearn/base.html | 1637 ++++++ docs/sslearn/datasets.html | 698 +++ docs/sslearn/model_selection.html | 470 ++ docs/sslearn/restricted.html | 1144 ++++ docs/sslearn/subview.html | 664 +++ docs/sslearn/utils.html | 807 +++ docs/sslearn/wrapper.html | 7161 ++++++++++++++++++++++++++ docs/sslearn_mini.svg | 101 + setup.py | 4 +- sitemap.xml | 14 + sslearn/__init__.py | 7 + sslearn/base.py | 87 + sslearn/datasets/__init__.py | 16 + sslearn/model_selection/__init__.py | 17 + sslearn/model_selection/_split.py | 18 +- sslearn/restricted.py | 19 + sslearn/subview/__init__.py | 14 + sslearn/utils.py | 72 + sslearn/wrapper/__init__.py | 28 +- sslearn/wrapper/_co.py | 108 +- sslearn/wrapper/_self.py | 167 +- sslearn/wrapper/_tritraining.py | 62 +- 28 files changed, 13953 insertions(+), 177 deletions(-) create mode 100644 docs/index.html create mode 100644 docs/make.py create mode 100644 docs/search.js create mode 100644 docs/sslearn.html create mode 100644 docs/sslearn.svg create mode 100644 docs/sslearn/base.html create mode 100644 docs/sslearn/datasets.html create mode 100644 docs/sslearn/model_selection.html create mode 100644 docs/sslearn/restricted.html create mode 100644 docs/sslearn/subview.html create mode 100644 docs/sslearn/utils.html create mode 100644 docs/sslearn/wrapper.html create mode 100644 docs/sslearn_mini.svg create mode 100644 sitemap.xml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index b688916..973d168 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -15,7 +15,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 diff --git a/docs/index.html b/docs/index.html new file mode 100644 index 0000000..ceadb09 --- /dev/null +++ b/docs/index.html @@ -0,0 +1,7 @@ + + + + + + + diff --git a/docs/make.py b/docs/make.py new file mode 100644 index 0000000..d28c0b8 --- /dev/null +++ b/docs/make.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 +from pathlib import Path +import shutil +import textwrap +import base64 + +from jinja2 import Environment +from jinja2 import FileSystemLoader +from markupsafe import Markup +import pygments.formatters.html +import pygments.lexers.python + +import pdoc.render + +here = Path("..") + +if __name__ == "__main__": + + favicon = (here / "docs" / "sslearn_mini.svg").read_bytes() + favicon = base64.b64encode(favicon).decode("utf8") + logo = (here / "docs" / "sslearn.svg").read_bytes() + logo = base64.b64encode(logo).decode("utf8") + + # Render main docs + pdoc.render.configure( + + favicon=f"data:image/svg+xml;base64,{favicon}", + logo=f"data:image/svg+xml;base64,{logo}", + logo_link="/", + footer_text=f"pdoc {pdoc.__version__}", + search=True, + math=True, + include_undocumented=False, + docformat="numpy", + ) + + pdoc.pdoc( + here / "sslearn", + output_directory=here / "docs", + ) + + + with (here / "sitemap.xml").open("w", newline="\n") as f: + f.write( + textwrap.dedent( + """ + + + """ + ).strip() + ) + for file in here.glob("**/*.html"): + if file.name.startswith("_"): + continue + filename = str(file.relative_to(here).as_posix()).replace("index.html", "") + f.write(f"""\nhttps://pdoc.dev/{filename}""") + f.write("""\n""") diff --git a/docs/search.js b/docs/search.js new file mode 100644 index 0000000..1ec3c1a --- /dev/null +++ b/docs/search.js @@ -0,0 +1,46 @@ +window.pdocSearch = (function(){ +/** elasticlunr - 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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oSemi-Supervised Learning Library (sslearn)\n\n

\n

\n\n

\"Code \"Code \"GitHub \"PyPI

\n\n

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

\n\n
Installation
\n\n

Dependencies

\n\n
    \n
  • joblib >= 1.2.0
    • \n
  • numpy >= 1.23.3
    • \n
  • pandas >= 1.4.3
    • \n
  • scikit_learn >= 1.2.0
    • \n
  • scipy >= 1.10.1
    • \n
  • statsmodels >= 0.13.2
    • \n
  • pytest = 7.2.0 (only for testing)
    • \n
\n\n

pip installation

\n\n

It can be installed using Pypi:

\n\n
pip install sslearn\n
\n\n
Code example
\n\n
\n
from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
\n
\n\n
Citing
\n\n
\n
@software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
\n
\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

\n\n
Functions
\n\n

get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

\n\n
Classes
\n\n

FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

\n\n

OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

\n\n

All doc

\n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

Create a classifier that fakes predict_proba method if it does not exist.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
    • \n
\n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

Fit a FakedProbaClassifier.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
    • \n
\n\n
Returns
\n\n
    \n
  • self (FakedProbaClassifier):\nReturns self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

Check and divide dataset between labeled and unlabeled data.

\n\n
Parameters
\n\n
    \n
  • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
    • \n
  • y (ndarray of shape (n_samples,)):\nTarget vector.
    • \n
\n\n
Returns
\n\n
    \n
  • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
    • \n
  • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
    • \n
  • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

One-vs-the-rest (OvR) multiclass strategy.

\n\n

Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

\n\n

OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

\n\n

Read more in the :ref:User Guide <ovr_classification>.

\n\n
Parameters
\n\n
    \n
  • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
    • \n

  • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

    \n\n

    None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

    \n\n

    Changed in version 0.20:\nn_jobs default changed from 1 to None

    • \n

  • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

    \n\n

    New in version 1.1.

    • \n
\n\n
Attributes
\n\n
    \n
  • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
    • \n
  • classes_ (array, shape = [n_classes]):\nClass labels.
    • \n
  • n_classes_ (int):\nNumber of classes.
    • \n
  • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
    • \n
  • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 1.0.

    • \n
\n\n
See Also
\n\n

OneVsOneClassifier: One-vs-one multiclass strategy.
\nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
\nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
\nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
\n
\n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

Adapted OneVsRestClassifier for SSL datasets

\n\n
Parameters
\n\n
    \n
  • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
    • \n
  • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

Fit underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nInstance of fitted estimator.
    • \n
\n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

Predict multi-class targets using underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
\n\n
Returns
\n\n
    \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

Probability estimates.

\n\n

The returned estimates for all classes are ordered by label of classes.

\n\n

Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

\n\n

In the single label multiclass case, the rows of the returned matrix\nsum to 1.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
    • \n
\n\n
Returns
\n\n
    \n
  • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

Summary of module sslearn.datasets:

\n\n

This module contains functions to load and save datasets in different formats.

\n\n
Functions
\n\n
    \n
  1. read_csv : Load a dataset from a CSV file.
    2. \n
  2. read_keel : Load a dataset from a KEEL file.
    3. \n
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. \n
  4. save_keel : Save a dataset in KEEL format.
    5. \n
\n\n

All doc

\n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

Read a .csv file

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

Read a .dat file from KEEL (http://www.keel.es/)

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
  • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

\n\n
Parameters
\n\n
    \n
  • X (Array-like):\nIgnored
    • \n
  • y (Array-like):\nTarget array.
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset securized.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

Save a dataset in the KEEL format

\n\n
Parameters
\n\n
    \n
  • X (array-like):\nDataset features
    • \n
  • y (array-like):\nDataset targets
    • \n
  • route (str):\nPath to save the dataset
    • \n
  • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
    • \n
  • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
    • \n
  • target_name (str, optional):\nTarget name, by default \"Class\"
    • \n
  • classification (bool, optional):\nIf the dataset is classification or regression, by default True
    • \n
  • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
    • \n
  • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
    • \n
\n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

Summary of module sslearn.model_selection:

\n\n

This module contains functions to split datasets into training and testing sets.

\n\n
Functions
\n\n

artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

\n\n
Classes
\n\n

StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

\n\n

All doc

\n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

Create an artificial Semi-supervised dataset from a supervised dataset.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
    • \n
  • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
    • \n
  • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
    • \n
  • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
    • \n
  • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
    • \n
  • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
    • \n
  • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
    • \n
\n\n
Returns
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
    • \n
  • y_unlabel (ndarray):\nThe true label for each y in the same order.
    • \n
  • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
    • \n
  • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
    • \n
\n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

This module contains classes to train a classifier using the restricted set classification approach.

\n\n
Classes
\n\n

WhoIsWhoClassifier : Who is Who Classifier

\n\n
Functions
\n\n

conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

\n\n

All doc

\n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

Fit the model according to the given training data.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nReturns self.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

Calculate the conflict rate of the model.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • float: The conflict rate.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

Predict class for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

Computes the conflict rate of a prediction, given a set of restrictions.

\n\n
Parameters
\n\n
    \n
  • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
    • \n
  • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
    • \n
  • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • conflict rate (float):\nThe conflict rate.
    • \n
\n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

Summary of module sslearn.subview:

\n\n

This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

\n\n
Classes
\n\n

SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

\n\n

All doc

\n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

Predict using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted values.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

Some utility functions

\n\n

This module contains utility functions that are used in different parts of the library.

\n\n
Functions
\n\n

safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

\n\n

All doc

\n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

Safely divide two numbers preventing division by zero

\n\n
Parameters
\n\n
    \n
  • dividend (numeric):\nDividend value
    • \n
  • divisor (numeric):\nDivisor value
    • \n
  • epsilon (numeric):\nClose to zero value to be used in case of division by zero
    • \n
\n\n
Returns
\n\n
    \n
  • result (numeric):\nResult of the division
    • \n
\n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

Calculate the confidence interval of the predictions

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • hyp (classifier):\nThe classifier to be used for prediction
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values
    • \n
  • alpha (float, optional):\nconfidence (1 - significance), by default .95
    • \n
\n\n
Returns
\n\n
    \n
  • li, hi (float):\nlower and upper bound of the confidence interval
    • \n
\n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

Choice the best predictions according to the proportion of each class.

\n\n
Parameters
\n\n
    \n
  • predictions (array-like of shape (n_samples,)):\narray of predictions
    • \n
  • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
    • \n
  • proportion (dict):\ndictionary with the proportion of each class
    • \n
  • extra (int, optional):\nnumber of extra instances to be added, by default 0
    • \n
\n\n
Returns
\n\n
    \n
  • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
    • \n
\n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

Calculate the priori probability of each label

\n\n
Parameters
\n\n
    \n
  • y (array-like of shape (n_samples,)):\narray of labels
    • \n
\n\n
Returns
\n\n
    \n
  • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
    • \n
\n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

\n\n
Parameters
\n\n
    \n
  • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
    • \n
\n\n
Returns
\n\n
    \n
  • n_jobs (int):\nChecked number of jobs.
    • \n
\n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

Summary of module sslearn.wrapper:

\n\n

This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

\n\n

Self-Training Algorithms

\n\n
    \n
  1. SelfTraining : Self-training algorithm.
    2. \n
  2. Setred : Self-training with redundancy reduction.
    3. \n
\n\n

Co-Training Algorithms

\n\n
    \n
  1. CoTraining : Co-training
    2. \n
  2. CoTrainingByCommittee : Co-training by committee
    3. \n
  3. DemocraticCoLearning : Democratic co-learning
    4. \n
  4. Rasco : Random subspace co-training
    5. \n
  5. RelRasco : Relevant random subspace co-training
    6. \n
  6. CoForest : Co-Forest
    7. \n
  7. TriTraining : Tri-training
    8. \n
  8. DeTriTraining : Data Editing Tri-training
    9. \n
  9. WiWTriTraining : Who-Is-Who Tri-training
    10. \n
\n\n

All doc

\n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

Self-training classifier.

\n\n

This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n

Read more in the :ref:User Guide <self_training>.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
Attributes
\n\n
    \n
  • base_estimator_ (estimator object):\nThe fitted estimator.
    • \n
  • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
    • \n
  • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
    • \n
  • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

    \n\n

    New in version 1.0.

    • \n
  • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
    • \n

  • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

    \n\n
      \n
    • 'max_iter': n_iter_ reached max_iter.
      • \n
    • 'no_change': no new labels were predicted.
      • \n
    • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
      • \n
    • \n
\n\n
See Also
\n\n

LabelPropagation: Label propagation classifier.
\nLabelSpreading: Label spreading model for semi-supervised learning.

\n\n
References
\n\n

:doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
\n
\n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

\n\n

This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
References
\n\n

David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

\n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

Fits this SelfTrainingClassifier to a dataset.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
    • \n
\n\n
Returns
\n\n
    \n
  • self (SelfTrainingClassifier):\nReturns an instance of self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

Create a committee trained by cotraining based on\nthe diversity of classifiers.

\n\n
Parameters
\n\n
    \n
  • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
    • \n
  • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
    • \n
  • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

\n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

Build a CoTrainingByCommittee classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTrainingByCommittee):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

Co-Training based on random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

Build a Rasco classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Rasco):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

Co-Training with relevant random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

TriTraining. Trio of classifiers with bootstrapping.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

TriTraining with restriction Who-is-Who.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
  • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
      \n
    • \"labeled\" over complete L,
      • \n
    • \"labeled_plus\" over complete L union L',
      • \n
    • \"unlabeled\u00a8: over complete U,
      • \n
    • \"all\": over complete X (LuU) and
      • \n
    • \"none\": don't penalize the \"meause error\", by default \"labeled\"
      • \n
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

\n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • \n
  • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • \n
  • max_iterations (int, optional):\nThe number of iterations, by default 30
    • \n
  • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • \n
  • threshold (float, optional):\nThe threshold for label instances, by default 0.5
    • \n
  • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

\n\n

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

Build a CoTraining classifier from the training set.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
    • \n
  • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
    • \n
  • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTraining):\nFitted estimator.
    • \n
\n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
    • \n
  • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations, by default 100
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

Build a DeTriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

Democratic Co-learning. Ensemble of classifiers of different types.

\n\n
Parameters
\n\n
    \n
  • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
    • \n
  • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
    • \n
  • alpha (float, optional):\nconfidence level, by default 0.95
    • \n
  • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
Raises
\n\n
    \n
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • \n
\n\n
References
\n\n

Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

\n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

Fit Democratic-Co classifier

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
  • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (DemocraticCoLearning):\nfitted classifier
    • \n
\n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.1
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n\n
References
\n\n

Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

Build a Setred classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Setred):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
    • \n
  • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
    • \n
  • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
    • \n
\n\n
References
\n\n

Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

Build a CoForest classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoForest):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
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"df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.WiWTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; + + // mirrored in build-search-index.js (part 1) + // Also split on html tags. this is a cheap heuristic, but good enough. + elasticlunr.tokenizer.setSeperator(/[\s\-.;&_'"=,()]+|<[^>]*>/); + + let searchIndex; + if (docs._isPrebuiltIndex) { + console.info("using precompiled search index"); + searchIndex = elasticlunr.Index.load(docs); + } else { + console.time("building search index"); + // mirrored in build-search-index.js (part 2) + searchIndex = elasticlunr(function () { + this.pipeline.remove(elasticlunr.stemmer); + this.pipeline.remove(elasticlunr.stopWordFilter); + this.addField("qualname"); + this.addField("fullname"); + this.addField("annotation"); + this.addField("default_value"); + this.addField("signature"); + this.addField("bases"); + this.addField("doc"); + this.setRef("fullname"); + }); + for (let doc of docs) { + searchIndex.addDoc(doc); + } + console.timeEnd("building search index"); + } + + return (term) => searchIndex.search(term, { + fields: { + qualname: {boost: 4}, + fullname: {boost: 2}, + annotation: {boost: 2}, + default_value: {boost: 2}, + signature: {boost: 2}, + bases: {boost: 2}, + doc: {boost: 1}, + }, + expand: true + }); +})(); \ No newline at end of file diff --git a/docs/sslearn.html b/docs/sslearn.html new file mode 100644 index 0000000..c678631 --- /dev/null +++ b/docs/sslearn.html @@ -0,0 +1,346 @@ + + + + + + + sslearn API documentation + + + + + + + + + + + + + + +
+
+

+sslearn

+ +

Semi-Supervised Learning Library (sslearn)

+ +

+

+ +

Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version

+ +

The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

+ +
Installation
+ +

Dependencies

+ +
    +
  • joblib >= 1.2.0
    • +
  • numpy >= 1.23.3
    • +
  • pandas >= 1.4.3
    • +
  • scikit_learn >= 1.2.0
    • +
  • scipy >= 1.10.1
    • +
  • statsmodels >= 0.13.2
    • +
  • pytest = 7.2.0 (only for testing)
    • +
+ +

pip installation

+ +

It can be installed using Pypi:

+ +
pip install sslearn
+
+ +
Code example
+ +
+
from sslearn.wrapper import TriTraining
+from sslearn.model_selection import artificial_ssl_dataset
+from sklearn.datasets import load_iris
+
+X, y = load_iris(return_X_y=True)
+X, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)
+
+model = TriTraining().fit(X, y)
+model.score(X_unlabel, true_label)
+
+
+ +
Citing
+ +
+
@software{jose_luis_garrido_labrador_2024_10623889,
+  author       = {José Luis Garrido-Labrador},
+  title        = {jlgarridol/sslearn: v1.0.4},
+  month        = feb,
+  year         = 2024,
+  publisher    = {Zenodo},
+  version      = {1.0.4},
+  doi          = {10.5281/zenodo.10623889},
+  url          = {https://doi.org/10.5281/zenodo.10623889}
+}
+
+
+
+ + + + + + 
1# Open README.md and added to __doc__
+2with open("../README.md", "r") as f:
+3    __doc__ = f.read()
+4
+5
+6__version__='1.0.4.1'
+7__AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
+8__AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
+9__URL__="https://pypi.org/project/sslearn/"
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/sslearn.svg b/docs/sslearn.svg new file mode 100644 index 0000000..52a09d5 --- /dev/null +++ b/docs/sslearn.svg @@ -0,0 +1,355 @@ + + + + + + + + + sslearn + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + * + * + * + + + + diff --git a/docs/sslearn/base.html b/docs/sslearn/base.html new file mode 100644 index 0000000..450b433 --- /dev/null +++ b/docs/sslearn/base.html @@ -0,0 +1,1637 @@ + + + + + + + sslearn.base API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.base

+ +

Summary of module sslearn.base:

+ +
Functions
+ +

get_dataset(X, y): + Check and divide dataset between labeled and unlabeled data.

+ +
Classes
+ +

FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): + Create a classifier that fakes predict_proba method if it does not exist.

+ +

OneVsRestSSLClassifier(OneVsRestClassifier): + Adapted OneVsRestClassifier for SSL datasets

+ +

All doc

+
+ + + + + + 
  1"""
+  2Summary of module `sslearn.base`:
+  3
+  4Functions
+  5---------
+  6get_dataset(X, y):
+  7    Check and divide dataset between labeled and unlabeled data.
+  8
+  9Classes
+ 10-------
+ 11FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
+ 12    Create a classifier that fakes predict_proba method if it does not exist.
+ 13
+ 14OneVsRestSSLClassifier(OneVsRestClassifier):
+ 15    Adapted OneVsRestClassifier for SSL datasets
+ 16
+ 17All doc
+ 18----
+ 19"""
+ 20
+ 21import array
+ 22import warnings
+ 23from abc import ABC, abstractmethod
+ 24
+ 25import numpy as np
+ 26import pandas as pd
+ 27import scipy.sparse as sp
+ 28from joblib import Parallel, delayed
+ 29from sklearn.base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin
+ 30from sklearn.base import clone as skclone
+ 31from sklearn.base import is_classifier
+ 32from sklearn.multiclass import (LabelBinarizer, OneVsRestClassifier,
+ 33                                _ConstantPredictor, _num_samples,
+ 34                                _predict_binary)
+ 35from sklearn.preprocessing import OneHotEncoder
+ 36from sklearn.utils import check_X_y, check_array
+ 37from sklearn.utils.validation import check_is_fitted
+ 38from sklearn.utils.metaestimators import available_if
+ 39from sklearn.ensemble._base import _set_random_states
+ 40from sklearn.utils import check_random_state
+ 41
+ 42__all__ = ["FakedProbaClassifier", "get_dataset", "OneVsRestSSLClassifier"]
+ 43
+ 44
+ 45
+ 46def get_dataset(X, y):
+ 47    """Check and divide dataset between labeled and unlabeled data.
+ 48
+ 49    Parameters
+ 50    ----------
+ 51    X : ndarray or DataFrame of shape (n_samples, n_features)
+ 52        Features matrix.
+ 53    y : ndarray of shape (n_samples,)
+ 54        Target vector.
+ 55
+ 56    Returns
+ 57    -------
+ 58    X_label : ndarray or DataFrame of shape (n_label, n_features)
+ 59        Labeled features matrix.
+ 60    y_label : ndarray or Serie of shape (n_label,)
+ 61        Labeled target vector.
+ 62    X_unlabel : ndarray or Serie DataFrame of shape (n_unlabel, n_features)
+ 63        Unlabeled features matrix.
+ 64    """
+ 65
+ 66    is_df = False
+ 67    if isinstance(X, pd.DataFrame):
+ 68        is_df = True
+ 69        columns = X.columns
+ 70
+ 71    X = check_array(X)
+ 72    y = check_array(y, ensure_2d=False, dtype=y.dtype.type)
+ 73    
+ 74    X_label = X[y != y.dtype.type(-1)]
+ 75    y_label = y[y != y.dtype.type(-1)]
+ 76    X_unlabel = X[y == y.dtype.type(-1)]
+ 77
+ 78    X_label, y_label = check_X_y(X_label, y_label)
+ 79
+ 80    if is_df:
+ 81        X_label = pd.DataFrame(X_label, columns=columns)
+ 82        X_unlabel = pd.DataFrame(X_unlabel, columns=columns)
+ 83
+ 84    return X_label, y_label, X_unlabel
+ 85
+ 86
+ 87class BaseEnsemble(ABC, MetaEstimatorMixin):
+ 88
+ 89    @abstractmethod
+ 90    def predict_proba(self, X):
+ 91        pass
+ 92
+ 93    def predict(self, X):
+ 94        """Predict the classes of X.
+ 95        Parameters
+ 96        ----------
+ 97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 98            Array representing the data.
+ 99        Returns
+100        -------
+101        y : ndarray of shape (n_samples,)
+102            Array with predicted labels.
+103        """
+104        predicted_probabilitiy = self.predict_proba(X)
+105        classes = self.classes_.take((np.argmax(predicted_probabilitiy, axis=1)),
+106                                  axis=0)
+107
+108        # If exists label_encoder_ attribute, use it to transform classes
+109        if hasattr(self, "label_encoder_"):
+110            classes = self.label_encoder_.inverse_transform(classes)
+111            
+112        return classes
+113
+114
+115class FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
+116
+117    def __init__(self, base_estimator):
+118        """Create a classifier that fakes predict_proba method if it does not exist.
+119
+120        Parameters
+121        ----------
+122        base_estimator : ClassifierMixin
+123            A classifier that implements fit and predict methods.
+124        """
+125        self.base_estimator = base_estimator
+126
+127    def fit(self, X, y):
+128        """Fit a FakedProbaClassifier.
+129
+130        Parameters
+131        ----------
+132        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+133            The input samples.
+134        y : {array-like, sparse matrix} of shape (n_samples,)
+135            The target values.
+136
+137        Returns
+138        -------
+139        self : FakedProbaClassifier
+140            Returns self.
+141        """
+142        self.classes_ = np.unique(y)
+143        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+144        self.base_estimator.fit(X, y)
+145        return self
+146
+147    def predict(self, X):
+148        """Predict the classes of X.
+149
+150        Parameters
+151        ----------
+152        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+153            Array representing the data.
+154
+155        Returns
+156        -------
+157        y : ndarray of shape (n_samples,)
+158            Array with predicted labels.
+159        """
+160        return self.base_estimator.predict(X)
+161
+162    def predict_proba(self, X):
+163        """Predict the probabilities of each class for X. 
+164        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+165
+166        Parameters
+167        ----------
+168        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+169
+170        Returns
+171        -------
+172        y : ndarray of shape (n_samples, n_classes)
+173            Array with predicted probabilities.
+174        """
+175        if "predict_proba" in dir(self.base_estimator):
+176            return self.base_estimator.predict_proba(X)
+177        else:
+178            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+179
+180
+181def _fit_binary_ssl(estimator, X, y_label, size, classes=None, **fit_params):
+182    # unique_y = np.unique(y_label)
+183    # X = np.concatenate((X_label, X_unlabel), axis=0)
+184    y = np.concatenate((y_label, np.array([y_label.dtype.type(-1)] * size)))
+185    unique_y = np.unique(y_label)
+186    if len(unique_y) == 1:
+187        if classes is not None:
+188            if y_label[0] == -1:
+189                c = 0
+190            else:
+191                c = y_label[0]
+192            warnings.warn(
+193                "Label %s is present in all training examples." % str(classes[c])
+194            )
+195        estimator = _ConstantPredictor().fit(None, unique_y)
+196    else:
+197        estimator = skclone(estimator)
+198        estimator.fit(X, y, **fit_params)
+199    return estimator
+200
+201def _predict_binary_ssl(estimator, X, **predict_params):
+202    """Make predictions using a single binary estimator."""
+203    try:
+204        score = np.ravel(estimator.decision_function(X, **predict_params))
+205    except (AttributeError, NotImplementedError):
+206        # probabilities of the positive class
+207        score = estimator.predict_proba(X, **predict_params)[:, 1]
+208    return score
+209
+210
+211class OneVsRestSSLClassifier(OneVsRestClassifier):
+212
+213    def __init__(self, estimator, *, n_jobs=None):
+214        """Adapted OneVsRestClassifier for SSL datasets
+215
+216        Parameters
+217        ----------
+218        estimator : {ClassifierMixin, list},
+219            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+220        n_jobs : n_jobs : int, optional
+221            The number of jobs to run in parallel. -1 means using all processors., by default None
+222        """
+223        super().__init__(estimator, n_jobs=n_jobs)
+224
+225    def fit(self, X, y, **fit_params):
+226        #
+227        y_label = y[y != y.dtype.type(-1)]
+228        size = len(y) - len(y_label)
+229
+230        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+231        Y = self.label_binarizer_.fit_transform(y_label)
+232        Y = Y.tocsc()
+233        self.classes_ = self.label_binarizer_.classes_
+234        columns = (col.toarray().ravel() for col in Y.T)
+235
+236        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+237        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+238        for e in estimators:
+239            _set_random_states(e, rs)
+240
+241        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+242            delayed(_fit_binary_ssl)(
+243                estimators[i],
+244                X,
+245                column,
+246                size,
+247                classes=[
+248                    "not %s" % self.label_binarizer_.classes_[i],
+249                    self.label_binarizer_.classes_[i],
+250                ],
+251                **fit_params
+252            )
+253            for i, column in enumerate(columns)
+254        )
+255
+256        if hasattr(self.estimators_[0], "n_features_in_"):
+257            self.n_features_in_ = self.estimators_[0].n_features_in_
+258        if hasattr(self.estimators_[0], "feature_names_in_"):
+259            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+260
+261        return self
+262
+263    def predict(self, X, **kwards):
+264        check_is_fitted(self)
+265
+266        n_samples = _num_samples(X)
+267        if self.label_binarizer_.y_type_ == "multiclass":
+268            maxima = np.empty(n_samples, dtype=float)
+269            maxima.fill(-np.inf)
+270            argmaxima = np.zeros(n_samples, dtype=int)
+271            for i, e in enumerate(self.estimators_):
+272                pred = _predict_binary_ssl(e, X, **kwards)
+273                np.maximum(maxima, pred, out=maxima)
+274                argmaxima[maxima == pred] = i
+275            return self.classes_[argmaxima]
+276        else:
+277            if (hasattr(self.estimators_[0], "decision_function") and
+278                    is_classifier(self.estimators_[0])):
+279                thresh = 0
+280            else:
+281                thresh = .5
+282            indices = array.array('i')
+283            indptr = array.array('i', [0])
+284            for e in self.estimators_:
+285                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+286                indptr.append(len(indices))
+287            data = np.ones(len(indices), dtype=int)
+288            indicator = sp.csc_matrix((data, indices, indptr),
+289                                      shape=(n_samples, len(self.estimators_)))
+290            return self.label_binarizer_.inverse_transform(indicator)
+291
+292    def predict_proba(self, X, **kwards):
+293        check_is_fitted(self)
+294        # Y[i, j] gives the probability that sample i has the label j.
+295        # In the multi-label case, these are not disjoint.
+296        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+297
+298        if len(self.estimators_) == 1:
+299            # Only one estimator, but we still want to return probabilities
+300            # for two classes.
+301            Y = np.concatenate(((1 - Y), Y), axis=1)
+302
+303        if not self.multilabel_:
+304            # Then, probabilities should be normalized to 1.
+305            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+306        return Y
+
+ + +
+
+ +
+ + class + FakedProbaClassifier(sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator): + + + +
+ + 
116class FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
+117
+118    def __init__(self, base_estimator):
+119        """Create a classifier that fakes predict_proba method if it does not exist.
+120
+121        Parameters
+122        ----------
+123        base_estimator : ClassifierMixin
+124            A classifier that implements fit and predict methods.
+125        """
+126        self.base_estimator = base_estimator
+127
+128    def fit(self, X, y):
+129        """Fit a FakedProbaClassifier.
+130
+131        Parameters
+132        ----------
+133        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+134            The input samples.
+135        y : {array-like, sparse matrix} of shape (n_samples,)
+136            The target values.
+137
+138        Returns
+139        -------
+140        self : FakedProbaClassifier
+141            Returns self.
+142        """
+143        self.classes_ = np.unique(y)
+144        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+145        self.base_estimator.fit(X, y)
+146        return self
+147
+148    def predict(self, X):
+149        """Predict the classes of X.
+150
+151        Parameters
+152        ----------
+153        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+154            Array representing the data.
+155
+156        Returns
+157        -------
+158        y : ndarray of shape (n_samples,)
+159            Array with predicted labels.
+160        """
+161        return self.base_estimator.predict(X)
+162
+163    def predict_proba(self, X):
+164        """Predict the probabilities of each class for X. 
+165        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+166
+167        Parameters
+168        ----------
+169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+170
+171        Returns
+172        -------
+173        y : ndarray of shape (n_samples, n_classes)
+174            Array with predicted probabilities.
+175        """
+176        if "predict_proba" in dir(self.base_estimator):
+177            return self.base_estimator.predict_proba(X)
+178        else:
+179            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+
+ + +

Mixin class for all classifiers in scikit-learn.

+
+ + +
+ +
+ + FakedProbaClassifier(base_estimator) + + + +
+ + 
118    def __init__(self, base_estimator):
+119        """Create a classifier that fakes predict_proba method if it does not exist.
+120
+121        Parameters
+122        ----------
+123        base_estimator : ClassifierMixin
+124            A classifier that implements fit and predict methods.
+125        """
+126        self.base_estimator = base_estimator
+
+ + +

Create a classifier that fakes predict_proba method if it does not exist.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin): +A classifier that implements fit and predict methods.
    • +
+
+ + +
+
+ +
+ + def + fit(self, X, y): + + + +
+ + 
128    def fit(self, X, y):
+129        """Fit a FakedProbaClassifier.
+130
+131        Parameters
+132        ----------
+133        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+134            The input samples.
+135        y : {array-like, sparse matrix} of shape (n_samples,)
+136            The target values.
+137
+138        Returns
+139        -------
+140        self : FakedProbaClassifier
+141            Returns self.
+142        """
+143        self.classes_ = np.unique(y)
+144        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+145        self.base_estimator.fit(X, y)
+146        return self
+
+ + +

Fit a FakedProbaClassifier.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
  • y ({array-like, sparse matrix} of shape (n_samples,)): +The target values.
    • +
+ +
Returns
+ +
    +
  • self (FakedProbaClassifier): +Returns self.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X): + + + +
+ + 
148    def predict(self, X):
+149        """Predict the classes of X.
+150
+151        Parameters
+152        ----------
+153        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+154            Array representing the data.
+155
+156        Returns
+157        -------
+158        y : ndarray of shape (n_samples,)
+159            Array with predicted labels.
+160        """
+161        return self.base_estimator.predict(X)
+
+ + +

Predict the classes of X.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples,)): +Array with predicted labels.
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X): + + + +
+ + 
163    def predict_proba(self, X):
+164        """Predict the probabilities of each class for X. 
+165        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+166
+167        Parameters
+168        ----------
+169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+170
+171        Returns
+172        -------
+173        y : ndarray of shape (n_samples, n_classes)
+174            Array with predicted probabilities.
+175        """
+176        if "predict_proba" in dir(self.base_estimator):
+177            return self.base_estimator.predict_proba(X)
+178        else:
+179            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+
+ + +

Predict the probabilities of each class for X. +If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples, n_classes)): +Array with predicted probabilities.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.ClassifierMixin
+
score
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ +
+ + def + get_dataset(X, y): + + + +
+ + 
47def get_dataset(X, y):
+48    """Check and divide dataset between labeled and unlabeled data.
+49
+50    Parameters
+51    ----------
+52    X : ndarray or DataFrame of shape (n_samples, n_features)
+53        Features matrix.
+54    y : ndarray of shape (n_samples,)
+55        Target vector.
+56
+57    Returns
+58    -------
+59    X_label : ndarray or DataFrame of shape (n_label, n_features)
+60        Labeled features matrix.
+61    y_label : ndarray or Serie of shape (n_label,)
+62        Labeled target vector.
+63    X_unlabel : ndarray or Serie DataFrame of shape (n_unlabel, n_features)
+64        Unlabeled features matrix.
+65    """
+66
+67    is_df = False
+68    if isinstance(X, pd.DataFrame):
+69        is_df = True
+70        columns = X.columns
+71
+72    X = check_array(X)
+73    y = check_array(y, ensure_2d=False, dtype=y.dtype.type)
+74    
+75    X_label = X[y != y.dtype.type(-1)]
+76    y_label = y[y != y.dtype.type(-1)]
+77    X_unlabel = X[y == y.dtype.type(-1)]
+78
+79    X_label, y_label = check_X_y(X_label, y_label)
+80
+81    if is_df:
+82        X_label = pd.DataFrame(X_label, columns=columns)
+83        X_unlabel = pd.DataFrame(X_unlabel, columns=columns)
+84
+85    return X_label, y_label, X_unlabel
+
+ + +

Check and divide dataset between labeled and unlabeled data.

+ +
Parameters
+ +
    +
  • X (ndarray or DataFrame of shape (n_samples, n_features)): +Features matrix.
    • +
  • y (ndarray of shape (n_samples,)): +Target vector.
    • +
+ +
Returns
+ +
    +
  • X_label (ndarray or DataFrame of shape (n_label, n_features)): +Labeled features matrix.
    • +
  • y_label (ndarray or Serie of shape (n_label,)): +Labeled target vector.
    • +
  • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)): +Unlabeled features matrix.
    • +
+
+ + +
+
+ +
+ + class + OneVsRestSSLClassifier(sklearn.multiclass.OneVsRestClassifier): + + + +
+ + 
212class OneVsRestSSLClassifier(OneVsRestClassifier):
+213
+214    def __init__(self, estimator, *, n_jobs=None):
+215        """Adapted OneVsRestClassifier for SSL datasets
+216
+217        Parameters
+218        ----------
+219        estimator : {ClassifierMixin, list},
+220            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+221        n_jobs : n_jobs : int, optional
+222            The number of jobs to run in parallel. -1 means using all processors., by default None
+223        """
+224        super().__init__(estimator, n_jobs=n_jobs)
+225
+226    def fit(self, X, y, **fit_params):
+227        #
+228        y_label = y[y != y.dtype.type(-1)]
+229        size = len(y) - len(y_label)
+230
+231        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+232        Y = self.label_binarizer_.fit_transform(y_label)
+233        Y = Y.tocsc()
+234        self.classes_ = self.label_binarizer_.classes_
+235        columns = (col.toarray().ravel() for col in Y.T)
+236
+237        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+238        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+239        for e in estimators:
+240            _set_random_states(e, rs)
+241
+242        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+243            delayed(_fit_binary_ssl)(
+244                estimators[i],
+245                X,
+246                column,
+247                size,
+248                classes=[
+249                    "not %s" % self.label_binarizer_.classes_[i],
+250                    self.label_binarizer_.classes_[i],
+251                ],
+252                **fit_params
+253            )
+254            for i, column in enumerate(columns)
+255        )
+256
+257        if hasattr(self.estimators_[0], "n_features_in_"):
+258            self.n_features_in_ = self.estimators_[0].n_features_in_
+259        if hasattr(self.estimators_[0], "feature_names_in_"):
+260            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+261
+262        return self
+263
+264    def predict(self, X, **kwards):
+265        check_is_fitted(self)
+266
+267        n_samples = _num_samples(X)
+268        if self.label_binarizer_.y_type_ == "multiclass":
+269            maxima = np.empty(n_samples, dtype=float)
+270            maxima.fill(-np.inf)
+271            argmaxima = np.zeros(n_samples, dtype=int)
+272            for i, e in enumerate(self.estimators_):
+273                pred = _predict_binary_ssl(e, X, **kwards)
+274                np.maximum(maxima, pred, out=maxima)
+275                argmaxima[maxima == pred] = i
+276            return self.classes_[argmaxima]
+277        else:
+278            if (hasattr(self.estimators_[0], "decision_function") and
+279                    is_classifier(self.estimators_[0])):
+280                thresh = 0
+281            else:
+282                thresh = .5
+283            indices = array.array('i')
+284            indptr = array.array('i', [0])
+285            for e in self.estimators_:
+286                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+287                indptr.append(len(indices))
+288            data = np.ones(len(indices), dtype=int)
+289            indicator = sp.csc_matrix((data, indices, indptr),
+290                                      shape=(n_samples, len(self.estimators_)))
+291            return self.label_binarizer_.inverse_transform(indicator)
+292
+293    def predict_proba(self, X, **kwards):
+294        check_is_fitted(self)
+295        # Y[i, j] gives the probability that sample i has the label j.
+296        # In the multi-label case, these are not disjoint.
+297        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+298
+299        if len(self.estimators_) == 1:
+300            # Only one estimator, but we still want to return probabilities
+301            # for two classes.
+302            Y = np.concatenate(((1 - Y), Y), axis=1)
+303
+304        if not self.multilabel_:
+305            # Then, probabilities should be normalized to 1.
+306            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+307        return Y
+
+ + +

One-vs-the-rest (OvR) multiclass strategy.

+ +

Also known as one-vs-all, this strategy consists in fitting one classifier +per class. For each classifier, the class is fitted against all the other +classes. In addition to its computational efficiency (only n_classes +classifiers are needed), one advantage of this approach is its +interpretability. Since each class is represented by one and one classifier +only, it is possible to gain knowledge about the class by inspecting its +corresponding classifier. This is the most commonly used strategy for +multiclass classification and is a fair default choice.

+ +

OneVsRestClassifier can also be used for multilabel classification. To use +this feature, provide an indicator matrix for the target y when calling +.fit. In other words, the target labels should be formatted as a 2D +binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j +in sample i. This estimator uses the binary relevance method to perform +multilabel classification, which involves training one binary classifier +independently for each label.

+ +

Read more in the :ref:User Guide <ovr_classification>.

+ +
Parameters
+ +
    +
  • estimator (estimator object): +A regressor or a classifier that implements :term:fit. +When a classifier is passed, :term:decision_function will be used +in priority and it will fallback to :term:predict_proba if it is not +available. +When a regressor is passed, :term:predict is used.
    • +

  • n_jobs (int, default=None): +The number of jobs to use for the computation: the n_classes +one-vs-rest problems are computed in parallel.

    + +

    None means 1 unless in a joblib.parallel_backend context. +-1 means using all processors. See :term:Glossary <n_jobs> +for more details.

    + +

    Changed in version 0.20: +n_jobs default changed from 1 to None

    • +

  • verbose (int, default=0): +The verbosity level, if non zero, progress messages are printed. +Below 50, the output is sent to stderr. Otherwise, the output is sent +to stdout. The frequency of the messages increases with the verbosity +level, reporting all iterations at 10. See joblib.Parallel for +more details.

    + +

    New in version 1.1.

    • +
+ +
Attributes
+ +
    +
  • estimators_ (list of n_classes estimators): +Estimators used for predictions.
    • +
  • classes_ (array, shape = [n_classes]): +Class labels.
    • +
  • n_classes_ (int): +Number of classes.
    • +
  • label_binarizer_ (LabelBinarizer object): +Object used to transform multiclass labels to binary labels and +vice-versa.
    • +
  • multilabel_ (boolean): +Whether a OneVsRestClassifier is a multilabel classifier.
    • +

  • n_features_in_ (int): +Number of features seen during :term:fit. Only defined if the +underlying estimator exposes such an attribute when fit.

    + +

    New in version 0.24.

    • +

  • feature_names_in_ (ndarray of shape (n_features_in_,)): +Names of features seen during :term:fit. Only defined if the +underlying estimator exposes such an attribute when fit.

    + +

    New in version 1.0.

    • +
+ +
See Also
+ +

OneVsOneClassifier: One-vs-one multiclass strategy.
+OutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
+sklearn.multioutput.MultiOutputClassifier: Alternate way of extending an +estimator for multilabel classification.
+sklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables +to binary indicator matrix.

+ +
Examples
+ +
+
>>> import numpy as np
+>>> from sklearn.multiclass import OneVsRestClassifier
+>>> from sklearn.svm import SVC
+>>> X = np.array([
+...     [10, 10],
+...     [8, 10],
+...     [-5, 5.5],
+...     [-5.4, 5.5],
+...     [-20, -20],
+...     [-15, -20]
+... ])
+>>> y = np.array([0, 0, 1, 1, 2, 2])
+>>> clf = OneVsRestClassifier(SVC()).fit(X, y)
+>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])
+array([2, 0, 1])
+
+
+
+ + +
+ +
+ + OneVsRestSSLClassifier(estimator, *, n_jobs=None) + + + +
+ + 
214    def __init__(self, estimator, *, n_jobs=None):
+215        """Adapted OneVsRestClassifier for SSL datasets
+216
+217        Parameters
+218        ----------
+219        estimator : {ClassifierMixin, list},
+220            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+221        n_jobs : n_jobs : int, optional
+222            The number of jobs to run in parallel. -1 means using all processors., by default None
+223        """
+224        super().__init__(estimator, n_jobs=n_jobs)
+
+ + +

Adapted OneVsRestClassifier for SSL datasets

+ +
Parameters
+ +
    +
  • estimator ({ClassifierMixin, list},): +An estimator object implementing fit and predict_proba or a list of ClassifierMixin
    • +
  • n_jobs : n_jobs (int, optional): +The number of jobs to run in parallel. -1 means using all processors., by default None
    • +
+
+ + +
+
+ +
+ + def + fit(self, X, y, **fit_params): + + + +
+ + 
226    def fit(self, X, y, **fit_params):
+227        #
+228        y_label = y[y != y.dtype.type(-1)]
+229        size = len(y) - len(y_label)
+230
+231        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+232        Y = self.label_binarizer_.fit_transform(y_label)
+233        Y = Y.tocsc()
+234        self.classes_ = self.label_binarizer_.classes_
+235        columns = (col.toarray().ravel() for col in Y.T)
+236
+237        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+238        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+239        for e in estimators:
+240            _set_random_states(e, rs)
+241
+242        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+243            delayed(_fit_binary_ssl)(
+244                estimators[i],
+245                X,
+246                column,
+247                size,
+248                classes=[
+249                    "not %s" % self.label_binarizer_.classes_[i],
+250                    self.label_binarizer_.classes_[i],
+251                ],
+252                **fit_params
+253            )
+254            for i, column in enumerate(columns)
+255        )
+256
+257        if hasattr(self.estimators_[0], "n_features_in_"):
+258            self.n_features_in_ = self.estimators_[0].n_features_in_
+259        if hasattr(self.estimators_[0], "feature_names_in_"):
+260            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+261
+262        return self
+
+ + +

Fit underlying estimators.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Data.
    • +
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)): +Multi-class targets. An indicator matrix turns on multilabel +classification.
    • +
+ +
Returns
+ +
    +
  • self (object): +Instance of fitted estimator.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X, **kwards): + + + +
+ + 
264    def predict(self, X, **kwards):
+265        check_is_fitted(self)
+266
+267        n_samples = _num_samples(X)
+268        if self.label_binarizer_.y_type_ == "multiclass":
+269            maxima = np.empty(n_samples, dtype=float)
+270            maxima.fill(-np.inf)
+271            argmaxima = np.zeros(n_samples, dtype=int)
+272            for i, e in enumerate(self.estimators_):
+273                pred = _predict_binary_ssl(e, X, **kwards)
+274                np.maximum(maxima, pred, out=maxima)
+275                argmaxima[maxima == pred] = i
+276            return self.classes_[argmaxima]
+277        else:
+278            if (hasattr(self.estimators_[0], "decision_function") and
+279                    is_classifier(self.estimators_[0])):
+280                thresh = 0
+281            else:
+282                thresh = .5
+283            indices = array.array('i')
+284            indptr = array.array('i', [0])
+285            for e in self.estimators_:
+286                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+287                indptr.append(len(indices))
+288            data = np.ones(len(indices), dtype=int)
+289            indicator = sp.csc_matrix((data, indices, indptr),
+290                                      shape=(n_samples, len(self.estimators_)))
+291            return self.label_binarizer_.inverse_transform(indicator)
+
+ + +

Predict multi-class targets using underlying estimators.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Data.
    • +
+ +
Returns
+ +
    +
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)): +Predicted multi-class targets.
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X, **kwards): + + + +
+ + 
293    def predict_proba(self, X, **kwards):
+294        check_is_fitted(self)
+295        # Y[i, j] gives the probability that sample i has the label j.
+296        # In the multi-label case, these are not disjoint.
+297        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+298
+299        if len(self.estimators_) == 1:
+300            # Only one estimator, but we still want to return probabilities
+301            # for two classes.
+302            Y = np.concatenate(((1 - Y), Y), axis=1)
+303
+304        if not self.multilabel_:
+305            # Then, probabilities should be normalized to 1.
+306            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+307        return Y
+
+ + +

Probability estimates.

+ +

The returned estimates for all classes are ordered by label of classes.

+ +

Note that in the multilabel case, each sample can have any number of +labels. This returns the marginal probability that the given sample has +the label in question. For example, it is entirely consistent that two +labels both have a 90% probability of applying to a given sample.

+ +

In the single label multiclass case, the rows of the returned matrix +sum to 1.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Input data.
    • +
+ +
Returns
+ +
    +
  • T (array-like of shape (n_samples, n_classes)): +Returns the probability of the sample for each class in the model, +where classes are ordered as they are in self.classes_.
    • +
+
+ + +
+
+
+ + def + set_partial_fit_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.multiclass.OneVsRestClassifier
+
partial_fit
+
decision_function
+
multilabel_
+
n_classes_
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/docs/sslearn/datasets.html b/docs/sslearn/datasets.html new file mode 100644 index 0000000..0791423 --- /dev/null +++ b/docs/sslearn/datasets.html @@ -0,0 +1,698 @@ + + + + + + + sslearn.datasets API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.datasets

+ +

Summary of module sslearn.datasets:

+ +

This module contains functions to load and save datasets in different formats.

+ +
Functions
+ +
    +
  1. read_csv : Load a dataset from a CSV file.
    2. +
  2. read_keel : Load a dataset from a KEEL file.
    3. +
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. +
  4. save_keel : Save a dataset in KEEL format.
    5. +
+ +

All doc

+
+ + + + + + 
 1"""
+ 2Summary of module `sslearn.datasets`:
+ 3
+ 4This module contains functions to load and save datasets in different formats.
+ 5
+ 6Functions
+ 7---------
+ 81. read_csv : Load a dataset from a CSV file.
+ 92. read_keel : Load a dataset from a KEEL file.
+103. secure_dataset : Secure the dataset by converting it into a secure format.
+114. save_keel : Save a dataset in KEEL format.
+12
+13All doc
+14-------
+15"""
+16
+17from ._loader import read_csv, read_keel
+18from ._writer import save_keel
+19from ._preprocess import secure_dataset
+20
+21__all__ = ["read_csv", "read_keel", "secure_dataset", "save_keel"]
+
+ + +
+
+ +
+ + def + read_csv(path, format='pandas', secure=False, target_col=-1, **kwards): + + + +
+ + 
 94def read_csv(path, format="pandas", secure=False, target_col=-1, **kwards):
+ 95    """Read a .csv file
+ 96
+ 97    Parameters
+ 98    ----------
+ 99    path : str
+100        File path
+101    format : str, optional
+102        Object that will contain the data, it can be `numpy` or `pandas`, by default "pandas"
+103    secure : bool, optional
+104        It guarantees that the dataset has not  `-1` as valid class, in order to make it semi-supervised after, by default False
+105    target_col : {str, int, None}, optional
+106        Column name or index to select class column, if None use the default value stored in the file, by default None
+107
+108    Returns
+109    -------
+110    X, y: array_like
+111        Dataset loaded.
+112    """
+113    if format not in ["pandas", "numpy"]:
+114        raise AttributeError("Formats allowed are `pandas` or `numpy`")
+115    data = pd.read_csv(path, **kwards)
+116
+117    if target_col is None:
+118        raise AttributeError("`read_csv` do not allow a `None` value for `target_col`, use `integer` or `string` instead.")
+119    elif isinstance(target_col, str):
+120        target_col = data.columns.index(target_col)
+121
+122    X = data.iloc[:, data.columns != data.columns[target_col]]
+123    y = data.iloc[:, target_col]
+124
+125    if secure:
+126        X, y = secure_dataset(X, y)
+127    if format == "numpy":
+128        X = X.to_numpy()
+129        y = y.to_numpy()
+130    return X, y
+
+ + +

Read a .csv file

+ +
Parameters
+ +
    +
  • path (str): +File path
    • +
  • format (str, optional): +Object that will contain the data, it can be numpy or pandas, by default "pandas"
    • +
  • secure (bool, optional): +It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • +
  • target_col ({str, int, None}, optional): +Column name or index to select class column, if None use the default value stored in the file, by default None
    • +
+ +
Returns
+ +
    +
  • X, y (array_like): +Dataset loaded.
    • +
+
+ + +
+
+ +
+ + def + read_keel( path, format='pandas', secure=False, target_col=None, encoding='utf-8', **kwards): + + + +
+ + 
14def read_keel(path, format="pandas", secure=False, target_col=None, encoding="utf-8", **kwards):
+15    """Read a .dat file from KEEL (http://www.keel.es/)
+16
+17    Parameters
+18    ----------
+19    path : str
+20        File path
+21    format : str, optional
+22        Object that will contain the data, it can be `numpy` or `pandas`, by default "pandas"
+23    secure : bool, optional
+24        It guarantees that the dataset has not  `-1` as valid class, in order to make it semi-supervised after, by default False
+25    target_col : {str, int, None}, optional
+26        Column name or index to select class column, if None use the default value stored in the file, by default None
+27    encoding: str, optional
+28        Encoding of file, by default "utf-8"
+29
+30    Returns
+31    -------
+32    X, y: array_like
+33        Dataset loaded.
+34    """
+35    if format not in ["pandas", "numpy"]:
+36        raise AttributeError("Formats allowed are `pandas` or `numpy`")
+37
+38    attributes = []
+39    types = []
+40    target = None
+41    with open(path, "r") as file:
+42        lines = file.readlines()
+43        counter = 1
+44        for line in lines:
+45            counter += 1
+46            if "@attribute" in line:
+47                parts = line.split(" ")
+48                name_ = parts[1]
+49                type_ = parts[2]
+50                if type_[0] == "{":
+51                    type_ = "string"
+52                attributes.append(name_)
+53                types.append(keel_type_cheat[type_])
+54            elif "@outputs" in line:
+55                target = line.split(" ")[1].strip('\n')
+56            elif "@data" in line:
+57                break
+58    if target is None:
+59        target = attributes[-1]
+60    data = pd.read_csv(path, skiprows=counter-1, header=None, **kwards)
+61    if len(data.columns) != len(attributes):
+62        warnings.warn(f"The dataset's have {len(data.columns)} columns but file declares {len(attributes)}.", RuntimeWarning)
+63        X = data
+64        y = None
+65    else:
+66        data.columns = attributes
+67        data = data.astype(dict(zip(attributes, types)))
+68        for att, tp in zip(attributes, types):
+69            if tp == "string":
+70                data[att] = data[att].str.strip()
+71        if target_col is None:
+72            target_col = target
+73        elif isinstance(target_col, int):
+74            target_col = data.columns[target_col]
+75
+76        att_columns = attributes.copy()
+77        att_columns.remove(target_col)
+78
+79        X = data[att_columns]
+80        y = data[target_col]
+81
+82        y[y == "unlabeled"] = y.dtype.type(-1)
+83        if secure:
+84            X, y = secure_dataset(X, y)
+85
+86    if format == "numpy":
+87        X = X.to_numpy().astype(float)
+88        y = y.to_numpy()
+89        if y.dtype == object:
+90            y = y.astype("str")
+91    return X, y
+
+ + +

Read a .dat file from KEEL (http://www.keel.es/)

+ +
Parameters
+ +
    +
  • path (str): +File path
    • +
  • format (str, optional): +Object that will contain the data, it can be numpy or pandas, by default "pandas"
    • +
  • secure (bool, optional): +It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • +
  • target_col ({str, int, None}, optional): +Column name or index to select class column, if None use the default value stored in the file, by default None
    • +
  • encoding (str, optional): +Encoding of file, by default "utf-8"
    • +
+ +
Returns
+ +
    +
  • X, y (array_like): +Dataset loaded.
    • +
+
+ + +
+
+ +
+ + def + secure_dataset(X, y): + + + +
+ + 
 2def secure_dataset(X, y):
+ 3    """It guarantees that the dataset has not  `-1` as valid class, in order to make it semi-supervised after
+ 4
+ 5    Parameters
+ 6    ----------
+ 7    X : Array-like
+ 8        Ignored
+ 9    y : Array-like
+10        Target array.
+11
+12    Returns
+13    -------
+14    X, y: array_like
+15        Dataset securized.
+16    """
+17    if y.dtype.type(-1) in y.tolist():
+18        raise ValueError("The dataset contains -1 as valid class. Please, change it to another value.")
+19    return X, y
+20    # if np.issubdtype(y.dtype, np.number):
+21    #     y = y + 2
+22
+23    # return X, y
+
+ + +

It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

+ +
Parameters
+ +
    +
  • X (Array-like): +Ignored
    • +
  • y (Array-like): +Target array.
    • +
+ +
Returns
+ +
    +
  • X, y (array_like): +Dataset securized.
    • +
+
+ + +
+
+ +
+ + def + save_keel( X, y, route, name=None, attribute_name=None, target_name='Class', classification=True, unlabeled=True, force_targets=None): + + + +
+ + 
 8def save_keel(X, y, route, name=None, attribute_name=None, target_name="Class",  classification=True, unlabeled=True, force_targets=None):
+ 9    """Save a dataset in the KEEL format
+10
+11    Parameters
+12    ----------
+13    X : array-like
+14        Dataset features
+15    y : array-like
+16        Dataset targets
+17    route : str
+18        Path to save the dataset
+19    name : str, optional
+20        Dataset name, if None the route basename will be selected, by default None
+21    attribute_name : list, optional
+22        List of attribute names, if None the default names will be used, by default None
+23    target_name : str, optional
+24        Target name, by default "Class"
+25    classification : bool, optional
+26        If the dataset is classification or regression, by default True
+27    unlabeled : bool, optional
+28        If the dataset has unlabeled instances, by default True
+29    force_targets : collection, optional
+30        Force the targets to be a specific value, by default None
+31    """    
+32    columns = []
+33    types = []
+34    min_max = []
+35    if name is None:
+36        name = os.path.basename(route).split(".")[0]
+37
+38    unlabel_target = y == y.dtype.type(-1)
+39    if classification:
+40        y = y.astype("str")
+41
+42        if unlabeled:            
+43            y = y.astype("str")
+44            if y.dtype.itemsize < np.array("unlabeled").dtype.itemsize:
+45                y = y.astype(f"<U{len('unlabeled')}")
+46            y[unlabel_target] = "unlabeled"
+47            if force_targets is not None:
+48                force_targets = force_targets.copy()
+49                force_targets.append("unlabeled")
+50
+51    # Generate attributes:
+52    if attribute_name is None:
+53        if isinstance(X, pd.DataFrame):
+54            attribute_name = X.columns
+55        elif isinstance(X, np.ndarray):
+56            attribute_name = [f"a{i}" for i in range(X.shape[-1])]
+57    if not isinstance(X, pd.DataFrame):
+58        X = pd.DataFrame(X)
+59    data = pd.concat([X, pd.Series(y).rename(target_name)], axis=1)
+60    for i, col in enumerate(data):
+61        if i < len(attribute_name) and attribute_name[i] != col:
+62            columns.append(attribute_name[i])
+63        else:
+64            columns.append(col)
+65        numeric = False
+66        if data[col].dtype.kind in "ui":
+67            numeric = True
+68            types.append(" integer")
+69        elif data[col].dtype.kind == "f":
+70            numeric = True
+71            types.append(" real")
+72        elif data[col].dtype.kind in "bSOU":
+73            types.append("")
+74        if numeric:
+75            min_max.append(f" [{data[col].min()},{data[col].max()}]")
+76        else:
+77            if col == target_name and force_targets is not None:
+78                min_max.append(" {" + ",".join(force_targets) + "}")
+79            else:
+80                min_max.append(" {" + ",".join(data[col].unique()) + "}")
+81
+82    # Generate header
+83    value = f"@relation {name}"
+84    for c, t, mm in zip(columns, types, min_max):
+85        value += f"\n@attribute {c}{t}{mm}"
+86    
+87    value += "\n@inputs " + ",".join(attribute_name)
+88    value += f"\n@outputs {target_name}"
+89    value += "\n@data\n"
+90    with open(route, "w") as f:
+91        f.write(value)
+92        data.to_csv(f, index=False, header=False)
+
+ + +

Save a dataset in the KEEL format

+ +
Parameters
+ +
    +
  • X (array-like): +Dataset features
    • +
  • y (array-like): +Dataset targets
    • +
  • route (str): +Path to save the dataset
    • +
  • name (str, optional): +Dataset name, if None the route basename will be selected, by default None
    • +
  • attribute_name (list, optional): +List of attribute names, if None the default names will be used, by default None
    • +
  • target_name (str, optional): +Target name, by default "Class"
    • +
  • classification (bool, optional): +If the dataset is classification or regression, by default True
    • +
  • unlabeled (bool, optional): +If the dataset has unlabeled instances, by default True
    • +
  • force_targets (collection, optional): +Force the targets to be a specific value, by default None
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/sslearn/model_selection.html b/docs/sslearn/model_selection.html new file mode 100644 index 0000000..3be92fd --- /dev/null +++ b/docs/sslearn/model_selection.html @@ -0,0 +1,470 @@ + + + + + + + sslearn.model_selection API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.model_selection

+ +

Summary of module sslearn.model_selection:

+ +

This module contains functions to split datasets into training and testing sets.

+ +
Functions
+ +

artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

+ +
Classes
+ +

StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

+ +

All doc

+
+ + + + + + 
 1"""
+ 2Summary of module `sslearn.model_selection`:
+ 3
+ 4This module contains functions to split datasets into training and testing sets.
+ 5
+ 6Functions
+ 7---------
+ 8artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.
+ 9
+10Classes
+11-------
+12StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.
+13
+14All doc
+15----
+16"""
+17
+18from ._split import *
+19
+20__all__ = ['StratifiedKFoldSS', 'artificial_ssl_dataset']
+
+ + +
+
+ +
+ + def + artificial_ssl_dataset( X, y, label_rate=0.1, random_state=None, force_minimum=None, indexes=False, **kwards): + + + +
+ + 
 65def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimum=None, indexes=False, **kwards):
+ 66    """Create an artificial Semi-supervised dataset from a supervised dataset.
+ 67
+ 68    Parameters
+ 69    ----------
+ 70    X : array-like of shape (n_samples, n_features)
+ 71        Training data, where n_samples is the number of samples
+ 72        and n_features is the number of features.
+ 73    y : array-like of shape (n_samples,)
+ 74        The target variable for supervised learning problems.
+ 75    label_rate : float, optional
+ 76        Proportion between labeled instances and unlabel instances, by default 0.1
+ 77    random_state : int or RandomState, optional
+ 78        Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
+ 79    force_minimum: int, optional
+ 80        Force a minimum of instances of each class, by default None
+ 81    indexes: bool, optional
+ 82        If True, return the indexes of the labeled and unlabeled instances, by default False
+ 83    shuffle: bool, default=True
+ 84        Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
+ 85    stratify: array-like, default=None
+ 86        If not None, data is split in a stratified fashion, using this as the class labels.
+ 87
+ 88    Returns
+ 89    -------
+ 90    X : ndarray
+ 91        The feature set.
+ 92    y : ndarray
+ 93        The label set, -1 for unlabel instance.
+ 94    X_unlabel: ndarray
+ 95        The feature set for each y mark as unlabel
+ 96    y_unlabel: ndarray
+ 97        The true label for each y in the same order.
+ 98    label: ndarray (optional)
+ 99        The training set indexes for split mark as labeled.
+100    unlabel: ndarray (optional)
+101        The training set indexes for split mark as unlabeled.
+102    """
+103    assert (label_rate > 0) and (label_rate < 1),\
+104        "Label rate must be in (0, 1)."
+105    assert "test_size" not in kwards and "train_size" not in kwards,\
+106        "Test size and train size are illegal parameters in this method."
+107
+108    indices = np.arange(len(y))
+109
+110    if force_minimum is not None:
+111        try:
+112            selected = __random_select_n_instances(y, force_minimum, random_state)
+113        except ValueError:
+114            raise ValueError("The number of instances of each class is less than force_minimum.")
+115
+116        # Remove selected instances from indices
+117        indices = np.delete(indices, selected, axis=0)    
+118
+119    # Train test split with indexes
+120    label, unlabel = ms.train_test_split(indices, train_size=label_rate,
+121                                         random_state=random_state, **kwards)
+122
+123    if force_minimum is not None:
+124        label = np.concatenate((selected, label))
+125    
+126    # Create the label and unlabel sets
+127    X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
+128        X[unlabel], np.array([-1] * len(unlabel))
+129
+130    # Create the artificial dataset
+131    X = np.concatenate((X_label, X_unlabel), axis=0)
+132    y = np.concatenate((y_label, y_unlabel), axis=0)
+133
+134    if indexes:
+135        return X, y, X_unlabel, y_unlabel, label, unlabel
+136
+137    return X, y, X_unlabel, y_unlabel
+138
+139
+140    """    
+141    if force_minimum is not None:
+142        try:
+143            selected = __random_select_n_instances(y, force_minimum, random_state)
+144        except ValueError:
+145            raise ValueError("The number of instances of each class is less than force_minimum.")
+146        X_selected = X[selected]
+147        y_selected = y[selected]
+148
+149        # Remove selected instances from X and y
+150        X = np.delete(X, selected, axis=0)
+151        y = np.delete(y, selected, axis=0)
+152    
+153    X_label, X_unlabel, y_label, true_label = \
+154        ms.train_test_split(X, y,
+155                            train_size=label_rate,
+156                            random_state=random_state, **kwards)
+157    X = np.concatenate((X_label, X_unlabel), axis=0)
+158    y = np.concatenate((y_label, np.array([-1] * len(true_label))), axis=0)
+159
+160    if force_minimum is not None:
+161        X = np.concatenate((X, X_selected), axis=0)
+162        y = np.concatenate((y, y_selected), axis=0)
+163    
+164    if indexes:
+165        return X, y, X_unlabel, true_label, X_label, X_unlabel
+166
+167    return X, y, X_unlabel, true_label
+168    """
+
+ + +

Create an artificial Semi-supervised dataset from a supervised dataset.

+ +
Parameters
+ +
    +
  • X (array-like of shape (n_samples, n_features)): +Training data, where n_samples is the number of samples +and n_features is the number of features.
    • +
  • y (array-like of shape (n_samples,)): +The target variable for supervised learning problems.
    • +
  • label_rate (float, optional): +Proportion between labeled instances and unlabel instances, by default 0.1
    • +
  • random_state (int or RandomState, optional): +Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
    • +
  • force_minimum (int, optional): +Force a minimum of instances of each class, by default None
    • +
  • indexes (bool, optional): +If True, return the indexes of the labeled and unlabeled instances, by default False
    • +
  • shuffle (bool, default=True): +Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
    • +
  • stratify (array-like, default=None): +If not None, data is split in a stratified fashion, using this as the class labels.
    • +
+ +
Returns
+ +
    +
  • X (ndarray): +The feature set.
    • +
  • y (ndarray): +The label set, -1 for unlabel instance.
    • +
  • X_unlabel (ndarray): +The feature set for each y mark as unlabel
    • +
  • y_unlabel (ndarray): +The true label for each y in the same order.
    • +
  • label (ndarray (optional)): +The training set indexes for split mark as labeled.
    • +
  • unlabel (ndarray (optional)): +The training set indexes for split mark as unlabeled.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/sslearn/restricted.html b/docs/sslearn/restricted.html new file mode 100644 index 0000000..9e412fc --- /dev/null +++ b/docs/sslearn/restricted.html @@ -0,0 +1,1144 @@ + + + + + + + sslearn.restricted API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.restricted

+ +

Summary of module sslearn.restricted:

+ +

This module contains classes to train a classifier using the restricted set classification approach.

+ +
Classes
+ +

WhoIsWhoClassifier : Who is Who Classifier

+ +
Functions
+ +

conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions. +combine_predictions : Combine the predictions of a group of instances to keep the restrictions.

+ +

All doc

+
+ + + + + + 
  1"""Summary of module `sslearn.restricted`:
+  2
+  3This module contains classes to train a classifier using the restricted set classification approach.
+  4
+  5Classes
+  6-------
+  7WhoIsWhoClassifier : Who is Who Classifier
+  8
+  9Functions
+ 10---------
+ 11conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.
+ 12combine_predictions : Combine the predictions of a group of instances to keep the restrictions.
+ 13
+ 14All doc
+ 15-------
+ 16"""
+ 17
+ 18import numpy as np
+ 19from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator
+ 20from scipy.optimize import linear_sum_assignment
+ 21import warnings
+ 22import pandas as pd
+ 23
+ 24__all__ = ["WhoIsWhoClassifier", "conflict_rate", "combine_predictions"]
+ 25
+ 26class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ 27
+ 28    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+ 29        """
+ 30        Who is Who Classifier
+ 31        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+ 32        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+ 33
+ 34        Parameters
+ 35        ----------
+ 36        base_estimator : ClassifierMixin
+ 37            The base estimator to be used for training.
+ 38        method : str, optional
+ 39            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+ 40        conflict_weighted : bool, default=True
+ 41            Whether to weighted the confusion rate by the number of instances with the same group.
+ 42        """        
+ 43        allowed_methods = ["greedy", "hungarian"]
+ 44        self.base_estimator = base_estimator
+ 45        self.method = method
+ 46        if method not in allowed_methods:
+ 47            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+ 48        self.conflict_weighted = conflict_weighted
+ 49
+ 50
+ 51    def fit(self, X, y, instance_group=None, **kwards):
+ 52        """Fit the model according to the given training data.
+ 53        Parameters
+ 54        ----------
+ 55        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 56            The input samples.
+ 57        y : array-like of shape (n_samples,)
+ 58            The target values.
+ 59        instance_group : array-like of shape (n_samples)
+ 60            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+ 61        Returns
+ 62        -------
+ 63        self : object
+ 64            Returns self.
+ 65        """
+ 66        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+ 67        self.classes_ = self.base_estimator.classes_
+ 68        if instance_group is not None:
+ 69            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+ 70        else:
+ 71            self.conflict_in_train = None
+ 72        return self
+ 73
+ 74    def conflict_rate(self, X, instance_group):
+ 75        """Calculate the conflict rate of the model.
+ 76        Parameters
+ 77        ----------
+ 78        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 79            The input samples.
+ 80        instance_group : array-like of shape (n_samples)
+ 81            The group. Two instances with the same label are not allowed to be in the same group.
+ 82        Returns
+ 83        -------
+ 84        float
+ 85            The conflict rate.
+ 86        """
+ 87        y_pred = self.base_estimator.predict(X)
+ 88        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+ 89
+ 90    def predict(self, X, instance_group):
+ 91        """Predict class for X.
+ 92        Parameters
+ 93        ----------
+ 94        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 95            The input samples.
+ 96        **kwards : array-like of shape (n_samples)
+ 97            The group. Two instances with the same label are not allowed to be in the same group.
+ 98        Returns
+ 99        -------
+100        array-like of shape (n_samples, n_classes)
+101            The class probabilities of the input samples.
+102        """
+103        
+104        y_prob = self.predict_proba(X)
+105        
+106        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+107
+108        return self.classes_.take(y_predicted)
+109
+110
+111    def predict_proba(self, X):
+112        """Predict class probabilities for X.
+113        Parameters
+114        ----------
+115        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+116            The input samples.
+117        Returns
+118        -------
+119        array-like of shape (n_samples, n_classes)
+120            The class probabilities of the input samples.
+121        """
+122        return self.base_estimator.predict_proba(X)
+123
+124
+125def conflict_rate(y_pred, restrictions, weighted=True):
+126    """
+127    Computes the conflict rate of a prediction, given a set of restrictions.
+128    Parameters
+129    ----------
+130    y_pred : array-like of shape (n_samples,)
+131        Predicted target values.
+132    restrictions : array-like of shape (n_samples,)
+133        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+134    weighted : bool, default=True
+135        Whether to weighted the confusion rate by the number of instances with the same group.
+136    Returns
+137    -------
+138    conflict rate : float
+139        The conflict rate.
+140    """
+141    
+142    # Check that y_pred and restrictions have the same length
+143    if len(y_pred) != len(restrictions):
+144        raise ValueError("y_pred and restrictions must have the same length.")
+145    
+146    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+147
+148    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
+149    if weighted:
+150        return conflicted.sum().y_pred / len(y_pred)
+151    else:
+152        rcount = restricted_df.groupby('restrictions').count()
+153        return (conflicted.y_pred / rcount.y_pred).sum()
+154
+155def combine_predictions(y_probas, instance_group, class_number, method="hungarian"):
+156    y_predicted = []
+157    for group in np.unique(instance_group):
+158           
+159        mask = instance_group == group
+160        probas_matrix = y_probas[mask]
+161        
+162
+163        preds = list(np.argmax(probas_matrix, axis=1))
+164
+165        if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number:
+166            y_predicted.extend(preds)
+167            if probas_matrix.shape[0] > class_number:
+168                warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning)
+169            continue
+170
+171        if method == "greedy":
+172            y = _greedy(probas_matrix)
+173        elif method == "hungarian":
+174            y = _hungarian(probas_matrix)
+175        
+176        y_predicted.extend(y)
+177    return y_predicted
+178
+179def _greedy(probas_matrix):        
+180
+181    probas = probas_matrix.reshape(probas_matrix.size,)
+182    order = probas.argsort()[::-1]
+183
+184    y_pred_group = [None for i in range(probas_matrix.shape[0])]
+185
+186    instance_to_predict = {i for i in range(probas_matrix.shape[0])}
+187    class_predicted = set()
+188    for item in order:
+189        class_ = item % probas_matrix.shape[0]
+190        instance = item // probas_matrix.shape[0]
+191        if instance in instance_to_predict and class_ not in class_predicted:
+192            y_pred_group[instance] = class_
+193            instance_to_predict.remove(instance)
+194            class_predicted.add(class_)
+195            
+196    return y_pred_group
+197        
+198
+199def _hungarian(probas_matrix):
+200    
+201    costs = np.log(probas_matrix)
+202    costs[costs == -np.inf] = 0  # if proba is 0, then the cost is 0
+203    _, col_ind = linear_sum_assignment(costs, maximize=True)
+204    col_ind = list(col_ind)
+205        
+206    return col_ind
+
+ + +
+
+ +
+ + class + WhoIsWhoClassifier(sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin): + + + +
+ + 
 27class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ 28
+ 29    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+ 30        """
+ 31        Who is Who Classifier
+ 32        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+ 33        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+ 34
+ 35        Parameters
+ 36        ----------
+ 37        base_estimator : ClassifierMixin
+ 38            The base estimator to be used for training.
+ 39        method : str, optional
+ 40            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+ 41        conflict_weighted : bool, default=True
+ 42            Whether to weighted the confusion rate by the number of instances with the same group.
+ 43        """        
+ 44        allowed_methods = ["greedy", "hungarian"]
+ 45        self.base_estimator = base_estimator
+ 46        self.method = method
+ 47        if method not in allowed_methods:
+ 48            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+ 49        self.conflict_weighted = conflict_weighted
+ 50
+ 51
+ 52    def fit(self, X, y, instance_group=None, **kwards):
+ 53        """Fit the model according to the given training data.
+ 54        Parameters
+ 55        ----------
+ 56        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 57            The input samples.
+ 58        y : array-like of shape (n_samples,)
+ 59            The target values.
+ 60        instance_group : array-like of shape (n_samples)
+ 61            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+ 62        Returns
+ 63        -------
+ 64        self : object
+ 65            Returns self.
+ 66        """
+ 67        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+ 68        self.classes_ = self.base_estimator.classes_
+ 69        if instance_group is not None:
+ 70            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+ 71        else:
+ 72            self.conflict_in_train = None
+ 73        return self
+ 74
+ 75    def conflict_rate(self, X, instance_group):
+ 76        """Calculate the conflict rate of the model.
+ 77        Parameters
+ 78        ----------
+ 79        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 80            The input samples.
+ 81        instance_group : array-like of shape (n_samples)
+ 82            The group. Two instances with the same label are not allowed to be in the same group.
+ 83        Returns
+ 84        -------
+ 85        float
+ 86            The conflict rate.
+ 87        """
+ 88        y_pred = self.base_estimator.predict(X)
+ 89        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+ 90
+ 91    def predict(self, X, instance_group):
+ 92        """Predict class for X.
+ 93        Parameters
+ 94        ----------
+ 95        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 96            The input samples.
+ 97        **kwards : array-like of shape (n_samples)
+ 98            The group. Two instances with the same label are not allowed to be in the same group.
+ 99        Returns
+100        -------
+101        array-like of shape (n_samples, n_classes)
+102            The class probabilities of the input samples.
+103        """
+104        
+105        y_prob = self.predict_proba(X)
+106        
+107        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+108
+109        return self.classes_.take(y_predicted)
+110
+111
+112    def predict_proba(self, X):
+113        """Predict class probabilities for X.
+114        Parameters
+115        ----------
+116        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+117            The input samples.
+118        Returns
+119        -------
+120        array-like of shape (n_samples, n_classes)
+121            The class probabilities of the input samples.
+122        """
+123        return self.base_estimator.predict_proba(X)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + WhoIsWhoClassifier(base_estimator, method='hungarian', conflict_weighted=True) + + + +
+ + 
29    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+30        """
+31        Who is Who Classifier
+32        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+33        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+34
+35        Parameters
+36        ----------
+37        base_estimator : ClassifierMixin
+38            The base estimator to be used for training.
+39        method : str, optional
+40            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+41        conflict_weighted : bool, default=True
+42            Whether to weighted the confusion rate by the number of instances with the same group.
+43        """        
+44        allowed_methods = ["greedy", "hungarian"]
+45        self.base_estimator = base_estimator
+46        self.method = method
+47        if method not in allowed_methods:
+48            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+49        self.conflict_weighted = conflict_weighted
+
+ + +

Who is Who Classifier +Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017). +Restricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin): +The base estimator to be used for training.
    • +
  • method (str, optional): +The method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default "hungarian"
    • +
  • conflict_weighted (bool, default=True): +Whether to weighted the confusion rate by the number of instances with the same group.
    • +
+
+ + +
+
+ +
+ + def + fit(self, X, y, instance_group=None, **kwards): + + + +
+ + 
52    def fit(self, X, y, instance_group=None, **kwards):
+53        """Fit the model according to the given training data.
+54        Parameters
+55        ----------
+56        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+57            The input samples.
+58        y : array-like of shape (n_samples,)
+59            The target values.
+60        instance_group : array-like of shape (n_samples)
+61            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+62        Returns
+63        -------
+64        self : object
+65            Returns self.
+66        """
+67        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+68        self.classes_ = self.base_estimator.classes_
+69        if instance_group is not None:
+70            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+71        else:
+72            self.conflict_in_train = None
+73        return self
+
+ + +

Fit the model according to the given training data.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values.
    • +
  • instance_group (array-like of shape (n_samples)): +The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
    • +
+ +
Returns
+ +
    +
  • self (object): +Returns self.
    • +
+
+ + +
+
+ +
+ + def + conflict_rate(self, X, instance_group): + + + +
+ + 
75    def conflict_rate(self, X, instance_group):
+76        """Calculate the conflict rate of the model.
+77        Parameters
+78        ----------
+79        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+80            The input samples.
+81        instance_group : array-like of shape (n_samples)
+82            The group. Two instances with the same label are not allowed to be in the same group.
+83        Returns
+84        -------
+85        float
+86            The conflict rate.
+87        """
+88        y_pred = self.base_estimator.predict(X)
+89        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+
+ + +

Calculate the conflict rate of the model.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
  • instance_group (array-like of shape (n_samples)): +The group. Two instances with the same label are not allowed to be in the same group.
    • +
+ +
Returns
+ +
    +
  • float: The conflict rate.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X, instance_group): + + + +
+ + 
 91    def predict(self, X, instance_group):
+ 92        """Predict class for X.
+ 93        Parameters
+ 94        ----------
+ 95        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 96            The input samples.
+ 97        **kwards : array-like of shape (n_samples)
+ 98            The group. Two instances with the same label are not allowed to be in the same group.
+ 99        Returns
+100        -------
+101        array-like of shape (n_samples, n_classes)
+102            The class probabilities of the input samples.
+103        """
+104        
+105        y_prob = self.predict_proba(X)
+106        
+107        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+108
+109        return self.classes_.take(y_predicted)
+
+ + +

Predict class for X.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
  • **kwards (array-like of shape (n_samples)): +The group. Two instances with the same label are not allowed to be in the same group.
    • +
+ +
Returns
+ +
    +
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X): + + + +
+ + 
112    def predict_proba(self, X):
+113        """Predict class probabilities for X.
+114        Parameters
+115        ----------
+116        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+117            The input samples.
+118        Returns
+119        -------
+120        array-like of shape (n_samples, n_classes)
+121            The class probabilities of the input samples.
+122        """
+123        return self.base_estimator.predict_proba(X)
+
+ + +

Predict class probabilities for X.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • +
+
+ + +
+
+
+ + def + set_fit_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_predict_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+
+
+
+
+ +
+ + def + conflict_rate(y_pred, restrictions, weighted=True): + + + +
+ + 
126def conflict_rate(y_pred, restrictions, weighted=True):
+127    """
+128    Computes the conflict rate of a prediction, given a set of restrictions.
+129    Parameters
+130    ----------
+131    y_pred : array-like of shape (n_samples,)
+132        Predicted target values.
+133    restrictions : array-like of shape (n_samples,)
+134        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+135    weighted : bool, default=True
+136        Whether to weighted the confusion rate by the number of instances with the same group.
+137    Returns
+138    -------
+139    conflict rate : float
+140        The conflict rate.
+141    """
+142    
+143    # Check that y_pred and restrictions have the same length
+144    if len(y_pred) != len(restrictions):
+145        raise ValueError("y_pred and restrictions must have the same length.")
+146    
+147    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+148
+149    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
+150    if weighted:
+151        return conflicted.sum().y_pred / len(y_pred)
+152    else:
+153        rcount = restricted_df.groupby('restrictions').count()
+154        return (conflicted.y_pred / rcount.y_pred).sum()
+
+ + +

Computes the conflict rate of a prediction, given a set of restrictions.

+ +
Parameters
+ +
    +
  • y_pred (array-like of shape (n_samples,)): +Predicted target values.
    • +
  • restrictions (array-like of shape (n_samples,)): +Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
    • +
  • weighted (bool, default=True): +Whether to weighted the confusion rate by the number of instances with the same group.
    • +
+ +
Returns
+ +
    +
  • conflict rate (float): +The conflict rate.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/sslearn/subview.html b/docs/sslearn/subview.html new file mode 100644 index 0000000..1602bf0 --- /dev/null +++ b/docs/sslearn/subview.html @@ -0,0 +1,664 @@ + + + + + + + sslearn.subview API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.subview

+ +

Summary of module sslearn.subview:

+ +

This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

+ +
Classes
+ +

SubViewClassifier : Train a sub-view classifier. +SubViewRegressor : Train a sub-view regressor.

+ +

All doc

+
+ + + + + + 
 1"""
+ 2Summary of module `sslearn.subview`:
+ 3
+ 4This module contains classes to train a classifier or a regressor selecting a sub-view of the data.
+ 5
+ 6Classes
+ 7-------
+ 8SubViewClassifier : Train a sub-view classifier.
+ 9SubViewRegressor : Train a sub-view regressor.
+10
+11All doc
+12-------
+13"""
+14
+15from ._subview import SubViewClassifier, SubViewRegressor
+16
+17__all__ = ["SubViewClassifier", "SubViewRegressor"]
+
+ + +
+
+ +
+ + class + SubViewClassifier(sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin): + + + +
+ + 
131class SubViewClassifier(SubView, ClassifierMixin):
+132
+133    def predict_proba(self, X):
+134        """Predict class probabilities using the base estimator.
+135
+136        Parameters
+137        ----------
+138        X : array-like of shape (n_samples, n_features)
+139            The input samples.
+140
+141        Returns
+142        -------
+143        p : array-like of shape (n_samples, n_classes)
+144            The class probabilities of the input samples.
+145        """        
+146        if self.mode == "regex":
+147            X = self._regex_subview(X)
+148        elif self.mode == "index":
+149            X = self._index_subview(X)
+150        elif self.mode == "include":
+151            X = self._include_subview(X)
+152
+153        return self.base_estimator_.predict_proba(X)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + def + predict_proba(self, X): + + + +
+ + 
133    def predict_proba(self, X):
+134        """Predict class probabilities using the base estimator.
+135
+136        Parameters
+137        ----------
+138        X : array-like of shape (n_samples, n_features)
+139            The input samples.
+140
+141        Returns
+142        -------
+143        p : array-like of shape (n_samples, n_classes)
+144            The class probabilities of the input samples.
+145        """        
+146        if self.mode == "regex":
+147            X = self._regex_subview(X)
+148        elif self.mode == "index":
+149            X = self._index_subview(X)
+150        elif self.mode == "include":
+151            X = self._include_subview(X)
+152
+153        return self.base_estimator_.predict_proba(X)
+
+ + +

Predict class probabilities using the base estimator.

+ +
Parameters
+ +
    +
  • X (array-like of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • p (array-like of shape (n_samples, n_classes)): +The class probabilities of the input samples.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.subview._subview.SubView
+
SubView
+
fit
+
predict
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+
+
+
+
+ +
+ + class + SubViewRegressor(sslearn.subview._subview.SubView, sklearn.base.RegressorMixin): + + + +
+ + 
155class SubViewRegressor(SubView, RegressorMixin):
+156
+157    def predict(self, X):
+158        """Predict using the base estimator.
+159
+160        Parameters
+161        ----------
+162        X : array-like of shape (n_samples, n_features)
+163            The input samples.
+164
+165        Returns
+166        -------
+167        y : array-like of shape (n_samples,)
+168            The predicted values.
+169        """        
+170        return super().predict(X)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + def + predict(self, X): + + + +
+ + 
157    def predict(self, X):
+158        """Predict using the base estimator.
+159
+160        Parameters
+161        ----------
+162        X : array-like of shape (n_samples, n_features)
+163            The input samples.
+164
+165        Returns
+166        -------
+167        y : array-like of shape (n_samples,)
+168            The predicted values.
+169        """        
+170        return super().predict(X)
+
+ + +

Predict using the base estimator.

+ +
Parameters
+ +
    +
  • X (array-like of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • y (array-like of shape (n_samples,)): +The predicted values.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.subview._subview.SubView
+
SubView
+
fit
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.RegressorMixin
+
score
+ +
+
+
+
+
+ + \ No newline at end of file diff --git a/docs/sslearn/utils.html b/docs/sslearn/utils.html new file mode 100644 index 0000000..aa1f645 --- /dev/null +++ b/docs/sslearn/utils.html @@ -0,0 +1,807 @@ + + + + + + + sslearn.utils API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.utils

+ +

Some utility functions

+ +

This module contains utility functions that are used in different parts of the library.

+ +
Functions
+ +

safe_division : Safely divide two numbers preventing division by zero. +confidence_interval : Calculate the confidence interval of the predictions. +choice_with_proportion : Choice the best predictions according to the proportion of each class. +calculate_prior_probability : Calculate the priori probability of each label. +check_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

+ +

All doc

+
+ + + + + + 
  1"""
+  2Some utility functions
+  3
+  4This module contains utility functions that are used in different parts of the library.
+  5
+  6Functions
+  7---------
+  8safe_division : Safely divide two numbers preventing division by zero.
+  9confidence_interval : Calculate the confidence interval of the predictions.
+ 10choice_with_proportion : Choice the best predictions according to the proportion of each class.
+ 11calculate_prior_probability : Calculate the priori probability of each label.
+ 12check_n_jobs : Check `n_jobs` parameter according to the scikit-learn convention.
+ 13
+ 14All doc
+ 15-------
+ 16"""
+ 17
+ 18import numpy as np
+ 19import os
+ 20import math
+ 21
+ 22import pandas as pd
+ 23
+ 24from statsmodels.stats.proportion import proportion_confint
+ 25from sklearn.tree import DecisionTreeClassifier
+ 26from sklearn.base import ClassifierMixin
+ 27
+ 28__all__ = ["safe_division", "confidence_interval", "choice_with_proportion", "calculate_prior_probability",
+ 29           "check_n_jobs"]
+ 30
+ 31
+ 32def safe_division(dividend, divisor, epsilon):
+ 33    """Safely divide two numbers preventing division by zero
+ 34
+ 35    Parameters
+ 36    ----------
+ 37    dividend : numeric
+ 38        Dividend value
+ 39    divisor : numeric
+ 40        Divisor value
+ 41    epsilon : numeric
+ 42        Close to zero value to be used in case of division by zero
+ 43
+ 44    Returns
+ 45    -------
+ 46    result : numeric
+ 47        Result of the division
+ 48    """
+ 49    if divisor == 0:
+ 50        return dividend / epsilon
+ 51    return dividend / divisor
+ 52
+ 53
+ 54def confidence_interval(X, hyp, y, alpha=.95):
+ 55    """Calculate the confidence interval of the predictions
+ 56
+ 57    Parameters
+ 58    ----------
+ 59    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 60        The input samples.
+ 61    hyp : classifier
+ 62        The classifier to be used for prediction
+ 63    y : array-like of shape (n_samples,)
+ 64        The target values
+ 65    alpha : float, optional
+ 66        confidence (1 - significance), by default .95
+ 67
+ 68    Returns
+ 69    -------
+ 70    li, hi: float
+ 71        lower and upper bound of the confidence interval
+ 72    """
+ 73    data = hyp.predict(X)
+ 74
+ 75    successes = np.count_nonzero(data == y)
+ 76    trials = X.shape[0]
+ 77    li, hi = proportion_confint(successes, trials, alpha=1 - alpha, method="wilson")
+ 78    return li, hi
+ 79
+ 80
+ 81def choice_with_proportion(predictions, class_predicted, proportion, extra=0):
+ 82    """Choice the best predictions according to the proportion of each class.
+ 83
+ 84    Parameters
+ 85    ----------
+ 86    predictions : array-like of shape (n_samples,)
+ 87        array of predictions
+ 88    class_predicted : array-like of shape (n_samples,)
+ 89        array of predicted classes
+ 90    proportion : dict
+ 91        dictionary with the proportion of each class
+ 92    extra : int, optional
+ 93        number of extra instances to be added, by default 0
+ 94
+ 95    Returns
+ 96    -------
+ 97    indices: array-like of shape (n_samples,)
+ 98        array of indices of the best predictions
+ 99    """
+100    n = len(predictions)
+101    for_each_class = {c: int(n * j) for c, j in proportion.items()}
+102    indices = np.zeros(0)
+103    for c in proportion:
+104        instances = class_predicted == c
+105        to_add = np.argsort(predictions, kind="mergesort")[instances][::-1][0:for_each_class[c] + extra]
+106        indices = np.concatenate((indices, to_add))
+107
+108    return indices.astype(int)
+109
+110
+111def calculate_prior_probability(y):
+112    """Calculate the priori probability of each label
+113
+114    Parameters
+115    ----------
+116    y : array-like of shape (n_samples,)
+117        array of labels
+118
+119    Returns
+120    -------
+121    class_probability: dict
+122        dictionary with priori probability (value) of each label (key)
+123    """
+124    unique, counts = np.unique(y, return_counts=True)
+125    u_c = dict(zip(unique, counts))
+126    instances = len(y)
+127    for u in u_c:
+128        u_c[u] = float(u_c[u] / instances)
+129    return u_c
+130
+131
+132def is_int(x):
+133    """Check if x is of integer type, but not boolean"""
+134    # From sktime: BSD 3-Clause
+135    # boolean are subclasses of integers in Python, so explicitly exclude them
+136    return isinstance(x, (int, np.integer)) and not isinstance(x, bool)
+137
+138
+139def mode(y):
+140    """Calculate the mode of a list of values
+141
+142    Parameters
+143    ----------
+144    y : array-like of shape (n_samples, n_estimators)
+145        array of values
+146
+147    Returns
+148    -------
+149    mode: array-like of shape (n_samples,)
+150        array of mode of each label
+151    count: array-like of shape (n_samples,)
+152        array of count of the mode of each label
+153    """
+154    array = pd.DataFrame(np.array(y))
+155    mode = array.mode(axis=0).loc[0, :]
+156    count = array.apply(lambda x: x.value_counts().max())
+157    return mode.values, count.values
+158
+159
+160def check_n_jobs(n_jobs):
+161    """Check `n_jobs` parameter according to the scikit-learn convention.
+162    From sktime: BSD 3-Clause
+163    Parameters
+164    ----------
+165    n_jobs : int, positive or -1
+166        The number of jobs for parallelization.
+167
+168    Returns
+169    -------
+170    n_jobs : int
+171        Checked number of jobs.
+172    """
+173    # scikit-learn convention
+174    # https://scikit-learn.org/stable/glossary.html#term-n-jobs
+175    if n_jobs is None:
+176        return 1
+177    elif not is_int(n_jobs):
+178        raise ValueError(f"`n_jobs` must be None or an integer, but found: {n_jobs}")
+179    elif n_jobs < 0:
+180        return os.cpu_count()
+181    else:
+182        return n_jobs
+183
+184
+185def calc_number_per_class(y_label):
+186    classes = np.unique(y_label)
+187    proportion = calculate_prior_probability(y_label)
+188    factor = 1/min(proportion.values())
+189    number_per_class = dict()
+190    for c in classes:
+191        number_per_class[c] = math.ceil(proportion[c] * factor)
+192
+193    return number_per_class
+194
+195
+196def check_classifier(base_classifier, can_be_list=True, collection_size=None):
+197
+198    if base_classifier is None:
+199        return DecisionTreeClassifier()
+200    elif can_be_list and (type(base_classifier) == list or type(base_classifier) == tuple):
+201        if collection_size is not None:
+202            if len(base_classifier) != collection_size:
+203                raise AttributeError(f"base_classifier is a list of classifiers, but its length ({len(base_classifier)}) is different from expected ({collection_size})")
+204        for i, bc in enumerate(base_classifier):
+205            base_classifier[i] = check_classifier(bc, False)
+206        return list(base_classifier)  # Transform to list
+207    else:
+208        if not isinstance(base_classifier, ClassifierMixin):
+209            raise AttributeError(f"base_classifier must be a ClassifierMixin, but found {type(base_classifier)}")
+210        return base_classifier
+
+ + +
+
+ +
+ + def + safe_division(dividend, divisor, epsilon): + + + +
+ + 
33def safe_division(dividend, divisor, epsilon):
+34    """Safely divide two numbers preventing division by zero
+35
+36    Parameters
+37    ----------
+38    dividend : numeric
+39        Dividend value
+40    divisor : numeric
+41        Divisor value
+42    epsilon : numeric
+43        Close to zero value to be used in case of division by zero
+44
+45    Returns
+46    -------
+47    result : numeric
+48        Result of the division
+49    """
+50    if divisor == 0:
+51        return dividend / epsilon
+52    return dividend / divisor
+
+ + +

Safely divide two numbers preventing division by zero

+ +
Parameters
+ +
    +
  • dividend (numeric): +Dividend value
    • +
  • divisor (numeric): +Divisor value
    • +
  • epsilon (numeric): +Close to zero value to be used in case of division by zero
    • +
+ +
Returns
+ +
    +
  • result (numeric): +Result of the division
    • +
+
+ + +
+
+ +
+ + def + confidence_interval(X, hyp, y, alpha=0.95): + + + +
+ + 
55def confidence_interval(X, hyp, y, alpha=.95):
+56    """Calculate the confidence interval of the predictions
+57
+58    Parameters
+59    ----------
+60    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+61        The input samples.
+62    hyp : classifier
+63        The classifier to be used for prediction
+64    y : array-like of shape (n_samples,)
+65        The target values
+66    alpha : float, optional
+67        confidence (1 - significance), by default .95
+68
+69    Returns
+70    -------
+71    li, hi: float
+72        lower and upper bound of the confidence interval
+73    """
+74    data = hyp.predict(X)
+75
+76    successes = np.count_nonzero(data == y)
+77    trials = X.shape[0]
+78    li, hi = proportion_confint(successes, trials, alpha=1 - alpha, method="wilson")
+79    return li, hi
+
+ + +

Calculate the confidence interval of the predictions

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
  • hyp (classifier): +The classifier to be used for prediction
    • +
  • y (array-like of shape (n_samples,)): +The target values
    • +
  • alpha (float, optional): +confidence (1 - significance), by default .95
    • +
+ +
Returns
+ +
    +
  • li, hi (float): +lower and upper bound of the confidence interval
    • +
+
+ + +
+
+ +
+ + def + choice_with_proportion(predictions, class_predicted, proportion, extra=0): + + + +
+ + 
 82def choice_with_proportion(predictions, class_predicted, proportion, extra=0):
+ 83    """Choice the best predictions according to the proportion of each class.
+ 84
+ 85    Parameters
+ 86    ----------
+ 87    predictions : array-like of shape (n_samples,)
+ 88        array of predictions
+ 89    class_predicted : array-like of shape (n_samples,)
+ 90        array of predicted classes
+ 91    proportion : dict
+ 92        dictionary with the proportion of each class
+ 93    extra : int, optional
+ 94        number of extra instances to be added, by default 0
+ 95
+ 96    Returns
+ 97    -------
+ 98    indices: array-like of shape (n_samples,)
+ 99        array of indices of the best predictions
+100    """
+101    n = len(predictions)
+102    for_each_class = {c: int(n * j) for c, j in proportion.items()}
+103    indices = np.zeros(0)
+104    for c in proportion:
+105        instances = class_predicted == c
+106        to_add = np.argsort(predictions, kind="mergesort")[instances][::-1][0:for_each_class[c] + extra]
+107        indices = np.concatenate((indices, to_add))
+108
+109    return indices.astype(int)
+
+ + +

Choice the best predictions according to the proportion of each class.

+ +
Parameters
+ +
    +
  • predictions (array-like of shape (n_samples,)): +array of predictions
    • +
  • class_predicted (array-like of shape (n_samples,)): +array of predicted classes
    • +
  • proportion (dict): +dictionary with the proportion of each class
    • +
  • extra (int, optional): +number of extra instances to be added, by default 0
    • +
+ +
Returns
+ +
    +
  • indices (array-like of shape (n_samples,)): +array of indices of the best predictions
    • +
+
+ + +
+
+ +
+ + def + calculate_prior_probability(y): + + + +
+ + 
112def calculate_prior_probability(y):
+113    """Calculate the priori probability of each label
+114
+115    Parameters
+116    ----------
+117    y : array-like of shape (n_samples,)
+118        array of labels
+119
+120    Returns
+121    -------
+122    class_probability: dict
+123        dictionary with priori probability (value) of each label (key)
+124    """
+125    unique, counts = np.unique(y, return_counts=True)
+126    u_c = dict(zip(unique, counts))
+127    instances = len(y)
+128    for u in u_c:
+129        u_c[u] = float(u_c[u] / instances)
+130    return u_c
+
+ + +

Calculate the priori probability of each label

+ +
Parameters
+ +
    +
  • y (array-like of shape (n_samples,)): +array of labels
    • +
+ +
Returns
+ +
    +
  • class_probability (dict): +dictionary with priori probability (value) of each label (key)
    • +
+
+ + +
+
+ +
+ + def + check_n_jobs(n_jobs): + + + +
+ + 
161def check_n_jobs(n_jobs):
+162    """Check `n_jobs` parameter according to the scikit-learn convention.
+163    From sktime: BSD 3-Clause
+164    Parameters
+165    ----------
+166    n_jobs : int, positive or -1
+167        The number of jobs for parallelization.
+168
+169    Returns
+170    -------
+171    n_jobs : int
+172        Checked number of jobs.
+173    """
+174    # scikit-learn convention
+175    # https://scikit-learn.org/stable/glossary.html#term-n-jobs
+176    if n_jobs is None:
+177        return 1
+178    elif not is_int(n_jobs):
+179        raise ValueError(f"`n_jobs` must be None or an integer, but found: {n_jobs}")
+180    elif n_jobs < 0:
+181        return os.cpu_count()
+182    else:
+183        return n_jobs
+
+ + +

Check n_jobs parameter according to the scikit-learn convention. +From sktime: BSD 3-Clause

+ +
Parameters
+ +
    +
  • n_jobs (int, positive or -1): +The number of jobs for parallelization.
    • +
+ +
Returns
+ +
    +
  • n_jobs (int): +Checked number of jobs.
    • +
+
+ + +
+
+ + \ No newline at end of file diff --git a/docs/sslearn/wrapper.html b/docs/sslearn/wrapper.html new file mode 100644 index 0000000..f3f640c --- /dev/null +++ b/docs/sslearn/wrapper.html @@ -0,0 +1,7161 @@ + + + + + + + sslearn.wrapper API documentation + + + + + + + + + + + + + + +
+
+

+sslearn.wrapper

+ +

Summary of module sslearn.wrapper:

+ +

This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

+ +

Self-Training Algorithms

+ +
    +
  1. SelfTraining : Self-training algorithm.
    2. +
  2. Setred : Self-training with redundancy reduction.
    3. +
+ +

Co-Training Algorithms

+ +
    +
  1. CoTraining : Co-training
    2. +
  2. CoTrainingByCommittee : Co-training by committee
    3. +
  3. DemocraticCoLearning : Democratic co-learning
    4. +
  4. Rasco : Random subspace co-training
    5. +
  5. RelRasco : Relevant random subspace co-training
    6. +
  6. CoForest : Co-Forest
    7. +
  7. TriTraining : Tri-training
    8. +
  8. DeTriTraining : Data Editing Tri-training
    9. +
  9. WiWTriTraining : Who-Is-Who Tri-training
    10. +
+ +

All doc

+
+ + + + + + 
 1"""
+ 2Summary of module `sslearn.wrapper`:
+ 3
+ 4This module contains classes to train semi-supervised learning algorithms using a wrapper approach.
+ 5
+ 6Self-Training Algorithms
+ 7------------------------
+ 81. SelfTraining : Self-training algorithm.
+ 92. Setred : Self-training with redundancy reduction.
+10
+11Co-Training Algorithms
+12-----------------------
+131. CoTraining : Co-training
+142. CoTrainingByCommittee : Co-training by committee
+153. DemocraticCoLearning : Democratic co-learning
+164. Rasco : Random subspace co-training
+175. RelRasco : Relevant random subspace co-training
+186. CoForest : Co-Forest
+197. TriTraining : Tri-training
+208. DeTriTraining : Data Editing Tri-training
+219. WiWTriTraining : Who-Is-Who Tri-training
+22
+23All doc
+24----
+25"""
+26
+27from ._co import (CoForest, CoTraining, CoTrainingByCommittee,
+28                  DemocraticCoLearning, Rasco, RelRasco)
+29from ._self import SelfTraining, Setred
+30from ._tritraining import DeTriTraining, TriTraining, WiWTriTraining
+31
+32__all__ = ["SelfTraining", "CoTrainingByCommittee", "Rasco", "RelRasco", "TriTraining", "WiWTriTraining",
+33           "CoTraining", "DeTriTraining", "DemocraticCoLearning", "Setred", "CoForest"]
+
+ + +
+
+ +
+ + class + SelfTraining(sklearn.semi_supervised._self_training.SelfTrainingClassifier): + + + +
+ + 
 16class SelfTraining(SelfTrainingClassifier):
+ 17
+ 18    _estimator_type = "classifier"
+ 19
+ 20    def __init__(self,
+ 21                 base_estimator,
+ 22                 threshold=0.75,
+ 23                 criterion='threshold',
+ 24                 k_best=10,
+ 25                 max_iter=10,
+ 26                 verbose=False):
+ 27        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
+ 28
+ 29        This class allows a given supervised classifier to function as a
+ 30        semi-supervised classifier, allowing it to learn from unlabeled data. It
+ 31        does this by iteratively predicting pseudo-labels for the unlabeled data
+ 32        and adding them to the training set.
+ 33
+ 34        The classifier will continue iterating until either max_iter is reached, or
+ 35        no pseudo-labels were added to the training set in the previous iteration.
+ 36
+ 37        Parameters
+ 38        ----------
+ 39        base_estimator : estimator object
+ 40            An estimator object implementing ``fit`` and ``predict_proba``.
+ 41            Invoking the ``fit`` method will fit a clone of the passed estimator,
+ 42            which will be stored in the ``base_estimator_`` attribute.
+ 43
+ 44        threshold : float, default=0.75
+ 45            The decision threshold for use with `criterion='threshold'`.
+ 46            Should be in [0, 1). When using the 'threshold' criterion, a
+ 47            :ref:`well calibrated classifier <calibration>` should be used.
+ 48
+ 49        criterion : {'threshold', 'k_best'}, default='threshold'
+ 50            The selection criterion used to select which labels to add to the
+ 51            training set. If 'threshold', pseudo-labels with prediction
+ 52            probabilities above `threshold` are added to the dataset. If 'k_best',
+ 53            the `k_best` pseudo-labels with highest prediction probabilities are
+ 54            added to the dataset. When using the 'threshold' criterion, a
+ 55            :ref:`well calibrated classifier <calibration>` should be used.
+ 56
+ 57        k_best : int, default=10
+ 58            The amount of samples to add in each iteration. Only used when
+ 59            `criterion` is k_best'.
+ 60
+ 61        max_iter : int or None, default=10
+ 62            Maximum number of iterations allowed. Should be greater than or equal
+ 63            to 0. If it is ``None``, the classifier will continue to predict labels
+ 64            until no new pseudo-labels are added, or all unlabeled samples have
+ 65            been labeled.
+ 66
+ 67        verbose : bool, default=False
+ 68            Enable verbose output.
+ 69
+ 70        References
+ 71        ----------
+ 72        David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling
+ 73        supervised methods. In Proceedings of the 33rd annual meeting on
+ 74        Association for Computational Linguistics (ACL '95). Association for
+ 75        Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:
+ 76        https://doi.org/10.3115/981658.981684
+ 77        """
+ 78        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
+ 79
+ 80    def fit(self, X, y):
+ 81        """
+ 82        Fits this ``SelfTrainingClassifier`` to a dataset.
+ 83
+ 84        Parameters
+ 85        ----------
+ 86        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 87            Array representing the data.
+ 88
+ 89        y : {array-like, sparse matrix} of shape (n_samples,)
+ 90            Array representing the labels. Unlabeled samples should have the
+ 91            label -1.
+ 92
+ 93        Returns
+ 94        -------
+ 95        self : SelfTrainingClassifier
+ 96            Returns an instance of self.
+ 97        """
+ 98        y_adapted = y.copy()
+ 99        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
+100            y_adapted = y_adapted.astype(object)
+101            y_adapted[y_adapted == '-1'] = -1
+102        return super().fit(X, y_adapted)
+
+ + +

Self-training classifier.

+ +

This :term:metaestimator allows a given supervised classifier to function as a +semi-supervised classifier, allowing it to learn from unlabeled data. It +does this by iteratively predicting pseudo-labels for the unlabeled data +and adding them to the training set.

+ +

The classifier will continue iterating until either max_iter is reached, or +no pseudo-labels were added to the training set in the previous iteration.

+ +

Read more in the :ref:User Guide <self_training>.

+ +
Parameters
+ +
    +
  • base_estimator (estimator object): +An estimator object implementing fit and predict_proba. +Invoking the fit method will fit a clone of the passed estimator, +which will be stored in the base_estimator_ attribute.
    • +
  • threshold (float, default=0.75): +The decision threshold for use with criterion='threshold'. +Should be in [0, 1). When using the 'threshold' criterion, a +:ref:well calibrated classifier <calibration> should be used.
    • +
  • criterion ({'threshold', 'k_best'}, default='threshold'): +The selection criterion used to select which labels to add to the +training set. If 'threshold', pseudo-labels with prediction +probabilities above threshold are added to the dataset. If 'k_best', +the k_best pseudo-labels with highest prediction probabilities are +added to the dataset. When using the 'threshold' criterion, a +:ref:well calibrated classifier <calibration> should be used.
    • +
  • k_best (int, default=10): +The amount of samples to add in each iteration. Only used when +criterion='k_best'.
    • +
  • max_iter (int or None, default=10): +Maximum number of iterations allowed. Should be greater than or equal +to 0. If it is None, the classifier will continue to predict labels +until no new pseudo-labels are added, or all unlabeled samples have +been labeled.
    • +
  • verbose (bool, default=False): +Enable verbose output.
    • +
+ +
Attributes
+ +
    +
  • base_estimator_ (estimator object): +The fitted estimator.
    • +
  • classes_ (ndarray or list of ndarray of shape (n_classes,)): +Class labels for each output. (Taken from the trained +base_estimator_).
    • +
  • transduction_ (ndarray of shape (n_samples,)): +The labels used for the final fit of the classifier, including +pseudo-labels added during fit.
    • +
  • labeled_iter_ (ndarray of shape (n_samples,)): +The iteration in which each sample was labeled. When a sample has +iteration 0, the sample was already labeled in the original dataset. +When a sample has iteration -1, the sample was not labeled in any +iteration.
    • +

  • n_features_in_ (int): +Number of features seen during :term:fit.

    + +

    New in version 0.24.

    • +

  • feature_names_in_ (ndarray of shape (n_features_in_,)): +Names of features seen during :term:fit. Defined only when X +has feature names that are all strings.

    + +

    New in version 1.0.

    • +
  • n_iter_ (int): +The number of rounds of self-training, that is the number of times the +base estimator is fitted on relabeled variants of the training set.
    • +

  • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}): +The reason that fitting was stopped.

    + +
      +
    • 'max_iter': n_iter_ reached max_iter.
      • +
    • 'no_change': no new labels were predicted.
      • +
    • 'all_labeled': all unlabeled samples were labeled before max_iter +was reached.
      • +
    • +
+ +
See Also
+ +

LabelPropagation: Label propagation classifier.
+LabelSpreading: Label spreading model for semi-supervised learning.

+ +
References
+ +

:doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling +supervised methods. In Proceedings of the 33rd annual meeting on +Association for Computational Linguistics (ACL '95). Association for +Computational Linguistics, Stroudsburg, PA, USA, 189-196. +<10.3115/981658.981684>

+ +
Examples
+ +
+
>>> import numpy as np
+>>> from sklearn import datasets
+>>> from sklearn.semi_supervised import SelfTrainingClassifier
+>>> from sklearn.svm import SVC
+>>> rng = np.random.RandomState(42)
+>>> iris = datasets.load_iris()
+>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
+>>> iris.target[random_unlabeled_points] = -1
+>>> svc = SVC(probability=True, gamma="auto")
+>>> self_training_model = SelfTrainingClassifier(svc)
+>>> self_training_model.fit(iris.data, iris.target)
+SelfTrainingClassifier(...)
+
+
+
+ + +
+ +
+ + SelfTraining( base_estimator, threshold=0.75, criterion='threshold', k_best=10, max_iter=10, verbose=False) + + + +
+ + 
20    def __init__(self,
+21                 base_estimator,
+22                 threshold=0.75,
+23                 criterion='threshold',
+24                 k_best=10,
+25                 max_iter=10,
+26                 verbose=False):
+27        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
+28
+29        This class allows a given supervised classifier to function as a
+30        semi-supervised classifier, allowing it to learn from unlabeled data. It
+31        does this by iteratively predicting pseudo-labels for the unlabeled data
+32        and adding them to the training set.
+33
+34        The classifier will continue iterating until either max_iter is reached, or
+35        no pseudo-labels were added to the training set in the previous iteration.
+36
+37        Parameters
+38        ----------
+39        base_estimator : estimator object
+40            An estimator object implementing ``fit`` and ``predict_proba``.
+41            Invoking the ``fit`` method will fit a clone of the passed estimator,
+42            which will be stored in the ``base_estimator_`` attribute.
+43
+44        threshold : float, default=0.75
+45            The decision threshold for use with `criterion='threshold'`.
+46            Should be in [0, 1). When using the 'threshold' criterion, a
+47            :ref:`well calibrated classifier <calibration>` should be used.
+48
+49        criterion : {'threshold', 'k_best'}, default='threshold'
+50            The selection criterion used to select which labels to add to the
+51            training set. If 'threshold', pseudo-labels with prediction
+52            probabilities above `threshold` are added to the dataset. If 'k_best',
+53            the `k_best` pseudo-labels with highest prediction probabilities are
+54            added to the dataset. When using the 'threshold' criterion, a
+55            :ref:`well calibrated classifier <calibration>` should be used.
+56
+57        k_best : int, default=10
+58            The amount of samples to add in each iteration. Only used when
+59            `criterion` is k_best'.
+60
+61        max_iter : int or None, default=10
+62            Maximum number of iterations allowed. Should be greater than or equal
+63            to 0. If it is ``None``, the classifier will continue to predict labels
+64            until no new pseudo-labels are added, or all unlabeled samples have
+65            been labeled.
+66
+67        verbose : bool, default=False
+68            Enable verbose output.
+69
+70        References
+71        ----------
+72        David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling
+73        supervised methods. In Proceedings of the 33rd annual meeting on
+74        Association for Computational Linguistics (ACL '95). Association for
+75        Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:
+76        https://doi.org/10.3115/981658.981684
+77        """
+78        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
+
+ + +

Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

+ +

This class allows a given supervised classifier to function as a +semi-supervised classifier, allowing it to learn from unlabeled data. It +does this by iteratively predicting pseudo-labels for the unlabeled data +and adding them to the training set.

+ +

The classifier will continue iterating until either max_iter is reached, or +no pseudo-labels were added to the training set in the previous iteration.

+ +
Parameters
+ +
    +
  • base_estimator (estimator object): +An estimator object implementing fit and predict_proba. +Invoking the fit method will fit a clone of the passed estimator, +which will be stored in the base_estimator_ attribute.
    • +
  • threshold (float, default=0.75): +The decision threshold for use with criterion='threshold'. +Should be in [0, 1). When using the 'threshold' criterion, a +:ref:well calibrated classifier <calibration> should be used.
    • +
  • criterion ({'threshold', 'k_best'}, default='threshold'): +The selection criterion used to select which labels to add to the +training set. If 'threshold', pseudo-labels with prediction +probabilities above threshold are added to the dataset. If 'k_best', +the k_best pseudo-labels with highest prediction probabilities are +added to the dataset. When using the 'threshold' criterion, a +:ref:well calibrated classifier <calibration> should be used.
    • +
  • k_best (int, default=10): +The amount of samples to add in each iteration. Only used when +criterion is k_best'.
    • +
  • max_iter (int or None, default=10): +Maximum number of iterations allowed. Should be greater than or equal +to 0. If it is None, the classifier will continue to predict labels +until no new pseudo-labels are added, or all unlabeled samples have +been labeled.
    • +
  • verbose (bool, default=False): +Enable verbose output.
    • +
+ +
References
+ +

David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling +supervised methods. In Proceedings of the 33rd annual meeting on +Association for Computational Linguistics (ACL '95). Association for +Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI: +https://doi.org/10.3115/981658.981684

+
+ + +
+
+ +
+ + def + fit(self, X, y): + + + +
+ + 
 80    def fit(self, X, y):
+ 81        """
+ 82        Fits this ``SelfTrainingClassifier`` to a dataset.
+ 83
+ 84        Parameters
+ 85        ----------
+ 86        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 87            Array representing the data.
+ 88
+ 89        y : {array-like, sparse matrix} of shape (n_samples,)
+ 90            Array representing the labels. Unlabeled samples should have the
+ 91            label -1.
+ 92
+ 93        Returns
+ 94        -------
+ 95        self : SelfTrainingClassifier
+ 96            Returns an instance of self.
+ 97        """
+ 98        y_adapted = y.copy()
+ 99        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
+100            y_adapted = y_adapted.astype(object)
+101            y_adapted[y_adapted == '-1'] = -1
+102        return super().fit(X, y_adapted)
+
+ + +

Fits this SelfTrainingClassifier to a dataset.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
  • y ({array-like, sparse matrix} of shape (n_samples,)): +Array representing the labels. Unlabeled samples should have the +label -1.
    • +
+ +
Returns
+ +
    +
  • self (SelfTrainingClassifier): +Returns an instance of self.
    • +
+
+ + +
+
+
Inherited Members
+
+
sklearn.semi_supervised._self_training.SelfTrainingClassifier
+
predict
+
predict_proba
+
decision_function
+
predict_log_proba
+
score
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ +
+ + class + CoTrainingByCommittee(sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator): + + + +
+ + 
 858class CoTrainingByCommittee(ClassifierMixin, BaseEnsemble, BaseEstimator):
+ 859    def __init__(
+ 860        self,
+ 861        ensemble_estimator=BaggingClassifier(),
+ 862        max_iterations=100,
+ 863        poolsize=100,
+ 864        min_instances_for_class=3,
+ 865        random_state=None,
+ 866    ):
+ 867        """
+ 868        Create a committee trained by cotraining based on
+ 869        the diversity of classifiers.
+ 870
+ 871        Parameters
+ 872        ----------
+ 873        ensemble_estimator : ClassifierMixin, optional
+ 874            ensemble method, works without a ensemble as
+ 875            self training with pool, by default BaggingClassifier().
+ 876        max_iterations : int, optional
+ 877            number of iterations of training, -1 if no max iterations, by default 100
+ 878        poolsize : int, optional
+ 879            max number of unlabeled instances candidates to pseudolabel, by default 100
+ 880        random_state : int, RandomState instance, optional
+ 881            controls the randomness of the estimator, by default None
+ 882
+ 883        References
+ 884        ----------
+ 885        M. F. A. Hady and F. Schwenker,
+ 886        "Co-training by Committee: A New Semi-supervised Learning Framework,"
+ 887        2008 IEEE International Conference on Data Mining Workshops,
+ 888        Pisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.
+ 889        """
+ 890        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+ 891        self.max_iterations = max_iterations
+ 892        self.poolsize = poolsize
+ 893        self.random_state = random_state
+ 894        self.min_instances_for_class = min_instances_for_class
+ 895
+ 896    def fit(self, X, y, **kwards):
+ 897        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+ 898        Parameters
+ 899        ----------
+ 900        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 901            The training input samples.
+ 902        y : array-like of shape (n_samples,)
+ 903            The target values (class labels), -1 if unlabel.
+ 904        Returns
+ 905        -------
+ 906        self : CoTrainingByCommittee
+ 907            Fitted estimator.
+ 908        """
+ 909        self.ensemble_estimator = skclone(self.ensemble_estimator)
+ 910        random_state = check_random_state(self.random_state)
+ 911
+ 912        X_label, y_prev, X_unlabel = get_dataset(X, y)
+ 913
+ 914        is_df = isinstance(X_label, pd.DataFrame)
+ 915
+ 916        self.label_encoder_ = LabelEncoder()
+ 917        y_label = self.label_encoder_.fit_transform(y_prev)
+ 918
+ 919        self.classes_ = self.label_encoder_.classes_
+ 920
+ 921        prior = calculate_prior_probability(y_label)
+ 922        permutation = random_state.permutation(len(X_unlabel))
+ 923
+ 924        self.ensemble_estimator.fit(X_label, y_label, **kwards)
+ 925
+ 926        if X_unlabel.shape[0] == 0:
+ 927            return self
+ 928
+ 929        for _ in range(self.max_iterations):
+ 930            if len(permutation) == 0:
+ 931                break
+ 932            raw_predictions = self.ensemble_estimator.predict_proba(
+ 933                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+ 934            )
+ 935
+ 936            predictions = np.max(raw_predictions, axis=1)
+ 937            class_predicted = np.argmax(raw_predictions, axis=1)
+ 938
+ 939            added = np.zeros(predictions.shape, dtype=bool)
+ 940            # First the n (or less) most confidence instances will be selected
+ 941            for c in self.ensemble_estimator.classes_:
+ 942                condition = class_predicted == c
+ 943
+ 944                candidates = predictions[condition]
+ 945                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+ 946                candidates_sub_set = candidates_bool[condition]
+ 947
+ 948                instances_index_selected = candidates.argsort(kind="mergesort")[
+ 949                    -self.min_instances_for_class:
+ 950                ]
+ 951
+ 952                candidates_sub_set[instances_index_selected] = True
+ 953                candidates_bool[condition] += candidates_sub_set
+ 954
+ 955                added[candidates_bool] = True
+ 956
+ 957            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+ 958            # Pero si se añaden ya en el proceso de proporción no se duplica.
+ 959
+ 960            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+ 961            to_label = choice_with_proportion(
+ 962                predictions, class_predicted, prior, extra=self.min_instances_for_class
+ 963            )
+ 964            added[to_label] = True
+ 965
+ 966            index = permutation[0: self.poolsize][added]
+ 967            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+ 968                [X_label, X_unlabel.iloc[index, :]]
+ 969            )
+ 970            pseudoy = class_predicted[added]
+ 971
+ 972            y_label = np.append(y_label, pseudoy)
+ 973            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+ 974
+ 975            self.ensemble_estimator.fit(X_label, y_label, **kwards)
+ 976
+ 977        return self
+ 978
+ 979    def predict(self, X):
+ 980        """Predict class value for X.
+ 981        For a classification model, the predicted class for each sample in X is returned.
+ 982        Parameters
+ 983        ----------
+ 984        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 985            The input samples.
+ 986        Returns
+ 987        -------
+ 988        y : array-like of shape (n_samples,)
+ 989            The predicted classes
+ 990        """
+ 991        check_is_fitted(self.ensemble_estimator)
+ 992        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+ 993
+ 994    def predict_proba(self, X):
+ 995        """Predict class probabilities of the input samples X.
+ 996        The predicted class probability depends on the ensemble estimator.
+ 997        Parameters
+ 998        ----------
+ 999        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1000            The input samples.
+1001        Returns
+1002        -------
+1003        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1004            The predicted classes
+1005        """
+1006        check_is_fitted(self.ensemble_estimator)
+1007        return self.ensemble_estimator.predict_proba(X)
+1008
+1009    def score(self, X, y, sample_weight=None):
+1010        """Return the mean accuracy on the given test data and labels.
+1011        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1012        Parameters
+1013        ----------
+1014        X : array-like of shape (n_samples, n_features)
+1015            Test samples.
+1016        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1017            True labels for X.
+1018        sample_weight : array-like of shape (n_samples,), optional
+1019            Sample weights., by default None
+1020        Returns
+1021        -------
+1022        score: float
+1023            Mean accuracy of self.predict(X) wrt. y.
+1024        """
+1025        try:
+1026            y = self.label_encoder_.transform(y)
+1027        except ValueError:
+1028            if "le_dict_" not in dir(self):
+1029                self.le_dict_ = dict(
+1030                    zip(
+1031                        self.label_encoder_.classes_,
+1032                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1033                    )
+1034                )
+1035            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1036
+1037        return self.ensemble_estimator.score(X, y, sample_weight)
+
+ + +

Mixin class for all classifiers in scikit-learn.

+
+ + +
+ +
+ + CoTrainingByCommittee( ensemble_estimator=BaggingClassifier(), max_iterations=100, poolsize=100, min_instances_for_class=3, random_state=None) + + + +
+ + 
859    def __init__(
+860        self,
+861        ensemble_estimator=BaggingClassifier(),
+862        max_iterations=100,
+863        poolsize=100,
+864        min_instances_for_class=3,
+865        random_state=None,
+866    ):
+867        """
+868        Create a committee trained by cotraining based on
+869        the diversity of classifiers.
+870
+871        Parameters
+872        ----------
+873        ensemble_estimator : ClassifierMixin, optional
+874            ensemble method, works without a ensemble as
+875            self training with pool, by default BaggingClassifier().
+876        max_iterations : int, optional
+877            number of iterations of training, -1 if no max iterations, by default 100
+878        poolsize : int, optional
+879            max number of unlabeled instances candidates to pseudolabel, by default 100
+880        random_state : int, RandomState instance, optional
+881            controls the randomness of the estimator, by default None
+882
+883        References
+884        ----------
+885        M. F. A. Hady and F. Schwenker,
+886        "Co-training by Committee: A New Semi-supervised Learning Framework,"
+887        2008 IEEE International Conference on Data Mining Workshops,
+888        Pisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.
+889        """
+890        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+891        self.max_iterations = max_iterations
+892        self.poolsize = poolsize
+893        self.random_state = random_state
+894        self.min_instances_for_class = min_instances_for_class
+
+ + +

Create a committee trained by cotraining based on +the diversity of classifiers.

+ +
Parameters
+ +
    +
  • ensemble_estimator (ClassifierMixin, optional): +ensemble method, works without a ensemble as +self training with pool, by default BaggingClassifier().
    • +
  • max_iterations (int, optional): +number of iterations of training, -1 if no max iterations, by default 100
    • +
  • poolsize (int, optional): +max number of unlabeled instances candidates to pseudolabel, by default 100
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
References
+ +

M. F. A. Hady and F. Schwenker, +"Co-training by Committee: A New Semi-supervised Learning Framework," +2008 IEEE International Conference on Data Mining Workshops, +Pisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwards): + + + +
+ + 
896    def fit(self, X, y, **kwards):
+897        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+898        Parameters
+899        ----------
+900        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+901            The training input samples.
+902        y : array-like of shape (n_samples,)
+903            The target values (class labels), -1 if unlabel.
+904        Returns
+905        -------
+906        self : CoTrainingByCommittee
+907            Fitted estimator.
+908        """
+909        self.ensemble_estimator = skclone(self.ensemble_estimator)
+910        random_state = check_random_state(self.random_state)
+911
+912        X_label, y_prev, X_unlabel = get_dataset(X, y)
+913
+914        is_df = isinstance(X_label, pd.DataFrame)
+915
+916        self.label_encoder_ = LabelEncoder()
+917        y_label = self.label_encoder_.fit_transform(y_prev)
+918
+919        self.classes_ = self.label_encoder_.classes_
+920
+921        prior = calculate_prior_probability(y_label)
+922        permutation = random_state.permutation(len(X_unlabel))
+923
+924        self.ensemble_estimator.fit(X_label, y_label, **kwards)
+925
+926        if X_unlabel.shape[0] == 0:
+927            return self
+928
+929        for _ in range(self.max_iterations):
+930            if len(permutation) == 0:
+931                break
+932            raw_predictions = self.ensemble_estimator.predict_proba(
+933                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+934            )
+935
+936            predictions = np.max(raw_predictions, axis=1)
+937            class_predicted = np.argmax(raw_predictions, axis=1)
+938
+939            added = np.zeros(predictions.shape, dtype=bool)
+940            # First the n (or less) most confidence instances will be selected
+941            for c in self.ensemble_estimator.classes_:
+942                condition = class_predicted == c
+943
+944                candidates = predictions[condition]
+945                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+946                candidates_sub_set = candidates_bool[condition]
+947
+948                instances_index_selected = candidates.argsort(kind="mergesort")[
+949                    -self.min_instances_for_class:
+950                ]
+951
+952                candidates_sub_set[instances_index_selected] = True
+953                candidates_bool[condition] += candidates_sub_set
+954
+955                added[candidates_bool] = True
+956
+957            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+958            # Pero si se añaden ya en el proceso de proporción no se duplica.
+959
+960            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+961            to_label = choice_with_proportion(
+962                predictions, class_predicted, prior, extra=self.min_instances_for_class
+963            )
+964            added[to_label] = True
+965
+966            index = permutation[0: self.poolsize][added]
+967            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+968                [X_label, X_unlabel.iloc[index, :]]
+969            )
+970            pseudoy = class_predicted[added]
+971
+972            y_label = np.append(y_label, pseudoy)
+973            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+974
+975            self.ensemble_estimator.fit(X_label, y_label, **kwards)
+976
+977        return self
+
+ + +

Build a CoTrainingByCommittee classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabel.
    • +
+ +
Returns
+ +
    +
  • self (CoTrainingByCommittee): +Fitted estimator.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X): + + + +
+ + 
979    def predict(self, X):
+980        """Predict class value for X.
+981        For a classification model, the predicted class for each sample in X is returned.
+982        Parameters
+983        ----------
+984        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+985            The input samples.
+986        Returns
+987        -------
+988        y : array-like of shape (n_samples,)
+989            The predicted classes
+990        """
+991        check_is_fitted(self.ensemble_estimator)
+992        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+
+ + +

Predict class value for X. +For a classification model, the predicted class for each sample in X is returned.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • y (array-like of shape (n_samples,)): +The predicted classes
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X): + + + +
+ + 
 994    def predict_proba(self, X):
+ 995        """Predict class probabilities of the input samples X.
+ 996        The predicted class probability depends on the ensemble estimator.
+ 997        Parameters
+ 998        ----------
+ 999        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1000            The input samples.
+1001        Returns
+1002        -------
+1003        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1004            The predicted classes
+1005        """
+1006        check_is_fitted(self.ensemble_estimator)
+1007        return self.ensemble_estimator.predict_proba(X)
+
+ + +

Predict class probabilities of the input samples X. +The predicted class probability depends on the ensemble estimator.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1): +The predicted classes
    • +
+
+ + +
+
+ +
+ + def + score(self, X, y, sample_weight=None): + + + +
+ + 
1009    def score(self, X, y, sample_weight=None):
+1010        """Return the mean accuracy on the given test data and labels.
+1011        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1012        Parameters
+1013        ----------
+1014        X : array-like of shape (n_samples, n_features)
+1015            Test samples.
+1016        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1017            True labels for X.
+1018        sample_weight : array-like of shape (n_samples,), optional
+1019            Sample weights., by default None
+1020        Returns
+1021        -------
+1022        score: float
+1023            Mean accuracy of self.predict(X) wrt. y.
+1024        """
+1025        try:
+1026            y = self.label_encoder_.transform(y)
+1027        except ValueError:
+1028            if "le_dict_" not in dir(self):
+1029                self.le_dict_ = dict(
+1030                    zip(
+1031                        self.label_encoder_.classes_,
+1032                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1033                    )
+1034                )
+1035            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1036
+1037        return self.ensemble_estimator.score(X, y, sample_weight)
+
+ + +

Return the mean accuracy on the given test data and labels. +In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

+ +
Parameters
+ +
    +
  • X (array-like of shape (n_samples, n_features)): +Test samples.
    • +
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)): +True labels for X.
    • +
  • sample_weight (array-like of shape (n_samples,), optional): +Sample weights., by default None
    • +
+ +
Returns
+ +
    +
  • score (float): +Mean accuracy of self.predict(X) wrt. y.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ +
+ + class + Rasco(sslearn.wrapper._co.BaseCoTraining): + + + +
+ + 
636class Rasco(BaseCoTraining):
+637    def __init__(
+638        self,
+639        base_estimator=DecisionTreeClassifier(),
+640        max_iterations=10,
+641        n_estimators=30,
+642        subspace_size=None,
+643        random_state=None,
+644        n_jobs=None,
+645    ):
+646        """
+647        Co-Training based on random subspaces
+648
+649        Parameters
+650        ----------
+651        base_estimator : ClassifierMixin, optional
+652            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+653        max_iterations : int, optional
+654            Maximum number of iterations allowed. Should be greater than or equal to 0.
+655            If is -1 then will be infinite iterations until U be empty, by default 10
+656        n_estimators : int, optional
+657            The number of base estimators in the ensemble., by default 30
+658        subspace_size : int, optional
+659            The number of features for each subspace. If it is None will be the half of the features size., by default None
+660        random_state : int, RandomState instance, optional
+661            controls the randomness of the estimator, by default None
+662
+663        References
+664        ----------
+665        Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).
+666        A random subspace method for co-training.
+667        In <i>2008 IEEE International Joint Conference on Neural Networks</i>
+668        (IEEE World Congress on Computational Intelligence)
+669        (pp. 195-200). IEEE.
+670        """
+671        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
+672        self.max_iterations = max_iterations  # J in paper
+673        self.n_estimators = n_estimators  # K in paper
+674        self.subspace_size = subspace_size  # m in paper
+675        self.n_jobs = check_n_jobs(n_jobs)
+676
+677        self.random_state = random_state
+678
+679    def _generate_random_subspaces(self, X, y=None, random_state=None):
+680        """Generate the random subspaces
+681
+682        Parameters
+683        ----------
+684        X : array like
+685            Labeled dataset
+686        y : array like, optional
+687            Target for each X, not needed on Rasco, by default None
+688
+689        Returns
+690        -------
+691        subspaces : list
+692            List of index of features
+693        """
+694        random_state = check_random_state(random_state)
+695        features = list(range(X.shape[1]))
+696        idxs = []
+697        for _ in range(self.n_estimators):
+698            idxs.append(random_state.permutation(features)[: self.subspace_size])
+699        return idxs
+700
+701    def _fit_estimator(self, X, y, i, **kwards):
+702        estimator = self.base_estimator
+703        if type(self.base_estimator) == list:
+704            estimator = skclone(self.base_estimator[i])
+705        return skclone(estimator).fit(X, y, **kwards)
+706
+707    def fit(self, X, y, **kwards):
+708        """Build a Rasco classifier from the training set (X, y).
+709
+710        Parameters
+711        ----------
+712        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+713            The training input samples.
+714        y : array-like of shape (n_samples,)
+715            The target values (class labels), -1 if unlabel.
+716
+717        Returns
+718        -------
+719        self: Rasco
+720            Fitted estimator.
+721        """
+722        X_label, y_label, X_unlabel = get_dataset(X, y)
+723        self.classes_ = np.unique(y_label)
+724
+725        is_df = isinstance(X_label, pd.DataFrame)
+726
+727        random_state = check_random_state(self.random_state)
+728
+729        self.classes_ = np.unique(y_label)
+730        number_per_class = calc_number_per_class(y_label)
+731
+732        if self.subspace_size is None:
+733            self.subspace_size = int(X.shape[1] / 2)
+734        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
+735
+736        cfs = Parallel(n_jobs=self.n_jobs)(
+737            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+738            for i in range(self.n_estimators)
+739        )
+740
+741        it = 0
+742        while True:
+743            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
+744                X_unlabel
+745            ) == 0:
+746                break
+747
+748            raw_predicions = []
+749            for i in range(self.n_estimators):
+750                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
+751                raw_predicions.append(rp)
+752            raw_predicions = sum(raw_predicions) / self.n_estimators
+753            predictions = np.max(raw_predicions, axis=1)
+754            class_predicted = np.argmax(raw_predicions, axis=1)
+755            pseudoy = self.classes_.take(class_predicted, axis=0)
+756
+757            final_instances = list()
+758            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
+759            for c in self.classes_:
+760                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
+761
+762            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
+763            yj = pseudoy[final_instances]
+764
+765            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
+766            y_label = np.append(y_label, yj)
+767            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
+768
+769            cfs = Parallel(n_jobs=self.n_jobs)(
+770                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+771                for i in range(self.n_estimators)
+772            )
+773
+774            it += 1
+775
+776        self.h_ = cfs
+777        self.columns_ = idxs
+778
+779        return self
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + Rasco( base_estimator=DecisionTreeClassifier(), max_iterations=10, n_estimators=30, subspace_size=None, random_state=None, n_jobs=None) + + + +
+ + 
637    def __init__(
+638        self,
+639        base_estimator=DecisionTreeClassifier(),
+640        max_iterations=10,
+641        n_estimators=30,
+642        subspace_size=None,
+643        random_state=None,
+644        n_jobs=None,
+645    ):
+646        """
+647        Co-Training based on random subspaces
+648
+649        Parameters
+650        ----------
+651        base_estimator : ClassifierMixin, optional
+652            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+653        max_iterations : int, optional
+654            Maximum number of iterations allowed. Should be greater than or equal to 0.
+655            If is -1 then will be infinite iterations until U be empty, by default 10
+656        n_estimators : int, optional
+657            The number of base estimators in the ensemble., by default 30
+658        subspace_size : int, optional
+659            The number of features for each subspace. If it is None will be the half of the features size., by default None
+660        random_state : int, RandomState instance, optional
+661            controls the randomness of the estimator, by default None
+662
+663        References
+664        ----------
+665        Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).
+666        A random subspace method for co-training.
+667        In <i>2008 IEEE International Joint Conference on Neural Networks</i>
+668        (IEEE World Congress on Computational Intelligence)
+669        (pp. 195-200). IEEE.
+670        """
+671        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
+672        self.max_iterations = max_iterations  # J in paper
+673        self.n_estimators = n_estimators  # K in paper
+674        self.subspace_size = subspace_size  # m in paper
+675        self.n_jobs = check_n_jobs(n_jobs)
+676
+677        self.random_state = random_state
+
+ + +

Co-Training based on random subspaces

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • max_iterations (int, optional): +Maximum number of iterations allowed. Should be greater than or equal to 0. +If is -1 then will be infinite iterations until U be empty, by default 10
    • +
  • n_estimators (int, optional): +The number of base estimators in the ensemble., by default 30
    • +
  • subspace_size (int, optional): +The number of features for each subspace. If it is None will be the half of the features size., by default None
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
References
+ +

Wang, J., Luo, S. W., & Zeng, X. H. (2008, June). +A random subspace method for co-training. +In 2008 IEEE International Joint Conference on Neural Networks +(IEEE World Congress on Computational Intelligence) +(pp. 195-200). IEEE.

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwards): + + + +
+ + 
707    def fit(self, X, y, **kwards):
+708        """Build a Rasco classifier from the training set (X, y).
+709
+710        Parameters
+711        ----------
+712        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+713            The training input samples.
+714        y : array-like of shape (n_samples,)
+715            The target values (class labels), -1 if unlabel.
+716
+717        Returns
+718        -------
+719        self: Rasco
+720            Fitted estimator.
+721        """
+722        X_label, y_label, X_unlabel = get_dataset(X, y)
+723        self.classes_ = np.unique(y_label)
+724
+725        is_df = isinstance(X_label, pd.DataFrame)
+726
+727        random_state = check_random_state(self.random_state)
+728
+729        self.classes_ = np.unique(y_label)
+730        number_per_class = calc_number_per_class(y_label)
+731
+732        if self.subspace_size is None:
+733            self.subspace_size = int(X.shape[1] / 2)
+734        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
+735
+736        cfs = Parallel(n_jobs=self.n_jobs)(
+737            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+738            for i in range(self.n_estimators)
+739        )
+740
+741        it = 0
+742        while True:
+743            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
+744                X_unlabel
+745            ) == 0:
+746                break
+747
+748            raw_predicions = []
+749            for i in range(self.n_estimators):
+750                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
+751                raw_predicions.append(rp)
+752            raw_predicions = sum(raw_predicions) / self.n_estimators
+753            predictions = np.max(raw_predicions, axis=1)
+754            class_predicted = np.argmax(raw_predicions, axis=1)
+755            pseudoy = self.classes_.take(class_predicted, axis=0)
+756
+757            final_instances = list()
+758            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
+759            for c in self.classes_:
+760                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
+761
+762            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
+763            yj = pseudoy[final_instances]
+764
+765            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
+766            y_label = np.append(y_label, yj)
+767            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
+768
+769            cfs = Parallel(n_jobs=self.n_jobs)(
+770                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+771                for i in range(self.n_estimators)
+772            )
+773
+774            it += 1
+775
+776        self.h_ = cfs
+777        self.columns_ = idxs
+778
+779        return self
+
+ + +

Build a Rasco classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabel.
    • +
+ +
Returns
+ +
    +
  • self (Rasco): +Fitted estimator.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ +
+ + class + RelRasco(sslearn.wrapper.Rasco): + + + +
+ + 
782class RelRasco(Rasco):
+783    def __init__(
+784        self,
+785        base_estimator=DecisionTreeClassifier(),
+786        max_iterations=10,
+787        n_estimators=30,
+788        subspace_size=None,
+789        random_state=None,
+790        n_jobs=None,
+791    ):
+792        """
+793        Co-Training with relevant random subspaces
+794
+795        Parameters
+796        ----------
+797        base_estimator : ClassifierMixin, optional
+798            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+799        max_iterations : int, optional
+800            Maximum number of iterations allowed. Should be greater than or equal to 0.
+801            If is -1 then will be infinite iterations until U be empty, by default 10
+802        n_estimators : int, optional
+803            The number of base estimators in the ensemble., by default 30
+804        subspace_size : int, optional
+805            The number of features for each subspace. If it is None will be the half of the features size., by default None
+806        random_state : int, RandomState instance, optional
+807            controls the randomness of the estimator, by default None
+808        n_jobs : int, optional
+809            The number of jobs to run in parallel. -1 means using all processors., by default None
+810
+811        References
+812        ----------
+813        Yaslan, Y., & Cataltepe, Z. (2010).
+814        Co-training with relevant random subspaces.
+815        <i>Neurocomputing</i>, 73(10-12), 1652-1661.
+816        """
+817        super().__init__(
+818            base_estimator,
+819            max_iterations,
+820            n_estimators,
+821            subspace_size,
+822            random_state,
+823            n_jobs,
+824        )
+825
+826    def _generate_random_subspaces(self, X, y, random_state=None):
+827        """Generate the relevant random subspcaes
+828
+829        Parameters
+830        ----------
+831        X : array like
+832            Labeled dataset
+833        y : array like, optional
+834            Target for each X, only needed on Rel-Rasco, by default None
+835
+836        Returns
+837        -------
+838        subspaces: list
+839            List of index of features
+840        """
+841        random_state = check_random_state(random_state)
+842        relevance = mutual_info_classif(X, y, random_state=random_state)
+843        idxs = []
+844        for _ in range(self.n_estimators):
+845            subspace = []
+846            for __ in range(self.subspace_size):
+847                f1 = random_state.randint(0, X.shape[1])
+848                f2 = random_state.randint(0, X.shape[1])
+849                if relevance[f1] > relevance[f2]:
+850                    subspace.append(f1)
+851                else:
+852                    subspace.append(f2)
+853            idxs.append(subspace)
+854        return idxs
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + RelRasco( base_estimator=DecisionTreeClassifier(), max_iterations=10, n_estimators=30, subspace_size=None, random_state=None, n_jobs=None) + + + +
+ + 
783    def __init__(
+784        self,
+785        base_estimator=DecisionTreeClassifier(),
+786        max_iterations=10,
+787        n_estimators=30,
+788        subspace_size=None,
+789        random_state=None,
+790        n_jobs=None,
+791    ):
+792        """
+793        Co-Training with relevant random subspaces
+794
+795        Parameters
+796        ----------
+797        base_estimator : ClassifierMixin, optional
+798            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+799        max_iterations : int, optional
+800            Maximum number of iterations allowed. Should be greater than or equal to 0.
+801            If is -1 then will be infinite iterations until U be empty, by default 10
+802        n_estimators : int, optional
+803            The number of base estimators in the ensemble., by default 30
+804        subspace_size : int, optional
+805            The number of features for each subspace. If it is None will be the half of the features size., by default None
+806        random_state : int, RandomState instance, optional
+807            controls the randomness of the estimator, by default None
+808        n_jobs : int, optional
+809            The number of jobs to run in parallel. -1 means using all processors., by default None
+810
+811        References
+812        ----------
+813        Yaslan, Y., & Cataltepe, Z. (2010).
+814        Co-training with relevant random subspaces.
+815        <i>Neurocomputing</i>, 73(10-12), 1652-1661.
+816        """
+817        super().__init__(
+818            base_estimator,
+819            max_iterations,
+820            n_estimators,
+821            subspace_size,
+822            random_state,
+823            n_jobs,
+824        )
+
+ + +

Co-Training with relevant random subspaces

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • max_iterations (int, optional): +Maximum number of iterations allowed. Should be greater than or equal to 0. +If is -1 then will be infinite iterations until U be empty, by default 10
    • +
  • n_estimators (int, optional): +The number of base estimators in the ensemble., by default 30
    • +
  • subspace_size (int, optional): +The number of features for each subspace. If it is None will be the half of the features size., by default None
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
  • n_jobs (int, optional): +The number of jobs to run in parallel. -1 means using all processors., by default None
    • +
+ +
References
+ +

Yaslan, Y., & Cataltepe, Z. (2010). +Co-training with relevant random subspaces. +Neurocomputing, 73(10-12), 1652-1661.

+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
Rasco
+
fit
+ +
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ +
+ + class + TriTraining(sslearn.wrapper._co.BaseCoTraining): + + + +
+ + 
 25class TriTraining(BaseCoTraining):
+ 26
+ 27    def __init__(
+ 28        self,
+ 29        base_estimator=DecisionTreeClassifier(),
+ 30        n_samples=None,
+ 31        random_state=None,
+ 32        n_jobs=None,
+ 33    ):
+ 34        """TriTraining. Trio of classifiers with bootstrapping.
+ 35
+ 36        Parameters
+ 37        ----------
+ 38        base_estimator : ClassifierMixin, optional
+ 39            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+ 40        n_samples : int, optional
+ 41            Number of samples to generate.
+ 42            If left to None this is automatically set to the first dimension of the arrays., by default None
+ 43        random_state : int, RandomState instance, optional
+ 44            controls the randomness of the estimator, by default None
+ 45        n_jobs : int, optional
+ 46            The number of jobs to run in parallel for both `fit` and `predict`.
+ 47            `None` means 1 unless in a :obj:`joblib.parallel_backend` context.
+ 48            `-1` means using all processors., by default None
+ 49
+ 50        References
+ 51        ----------
+ 52        Zhi-Hua Zhou and Ming Li,
+ 53        "Tri-training: exploiting unlabeled data using three classifiers,"
+ 54        in <i>IEEE Transactions on Knowledge and Data Engineering</i>,
+ 55        vol. 17, no. 11, pp. 1529-1541, Nov. 2005,
+ 56        doi: 10.1109/TKDE.2005.186.
+ 57        """
+ 58        self._N_LEARNER = 3
+ 59        self.base_estimator = check_classifier(base_estimator, collection_size=self._N_LEARNER)
+ 60        self.n_samples = n_samples
+ 61        self._epsilon = sys.float_info.epsilon
+ 62        self.random_state = random_state
+ 63        self.n_jobs = n_jobs
+ 64
+ 65    def fit(self, X, y, **kwards):
+ 66        """Build a TriTraining classifier from the training set (X, y).
+ 67        Parameters
+ 68        ----------
+ 69        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 70            The training input samples.
+ 71        y : array-like of shape (n_samples,)
+ 72            The target values (class labels), -1 if unlabeled.
+ 73        Returns
+ 74        -------
+ 75        self : TriTraining
+ 76            Fitted estimator.
+ 77        """
+ 78        random_state = check_random_state(self.random_state)
+ 79        self.n_jobs = min(check_n_jobs(self.n_jobs), self._N_LEARNER)
+ 80
+ 81        X_label, y_label, X_unlabel = get_dataset(X, y)
+ 82
+ 83        is_df = isinstance(X_label, pd.DataFrame)
+ 84
+ 85        hypotheses = []
+ 86        e_ = [0.5] * self._N_LEARNER
+ 87        l_ = [0] * self._N_LEARNER
+ 88
+ 89        # Get a random instance for each class to keep class index
+ 90        self.classes_ = np.unique(y_label)
+ 91        classes = set(self.classes_)
+ 92        instances = list()
+ 93        labels = list()
+ 94        iteration = zip(X_label, y_label)
+ 95        if is_df:
+ 96            iteration = zip(X_label.values, y_label)
+ 97        for x_, y_ in iteration:
+ 98            if y_ in classes:
+ 99                classes.remove(y_)
+100                instances.append(x_)
+101                labels.append(y_)
+102            if len(classes) == 0:
+103                break
+104
+105        for i in range(self._N_LEARNER):
+106            X_sampled, y_sampled = resample(
+107                X_label,
+108                y_label,
+109                replace=True,
+110                n_samples=self.n_samples,
+111                random_state=random_state,
+112            )
+113
+114            if is_df:
+115                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+116                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled])
+117            else:
+118                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+119            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+120
+121            hypotheses.append(
+122                skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(X_sampled, y_sampled, **kwards)
+123            )
+124
+125        something_has_changed = True if X_unlabel.size > 0 else False
+126        while something_has_changed:
+127            something_has_changed = False
+128            L = [[]] * self._N_LEARNER
+129            Ly = [[]] * self._N_LEARNER
+130            e = []
+131            updates = [False] * 3
+132
+133            for i in range(self._N_LEARNER):
+134                hj, hk = TriTraining._another_hs(hypotheses, i)
+135                e.append(
+136                    self._measure_error(X_label, y_label, hj, hk, self._epsilon)
+137                )
+138                if e_[i] <= e[i]:
+139                    continue
+140                y_p = hj.predict(X_unlabel)
+141                validx = y_p == hk.predict(X_unlabel)
+142                L[i] = X_unlabel[validx]
+143                Ly[i] = y_p[validx]
+144
+145                if l_[i] == 0:
+146                    l_[i] = math.floor(
+147                        safe_division(e[i], (e_[i] - e[i]), self._epsilon) + 1
+148                    )
+149                if l_[i] >= len(L[i]):
+150                    continue
+151                if e[i] * len(L[i]) < e_[i] * l_[i]:
+152                    updates[i] = True
+153                elif l_[i] > safe_division(e[i], e_[i] - e[i], self._epsilon):
+154                    L[i], Ly[i] = TriTraining._subsample(
+155                        (L[i], Ly[i]),
+156                        math.ceil(
+157                            safe_division(e_[i] * l_[i], e[i], self._epsilon) - 1
+158                        ),
+159                        random_state,
+160                    )
+161                    if is_df:
+162                        L[i] = pd.DataFrame(L[i], columns=X_label.columns)
+163                    updates[i] = True
+164
+165            hypotheses = Parallel(n_jobs=self.n_jobs)(
+166                delayed(self._fit_estimator)(
+167                    hypotheses[i], X_label, y_label, L[i], Ly[i], updates[i], **kwards
+168                )
+169                for i in range(self._N_LEARNER)
+170            )
+171
+172            for i in range(self._N_LEARNER):
+173                if updates[i]:
+174                    e_[i] = e[i]
+175                    l_[i] = len(L[i])
+176                    something_has_changed = True
+177
+178        self.h_ = hypotheses
+179        self.columns_ = [list(range(X.shape[1]))] * self._N_LEARNER
+180
+181        return self
+182
+183    def _fit_estimator(self, hyp, X_label, y_label, L, Ly, update, **kwards):
+184        if update:
+185            if isinstance(L, pd.DataFrame):
+186                _tempL = pd.concat([X_label, L])
+187            else:
+188                _tempL = np.concatenate((X_label, L))
+189            _tempY = np.concatenate((y_label, Ly))
+190
+191            return hyp.fit(_tempL, _tempY, **kwards)
+192        return hyp
+193
+194    @staticmethod
+195    def _another_hs(hs, index):
+196        """Get the other hypotheses
+197        Parameters
+198        ----------
+199        hs : list
+200            hypotheses collection
+201        index : int
+202            base hypothesis  index
+203        Returns
+204        -------
+205        classifiers: list
+206            Collection of other hypotheses
+207        """
+208        another_hs = []
+209        for i in range(len(hs)):
+210            if i != index:
+211                another_hs.append(hs[i])
+212        return another_hs
+213
+214    @staticmethod
+215    def _subsample(L, s, random_state=None):
+216        """Randomly removes |L| - s number of examples from L
+217        Parameters
+218        ----------
+219        L : tuple of array-like
+220            Collection pseudo-labeled candidates and its labels
+221        s : int
+222            Equation 10 in paper
+223        random_state : int, RandomState instance, optional
+224            controls the randomness of the estimator, by default None
+225        Returns
+226        -------
+227        subsamples: tuple
+228            Collection of pseudo-labeled selected for enlarged labeled examples.
+229        """
+230        to_remove = len(L[0]) - s
+231        select = len(L[0]) - to_remove
+232
+233        return resample(*L, replace=False, n_samples=select, random_state=random_state)
+234
+235    def _measure_error(
+236        self, X, y, h1: ClassifierMixin, h2: ClassifierMixin, epsilon=sys.float_info.epsilon, **kwards
+237    ):
+238        """Calculate the error between two hypotheses
+239        Parameters
+240        ----------
+241        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+242            The training labeled input samples.
+243        y : array-like of shape (n_samples,)
+244            The target values (class labels).
+245        h1 : ClassifierMixin
+246            First hypothesis
+247        h2 : ClassifierMixin
+248            Second hypothesis
+249        epsilon : float
+250            A small number to avoid division by zero
+251        Returns
+252        -------
+253        error : float
+254            Division of the number of labeled examples on which both h1 and h2 make incorrect classification,
+255            by the number of labeled examples on which the classification made by h1 is the same as that made by h2.
+256        """
+257        y1 = h1.predict(X)
+258        y2 = h2.predict(X)
+259
+260        error = np.count_nonzero(np.logical_and(y1 == y2, y2 != y))
+261        coincidence = np.count_nonzero(y1 == y2)
+262        return safe_division(error, coincidence, epsilon)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + TriTraining( base_estimator=DecisionTreeClassifier(), n_samples=None, random_state=None, n_jobs=None) + + + +
+ + 
27    def __init__(
+28        self,
+29        base_estimator=DecisionTreeClassifier(),
+30        n_samples=None,
+31        random_state=None,
+32        n_jobs=None,
+33    ):
+34        """TriTraining. Trio of classifiers with bootstrapping.
+35
+36        Parameters
+37        ----------
+38        base_estimator : ClassifierMixin, optional
+39            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+40        n_samples : int, optional
+41            Number of samples to generate.
+42            If left to None this is automatically set to the first dimension of the arrays., by default None
+43        random_state : int, RandomState instance, optional
+44            controls the randomness of the estimator, by default None
+45        n_jobs : int, optional
+46            The number of jobs to run in parallel for both `fit` and `predict`.
+47            `None` means 1 unless in a :obj:`joblib.parallel_backend` context.
+48            `-1` means using all processors., by default None
+49
+50        References
+51        ----------
+52        Zhi-Hua Zhou and Ming Li,
+53        "Tri-training: exploiting unlabeled data using three classifiers,"
+54        in <i>IEEE Transactions on Knowledge and Data Engineering</i>,
+55        vol. 17, no. 11, pp. 1529-1541, Nov. 2005,
+56        doi: 10.1109/TKDE.2005.186.
+57        """
+58        self._N_LEARNER = 3
+59        self.base_estimator = check_classifier(base_estimator, collection_size=self._N_LEARNER)
+60        self.n_samples = n_samples
+61        self._epsilon = sys.float_info.epsilon
+62        self.random_state = random_state
+63        self.n_jobs = n_jobs
+
+ + +

TriTraining. Trio of classifiers with bootstrapping.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • n_samples (int, optional): +Number of samples to generate. +If left to None this is automatically set to the first dimension of the arrays., by default None
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
  • n_jobs (int, optional): +The number of jobs to run in parallel for both fit and predict. +None means 1 unless in a joblib.parallel_backend context. +-1 means using all processors., by default None
    • +
+ +
References
+ +

Zhi-Hua Zhou and Ming Li, +"Tri-training: exploiting unlabeled data using three classifiers," +in IEEE Transactions on Knowledge and Data Engineering, +vol. 17, no. 11, pp. 1529-1541, Nov. 2005, +doi: 10.1109/TKDE.2005.186.

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwards): + + + +
+ + 
 65    def fit(self, X, y, **kwards):
+ 66        """Build a TriTraining classifier from the training set (X, y).
+ 67        Parameters
+ 68        ----------
+ 69        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 70            The training input samples.
+ 71        y : array-like of shape (n_samples,)
+ 72            The target values (class labels), -1 if unlabeled.
+ 73        Returns
+ 74        -------
+ 75        self : TriTraining
+ 76            Fitted estimator.
+ 77        """
+ 78        random_state = check_random_state(self.random_state)
+ 79        self.n_jobs = min(check_n_jobs(self.n_jobs), self._N_LEARNER)
+ 80
+ 81        X_label, y_label, X_unlabel = get_dataset(X, y)
+ 82
+ 83        is_df = isinstance(X_label, pd.DataFrame)
+ 84
+ 85        hypotheses = []
+ 86        e_ = [0.5] * self._N_LEARNER
+ 87        l_ = [0] * self._N_LEARNER
+ 88
+ 89        # Get a random instance for each class to keep class index
+ 90        self.classes_ = np.unique(y_label)
+ 91        classes = set(self.classes_)
+ 92        instances = list()
+ 93        labels = list()
+ 94        iteration = zip(X_label, y_label)
+ 95        if is_df:
+ 96            iteration = zip(X_label.values, y_label)
+ 97        for x_, y_ in iteration:
+ 98            if y_ in classes:
+ 99                classes.remove(y_)
+100                instances.append(x_)
+101                labels.append(y_)
+102            if len(classes) == 0:
+103                break
+104
+105        for i in range(self._N_LEARNER):
+106            X_sampled, y_sampled = resample(
+107                X_label,
+108                y_label,
+109                replace=True,
+110                n_samples=self.n_samples,
+111                random_state=random_state,
+112            )
+113
+114            if is_df:
+115                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+116                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled])
+117            else:
+118                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+119            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+120
+121            hypotheses.append(
+122                skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(X_sampled, y_sampled, **kwards)
+123            )
+124
+125        something_has_changed = True if X_unlabel.size > 0 else False
+126        while something_has_changed:
+127            something_has_changed = False
+128            L = [[]] * self._N_LEARNER
+129            Ly = [[]] * self._N_LEARNER
+130            e = []
+131            updates = [False] * 3
+132
+133            for i in range(self._N_LEARNER):
+134                hj, hk = TriTraining._another_hs(hypotheses, i)
+135                e.append(
+136                    self._measure_error(X_label, y_label, hj, hk, self._epsilon)
+137                )
+138                if e_[i] <= e[i]:
+139                    continue
+140                y_p = hj.predict(X_unlabel)
+141                validx = y_p == hk.predict(X_unlabel)
+142                L[i] = X_unlabel[validx]
+143                Ly[i] = y_p[validx]
+144
+145                if l_[i] == 0:
+146                    l_[i] = math.floor(
+147                        safe_division(e[i], (e_[i] - e[i]), self._epsilon) + 1
+148                    )
+149                if l_[i] >= len(L[i]):
+150                    continue
+151                if e[i] * len(L[i]) < e_[i] * l_[i]:
+152                    updates[i] = True
+153                elif l_[i] > safe_division(e[i], e_[i] - e[i], self._epsilon):
+154                    L[i], Ly[i] = TriTraining._subsample(
+155                        (L[i], Ly[i]),
+156                        math.ceil(
+157                            safe_division(e_[i] * l_[i], e[i], self._epsilon) - 1
+158                        ),
+159                        random_state,
+160                    )
+161                    if is_df:
+162                        L[i] = pd.DataFrame(L[i], columns=X_label.columns)
+163                    updates[i] = True
+164
+165            hypotheses = Parallel(n_jobs=self.n_jobs)(
+166                delayed(self._fit_estimator)(
+167                    hypotheses[i], X_label, y_label, L[i], Ly[i], updates[i], **kwards
+168                )
+169                for i in range(self._N_LEARNER)
+170            )
+171
+172            for i in range(self._N_LEARNER):
+173                if updates[i]:
+174                    e_[i] = e[i]
+175                    l_[i] = len(L[i])
+176                    something_has_changed = True
+177
+178        self.h_ = hypotheses
+179        self.columns_ = [list(range(X.shape[1]))] * self._N_LEARNER
+180
+181        return self
+
+ + +

Build a TriTraining classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabeled.
    • +
+ +
Returns
+ +
    +
  • self (TriTraining): +Fitted estimator.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ +
+ + class + WiWTriTraining(sslearn.wrapper.TriTraining): + + + +
+ + 
265class WiWTriTraining(TriTraining):
+266
+267    def __init__(
+268        self,
+269        base_estimator,
+270        n_samples=100,
+271        n_jobs=None,
+272        method="hungarian",
+273        conflict_weighted=True,
+274        conflict_over="labeled",
+275        random_state=None,
+276    ):
+277        """TriTraining with restriction Who-is-Who.
+278
+279        Parameters
+280        ----------
+281        base_estimator : ClassifierMixin, optional
+282            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+283        n_samples : int, optional
+284            Number of samples to generate.
+285            If left to None this is automatically set to the first dimension of the arrays., by default None
+286        n_jobs : int, optional
+287           Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
+288        method : str, optional
+289            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+290        conflict_weighted : bool, default=True
+291            Whether to weighted the confusion rate by the number of instances with the same group.
+292        conflict_over : str, optional
+293            The conflict rate penalizes the "measure error" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:
+294            * "labeled" over complete L,
+295            * "labeled_plus" over complete L union L',
+296            * "unlabeled¨: over complete U,
+297            * "all": over complete X (LuU) and
+298            * "none": don't penalize the "meause error", by default "labeled"
+299        random_state : int, RandomState instance, optional
+300            controls the randomness of the estimator, by default None
+301
+302        References
+303        ----------
+304        Ludmila I. Kuncheva, Juan J. Rodríguez, Aaron S. Jackson,
+305        Restricted set classification: Who is there?,
+306        Pattern Recognition, 63, 158-170, 
+307        10.1016/j.patcog.2016.08.028
+308        """
+309        super().__init__(base_estimator, n_samples, random_state, n_jobs)
+310        conflict_over_choices = ["labeled", "labeled_plus", "unlabeled", "all", "none"]
+311        if conflict_over not in conflict_over_choices:
+312            raise ValueError(
+313                f"conflict_over must be one of {conflict_over_choices}, got {conflict_over}"
+314            )
+315        self.conflict_over = conflict_over
+316        self.method = method
+317        self.conflict_weighted = conflict_weighted
+318
+319    def fit(self, X, y, instance_group=None, **kwards):
+320        """Build a TriTraining classifier from the training set (X, y).
+321        Parameters
+322        ----------
+323        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+324            The training input samples.
+325        y : array-like of shape (n_samples,)
+326            The target values (class labels), -1 if unlabeled.
+327        instance_group : array-like of shape (n_samples)
+328            The group. Two instances with the same label are not allowed to be in the same group.
+329        Returns
+330        -------
+331        self : TriTraining
+332            Fitted estimator.
+333        """
+334        random_state = check_random_state(self.random_state)
+335        self.n_jobs = check_n_jobs(self.n_jobs)
+336
+337        if instance_group is None:
+338            warn(
+339                "Instance group is not provided. Each instance will belong their own group. Consider using `TriTraining`."
+340            )
+341            instance_group = np.arange(len(y))
+342
+343        X_label, y_label, X_unlabel = get_dataset(X, y)
+344
+345        is_df = isinstance(X_label, pd.DataFrame)
+346
+347        group_label = instance_group[y != y.dtype.type(-1)]
+348        group_unlabel = instance_group[y == y.dtype.type(-1)]
+349
+350        hypotheses = []
+351        e_ = [0.5] * self._N_LEARNER
+352        l_ = [0] * self._N_LEARNER
+353
+354        # Get a random instance for each class to keep class index
+355        self.classes_ = np.unique(y_label)
+356        classes = set(self.classes_)
+357        instances = list()
+358        labels = list()
+359        groups = list()
+360        iteration = zip(X_label, y_label, group_label)
+361        if is_df:
+362            iteration = zip(X_label.values, y_label, group_label)
+363        for x_, y_, g_ in iteration:
+364            if y_ in classes:
+365                classes.remove(y_)
+366                instances.append(x_)
+367                labels.append(y_)
+368                groups.append(g_)
+369            if len(classes) == 0:
+370                break
+371
+372        for i in range(self._N_LEARNER):
+373            X_sampled, y_sampled, group_sample = resample(
+374                X_label,
+375                y_label,
+376                group_label,
+377                replace=False,  # It must be False to keep the group restriction
+378                n_samples=self.n_samples,
+379                random_state=random_state,
+380            )
+381
+382            if is_df:
+383                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+384                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled])
+385            else:
+386                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+387            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+388            group_sample = np.concatenate((np.array(groups), group_sample), axis=0)
+389
+390            hypotheses.append(
+391                WhoIsWhoClassifier(self.base_estimator if not isinstance(self.base_estimator, list) else self.base_estimator[i], method=self.method, conflict_weighted=self.conflict_weighted).
+392                fit(X_sampled, y_sampled, instance_group=group_sample, **kwards)
+393            )
+394
+395        something_has_changed = True if X_unlabel.shape[0] > 0 else False
+396
+397        while something_has_changed:
+398            something_has_changed = False
+399            L = [[]] * self._N_LEARNER
+400            Ly = [[]] * self._N_LEARNER
+401            G = [[]] * self._N_LEARNER
+402            e = []
+403            updates = [False] * 3
+404
+405            for i in range(self._N_LEARNER):
+406                hj, hk = TriTraining._another_hs(hypotheses, i)
+407                e.append(
+408                    self._measure_error(X_label, y_label, hj, hk, self._epsilon, U=X_unlabel, LU=X, GL=group_label, GU=group_unlabel, GLU=instance_group)
+409                )
+410                if e_[i] <= e[i]:
+411                    continue
+412                y_p = hj.predict(X_unlabel, instance_group=group_unlabel)
+413                validx = y_p == hk.predict(X_unlabel, instance_group=group_unlabel)
+414                L[i] = X_unlabel[validx]
+415                Ly[i] = y_p[validx]
+416                G[i] = group_unlabel[validx]
+417
+418                if l_[i] == 0:
+419                    l_[i] = math.floor(
+420                        safe_division(e[i], (e_[i] - e[i]), self._epsilon) + 1
+421                    )
+422                if l_[i] >= len(L[i]):
+423                    continue
+424                if e[i] * len(L[i]) < e_[i] * l_[i]:
+425                    updates[i] = True
+426                elif l_[i] > safe_division(e[i], e_[i] - e[i], self._epsilon):
+427                    L[i], Ly[i], G[i] = TriTraining._subsample(
+428                        (L[i], Ly[i], G[i]),
+429                        math.ceil(
+430                            safe_division(e_[i] * l_[i], e[i], self._epsilon) - 1
+431                        ),
+432                        random_state,
+433                    )
+434                    updates[i] = True
+435                    if is_df:
+436                        L[i] = pd.DataFrame(L[i], columns=X_label.columns)
+437
+438            hypotheses = Parallel(n_jobs=self.n_jobs)(
+439                delayed(self._fit_estimator)(
+440                    hypotheses[i], X_label, y_label, L[i], Ly[i], updates[i], group_label=group_label, Lg=G[i], **kwards
+441                )
+442                for i in range(self._N_LEARNER)
+443            )
+444
+445            for i in range(self._N_LEARNER):
+446                if updates[i]:
+447                    e_[i] = e[i]
+448                    l_[i] = len(L[i])
+449                    something_has_changed = True
+450
+451        self.h_ = hypotheses
+452        self.columns_ = [list(range(X.shape[1]))] * self._N_LEARNER
+453
+454        return self
+455
+456    def _measure_error(self, L, y, h1: ClassifierMixin, h2: ClassifierMixin, epsilon=sys.float_info.epsilon, **kwards):
+457        """Calculate the error between two hypotheses
+458        Parameters
+459        ----------
+460        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+461            The training labeled input samples.
+462        y : array-like of shape (n_samples,)
+463            The target values (class labels).
+464        h1 : ClassifierMixin
+465            First hypothesis
+466        h2 : ClassifierMixin
+467            Second hypothesis
+468        epsilon : float
+469            A small number to avoid division by zero
+470        Returns
+471        -------
+472        error: float
+473            Division of the number of labeled examples on which both h1 and h2 make incorrect classification,
+474            by the number of labeled examples on which the classification made by h1 is the same as that made by h2.
+475        """
+476        me = super()._measure_error(L, y, h1.base_estimator, h2.base_estimator, epsilon)
+477
+478        LU = kwards.get("LU", None)
+479        U = kwards.get("U", None)
+480        GL = kwards.get("GL", None)
+481        GU = kwards.get("GU", None)
+482        GLU = kwards.get("GLU", None)
+483        if self.conflict_over == "labeled":
+484            conflict = (h1.conflict_rate(L, GL) + h2.conflict_rate(L, GL))
+485        elif self.conflict_over == "labeled_plus":
+486            conflict = (h1.conflict_in_train + h2.conflict_in_train)
+487        elif self.conflict_over == "unlabeled":
+488            conflict = (h1.conflict_rate(U, GU) + h2.conflict_rate(U, GU))
+489        elif self.conflict_over == "all":
+490            conflict = (h1.conflict_rate(LU, GLU) + h2.conflict_rate(LU, GLU))
+491        else:
+492            conflict = 0
+493        return me * (1 + conflict / 2)
+494
+495    def _fit_estimator(self, hyp, X_label, y_label, L, Ly, update, **kwards):
+496        Lg = kwards.pop("Lg", None)
+497        group_label = kwards.pop("group_label", None)
+498        kwards
+499        if update:
+500            if isinstance(X_label, pd.DataFrame):
+501                _tempL = pd.concat([X_label, L])
+502            else:
+503                _tempL = np.concatenate((X_label, L))
+504            _tempY = np.concatenate((y_label, Ly))
+505            _tempG = np.concatenate((group_label, Lg))
+506
+507            return hyp.fit(_tempL, _tempY, instance_group=_tempG, **kwards)
+508        return hyp
+509
+510    def predict(self, X, instance_group):
+511        y_probas = self.predict_proba(X)
+512
+513        y_preds = combine_predictions(y_probas, instance_group, len(self.classes_), self.method)
+514
+515        return self.classes_.take(y_preds)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + WiWTriTraining( base_estimator, n_samples=100, n_jobs=None, method='hungarian', conflict_weighted=True, conflict_over='labeled', random_state=None) + + + +
+ + 
267    def __init__(
+268        self,
+269        base_estimator,
+270        n_samples=100,
+271        n_jobs=None,
+272        method="hungarian",
+273        conflict_weighted=True,
+274        conflict_over="labeled",
+275        random_state=None,
+276    ):
+277        """TriTraining with restriction Who-is-Who.
+278
+279        Parameters
+280        ----------
+281        base_estimator : ClassifierMixin, optional
+282            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+283        n_samples : int, optional
+284            Number of samples to generate.
+285            If left to None this is automatically set to the first dimension of the arrays., by default None
+286        n_jobs : int, optional
+287           Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
+288        method : str, optional
+289            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+290        conflict_weighted : bool, default=True
+291            Whether to weighted the confusion rate by the number of instances with the same group.
+292        conflict_over : str, optional
+293            The conflict rate penalizes the "measure error" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:
+294            * "labeled" over complete L,
+295            * "labeled_plus" over complete L union L',
+296            * "unlabeled¨: over complete U,
+297            * "all": over complete X (LuU) and
+298            * "none": don't penalize the "meause error", by default "labeled"
+299        random_state : int, RandomState instance, optional
+300            controls the randomness of the estimator, by default None
+301
+302        References
+303        ----------
+304        Ludmila I. Kuncheva, Juan J. Rodríguez, Aaron S. Jackson,
+305        Restricted set classification: Who is there?,
+306        Pattern Recognition, 63, 158-170, 
+307        10.1016/j.patcog.2016.08.028
+308        """
+309        super().__init__(base_estimator, n_samples, random_state, n_jobs)
+310        conflict_over_choices = ["labeled", "labeled_plus", "unlabeled", "all", "none"]
+311        if conflict_over not in conflict_over_choices:
+312            raise ValueError(
+313                f"conflict_over must be one of {conflict_over_choices}, got {conflict_over}"
+314            )
+315        self.conflict_over = conflict_over
+316        self.method = method
+317        self.conflict_weighted = conflict_weighted
+
+ + +

TriTraining with restriction Who-is-Who.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • n_samples (int, optional): +Number of samples to generate. +If left to None this is automatically set to the first dimension of the arrays., by default None
    • +
  • n_jobs (int, optional): +Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
    • +
  • method (str, optional): +The method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default "hungarian"
    • +
  • conflict_weighted (bool, default=True): +Whether to weighted the confusion rate by the number of instances with the same group.
    • +
  • conflict_over (str, optional): +The conflict rate penalizes the "measure error" of basic TriTraining, it can be calculated over differentes subsamples of X, can be: +
      +
    • "labeled" over complete L,
      • +
    • "labeled_plus" over complete L union L',
      • +
    • "unlabeled¨: over complete U,
      • +
    • "all": over complete X (LuU) and
      • +
    • "none": don't penalize the "meause error", by default "labeled"
      • +
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
References
+ +

Ludmila I. Kuncheva, Juan J. Rodríguez, Aaron S. Jackson, +Restricted set classification: Who is there?, +Pattern Recognition, 63, 158-170, +10.1016/j.patcog.2016.08.028

+
+ + +
+
+ +
+ + def + fit(self, X, y, instance_group=None, **kwards): + + + +
+ + 
319    def fit(self, X, y, instance_group=None, **kwards):
+320        """Build a TriTraining classifier from the training set (X, y).
+321        Parameters
+322        ----------
+323        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+324            The training input samples.
+325        y : array-like of shape (n_samples,)
+326            The target values (class labels), -1 if unlabeled.
+327        instance_group : array-like of shape (n_samples)
+328            The group. Two instances with the same label are not allowed to be in the same group.
+329        Returns
+330        -------
+331        self : TriTraining
+332            Fitted estimator.
+333        """
+334        random_state = check_random_state(self.random_state)
+335        self.n_jobs = check_n_jobs(self.n_jobs)
+336
+337        if instance_group is None:
+338            warn(
+339                "Instance group is not provided. Each instance will belong their own group. Consider using `TriTraining`."
+340            )
+341            instance_group = np.arange(len(y))
+342
+343        X_label, y_label, X_unlabel = get_dataset(X, y)
+344
+345        is_df = isinstance(X_label, pd.DataFrame)
+346
+347        group_label = instance_group[y != y.dtype.type(-1)]
+348        group_unlabel = instance_group[y == y.dtype.type(-1)]
+349
+350        hypotheses = []
+351        e_ = [0.5] * self._N_LEARNER
+352        l_ = [0] * self._N_LEARNER
+353
+354        # Get a random instance for each class to keep class index
+355        self.classes_ = np.unique(y_label)
+356        classes = set(self.classes_)
+357        instances = list()
+358        labels = list()
+359        groups = list()
+360        iteration = zip(X_label, y_label, group_label)
+361        if is_df:
+362            iteration = zip(X_label.values, y_label, group_label)
+363        for x_, y_, g_ in iteration:
+364            if y_ in classes:
+365                classes.remove(y_)
+366                instances.append(x_)
+367                labels.append(y_)
+368                groups.append(g_)
+369            if len(classes) == 0:
+370                break
+371
+372        for i in range(self._N_LEARNER):
+373            X_sampled, y_sampled, group_sample = resample(
+374                X_label,
+375                y_label,
+376                group_label,
+377                replace=False,  # It must be False to keep the group restriction
+378                n_samples=self.n_samples,
+379                random_state=random_state,
+380            )
+381
+382            if is_df:
+383                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+384                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled])
+385            else:
+386                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+387            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+388            group_sample = np.concatenate((np.array(groups), group_sample), axis=0)
+389
+390            hypotheses.append(
+391                WhoIsWhoClassifier(self.base_estimator if not isinstance(self.base_estimator, list) else self.base_estimator[i], method=self.method, conflict_weighted=self.conflict_weighted).
+392                fit(X_sampled, y_sampled, instance_group=group_sample, **kwards)
+393            )
+394
+395        something_has_changed = True if X_unlabel.shape[0] > 0 else False
+396
+397        while something_has_changed:
+398            something_has_changed = False
+399            L = [[]] * self._N_LEARNER
+400            Ly = [[]] * self._N_LEARNER
+401            G = [[]] * self._N_LEARNER
+402            e = []
+403            updates = [False] * 3
+404
+405            for i in range(self._N_LEARNER):
+406                hj, hk = TriTraining._another_hs(hypotheses, i)
+407                e.append(
+408                    self._measure_error(X_label, y_label, hj, hk, self._epsilon, U=X_unlabel, LU=X, GL=group_label, GU=group_unlabel, GLU=instance_group)
+409                )
+410                if e_[i] <= e[i]:
+411                    continue
+412                y_p = hj.predict(X_unlabel, instance_group=group_unlabel)
+413                validx = y_p == hk.predict(X_unlabel, instance_group=group_unlabel)
+414                L[i] = X_unlabel[validx]
+415                Ly[i] = y_p[validx]
+416                G[i] = group_unlabel[validx]
+417
+418                if l_[i] == 0:
+419                    l_[i] = math.floor(
+420                        safe_division(e[i], (e_[i] - e[i]), self._epsilon) + 1
+421                    )
+422                if l_[i] >= len(L[i]):
+423                    continue
+424                if e[i] * len(L[i]) < e_[i] * l_[i]:
+425                    updates[i] = True
+426                elif l_[i] > safe_division(e[i], e_[i] - e[i], self._epsilon):
+427                    L[i], Ly[i], G[i] = TriTraining._subsample(
+428                        (L[i], Ly[i], G[i]),
+429                        math.ceil(
+430                            safe_division(e_[i] * l_[i], e[i], self._epsilon) - 1
+431                        ),
+432                        random_state,
+433                    )
+434                    updates[i] = True
+435                    if is_df:
+436                        L[i] = pd.DataFrame(L[i], columns=X_label.columns)
+437
+438            hypotheses = Parallel(n_jobs=self.n_jobs)(
+439                delayed(self._fit_estimator)(
+440                    hypotheses[i], X_label, y_label, L[i], Ly[i], updates[i], group_label=group_label, Lg=G[i], **kwards
+441                )
+442                for i in range(self._N_LEARNER)
+443            )
+444
+445            for i in range(self._N_LEARNER):
+446                if updates[i]:
+447                    e_[i] = e[i]
+448                    l_[i] = len(L[i])
+449                    something_has_changed = True
+450
+451        self.h_ = hypotheses
+452        self.columns_ = [list(range(X.shape[1]))] * self._N_LEARNER
+453
+454        return self
+
+ + +

Build a TriTraining classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabeled.
    • +
  • instance_group (array-like of shape (n_samples)): +The group. Two instances with the same label are not allowed to be in the same group.
    • +
+ +
Returns
+ +
    +
  • self (TriTraining): +Fitted estimator.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X, instance_group): + + + +
+ + 
510    def predict(self, X, instance_group):
+511        y_probas = self.predict_proba(X)
+512
+513        y_preds = combine_predictions(y_probas, instance_group, len(self.classes_), self.method)
+514
+515        return self.classes_.take(y_preds)
+
+ + +

Predict the classes of X.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples,)): +Array with predicted labels.
    • +
+
+ + +
+
+
+ + def + set_fit_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_predict_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+
+
+
+
+ +
+ + class + CoTraining(sslearn.wrapper._co.BaseCoTraining): + + + +
+ + 
365class CoTraining(BaseCoTraining):
+366
+367    def __init__(
+368        self,
+369        base_estimator=DecisionTreeClassifier(),
+370        second_base_estimator=None,
+371        max_iterations=30,
+372        poolsize=75,
+373        threshold=0.5,
+374        force_second_view=True,
+375        random_state=None
+376    ):
+377        """
+378        Create a CoTraining classifier. 
+379        Multi-view learning algorithm that uses two classifiers to label instances.
+380
+381        Parameters
+382        ----------
+383        base_estimator : ClassifierMixin, optional
+384            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
+385        second_base_estimator : ClassifierMixin, optional
+386            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
+387        max_iterations : int, optional
+388            The number of iterations, by default 30
+389        poolsize : int, optional
+390            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
+391        threshold : float, optional
+392            The threshold for label instances, by default 0.5
+393        force_second_view : bool, optional
+394            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
+395        random_state : int, RandomState instance, optional
+396            controls the randomness of the estimator, by default None
+397
+398        References
+399        ----------
+400        Avrim Blum and Tom Mitchell. 1998.
+401        Combining labeled and unlabeled data with co-training.
+402        In Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).
+403        Association for Computing Machinery, New York, NY, USA, 92-100.
+404        DOI:https://doi.org/10.1145/279943.279962
+405
+406        Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. 
+407        'Multi-Class Co-Training Learning for Object and Scene Recognition'.
+408        Pp. 67-70 in. Nara, Japan.
+409        """
+410        self.base_estimator = check_classifier(base_estimator, False)
+411        if second_base_estimator is not None:
+412            second_base_estimator = check_classifier(second_base_estimator, False)
+413        self.second_base_estimator = second_base_estimator
+414        self.max_iterations = max_iterations
+415        self.poolsize = poolsize
+416        self.threshold = threshold
+417        self.force_second_view = force_second_view
+418        self.random_state = random_state
+419
+420    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
+421        """
+422        Build a CoTraining classifier from the training set.
+423
+424        Parameters
+425        ----------
+426        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+427            Array representing the data.
+428        y : array-like of shape (n_samples,)
+429            The target values (class labels), -1 if unlabeled.
+430        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+431            Array representing the data from another view, not compatible with `features`, by default None
+432        features : {list, tuple}, optional
+433            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
+434        number_per_class : {dict}, optional
+435            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
+436
+437        Returns
+438        -------
+439        self: CoTraining
+440            Fitted estimator.
+441        """
+442        rs = check_random_state(self.random_state)
+443
+444        X_label, y_label, X_unlabel = get_dataset(X, y)
+445
+446        is_df = isinstance(X_label, pd.DataFrame)
+447
+448        if X2 is not None:
+449            X2_label, _, X2_unlabel = get_dataset(X2, y)
+450        elif features is not None:
+451            if is_df:
+452                X2_label = X_label.iloc[:, features[1]]
+453                X2_unlabel = X_unlabel.iloc[:, features[1]]
+454                X_label = X_label.iloc[:, features[0]]
+455                X_unlabel = X_unlabel.iloc[:, features[0]]
+456            else:
+457                X2_label = X_label[:, features[1]]
+458                X2_unlabel = X_unlabel[:, features[1]]
+459                X_label = X_label[:, features[0]]
+460                X_unlabel = X_unlabel[:, features[0]]
+461            self.columns_ = features
+462        elif self.force_second_view:
+463            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
+464        else:
+465            self.columns_ = [list(range(X.shape[1]))] * 2
+466            X2_label = X_label.copy()
+467            X2_unlabel = X_unlabel.copy()
+468
+469        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
+470            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
+471
+472        self.h = [
+473            skclone(self.base_estimator),
+474            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
+475        ]
+476        assert (
+477            X2 is None or features is None
+478        ), "The list of features and X2 cannot be defined at the same time"
+479
+480        self.classes_ = np.unique(y_label)
+481        if number_per_class is None:
+482            number_per_class = calc_number_per_class(y_label)
+483
+484        if X_unlabel.shape[0] < self.poolsize:
+485            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+486
+487        permutation = rs.permutation(len(X_unlabel))
+488
+489        self.h[0].fit(X_label, y_label)
+490        self.h[1].fit(X2_label, y_label)
+491
+492        it = 0
+493        while it < self.max_iterations and any(permutation):
+494            it += 1
+495
+496            get_index = permutation[:self.poolsize]
+497            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
+498            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
+499
+500            predictions1 = np.max(y1_prob, axis=1)
+501            class_predicted1 = np.argmax(y1_prob, axis=1)
+502
+503            predictions2 = np.max(y2_prob, axis=1)
+504            class_predicted2 = np.argmax(y2_prob, axis=1)
+505
+506            # If two classifier select same instance and bring different predictions then the instance is not labeled
+507            candidates1 = predictions1 > self.threshold
+508            candidates2 = predictions2 > self.threshold
+509            aggreement = class_predicted1 == class_predicted2
+510
+511            full_candidates = candidates1 ^ candidates2
+512            medium_candidates = candidates1 & candidates2 & aggreement
+513            true_candidates1 = full_candidates & candidates1
+514            true_candidates2 = full_candidates & candidates2
+515
+516            # Fill probas and candidate classes.
+517            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
+518            y_class = class_predicted1.copy()
+519
+520            temp_probas1 = predictions1[true_candidates1]
+521            temp_probas2 = predictions2[true_candidates2]
+522            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+523
+524            temp_classes2 = class_predicted2[true_candidates2]
+525
+526            y_probas[true_candidates1] = temp_probas1
+527            y_probas[true_candidates2] = temp_probas2
+528            y_probas[medium_candidates] = temp_probasB
+529            y_class[true_candidates2] = temp_classes2
+530
+531            # Select the best candidates
+532            final_instances = list()
+533            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
+534            for c in self.classes_:
+535                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
+536
+537            # Fill the new labeled instances
+538            pseudoy = y_class[final_instances]
+539            y_label = np.append(y_label, pseudoy)
+540
+541            index = permutation[0: self.poolsize][final_instances]
+542            if is_df:
+543                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
+544                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
+545            else:
+546                X_label = np.append(X_label, X_unlabel[index], axis=0)
+547                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+548
+549            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+550
+551            # Poolsize increments in order double of max instances candidates:
+552            self.poolsize += sum(number_per_class.values()) * 2
+553
+554            self.h[0].fit(X_label, y_label)
+555            self.h[1].fit(X2_label, y_label)
+556
+557        self.h_ = self.h
+558
+559        return self
+560
+561    def predict_proba(self, X, X2=None, **kwards):
+562        """Predict probability for each possible outcome.
+563
+564        Parameters
+565        ----------
+566        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+567            Array representing the data.
+568        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+569            Array representing the data from another view, by default None
+570        Returns
+571        -------
+572        class probabilities: ndarray of shape (n_samples, n_classes)
+573            Array with prediction probabilities.
+574        """
+575        if "columns_" in dir(self):
+576            return super().predict_proba(X, **kwards)
+577        elif "h_" in dir(self):
+578            ys = []
+579            ys.append(self.h_[0].predict_proba(X, **kwards))
+580            ys.append(self.h_[1].predict_proba(X2, **kwards))
+581            y = sum(ys) / len(ys)
+582            return y
+583        else:
+584            raise NotFittedError("Classifier not fitted")
+585
+586    def predict(self, X, X2=None, **kwards):
+587        """Predict the classes of X.
+588        Parameters
+589        ----------
+590        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+591            Array representing the data.
+592        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+593            Array representing the data from another view, by default None
+594
+595        Returns
+596        -------
+597        y : ndarray of shape (n_samples,)
+598            Array with predicted labels.
+599        """
+600        if "columns_" in dir(self):
+601            result = super().predict(X, **kwards)
+602        else:
+603            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
+604            result = self.classes_.take(
+605                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
+606            )
+607        return result
+608
+609    def score(self, X, y, sample_weight=None, **kwards):
+610        """
+611        Return the mean accuracy on the given test data and labels.
+612        In multi-label classification, this is the subset accuracy
+613        which is a harsh metric since you require for each sample that
+614        each label set be correctly predicted.
+615        Parameters
+616        ----------
+617        X : array-like of shape (n_samples, n_features)
+618            Test samples.
+619        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+620            True labels for `X`.
+621        sample_weight : array-like of shape (n_samples,), default=None
+622            Sample weights.
+623        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+624            Array representing the data from another view, by default None
+625        Returns
+626        -------
+627        score : float
+628            Mean accuracy of ``self.predict(X)`` wrt. `y`.
+629        """
+630        if "X2" in kwards:
+631            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
+632        else:
+633            return super().score(X, y, sample_weight=sample_weight)
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + CoTraining( base_estimator=DecisionTreeClassifier(), second_base_estimator=None, max_iterations=30, poolsize=75, threshold=0.5, force_second_view=True, random_state=None) + + + +
+ + 
367    def __init__(
+368        self,
+369        base_estimator=DecisionTreeClassifier(),
+370        second_base_estimator=None,
+371        max_iterations=30,
+372        poolsize=75,
+373        threshold=0.5,
+374        force_second_view=True,
+375        random_state=None
+376    ):
+377        """
+378        Create a CoTraining classifier. 
+379        Multi-view learning algorithm that uses two classifiers to label instances.
+380
+381        Parameters
+382        ----------
+383        base_estimator : ClassifierMixin, optional
+384            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
+385        second_base_estimator : ClassifierMixin, optional
+386            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
+387        max_iterations : int, optional
+388            The number of iterations, by default 30
+389        poolsize : int, optional
+390            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
+391        threshold : float, optional
+392            The threshold for label instances, by default 0.5
+393        force_second_view : bool, optional
+394            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
+395        random_state : int, RandomState instance, optional
+396            controls the randomness of the estimator, by default None
+397
+398        References
+399        ----------
+400        Avrim Blum and Tom Mitchell. 1998.
+401        Combining labeled and unlabeled data with co-training.
+402        In Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).
+403        Association for Computing Machinery, New York, NY, USA, 92-100.
+404        DOI:https://doi.org/10.1145/279943.279962
+405
+406        Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. 
+407        'Multi-Class Co-Training Learning for Object and Scene Recognition'.
+408        Pp. 67-70 in. Nara, Japan.
+409        """
+410        self.base_estimator = check_classifier(base_estimator, False)
+411        if second_base_estimator is not None:
+412            second_base_estimator = check_classifier(second_base_estimator, False)
+413        self.second_base_estimator = second_base_estimator
+414        self.max_iterations = max_iterations
+415        self.poolsize = poolsize
+416        self.threshold = threshold
+417        self.force_second_view = force_second_view
+418        self.random_state = random_state
+
+ + +

Create a CoTraining classifier. +Multi-view learning algorithm that uses two classifiers to label instances.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • +
  • second_base_estimator (ClassifierMixin, optional): +The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • +
  • max_iterations (int, optional): +The number of iterations, by default 30
    • +
  • poolsize (int, optional): +The size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • +
  • threshold (float, optional): +The threshold for label instances, by default 0.5
    • +
  • force_second_view (bool, optional): +The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
References
+ +

Avrim Blum and Tom Mitchell. 1998. +Combining labeled and unlabeled data with co-training. +In Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98). +Association for Computing Machinery, New York, NY, USA, 92-100. +DOI:https://doi.org/10.1145/279943.279962

+ +

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. +'Multi-Class Co-Training Learning for Object and Scene Recognition'. +Pp. 67-70 in. Nara, Japan.

+
+ + +
+
+ +
+ + def + fit( self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards): + + + +
+ + 
420    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
+421        """
+422        Build a CoTraining classifier from the training set.
+423
+424        Parameters
+425        ----------
+426        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+427            Array representing the data.
+428        y : array-like of shape (n_samples,)
+429            The target values (class labels), -1 if unlabeled.
+430        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+431            Array representing the data from another view, not compatible with `features`, by default None
+432        features : {list, tuple}, optional
+433            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
+434        number_per_class : {dict}, optional
+435            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
+436
+437        Returns
+438        -------
+439        self: CoTraining
+440            Fitted estimator.
+441        """
+442        rs = check_random_state(self.random_state)
+443
+444        X_label, y_label, X_unlabel = get_dataset(X, y)
+445
+446        is_df = isinstance(X_label, pd.DataFrame)
+447
+448        if X2 is not None:
+449            X2_label, _, X2_unlabel = get_dataset(X2, y)
+450        elif features is not None:
+451            if is_df:
+452                X2_label = X_label.iloc[:, features[1]]
+453                X2_unlabel = X_unlabel.iloc[:, features[1]]
+454                X_label = X_label.iloc[:, features[0]]
+455                X_unlabel = X_unlabel.iloc[:, features[0]]
+456            else:
+457                X2_label = X_label[:, features[1]]
+458                X2_unlabel = X_unlabel[:, features[1]]
+459                X_label = X_label[:, features[0]]
+460                X_unlabel = X_unlabel[:, features[0]]
+461            self.columns_ = features
+462        elif self.force_second_view:
+463            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
+464        else:
+465            self.columns_ = [list(range(X.shape[1]))] * 2
+466            X2_label = X_label.copy()
+467            X2_unlabel = X_unlabel.copy()
+468
+469        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
+470            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
+471
+472        self.h = [
+473            skclone(self.base_estimator),
+474            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
+475        ]
+476        assert (
+477            X2 is None or features is None
+478        ), "The list of features and X2 cannot be defined at the same time"
+479
+480        self.classes_ = np.unique(y_label)
+481        if number_per_class is None:
+482            number_per_class = calc_number_per_class(y_label)
+483
+484        if X_unlabel.shape[0] < self.poolsize:
+485            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+486
+487        permutation = rs.permutation(len(X_unlabel))
+488
+489        self.h[0].fit(X_label, y_label)
+490        self.h[1].fit(X2_label, y_label)
+491
+492        it = 0
+493        while it < self.max_iterations and any(permutation):
+494            it += 1
+495
+496            get_index = permutation[:self.poolsize]
+497            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
+498            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
+499
+500            predictions1 = np.max(y1_prob, axis=1)
+501            class_predicted1 = np.argmax(y1_prob, axis=1)
+502
+503            predictions2 = np.max(y2_prob, axis=1)
+504            class_predicted2 = np.argmax(y2_prob, axis=1)
+505
+506            # If two classifier select same instance and bring different predictions then the instance is not labeled
+507            candidates1 = predictions1 > self.threshold
+508            candidates2 = predictions2 > self.threshold
+509            aggreement = class_predicted1 == class_predicted2
+510
+511            full_candidates = candidates1 ^ candidates2
+512            medium_candidates = candidates1 & candidates2 & aggreement
+513            true_candidates1 = full_candidates & candidates1
+514            true_candidates2 = full_candidates & candidates2
+515
+516            # Fill probas and candidate classes.
+517            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
+518            y_class = class_predicted1.copy()
+519
+520            temp_probas1 = predictions1[true_candidates1]
+521            temp_probas2 = predictions2[true_candidates2]
+522            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+523
+524            temp_classes2 = class_predicted2[true_candidates2]
+525
+526            y_probas[true_candidates1] = temp_probas1
+527            y_probas[true_candidates2] = temp_probas2
+528            y_probas[medium_candidates] = temp_probasB
+529            y_class[true_candidates2] = temp_classes2
+530
+531            # Select the best candidates
+532            final_instances = list()
+533            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
+534            for c in self.classes_:
+535                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
+536
+537            # Fill the new labeled instances
+538            pseudoy = y_class[final_instances]
+539            y_label = np.append(y_label, pseudoy)
+540
+541            index = permutation[0: self.poolsize][final_instances]
+542            if is_df:
+543                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
+544                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
+545            else:
+546                X_label = np.append(X_label, X_unlabel[index], axis=0)
+547                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+548
+549            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+550
+551            # Poolsize increments in order double of max instances candidates:
+552            self.poolsize += sum(number_per_class.values()) * 2
+553
+554            self.h[0].fit(X_label, y_label)
+555            self.h[1].fit(X2_label, y_label)
+556
+557        self.h_ = self.h
+558
+559        return self
+
+ + +

Build a CoTraining classifier from the training set.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabeled.
    • +
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional): +Array representing the data from another view, not compatible with features, by default None
    • +
  • features ({list, tuple}, optional): +list or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
    • +
  • number_per_class ({dict}, optional): +dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
    • +
+ +
Returns
+ +
    +
  • self (CoTraining): +Fitted estimator.
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X, X2=None, **kwards): + + + +
+ + 
561    def predict_proba(self, X, X2=None, **kwards):
+562        """Predict probability for each possible outcome.
+563
+564        Parameters
+565        ----------
+566        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+567            Array representing the data.
+568        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+569            Array representing the data from another view, by default None
+570        Returns
+571        -------
+572        class probabilities: ndarray of shape (n_samples, n_classes)
+573            Array with prediction probabilities.
+574        """
+575        if "columns_" in dir(self):
+576            return super().predict_proba(X, **kwards)
+577        elif "h_" in dir(self):
+578            ys = []
+579            ys.append(self.h_[0].predict_proba(X, **kwards))
+580            ys.append(self.h_[1].predict_proba(X2, **kwards))
+581            y = sum(ys) / len(ys)
+582            return y
+583        else:
+584            raise NotFittedError("Classifier not fitted")
+
+ + +

Predict probability for each possible outcome.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional): +Array representing the data from another view, by default None
    • +
+ +
Returns
+ +
    +
  • class probabilities (ndarray of shape (n_samples, n_classes)): +Array with prediction probabilities.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X, X2=None, **kwards): + + + +
+ + 
586    def predict(self, X, X2=None, **kwards):
+587        """Predict the classes of X.
+588        Parameters
+589        ----------
+590        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+591            Array representing the data.
+592        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+593            Array representing the data from another view, by default None
+594
+595        Returns
+596        -------
+597        y : ndarray of shape (n_samples,)
+598            Array with predicted labels.
+599        """
+600        if "columns_" in dir(self):
+601            result = super().predict(X, **kwards)
+602        else:
+603            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
+604            result = self.classes_.take(
+605                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
+606            )
+607        return result
+
+ + +

Predict the classes of X.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional): +Array representing the data from another view, by default None
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples,)): +Array with predicted labels.
    • +
+
+ + +
+
+ +
+ + def + score(self, X, y, sample_weight=None, **kwards): + + + +
+ + 
609    def score(self, X, y, sample_weight=None, **kwards):
+610        """
+611        Return the mean accuracy on the given test data and labels.
+612        In multi-label classification, this is the subset accuracy
+613        which is a harsh metric since you require for each sample that
+614        each label set be correctly predicted.
+615        Parameters
+616        ----------
+617        X : array-like of shape (n_samples, n_features)
+618            Test samples.
+619        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+620            True labels for `X`.
+621        sample_weight : array-like of shape (n_samples,), default=None
+622            Sample weights.
+623        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+624            Array representing the data from another view, by default None
+625        Returns
+626        -------
+627        score : float
+628            Mean accuracy of ``self.predict(X)`` wrt. `y`.
+629        """
+630        if "X2" in kwards:
+631            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
+632        else:
+633            return super().score(X, y, sample_weight=sample_weight)
+
+ + +

Return the mean accuracy on the given test data and labels. +In multi-label classification, this is the subset accuracy +which is a harsh metric since you require for each sample that +each label set be correctly predicted.

+ +
Parameters
+ +
    +
  • X (array-like of shape (n_samples, n_features)): +Test samples.
    • +
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)): +True labels for X.
    • +
  • sample_weight (array-like of shape (n_samples,), default=None): +Sample weights.
    • +
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional): +Array representing the data from another view, by default None
    • +
+ +
Returns
+ +
    +
  • score (float): +Mean accuracy of self.predict(X) wrt. y.
    • +
+
+ + +
+
+
+ + def + set_fit_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_predict_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_predict_proba_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ +
+ + class + DeTriTraining(sslearn.wrapper.TriTraining): + + + +
+ + 
518class DeTriTraining(TriTraining):
+519
+520    def __init__(self, base_estimator=DecisionTreeClassifier(), k_neighbors=3,
+521                 n_samples=None, mode="seeded", max_iterations=100, n_jobs=None, random_state=None):
+522        """
+523        DeTriTraining - TriTraining with Depurated and Clustering.
+524        Avoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.        
+525
+526        Parameters
+527        ----------
+528        base_estimator : ClassifierMixin, optional
+529            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+530        n_samples : int, optional
+531            Number of samples to generate. 
+532            If left to None this is automatically set to the first dimension of the arrays., by default None
+533        k_neighbors : int, optional
+534            Number of neighbors for depurate classification. 
+535            If at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
+536        mode : string, optional
+537            How to calculate the cluster each instance belongs to.
+538            If `seeded` each instance belong to nearest cluster.
+539            If `constrained` each instance belong to nearest cluster unless the instance is in to enlarged dataset, 
+540            then the instance belongs to the cluster of its class., by default `seeded`
+541        max_iterations : int, optional
+542            Maximum number of iterations, by default 100
+543        n_jobs : int, optional
+544            The number of parallel jobs to run for neighbors search. 
+545            None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. 
+546            Doesn't affect fit method., by default None
+547        random_state : int, RandomState instance, optional
+548            controls the randomness of the estimator, by default None
+549
+550        References
+551        ----------
+552        Deng C., Guo M.Z. (2006)
+553        Tri-training and Data Editing Based Semi-supervised Clustering Algorithm. 
+554        In: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. 
+555        Lecture Notes in Computer Science, vol 4293.
+556        Springer, Berlin, Heidelberg.
+557        https://doi.org/10.1007/11925231_61
+558        """
+559        super().__init__(base_estimator, n_samples, random_state)
+560        self.k_neighbors = k_neighbors
+561        self.mode = mode
+562        self.max_iterations = max_iterations
+563        self.n_jobs = n_jobs
+564        if mode != "seeded" and mode != "constrained":
+565            raise AttributeError("`mode` must be \"seeded\" or \"constrained\".")
+566
+567    def _depure(self, S):
+568        """Depure the S dataset
+569
+570        Parameters
+571        ----------
+572        S : tuple (X, y)
+573            Enlarged dataset
+574
+575        Returns
+576        -------
+577        tuple : (X, y)
+578            Enlarged dataset with instances where at least k_neighbors/2+1 have the same class.
+579        """
+580        init = time.time()
+581        knn = KNeighborsClassifier(n_neighbors=self.k_neighbors, n_jobs=self.n_jobs)
+582        valid = knn.fit(*S).predict(S[0]) == S[1]
+583        print(f"Depure time: {time.time() - init}")
+584        return S[0][valid], S[1][valid]
+585
+586    def _clustering(self, S, X):
+587        """Clustering phase of the fitting
+588
+589        Parameters
+590        ----------
+591        S : tuple (X, y)
+592            Enlarged dataset
+593        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+594            Complete dataset, only features
+595
+596        Returns
+597        -------
+598        y: array-like of shape (n_samples,)
+599            class predicted for each instance
+600        """
+601        centroids = dict()
+602        clusters = set(S[1])
+603
+604        # uses as numpy
+605        if isinstance(X, pd.DataFrame):
+606            X = X.to_numpy()
+607        if isinstance(S[0], pd.DataFrame):
+608            S = (S[0].to_numpy(), S[1])
+609
+610        for k in clusters:
+611            centroids[k] = np.mean(S[0][S[1] == k], axis=0)
+612
+613        def seeded(X):
+614            # For each instance, calculate the distance to each centroid
+615            distances = np.linalg.norm(X[:, None, :] - np.array(list(centroids.values())), axis=2)
+616            # Get the index of the nearest centroid
+617            return np.argmin(distances, axis=1)
+618
+619        def constrained(X):
+620            # Calculate the distances to centroids using broadcasting
+621            distances = np.linalg.norm(X[:, None, :] - np.array(list(centroids.values())), axis=2)
+622            # Get the index of the nearest centroid
+623            nearest = np.argmin(distances, axis=1)
+624            # Create a mask to find instances in X that belong to S[0]
+625            mask = (S[0] == X[:, None])
+626            # Find the row and column indices where all elements are True
+627            i, j = np.where(mask.all(axis=2))
+628            # Initialize cluster with -1
+629            cluster = np.full(X.shape[0], -1, dtype=int)
+630            # Update cluster for the instances found in S[0]
+631            cluster[i] = S[1][j]
+632            # Update cluster for instances not found in S[0]
+633            cluster[cluster == -1] = nearest[cluster == -1]
+634
+635            return cluster
+636
+637        if self.mode == "seeded":
+638            op = seeded
+639        elif self.mode == "constrained":
+640            op = constrained
+641
+642        changes = True
+643        iterations = 0
+644        while changes and iterations < self.max_iterations:
+645            changes = False
+646            iterations += 1
+647            # Need to vectorize
+648            new_clusters = op(X)
+649            new_centroids = dict()
+650            for k in clusters:
+651                if np.any(new_clusters == k):
+652                    new_centroids[k] = np.mean(X[new_clusters == k], axis=0)
+653                    if not np.array_equal(new_centroids[k], centroids[k]):
+654                        changes = True
+655            centroids = new_centroids
+656
+657        return new_clusters
+658
+659    def fit(self, X, y, **kwards):
+660        """Build a DeTriTraining classifier from the training set (X, y).
+661
+662        Parameters
+663        ----------
+664        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+665            The training input samples.
+666        y : array-like of shape (n_samples,)
+667            The target values (class labels), -1 if unlabel.
+668
+669        Returns
+670        -------
+671        self: DeTriTraining
+672            Fitted estimator.
+673        """
+674        X_label, y_label, X_unlabel = get_dataset(X, y)
+675
+676        self.label_encoder_ = LabelEncoder()
+677        self.label_encoder_.fit(y_label)
+678        y_label = self.label_encoder_.transform(y_label)
+679
+680        is_df = isinstance(X_label, pd.DataFrame)
+681
+682        self.classes_ = np.unique(y_label)
+683
+684        classes = set(self.classes_)
+685        instances = list()
+686        labels = list()
+687        iteration = zip(X_label, y_label)
+688        if is_df:
+689            iteration = zip(X_label.values, y_label)
+690        for x_, y_ in iteration:
+691            if y_ in classes:
+692                classes.remove(y_)
+693                instances.append(x_)
+694                labels.append(y_)
+695            if len(classes) == 0:
+696                break
+697
+698        S_ = []
+699        hypothesis = []
+700        for i in range(self._N_LEARNER):
+701            X_sampled, y_sampled = \
+702                resample(X_label, y_label, replace=True,
+703                         n_samples=self.n_samples,
+704                         random_state=self.random_state)
+705            if is_df:
+706                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+707            hypothesis.append(
+708                skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(
+709                    X_sampled, y_sampled, **kwards)
+710            )
+711
+712            # Keep class order
+713            if not is_df:
+714                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+715            else:
+716                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled], axis=0)
+717
+718            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+719
+720            S_.append((X_sampled, y_sampled))
+721
+722        changes = True
+723        last_addition = [0] * self._N_LEARNER
+724        it = 0 if X_unlabel.shape[0] > 0 else self.max_iterations
+725        while it < self.max_iterations:
+726            it += 1
+727            changes = False
+728
+729            # Enlarged
+730            L = [[]] * self._N_LEARNER
+731
+732            for i in range(self._N_LEARNER):
+733                hj, hk = TriTraining._another_hs(hypothesis, i)
+734                y_p = hj.predict(X_unlabel)
+735                validx = y_p == hk.predict(X_unlabel)
+736                L[i] = (X_unlabel[validx] if not is_df else X_unlabel.iloc[validx, :], y_p[validx])
+737
+738            for i, _ in enumerate(L):
+739
+740                if len(L[i][0]) > 0:
+741                    S_[i] = np.concatenate((X_label, L[i][0])) if not is_df else pd.concat([X_label, L[i][0]]), np.concatenate((y_label, L[i][1]))
+742                    S_[i] = self._depure(S_[i])
+743
+744            for i in range(self._N_LEARNER):
+745                if len(S_[i][0]) > len(X_label):
+746                    last_addition[i] = len(S_[i][0])
+747                    changes = True
+748                    hypothesis[i].fit(*S_[i], **kwards)
+749
+750            if not changes:
+751                break
+752        else:
+753            warn.warn("Maximum number of iterations reached before convergence. Consider increasing max_iter to improve the fit.", ConvergenceWarning)
+754
+755        S = np.concatenate([x[0] for x in S_]) if not is_df else pd.concat([x[0] for x in S_]), np.concatenate([x[1] for x in S_])
+756        S_0, index_ = np.unique(S[0], axis=0, return_index=True)
+757        S_1 = S[1][index_]
+758        S = S_0, S_1
+759        S = self._depure(S)  # Change, is S - L (only new)
+760
+761        new_y = self._clustering(S, X)
+762
+763        self.h_ = [skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(X, new_y, **kwards) for i in range(self._N_LEARNER)]
+764        self.columns_ = [list(range(X.shape[1]))]
+765
+766        return self
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + DeTriTraining( base_estimator=DecisionTreeClassifier(), k_neighbors=3, n_samples=None, mode='seeded', max_iterations=100, n_jobs=None, random_state=None) + + + +
+ + 
520    def __init__(self, base_estimator=DecisionTreeClassifier(), k_neighbors=3,
+521                 n_samples=None, mode="seeded", max_iterations=100, n_jobs=None, random_state=None):
+522        """
+523        DeTriTraining - TriTraining with Depurated and Clustering.
+524        Avoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.        
+525
+526        Parameters
+527        ----------
+528        base_estimator : ClassifierMixin, optional
+529            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+530        n_samples : int, optional
+531            Number of samples to generate. 
+532            If left to None this is automatically set to the first dimension of the arrays., by default None
+533        k_neighbors : int, optional
+534            Number of neighbors for depurate classification. 
+535            If at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
+536        mode : string, optional
+537            How to calculate the cluster each instance belongs to.
+538            If `seeded` each instance belong to nearest cluster.
+539            If `constrained` each instance belong to nearest cluster unless the instance is in to enlarged dataset, 
+540            then the instance belongs to the cluster of its class., by default `seeded`
+541        max_iterations : int, optional
+542            Maximum number of iterations, by default 100
+543        n_jobs : int, optional
+544            The number of parallel jobs to run for neighbors search. 
+545            None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. 
+546            Doesn't affect fit method., by default None
+547        random_state : int, RandomState instance, optional
+548            controls the randomness of the estimator, by default None
+549
+550        References
+551        ----------
+552        Deng C., Guo M.Z. (2006)
+553        Tri-training and Data Editing Based Semi-supervised Clustering Algorithm. 
+554        In: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. 
+555        Lecture Notes in Computer Science, vol 4293.
+556        Springer, Berlin, Heidelberg.
+557        https://doi.org/10.1007/11925231_61
+558        """
+559        super().__init__(base_estimator, n_samples, random_state)
+560        self.k_neighbors = k_neighbors
+561        self.mode = mode
+562        self.max_iterations = max_iterations
+563        self.n_jobs = n_jobs
+564        if mode != "seeded" and mode != "constrained":
+565            raise AttributeError("`mode` must be \"seeded\" or \"constrained\".")
+
+ + +

DeTriTraining - TriTraining with Depurated and Clustering. +Avoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • n_samples (int, optional): +Number of samples to generate. +If left to None this is automatically set to the first dimension of the arrays., by default None
    • +
  • k_neighbors (int, optional): +Number of neighbors for depurate classification. +If at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
    • +
  • mode (string, optional): +How to calculate the cluster each instance belongs to. +If seeded each instance belong to nearest cluster. +If constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, +then the instance belongs to the cluster of its class., by default seeded
    • +
  • max_iterations (int, optional): +Maximum number of iterations, by default 100
    • +
  • n_jobs (int, optional): +The number of parallel jobs to run for neighbors search. +None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. +Doesn't affect fit method., by default None
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
References
+ +

Deng C., Guo M.Z. (2006) +Tri-training and Data Editing Based Semi-supervised Clustering Algorithm. +In: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. +Lecture Notes in Computer Science, vol 4293. +Springer, Berlin, Heidelberg. +https://doi.org/10.1007/11925231_61

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwards): + + + +
+ + 
659    def fit(self, X, y, **kwards):
+660        """Build a DeTriTraining classifier from the training set (X, y).
+661
+662        Parameters
+663        ----------
+664        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+665            The training input samples.
+666        y : array-like of shape (n_samples,)
+667            The target values (class labels), -1 if unlabel.
+668
+669        Returns
+670        -------
+671        self: DeTriTraining
+672            Fitted estimator.
+673        """
+674        X_label, y_label, X_unlabel = get_dataset(X, y)
+675
+676        self.label_encoder_ = LabelEncoder()
+677        self.label_encoder_.fit(y_label)
+678        y_label = self.label_encoder_.transform(y_label)
+679
+680        is_df = isinstance(X_label, pd.DataFrame)
+681
+682        self.classes_ = np.unique(y_label)
+683
+684        classes = set(self.classes_)
+685        instances = list()
+686        labels = list()
+687        iteration = zip(X_label, y_label)
+688        if is_df:
+689            iteration = zip(X_label.values, y_label)
+690        for x_, y_ in iteration:
+691            if y_ in classes:
+692                classes.remove(y_)
+693                instances.append(x_)
+694                labels.append(y_)
+695            if len(classes) == 0:
+696                break
+697
+698        S_ = []
+699        hypothesis = []
+700        for i in range(self._N_LEARNER):
+701            X_sampled, y_sampled = \
+702                resample(X_label, y_label, replace=True,
+703                         n_samples=self.n_samples,
+704                         random_state=self.random_state)
+705            if is_df:
+706                X_sampled = pd.DataFrame(X_sampled, columns=X_label.columns)
+707            hypothesis.append(
+708                skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(
+709                    X_sampled, y_sampled, **kwards)
+710            )
+711
+712            # Keep class order
+713            if not is_df:
+714                X_sampled = np.concatenate((np.array(instances), X_sampled), axis=0)
+715            else:
+716                X_sampled = pd.concat([pd.DataFrame(instances, columns=X_label.columns), X_sampled], axis=0)
+717
+718            y_sampled = np.concatenate((np.array(labels), y_sampled), axis=0)
+719
+720            S_.append((X_sampled, y_sampled))
+721
+722        changes = True
+723        last_addition = [0] * self._N_LEARNER
+724        it = 0 if X_unlabel.shape[0] > 0 else self.max_iterations
+725        while it < self.max_iterations:
+726            it += 1
+727            changes = False
+728
+729            # Enlarged
+730            L = [[]] * self._N_LEARNER
+731
+732            for i in range(self._N_LEARNER):
+733                hj, hk = TriTraining._another_hs(hypothesis, i)
+734                y_p = hj.predict(X_unlabel)
+735                validx = y_p == hk.predict(X_unlabel)
+736                L[i] = (X_unlabel[validx] if not is_df else X_unlabel.iloc[validx, :], y_p[validx])
+737
+738            for i, _ in enumerate(L):
+739
+740                if len(L[i][0]) > 0:
+741                    S_[i] = np.concatenate((X_label, L[i][0])) if not is_df else pd.concat([X_label, L[i][0]]), np.concatenate((y_label, L[i][1]))
+742                    S_[i] = self._depure(S_[i])
+743
+744            for i in range(self._N_LEARNER):
+745                if len(S_[i][0]) > len(X_label):
+746                    last_addition[i] = len(S_[i][0])
+747                    changes = True
+748                    hypothesis[i].fit(*S_[i], **kwards)
+749
+750            if not changes:
+751                break
+752        else:
+753            warn.warn("Maximum number of iterations reached before convergence. Consider increasing max_iter to improve the fit.", ConvergenceWarning)
+754
+755        S = np.concatenate([x[0] for x in S_]) if not is_df else pd.concat([x[0] for x in S_]), np.concatenate([x[1] for x in S_])
+756        S_0, index_ = np.unique(S[0], axis=0, return_index=True)
+757        S_1 = S[1][index_]
+758        S = S_0, S_1
+759        S = self._depure(S)  # Change, is S - L (only new)
+760
+761        new_y = self._clustering(S, X)
+762
+763        self.h_ = [skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]).fit(X, new_y, **kwards) for i in range(self._N_LEARNER)]
+764        self.columns_ = [list(range(X.shape[1]))]
+765
+766        return self
+
+ + +

Build a DeTriTraining classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabel.
    • +
+ +
Returns
+ +
    +
  • self (DeTriTraining): +Fitted estimator.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ +
+ + class + DemocraticCoLearning(sslearn.wrapper._co.BaseCoTraining): + + + +
+ + 
 75class DemocraticCoLearning(BaseCoTraining):
+ 76    def __init__(
+ 77        self,
+ 78        base_estimator=[
+ 79            DecisionTreeClassifier(),
+ 80            GaussianNB(),
+ 81            KNeighborsClassifier(n_neighbors=3),
+ 82        ],
+ 83        n_estimators=None,
+ 84        expand_only_mislabeled=True,
+ 85        alpha=0.95,
+ 86        q_exp=2,
+ 87        random_state=None
+ 88    ):
+ 89        """
+ 90        Democratic Co-learning. Ensemble of classifiers of different types.
+ 91
+ 92        Parameters
+ 93        ----------
+ 94        base_estimator : {ClassifierMixin, list}, optional
+ 95            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
+ 96        n_estimators : int, optional
+ 97            number of base_estimators to use. None if base_estimator is a list, by default None
+ 98        expand_only_mislabeled : bool, optional
+ 99            expand only mislabeled instances by itself, by default True
+100        alpha : float, optional
+101            confidence level, by default 0.95
+102        q_exp : int, optional
+103            exponent for the estimation for error rate, by default 2
+104        random_state : int, RandomState instance, optional
+105            controls the randomness of the estimator, by default None
+106        Raises
+107        ------
+108        AttributeError
+109            If n_estimators is None and base_estimator is not a list
+110
+111        References
+112        ----------
+113        Y. Zhou and S. Goldman, "Democratic co-learning,"
+114        16th IEEE International Conference on Tools with Artificial Intelligence,
+115        2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.
+116        """
+117
+118        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
+119            estimators = list()
+120            random_available = True
+121            rand = check_random_state(random_state)
+122            if "random_state" not in dir(base_estimator):
+123                warnings.warn(
+124                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
+125                    ConvergenceWarning,
+126                )
+127                random_available = False
+128            for i in range(n_estimators):
+129                estimators.append(skclone(base_estimator))
+130                if random_available:
+131                    estimators[i].random_state = rand.randint(0, 1e5)
+132            self.base_estimator = estimators
+133
+134        elif isinstance(base_estimator, list):
+135            self.base_estimator = base_estimator
+136        else:
+137            raise AttributeError(
+138                "If `n_estimators` is None then `base_estimator` must be a `list`."
+139            )
+140        self.base_estimator = check_classifier(self.base_estimator)
+141        self.n_estimators = len(self.base_estimator)
+142        self.one_hot = OneHotEncoder(sparse_output=False)
+143        self.expand_only_mislabeled = expand_only_mislabeled
+144
+145        self.alpha = alpha
+146        self.q_exp = q_exp
+147        self.random_state = random_state
+148
+149    def __weighted_y(self, predictions, weights):
+150        y_complete = np.sum(
+151            [
+152                self.one_hot.transform(p.reshape(-1, 1)) * wi
+153                for p, wi in zip(predictions, weights)
+154            ],
+155            0,
+156        )
+157        y_zeros = np.zeros(y_complete.shape)
+158        y_zeros[np.arange(y_complete.shape[0]), y_complete.argmax(1)] = 1
+159
+160        return self.one_hot.inverse_transform(y_zeros).flatten()
+161
+162    def __calcule_last_confidences(self, X, y):
+163        """Calculate the confidence of each learner
+164
+165        Parameters
+166        ----------
+167        X : array-like
+168            Set of instances
+169        y : array-like
+170            Set of classes for each instance
+171        """
+172        w = []
+173        w = [sum(confidence_interval(X, H, y, self.alpha)) / 2 for H in self.h_]
+174        self.confidences_ = w
+175
+176    def fit(self, X, y, estimator_kwards=None):
+177        """Fit Democratic-Co classifier
+178
+179        Parameters
+180        ----------
+181        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+182            The training input samples.
+183        y : array-like of shape (n_samples,)
+184            The target values (class labels), -1 if unlabel.
+185        estimator_kwards : {list, dict}, optional
+186            list of kwards for each estimator or kwards for all estimators, by default None
+187
+188        Returns
+189        -------
+190        self : DemocraticCoLearning
+191            fitted classifier
+192        """
+193
+194        X_label, y_label, X_unlabel = get_dataset(X, y)
+195
+196        is_df = isinstance(X_label, pd.DataFrame)
+197
+198        self.classes_ = np.unique(y_label)
+199        self.encoder = LabelEncoder().fit(y_label)
+200        y_label = self.encoder.transform(y_label)
+201
+202        self.one_hot.fit(y_label.reshape(-1, 1))
+203
+204        L = [X_label] * self.n_estimators
+205        Ly = [y_label] * self.n_estimators
+206        # This variable prevents duplicate instances.
+207        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
+208        e = [0] * self.n_estimators
+209
+210        if estimator_kwards is None:
+211            estimator_kwards = [{}] * self.n_estimators
+212
+213        changed = True
+214        iteration = 0
+215        while changed:
+216            changed = False
+217            iteration_dict = {}
+218            iteration += 1
+219
+220            for i in range(self.n_estimators):
+221                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
+222            if X_unlabel.shape[0] == 0:
+223                break
+224            # Majority Vote
+225            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
+226            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
+227            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
+228            #     0
+229            # ].flatten()  # K in pseudocode
+230
+231            L_ = [[]] * self.n_estimators
+232            Ly_ = [[]] * self.n_estimators
+233
+234            # Calculate confidence interval
+235            conf_interval = [
+236                confidence_interval(
+237                    X_label,
+238                    H,
+239                    y_label,
+240                    self.alpha
+241                )
+242                for H in self.base_estimator
+243            ]
+244
+245            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
+246            iteration_dict["weights"] = {
+247                "cl" + str(i): (l, h, w)
+248                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
+249            }
+250            # weighted vote
+251            weighted_class = self.__weighted_y(predictions, weights)
+252
+253            # If `weighted_class` is equal as `majority_class` then
+254            # the sum of classifier's weights of max voted class
+255            # is greater than the max of sum of classifier's weights
+256            # from another classes.
+257
+258            candidates = weighted_class == majority_class
+259            candidates_bool = list()
+260
+261            if not self.expand_only_mislabeled:
+262                all_same_list = list()
+263                for i in range(1, self.n_estimators):
+264                    all_same_list.append(predictions[i] == predictions[i - 1])
+265                all_same = np.logical_and(*all_same_list)
+266            # new_instances = []
+267            for i in range(self.n_estimators):
+268
+269                mispredictions = predictions[i] != weighted_class
+270                # An instance from U are added to Li' only if:
+271                #   It is a misprediction for i
+272                #   It is a candidate (weighted_class are same majority_class)
+273                #   It hasn't been added yet in Li
+274
+275                candidates_temp = np.logical_and(mispredictions, candidates)
+276
+277                if not self.expand_only_mislabeled:
+278                    candidates_temp = np.logical_or(candidates_temp, all_same)
+279
+280                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
+281
+282                candidates_bool.append(to_add)
+283                if is_df:
+284                    L_[i] = X_unlabel.iloc[to_add, :]
+285                else:
+286                    L_[i] = X_unlabel[to_add, :]
+287                Ly_[i] = weighted_class[to_add]
+288
+289            new_conf_interval = [
+290                confidence_interval(L[i], H, Ly[i], self.alpha)
+291                for i, H in enumerate(self.base_estimator)
+292            ]
+293            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
+294            for i, _ in enumerate(self.base_estimator):
+295                if len(L_[i]) > 0:
+296
+297                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
+298                    e_i = e_factor * len(L_[i])
+299                    # |Li|+|L'i| == |Li U L'i| because of to_add
+300                    q_i = (len(L[i]) + len(L_[i])) * (
+301                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
+302                    ) ** self.q_exp
+303                    if q_i <= qi:
+304                        continue
+305                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
+306                    if is_df:
+307                        L[i] = pd.concat([L[i], L_[i]])
+308                    else:
+309                        L[i] = np.concatenate((L[i], np.array(L_[i])))
+310                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
+311
+312                    e[i] = e[i] + e_i
+313                    changed = True
+314
+315        self.h_ = self.base_estimator
+316        self.__calcule_last_confidences(X_label, y_label)
+317
+318        # Ignore hypothesis
+319        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
+320        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+321
+322        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+323
+324        return self
+325
+326    def __combine_probabilities(self, X):
+327
+328        n_instances = X.shape[0]  # uppercase X as it will be an np.array
+329        sizes = np.zeros((n_instances, len(self.classes_)), dtype=int)
+330        C = np.zeros((n_instances, len(self.classes_)), dtype=float)
+331        Cavg = np.zeros((n_instances, len(self.classes_)), dtype=float)
+332
+333        for w, H in zip(self.confidences_, self.h_):
+334            cj = H.predict(X)
+335            factor = self.one_hot.transform(cj.reshape(-1, 1)).astype(int)
+336            C += w * factor
+337            sizes += factor
+338
+339        Cavg[sizes == 0] = 0.5  # «voting power» of 0.5 for small groups
+340        ne = (sizes != 0)  # non empty groups
+341        Cavg[ne] = (sizes[ne] + 0.5) / (sizes[ne] + 1) * C[ne] / sizes[ne]
+342
+343        return softmax(Cavg, axis=1)
+344
+345    def predict_proba(self, X):
+346        """Predict probability for each possible outcome.
+347
+348        Parameters
+349        ----------
+350        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+351            Array representing the data.
+352        Returns
+353        -------
+354        class probabilities: ndarray of shape (n_samples, n_classes)
+355            Array with prediction probabilities.
+356        """
+357        if "h_" in dir(self):
+358            if len(X) == 1:
+359                X = [X]
+360            return self.__combine_probabilities(X)
+361        else:
+362            raise NotFittedError("Classifier not fitted")
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + DemocraticCoLearning( base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)], n_estimators=None, expand_only_mislabeled=True, alpha=0.95, q_exp=2, random_state=None) + + + +
+ + 
 76    def __init__(
+ 77        self,
+ 78        base_estimator=[
+ 79            DecisionTreeClassifier(),
+ 80            GaussianNB(),
+ 81            KNeighborsClassifier(n_neighbors=3),
+ 82        ],
+ 83        n_estimators=None,
+ 84        expand_only_mislabeled=True,
+ 85        alpha=0.95,
+ 86        q_exp=2,
+ 87        random_state=None
+ 88    ):
+ 89        """
+ 90        Democratic Co-learning. Ensemble of classifiers of different types.
+ 91
+ 92        Parameters
+ 93        ----------
+ 94        base_estimator : {ClassifierMixin, list}, optional
+ 95            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
+ 96        n_estimators : int, optional
+ 97            number of base_estimators to use. None if base_estimator is a list, by default None
+ 98        expand_only_mislabeled : bool, optional
+ 99            expand only mislabeled instances by itself, by default True
+100        alpha : float, optional
+101            confidence level, by default 0.95
+102        q_exp : int, optional
+103            exponent for the estimation for error rate, by default 2
+104        random_state : int, RandomState instance, optional
+105            controls the randomness of the estimator, by default None
+106        Raises
+107        ------
+108        AttributeError
+109            If n_estimators is None and base_estimator is not a list
+110
+111        References
+112        ----------
+113        Y. Zhou and S. Goldman, "Democratic co-learning,"
+114        16th IEEE International Conference on Tools with Artificial Intelligence,
+115        2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.
+116        """
+117
+118        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
+119            estimators = list()
+120            random_available = True
+121            rand = check_random_state(random_state)
+122            if "random_state" not in dir(base_estimator):
+123                warnings.warn(
+124                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
+125                    ConvergenceWarning,
+126                )
+127                random_available = False
+128            for i in range(n_estimators):
+129                estimators.append(skclone(base_estimator))
+130                if random_available:
+131                    estimators[i].random_state = rand.randint(0, 1e5)
+132            self.base_estimator = estimators
+133
+134        elif isinstance(base_estimator, list):
+135            self.base_estimator = base_estimator
+136        else:
+137            raise AttributeError(
+138                "If `n_estimators` is None then `base_estimator` must be a `list`."
+139            )
+140        self.base_estimator = check_classifier(self.base_estimator)
+141        self.n_estimators = len(self.base_estimator)
+142        self.one_hot = OneHotEncoder(sparse_output=False)
+143        self.expand_only_mislabeled = expand_only_mislabeled
+144
+145        self.alpha = alpha
+146        self.q_exp = q_exp
+147        self.random_state = random_state
+
+ + +

Democratic Co-learning. Ensemble of classifiers of different types.

+ +
Parameters
+ +
    +
  • base_estimator ({ClassifierMixin, list}, optional): +An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • +
  • n_estimators (int, optional): +number of base_estimators to use. None if base_estimator is a list, by default None
    • +
  • expand_only_mislabeled (bool, optional): +expand only mislabeled instances by itself, by default True
    • +
  • alpha (float, optional): +confidence level, by default 0.95
    • +
  • q_exp (int, optional): +exponent for the estimation for error rate, by default 2
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
+ +
Raises
+ +
    +
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • +
+ +
References
+ +

Y. Zhou and S. Goldman, "Democratic co-learning," +16th IEEE International Conference on Tools with Artificial Intelligence, +2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

+
+ + +
+
+ +
+ + def + fit(self, X, y, estimator_kwards=None): + + + +
+ + 
176    def fit(self, X, y, estimator_kwards=None):
+177        """Fit Democratic-Co classifier
+178
+179        Parameters
+180        ----------
+181        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+182            The training input samples.
+183        y : array-like of shape (n_samples,)
+184            The target values (class labels), -1 if unlabel.
+185        estimator_kwards : {list, dict}, optional
+186            list of kwards for each estimator or kwards for all estimators, by default None
+187
+188        Returns
+189        -------
+190        self : DemocraticCoLearning
+191            fitted classifier
+192        """
+193
+194        X_label, y_label, X_unlabel = get_dataset(X, y)
+195
+196        is_df = isinstance(X_label, pd.DataFrame)
+197
+198        self.classes_ = np.unique(y_label)
+199        self.encoder = LabelEncoder().fit(y_label)
+200        y_label = self.encoder.transform(y_label)
+201
+202        self.one_hot.fit(y_label.reshape(-1, 1))
+203
+204        L = [X_label] * self.n_estimators
+205        Ly = [y_label] * self.n_estimators
+206        # This variable prevents duplicate instances.
+207        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
+208        e = [0] * self.n_estimators
+209
+210        if estimator_kwards is None:
+211            estimator_kwards = [{}] * self.n_estimators
+212
+213        changed = True
+214        iteration = 0
+215        while changed:
+216            changed = False
+217            iteration_dict = {}
+218            iteration += 1
+219
+220            for i in range(self.n_estimators):
+221                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
+222            if X_unlabel.shape[0] == 0:
+223                break
+224            # Majority Vote
+225            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
+226            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
+227            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
+228            #     0
+229            # ].flatten()  # K in pseudocode
+230
+231            L_ = [[]] * self.n_estimators
+232            Ly_ = [[]] * self.n_estimators
+233
+234            # Calculate confidence interval
+235            conf_interval = [
+236                confidence_interval(
+237                    X_label,
+238                    H,
+239                    y_label,
+240                    self.alpha
+241                )
+242                for H in self.base_estimator
+243            ]
+244
+245            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
+246            iteration_dict["weights"] = {
+247                "cl" + str(i): (l, h, w)
+248                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
+249            }
+250            # weighted vote
+251            weighted_class = self.__weighted_y(predictions, weights)
+252
+253            # If `weighted_class` is equal as `majority_class` then
+254            # the sum of classifier's weights of max voted class
+255            # is greater than the max of sum of classifier's weights
+256            # from another classes.
+257
+258            candidates = weighted_class == majority_class
+259            candidates_bool = list()
+260
+261            if not self.expand_only_mislabeled:
+262                all_same_list = list()
+263                for i in range(1, self.n_estimators):
+264                    all_same_list.append(predictions[i] == predictions[i - 1])
+265                all_same = np.logical_and(*all_same_list)
+266            # new_instances = []
+267            for i in range(self.n_estimators):
+268
+269                mispredictions = predictions[i] != weighted_class
+270                # An instance from U are added to Li' only if:
+271                #   It is a misprediction for i
+272                #   It is a candidate (weighted_class are same majority_class)
+273                #   It hasn't been added yet in Li
+274
+275                candidates_temp = np.logical_and(mispredictions, candidates)
+276
+277                if not self.expand_only_mislabeled:
+278                    candidates_temp = np.logical_or(candidates_temp, all_same)
+279
+280                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
+281
+282                candidates_bool.append(to_add)
+283                if is_df:
+284                    L_[i] = X_unlabel.iloc[to_add, :]
+285                else:
+286                    L_[i] = X_unlabel[to_add, :]
+287                Ly_[i] = weighted_class[to_add]
+288
+289            new_conf_interval = [
+290                confidence_interval(L[i], H, Ly[i], self.alpha)
+291                for i, H in enumerate(self.base_estimator)
+292            ]
+293            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
+294            for i, _ in enumerate(self.base_estimator):
+295                if len(L_[i]) > 0:
+296
+297                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
+298                    e_i = e_factor * len(L_[i])
+299                    # |Li|+|L'i| == |Li U L'i| because of to_add
+300                    q_i = (len(L[i]) + len(L_[i])) * (
+301                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
+302                    ) ** self.q_exp
+303                    if q_i <= qi:
+304                        continue
+305                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
+306                    if is_df:
+307                        L[i] = pd.concat([L[i], L_[i]])
+308                    else:
+309                        L[i] = np.concatenate((L[i], np.array(L_[i])))
+310                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
+311
+312                    e[i] = e[i] + e_i
+313                    changed = True
+314
+315        self.h_ = self.base_estimator
+316        self.__calcule_last_confidences(X_label, y_label)
+317
+318        # Ignore hypothesis
+319        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
+320        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+321
+322        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+323
+324        return self
+
+ + +

Fit Democratic-Co classifier

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabel.
    • +
  • estimator_kwards ({list, dict}, optional): +list of kwards for each estimator or kwards for all estimators, by default None
    • +
+ +
Returns
+ +
    +
  • self (DemocraticCoLearning): +fitted classifier
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X): + + + +
+ + 
345    def predict_proba(self, X):
+346        """Predict probability for each possible outcome.
+347
+348        Parameters
+349        ----------
+350        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+351            Array representing the data.
+352        Returns
+353        -------
+354        class probabilities: ndarray of shape (n_samples, n_classes)
+355            Array with prediction probabilities.
+356        """
+357        if "h_" in dir(self):
+358            if len(X) == 1:
+359                X = [X]
+360            return self.__combine_probabilities(X)
+361        else:
+362            raise NotFittedError("Classifier not fitted")
+
+ + +

Predict probability for each possible outcome.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +Array representing the data.
    • +
+ +
Returns
+ +
    +
  • class probabilities (ndarray of shape (n_samples, n_classes)): +Array with prediction probabilities.
    • +
+
+ + +
+
+
+ + def + set_fit_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ +
+ + class + Setred(sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator): + + + +
+ + 
105class Setred(ClassifierMixin, BaseEstimator):
+106    def __init__(
+107        self,
+108        base_estimator=KNeighborsClassifier(n_neighbors=3),
+109        max_iterations=40,
+110        distance="euclidean",
+111        poolsize=0.25,
+112        rejection_threshold=0.05,
+113        graph_neighbors=1,
+114        random_state=None,
+115        n_jobs=None,
+116    ):
+117        """
+118        Create a SETRED classifier.
+119        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
+120        
+121        Parameters
+122        ----------
+123        base_estimator : ClassifierMixin, optional
+124            An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
+125        max_iterations : int, optional
+126            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
+127        distance : str, optional
+128            The distance metric to use for the graph.
+129            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
+130            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
+131            Note that the “cosine” metric uses cosine_distances., by default "euclidean"
+132        poolsize : float, optional
+133            Max number of unlabel instances candidates to pseudolabel, by default 0.25
+134        rejection_threshold : float, optional
+135            significance level, by default 0.1
+136        graph_neighbors : int, optional
+137            Number of neighbors for each sample., by default 1
+138        random_state : int, RandomState instance, optional
+139            controls the randomness of the estimator, by default None
+140        n_jobs : int, optional
+141            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
+142        
+143        References
+144        ----------
+145        Li, Ming, and Zhi-Hua Zhou. "SETRED: Self-training with editing."
+146        Pacific-Asia Conference on Knowledge Discovery and Data Mining.
+147        Springer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.
+148        """
+149        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
+150        self.max_iterations = max_iterations
+151        self.poolsize = poolsize
+152        self.distance = distance
+153        self.rejection_threshold = rejection_threshold
+154        self.graph_neighbors = graph_neighbors
+155        self.random_state = random_state
+156        self.n_jobs = n_jobs
+157
+158    def __create_neighborhood(self, X):
+159        # kneighbors_graph(X, 1, metric=self.distance, n_jobs=self.n_jobs).toarray()
+160        return kneighbors_graph(
+161            X, self.graph_neighbors, metric=self.distance, n_jobs=self.n_jobs, mode="distance"
+162        ).toarray()
+163
+164    def fit(self, X, y, **kwars):
+165        """Build a Setred classifier from the training set (X, y).
+166
+167        Parameters
+168        ----------
+169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+170            The training input samples.
+171        y : array-like of shape (n_samples,)
+172            The target values (class labels), -1 if unlabeled.
+173
+174        Returns
+175        -------
+176        self: Setred
+177            Fitted estimator.
+178        """        
+179        random_state = check_random_state(self.random_state)
+180
+181        X_label, y_label, X_unlabel = get_dataset(X, y)
+182
+183        is_df = isinstance(X_label, pd.DataFrame)
+184
+185        self.classes_ = np.unique(y_label)
+186
+187        each_iteration_candidates = X_label.shape[0]
+188
+189        pool = int(len(X_unlabel) * self.poolsize)
+190        self._base_estimator = skclone(self.base_estimator)
+191
+192        self._base_estimator.fit(X_label, y_label, **kwars)
+193
+194        y_probabilities = calculate_prior_probability(
+195            y_label
+196        )  # Should probabilities change every iteration or may it keep with the first L?
+197
+198        sort_idx = np.argsort(list(y_probabilities.keys()))
+199
+200        if X_unlabel.shape[0] == 0:
+201            return self
+202
+203        for _ in range(self.max_iterations):
+204            U_ = resample(
+205                X_unlabel, replace=False, n_samples=pool, random_state=random_state
+206            )
+207
+208            if is_df:
+209                U_ = pd.DataFrame(U_, columns=X_label.columns)
+210
+211            raw_predictions = self._base_estimator.predict_proba(U_)
+212            predictions = np.max(raw_predictions, axis=1)
+213            class_predicted = np.argmax(raw_predictions, axis=1)
+214            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
+215            indexes = predictions.argsort()[-each_iteration_candidates:]
+216
+217            if is_df:
+218                L_ = U_.iloc[indexes]
+219            else:
+220                L_ = U_[indexes]
+221            y_ = np.array(
+222                list(
+223                    map(
+224                        lambda x: self._base_estimator.classes_[x],
+225                        class_predicted[indexes],
+226                    )
+227                )
+228            )
+229
+230            if is_df:
+231                pre_L = pd.concat([X_label, L_])
+232            else:
+233                pre_L = np.concatenate((X_label, L_), axis=0)
+234
+235            weights = self.__create_neighborhood(pre_L)
+236            #  Keep only weights for L_
+237            weights = weights[-L_.shape[0]:, :]
+238
+239            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
+240            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
+241            #  Must weights be the inverse of distance?
+242            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
+243
+244            weights_sum = weights.sum(axis=1)
+245            weights_square_sum = (weights ** 2).sum(axis=1)
+246
+247            iid_random = random_state.binomial(
+248                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
+249            )
+250            ji = (iid_random * weights).sum(axis=1)
+251
+252            mu_h0 = p_wrong * weights_sum
+253            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
+254            
+255            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
+256            # z_score = (ji - mu_h0) / sigma_h0
+257            
+258            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
+259            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
+260
+261            if is_df:
+262                L_filtered = L_.iloc[to_add, :]
+263            else:
+264                L_filtered = L_[to_add, :]
+265            y_filtered = y_[to_add]
+266            
+267            if is_df:
+268                X_label = pd.concat([X_label, L_filtered])
+269            else:
+270                X_label = np.concatenate((X_label, L_filtered), axis=0)
+271            y_label = np.concatenate((y_label, y_filtered), axis=0)
+272
+273            #  Remove the instances from the unlabeled set.
+274            to_delete = indexes[to_add]
+275            if is_df:
+276                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
+277            else:
+278                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
+279
+280        return self
+281
+282    def predict(self, X, **kwards):
+283        """Predict class value for X.
+284        For a classification model, the predicted class for each sample in X is returned.
+285        Parameters
+286        ----------
+287        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+288            The input samples.
+289        Returns
+290        -------
+291        y : array-like of shape (n_samples,)
+292            The predicted classes
+293        """
+294        return self._base_estimator.predict(X, **kwards)
+295
+296    def predict_proba(self, X, **kwards):
+297        """Predict class probabilities of the input samples X.
+298        The predicted class probability depends on the ensemble estimator.
+299        Parameters
+300        ----------
+301        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+302            The input samples.
+303        Returns
+304        -------
+305        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+306            The predicted classes
+307        """
+308        return self._base_estimator.predict_proba(X, **kwards)
+
+ + +

Mixin class for all classifiers in scikit-learn.

+
+ + +
+ +
+ + Setred( base_estimator=KNeighborsClassifier(n_neighbors=3), max_iterations=40, distance='euclidean', poolsize=0.25, rejection_threshold=0.05, graph_neighbors=1, random_state=None, n_jobs=None) + + + +
+ + 
106    def __init__(
+107        self,
+108        base_estimator=KNeighborsClassifier(n_neighbors=3),
+109        max_iterations=40,
+110        distance="euclidean",
+111        poolsize=0.25,
+112        rejection_threshold=0.05,
+113        graph_neighbors=1,
+114        random_state=None,
+115        n_jobs=None,
+116    ):
+117        """
+118        Create a SETRED classifier.
+119        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
+120        
+121        Parameters
+122        ----------
+123        base_estimator : ClassifierMixin, optional
+124            An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
+125        max_iterations : int, optional
+126            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
+127        distance : str, optional
+128            The distance metric to use for the graph.
+129            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
+130            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
+131            Note that the “cosine” metric uses cosine_distances., by default "euclidean"
+132        poolsize : float, optional
+133            Max number of unlabel instances candidates to pseudolabel, by default 0.25
+134        rejection_threshold : float, optional
+135            significance level, by default 0.1
+136        graph_neighbors : int, optional
+137            Number of neighbors for each sample., by default 1
+138        random_state : int, RandomState instance, optional
+139            controls the randomness of the estimator, by default None
+140        n_jobs : int, optional
+141            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
+142        
+143        References
+144        ----------
+145        Li, Ming, and Zhi-Hua Zhou. "SETRED: Self-training with editing."
+146        Pacific-Asia Conference on Knowledge Discovery and Data Mining.
+147        Springer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.
+148        """
+149        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
+150        self.max_iterations = max_iterations
+151        self.poolsize = poolsize
+152        self.distance = distance
+153        self.rejection_threshold = rejection_threshold
+154        self.graph_neighbors = graph_neighbors
+155        self.random_state = random_state
+156        self.n_jobs = n_jobs
+
+ + +

Create a SETRED classifier. +It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
    • +
  • max_iterations (int, optional): +Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • +
  • distance (str, optional): +The distance metric to use for the graph. +The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric. +For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS. +Note that the “cosine” metric uses cosine_distances., by default "euclidean"
    • +
  • poolsize (float, optional): +Max number of unlabel instances candidates to pseudolabel, by default 0.25
    • +
  • rejection_threshold (float, optional): +significance level, by default 0.1
    • +
  • graph_neighbors (int, optional): +Number of neighbors for each sample., by default 1
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
  • n_jobs (int, optional): +The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • +
+ +
References
+ +

Li, Ming, and Zhi-Hua Zhou. "SETRED: Self-training with editing." +Pacific-Asia Conference on Knowledge Discovery and Data Mining. +Springer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwars): + + + +
+ + 
164    def fit(self, X, y, **kwars):
+165        """Build a Setred classifier from the training set (X, y).
+166
+167        Parameters
+168        ----------
+169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+170            The training input samples.
+171        y : array-like of shape (n_samples,)
+172            The target values (class labels), -1 if unlabeled.
+173
+174        Returns
+175        -------
+176        self: Setred
+177            Fitted estimator.
+178        """        
+179        random_state = check_random_state(self.random_state)
+180
+181        X_label, y_label, X_unlabel = get_dataset(X, y)
+182
+183        is_df = isinstance(X_label, pd.DataFrame)
+184
+185        self.classes_ = np.unique(y_label)
+186
+187        each_iteration_candidates = X_label.shape[0]
+188
+189        pool = int(len(X_unlabel) * self.poolsize)
+190        self._base_estimator = skclone(self.base_estimator)
+191
+192        self._base_estimator.fit(X_label, y_label, **kwars)
+193
+194        y_probabilities = calculate_prior_probability(
+195            y_label
+196        )  # Should probabilities change every iteration or may it keep with the first L?
+197
+198        sort_idx = np.argsort(list(y_probabilities.keys()))
+199
+200        if X_unlabel.shape[0] == 0:
+201            return self
+202
+203        for _ in range(self.max_iterations):
+204            U_ = resample(
+205                X_unlabel, replace=False, n_samples=pool, random_state=random_state
+206            )
+207
+208            if is_df:
+209                U_ = pd.DataFrame(U_, columns=X_label.columns)
+210
+211            raw_predictions = self._base_estimator.predict_proba(U_)
+212            predictions = np.max(raw_predictions, axis=1)
+213            class_predicted = np.argmax(raw_predictions, axis=1)
+214            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
+215            indexes = predictions.argsort()[-each_iteration_candidates:]
+216
+217            if is_df:
+218                L_ = U_.iloc[indexes]
+219            else:
+220                L_ = U_[indexes]
+221            y_ = np.array(
+222                list(
+223                    map(
+224                        lambda x: self._base_estimator.classes_[x],
+225                        class_predicted[indexes],
+226                    )
+227                )
+228            )
+229
+230            if is_df:
+231                pre_L = pd.concat([X_label, L_])
+232            else:
+233                pre_L = np.concatenate((X_label, L_), axis=0)
+234
+235            weights = self.__create_neighborhood(pre_L)
+236            #  Keep only weights for L_
+237            weights = weights[-L_.shape[0]:, :]
+238
+239            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
+240            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
+241            #  Must weights be the inverse of distance?
+242            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
+243
+244            weights_sum = weights.sum(axis=1)
+245            weights_square_sum = (weights ** 2).sum(axis=1)
+246
+247            iid_random = random_state.binomial(
+248                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
+249            )
+250            ji = (iid_random * weights).sum(axis=1)
+251
+252            mu_h0 = p_wrong * weights_sum
+253            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
+254            
+255            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
+256            # z_score = (ji - mu_h0) / sigma_h0
+257            
+258            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
+259            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
+260
+261            if is_df:
+262                L_filtered = L_.iloc[to_add, :]
+263            else:
+264                L_filtered = L_[to_add, :]
+265            y_filtered = y_[to_add]
+266            
+267            if is_df:
+268                X_label = pd.concat([X_label, L_filtered])
+269            else:
+270                X_label = np.concatenate((X_label, L_filtered), axis=0)
+271            y_label = np.concatenate((y_label, y_filtered), axis=0)
+272
+273            #  Remove the instances from the unlabeled set.
+274            to_delete = indexes[to_add]
+275            if is_df:
+276                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
+277            else:
+278                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
+279
+280        return self
+
+ + +

Build a Setred classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabeled.
    • +
+ +
Returns
+ +
    +
  • self (Setred): +Fitted estimator.
    • +
+
+ + +
+
+ +
+ + def + predict(self, X, **kwards): + + + +
+ + 
282    def predict(self, X, **kwards):
+283        """Predict class value for X.
+284        For a classification model, the predicted class for each sample in X is returned.
+285        Parameters
+286        ----------
+287        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+288            The input samples.
+289        Returns
+290        -------
+291        y : array-like of shape (n_samples,)
+292            The predicted classes
+293        """
+294        return self._base_estimator.predict(X, **kwards)
+
+ + +

Predict class value for X. +For a classification model, the predicted class for each sample in X is returned.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • y (array-like of shape (n_samples,)): +The predicted classes
    • +
+
+ + +
+
+ +
+ + def + predict_proba(self, X, **kwards): + + + +
+ + 
296    def predict_proba(self, X, **kwards):
+297        """Predict class probabilities of the input samples X.
+298        The predicted class probability depends on the ensemble estimator.
+299        Parameters
+300        ----------
+301        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+302            The input samples.
+303        Returns
+304        -------
+305        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+306            The predicted classes
+307        """
+308        return self._base_estimator.predict_proba(X, **kwards)
+
+ + +

Predict class probabilities of the input samples X. +The predicted class probability depends on the ensemble estimator.

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The input samples.
    • +
+ +
Returns
+ +
    +
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1): +The predicted classes
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sklearn.base.ClassifierMixin
+
score
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
+
+
+
+ +
+ + class + CoForest(sslearn.wrapper._co.BaseCoTraining): + + + +
+ + 
1040class CoForest(BaseCoTraining):
+1041    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1042        """
+1043        Generate a CoForest classifier.
+1044        A SSL Random Forest adaption for CoTraining. 
+1045
+1046        Parameters
+1047        ----------
+1048        base_estimator : ClassifierMixin, optional
+1049            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1050        n_estimators : int, optional
+1051            The number of base estimators in the ensemble., by default 7
+1052        threshold : float, optional
+1053            The decision threshold. Should be in [0, 1)., by default 0.5
+1054        n_jobs : int, optional
+1055            The number of jobs to run in parallel for both fit and predict., by default None
+1056        bootstrap : bool, optional
+1057            Whether bootstrap samples are used when building estimators., by default True
+1058        random_state : int, RandomState instance, optional
+1059            controls the randomness of the estimator, by default None
+1060        **kwards : dict, optional
+1061            Additional parameters to be passed to base_estimator, by default None.
+1062
+1063        References
+1064        ----------
+1065        Li, M., & Zhou, Z.-H. (2007).
+1066        Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.
+1067        <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,
+1068        37(6), 1088-1098. doi:10.1109/tsmca.2007.904745
+1069        """
+1070        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1071        self.n_estimators = n_estimators
+1072        self.threshold = threshold
+1073        self.bootstrap = bootstrap
+1074        self._epsilon = sys.float_info.epsilon
+1075        self.n_jobs = n_jobs
+1076        self.random_state = random_state
+1077        self.version = version
+1078        if self.version == "1.0.2":
+1079            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+1080
+1081    def __bootstraping(self, X, y, r_state):
+1082        # It is necessary to bootstrap the data
+1083        if self.bootstrap and self.version == "1.0.3":
+1084            is_df = isinstance(X, pd.DataFrame)
+1085            columns = None
+1086            if is_df:
+1087                columns = X.columns
+1088                X = X.to_numpy()
+1089            y = y.copy()
+1090            # Get a reprentation of each class
+1091            classes = np.unique(y)
+1092            # Choose at least one sample from each class
+1093            X_label, y_label = [], []
+1094            for c in classes:
+1095                index = np.where(y == c)[0]
+1096                # Choose one sample from each class
+1097                X_label.append(X[index[0], :])
+1098                y_label.append(y[index[0]])
+1099                # Remove the sample from the original data
+1100                X = np.delete(X, index[0], axis=0)
+1101                y = np.delete(y, index[0], axis=0)
+1102            X, y = resample(X, y, random_state=r_state)
+1103            X = np.concatenate((X, np.array(X_label)), axis=0)
+1104            y = np.concatenate((y, np.array(y_label)), axis=0)
+1105            if is_df:
+1106                X = pd.DataFrame(X, columns=columns)
+1107        return X, y
+1108
+1109    def __estimate_error(self, hypothesis, X, y, index):
+1110        if self.version == "1.0.3":
+1111            concomitants = [h for i, h in enumerate(self.hypotheses) if i != index]
+1112            predicted = [h.predict(X) for h in concomitants]
+1113            predicted = np.array(predicted, dtype=y.dtype)
+1114            # Get the majority vote
+1115            predicted, _ = mode(predicted)
+1116            # predicted, _ = st.mode(predicted, axis=1)
+1117            # Get the error rate
+1118            return 1 - accuracy_score(y, predicted)
+1119        else:
+1120            probas = hypothesis.predict_proba(X)
+1121            ei_t = 0
+1122            classes = list(hypothesis.classes_)
+1123            for j in range(y.shape[0]):
+1124                true_y = y[j]
+1125                true_y_index = classes.index(true_y)
+1126                ei_t += 1 - probas[j, true_y_index]
+1127            if ei_t == 0:
+1128                ei_t = self._epsilon
+1129            return ei_t
+1130
+1131    def __confidence(self, h_index, X):
+1132        concomitants = [h for i, h in enumerate(self.hypotheses) if i != h_index]
+1133
+1134        predicted = [h.predict(X) for h in concomitants]
+1135        predicted = np.array(predicted, dtype=predicted[0].dtype)
+1136        # Get the majority vote and the number of votes
+1137        _, counts = mode(predicted)
+1138        # _, counts = st.mode(predicted, axis=1)
+1139        confidences = counts / len(concomitants)
+1140        return confidences
+1141
+1142    def _fit_estimator(self, X, y, i, beginning=False, **kwards):
+1143        estimator = self.base_estimator
+1144        if type(self.base_estimator) == list:
+1145            estimator = skclone(self.hypotheses[i])
+1146
+1147        if "random_state" in estimator.get_params():
+1148            r_state = estimator.random_state
+1149        else:
+1150            r_state = self.random_state
+1151            if r_state is None:
+1152                r_state = np.random.randint(0, 1000)
+1153            r_state += i
+1154        # Only in the beginning
+1155        if beginning:
+1156            X, y = self.__bootstraping(X, y, r_state)
+1157
+1158        return skclone(estimator).fit(X, y, **kwards)
+1159
+1160    def fit(self, X, y, **kwards):
+1161        """Build a CoForest classifier from the training set (X, y).
+1162
+1163        Parameters
+1164        ----------
+1165        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1166            The training input samples.
+1167        y : array-like of shape (n_samples,)
+1168            The target values (class labels), -1 if unlabel.
+1169
+1170        Returns
+1171        -------
+1172        self: CoForest
+1173            Fitted estimator.
+1174        """
+1175        random_state = check_random_state(self.random_state)
+1176        n_jobs = check_n_jobs(self.n_jobs)
+1177
+1178        X_label, y_label, X_unlabel = get_dataset(X, y)
+1179
+1180        is_df = isinstance(X_label, pd.DataFrame)
+1181
+1182        self.classes_ = np.unique(y_label)
+1183
+1184        self.hypotheses = []
+1185        errors = []
+1186        weights = []
+1187        for i in range(self.n_estimators):
+1188            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1189            if "random_state" in dir(self.hypotheses[-1]):
+1190                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
+1191            errors.append(0.5)
+1192
+1193        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1194            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1195            for i in range(self.n_estimators)
+1196        )
+1197
+1198        for i in range(self.n_estimators):
+1199            # The paper stablishes that the weight of each hypothesis is 0,
+1200            # but it is not possible to do that because it will be impossible increase the training set
+1201            if self.version == "1.0.2":
+1202                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1203            else:
+1204                weights.append(self.__confidence(i, X_label).sum())
+1205
+1206        changing = True if X_unlabel.shape[0] > 0 else False
+1207        while changing:
+1208            changing = False
+1209            for i in range(self.n_estimators):
+1210                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1211
+1212                ei_t = self.__estimate_error(hi, X_label, y_label, i)
+1213
+1214                if ei_t < ei:
+1215                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1216                    random_index_subsample = random_state.permutation(
+1217                        random_index_subsample
+1218                    )
+1219                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1220                    if is_df:
+1221                        Ui_t = X_unlabel.iloc[cond, :]
+1222                    else:
+1223                        Ui_t = X_unlabel[cond, :]
+1224
+1225                    raw_predictions = hi.predict_proba(Ui_t)
+1226                    predictions = np.max(raw_predictions, axis=1)
+1227                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1228
+1229                    to_label = predictions > self.threshold
+1230                    wi_t = predictions[to_label].sum()
+1231
+1232                    if ei_t * wi_t < ei * wi:
+1233                        changing = True
+1234                        if is_df:
+1235                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1236                        else:
+1237                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1238                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1239                        hi.fit(
+1240                            x_temp,
+1241                            y_temp,
+1242                            **kwards
+1243                        )
+1244
+1245                    errors[i] = ei_t
+1246                    weights[i] = wi_t
+1247
+1248        self.h_ = self.hypotheses
+1249        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1250
+1251        return self
+
+ + +

Base class for all estimators in scikit-learn.

+ +
Notes
+ +

All estimators should specify all the parameters that can be set +at the class level in their __init__ as explicit keyword +arguments (no *args or **kwargs).

+
+ + +
+ +
+ + CoForest( base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version='1.0.3') + + + +
+ + 
1041    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1042        """
+1043        Generate a CoForest classifier.
+1044        A SSL Random Forest adaption for CoTraining. 
+1045
+1046        Parameters
+1047        ----------
+1048        base_estimator : ClassifierMixin, optional
+1049            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1050        n_estimators : int, optional
+1051            The number of base estimators in the ensemble., by default 7
+1052        threshold : float, optional
+1053            The decision threshold. Should be in [0, 1)., by default 0.5
+1054        n_jobs : int, optional
+1055            The number of jobs to run in parallel for both fit and predict., by default None
+1056        bootstrap : bool, optional
+1057            Whether bootstrap samples are used when building estimators., by default True
+1058        random_state : int, RandomState instance, optional
+1059            controls the randomness of the estimator, by default None
+1060        **kwards : dict, optional
+1061            Additional parameters to be passed to base_estimator, by default None.
+1062
+1063        References
+1064        ----------
+1065        Li, M., & Zhou, Z.-H. (2007).
+1066        Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.
+1067        <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,
+1068        37(6), 1088-1098. doi:10.1109/tsmca.2007.904745
+1069        """
+1070        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1071        self.n_estimators = n_estimators
+1072        self.threshold = threshold
+1073        self.bootstrap = bootstrap
+1074        self._epsilon = sys.float_info.epsilon
+1075        self.n_jobs = n_jobs
+1076        self.random_state = random_state
+1077        self.version = version
+1078        if self.version == "1.0.2":
+1079            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+
+ + +

Generate a CoForest classifier. +A SSL Random Forest adaption for CoTraining.

+ +
Parameters
+ +
    +
  • base_estimator (ClassifierMixin, optional): +An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • +
  • n_estimators (int, optional): +The number of base estimators in the ensemble., by default 7
    • +
  • threshold (float, optional): +The decision threshold. Should be in [0, 1)., by default 0.5
    • +
  • n_jobs (int, optional): +The number of jobs to run in parallel for both fit and predict., by default None
    • +
  • bootstrap (bool, optional): +Whether bootstrap samples are used when building estimators., by default True
    • +
  • random_state (int, RandomState instance, optional): +controls the randomness of the estimator, by default None
    • +
  • **kwards (dict, optional): +Additional parameters to be passed to base_estimator, by default None.
    • +
+ +
References
+ +

Li, M., & Zhou, Z.-H. (2007). +Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. +IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, +37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

+
+ + +
+
+ +
+ + def + fit(self, X, y, **kwards): + + + +
+ + 
1160    def fit(self, X, y, **kwards):
+1161        """Build a CoForest classifier from the training set (X, y).
+1162
+1163        Parameters
+1164        ----------
+1165        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1166            The training input samples.
+1167        y : array-like of shape (n_samples,)
+1168            The target values (class labels), -1 if unlabel.
+1169
+1170        Returns
+1171        -------
+1172        self: CoForest
+1173            Fitted estimator.
+1174        """
+1175        random_state = check_random_state(self.random_state)
+1176        n_jobs = check_n_jobs(self.n_jobs)
+1177
+1178        X_label, y_label, X_unlabel = get_dataset(X, y)
+1179
+1180        is_df = isinstance(X_label, pd.DataFrame)
+1181
+1182        self.classes_ = np.unique(y_label)
+1183
+1184        self.hypotheses = []
+1185        errors = []
+1186        weights = []
+1187        for i in range(self.n_estimators):
+1188            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1189            if "random_state" in dir(self.hypotheses[-1]):
+1190                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
+1191            errors.append(0.5)
+1192
+1193        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1194            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1195            for i in range(self.n_estimators)
+1196        )
+1197
+1198        for i in range(self.n_estimators):
+1199            # The paper stablishes that the weight of each hypothesis is 0,
+1200            # but it is not possible to do that because it will be impossible increase the training set
+1201            if self.version == "1.0.2":
+1202                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1203            else:
+1204                weights.append(self.__confidence(i, X_label).sum())
+1205
+1206        changing = True if X_unlabel.shape[0] > 0 else False
+1207        while changing:
+1208            changing = False
+1209            for i in range(self.n_estimators):
+1210                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1211
+1212                ei_t = self.__estimate_error(hi, X_label, y_label, i)
+1213
+1214                if ei_t < ei:
+1215                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1216                    random_index_subsample = random_state.permutation(
+1217                        random_index_subsample
+1218                    )
+1219                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1220                    if is_df:
+1221                        Ui_t = X_unlabel.iloc[cond, :]
+1222                    else:
+1223                        Ui_t = X_unlabel[cond, :]
+1224
+1225                    raw_predictions = hi.predict_proba(Ui_t)
+1226                    predictions = np.max(raw_predictions, axis=1)
+1227                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1228
+1229                    to_label = predictions > self.threshold
+1230                    wi_t = predictions[to_label].sum()
+1231
+1232                    if ei_t * wi_t < ei * wi:
+1233                        changing = True
+1234                        if is_df:
+1235                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1236                        else:
+1237                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1238                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1239                        hi.fit(
+1240                            x_temp,
+1241                            y_temp,
+1242                            **kwards
+1243                        )
+1244
+1245                    errors[i] = ei_t
+1246                    weights[i] = wi_t
+1247
+1248        self.h_ = self.hypotheses
+1249        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1250
+1251        return self
+
+ + +

Build a CoForest classifier from the training set (X, y).

+ +
Parameters
+ +
    +
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)): +The training input samples.
    • +
  • y (array-like of shape (n_samples,)): +The target values (class labels), -1 if unlabel.
    • +
+ +
Returns
+ +
    +
  • self (CoForest): +Fitted estimator.
    • +
+
+ + +
+
+
+ + def + set_score_request(unknown): + + +
+ + +

A descriptor for request methods.

+ +

New in version 1.3.

+ +
Parameters
+ +
    +
  • name (str): +The name of the method for which the request function should be +created, e.g. "fit" would create a set_fit_request function.
    • +
  • keys (list of str): +A list of strings which are accepted parameters by the created +function, e.g. ["sample_weight"] if the corresponding method +accepts it as a metadata.
    • +
  • validate_keys (bool, default=True): +Whether to check if the requested parameters fit the actual parameters +of the method.
    • +
+ +
Notes
+ +

This class is a descriptor 1 and uses PEP-362 to set the signature of +the returned function 2.

+ +
References
+ +
+
+
    +
  1. +

    https://docs.python.org/3/howto/descriptor.html ↩

    +
    2. + +
  2. +

    https://www.python.org/dev/peps/pep-0362/ ↩

    +
    3. +
+
+
+ + +
+
+
Inherited Members
+
+
sslearn.wrapper._co.BaseCoTraining
+
predict_proba
+ +
+
sklearn.base.BaseEstimator
+
get_params
+
set_params
+ +
+
sklearn.utils._metadata_requests._MetadataRequester
+
get_metadata_routing
+ +
+
sklearn.base.ClassifierMixin
+
score
+ +
+ +
+
+
+
+ + \ No newline at end of file diff --git a/docs/sslearn_mini.svg b/docs/sslearn_mini.svg new file mode 100644 index 0000000..f8b0b13 --- /dev/null +++ b/docs/sslearn_mini.svg @@ -0,0 +1,101 @@ + + + + + + + + * + * + * + ssl + + + + diff --git a/setup.py b/setup.py index 8af2b50..e483cc0 100644 --- a/setup.py +++ b/setup.py @@ -12,7 +12,7 @@ def get_version(): version = get_version() -url = f"https://github.com/jlgarridol/sslearn/archive/refs/tags/f{version}.tar.gz" +url = f"https://github.com/jlgarridol/sslearn/archive/refs/tags/{version}.tar.gz" setuptools.setup( name='sslearn', @@ -42,5 +42,7 @@ def get_version(): 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', ] ) diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..6e7fb7f --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,14 @@ + + +https://pdoc.dev/docs/ +https://pdoc.dev/docs/sslearn.html +https://pdoc.dev/docs/sslearn/subview.html +https://pdoc.dev/docs/sslearn/model_selection.html +https://pdoc.dev/docs/sslearn/base.html +https://pdoc.dev/docs/sslearn/datasets.html +https://pdoc.dev/docs/sslearn/wrapper.html +https://pdoc.dev/docs/sslearn/restricted.html +https://pdoc.dev/docs/sslearn/utils.html + \ No newline at end of file diff --git a/sslearn/__init__.py b/sslearn/__init__.py index 7218536..92194ae 100644 --- a/sslearn/__init__.py +++ b/sslearn/__init__.py @@ -1,4 +1,11 @@ +# Open README.md and added to __doc__ +with open("../README.md", "r") as f: + __doc__ = f.read() + + __version__='1.0.4.1' __AUTHOR__="José Luis Garrido-Labrador" # Author of the package __AUTHOR_EMAIL__="jlgarrido@ubu.es" # Author's email __URL__="https://pypi.org/project/sslearn/" + + diff --git a/sslearn/base.py b/sslearn/base.py index 158010d..32d46fa 100644 --- a/sslearn/base.py +++ b/sslearn/base.py @@ -1,3 +1,23 @@ +""" +Summary of module `sslearn.base`: + +Functions +--------- +get_dataset(X, y): + Check and divide dataset between labeled and unlabeled data. + +Classes +------- +FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): + Create a classifier that fakes predict_proba method if it does not exist. + +OneVsRestSSLClassifier(OneVsRestClassifier): + Adapted OneVsRestClassifier for SSL datasets + +All doc +---- +""" + import array import warnings from abc import ABC, abstractmethod @@ -19,7 +39,29 @@ from sklearn.ensemble._base import _set_random_states from sklearn.utils import check_random_state +__all__ = ["FakedProbaClassifier", "get_dataset", "OneVsRestSSLClassifier"] + + + def get_dataset(X, y): + """Check and divide dataset between labeled and unlabeled data. + + Parameters + ---------- + X : ndarray or DataFrame of shape (n_samples, n_features) + Features matrix. + y : ndarray of shape (n_samples,) + Target vector. + + Returns + ------- + X_label : ndarray or DataFrame of shape (n_label, n_features) + Labeled features matrix. + y_label : ndarray or Serie of shape (n_label,) + Labeled target vector. + X_unlabel : ndarray or Serie DataFrame of shape (n_unlabel, n_features) + Unlabeled features matrix. + """ is_df = False if isinstance(X, pd.DataFrame): @@ -73,18 +115,63 @@ def predict(self, X): class FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): def __init__(self, base_estimator): + """Create a classifier that fakes predict_proba method if it does not exist. + + Parameters + ---------- + base_estimator : ClassifierMixin + A classifier that implements fit and predict methods. + """ self.base_estimator = base_estimator def fit(self, X, y): + """Fit a FakedProbaClassifier. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + y : {array-like, sparse matrix} of shape (n_samples,) + The target values. + + Returns + ------- + self : FakedProbaClassifier + Returns self. + """ self.classes_ = np.unique(y) self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1)) self.base_estimator.fit(X, y) return self def predict(self, X): + """Predict the classes of X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Array representing the data. + + Returns + ------- + y : ndarray of shape (n_samples,) + Array with predicted labels. + """ return self.base_estimator.predict(X) def predict_proba(self, X): + """Predict the probabilities of each class for X. + If the base estimator does not have a predict_proba method, it will be faked using one hot encoding. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + + Returns + ------- + y : ndarray of shape (n_samples, n_classes) + Array with predicted probabilities. + """ if "predict_proba" in dir(self.base_estimator): return self.base_estimator.predict_proba(X) else: diff --git a/sslearn/datasets/__init__.py b/sslearn/datasets/__init__.py index bd13379..2db54e5 100644 --- a/sslearn/datasets/__init__.py +++ b/sslearn/datasets/__init__.py @@ -1,3 +1,19 @@ +""" +Summary of module `sslearn.datasets`: + +This module contains functions to load and save datasets in different formats. + +Functions +--------- +1. read_csv : Load a dataset from a CSV file. +2. read_keel : Load a dataset from a KEEL file. +3. secure_dataset : Secure the dataset by converting it into a secure format. +4. save_keel : Save a dataset in KEEL format. + +All doc +------- +""" + from ._loader import read_csv, read_keel from ._writer import save_keel from ._preprocess import secure_dataset diff --git a/sslearn/model_selection/__init__.py b/sslearn/model_selection/__init__.py index 5bc0185..e558f8e 100644 --- a/sslearn/model_selection/__init__.py +++ b/sslearn/model_selection/__init__.py @@ -1,3 +1,20 @@ +""" +Summary of module `sslearn.model_selection`: + +This module contains functions to split datasets into training and testing sets. + +Functions +--------- +artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset. + +Classes +------- +StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning. + +All doc +---- +""" + from ._split import * __all__ = ['StratifiedKFoldSS', 'artificial_ssl_dataset'] \ No newline at end of file diff --git a/sslearn/model_selection/_split.py b/sslearn/model_selection/_split.py index d24c773..6e4a1ac 100644 --- a/sslearn/model_selection/_split.py +++ b/sslearn/model_selection/_split.py @@ -5,6 +5,20 @@ class StratifiedKFoldSS(): def __init__(self, n_splits=5, shuffle=False, random_state=None): + """Stratified K-Folds cross-validator for semi-supervised learning. + + Provides train/test indices to split data in train/test sets. + + Parameters + ---------- + n_splits : int, default=5 + Number of folds. Must be at least 2. + shuffle : bool, default=False + Whether to shuffle each class's samples before splitting into batches. + random_state : int or RandomState instance, default=None + When shuffle is True, random_state affects the ordering of the indices. + + """ self.K = ms.StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state) @@ -29,9 +43,9 @@ def split(self, X, y): The feature set. y : ndarray The label set, -1 for unlabel instance. - label: ndarray + label : ndarray The training set indices for split mark as labeled. - unlabel: ndarray + unlabel : ndarray The training set indices for split mark as unlabeled. """ for train, test in self.K.split(X, y): diff --git a/sslearn/restricted.py b/sslearn/restricted.py index 6dd5a36..edab23f 100644 --- a/sslearn/restricted.py +++ b/sslearn/restricted.py @@ -1,9 +1,28 @@ +"""Summary of module `sslearn.restricted`: + +This module contains classes to train a classifier using the restricted set classification approach. + +Classes +------- +WhoIsWhoClassifier : Who is Who Classifier + +Functions +--------- +conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions. +combine_predictions : Combine the predictions of a group of instances to keep the restrictions. + +All doc +------- +""" + import numpy as np from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator from scipy.optimize import linear_sum_assignment import warnings import pandas as pd +__all__ = ["WhoIsWhoClassifier", "conflict_rate", "combine_predictions"] + class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): def __init__(self, base_estimator, method="hungarian", conflict_weighted=True): diff --git a/sslearn/subview/__init__.py b/sslearn/subview/__init__.py index e112345..9edad96 100644 --- a/sslearn/subview/__init__.py +++ b/sslearn/subview/__init__.py @@ -1,3 +1,17 @@ +""" +Summary of module `sslearn.subview`: + +This module contains classes to train a classifier or a regressor selecting a sub-view of the data. + +Classes +------- +SubViewClassifier : Train a sub-view classifier. +SubViewRegressor : Train a sub-view regressor. + +All doc +------- +""" + from ._subview import SubViewClassifier, SubViewRegressor __all__ = ["SubViewClassifier", "SubViewRegressor"] \ No newline at end of file diff --git a/sslearn/utils.py b/sslearn/utils.py index 83951bb..b54bd29 100644 --- a/sslearn/utils.py +++ b/sslearn/utils.py @@ -1,3 +1,20 @@ +""" +Some utility functions + +This module contains utility functions that are used in different parts of the library. + +Functions +--------- +safe_division : Safely divide two numbers preventing division by zero. +confidence_interval : Calculate the confidence interval of the predictions. +choice_with_proportion : Choice the best predictions according to the proportion of each class. +calculate_prior_probability : Calculate the priori probability of each label. +check_n_jobs : Check `n_jobs` parameter according to the scikit-learn convention. + +All doc +------- +""" + import numpy as np import os import math @@ -8,14 +25,51 @@ from sklearn.tree import DecisionTreeClassifier from sklearn.base import ClassifierMixin +__all__ = ["safe_division", "confidence_interval", "choice_with_proportion", "calculate_prior_probability", + "check_n_jobs"] + def safe_division(dividend, divisor, epsilon): + """Safely divide two numbers preventing division by zero + + Parameters + ---------- + dividend : numeric + Dividend value + divisor : numeric + Divisor value + epsilon : numeric + Close to zero value to be used in case of division by zero + + Returns + ------- + result : numeric + Result of the division + """ if divisor == 0: return dividend / epsilon return dividend / divisor def confidence_interval(X, hyp, y, alpha=.95): + """Calculate the confidence interval of the predictions + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + hyp : classifier + The classifier to be used for prediction + y : array-like of shape (n_samples,) + The target values + alpha : float, optional + confidence (1 - significance), by default .95 + + Returns + ------- + li, hi: float + lower and upper bound of the confidence interval + """ data = hyp.predict(X) successes = np.count_nonzero(data == y) @@ -25,6 +79,24 @@ def confidence_interval(X, hyp, y, alpha=.95): def choice_with_proportion(predictions, class_predicted, proportion, extra=0): + """Choice the best predictions according to the proportion of each class. + + Parameters + ---------- + predictions : array-like of shape (n_samples,) + array of predictions + class_predicted : array-like of shape (n_samples,) + array of predicted classes + proportion : dict + dictionary with the proportion of each class + extra : int, optional + number of extra instances to be added, by default 0 + + Returns + ------- + indices: array-like of shape (n_samples,) + array of indices of the best predictions + """ n = len(predictions) for_each_class = {c: int(n * j) for c, j in proportion.items()} indices = np.zeros(0) diff --git a/sslearn/wrapper/__init__.py b/sslearn/wrapper/__init__.py index f5336c9..68a176e 100644 --- a/sslearn/wrapper/__init__.py +++ b/sslearn/wrapper/__init__.py @@ -1,7 +1,33 @@ +""" +Summary of module `sslearn.wrapper`: + +This module contains classes to train semi-supervised learning algorithms using a wrapper approach. + +Self-Training Algorithms +------------------------ +1. SelfTraining : Self-training algorithm. +2. Setred : Self-training with redundancy reduction. + +Co-Training Algorithms +----------------------- +1. CoTraining : Co-training +2. CoTrainingByCommittee : Co-training by committee +3. DemocraticCoLearning : Democratic co-learning +4. Rasco : Random subspace co-training +5. RelRasco : Relevant random subspace co-training +6. CoForest : Co-Forest +7. TriTraining : Tri-training +8. DeTriTraining : Data Editing Tri-training +9. WiWTriTraining : Who-Is-Who Tri-training + +All doc +---- +""" + from ._co import (CoForest, CoTraining, CoTrainingByCommittee, DemocraticCoLearning, Rasco, RelRasco) from ._self import SelfTraining, Setred from ._tritraining import DeTriTraining, TriTraining, WiWTriTraining -__all__ = ['SelfTraining', 'CoTrainingByCommittee', 'Rasco', 'RelRasco', 'TriTraining', 'WiWTriTraining' +__all__ = ["SelfTraining", "CoTrainingByCommittee", "Rasco", "RelRasco", "TriTraining", "WiWTriTraining", "CoTraining", "DeTriTraining", "DemocraticCoLearning", "Setred", "CoForest"] diff --git a/sslearn/wrapper/_co.py b/sslearn/wrapper/_co.py index 7b266f1..63573d1 100644 --- a/sslearn/wrapper/_co.py +++ b/sslearn/wrapper/_co.py @@ -40,7 +40,7 @@ def predict_proba(self, X): Array representing the data. Returns ------- - ndarray of shape (n_samples, n_features) + class probabilities: ndarray of shape (n_samples, n_classes) Array with prediction probabilities. """ is_df = isinstance(X, pd.DataFrame) @@ -86,9 +86,7 @@ def __init__( random_state=None ): """ - Y. Zhou and S. Goldman, "Democratic co-learning," - 16th IEEE International Conference on Tools with Artificial Intelligence, - 2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48. + Democratic Co-learning. Ensemble of classifiers of different types. Parameters ---------- @@ -108,6 +106,12 @@ def __init__( ------ AttributeError If n_estimators is None and base_estimator is not a list + + References + ---------- + Y. Zhou and S. Goldman, "Democratic co-learning," + 16th IEEE International Conference on Tools with Artificial Intelligence, + 2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48. """ if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None: @@ -182,7 +186,7 @@ def fit(self, X, y, estimator_kwards=None): Returns ------- - self + self : DemocraticCoLearning fitted classifier """ @@ -346,7 +350,7 @@ def predict_proba(self, X): Array representing the data. Returns ------- - ndarray of shape (n_samples, n_features) + class probabilities: ndarray of shape (n_samples, n_classes) Array with prediction probabilities. """ if "h_" in dir(self): @@ -358,17 +362,6 @@ def predict_proba(self, X): class CoTraining(BaseCoTraining): - """ - Avrim Blum and Tom Mitchell. 1998. - Combining labeled and unlabeled data with co-training. - In Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98). - Association for Computing Machinery, New York, NY, USA, 92–100. - DOI:https://doi.org/10.1145/279943.279962 - - Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. - ‘Multi-Class Co-Training Learning for Object and Scene Recognition’. - Pp. 67–70 in. Nara, Japan. - """ def __init__( self, @@ -380,7 +373,9 @@ def __init__( force_second_view=True, random_state=None ): - """Create a CoTraining classifier + """ + Create a CoTraining classifier. + Multi-view learning algorithm that uses two classifiers to label instances. Parameters ---------- @@ -398,6 +393,18 @@ def __init__( The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True random_state : int, RandomState instance, optional controls the randomness of the estimator, by default None + + References + ---------- + Avrim Blum and Tom Mitchell. 1998. + Combining labeled and unlabeled data with co-training. + In Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98). + Association for Computing Machinery, New York, NY, USA, 92-100. + DOI:https://doi.org/10.1145/279943.279962 + + Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. + 'Multi-Class Co-Training Learning for Object and Scene Recognition'. + Pp. 67-70 in. Nara, Japan. """ self.base_estimator = check_classifier(base_estimator, False) if second_base_estimator is not None: @@ -561,7 +568,7 @@ def predict_proba(self, X, X2=None, **kwards): Array representing the data from another view, by default None Returns ------- - ndarray of shape (n_samples, n_features) + class probabilities: ndarray of shape (n_samples, n_classes) Array with prediction probabilities. """ if "columns_" in dir(self): @@ -638,12 +645,6 @@ def __init__( """ Co-Training based on random subspaces - Wang, J., Luo, S. W., & Zeng, X. H. (2008, June). - A random subspace method for co-training. - In 2008 IEEE International Joint Conference on Neural Networks - (IEEE World Congress on Computational Intelligence) - (pp. 195-200). IEEE. - Parameters ---------- base_estimator : ClassifierMixin, optional @@ -657,6 +658,14 @@ def __init__( The number of features for each subspace. If it is None will be the half of the features size., by default None random_state : int, RandomState instance, optional controls the randomness of the estimator, by default None + + References + ---------- + Wang, J., Luo, S. W., & Zeng, X. H. (2008, June). + A random subspace method for co-training. + In 2008 IEEE International Joint Conference on Neural Networks + (IEEE World Congress on Computational Intelligence) + (pp. 195-200). IEEE. """ self.base_estimator = check_classifier(base_estimator, True, n_estimators) # C in paper self.max_iterations = max_iterations # J in paper @@ -678,7 +687,7 @@ def _generate_random_subspaces(self, X, y=None, random_state=None): Returns ------- - list + subspaces : list List of index of features """ random_state = check_random_state(random_state) @@ -782,11 +791,6 @@ def __init__( """ Co-Training with relevant random subspaces - Yaslan, Y., & Cataltepe, Z. (2010). - Co-training with relevant random subspaces. - Neurocomputing, 73(10-12), 1652-1661. - - Parameters ---------- base_estimator : ClassifierMixin, optional @@ -802,6 +806,12 @@ def __init__( controls the randomness of the estimator, by default None n_jobs : int, optional The number of jobs to run in parallel. -1 means using all processors., by default None + + References + ---------- + Yaslan, Y., & Cataltepe, Z. (2010). + Co-training with relevant random subspaces. + Neurocomputing, 73(10-12), 1652-1661. """ super().__init__( base_estimator, @@ -824,7 +834,7 @@ def _generate_random_subspaces(self, X, y, random_state=None): Returns ------- - list + subspaces: list List of index of features """ random_state = check_random_state(random_state) @@ -853,12 +863,10 @@ def __init__( min_instances_for_class=3, random_state=None, ): - """Create a committee trained by cotraining based on + """ + Create a committee trained by cotraining based on the diversity of classifiers. - M. F. A. Hady and F. Schwenker, - "Co-training by Committee: A New Semi-supervised Learning Framework," - 2008 IEEE International Conference on Data Mining Workshops, - Pisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27. + Parameters ---------- ensemble_estimator : ClassifierMixin, optional @@ -870,6 +878,13 @@ def __init__( max number of unlabeled instances candidates to pseudolabel, by default 100 random_state : int, RandomState instance, optional controls the randomness of the estimator, by default None + + References + ---------- + M. F. A. Hady and F. Schwenker, + "Co-training by Committee: A New Semi-supervised Learning Framework," + 2008 IEEE International Conference on Data Mining Workshops, + Pisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27. """ self.ensemble_estimator = check_classifier(ensemble_estimator, False) self.max_iterations = max_iterations @@ -887,7 +902,7 @@ def fit(self, X, y, **kwards): The target values (class labels), -1 if unlabel. Returns ------- - self: CoTrainingByCommittee + self : CoTrainingByCommittee Fitted estimator. """ self.ensemble_estimator = skclone(self.ensemble_estimator) @@ -969,7 +984,7 @@ def predict(self, X): The input samples. Returns ------- - y: array-like of shape (n_samples,) + y : array-like of shape (n_samples,) The predicted classes """ check_is_fitted(self.ensemble_estimator) @@ -984,7 +999,7 @@ def predict_proba(self, X): The input samples. Returns ------- - y: ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 + y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The predicted classes """ check_is_fitted(self.ensemble_estimator) @@ -1024,10 +1039,8 @@ def score(self, X, y, sample_weight=None): class CoForest(BaseCoTraining): def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"): """ - Li, M., & Zhou, Z.-H. (2007). - Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. - IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, - 37(6), 1088–1098. doi:10.1109/tsmca.2007.904745 + Generate a CoForest classifier. + A SSL Random Forest adaption for CoTraining. Parameters ---------- @@ -1045,6 +1058,13 @@ def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, thre controls the randomness of the estimator, by default None **kwards : dict, optional Additional parameters to be passed to base_estimator, by default None. + + References + ---------- + Li, M., & Zhou, Z.-H. (2007). + Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples. + IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, + 37(6), 1088-1098. doi:10.1109/tsmca.2007.904745 """ self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators) self.n_estimators = n_estimators diff --git a/sslearn/wrapper/_self.py b/sslearn/wrapper/_self.py index 080db55..bb621c4 100644 --- a/sslearn/wrapper/_self.py +++ b/sslearn/wrapper/_self.py @@ -14,105 +14,6 @@ class SelfTraining(SelfTrainingClassifier): - """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible. - - This class allows a given supervised classifier to function as a - semi-supervised classifier, allowing it to learn from unlabeled data. It - does this by iteratively predicting pseudo-labels for the unlabeled data - and adding them to the training set. - - The classifier will continue iterating until either max_iter is reached, or - no pseudo-labels were added to the training set in the previous iteration. - - Read more in the :ref:`User Guide `. - - Parameters - ---------- - base_estimator : estimator object - An estimator object implementing ``fit`` and ``predict_proba``. - Invoking the ``fit`` method will fit a clone of the passed estimator, - which will be stored in the ``base_estimator_`` attribute. - - threshold : float, default=0.75 - The decision threshold for use with `criterion='threshold'`. - Should be in [0, 1). When using the 'threshold' criterion, a - :ref:`well calibrated classifier ` should be used. - - criterion : {'threshold', 'k_best'}, default='threshold' - The selection criterion used to select which labels to add to the - training set. If 'threshold', pseudo-labels with prediction - probabilities above `threshold` are added to the dataset. If 'k_best', - the `k_best` pseudo-labels with highest prediction probabilities are - added to the dataset. When using the 'threshold' criterion, a - :ref:`well calibrated classifier ` should be used. - - k_best : int, default=10 - The amount of samples to add in each iteration. Only used when - `criterion` is k_best'. - - max_iter : int or None, default=10 - Maximum number of iterations allowed. Should be greater than or equal - to 0. If it is ``None``, the classifier will continue to predict labels - until no new pseudo-labels are added, or all unlabeled samples have - been labeled. - - verbose : bool, default=False - Enable verbose output. - - Attributes - ---------- - base_estimator_ : estimator object - The fitted estimator. - - classes_ : ndarray or list of ndarray of shape (n_classes,) - Class labels for each output. (Taken from the trained - ``base_estimator_``). - - transduction_ : ndarray of shape (n_samples,) - The labels used for the final fit of the classifier, including - pseudo-labels added during fit. - - labeled_iter_ : ndarray of shape (n_samples,) - The iteration in which each sample was labeled. When a sample has - iteration 0, the sample was already labeled in the original dataset. - When a sample has iteration -1, the sample was not labeled in any - iteration. - - n_iter_ : int - The number of rounds of self-training, that is the number of times the - base estimator is fitted on relabeled variants of the training set. - - termination_condition_ : {'max_iter', 'no_change', 'all_labeled'} - The reason that fitting was stopped. - - - 'max_iter': `n_iter_` reached `max_iter`. - - 'no_change': no new labels were predicted. - - 'all_labeled': all unlabeled samples were labeled before `max_iter` - was reached. - - Examples - -------- - >>> import numpy as np - >>> from sklearn import datasets - >>> from sklearn.semi_supervised import SelfTrainingClassifier - >>> from sklearn.svm import SVC - >>> rng = np.random.RandomState(42) - >>> iris = datasets.load_iris() - >>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3 - >>> iris.target[random_unlabeled_points] = -1 - >>> svc = SVC(probability=True, gamma="auto") - >>> self_training_model = SelfTrainingClassifier(svc) - >>> self_training_model.fit(iris.data, iris.target) - SelfTrainingClassifier(...) - - References - ---------- - David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling - supervised methods. In Proceedings of the 33rd annual meeting on - Association for Computational Linguistics (ACL '95). Association for - Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI: - https://doi.org/10.3115/981658.981684 - """ _estimator_type = "classifier" def __init__(self, @@ -122,6 +23,57 @@ def __init__(self, k_best=10, max_iter=10, verbose=False): + """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible. + + This class allows a given supervised classifier to function as a + semi-supervised classifier, allowing it to learn from unlabeled data. It + does this by iteratively predicting pseudo-labels for the unlabeled data + and adding them to the training set. + + The classifier will continue iterating until either max_iter is reached, or + no pseudo-labels were added to the training set in the previous iteration. + + Parameters + ---------- + base_estimator : estimator object + An estimator object implementing ``fit`` and ``predict_proba``. + Invoking the ``fit`` method will fit a clone of the passed estimator, + which will be stored in the ``base_estimator_`` attribute. + + threshold : float, default=0.75 + The decision threshold for use with `criterion='threshold'`. + Should be in [0, 1). When using the 'threshold' criterion, a + :ref:`well calibrated classifier ` should be used. + + criterion : {'threshold', 'k_best'}, default='threshold' + The selection criterion used to select which labels to add to the + training set. If 'threshold', pseudo-labels with prediction + probabilities above `threshold` are added to the dataset. If 'k_best', + the `k_best` pseudo-labels with highest prediction probabilities are + added to the dataset. When using the 'threshold' criterion, a + :ref:`well calibrated classifier ` should be used. + + k_best : int, default=10 + The amount of samples to add in each iteration. Only used when + `criterion` is k_best'. + + max_iter : int or None, default=10 + Maximum number of iterations allowed. Should be greater than or equal + to 0. If it is ``None``, the classifier will continue to predict labels + until no new pseudo-labels are added, or all unlabeled samples have + been labeled. + + verbose : bool, default=False + Enable verbose output. + + References + ---------- + David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling + supervised methods. In Proceedings of the 33rd annual meeting on + Association for Computational Linguistics (ACL '95). Association for + Computational Linguistics, Stroudsburg, PA, USA, 189-196. DOI: + https://doi.org/10.3115/981658.981684 + """ super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose) def fit(self, X, y): @@ -162,10 +114,9 @@ def __init__( n_jobs=None, ): """ - Li, Ming, and Zhi-Hua Zhou. "SETRED: Self-training with editing." - Pacific-Asia Conference on Knowledge Discovery and Data Mining. - Springer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71. - + Create a SETRED classifier. + It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set. + Parameters ---------- base_estimator : ClassifierMixin, optional @@ -187,6 +138,12 @@ def __init__( controls the randomness of the estimator, by default None n_jobs : int, optional The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None + + References + ---------- + Li, Ming, and Zhi-Hua Zhou. "SETRED: Self-training with editing." + Pacific-Asia Conference on Knowledge Discovery and Data Mining. + Springer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71. """ self.base_estimator = check_classifier(base_estimator, can_be_list=False) self.max_iterations = max_iterations @@ -330,7 +287,7 @@ def predict(self, X, **kwards): The input samples. Returns ------- - y: array-like of shape (n_samples,) + y : array-like of shape (n_samples,) The predicted classes """ return self._base_estimator.predict(X, **kwards) @@ -344,7 +301,7 @@ def predict_proba(self, X, **kwards): The input samples. Returns ------- - y: ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 + y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1 The predicted classes """ return self._base_estimator.predict_proba(X, **kwards) diff --git a/sslearn/wrapper/_tritraining.py b/sslearn/wrapper/_tritraining.py index 4370c7d..ef0b391 100644 --- a/sslearn/wrapper/_tritraining.py +++ b/sslearn/wrapper/_tritraining.py @@ -30,12 +30,8 @@ def __init__( random_state=None, n_jobs=None, ): - """TriTraining - Zhi-Hua Zhou and Ming Li, - "Tri-training: exploiting unlabeled data using three classifiers," - in IEEE Transactions on Knowledge and Data Engineering, - vol. 17, no. 11, pp. 1529-1541, Nov. 2005, - doi: 10.1109/TKDE.2005.186. + """TriTraining. Trio of classifiers with bootstrapping. + Parameters ---------- base_estimator : ClassifierMixin, optional @@ -49,6 +45,14 @@ def __init__( The number of jobs to run in parallel for both `fit` and `predict`. `None` means 1 unless in a :obj:`joblib.parallel_backend` context. `-1` means using all processors., by default None + + References + ---------- + Zhi-Hua Zhou and Ming Li, + "Tri-training: exploiting unlabeled data using three classifiers," + in IEEE Transactions on Knowledge and Data Engineering, + vol. 17, no. 11, pp. 1529-1541, Nov. 2005, + doi: 10.1109/TKDE.2005.186. """ self._N_LEARNER = 3 self.base_estimator = check_classifier(base_estimator, collection_size=self._N_LEARNER) @@ -67,7 +71,7 @@ def fit(self, X, y, **kwards): The target values (class labels), -1 if unlabeled. Returns ------- - self: TriTraining + self : TriTraining Fitted estimator. """ random_state = check_random_state(self.random_state) @@ -197,7 +201,8 @@ def _another_hs(hs, index): base hypothesis index Returns ------- - list + classifiers: list + Collection of other hypotheses """ another_hs = [] for i in range(len(hs)): @@ -218,7 +223,7 @@ def _subsample(L, s, random_state=None): controls the randomness of the estimator, by default None Returns ------- - tuple + subsamples: tuple Collection of pseudo-labeled selected for enlarged labeled examples. """ to_remove = len(L[0]) - s @@ -244,7 +249,7 @@ def _measure_error( A small number to avoid division by zero Returns ------- - float + error : float Division of the number of labeled examples on which both h1 and h2 make incorrect classification, by the number of labeled examples on which the classification made by h1 is the same as that made by h2. """ @@ -292,6 +297,13 @@ def __init__( * "none": don't penalize the "meause error", by default "labeled" random_state : int, RandomState instance, optional controls the randomness of the estimator, by default None + + References + ---------- + Ludmila I. Kuncheva, Juan J. Rodríguez, Aaron S. Jackson, + Restricted set classification: Who is there?, + Pattern Recognition, 63, 158-170, + 10.1016/j.patcog.2016.08.028 """ super().__init__(base_estimator, n_samples, random_state, n_jobs) conflict_over_choices = ["labeled", "labeled_plus", "unlabeled", "all", "none"] @@ -315,7 +327,7 @@ def fit(self, X, y, instance_group=None, **kwards): The group. Two instances with the same label are not allowed to be in the same group. Returns ------- - self: TriTraining + self : TriTraining Fitted estimator. """ random_state = check_random_state(self.random_state) @@ -456,7 +468,7 @@ def _measure_error(self, L, y, h1: ClassifierMixin, h2: ClassifierMixin, epsilon A small number to avoid division by zero Returns ------- - float + error: float Division of the number of labeled examples on which both h1 and h2 make incorrect classification, by the number of labeled examples on which the classification made by h1 is the same as that made by h2. """ @@ -506,14 +518,9 @@ class DeTriTraining(TriTraining): def __init__(self, base_estimator=DecisionTreeClassifier(), k_neighbors=3, n_samples=None, mode="seeded", max_iterations=100, n_jobs=None, random_state=None): - """DeTriTraining - - Deng C., Guo M.Z. (2006) - Tri-training and Data Editing Based Semi-supervised Clustering Algorithm. - In: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. - Lecture Notes in Computer Science, vol 4293. - Springer, Berlin, Heidelberg. - https://doi.org/10.1007/11925231_61 + """ + DeTriTraining - TriTraining with Depurated and Clustering. + Avoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances. Parameters ---------- @@ -535,9 +542,18 @@ def __init__(self, base_estimator=DecisionTreeClassifier(), k_neighbors=3, n_jobs : int, optional The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. - Doesn’t affect fit method., by default None + Doesn't affect fit method., by default None random_state : int, RandomState instance, optional controls the randomness of the estimator, by default None + + References + ---------- + Deng C., Guo M.Z. (2006) + Tri-training and Data Editing Based Semi-supervised Clustering Algorithm. + In: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. + Lecture Notes in Computer Science, vol 4293. + Springer, Berlin, Heidelberg. + https://doi.org/10.1007/11925231_61 """ super().__init__(base_estimator, n_samples, random_state) self.k_neighbors = k_neighbors @@ -557,7 +573,7 @@ def _depure(self, S): Returns ------- - tuple (X, y) + tuple : (X, y) Enlarged dataset with instances where at least k_neighbors/2+1 have the same class. """ init = time.time() @@ -578,7 +594,7 @@ def _clustering(self, S, X): Returns ------- - array-like of shape (n_samples,) + y: array-like of shape (n_samples,) class predicted for each instance """ centroids = dict() From 5d5891ba6534278a78a57dc722ef0f201dacf04f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:47:17 +0200 Subject: [PATCH 17/34] Document all repository using pdoc --- sslearn/__init__.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/sslearn/__init__.py b/sslearn/__init__.py index 92194ae..d698d2e 100644 --- a/sslearn/__init__.py +++ b/sslearn/__init__.py @@ -1,6 +1,10 @@ -# Open README.md and added to __doc__ -with open("../README.md", "r") as f: - __doc__ = f.read() +# Open README.md and added to __doc__ for +import os +if os.path.exists("../README.md"): + with open("../README.md", "r") as f: + __doc__ = f.read() +else: + __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models." __version__='1.0.4.1' From 8c61aaa223c3adb8f159fd866860260a06dcdf7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:55:58 +0200 Subject: [PATCH 18/34] Publish doc --- .github/workflows/docs.yml | 52 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) create mode 100644 .github/workflows/docs.yml diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..2e731cb --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,52 @@ +name: website + +# build the documentation whenever there are new commits on main +on: + push: + branches: + - main + # Alternative: only build for tags. + # tags: + # - '*' + +# security: restrict permissions for CI jobs. +permissions: + contents: read + +jobs: + # Build the documentation and upload the static HTML files as an artifact. + build: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + + # ADJUST THIS: install all dependencies (including pdoc) + - run: | + python -m pip install --upgrade pip + python -m pip pdoc + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + # ADJUST THIS: build your documentation into docs/. + # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here. + - run: python docs/make.py + + - uses: actions/upload-pages-artifact@v3 + with: + path: docs/ + + # Deploy the artifact to GitHub pages. + # This is a separate job so that only actions/deploy-pages has the necessary permissions. + deploy: + needs: build + runs-on: ubuntu-latest + permissions: + pages: write + id-token: write + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + steps: + - id: deployment + uses: actions/deploy-pages@v4 \ No newline at end of file From 559838b77926275890a5e4998d168e29c60a1968 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:57:08 +0200 Subject: [PATCH 19/34] Publish doc --- .github/workflows/docs.yml | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 2e731cb..6ce5d4b 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -24,10 +24,9 @@ jobs: python-version: '3.12' # ADJUST THIS: install all dependencies (including pdoc) - - run: | - python -m pip install --upgrade pip - python -m pip pdoc - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - run: python -m pip install --upgrade pip + - run: python -m pip pdoc + - run: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # ADJUST THIS: build your documentation into docs/. # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here. - run: python docs/make.py From 3ed43004557296906768fde36834d7a603aa1796 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:57:44 +0200 Subject: [PATCH 20/34] Publish doc --- .github/workflows/docs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 6ce5d4b..93ec942 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -25,7 +25,7 @@ jobs: # ADJUST THIS: install all dependencies (including pdoc) - run: python -m pip install --upgrade pip - - run: python -m pip pdoc + - run: python -m pip install pdoc - run: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # ADJUST THIS: build your documentation into docs/. # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here. From adf22a046efac1b59cbbd2c14ae7b6bf82bb7106 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 18:59:44 +0200 Subject: [PATCH 21/34] Publish doc --- docs/make.py | 2 +- docs/search.js | 2 +- docs/sslearn.html | 88 ++++++++--------------------------------------- 3 files changed, 16 insertions(+), 76 deletions(-) diff --git a/docs/make.py b/docs/make.py index d28c0b8..a712a23 100644 --- a/docs/make.py +++ b/docs/make.py @@ -12,7 +12,7 @@ import pdoc.render -here = Path("..") +here = Path(__file__).parent.parent if __name__ == "__main__": diff --git a/docs/search.js b/docs/search.js index 1ec3c1a..2647540 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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\"Code \"Code \"GitHub \"PyPI

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The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

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Installation
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Dependencies

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  • joblib >= 1.2.0
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  • numpy >= 1.23.3
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  • pandas >= 1.4.3
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  • scikit_learn >= 1.2.0
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  • scipy >= 1.10.1
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  • statsmodels >= 0.13.2
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  • pytest = 7.2.0 (only for testing)
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pip installation

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It can be installed using Pypi:

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pip install sslearn\n
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Code example
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from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
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Citing
\n\n
\n
@software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
\n
\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

\n\n
Functions
\n\n

get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

\n\n
Classes
\n\n

FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

\n\n

OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

\n\n

All doc

\n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

Create a classifier that fakes predict_proba method if it does not exist.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
    • \n
\n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

Fit a FakedProbaClassifier.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
    • \n
\n\n
Returns
\n\n
    \n
  • self (FakedProbaClassifier):\nReturns self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

Check and divide dataset between labeled and unlabeled data.

\n\n
Parameters
\n\n
    \n
  • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
    • \n
  • y (ndarray of shape (n_samples,)):\nTarget vector.
    • \n
\n\n
Returns
\n\n
    \n
  • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
    • \n
  • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
    • \n
  • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

One-vs-the-rest (OvR) multiclass strategy.

\n\n

Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

\n\n

OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

\n\n

Read more in the :ref:User Guide <ovr_classification>.

\n\n
Parameters
\n\n
    \n
  • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
    • \n

  • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

    \n\n

    None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

    \n\n

    Changed in version 0.20:\nn_jobs default changed from 1 to None

    • \n

  • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

    \n\n

    New in version 1.1.

    • \n
\n\n
Attributes
\n\n
    \n
  • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
    • \n
  • classes_ (array, shape = [n_classes]):\nClass labels.
    • \n
  • n_classes_ (int):\nNumber of classes.
    • \n
  • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
    • \n
  • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 1.0.

    • \n
\n\n
See Also
\n\n

OneVsOneClassifier: One-vs-one multiclass strategy.
\nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
\nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
\nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
\n
\n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

Adapted OneVsRestClassifier for SSL datasets

\n\n
Parameters
\n\n
    \n
  • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
    • \n
  • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

Fit underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nInstance of fitted estimator.
    • \n
\n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

Predict multi-class targets using underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
\n\n
Returns
\n\n
    \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

Probability estimates.

\n\n

The returned estimates for all classes are ordered by label of classes.

\n\n

Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

\n\n

In the single label multiclass case, the rows of the returned matrix\nsum to 1.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
    • \n
\n\n
Returns
\n\n
    \n
  • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

Summary of module sslearn.datasets:

\n\n

This module contains functions to load and save datasets in different formats.

\n\n
Functions
\n\n
    \n
  1. read_csv : Load a dataset from a CSV file.
    2. \n
  2. read_keel : Load a dataset from a KEEL file.
    3. \n
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. \n
  4. save_keel : Save a dataset in KEEL format.
    5. \n
\n\n

All doc

\n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

Read a .csv file

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

Read a .dat file from KEEL (http://www.keel.es/)

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
  • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

\n\n
Parameters
\n\n
    \n
  • X (Array-like):\nIgnored
    • \n
  • y (Array-like):\nTarget array.
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset securized.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

Save a dataset in the KEEL format

\n\n
Parameters
\n\n
    \n
  • X (array-like):\nDataset features
    • \n
  • y (array-like):\nDataset targets
    • \n
  • route (str):\nPath to save the dataset
    • \n
  • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
    • \n
  • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
    • \n
  • target_name (str, optional):\nTarget name, by default \"Class\"
    • \n
  • classification (bool, optional):\nIf the dataset is classification or regression, by default True
    • \n
  • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
    • \n
  • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
    • \n
\n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

Summary of module sslearn.model_selection:

\n\n

This module contains functions to split datasets into training and testing sets.

\n\n
Functions
\n\n

artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

\n\n
Classes
\n\n

StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

\n\n

All doc

\n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

Create an artificial Semi-supervised dataset from a supervised dataset.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
    • \n
  • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
    • \n
  • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
    • \n
  • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
    • \n
  • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
    • \n
  • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
    • \n
  • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
    • \n
\n\n
Returns
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
    • \n
  • y_unlabel (ndarray):\nThe true label for each y in the same order.
    • \n
  • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
    • \n
  • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
    • \n
\n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

This module contains classes to train a classifier using the restricted set classification approach.

\n\n
Classes
\n\n

WhoIsWhoClassifier : Who is Who Classifier

\n\n
Functions
\n\n

conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

\n\n

All doc

\n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

Fit the model according to the given training data.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nReturns self.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

Calculate the conflict rate of the model.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • float: The conflict rate.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

Predict class for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

Computes the conflict rate of a prediction, given a set of restrictions.

\n\n
Parameters
\n\n
    \n
  • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
    • \n
  • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
    • \n
  • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • conflict rate (float):\nThe conflict rate.
    • \n
\n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

Summary of module sslearn.subview:

\n\n

This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

\n\n
Classes
\n\n

SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

\n\n

All doc

\n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

Predict using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted values.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

Some utility functions

\n\n

This module contains utility functions that are used in different parts of the library.

\n\n
Functions
\n\n

safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

\n\n

All doc

\n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

Safely divide two numbers preventing division by zero

\n\n
Parameters
\n\n
    \n
  • dividend (numeric):\nDividend value
    • \n
  • divisor (numeric):\nDivisor value
    • \n
  • epsilon (numeric):\nClose to zero value to be used in case of division by zero
    • \n
\n\n
Returns
\n\n
    \n
  • result (numeric):\nResult of the division
    • \n
\n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

Calculate the confidence interval of the predictions

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • hyp (classifier):\nThe classifier to be used for prediction
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values
    • \n
  • alpha (float, optional):\nconfidence (1 - significance), by default .95
    • \n
\n\n
Returns
\n\n
    \n
  • li, hi (float):\nlower and upper bound of the confidence interval
    • \n
\n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

Choice the best predictions according to the proportion of each class.

\n\n
Parameters
\n\n
    \n
  • predictions (array-like of shape (n_samples,)):\narray of predictions
    • \n
  • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
    • \n
  • proportion (dict):\ndictionary with the proportion of each class
    • \n
  • extra (int, optional):\nnumber of extra instances to be added, by default 0
    • \n
\n\n
Returns
\n\n
    \n
  • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
    • \n
\n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

Calculate the priori probability of each label

\n\n
Parameters
\n\n
    \n
  • y (array-like of shape (n_samples,)):\narray of labels
    • \n
\n\n
Returns
\n\n
    \n
  • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
    • \n
\n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

\n\n
Parameters
\n\n
    \n
  • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
    • \n
\n\n
Returns
\n\n
    \n
  • n_jobs (int):\nChecked number of jobs.
    • \n
\n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

Summary of module sslearn.wrapper:

\n\n

This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

\n\n

Self-Training Algorithms

\n\n
    \n
  1. SelfTraining : Self-training algorithm.
    2. \n
  2. Setred : Self-training with redundancy reduction.
    3. \n
\n\n

Co-Training Algorithms

\n\n
    \n
  1. CoTraining : Co-training
    2. \n
  2. CoTrainingByCommittee : Co-training by committee
    3. \n
  3. DemocraticCoLearning : Democratic co-learning
    4. \n
  4. Rasco : Random subspace co-training
    5. \n
  5. RelRasco : Relevant random subspace co-training
    6. \n
  6. CoForest : Co-Forest
    7. \n
  7. TriTraining : Tri-training
    8. \n
  8. DeTriTraining : Data Editing Tri-training
    9. \n
  9. WiWTriTraining : Who-Is-Who Tri-training
    10. \n
\n\n

All doc

\n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

Self-training classifier.

\n\n

This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n

Read more in the :ref:User Guide <self_training>.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
Attributes
\n\n
    \n
  • base_estimator_ (estimator object):\nThe fitted estimator.
    • \n
  • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
    • \n
  • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
    • \n
  • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

    \n\n

    New in version 1.0.

    • \n
  • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
    • \n

  • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

    \n\n
      \n
    • 'max_iter': n_iter_ reached max_iter.
      • \n
    • 'no_change': no new labels were predicted.
      • \n
    • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
      • \n
    • \n
\n\n
See Also
\n\n

LabelPropagation: Label propagation classifier.
\nLabelSpreading: Label spreading model for semi-supervised learning.

\n\n
References
\n\n

:doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
\n
\n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

\n\n

This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
References
\n\n

David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

\n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

Fits this SelfTrainingClassifier to a dataset.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
    • \n
\n\n
Returns
\n\n
    \n
  • self (SelfTrainingClassifier):\nReturns an instance of self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

Create a committee trained by cotraining based on\nthe diversity of classifiers.

\n\n
Parameters
\n\n
    \n
  • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
    • \n
  • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
    • \n
  • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

\n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

Build a CoTrainingByCommittee classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTrainingByCommittee):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

Co-Training based on random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

Build a Rasco classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Rasco):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

Co-Training with relevant random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

TriTraining. Trio of classifiers with bootstrapping.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

TriTraining with restriction Who-is-Who.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
  • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
      \n
    • \"labeled\" over complete L,
      • \n
    • \"labeled_plus\" over complete L union L',
      • \n
    • \"unlabeled\u00a8: over complete U,
      • \n
    • \"all\": over complete X (LuU) and
      • \n
    • \"none\": don't penalize the \"meause error\", by default \"labeled\"
      • \n
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

\n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • \n
  • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • \n
  • max_iterations (int, optional):\nThe number of iterations, by default 30
    • \n
  • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • \n
  • threshold (float, optional):\nThe threshold for label instances, by default 0.5
    • \n
  • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

\n\n

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

Build a CoTraining classifier from the training set.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
    • \n
  • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
    • \n
  • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTraining):\nFitted estimator.
    • \n
\n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
    • \n
  • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations, by default 100
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

Build a DeTriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

Democratic Co-learning. Ensemble of classifiers of different types.

\n\n
Parameters
\n\n
    \n
  • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
    • \n
  • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
    • \n
  • alpha (float, optional):\nconfidence level, by default 0.95
    • \n
  • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
Raises
\n\n
    \n
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • \n
\n\n
References
\n\n

Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

\n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

Fit Democratic-Co classifier

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
  • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (DemocraticCoLearning):\nfitted classifier
    • \n
\n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.1
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n\n
References
\n\n

Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

Build a Setred classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Setred):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
    • \n
  • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
    • \n
  • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
    • \n
\n\n
References
\n\n

Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

Build a CoForest classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoForest):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
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Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models.

\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

\n\n
Functions
\n\n

get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

\n\n
Classes
\n\n

FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

\n\n

OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

\n\n

All doc

\n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

Create a classifier that fakes predict_proba method if it does not exist.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
    • \n
\n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

Fit a FakedProbaClassifier.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
    • \n
\n\n
Returns
\n\n
    \n
  • self (FakedProbaClassifier):\nReturns self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

Check and divide dataset between labeled and unlabeled data.

\n\n
Parameters
\n\n
    \n
  • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
    • \n
  • y (ndarray of shape (n_samples,)):\nTarget vector.
    • \n
\n\n
Returns
\n\n
    \n
  • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
    • \n
  • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
    • \n
  • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

One-vs-the-rest (OvR) multiclass strategy.

\n\n

Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

\n\n

OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

\n\n

Read more in the :ref:User Guide <ovr_classification>.

\n\n
Parameters
\n\n
    \n
  • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
    • \n

  • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

    \n\n

    None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

    \n\n

    Changed in version 0.20:\nn_jobs default changed from 1 to None

    • \n

  • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

    \n\n

    New in version 1.1.

    • \n
\n\n
Attributes
\n\n
    \n
  • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
    • \n
  • classes_ (array, shape = [n_classes]):\nClass labels.
    • \n
  • n_classes_ (int):\nNumber of classes.
    • \n
  • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
    • \n
  • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

    \n\n

    New in version 1.0.

    • \n
\n\n
See Also
\n\n

OneVsOneClassifier: One-vs-one multiclass strategy.
\nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
\nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
\nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
\n
\n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

Adapted OneVsRestClassifier for SSL datasets

\n\n
Parameters
\n\n
    \n
  • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
    • \n
  • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

Fit underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nInstance of fitted estimator.
    • \n
\n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

Predict multi-class targets using underlying estimators.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
    • \n
\n\n
Returns
\n\n
    \n
  • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

Probability estimates.

\n\n

The returned estimates for all classes are ordered by label of classes.

\n\n

Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

\n\n

In the single label multiclass case, the rows of the returned matrix\nsum to 1.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
    • \n
\n\n
Returns
\n\n
    \n
  • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

Summary of module sslearn.datasets:

\n\n

This module contains functions to load and save datasets in different formats.

\n\n
Functions
\n\n
    \n
  1. read_csv : Load a dataset from a CSV file.
    2. \n
  2. read_keel : Load a dataset from a KEEL file.
    3. \n
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. \n
  4. save_keel : Save a dataset in KEEL format.
    5. \n
\n\n

All doc

\n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

Read a .csv file

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

Read a .dat file from KEEL (http://www.keel.es/)

\n\n
Parameters
\n\n
    \n
  • path (str):\nFile path
    • \n
  • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
    • \n
  • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
    • \n
  • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
    • \n
  • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset loaded.
    • \n
\n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

\n\n
Parameters
\n\n
    \n
  • X (Array-like):\nIgnored
    • \n
  • y (Array-like):\nTarget array.
    • \n
\n\n
Returns
\n\n
    \n
  • X, y (array_like):\nDataset securized.
    • \n
\n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

Save a dataset in the KEEL format

\n\n
Parameters
\n\n
    \n
  • X (array-like):\nDataset features
    • \n
  • y (array-like):\nDataset targets
    • \n
  • route (str):\nPath to save the dataset
    • \n
  • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
    • \n
  • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
    • \n
  • target_name (str, optional):\nTarget name, by default \"Class\"
    • \n
  • classification (bool, optional):\nIf the dataset is classification or regression, by default True
    • \n
  • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
    • \n
  • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
    • \n
\n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

Summary of module sslearn.model_selection:

\n\n

This module contains functions to split datasets into training and testing sets.

\n\n
Functions
\n\n

artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

\n\n
Classes
\n\n

StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

\n\n

All doc

\n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

Create an artificial Semi-supervised dataset from a supervised dataset.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
    • \n
  • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
    • \n
  • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
    • \n
  • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
    • \n
  • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
    • \n
  • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
    • \n
  • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
    • \n
\n\n
Returns
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
    • \n
  • y_unlabel (ndarray):\nThe true label for each y in the same order.
    • \n
  • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
    • \n
  • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
    • \n
\n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

This module contains classes to train a classifier using the restricted set classification approach.

\n\n
Classes
\n\n

WhoIsWhoClassifier : Who is Who Classifier

\n\n
Functions
\n\n

conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

\n\n

All doc

\n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

Fit the model according to the given training data.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
    • \n
\n\n
Returns
\n\n
    \n
  • self (object):\nReturns self.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

Calculate the conflict rate of the model.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • float: The conflict rate.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

Predict class for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities for X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

Computes the conflict rate of a prediction, given a set of restrictions.

\n\n
Parameters
\n\n
    \n
  • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
    • \n
  • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
    • \n
  • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • conflict rate (float):\nThe conflict rate.
    • \n
\n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

Summary of module sslearn.subview:

\n\n

This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

\n\n
Classes
\n\n

SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

\n\n

All doc

\n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

Predict using the base estimator.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted values.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

Some utility functions

\n\n

This module contains utility functions that are used in different parts of the library.

\n\n
Functions
\n\n

safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

\n\n

All doc

\n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

Safely divide two numbers preventing division by zero

\n\n
Parameters
\n\n
    \n
  • dividend (numeric):\nDividend value
    • \n
  • divisor (numeric):\nDivisor value
    • \n
  • epsilon (numeric):\nClose to zero value to be used in case of division by zero
    • \n
\n\n
Returns
\n\n
    \n
  • result (numeric):\nResult of the division
    • \n
\n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

Calculate the confidence interval of the predictions

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
  • hyp (classifier):\nThe classifier to be used for prediction
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values
    • \n
  • alpha (float, optional):\nconfidence (1 - significance), by default .95
    • \n
\n\n
Returns
\n\n
    \n
  • li, hi (float):\nlower and upper bound of the confidence interval
    • \n
\n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

Choice the best predictions according to the proportion of each class.

\n\n
Parameters
\n\n
    \n
  • predictions (array-like of shape (n_samples,)):\narray of predictions
    • \n
  • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
    • \n
  • proportion (dict):\ndictionary with the proportion of each class
    • \n
  • extra (int, optional):\nnumber of extra instances to be added, by default 0
    • \n
\n\n
Returns
\n\n
    \n
  • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
    • \n
\n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

Calculate the priori probability of each label

\n\n
Parameters
\n\n
    \n
  • y (array-like of shape (n_samples,)):\narray of labels
    • \n
\n\n
Returns
\n\n
    \n
  • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
    • \n
\n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

\n\n
Parameters
\n\n
    \n
  • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
    • \n
\n\n
Returns
\n\n
    \n
  • n_jobs (int):\nChecked number of jobs.
    • \n
\n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

Summary of module sslearn.wrapper:

\n\n

This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

\n\n

Self-Training Algorithms

\n\n
    \n
  1. SelfTraining : Self-training algorithm.
    2. \n
  2. Setred : Self-training with redundancy reduction.
    3. \n
\n\n

Co-Training Algorithms

\n\n
    \n
  1. CoTraining : Co-training
    2. \n
  2. CoTrainingByCommittee : Co-training by committee
    3. \n
  3. DemocraticCoLearning : Democratic co-learning
    4. \n
  4. Rasco : Random subspace co-training
    5. \n
  5. RelRasco : Relevant random subspace co-training
    6. \n
  6. CoForest : Co-Forest
    7. \n
  7. TriTraining : Tri-training
    8. \n
  8. DeTriTraining : Data Editing Tri-training
    9. \n
  9. WiWTriTraining : Who-Is-Who Tri-training
    10. \n
\n\n

All doc

\n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

Self-training classifier.

\n\n

This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n

Read more in the :ref:User Guide <self_training>.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
Attributes
\n\n
    \n
  • base_estimator_ (estimator object):\nThe fitted estimator.
    • \n
  • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
    • \n
  • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
    • \n
  • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
    • \n

  • n_features_in_ (int):\nNumber of features seen during :term:fit.

    \n\n

    New in version 0.24.

    • \n

  • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

    \n\n

    New in version 1.0.

    • \n
  • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
    • \n

  • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

    \n\n
      \n
    • 'max_iter': n_iter_ reached max_iter.
      • \n
    • 'no_change': no new labels were predicted.
      • \n
    • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
      • \n
    • \n
\n\n
See Also
\n\n

LabelPropagation: Label propagation classifier.
\nLabelSpreading: Label spreading model for semi-supervised learning.

\n\n
References
\n\n

:doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

\n\n
Examples
\n\n
\n
>>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
\n
\n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

\n\n

This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

\n\n

The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

\n\n
Parameters
\n\n
    \n
  • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
    • \n
  • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
    • \n
  • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
    • \n
  • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
    • \n
  • verbose (bool, default=False):\nEnable verbose output.
    • \n
\n\n
References
\n\n

David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

\n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

Fits this SelfTrainingClassifier to a dataset.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
    • \n
\n\n
Returns
\n\n
    \n
  • self (SelfTrainingClassifier):\nReturns an instance of self.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

Create a committee trained by cotraining based on\nthe diversity of classifiers.

\n\n
Parameters
\n\n
    \n
  • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
    • \n
  • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
    • \n
  • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

\n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

Build a CoTrainingByCommittee classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTrainingByCommittee):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

Co-Training based on random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

Build a Rasco classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Rasco):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

Co-Training with relevant random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

TriTraining. Trio of classifiers with bootstrapping.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
    • \n
\n\n
References
\n\n

Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

TriTraining with restriction Who-is-Who.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
  • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
  • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
      \n
    • \"labeled\" over complete L,
      • \n
    • \"labeled_plus\" over complete L union L',
      • \n
    • \"unlabeled\u00a8: over complete U,
      • \n
    • \"all\": over complete X (LuU) and
      • \n
    • \"none\": don't penalize the \"meause error\", by default \"labeled\"
      • \n
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

\n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

Build a TriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • self (TriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • \n
  • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • \n
  • max_iterations (int, optional):\nThe number of iterations, by default 30
    • \n
  • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • \n
  • threshold (float, optional):\nThe threshold for label instances, by default 0.5
    • \n
  • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

\n\n

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

Build a CoTraining classifier from the training set.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
    • \n
  • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
    • \n
  • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoTraining):\nFitted estimator.
    • \n
\n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

Predict the classes of X.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTest samples.
    • \n
  • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
    • \n
  • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
    • \n
  • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) wrt. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
    • \n
  • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations, by default 100
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
References
\n\n

Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

Build a DeTriTraining classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

Democratic Co-learning. Ensemble of classifiers of different types.

\n\n
Parameters
\n\n
    \n
  • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
    • \n
  • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
    • \n
  • alpha (float, optional):\nconfidence level, by default 0.95
    • \n
  • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
Raises
\n\n
    \n
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • \n
\n\n
References
\n\n

Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

\n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

Fit Democratic-Co classifier

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
  • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
    • \n
\n\n
Returns
\n\n
    \n
  • self (DemocraticCoLearning):\nfitted classifier
    • \n
\n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

Predict probability for each possible outcome.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
\n\n
Returns
\n\n
    \n
  • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
    • \n
\n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

Mixin class for all classifiers in scikit-learn.

\n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.1
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n\n
References
\n\n

Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

Build a Setred classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
    • \n
\n\n
Returns
\n\n
    \n
  • self (Setred):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (array-like of shape (n_samples,)):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
    • \n
\n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
\n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

\n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
    • \n
  • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
    • \n
  • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
    • \n
\n\n
References
\n\n

Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

Build a CoForest classifier from the training set (X, y).

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (CoForest):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

A descriptor for request methods.

\n\n

New in version 1.3.

\n\n
Parameters
\n\n
    \n
  • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
    • \n
  • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
    • \n
  • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
    • \n
\n\n
Notes
\n\n

This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

\n\n
References
\n\n
\n
\n
    \n
  1. \n

    https://docs.python.org/3/howto/descriptor.html ↩

    \n
    2. \n\n
  2. \n

    https://www.python.org/dev/peps/pep-0362/ ↩

    \n
    3. \n
\n
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"sslearn.wrapper.CoTraining.set_predict_proba_request": {"tf": 1}, "sslearn.wrapper.CoTraining.set_score_request": {"tf": 1}, "sslearn.wrapper.DeTriTraining.set_score_request": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"tf": 1}, "sslearn.wrapper.Setred.set_score_request": {"tf": 1}, "sslearn.wrapper.CoForest.set_score_request": {"tf": 1}}, "df": 24}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.WiWTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}, "a": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}, "m": 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"sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.set_fit_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.set_predict_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.set_score_request": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.set_score_request": {"tf": 1.4142135623730951}}, "df": 24}, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.WiWTriTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 11}}}}}, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, 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"sslearn.wrapper.WiWTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}}, "df": 1}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.WiWTriTraining.__init__": {"tf": 1}}, "df": 1}}}}, "z": {"docs": {"sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}}, "df": 3}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "u": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough. diff --git a/docs/sslearn.html b/docs/sslearn.html index c678631..b962cdb 100644 --- a/docs/sslearn.html +++ b/docs/sslearn.html @@ -45,11 +45,6 @@ -

Contents

- -

Submodules

    @@ -81,81 +76,26 @@

    API Documentation

    sslearn

    -

    Semi-Supervised Learning Library (sslearn)

    - -

    -

    - -

    Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version

    - -

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    - -
    Installation
    - -

    Dependencies

    - -
      -
    • joblib >= 1.2.0
      • -
    • numpy >= 1.23.3
      • -
    • pandas >= 1.4.3
      • -
    • scikit_learn >= 1.2.0
      • -
    • scipy >= 1.10.1
      • -
    • statsmodels >= 0.13.2
      • -
    • pytest = 7.2.0 (only for testing)
      • -
    - -

    pip installation

    - -

    It can be installed using Pypi:

    - -
    pip install sslearn
-
    - -
    Code example
    - -
    -
    from sslearn.wrapper import TriTraining
-from sslearn.model_selection import artificial_ssl_dataset
-from sklearn.datasets import load_iris
-
-X, y = load_iris(return_X_y=True)
-X, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)
-
-model = TriTraining().fit(X, y)
-model.score(X_unlabel, true_label)
-
    -
    - -
    Citing
    - -
    -
    @software{jose_luis_garrido_labrador_2024_10623889,
-  author       = {José Luis Garrido-Labrador},
-  title        = {jlgarridol/sslearn: v1.0.4},
-  month        = feb,
-  year         = 2024,
-  publisher    = {Zenodo},
-  version      = {1.0.4},
-  doi          = {10.5281/zenodo.10623889},
-  url          = {https://doi.org/10.5281/zenodo.10623889}
-}
-
    -
    +

    Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models.

    - 
    1# Open README.md and added to __doc__
-2with open("../README.md", "r") as f:
-3    __doc__ = f.read()
-4
-5
-6__version__='1.0.4.1'
-7__AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
-8__AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
-9__URL__="https://pypi.org/project/sslearn/"
+                        
     1# Open README.md and added to __doc__ for 
+ 2import os
+ 3if os.path.exists("../README.md"):
+ 4    with open("../README.md", "r") as f:
+ 5        __doc__ = f.read()
+ 6else:
+ 7    __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."
+ 8
+ 9
+10__version__='1.0.4.1'
+11__AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
+12__AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
+13__URL__="https://pypi.org/project/sslearn/"
 
    
 
 

From 73dd359293a6bf26b731b6258752829026db47fa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:03:32 +0200
Subject: [PATCH 22/34] Change the process of doc

---
 .github/workflows/docs.yml | 51 --------------------------------------
 1 file changed, 51 deletions(-)
 delete mode 100644 .github/workflows/docs.yml

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
deleted file mode 100644
index 93ec942..0000000
--- a/.github/workflows/docs.yml
+++ /dev/null
@@ -1,51 +0,0 @@
-name: website
-
-# build the documentation whenever there are new commits on main
-on:
-  push:
-    branches:
-      - main
-    # Alternative: only build for tags.
-    # tags:
-    #   - '*'
-
-# security: restrict permissions for CI jobs.
-permissions:
-  contents: read
-
-jobs:
-  # Build the documentation and upload the static HTML files as an artifact.
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: actions/setup-python@v5
-        with:
-          python-version: '3.12'
-
-      # ADJUST THIS: install all dependencies (including pdoc)
-      - run: python -m pip install --upgrade pip
-      - run: python -m pip install pdoc
-      - run: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
-      # ADJUST THIS: build your documentation into docs/.
-      # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here.
-      - run: python docs/make.py
-
-      - uses: actions/upload-pages-artifact@v3
-        with:
-          path: docs/
-
-  # Deploy the artifact to GitHub pages.
-  # This is a separate job so that only actions/deploy-pages has the necessary permissions.
-  deploy:
-    needs: build
-    runs-on: ubuntu-latest
-    permissions:
-      pages: write
-      id-token: write
-    environment:
-      name: github-pages
-      url: ${{ steps.deployment.outputs.page_url }}
-    steps:
-      - id: deployment
-        uses: actions/deploy-pages@v4
\ No newline at end of file

From a9781455c0637d3e006faddec47f46eac3fb7a20 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:03:57 +0200
Subject: [PATCH 23/34] Create docs.yml

---
 .github/workflows/docs.yml | 51 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 .github/workflows/docs.yml

diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..f2eab0b
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,51 @@
+name: Publish documentation online
+
+# build the documentation whenever there are new commits on main
+on:
+  push:
+    branches:
+      - main
+    # Alternative: only build for tags.
+    # tags:
+    #   - '*'
+
+# security: restrict permissions for CI jobs.
+permissions:
+  contents: read
+
+jobs:
+  # Build the documentation and upload the static HTML files as an artifact.
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      # ADJUST THIS: install all dependencies (including pdoc)
+      - run: python -m pip install --upgrade pip
+      - run: python -m pip install pdoc
+      - run: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+      # ADJUST THIS: build your documentation into docs/.
+      # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here.
+      - run: python docs/make.py
+
+      - uses: actions/upload-pages-artifact@v3
+        with:
+          path: docs/
+
+  # Deploy the artifact to GitHub pages.
+  # This is a separate job so that only actions/deploy-pages has the necessary permissions.
+  deploy:
+    needs: build
+    runs-on: ubuntu-latest
+    permissions:
+      pages: write
+      id-token: write
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+    steps:
+      - id: deployment
+        uses: actions/deploy-pages@v4

From 0baee2d33d05f28d6d0c85c9f50268d921643992 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:06:40 +0200
Subject: [PATCH 24/34] Deploy documentation in github.io

---
 docs/make.py        | 2 +-
 sslearn/__init__.py | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/make.py b/docs/make.py
index a712a23..4d95c95 100644
--- a/docs/make.py
+++ b/docs/make.py
@@ -26,7 +26,7 @@
 
         favicon=f"data:image/svg+xml;base64,{favicon}",
         logo=f"data:image/svg+xml;base64,{logo}",
-        logo_link="/",
+        logo_link="/sslearn",
         footer_text=f"pdoc {pdoc.__version__}",
         search=True,
         math=True,
diff --git a/sslearn/__init__.py b/sslearn/__init__.py
index d698d2e..f6bbaae 100644
--- a/sslearn/__init__.py
+++ b/sslearn/__init__.py
@@ -3,6 +3,9 @@
 if os.path.exists("../README.md"):
     with open("../README.md", "r") as f:
         __doc__ = f.read()
+elif os.path.exists("README.md"):
+    with open("README.md", "r") as f:
+        __doc__ = f.read()
 else:
     __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."
 

From 425d58558dc10f264bef89969a260825f7a9ca76 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:07:48 +0200
Subject: [PATCH 25/34] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index cd0f45d..0fcf833 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@ Semi-Supervised Learning Library (sslearn)
 ===
 
 
-
+
 
 ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn)
 

From b4a794f7c05e28538204d8f99b0f19094f3fc633 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:16:06 +0200
Subject: [PATCH 26/34] Repair logos

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0fcf833..cd0f45d 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@ Semi-Supervised Learning Library (sslearn)
 ===
 
 
-
+
 
 ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn)
 

From 25cec04f48e2ea1eccadc03283f888c9a0cabbd8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:22:12 +0200
Subject: [PATCH 27/34] Add logo also in README

---
 sslearn.svg | 355 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 355 insertions(+)
 create mode 100644 sslearn.svg

diff --git a/sslearn.svg b/sslearn.svg
new file mode 100644
index 0000000..52a09d5
--- /dev/null
+++ b/sslearn.svg
@@ -0,0 +1,355 @@
+
+
+
+
+  
+  
+  
+  
+    sslearn
+  
+  
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    *
+    *
+    *
+    
+    
+  
+

From 597660858a63c52cab6eb04a95161e29823c01a1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= 
Date: Wed, 24 Apr 2024 19:24:25 +0200
Subject: [PATCH 28/34] Repair logo size

---
 README.md                         |   2 +-
 docs/search.js                    |   2 +-
 docs/sslearn.html                 |  87 +++++++-
 docs/sslearn/base.html            |   2 +-
 docs/sslearn/datasets.html        |   2 +-
 docs/sslearn/model_selection.html |   2 +-
 docs/sslearn/restricted.html      |   2 +-
 docs/sslearn/subview.html         |   2 +-
 docs/sslearn/utils.html           |   2 +-
 docs/sslearn/wrapper.html         |   2 +-
 sslearn.svg                       | 355 ------------------------------
 11 files changed, 86 insertions(+), 374 deletions(-)
 delete mode 100644 sslearn.svg

diff --git a/README.md b/README.md
index cd0f45d..db14ca3 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@ Semi-Supervised Learning Library (sslearn)
 ===
 
 
-
+
 
 ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn)
 
diff --git a/docs/search.js b/docs/search.js
index 2647540..ee831fc 100644
--- a/docs/search.js
+++ b/docs/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oSemi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models.
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "
    Summary of module sslearn.base:
    \n\n
    Functions
    \n\n
    get_dataset(X, y):\n    Check and divide dataset between labeled and unlabeled data.
    \n\n
    Classes
    \n\n
    FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n    Create a classifier that fakes predict_proba method if it does not exist.
    \n\n
    OneVsRestSSLClassifier(OneVsRestClassifier):\n    Adapted OneVsRestClassifier for SSL datasets
    \n\n
    All doc
    \n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "
    Create a classifier that fakes predict_proba method if it does not exist.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "
    Fit a FakedProbaClassifier.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "
    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "
    Check and divide dataset between labeled and unlabeled data.
    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "
    One-vs-the-rest (OvR) multiclass strategy.
    \n\n
    Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.
    \n\n
    OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.
    \n\n
    Read more in the :ref:User Guide <ovr_classification>.
    \n\n
    Parameters
    \n\n
      \n
    • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
      • \n
    • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.
      \n\n
      None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.
      \n\n
      Changed in version 0.20:\nn_jobs default changed from 1 to None
      • \n
    • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.
      \n\n
      New in version 1.1.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
      • \n
    • classes_ (array, shape = [n_classes]):\nClass labels.
      • \n
    • n_classes_ (int):\nNumber of classes.
      • \n
    • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
      • \n
    • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
      • \n
    • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.
      \n\n
      New in version 0.24.
      • \n
    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.
      \n\n
      New in version 1.0.
      • \n
    \n\n
    See Also
    \n\n
    OneVsOneClassifier:  One-vs-one multiclass strategy. 
    \nOutputCodeClassifier:  (Error-Correcting) Output-Code multiclass strategy. 
    \nsklearn.multioutput.MultiOutputClassifier:  Alternate way of extending an\nestimator for multilabel classification. 
    \nsklearn.preprocessing.MultiLabelBinarizer:  Transform iterable of iterables\nto binary indicator matrix.  
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
    \n
    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "
    Adapted OneVsRestClassifier for SSL datasets
    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "
    Fit underlying estimators.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "
    Predict multi-class targets using underlying estimators.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "
    Probability estimates.
    \n\n
    The returned estimates for all classes are ordered by label of classes.
    \n\n
    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.
    \n\n
    In the single label multiclass case, the rows of the returned matrix\nsum to 1.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "
    Summary of module sslearn.datasets:
    \n\n
    This module contains functions to load and save datasets in different formats.
    \n\n
    Functions
    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n\n
    All doc
    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "
    Read a .csv file
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "
    Read a .dat file from KEEL (http://www.keel.es/)
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "
    It guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after
    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "
    Save a dataset in the KEEL format
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "
    Summary of module sslearn.model_selection:
    \n\n
    This module contains functions to split datasets into training and testing sets.
    \n\n
    Functions
    \n\n
    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.
    \n\n
    Classes
    \n\n
    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.
    \n\n
    All doc
    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "
    Create an artificial Semi-supervised dataset from a supervised dataset.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "
    Summary of module sslearn.restricted:
    \n\n
    This module contains classes to train a classifier using the restricted set classification approach.
    \n\n
    Classes
    \n\n
    WhoIsWhoClassifier : Who is Who Classifier
    \n\n
    Functions
    \n\n
    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.
    \n\n
    All doc
    \n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "
    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "
    Fit the model according to the given training data.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "
    Calculate the conflict rate of the model.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "
    Predict class for X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "
    Predict class probabilities for X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "
    Computes the conflict rate of a prediction, given a set of restrictions.
    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "
    Summary of module sslearn.subview:
    \n\n
    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.
    \n\n
    Classes
    \n\n
    SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.
    \n\n
    All doc
    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "
    Predict class probabilities using the base estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "
    Predict using the base estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "
    Some utility functions
    \n\n
    This module contains utility functions that are used in different parts of the library.
    \n\n
    Functions
    \n\n
    safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.
    \n\n
    All doc
    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "
    Safely divide two numbers preventing division by zero
    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "
    Calculate the confidence interval of the predictions
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "
    Choice the best predictions according to the proportion of each class.
    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "
    Calculate the priori probability of each label
    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "
    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause
    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "
    Summary of module sslearn.wrapper:
    \n\n
    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.
    \n\n
    Self-Training Algorithms
    \n\n
      \n
    1. SelfTraining : Self-training algorithm.
      2. \n
    2. Setred : Self-training with redundancy reduction.
      3. \n
    \n\n
    Co-Training Algorithms
    \n\n
      \n
    1. CoTraining : Co-training
      2. \n
    2. CoTrainingByCommittee : Co-training by committee
      3. \n
    3. DemocraticCoLearning : Democratic co-learning
      4. \n
    4. Rasco : Random subspace co-training
      5. \n
    5. RelRasco : Relevant random subspace co-training
      6. \n
    6. CoForest : Co-Forest
      7. \n
    7. TriTraining : Tri-training
      8. \n
    8. DeTriTraining : Data Editing Tri-training
      9. \n
    9. WiWTriTraining : Who-Is-Who Tri-training
      10. \n
    \n\n
    All doc
    \n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "
    Self-training classifier.
    \n\n
    This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.
    \n\n
    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.
    \n\n
    Read more in the :ref:User Guide <self_training>.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • base_estimator_ (estimator object):\nThe fitted estimator.
      • \n
    • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
      • \n
    • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
      • \n
    • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
      • \n
    • n_features_in_ (int):\nNumber of features seen during :term:fit.
      \n\n
      New in version 0.24.
      • \n
    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.
      \n\n
      New in version 1.0.
      • \n
    • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
      • \n
    • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.
      \n\n
        \n
      • 'max_iter': n_iter_ reached max_iter.
        • \n
      • 'no_change': no new labels were predicted.
        • \n
      • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
        • \n
      • \n
    \n\n
    See Also
    \n\n
    LabelPropagation:  Label propagation classifier. 
    \nLabelSpreading:  Label spreading model for semi-supervised learning.  
    \n\n
    References
    \n\n
    :doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
    \n
    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "
    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
    \n\n
    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.
    \n\n
    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    References
    \n\n
    David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684
    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "
    Fits this SelfTrainingClassifier to a dataset.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "
    Create a committee trained by cotraining based on\nthe diversity of classifiers.
    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.
    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "
    Build a CoTrainingByCommittee classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "
    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "
    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "
    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "
    Co-Training based on random subspaces
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "
    Build a Rasco classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "
    Co-Training with relevant random subspaces
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n
    Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "
    TriTraining. Trio of classifiers with bootstrapping.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n
    Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "
    Build a TriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "
    TriTraining with restriction Who-is-Who.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028
    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "
    Build a TriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "
    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962
    \n\n
    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "
    Build a CoTraining classifier from the training set.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "
    Predict probability for each possible outcome.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "
    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "
    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.        
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "
    Build a DeTriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "
    Democratic Co-learning. Ensemble of classifiers of different types.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n\n
    References
    \n\n
    Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.
    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "
    Fit Democratic-Co classifier
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "
    Predict probability for each possible outcome.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "
    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n\n
    References
    \n\n
    Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.
    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "
    Build a Setred classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "
    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "
    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "
    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining. 
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n\n
    References
    \n\n
    Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "
    Build a CoForest classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"sslearn": {"fullname": "sslearn", "modulename": "sslearn", "kind": "module", "doc": "
    Semi-Supervised Learning Library (sslearn)
    \n\n
    \n
    \n\n
    \"Code \"Code \"GitHub \"PyPI
    \n\n
    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
    \n\n
    Installation
    \n\n
    Dependencies
    \n\n
      \n
    • joblib >= 1.2.0
      • \n
    • numpy >= 1.23.3
      • \n
    • pandas >= 1.4.3
      • \n
    • scikit_learn >= 1.2.0
      • \n
    • scipy >= 1.10.1
      • \n
    • statsmodels >= 0.13.2
      • \n
    • pytest = 7.2.0 (only for testing)
      • \n
    \n\n
    pip installation
    \n\n
    It can be installed using Pypi:
    \n\n
    pip install sslearn\n
    \n\n
    Code example
    \n\n
    \n
    from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
    \n
    \n\n
    Citing
    \n\n
    \n
    @software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
    \n
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "
    Summary of module sslearn.base:
    \n\n
    Functions
    \n\n
    get_dataset(X, y):\n    Check and divide dataset between labeled and unlabeled data.
    \n\n
    Classes
    \n\n
    FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n    Create a classifier that fakes predict_proba method if it does not exist.
    \n\n
    OneVsRestSSLClassifier(OneVsRestClassifier):\n    Adapted OneVsRestClassifier for SSL datasets
    \n\n
    All doc
    \n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "
    Create a classifier that fakes predict_proba method if it does not exist.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "
    Fit a FakedProbaClassifier.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "
    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "
    Check and divide dataset between labeled and unlabeled data.
    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "
    One-vs-the-rest (OvR) multiclass strategy.
    \n\n
    Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.
    \n\n
    OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.
    \n\n
    Read more in the :ref:User Guide <ovr_classification>.
    \n\n
    Parameters
    \n\n
      \n
    • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
      • \n
    • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.
      \n\n
      None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.
      \n\n
      Changed in version 0.20:\nn_jobs default changed from 1 to None
      • \n
    • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.
      \n\n
      New in version 1.1.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
      • \n
    • classes_ (array, shape = [n_classes]):\nClass labels.
      • \n
    • n_classes_ (int):\nNumber of classes.
      • \n
    • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
      • \n
    • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
      • \n
    • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.
      \n\n
      New in version 0.24.
      • \n
    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.
      \n\n
      New in version 1.0.
      • \n
    \n\n
    See Also
    \n\n
    OneVsOneClassifier:  One-vs-one multiclass strategy. 
    \nOutputCodeClassifier:  (Error-Correcting) Output-Code multiclass strategy. 
    \nsklearn.multioutput.MultiOutputClassifier:  Alternate way of extending an\nestimator for multilabel classification. 
    \nsklearn.preprocessing.MultiLabelBinarizer:  Transform iterable of iterables\nto binary indicator matrix.  
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
    \n
    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "
    Adapted OneVsRestClassifier for SSL datasets
    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "
    Fit underlying estimators.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "
    Predict multi-class targets using underlying estimators.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "
    Probability estimates.
    \n\n
    The returned estimates for all classes are ordered by label of classes.
    \n\n
    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.
    \n\n
    In the single label multiclass case, the rows of the returned matrix\nsum to 1.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "
    Summary of module sslearn.datasets:
    \n\n
    This module contains functions to load and save datasets in different formats.
    \n\n
    Functions
    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n\n
    All doc
    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "
    Read a .csv file
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "
    Read a .dat file from KEEL (http://www.keel.es/)
    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "
    It guarantees that the dataset has not  -1 as valid class, in order to make it semi-supervised after
    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "
    Save a dataset in the KEEL format
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "
    Summary of module sslearn.model_selection:
    \n\n
    This module contains functions to split datasets into training and testing sets.
    \n\n
    Functions
    \n\n
    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.
    \n\n
    Classes
    \n\n
    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.
    \n\n
    All doc
    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "
    Create an artificial Semi-supervised dataset from a supervised dataset.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "
    Summary of module sslearn.restricted:
    \n\n
    This module contains classes to train a classifier using the restricted set classification approach.
    \n\n
    Classes
    \n\n
    WhoIsWhoClassifier : Who is Who Classifier
    \n\n
    Functions
    \n\n
    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.
    \n\n
    All doc
    \n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "
    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "
    Fit the model according to the given training data.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "
    Calculate the conflict rate of the model.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "
    Predict class for X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "
    Predict class probabilities for X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "
    Computes the conflict rate of a prediction, given a set of restrictions.
    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "
    Summary of module sslearn.subview:
    \n\n
    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.
    \n\n
    Classes
    \n\n
    SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.
    \n\n
    All doc
    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "
    Predict class probabilities using the base estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "
    Predict using the base estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "
    Some utility functions
    \n\n
    This module contains utility functions that are used in different parts of the library.
    \n\n
    Functions
    \n\n
    safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.
    \n\n
    All doc
    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "
    Safely divide two numbers preventing division by zero
    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "
    Calculate the confidence interval of the predictions
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "
    Choice the best predictions according to the proportion of each class.
    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "
    Calculate the priori probability of each label
    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "
    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause
    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "
    Summary of module sslearn.wrapper:
    \n\n
    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.
    \n\n
    Self-Training Algorithms
    \n\n
      \n
    1. SelfTraining : Self-training algorithm.
      2. \n
    2. Setred : Self-training with redundancy reduction.
      3. \n
    \n\n
    Co-Training Algorithms
    \n\n
      \n
    1. CoTraining : Co-training
      2. \n
    2. CoTrainingByCommittee : Co-training by committee
      3. \n
    3. DemocraticCoLearning : Democratic co-learning
      4. \n
    4. Rasco : Random subspace co-training
      5. \n
    5. RelRasco : Relevant random subspace co-training
      6. \n
    6. CoForest : Co-Forest
      7. \n
    7. TriTraining : Tri-training
      8. \n
    8. DeTriTraining : Data Editing Tri-training
      9. \n
    9. WiWTriTraining : Who-Is-Who Tri-training
      10. \n
    \n\n
    All doc
    \n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "
    Self-training classifier.
    \n\n
    This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.
    \n\n
    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.
    \n\n
    Read more in the :ref:User Guide <self_training>.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • base_estimator_ (estimator object):\nThe fitted estimator.
      • \n
    • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
      • \n
    • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
      • \n
    • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
      • \n
    • n_features_in_ (int):\nNumber of features seen during :term:fit.
      \n\n
      New in version 0.24.
      • \n
    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.
      \n\n
      New in version 1.0.
      • \n
    • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
      • \n
    • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.
      \n\n
        \n
      • 'max_iter': n_iter_ reached max_iter.
        • \n
      • 'no_change': no new labels were predicted.
        • \n
      • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
        • \n
      • \n
    \n\n
    See Also
    \n\n
    LabelPropagation:  Label propagation classifier. 
    \nLabelSpreading:  Label spreading model for semi-supervised learning.  
    \n\n
    References
    \n\n
    :doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
    \n
    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "
    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
    \n\n
    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.
    \n\n
    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    References
    \n\n
    David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684
    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "
    Fits this SelfTrainingClassifier to a dataset.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "
    Create a committee trained by cotraining based on\nthe diversity of classifiers.
    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.
    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "
    Build a CoTrainingByCommittee classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "
    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "
    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "
    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "
    Co-Training based on random subspaces
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "
    Build a Rasco classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "
    Co-Training with relevant random subspaces
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n
    Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "
    TriTraining. Trio of classifiers with bootstrapping.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n
    Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "
    Build a TriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "
    TriTraining with restriction Who-is-Who.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028
    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "
    Build a TriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "
    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962
    \n\n
    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "
    Build a CoTraining classifier from the training set.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "
    Predict probability for each possible outcome.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "
    Predict the classes of X.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "
    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.
    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "
    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.        
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n
    Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "
    Build a DeTriTraining classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "
    Democratic Co-learning. Ensemble of classifiers of different types.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n\n
    References
    \n\n
    Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.
    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "
    Fit Democratic-Co classifier
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "
    Predict probability for each possible outcome.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "
    Mixin class for all classifiers in scikit-learn.
    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "
    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n\n
    References
    \n\n
    Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.
    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "
    Build a Setred classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "
    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "
    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "
    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining. 
    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n\n
    References
    \n\n
    Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "
    Build a CoForest classifier from the training set (X, y).
    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
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     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.
diff --git a/docs/sslearn.html b/docs/sslearn.html
index b962cdb..00be6aa 100644
--- a/docs/sslearn.html
+++ b/docs/sslearn.html
@@ -40,11 +40,16 @@
         
         
         
    
-            
+            
 
             
 
+            
    Contents
    
+            
+
 
             
    Submodules
    
             
      
@@ -76,7 +81,66 @@ 
      API Documentation
      
                     
      
 sslearn    
      
 
-                        
      Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models.
      
+                        
      Semi-Supervised Learning Library (sslearn)
      
+
+
      
+
      
+
+
      Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version
      
+
+
      The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.
      
+
+
      Installation
      
+
+
      Dependencies
      
+
+
        
+
      • joblib >= 1.2.0
        • 
+
      • numpy >= 1.23.3
        • 
+
      • pandas >= 1.4.3
        • 
+
      • scikit_learn >= 1.2.0
        • 
+
      • scipy >= 1.10.1
        • 
+
      • statsmodels >= 0.13.2
        • 
+
      • pytest = 7.2.0 (only for testing)
        • 
+
      
+
+
      pip installation
      
+
+
      It can be installed using Pypi:
      
+
+
      pip install sslearn
+
      
+
+
      Code example
      
+
+
      
+
      from sslearn.wrapper import TriTraining
+from sslearn.model_selection import artificial_ssl_dataset
+from sklearn.datasets import load_iris
+
+X, y = load_iris(return_X_y=True)
+X, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)
+
+model = TriTraining().fit(X, y)
+model.score(X_unlabel, true_label)
+
      
+
      
+
+
      Citing
      
+
+
      
+
      @software{jose_luis_garrido_labrador_2024_10623889,
+  author       = {José Luis Garrido-Labrador},
+  title        = {jlgarridol/sslearn: v1.0.4},
+  month        = feb,
+  year         = 2024,
+  publisher    = {Zenodo},
+  version      = {1.0.4},
+  doi          = {10.5281/zenodo.10623889},
+  url          = {https://doi.org/10.5281/zenodo.10623889}
+}
+
      
+
      
 
      
 
                         
@@ -88,14 +152,17 @@ 
      
  3if os.path.exists("../README.md"):
  4    with open("../README.md", "r") as f:
  5        __doc__ = f.read()
- 6else:
- 7    __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."
- 8
- 9
-10__version__='1.0.4.1'
-11__AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
-12__AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
-13__URL__="https://pypi.org/project/sslearn/"
+ 6elif os.path.exists("README.md"):
+ 7    with open("README.md", "r") as f:
+ 8        __doc__ = f.read()
+ 9else:
+10    __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."
+11
+12
+13__version__='1.0.4.1'
+14__AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
+15__AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
+16__URL__="https://pypi.org/project/sslearn/"
    diff --git a/docs/sslearn/base.html b/docs/sslearn/base.html index 450b433..7cd6639 100644 --- a/docs/sslearn/base.html +++ b/docs/sslearn/base.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/datasets.html b/docs/sslearn/datasets.html index 0791423..5ca2cb4 100644 --- a/docs/sslearn/datasets.html +++ b/docs/sslearn/datasets.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/model_selection.html b/docs/sslearn/model_selection.html index 3be92fd..bcd8218 100644 --- a/docs/sslearn/model_selection.html +++ b/docs/sslearn/model_selection.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/restricted.html b/docs/sslearn/restricted.html index 9e412fc..1075cd3 100644 --- a/docs/sslearn/restricted.html +++ b/docs/sslearn/restricted.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/subview.html b/docs/sslearn/subview.html index 1602bf0..d93b189 100644 --- a/docs/sslearn/subview.html +++ b/docs/sslearn/subview.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/utils.html b/docs/sslearn/utils.html index aa1f645..b69ce44 100644 --- a/docs/sslearn/utils.html +++ b/docs/sslearn/utils.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/wrapper.html b/docs/sslearn/wrapper.html index f3f640c..63c2ffb 100644 --- a/docs/sslearn/wrapper.html +++ b/docs/sslearn/wrapper.html @@ -45,7 +45,7 @@  sslearn - + diff --git a/sslearn.svg b/sslearn.svg deleted file mode 100644 index 52a09d5..0000000 --- a/sslearn.svg +++ /dev/null @@ -1,355 +0,0 @@ - - - - - - - - - sslearn - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - * - * - * - - - - From e5f9921d7c47fecd3b0fd79724126b2bf71de75b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Wed, 24 Apr 2024 19:31:48 +0200 Subject: [PATCH 29/34] Migrate svg to webp --- README.md | 2 +- docs/make.py | 8 ++++---- docs/search.js | 2 +- docs/sslearn.html | 6 +++--- docs/sslearn.webp | Bin 0 -> 60556 bytes docs/sslearn/base.html | 4 ++-- docs/sslearn/datasets.html | 4 ++-- docs/sslearn/model_selection.html | 4 ++-- docs/sslearn/restricted.html | 4 ++-- docs/sslearn/subview.html | 4 ++-- docs/sslearn/utils.html | 4 ++-- docs/sslearn/wrapper.html | 4 ++-- docs/sslearn_mini.webp | Bin 0 -> 12764 bytes 13 files changed, 23 insertions(+), 23 deletions(-) create mode 100644 docs/sslearn.webp create mode 100644 docs/sslearn_mini.webp diff --git a/README.md b/README.md index db14ca3..f6c4cab 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ Semi-Supervised Learning Library (sslearn) === - + ![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn) diff --git a/docs/make.py b/docs/make.py index 4d95c95..5eceecc 100644 --- a/docs/make.py +++ b/docs/make.py @@ -16,16 +16,16 @@ if __name__ == "__main__": - favicon = (here / "docs" / "sslearn_mini.svg").read_bytes() + favicon = (here / "docs" / "sslearn_mini.webp").read_bytes() favicon = base64.b64encode(favicon).decode("utf8") - logo = (here / "docs" / "sslearn.svg").read_bytes() + logo = (here / "docs" / "sslearn.webp").read_bytes() logo = base64.b64encode(logo).decode("utf8") # Render main docs pdoc.render.configure( - favicon=f"data:image/svg+xml;base64,{favicon}", - logo=f"data:image/svg+xml;base64,{logo}", + favicon="data:image/webp;base64," + favicon, + logo="data:image/webp;base64," + logo, logo_link="/sslearn", footer_text=f"pdoc {pdoc.__version__}", search=True, diff --git a/docs/search.js b/docs/search.js index ee831fc..e67c070 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - 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    \n

    \n\n

    \"Code \"Code \"GitHub \"PyPI

    \n\n

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    \n\n
    Installation
    \n\n

    Dependencies

    \n\n
      \n
    • joblib >= 1.2.0
      • \n
    • numpy >= 1.23.3
      • \n
    • pandas >= 1.4.3
      • \n
    • scikit_learn >= 1.2.0
      • \n
    • scipy >= 1.10.1
      • \n
    • statsmodels >= 0.13.2
      • \n
    • pytest = 7.2.0 (only for testing)
      • \n
    \n\n

    pip installation

    \n\n

    It can be installed using Pypi:

    \n\n
    pip install sslearn\n
    \n\n
    Code example
    \n\n
    \n
    from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
    \n
    \n\n
    Citing
    \n\n
    \n
    @software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
    \n
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

    Summary of module sslearn.base:

    \n\n
    Functions
    \n\n

    get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

    \n\n
    Classes
    \n\n

    FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

    \n\n

    OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

    \n\n

    All doc

    \n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

    Create a classifier that fakes predict_proba method if it does not exist.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

    Fit a FakedProbaClassifier.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

    Check and divide dataset between labeled and unlabeled data.

    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

    One-vs-the-rest (OvR) multiclass strategy.

    \n\n

    Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

    \n\n

    OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

    \n\n

    Read more in the :ref:User Guide <ovr_classification>.

    \n\n
    Parameters
    \n\n
      \n
    • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
      • \n

    • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

      \n\n

      None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

      \n\n

      Changed in version 0.20:\nn_jobs default changed from 1 to None

      • \n

    • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

      \n\n

      New in version 1.1.

      • \n
    \n\n
    Attributes
    \n\n
      \n
    • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
      • \n
    • classes_ (array, shape = [n_classes]):\nClass labels.
      • \n
    • n_classes_ (int):\nNumber of classes.
      • \n
    • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
      • \n
    • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 1.0.

      • \n
    \n\n
    See Also
    \n\n

    OneVsOneClassifier: One-vs-one multiclass strategy.
    \nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
    \nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
    \nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
    \n
    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

    Adapted OneVsRestClassifier for SSL datasets

    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

    Fit underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

    Predict multi-class targets using underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

    Probability estimates.

    \n\n

    The returned estimates for all classes are ordered by label of classes.

    \n\n

    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

    \n\n

    In the single label multiclass case, the rows of the returned matrix\nsum to 1.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

    Summary of module sslearn.datasets:

    \n\n

    This module contains functions to load and save datasets in different formats.

    \n\n
    Functions
    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n\n

    All doc

    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

    Read a .csv file

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

    Read a .dat file from KEEL (http://www.keel.es/)

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

    It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

    Save a dataset in the KEEL format

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

    Summary of module sslearn.model_selection:

    \n\n

    This module contains functions to split datasets into training and testing sets.

    \n\n
    Functions
    \n\n

    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

    \n\n
    Classes
    \n\n

    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

    \n\n

    All doc

    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

    Create an artificial Semi-supervised dataset from a supervised dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

    Summary of module sslearn.restricted:

    \n\n

    This module contains classes to train a classifier using the restricted set classification approach.

    \n\n
    Classes
    \n\n

    WhoIsWhoClassifier : Who is Who Classifier

    \n\n
    Functions
    \n\n

    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

    \n\n

    All doc

    \n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

    Fit the model according to the given training data.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

    Calculate the conflict rate of the model.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

    Predict class for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

    Computes the conflict rate of a prediction, given a set of restrictions.

    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

    Summary of module sslearn.subview:

    \n\n

    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

    \n\n
    Classes
    \n\n

    SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

    \n\n

    All doc

    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

    Predict using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

    Some utility functions

    \n\n

    This module contains utility functions that are used in different parts of the library.

    \n\n
    Functions
    \n\n

    safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

    \n\n

    All doc

    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

    Safely divide two numbers preventing division by zero

    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

    Calculate the confidence interval of the predictions

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

    Choice the best predictions according to the proportion of each class.

    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

    Calculate the priori probability of each label

    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

    Summary of module sslearn.wrapper:

    \n\n

    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

    \n\n

    Self-Training Algorithms

    \n\n
      \n
    1. SelfTraining : Self-training algorithm.
      2. \n
    2. Setred : Self-training with redundancy reduction.
      3. \n
    \n\n

    Co-Training Algorithms

    \n\n
      \n
    1. CoTraining : Co-training
      2. \n
    2. CoTrainingByCommittee : Co-training by committee
      3. \n
    3. DemocraticCoLearning : Democratic co-learning
      4. \n
    4. Rasco : Random subspace co-training
      5. \n
    5. RelRasco : Relevant random subspace co-training
      6. \n
    6. CoForest : Co-Forest
      7. \n
    7. TriTraining : Tri-training
      8. \n
    8. DeTriTraining : Data Editing Tri-training
      9. \n
    9. WiWTriTraining : Who-Is-Who Tri-training
      10. \n
    \n\n

    All doc

    \n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

    Self-training classifier.

    \n\n

    This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n

    Read more in the :ref:User Guide <self_training>.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • base_estimator_ (estimator object):\nThe fitted estimator.
      • \n
    • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
      • \n
    • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
      • \n
    • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

      \n\n

      New in version 1.0.

      • \n
    • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
      • \n

    • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

      \n\n
        \n
      • 'max_iter': n_iter_ reached max_iter.
        • \n
      • 'no_change': no new labels were predicted.
        • \n
      • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
        • \n
      • \n
    \n\n
    See Also
    \n\n

    LabelPropagation: Label propagation classifier.
    \nLabelSpreading: Label spreading model for semi-supervised learning.

    \n\n
    References
    \n\n

    :doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
    \n
    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

    \n\n

    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    References
    \n\n

    David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

    Fits this SelfTrainingClassifier to a dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

    Create a committee trained by cotraining based on\nthe diversity of classifiers.

    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

    Build a CoTrainingByCommittee classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

    Co-Training based on random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

    Build a Rasco classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

    Co-Training with relevant random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

    TriTraining. Trio of classifiers with bootstrapping.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

    TriTraining with restriction Who-is-Who.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

    \n\n

    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

    Build a CoTraining classifier from the training set.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

    Build a DeTriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

    Democratic Co-learning. Ensemble of classifiers of different types.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n\n
    References
    \n\n

    Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

    Fit Democratic-Co classifier

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n\n
    References
    \n\n

    Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

    Build a Setred classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n\n
    References
    \n\n

    Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

    Build a CoForest classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
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"module", "doc": "

    Semi-Supervised Learning Library (sslearn)

    \n\n

    \n

    \n\n

    \"Code \"Code \"GitHub \"PyPI

    \n\n

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    \n\n
    Installation
    \n\n

    Dependencies

    \n\n
      \n
    • joblib >= 1.2.0
      • \n
    • numpy >= 1.23.3
      • \n
    • pandas >= 1.4.3
      • \n
    • scikit_learn >= 1.2.0
      • \n
    • scipy >= 1.10.1
      • \n
    • statsmodels >= 0.13.2
      • \n
    • pytest = 7.2.0 (only for testing)
      • \n
    \n\n

    pip installation

    \n\n

    It can be installed using Pypi:

    \n\n
    pip install sslearn\n
    \n\n
    Code example
    \n\n
    \n
    from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
    \n
    \n\n
    Citing
    \n\n
    \n
    @software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
    \n
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

    Summary of module sslearn.base:

    \n\n
    Functions
    \n\n

    get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

    \n\n
    Classes
    \n\n

    FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

    \n\n

    OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

    \n\n

    All doc

    \n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

    Create a classifier that fakes predict_proba method if it does not exist.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

    Fit a FakedProbaClassifier.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

    Check and divide dataset between labeled and unlabeled data.

    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

    One-vs-the-rest (OvR) multiclass strategy.

    \n\n

    Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

    \n\n

    OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

    \n\n

    Read more in the :ref:User Guide <ovr_classification>.

    \n\n
    Parameters
    \n\n
      \n
    • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
      • \n

    • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

      \n\n

      None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

      \n\n

      Changed in version 0.20:\nn_jobs default changed from 1 to None

      • \n

    • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

      \n\n

      New in version 1.1.

      • \n
    \n\n
    Attributes
    \n\n
      \n
    • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
      • \n
    • classes_ (array, shape = [n_classes]):\nClass labels.
      • \n
    • n_classes_ (int):\nNumber of classes.
      • \n
    • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
      • \n
    • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 1.0.

      • \n
    \n\n
    See Also
    \n\n

    OneVsOneClassifier: One-vs-one multiclass strategy.
    \nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
    \nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
    \nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
    \n
    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

    Adapted OneVsRestClassifier for SSL datasets

    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

    Fit underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

    Predict multi-class targets using underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

    Probability estimates.

    \n\n

    The returned estimates for all classes are ordered by label of classes.

    \n\n

    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

    \n\n

    In the single label multiclass case, the rows of the returned matrix\nsum to 1.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

    Summary of module sslearn.datasets:

    \n\n

    This module contains functions to load and save datasets in different formats.

    \n\n
    Functions
    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n\n

    All doc

    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

    Read a .csv file

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

    Read a .dat file from KEEL (http://www.keel.es/)

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

    It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

    Save a dataset in the KEEL format

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

    Summary of module sslearn.model_selection:

    \n\n

    This module contains functions to split datasets into training and testing sets.

    \n\n
    Functions
    \n\n

    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

    \n\n
    Classes
    \n\n

    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

    \n\n

    All doc

    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

    Create an artificial Semi-supervised dataset from a supervised dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

    Summary of module sslearn.restricted:

    \n\n

    This module contains classes to train a classifier using the restricted set classification approach.

    \n\n
    Classes
    \n\n

    WhoIsWhoClassifier : Who is Who Classifier

    \n\n
    Functions
    \n\n

    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

    \n\n

    All doc

    \n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

    Fit the model according to the given training data.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

    Calculate the conflict rate of the model.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

    Predict class for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

    Computes the conflict rate of a prediction, given a set of restrictions.

    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

    Summary of module sslearn.subview:

    \n\n

    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

    \n\n
    Classes
    \n\n

    SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

    \n\n

    All doc

    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

    Predict using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

    Some utility functions

    \n\n

    This module contains utility functions that are used in different parts of the library.

    \n\n
    Functions
    \n\n

    safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

    \n\n

    All doc

    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

    Safely divide two numbers preventing division by zero

    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

    Calculate the confidence interval of the predictions

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

    Choice the best predictions according to the proportion of each class.

    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

    Calculate the priori probability of each label

    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

    Summary of module sslearn.wrapper:

    \n\n

    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

    \n\n

    Self-Training Algorithms

    \n\n
      \n
    1. SelfTraining : Self-training algorithm.
      2. \n
    2. Setred : Self-training with redundancy reduction.
      3. \n
    \n\n

    Co-Training Algorithms

    \n\n
      \n
    1. CoTraining : Co-training
      2. \n
    2. CoTrainingByCommittee : Co-training by committee
      3. \n
    3. DemocraticCoLearning : Democratic co-learning
      4. \n
    4. Rasco : Random subspace co-training
      5. \n
    5. RelRasco : Relevant random subspace co-training
      6. \n
    6. CoForest : Co-Forest
      7. \n
    7. TriTraining : Tri-training
      8. \n
    8. DeTriTraining : Data Editing Tri-training
      9. \n
    9. WiWTriTraining : Who-Is-Who Tri-training
      10. \n
    \n\n

    All doc

    \n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

    Self-training classifier.

    \n\n

    This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n

    Read more in the :ref:User Guide <self_training>.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • base_estimator_ (estimator object):\nThe fitted estimator.
      • \n
    • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
      • \n
    • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
      • \n
    • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

      \n\n

      New in version 1.0.

      • \n
    • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
      • \n

    • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

      \n\n
        \n
      • 'max_iter': n_iter_ reached max_iter.
        • \n
      • 'no_change': no new labels were predicted.
        • \n
      • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
        • \n
      • \n
    \n\n
    See Also
    \n\n

    LabelPropagation: Label propagation classifier.
    \nLabelSpreading: Label spreading model for semi-supervised learning.

    \n\n
    References
    \n\n

    :doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
    \n
    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

    \n\n

    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    References
    \n\n

    David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

    Fits this SelfTrainingClassifier to a dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

    Create a committee trained by cotraining based on\nthe diversity of classifiers.

    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

    Build a CoTrainingByCommittee classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

    Co-Training based on random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

    Build a Rasco classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

    Co-Training with relevant random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

    TriTraining. Trio of classifiers with bootstrapping.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

    TriTraining with restriction Who-is-Who.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

    \n\n

    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

    Build a CoTraining classifier from the training set.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

    Build a DeTriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

    Democratic Co-learning. Ensemble of classifiers of different types.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n\n
    References
    \n\n

    Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

    Fit Democratic-Co classifier

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n\n
    References
    \n\n

    Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

    Build a Setred classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n\n
    References
    \n\n

    Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

    Build a CoForest classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
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    Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version

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a/docs/sslearn/datasets.html b/docs/sslearn/datasets.html index 5ca2cb4..8bcfd1f 100644 --- a/docs/sslearn/datasets.html +++ b/docs/sslearn/datasets.html @@ -5,7 +5,7 @@ sslearn.datasets API documentation - + @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/model_selection.html b/docs/sslearn/model_selection.html index bcd8218..bdb747e 100644 --- a/docs/sslearn/model_selection.html +++ b/docs/sslearn/model_selection.html @@ -5,7 +5,7 @@ sslearn.model_selection API documentation - + @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/restricted.html b/docs/sslearn/restricted.html index 1075cd3..a7e0b00 100644 --- a/docs/sslearn/restricted.html +++ b/docs/sslearn/restricted.html @@ -5,7 +5,7 @@ sslearn.restricted API documentation - + @@ -45,7 +45,7 @@  sslearn - + diff --git a/docs/sslearn/subview.html b/docs/sslearn/subview.html index d93b189..f2dbaa1 100644 --- a/docs/sslearn/subview.html +++ b/docs/sslearn/subview.html @@ -5,7 +5,7 @@ sslearn.subview API documentation - 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++-- docs/sslearn/datasets.html | 35 +- docs/sslearn/model_selection.html | 54 +- docs/sslearn/restricted.html | 1085 ++- docs/sslearn/subview.html | 354 +- docs/sslearn/utils.html | 722 +- docs/sslearn/wrapper.html | 11234 ++++++++++++-------------- sslearn/base.py | 46 +- sslearn/datasets/__init__.py | 7 +- sslearn/model_selection/__init__.py | 19 +- sslearn/restricted.py | 22 +- sslearn/subview/__init__.py | 13 +- sslearn/subview/_subview.py | 23 + sslearn/utils.py | 29 +- sslearn/wrapper/__init__.py | 53 +- sslearn/wrapper/_co.py | 372 +- sslearn/wrapper/_self.py | 90 +- sslearn/wrapper/_tritraining.py | 100 +- 23 files changed, 7745 insertions(+), 8102 deletions(-) diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index f2eab0b..eb7c5aa 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -27,6 +27,7 @@ jobs: - run: python -m pip install --upgrade pip - run: python -m pip install pdoc - run: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - run: python -m pip install scikit-learn==1.2.2 # ADJUST THIS: build your documentation into docs/. # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here. - run: python docs/make.py diff --git a/README.md b/README.md index f6c4cab..e24719d 100644 --- a/README.md +++ b/README.md @@ -4,12 +4,13 @@ Semi-Supervised Learning Library (sslearn) -![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn) +![Code Climate maintainability](https://img.shields.io/codeclimate/maintainability-percentage/jlgarridol/sslearn) ![Code Climate coverage](https://img.shields.io/codeclimate/coverage/jlgarridol/sslearn) ![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/jlgarridol/sslearn/python-package.yml) ![PyPI - Version](https://img.shields.io/pypi/v/sslearn) [![Static Badge](https://img.shields.io/badge/doc-available-blue?style=flat)](https://jlgarridol.github.io/sslearn/) The `sslearn` library is a Python package for machine learning over Semi-supervised datasets. It is an extension of [scikit-learn](https://github.com/scikit-learn/scikit-learn). -Installation ---- +## Installation + + ### Dependencies * joblib >= 1.2.0 @@ -26,8 +27,9 @@ It can be installed using *Pypi*: pip install sslearn -Code example ---- +## Code example + + ```python from sslearn.wrapper import TriTraining from sslearn.model_selection import artificial_ssl_dataset @@ -40,8 +42,8 @@ model = TriTraining().fit(X, y) model.score(X_unlabel, true_label) ``` -Citing ---- +## Citing + ```bibtex @software{jose_luis_garrido_labrador_2024_10623889, author = {José Luis Garrido-Labrador}, diff --git a/docs/make.py b/docs/make.py index 5eceecc..b1c8bdb 100644 --- a/docs/make.py +++ b/docs/make.py @@ -14,6 +14,9 @@ here = Path(__file__).parent.parent +# Ignore set_score_request in the docs + + if __name__ == "__main__": favicon = (here / "docs" / "sslearn_mini.webp").read_bytes() diff --git a/docs/search.js b/docs/search.js index e67c070..657d744 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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    \n\n

    \n

    \n\n

    \"Code \"Code \"GitHub \"PyPI

    \n\n

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    \n\n
    Installation
    \n\n

    Dependencies

    \n\n
      \n
    • joblib >= 1.2.0
      • \n
    • numpy >= 1.23.3
      • \n
    • pandas >= 1.4.3
      • \n
    • scikit_learn >= 1.2.0
      • \n
    • scipy >= 1.10.1
      • \n
    • statsmodels >= 0.13.2
      • \n
    • pytest = 7.2.0 (only for testing)
      • \n
    \n\n

    pip installation

    \n\n

    It can be installed using Pypi:

    \n\n
    pip install sslearn\n
    \n\n
    Code example
    \n\n
    \n
    from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
    \n
    \n\n
    Citing
    \n\n
    \n
    @software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
    \n
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

    Summary of module sslearn.base:

    \n\n
    Functions
    \n\n

    get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

    \n\n
    Classes
    \n\n

    FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):\n Create a classifier that fakes predict_proba method if it does not exist.

    \n\n

    OneVsRestSSLClassifier(OneVsRestClassifier):\n Adapted OneVsRestClassifier for SSL datasets

    \n\n

    All doc

    \n"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

    Create a classifier that fakes predict_proba method if it does not exist.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

    Fit a FakedProbaClassifier.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.set_score_request": {"fullname": "sslearn.base.FakedProbaClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

    Check and divide dataset between labeled and unlabeled data.

    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

    One-vs-the-rest (OvR) multiclass strategy.

    \n\n

    Also known as one-vs-all, this strategy consists in fitting one classifier\nper class. For each classifier, the class is fitted against all the other\nclasses. In addition to its computational efficiency (only n_classes\nclassifiers are needed), one advantage of this approach is its\ninterpretability. Since each class is represented by one and one classifier\nonly, it is possible to gain knowledge about the class by inspecting its\ncorresponding classifier. This is the most commonly used strategy for\nmulticlass classification and is a fair default choice.

    \n\n

    OneVsRestClassifier can also be used for multilabel classification. To use\nthis feature, provide an indicator matrix for the target y when calling\n.fit. In other words, the target labels should be formatted as a 2D\nbinary (0/1) matrix, where [i, j] == 1 indicates the presence of label j\nin sample i. This estimator uses the binary relevance method to perform\nmultilabel classification, which involves training one binary classifier\nindependently for each label.

    \n\n

    Read more in the :ref:User Guide <ovr_classification>.

    \n\n
    Parameters
    \n\n
      \n
    • estimator (estimator object):\nA regressor or a classifier that implements :term:fit.\nWhen a classifier is passed, :term:decision_function will be used\nin priority and it will fallback to :term:predict_proba if it is not\navailable.\nWhen a regressor is passed, :term:predict is used.
      • \n

    • n_jobs (int, default=None):\nThe number of jobs to use for the computation: the n_classes\none-vs-rest problems are computed in parallel.

      \n\n

      None means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors. See :term:Glossary <n_jobs>\nfor more details.

      \n\n

      Changed in version 0.20:\nn_jobs default changed from 1 to None

      • \n

    • verbose (int, default=0):\nThe verbosity level, if non zero, progress messages are printed.\nBelow 50, the output is sent to stderr. Otherwise, the output is sent\nto stdout. The frequency of the messages increases with the verbosity\nlevel, reporting all iterations at 10. See joblib.Parallel for\nmore details.

      \n\n

      New in version 1.1.

      • \n
    \n\n
    Attributes
    \n\n
      \n
    • estimators_ (list of n_classes estimators):\nEstimators used for predictions.
      • \n
    • classes_ (array, shape = [n_classes]):\nClass labels.
      • \n
    • n_classes_ (int):\nNumber of classes.
      • \n
    • label_binarizer_ (LabelBinarizer object):\nObject used to transform multiclass labels to binary labels and\nvice-versa.
      • \n
    • multilabel_ (boolean):\nWhether a OneVsRestClassifier is a multilabel classifier.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Only defined if the\nunderlying estimator exposes such an attribute when fit.

      \n\n

      New in version 1.0.

      • \n
    \n\n
    See Also
    \n\n

    OneVsOneClassifier: One-vs-one multiclass strategy.
    \nOutputCodeClassifier: (Error-Correcting) Output-Code multiclass strategy.
    \nsklearn.multioutput.MultiOutputClassifier: Alternate way of extending an\nestimator for multilabel classification.
    \nsklearn.preprocessing.MultiLabelBinarizer: Transform iterable of iterables\nto binary indicator matrix.

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.multiclass import OneVsRestClassifier\n>>> from sklearn.svm import SVC\n>>> X = np.array([\n...     [10, 10],\n...     [8, 10],\n...     [-5, 5.5],\n...     [-5.4, 5.5],\n...     [-20, -20],\n...     [-15, -20]\n... ])\n>>> y = np.array([0, 0, 1, 1, 2, 2])\n>>> clf = OneVsRestClassifier(SVC()).fit(X, y)\n>>> clf.predict([[-19, -20], [9, 9], [-5, 5]])\narray([2, 0, 1])\n
    \n
    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

    Adapted OneVsRestClassifier for SSL datasets

    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

    Fit underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

    Predict multi-class targets using underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

    Probability estimates.

    \n\n

    The returned estimates for all classes are ordered by label of classes.

    \n\n

    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

    \n\n

    In the single label multiclass case, the rows of the returned matrix\nsum to 1.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_partial_fit_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_partial_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.set_score_request": {"fullname": "sslearn.base.OneVsRestSSLClassifier.set_score_request", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

    Summary of module sslearn.datasets:

    \n\n

    This module contains functions to load and save datasets in different formats.

    \n\n
    Functions
    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n\n

    All doc

    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

    Read a .csv file

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

    Read a .dat file from KEEL (http://www.keel.es/)

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

    It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

    Save a dataset in the KEEL format

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

    Summary of module sslearn.model_selection:

    \n\n

    This module contains functions to split datasets into training and testing sets.

    \n\n
    Functions
    \n\n

    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.

    \n\n
    Classes
    \n\n

    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.

    \n\n

    All doc

    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

    Create an artificial Semi-supervised dataset from a supervised dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

    Summary of module sslearn.restricted:

    \n\n

    This module contains classes to train a classifier using the restricted set classification approach.

    \n\n
    Classes
    \n\n

    WhoIsWhoClassifier : Who is Who Classifier

    \n\n
    Functions
    \n\n

    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.\ncombine_predictions : Combine the predictions of a group of instances to keep the restrictions.

    \n\n

    All doc

    \n"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

    Fit the model according to the given training data.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

    Calculate the conflict rate of the model.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

    Predict class for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_fit_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_fit_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_predict_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_predict_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.set_score_request": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.set_score_request", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

    Computes the conflict rate of a prediction, given a set of restrictions.

    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

    Summary of module sslearn.subview:

    \n\n

    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

    \n\n
    Classes
    \n\n

    SubViewClassifier : Train a sub-view classifier.\nSubViewRegressor : Train a sub-view regressor.

    \n\n

    All doc

    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewClassifier.set_score_request": {"fullname": "sslearn.subview.SubViewClassifier.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

    Predict using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor.set_score_request": {"fullname": "sslearn.subview.SubViewRegressor.set_score_request", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

    Some utility functions

    \n\n

    This module contains utility functions that are used in different parts of the library.

    \n\n
    Functions
    \n\n

    safe_division : Safely divide two numbers preventing division by zero.\nconfidence_interval : Calculate the confidence interval of the predictions.\nchoice_with_proportion : Choice the best predictions according to the proportion of each class.\ncalculate_prior_probability : Calculate the priori probability of each label.\ncheck_n_jobs : Check n_jobs parameter according to the scikit-learn convention.

    \n\n

    All doc

    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

    Safely divide two numbers preventing division by zero

    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

    Calculate the confidence interval of the predictions

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

    Choice the best predictions according to the proportion of each class.

    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

    Calculate the priori probability of each label

    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

    Summary of module sslearn.wrapper:

    \n\n

    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

    \n\n

    Self-Training Algorithms

    \n\n
      \n
    1. SelfTraining : Self-training algorithm.
      2. \n
    2. Setred : Self-training with redundancy reduction.
      3. \n
    \n\n

    Co-Training Algorithms

    \n\n
      \n
    1. CoTraining : Co-training
      2. \n
    2. CoTrainingByCommittee : Co-training by committee
      3. \n
    3. DemocraticCoLearning : Democratic co-learning
      4. \n
    4. Rasco : Random subspace co-training
      5. \n
    5. RelRasco : Relevant random subspace co-training
      6. \n
    6. CoForest : Co-Forest
      7. \n
    7. TriTraining : Tri-training
      8. \n
    8. DeTriTraining : Data Editing Tri-training
      9. \n
    9. WiWTriTraining : Who-Is-Who Tri-training
      10. \n
    \n\n

    All doc

    \n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

    Self-training classifier.

    \n\n

    This :term:metaestimator allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n

    Read more in the :ref:User Guide <self_training>.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion='k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    Attributes
    \n\n
      \n
    • base_estimator_ (estimator object):\nThe fitted estimator.
      • \n
    • classes_ (ndarray or list of ndarray of shape (n_classes,)):\nClass labels for each output. (Taken from the trained\nbase_estimator_).
      • \n
    • transduction_ (ndarray of shape (n_samples,)):\nThe labels used for the final fit of the classifier, including\npseudo-labels added during fit.
      • \n
    • labeled_iter_ (ndarray of shape (n_samples,)):\nThe iteration in which each sample was labeled. When a sample has\niteration 0, the sample was already labeled in the original dataset.\nWhen a sample has iteration -1, the sample was not labeled in any\niteration.
      • \n

    • n_features_in_ (int):\nNumber of features seen during :term:fit.

      \n\n

      New in version 0.24.

      • \n

    • feature_names_in_ (ndarray of shape (n_features_in_,)):\nNames of features seen during :term:fit. Defined only when X\nhas feature names that are all strings.

      \n\n

      New in version 1.0.

      • \n
    • n_iter_ (int):\nThe number of rounds of self-training, that is the number of times the\nbase estimator is fitted on relabeled variants of the training set.
      • \n

    • termination_condition_ ({'max_iter', 'no_change', 'all_labeled'}):\nThe reason that fitting was stopped.

      \n\n
        \n
      • 'max_iter': n_iter_ reached max_iter.
        • \n
      • 'no_change': no new labels were predicted.
        • \n
      • 'all_labeled': all unlabeled samples were labeled before max_iter\nwas reached.
        • \n
      • \n
    \n\n
    See Also
    \n\n

    LabelPropagation: Label propagation classifier.
    \nLabelSpreading: Label spreading model for semi-supervised learning.

    \n\n
    References
    \n\n

    :doi:David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196.\n<10.3115/981658.981684>

    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn import datasets\n>>> from sklearn.semi_supervised import SelfTrainingClassifier\n>>> from sklearn.svm import SVC\n>>> rng = np.random.RandomState(42)\n>>> iris = datasets.load_iris()\n>>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3\n>>> iris.target[random_unlabeled_points] = -1\n>>> svc = SVC(probability=True, gamma="auto")\n>>> self_training_model = SelfTrainingClassifier(svc)\n>>> self_training_model.fit(iris.data, iris.target)\nSelfTrainingClassifier(...)\n
    \n
    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

    \n\n

    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n\n
    References
    \n\n

    David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling\nsupervised methods. In Proceedings of the 33rd annual meeting on\nAssociation for Computational Linguistics (ACL '95). Association for\nComputational Linguistics, Stroudsburg, PA, USA, 189-196. DOI:\nhttps://doi.org/10.3115/981658.981684

    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

    Fits this SelfTrainingClassifier to a dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sslearn.base.BaseEnsemble, sklearn.base.BaseEstimator"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

    Create a committee trained by cotraining based on\nthe diversity of classifiers.

    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    M. F. A. Hady and F. Schwenker,\n\"Co-training by Committee: A New Semi-supervised Learning Framework,\"\n2008 IEEE International Conference on Data Mining Workshops,\nPisa, 2008, pp. 563-572, doi: 10.1109/ICDMW.2008.27.

    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

    Build a CoTrainingByCommittee classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.set_score_request": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

    Co-Training based on random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Wang, J., Luo, S. W., & Zeng, X. H. (2008, June).\nA random subspace method for co-training.\nIn 2008 IEEE International Joint Conference on Neural Networks\n(IEEE World Congress on Computational Intelligence)\n(pp. 195-200). IEEE.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

    Build a Rasco classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Rasco.set_score_request": {"fullname": "sslearn.wrapper.Rasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Rasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

    Co-Training with relevant random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Yaslan, Y., & Cataltepe, Z. (2010).\nCo-training with relevant random subspaces.\nNeurocomputing, 73(10-12), 1652-1661.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.RelRasco.set_score_request": {"fullname": "sslearn.wrapper.RelRasco.set_score_request", "modulename": "sslearn.wrapper", "qualname": "RelRasco.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

    TriTraining. Trio of classifiers with bootstrapping.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n\n
    References
    \n\n

    Zhi-Hua Zhou and Ming Li,\n\"Tri-training: exploiting unlabeled data using three classifiers,\"\nin IEEE Transactions on Knowledge and Data Engineering,\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,\ndoi: 10.1109/TKDE.2005.186.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining.set_score_request": {"fullname": "sslearn.wrapper.TriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "TriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

    TriTraining with restriction Who-is-Who.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson,\nRestricted set classification: Who is there?,\nPattern Recognition, 63, 158-170, \n10.1016/j.patcog.2016.08.028

    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_fit_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_predict_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.set_score_request": {"fullname": "sslearn.wrapper.WiWTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Avrim Blum and Tom Mitchell. 1998.\nCombining labeled and unlabeled data with co-training.\nIn Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).\nAssociation for Computing Machinery, New York, NY, USA, 92-100.\nDOI:https://doi.org/10.1145/279943.279962

    \n\n

    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. 2011. \n'Multi-Class Co-Training Learning for Object and Scene Recognition'.\nPp. 67-70 in. Nara, Japan.

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

    Build a CoTraining classifier from the training set.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_fit_request": {"fullname": "sslearn.wrapper.CoTraining.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_predict_proba_request": {"fullname": "sslearn.wrapper.CoTraining.set_predict_proba_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_predict_proba_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.set_score_request": {"fullname": "sslearn.wrapper.CoTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    References
    \n\n

    Deng C., Guo M.Z. (2006)\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm. \nIn: Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. \nLecture Notes in Computer Science, vol 4293.\nSpringer, Berlin, Heidelberg.\nhttps://doi.org/10.1007/11925231_61

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

    Build a DeTriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining.set_score_request": {"fullname": "sslearn.wrapper.DeTriTraining.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

    Democratic Co-learning. Ensemble of classifiers of different types.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n\n
    References
    \n\n

    Y. Zhou and S. Goldman, \"Democratic co-learning,\"\n16th IEEE International Conference on Tools with Artificial Intelligence,\n2004, pp. 594-602, doi: 10.1109/ICTAI.2004.48.

    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

    Fit Democratic-Co classifier

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_fit_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_fit_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_fit_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.set_score_request": {"fullname": "sslearn.wrapper.DemocraticCoLearning.set_score_request", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

    Mixin class for all classifiers in scikit-learn.

    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the \u201ccosine\u201d metric uses cosine_distances., by default \"euclidean\"
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n\n
    References
    \n\n

    Li, Ming, and Zhi-Hua Zhou. \"SETRED: Self-training with editing.\"\nPacific-Asia Conference on Knowledge Discovery and Data Mining.\nSpringer, Berlin, Heidelberg, 2005. doi: 10.1007/11430919_71.

    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

    Build a Setred classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.set_score_request": {"fullname": "sslearn.wrapper.Setred.set_score_request", "modulename": "sslearn.wrapper", "qualname": "Setred.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n\n
    References
    \n\n

    Li, M., & Zhou, Z.-H. (2007).\nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.\nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,\n37(6), 1088-1098. doi:10.1109/tsmca.2007.904745

    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

    Build a CoForest classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoForest.set_score_request": {"fullname": "sslearn.wrapper.CoForest.set_score_request", "modulename": "sslearn.wrapper", "qualname": "CoForest.set_score_request", "kind": "function", "doc": "

    A descriptor for request methods.

    \n\n

    New in version 1.3.

    \n\n
    Parameters
    \n\n
      \n
    • name (str):\nThe name of the method for which the request function should be\ncreated, e.g. \"fit\" would create a set_fit_request function.
      • \n
    • keys (list of str):\nA list of strings which are accepted parameters by the created\nfunction, e.g. [\"sample_weight\"] if the corresponding method\naccepts it as a metadata.
      • \n
    • validate_keys (bool, default=True):\nWhether to check if the requested parameters fit the actual parameters\nof the method.
      • \n
    \n\n
    Notes
    \n\n

    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.

    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n

      https://docs.python.org/3/howto/descriptor.html ↩

      \n
      2. \n\n
    2. \n

      https://www.python.org/dev/peps/pep-0362/ ↩

      \n
      3. \n
    \n
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"module", "doc": "

    Semi-Supervised Learning Library (sslearn)

    \n\n

    \n

    \n\n

    \"Code \"Code \"GitHub \"PyPI \"Static

    \n\n

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    \n\n

    Installation

    \n\n

    Dependencies

    \n\n
      \n
    • joblib >= 1.2.0
      • \n
    • numpy >= 1.23.3
      • \n
    • pandas >= 1.4.3
      • \n
    • scikit_learn >= 1.2.0
      • \n
    • scipy >= 1.10.1
      • \n
    • statsmodels >= 0.13.2
      • \n
    • pytest = 7.2.0 (only for testing)
      • \n
    \n\n

    pip installation

    \n\n

    It can be installed using Pypi:

    \n\n
    pip install sslearn\n
    \n\n

    Code example

    \n\n
    \n
    from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
    \n
    \n\n

    Citing

    \n\n
    \n
    @software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
    \n
    \n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

    Summary of module sslearn.base:

    \n\n

    Functions

    \n\n

    get_dataset(X, y):\n Check and divide dataset between labeled and unlabeled data.

    \n\n

    Classes

    \n\n

    FakedProbaClassifier:

    \n\n
    \n

    Create a classifier that fakes predict_proba method if it does not exist.

    \n
    \n\n

    OneVsRestSSLClassifier:

    \n\n
    \n

    Adapted OneVsRestClassifier for SSL datasets

    \n
    \n"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

    Check and divide dataset between labeled and unlabeled data.

    \n\n
    Parameters
    \n\n
      \n
    • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
      • \n
    • y (ndarray of shape (n_samples,)):\nTarget vector.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
      • \n
    • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
      • \n
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)):\nUnlabeled features matrix.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier": {"fullname": "sslearn.base.FakedProbaClassifier", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier", "kind": "class", "doc": "

    Fake predict_proba method for classifiers that do not have it. \nWhen predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.

    \n\n
    Examples
    \n\n
    \n
    from sklearn.svm import SVC\n# SVC does not have predict_proba method\n\nfrom sslearn.base import FakedProbaClassifier\nfaked_svc = FakedProbaClassifier(SVC())\nfaked_svc.fit(X, y)\nfaked_svc.predict_proba(X) # One hot encoding probabilities\n
    \n
    \n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

    Create a classifier that fakes predict_proba method if it does not exist.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nA classifier that implements fit and predict methods.
      • \n
    \n", "signature": "(base_estimator)"}, "sslearn.base.FakedProbaClassifier.fit": {"fullname": "sslearn.base.FakedProbaClassifier.fit", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.fit", "kind": "function", "doc": "

    Fit a FakedProbaClassifier.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nThe target values.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (FakedProbaClassifier):\nReturns self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict": {"fullname": "sslearn.base.FakedProbaClassifier.predict", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.FakedProbaClassifier.predict_proba": {"fullname": "sslearn.base.FakedProbaClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.predict_proba", "kind": "function", "doc": "

    Predict the probabilities of each class for X. \nIf the base estimator does not have a predict_proba method, it will be faked using one hot encoding.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes)):\nArray with predicted probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier": {"fullname": "sslearn.base.OneVsRestSSLClassifier", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier", "kind": "class", "doc": "

    Adapted OneVsRestClassifier for SSL datasets

    \n\n

    Prevent use unlabeled data as a independent class in the classifier.

    \n\n

    For more information of OvR classifier, see the documentation of OneVsRestClassifier.

    \n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

    Adapted OneVsRestClassifier for SSL datasets

    \n\n
    Parameters
    \n\n
      \n
    • estimator ({ClassifierMixin, list},):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin
      • \n
    • n_jobs : n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(estimator, *, n_jobs=None)"}, "sslearn.base.OneVsRestSSLClassifier.fit": {"fullname": "sslearn.base.OneVsRestSSLClassifier.fit", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.fit", "kind": "function", "doc": "

    Fit underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nMulti-class targets. An indicator matrix turns on multilabel\nclassification.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nInstance of fitted estimator.
      • \n
    \n", "signature": "(self, X, y, **fit_params):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict", "kind": "function", "doc": "

    Predict multi-class targets using underlying estimators.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nData.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y ({array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_classes)):\nPredicted multi-class targets.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"fullname": "sslearn.base.OneVsRestSSLClassifier.predict_proba", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.predict_proba", "kind": "function", "doc": "

    Probability estimates.

    \n\n

    The returned estimates for all classes are ordered by label of classes.

    \n\n

    Note that in the multilabel case, each sample can have any number of\nlabels. This returns the marginal probability that the given sample has\nthe label in question. For example, it is entirely consistent that two\nlabels both have a 90% probability of applying to a given sample.

    \n\n

    In the single label multiclass case, the rows of the returned matrix\nsum to 1.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nInput data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • T (array-like of shape (n_samples, n_classes)):\nReturns the probability of the sample for each class in the model,\nwhere classes are ordered as they are in self.classes_.
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.datasets": {"fullname": "sslearn.datasets", "modulename": "sslearn.datasets", "kind": "module", "doc": "

    Summary of module sslearn.datasets:

    \n\n

    This module contains functions to load and save datasets in different formats.

    \n\n

    Functions

    \n\n
      \n
    1. read_csv : Load a dataset from a CSV file.
      2. \n
    2. read_keel : Load a dataset from a KEEL file.
      3. \n
    3. secure_dataset : Secure the dataset by converting it into a secure format.
      4. \n
    4. save_keel : Save a dataset in KEEL format.
      5. \n
    \n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

    Read a .csv file

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(path, format='pandas', secure=False, target_col=-1, **kwards):", "funcdef": "def"}, "sslearn.datasets.read_keel": {"fullname": "sslearn.datasets.read_keel", "modulename": "sslearn.datasets", "qualname": "read_keel", "kind": "function", "doc": "

    Read a .dat file from KEEL (http://www.keel.es/)

    \n\n
    Parameters
    \n\n
      \n
    • path (str):\nFile path
      • \n
    • format (str, optional):\nObject that will contain the data, it can be numpy or pandas, by default \"pandas\"
      • \n
    • secure (bool, optional):\nIt guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after, by default False
      • \n
    • target_col ({str, int, None}, optional):\nColumn name or index to select class column, if None use the default value stored in the file, by default None
      • \n
    • encoding (str, optional):\nEncoding of file, by default \"utf-8\"
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset loaded.
      • \n
    \n", "signature": "(\tpath,\tformat='pandas',\tsecure=False,\ttarget_col=None,\tencoding='utf-8',\t**kwards):", "funcdef": "def"}, "sslearn.datasets.secure_dataset": {"fullname": "sslearn.datasets.secure_dataset", "modulename": "sslearn.datasets", "qualname": "secure_dataset", "kind": "function", "doc": "

    It guarantees that the dataset has not -1 as valid class, in order to make it semi-supervised after

    \n\n
    Parameters
    \n\n
      \n
    • X (Array-like):\nIgnored
      • \n
    • y (Array-like):\nTarget array.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X, y (array_like):\nDataset securized.
      • \n
    \n", "signature": "(X, y):", "funcdef": "def"}, "sslearn.datasets.save_keel": {"fullname": "sslearn.datasets.save_keel", "modulename": "sslearn.datasets", "qualname": "save_keel", "kind": "function", "doc": "

    Save a dataset in the KEEL format

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like):\nDataset features
      • \n
    • y (array-like):\nDataset targets
      • \n
    • route (str):\nPath to save the dataset
      • \n
    • name (str, optional):\nDataset name, if None the route basename will be selected, by default None
      • \n
    • attribute_name (list, optional):\nList of attribute names, if None the default names will be used, by default None
      • \n
    • target_name (str, optional):\nTarget name, by default \"Class\"
      • \n
    • classification (bool, optional):\nIf the dataset is classification or regression, by default True
      • \n
    • unlabeled (bool, optional):\nIf the dataset has unlabeled instances, by default True
      • \n
    • force_targets (collection, optional):\nForce the targets to be a specific value, by default None
      • \n
    \n", "signature": "(\tX,\ty,\troute,\tname=None,\tattribute_name=None,\ttarget_name='Class',\tclassification=True,\tunlabeled=True,\tforce_targets=None):", "funcdef": "def"}, "sslearn.model_selection": {"fullname": "sslearn.model_selection", "modulename": "sslearn.model_selection", "kind": "module", "doc": "

    Summary of module sslearn.model_selection:

    \n\n

    This module contains functions to split datasets into training and testing sets.

    \n\n

    Functions

    \n\n

    artificial_ssl_dataset:

    \n\n
    \n

    Generate an artificial semi-supervised learning dataset.

    \n
    \n\n

    Classes

    \n\n

    StratifiedKFoldSS:

    \n\n
    \n

    Stratified K-Folds cross-validator for semi-supervised learning.

    \n
    \n"}, "sslearn.model_selection.artificial_ssl_dataset": {"fullname": "sslearn.model_selection.artificial_ssl_dataset", "modulename": "sslearn.model_selection", "qualname": "artificial_ssl_dataset", "kind": "function", "doc": "

    Create an artificial Semi-supervised dataset from a supervised dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
      • \n
    • label_rate (float, optional):\nProportion between labeled instances and unlabel instances, by default 0.1
      • \n
    • random_state (int or RandomState, optional):\nControls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
      • \n
    • force_minimum (int, optional):\nForce a minimum of instances of each class, by default None
      • \n
    • indexes (bool, optional):\nIf True, return the indexes of the labeled and unlabeled instances, by default False
      • \n
    • shuffle (bool, default=True):\nWhether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
      • \n
    • stratify (array-like, default=None):\nIf not None, data is split in a stratified fashion, using this as the class labels.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • X (ndarray):\nThe feature set.
      • \n
    • y (ndarray):\nThe label set, -1 for unlabel instance.
      • \n
    • X_unlabel (ndarray):\nThe feature set for each y mark as unlabel
      • \n
    • y_unlabel (ndarray):\nThe true label for each y in the same order.
      • \n
    • label (ndarray (optional)):\nThe training set indexes for split mark as labeled.
      • \n
    • unlabel (ndarray (optional)):\nThe training set indexes for split mark as unlabeled.
      • \n
    \n", "signature": "(\tX,\ty,\tlabel_rate=0.1,\trandom_state=None,\tforce_minimum=None,\tindexes=False,\t**kwards):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

    Summary of module sslearn.restricted:

    \n\n

    This module contains classes to train a classifier using the restricted set classification approach.

    \n\n

    Classes

    \n\n

    WhoIsWhoClassifier:

    \n\n
    \n

    Who is Who Classifier

    \n
    \n\n

    Functions

    \n\n

    conflict_rate:

    \n\n
    \n

    Compute the conflict rate of a prediction, given a set of restrictions.\n combine_predictions: \n Combine the predictions of a group of instances to keep the restrictions.

    \n
    \n"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

    Computes the conflict rate of a prediction, given a set of restrictions.

    \n\n
    Parameters
    \n\n
      \n
    • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
      • \n
    • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • \n
    • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • conflict rate (float):\nThe conflict rate.
      • \n
    \n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

    Base class for all estimators in scikit-learn.

    \n\n
    Notes
    \n\n

    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).

    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin"}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.__init__", "kind": "function", "doc": "

    Who is Who Classifier\nKuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).\nRestricted set classification: Who is there?. Pattern Recognition, 63, 158-170.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin):\nThe base estimator to be used for training.
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    \n", "signature": "(base_estimator, method='hungarian', conflict_weighted=True)"}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.fit", "kind": "function", "doc": "

    Fit the model according to the given training data.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (object):\nReturns self.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.conflict_rate", "kind": "function", "doc": "

    Calculate the conflict rate of the model.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • float: The conflict rate.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict", "kind": "function", "doc": "

    Predict class for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • **kwards (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X, instance_group):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted.WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities for X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview": {"fullname": "sslearn.subview", "modulename": "sslearn.subview", "kind": "module", "doc": "

    Summary of module sslearn.subview:

    \n\n

    This module contains classes to train a classifier or a regressor selecting a sub-view of the data.

    \n\n

    Classes

    \n\n

    SubViewClassifier:

    \n\n
    \n

    Train a sub-view classifier.\n SubViewRegressor:\n Train a sub-view regressor.

    \n
    \n"}, "sslearn.subview.SubViewClassifier": {"fullname": "sslearn.subview.SubViewClassifier", "modulename": "sslearn.subview", "qualname": "SubViewClassifier", "kind": "class", "doc": "

    A classifier that uses a subview of the data.

    \n\n
    Example
    \n\n
    \n
    from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
    \n
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

    Predict class probabilities using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • p (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.subview.SubViewRegressor": {"fullname": "sslearn.subview.SubViewRegressor", "modulename": "sslearn.subview", "qualname": "SubViewRegressor", "kind": "class", "doc": "

    A classifier that uses a subview of the data.

    \n\n
    Example
    \n\n
    \n
    from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
    \n
    \n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

    Predict using the base estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted values.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.utils": {"fullname": "sslearn.utils", "modulename": "sslearn.utils", "kind": "module", "doc": "

    Some utility functions

    \n\n

    This module contains utility functions that are used in different parts of the library.

    \n\n

    Functions

    \n\n

    safe_division:

    \n\n
    \n

    Safely divide two numbers preventing division by zero.\n confidence_interval:\n Calculate the confidence interval of the predictions.\n choice_with_proportion: \n Choice the best predictions according to the proportion of each class.\n calculate_prior_probability:\n Calculate the priori probability of each label.\n mode:\n Calculate the mode of a list of values.\n check_n_jobs:\n Check n_jobs parameter according to the scikit-learn convention.\n check_classifier:\n Check if the classifier is a ClassifierMixin or a list of ClassifierMixin.

    \n
    \n"}, "sslearn.utils.safe_division": {"fullname": "sslearn.utils.safe_division", "modulename": "sslearn.utils", "qualname": "safe_division", "kind": "function", "doc": "

    Safely divide two numbers preventing division by zero

    \n\n
    Parameters
    \n\n
      \n
    • dividend (numeric):\nDividend value
      • \n
    • divisor (numeric):\nDivisor value
      • \n
    • epsilon (numeric):\nClose to zero value to be used in case of division by zero
      • \n
    \n\n
    Returns
    \n\n
      \n
    • result (numeric):\nResult of the division
      • \n
    \n", "signature": "(dividend, divisor, epsilon):", "funcdef": "def"}, "sslearn.utils.confidence_interval": {"fullname": "sslearn.utils.confidence_interval", "modulename": "sslearn.utils", "qualname": "confidence_interval", "kind": "function", "doc": "

    Calculate the confidence interval of the predictions

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    • hyp (classifier):\nThe classifier to be used for prediction
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values
      • \n
    • alpha (float, optional):\nconfidence (1 - significance), by default .95
      • \n
    \n\n
    Returns
    \n\n
      \n
    • li, hi (float):\nlower and upper bound of the confidence interval
      • \n
    \n", "signature": "(X, hyp, y, alpha=0.95):", "funcdef": "def"}, "sslearn.utils.choice_with_proportion": {"fullname": "sslearn.utils.choice_with_proportion", "modulename": "sslearn.utils", "qualname": "choice_with_proportion", "kind": "function", "doc": "

    Choice the best predictions according to the proportion of each class.

    \n\n
    Parameters
    \n\n
      \n
    • predictions (array-like of shape (n_samples,)):\narray of predictions
      • \n
    • class_predicted (array-like of shape (n_samples,)):\narray of predicted classes
      • \n
    • proportion (dict):\ndictionary with the proportion of each class
      • \n
    • extra (int, optional):\nnumber of extra instances to be added, by default 0
      • \n
    \n\n
    Returns
    \n\n
      \n
    • indices (array-like of shape (n_samples,)):\narray of indices of the best predictions
      • \n
    \n", "signature": "(predictions, class_predicted, proportion, extra=0):", "funcdef": "def"}, "sslearn.utils.calculate_prior_probability": {"fullname": "sslearn.utils.calculate_prior_probability", "modulename": "sslearn.utils", "qualname": "calculate_prior_probability", "kind": "function", "doc": "

    Calculate the priori probability of each label

    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\narray of labels
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class_probability (dict):\ndictionary with priori probability (value) of each label (key)
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.mode": {"fullname": "sslearn.utils.mode", "modulename": "sslearn.utils", "qualname": "mode", "kind": "function", "doc": "

    Calculate the mode of a list of values

    \n\n
    Parameters
    \n\n
      \n
    • y (array-like of shape (n_samples, n_estimators)):\narray of values
      • \n
    \n\n
    Returns
    \n\n
      \n
    • mode (array-like of shape (n_samples,)):\narray of mode of each label
      • \n
    • count (array-like of shape (n_samples,)):\narray of count of the mode of each label
      • \n
    \n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

    Check n_jobs parameter according to the scikit-learn convention.\nFrom sktime: BSD 3-Clause

    \n\n
    Parameters
    \n\n
      \n
    • n_jobs (int, positive or -1):\nThe number of jobs for parallelization.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • n_jobs (int):\nChecked number of jobs.
      • \n
    \n", "signature": "(n_jobs):", "funcdef": "def"}, "sslearn.wrapper": {"fullname": "sslearn.wrapper", "modulename": "sslearn.wrapper", "kind": "module", "doc": "

    Summary of module sslearn.wrapper:

    \n\n

    This module contains classes to train semi-supervised learning algorithms using a wrapper approach.

    \n\n

    Self-Training Algorithms

    \n\n
      \n
    • SelfTraining: \nSelf-training algorithm.
      • \n
    • Setred:\nSelf-training with redundancy reduction.
      • \n
    \n\n

    Co-Training Algorithms

    \n\n\n"}, "sslearn.wrapper.SelfTraining": {"fullname": "sslearn.wrapper.SelfTraining", "modulename": "sslearn.wrapper", "qualname": "SelfTraining", "kind": "class", "doc": "

    Self Training Classifier with data loader compatible.

    \n\n

    Is the same SelfTrainingClassifier from sklearn but with sslearn data loader compatible.\nFor more information, see the sklearn documentation.

    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import SelfTraining\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = SelfTraining()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    David Yarowsky. (1995).
    \nUnsupervised word sense disambiguation rivaling supervised methods.
    \nIn Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95).
    \nAssociation for Computational Linguistics,
    \nStroudsburg, PA, USA, 189-196.
    \n10.3115/981658.981684

    \n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

    Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.

    \n\n

    This class allows a given supervised classifier to function as a\nsemi-supervised classifier, allowing it to learn from unlabeled data. It\ndoes this by iteratively predicting pseudo-labels for the unlabeled data\nand adding them to the training set.

    \n\n

    The classifier will continue iterating until either max_iter is reached, or\nno pseudo-labels were added to the training set in the previous iteration.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (estimator object):\nAn estimator object implementing fit and predict_proba.\nInvoking the fit method will fit a clone of the passed estimator,\nwhich will be stored in the base_estimator_ attribute.
      • \n
    • threshold (float, default=0.75):\nThe decision threshold for use with criterion='threshold'.\nShould be in [0, 1). When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • criterion ({'threshold', 'k_best'}, default='threshold'):\nThe selection criterion used to select which labels to add to the\ntraining set. If 'threshold', pseudo-labels with prediction\nprobabilities above threshold are added to the dataset. If 'k_best',\nthe k_best pseudo-labels with highest prediction probabilities are\nadded to the dataset. When using the 'threshold' criterion, a\n:ref:well calibrated classifier <calibration> should be used.
      • \n
    • k_best (int, default=10):\nThe amount of samples to add in each iteration. Only used when\ncriterion is k_best'.
      • \n
    • max_iter (int or None, default=10):\nMaximum number of iterations allowed. Should be greater than or equal\nto 0. If it is None, the classifier will continue to predict labels\nuntil no new pseudo-labels are added, or all unlabeled samples have\nbeen labeled.
      • \n
    • verbose (bool, default=False):\nEnable verbose output.
      • \n
    \n", "signature": "(\tbase_estimator,\tthreshold=0.75,\tcriterion='threshold',\tk_best=10,\tmax_iter=10,\tverbose=False)"}, "sslearn.wrapper.SelfTraining.fit": {"fullname": "sslearn.wrapper.SelfTraining.fit", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.fit", "kind": "function", "doc": "

    Fits this SelfTrainingClassifier to a dataset.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y ({array-like, sparse matrix} of shape (n_samples,)):\nArray representing the labels. Unlabeled samples should have the\nlabel -1.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (SelfTrainingClassifier):\nReturns an instance of self.
      • \n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.wrapper.Setred": {"fullname": "sslearn.wrapper.Setred", "modulename": "sslearn.wrapper", "qualname": "Setred", "kind": "class", "doc": "

    Self-training with Editing.

    \n\n

    Create a SETRED classifier. It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.\nThe main process are:

    \n\n
      \n
    1. Train a classifier with the labeled data.
      2. \n
    2. Create a pool of unlabeled data and select the most confident predictions.
      3. \n
    3. Repeat until the maximum number of iterations is reached:\na. Select the most confident predictions from the unlabeled data.\nb. Calculate the neighborhood graph of the labeled data and the selected instances from the unlabeled data.\nc. Calculate the significance level of the selected instances.\nd. Reject the instances that are not significant according their position in the neighborhood graph.\ne. Add the selected instances to the labeled data and retrains the classifier.\nf. Add new instances to the pool of unlabeled data.
      4. \n
    4. Return the classifier trained with the labeled data.
      5. \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import Setred\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = Setred()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    Li, Ming, and Zhi-Hua Zhou. (2005)
    \nSETRED: Self-training with editing,
    \nin Advances in Knowledge Discovery and Data Mining.
    \nPacific-Asia Conference on Knowledge Discovery and Data Mining
    \nLNAI 3518, Springer, Berlin, Heidelberg,
    \n10.1007/11430919_71

    \n", "bases": "sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.wrapper.Setred.__init__": {"fullname": "sslearn.wrapper.Setred.__init__", "modulename": "sslearn.wrapper", "qualname": "Setred.__init__", "kind": "function", "doc": "

    Create a SETRED classifier.\nIt is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
      • \n
    • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the cosine metric uses cosine_distances., by default euclidean
      • \n
    • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
      • \n
    • rejection_threshold (float, optional):\nsignificance level, by default 0.1
      • \n
    • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
      • \n
    \n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

    Build a Setred classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Setred):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwars):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict": {"fullname": "sslearn.wrapper.Setred.predict", "modulename": "sslearn.wrapper", "qualname": "Setred.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.Setred.predict_proba": {"fullname": "sslearn.wrapper.Setred.predict_proba", "modulename": "sslearn.wrapper", "qualname": "Setred.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

    CoTraining classifier. Multi-view learning algorithm that uses two classifiers to label instances.

    \n\n

    The main process is:

    \n\n
      \n
    1. Train each classifier with the labeled instances and their respective view.
      2. \n
    2. While max iterations is not reached or any instance is unlabeled:\n
        \n
      1. Predict the instances from the unlabeled set.
        2. \n
      2. Select the instances that have the same prediction and the predictions are above the threshold.
        3. \n
      3. Label the instances with the highest probability, keeping the balance of the classes.
        4. \n
      4. Retrain the classifier with the new instances.
        5. \n
      3. \n
    3. Combine the probabilities of each classifier.
      4. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.wrapper import CoTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTraining(DecisionTreeClassifier())\nX1 = X[:, [0, 1]]\nX2 = X[:, [2, 3]]\ncotraining.fit(X1, y, X2) \n# or\ncotraining.fit(X, y, features=[[0, 1], [2, 3]])\n# or\ncotraining = CoTraining(DecisionTreeClassifier(), force_second_view=False)\ncotraining.fit(X, y)\n
    \n
    \n\n

    References

    \n\n

    Avrim Blum and Tom Mitchell. (1998).
    \nCombining labeled and unlabeled data with co-training
    \nin Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).
    \nAssociation for Computing Machinery, New York, NY, USA, 92-100.
    \n10.1145/279943.279962

    \n\n

    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. (2011).
    \nMulti-Class Co-Training Learning for Object and Scene Recognition,
    \npp. 67-70 in. Nara, Japan.
    \nhttp://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTraining.__init__": {"fullname": "sslearn.wrapper.CoTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTraining.__init__", "kind": "function", "doc": "

    Create a CoTraining classifier. \nMulti-view learning algorithm that uses two classifiers to label instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
      • \n
    • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
      • \n
    • max_iterations (int, optional):\nThe number of iterations, by default 30
      • \n
    • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
      • \n
    • threshold (float, optional):\nThe threshold for label instances, by default 0.5
      • \n
    • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

    Build a CoTraining classifier from the training set.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, not compatible with features, by default None
      • \n
    • features ({list, tuple}, optional):\nlist or tuple of two arrays with feature index for each subspace view, not compatible with X2, by default None
      • \n
    • number_per_class ({dict}, optional):\ndict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTraining):\nFitted estimator.
      • \n
    \n", "signature": "(\tself,\tX,\ty,\tX2=None,\tfeatures: list = None,\tnumber_per_class: dict = None,\t**kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict_proba": {"fullname": "sslearn.wrapper.CoTraining.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.predict": {"fullname": "sslearn.wrapper.CoTraining.predict", "modulename": "sslearn.wrapper", "qualname": "CoTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
    \n", "signature": "(self, X, X2=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTraining.score": {"fullname": "sslearn.wrapper.CoTraining.score", "modulename": "sslearn.wrapper", "qualname": "CoTraining.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy\nwhich is a harsh metric since you require for each sample that\neach label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), default=None):\nSample weights.
      • \n
    • X2 ({array-like, sparse matrix} of shape (n_samples, n_features), optional):\nArray representing the data from another view, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee": {"fullname": "sslearn.wrapper.CoTrainingByCommittee", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee", "kind": "class", "doc": "

    Co-Training by Committee classifier.

    \n\n

    Create a committee trained by co-training based on the diversity of the classifiers

    \n\n

    The main process is:

    \n\n
      \n
    1. Train a committee of classifiers.
      2. \n
    2. Create a pool of unlabeled instances.
      3. \n
    3. While max iterations is not reached or any instance is unlabeled:\n
        \n
      1. Predict the instances from the unlabeled set.
        2. \n
      2. Select the instances with the highest probability.
        3. \n
      3. Label the instances with the highest probability, keeping the balance of the classes but ensuring that at least n instances of each class are added.
        4. \n
      4. Retrain the classifier with the new instances.
        5. \n
      4. \n
    4. Combine the probabilities of each classifier.
      5. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoTrainingByCommittee\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTrainingByCommittee()\ncotraining.fit(X, y)\ncotraining.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    M. F. A. Hady and F. Schwenker,
    \nCo-training by Committee: A New Semi-supervised Learning Framework,
    \nin 2008 IEEE International Conference on Data Mining Workshops,
    \nPisa, 2008, pp. 563-572, 10.1109/ICDMW.2008.27

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.__init__", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.__init__", "kind": "function", "doc": "

    Create a committee trained by cotraining based on\nthe diversity of classifiers.

    \n\n
    Parameters
    \n\n
      \n
    • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
      • \n
    • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
      • \n
    • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

    Build a CoTrainingByCommittee classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoTrainingByCommittee):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict", "kind": "function", "doc": "

    Predict class value for X.\nFor a classification model, the predicted class for each sample in X is returned.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (array-like of shape (n_samples,)):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.predict_proba", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.predict_proba", "kind": "function", "doc": "

    Predict class probabilities of the input samples X.\nThe predicted class probability depends on the ensemble estimator.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe input samples.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1):\nThe predicted classes
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.CoTrainingByCommittee.score": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.score", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.score", "kind": "function", "doc": "

    Return the mean accuracy on the given test data and labels.\nIn multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

    \n\n
    Parameters
    \n\n
      \n
    • X (array-like of shape (n_samples, n_features)):\nTest samples.
      • \n
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)):\nTrue labels for X.
      • \n
    • sample_weight (array-like of shape (n_samples,), optional):\nSample weights., by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • score (float):\nMean accuracy of self.predict(X) wrt. y.
      • \n
    \n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning": {"fullname": "sslearn.wrapper.DemocraticCoLearning", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning", "kind": "class", "doc": "

    Democratic Co-learning. Ensemble of classifiers of different types.

    \n\n

    A iterative algorithm that uses a ensemble of classifiers to label instances.\nThe main process is:

    \n\n
      \n
    1. Train each classifier with the labeled instances.
      2. \n
    2. While any classifier is retrained:\n
        \n
      1. Predict the instances from the unlabeled set.
        2. \n
      2. Calculate the confidence interval for each classifier for define weights.
        3. \n
      3. Calculate the weighted vote for each instance.
        4. \n
      4. Calculate the majority vote for each instance.
        5. \n
      5. Select the instances to label if majority vote is the same as weighted vote.
        6. \n
      6. Select the instances to retrain the classifier, if only_mislabeled is False then select all instances, else select only mislabeled instances for each classifier.
        7. \n
      7. Retrain the classifier with the new instances if the error rate is lower than the previous iteration.
        8. \n
      3. \n
    3. Ignore the classifiers with confidence interval lower than 0.5.
      4. \n
    4. Combine the probabilities of each classifier.
      5. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sslearn.wrapper import DemocraticCoLearning\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ndcl = DemocraticCoLearning(base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)])\ndcl.fit(X, y)\ndcl.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    Y. Zhou and S. Goldman, (2004)
    \nDemocratic co-learning,
    \nin 16th IEEE International Conference on Tools with Artificial Intelligence,
    \npp. 594-602, 10.1109/ICTAI.2004.48.

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"fullname": "sslearn.wrapper.DemocraticCoLearning.__init__", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.__init__", "kind": "function", "doc": "

    Democratic Co-learning. Ensemble of classifiers of different types.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
      • \n
    • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
      • \n
    • alpha (float, optional):\nconfidence level, by default 0.95
      • \n
    • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n\n
    Raises
    \n\n
      \n
    • AttributeError: If n_estimators is None and base_estimator is not a list
      • \n
    \n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

    Fit Democratic-Co classifier

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    • estimator_kwards ({list, dict}, optional):\nlist of kwards for each estimator or kwards for all estimators, by default None
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DemocraticCoLearning):\nfitted classifier
      • \n
    \n", "signature": "(self, X, y, estimator_kwards=None):", "funcdef": "def"}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"fullname": "sslearn.wrapper.DemocraticCoLearning.predict_proba", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.predict_proba", "kind": "function", "doc": "

    Predict probability for each possible outcome.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • class probabilities (ndarray of shape (n_samples, n_classes)):\nArray with prediction probabilities.
      • \n
    \n", "signature": "(self, X):", "funcdef": "def"}, "sslearn.wrapper.Rasco": {"fullname": "sslearn.wrapper.Rasco", "modulename": "sslearn.wrapper", "qualname": "Rasco", "kind": "class", "doc": "

    Co-Training based on random subspaces

    \n\n

    Generate a set of random subspaces and train a classifier for each subspace.

    \n\n

    The main process is:

    \n\n
      \n
    1. Generate a set of random subspaces.
      2. \n
    2. Train a classifier for each subspace.
      3. \n
    3. While max iterations is not reached or any instance is unlabeled:\n
        \n
      1. Predict the instances from the unlabeled set for each classifier.
        2. \n
      2. Calculate the average of the predictions.
        3. \n
      3. Select the instances with the highest probability.
        4. \n
      4. Label the instances with the highest probability, keeping the balance of the classes.
        5. \n
      5. Retrain the classifier with the new instances.
        6. \n
      4. \n
    4. Combine the probabilities of each classifier.
      5. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.wrapper import Rasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrasco = Rasco()\nrasco.fit(X, y)\nrasco.score(X_unlabel, y_unlabel) \n
    \n
    \n\n

    References

    \n\n

    Wang, J., Luo, S. W., & Zeng, X. H. (2008).
    \nA random subspace method for co-training,
    \nin 2008 IEEE International Joint Conference on Neural Networks
    \nIEEE World Congress on Computational Intelligence
    \n(pp. 195-200). IEEE. 10.1109/IJCNN.2008.4633789

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.Rasco.__init__": {"fullname": "sslearn.wrapper.Rasco.__init__", "modulename": "sslearn.wrapper", "qualname": "Rasco.__init__", "kind": "function", "doc": "

    Co-Training based on random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

    Build a Rasco classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (Rasco):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.RelRasco": {"fullname": "sslearn.wrapper.RelRasco", "modulename": "sslearn.wrapper", "qualname": "RelRasco", "kind": "class", "doc": "

    Co-Training based on relevant random subspaces

    \n\n

    Is a variation of sslearn.wrapper.Rasco that uses the mutual information of each feature to select the random subspaces.\nThe process of training is the same as Rasco.

    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.wrapper import RelRasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrelrasco = RelRasco()\nrelrasco.fit(X, y)\nrelrasco.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    Yaslan, Y., & Cataltepe, Z. (2010).
    \nCo-training with relevant random subspaces.
    \nNeurocomputing, 73(10-12), 1652-1661.
    \n10.1016/j.neucom.2010.01.018

    \n", "bases": "sslearn.wrapper._co.Rasco"}, "sslearn.wrapper.RelRasco.__init__": {"fullname": "sslearn.wrapper.RelRasco.__init__", "modulename": "sslearn.wrapper", "qualname": "RelRasco.__init__", "kind": "function", "doc": "

    Co-Training with relevant random subspaces

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
      • \n
    • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

    CoForest classifier. Random Forest co-training

    \n\n

    Ensemble method for CoTraining based on Random Forest.

    \n\n

    The main process is:

    \n\n
      \n
    1. Train a committee of classifiers using bootstrap.
      2. \n
    2. While any base classifier is retrained:\n
        \n
      1. Predict the instances from the unlabeled set.
        2. \n
      2. Select the instances with the highest probability.
        3. \n
      3. Label the instances with the highest probability
        4. \n
      4. Add the instances to the labeled set only if the error is not bigger than the previous error.
        5. \n
      5. Retrain the classifier with the new instances.
        6. \n
      3. \n
    3. Combine the probabilities of each classifier.
      4. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    Example

    \n\n
    \n
    from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoForest\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncoforest = CoForest()\ncoforest.fit(X, y)\ncoforest.score(X_unlabel, y_unlabel)\n
    \n
    \n\n

    References

    \n\n

    Li, M., & Zhou, Z.-H. (2007).
    \nImprove Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.
    \nIEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans,
    \n37(6), 1088-1098. 10.1109/tsmca.2007.904745

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.CoForest.__init__": {"fullname": "sslearn.wrapper.CoForest.__init__", "modulename": "sslearn.wrapper", "qualname": "CoForest.__init__", "kind": "function", "doc": "

    Generate a CoForest classifier.\nA SSL Random Forest adaption for CoTraining.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
      • \n
    • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
      • \n
    • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

    Build a CoForest classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (CoForest):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.TriTraining": {"fullname": "sslearn.wrapper.TriTraining", "modulename": "sslearn.wrapper", "qualname": "TriTraining", "kind": "class", "doc": "

    TriTraining. Trio of classifiers with bootstrapping.

    \n\n

    The main process is:

    \n\n
      \n
    1. Generate three classifiers using bootstrapping.
      2. \n
    2. Iterate until convergence:\n
        \n
      1. Calculate the error between two hypotheses.
        2. \n
      2. If the error is less than the previous error, generate a dataset with the instances where both hypotheses agree.
        3. \n
      3. Retrain the classifiers with the new dataset and the original labeled dataset.
        4. \n
      3. \n
    3. Combine the predictions of the three classifiers.
      4. \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    References

    \n\n

    Zhi-Hua Zhou and Ming Li,
    \nTri-training: exploiting unlabeled data using three classifiers,
    \nin IEEE Transactions on Knowledge and Data Engineering,
    \nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,
    \n10.1109/TKDE.2005.186

    \n", "bases": "sslearn.wrapper._co.BaseCoTraining"}, "sslearn.wrapper.TriTraining.__init__": {"fullname": "sslearn.wrapper.TriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "TriTraining.__init__", "kind": "function", "doc": "

    TriTraining. Trio of classifiers with bootstrapping.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.DeTriTraining": {"fullname": "sslearn.wrapper.DeTriTraining", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining", "kind": "class", "doc": "

    TriTraining with Data Editing.

    \n\n

    It is a variation of the TriTraining, the main difference is that the instances are depurated in each iteration.\nIt means that the instances with their neighbors that have the same class are kept, the rest are removed.\nAt the end of the iterations, the instances are clustered and the class is assigned to the cluster centroid.

    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    References

    \n\n

    Deng C., Guo M.Z. (2006)
    \nTri-training and Data Editing Based Semi-supervised Clustering Algorithm,
    \nin Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006.
    \nLecture Notes in Computer Science, vol 4293. Springer, Berlin, Heidelberg.
    \n10.1007/11925231_61

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.DeTriTraining.__init__": {"fullname": "sslearn.wrapper.DeTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.__init__", "kind": "function", "doc": "

    DeTriTraining - TriTraining with Depurated and Clustering.\nAvoid the noise generated by the TriTraining algorithm by depurating the enlarged dataset and clustering the instances.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate. \nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • k_neighbors (int, optional):\nNumber of neighbors for depurate classification. \nIf at least k_neighbors/2+1 have a class other than the one predicted, the class is ignored., by default 3
      • \n
    • mode (string, optional):\nHow to calculate the cluster each instance belongs to.\nIf seeded each instance belong to nearest cluster.\nIf constrained each instance belong to nearest cluster unless the instance is in to enlarged dataset, \nthen the instance belongs to the cluster of its class., by default seeded
      • \n
    • max_iterations (int, optional):\nMaximum number of iterations, by default 100
      • \n
    • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. \nNone means 1 unless in a joblib.parallel_backend context. -1 means using all processors. \nDoesn't affect fit method., by default None
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tk_neighbors=3,\tn_samples=None,\tmode='seeded',\tmax_iterations=100,\tn_jobs=None,\trandom_state=None)"}, "sslearn.wrapper.DeTriTraining.fit": {"fullname": "sslearn.wrapper.DeTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "DeTriTraining.fit", "kind": "function", "doc": "

    Build a DeTriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (DeTriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining": {"fullname": "sslearn.wrapper.WiWTriTraining", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining", "kind": "class", "doc": "

    Who-Is-Who TriTraining.

    \n\n

    Trio of classifiers with bootstrapping and restricted set classification.\nIs the same as TriTraining but with the restricted set classification.\nManinly, the conflict rate penalizes the measure error of basic TriTraining, it can be calculated over differentes subsamples of X, can be:

    \n\n
      \n
    • labeled over complete L,
      • \n
    • labeled_plus over complete L union L',
      • \n
    • unlabeled: over complete U,
      • \n
    • all: over complete X (LuU) and
      • \n
    • none: don't penalize the meause error, only use the restrictions for avoid share classes in the same group.
      • \n
    \n\n

    Methods

    \n\n
      \n
    • fit: Fit the model with the labeled instances. Receives the instance group, an array-like of shape (n_samples) with the group of each instance. Two instances with the same label are not allowed to be in the same group.
      • \n
    • predict : Predict the class for each instance.
      • \n
    • predict_proba: Predict the probability for each class.
      • \n
    • score: Return the mean accuracy on the given test data and labels.
      • \n
    \n\n

    References

    \n\n

    Ludmila I. Kuncheva, Juan J. Rodr\u00edguez, Aaron S. Jackson, (2016)
    \nRestricted set classification: Who is there?
    \nPattern Recognition, 63, 158-170,
    \n10.1016/j.patcog.2016.08.028

    \n", "bases": "sslearn.wrapper._tritraining.TriTraining"}, "sslearn.wrapper.WiWTriTraining.__init__": {"fullname": "sslearn.wrapper.WiWTriTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.__init__", "kind": "function", "doc": "

    TriTraining with restriction Who-is-Who.

    \n\n
    Parameters
    \n\n
      \n
    • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
      • \n
    • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
      • \n
    • n_jobs (int, optional):\nNumber of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors., by default None
      • \n
    • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
      • \n
    • conflict_weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
      • \n
    • conflict_over (str, optional):\nThe conflict rate penalizes the \"measure error\" of basic TriTraining, it can be calculated over differentes subsamples of X, can be:\n
        \n
      • \"labeled\" over complete L,
        • \n
      • \"labeled_plus\" over complete L union L',
        • \n
      • \"unlabeled\u00a8: over complete U,
        • \n
      • \"all\": over complete X (LuU) and
        • \n
      • \"none\": don't penalize the \"meause error\", by default \"labeled\"
        • \n
      • \n
    • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
      • \n
    \n", "signature": "(\tbase_estimator,\tn_samples=100,\tn_jobs=None,\tmethod='hungarian',\tconflict_weighted=True,\tconflict_over='labeled',\trandom_state=None)"}, "sslearn.wrapper.WiWTriTraining.fit": {"fullname": "sslearn.wrapper.WiWTriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.fit", "kind": "function", "doc": "

    Build a TriTraining classifier from the training set (X, y).

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
      • \n
    • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabeled.
      • \n
    • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • self (TriTraining):\nFitted estimator.
      • \n
    \n", "signature": "(self, X, y, instance_group=None, **kwards):", "funcdef": "def"}, "sslearn.wrapper.WiWTriTraining.predict": {"fullname": "sslearn.wrapper.WiWTriTraining.predict", "modulename": "sslearn.wrapper", "qualname": "WiWTriTraining.predict", "kind": "function", "doc": "

    Predict the classes of X.

    \n\n
    Parameters
    \n\n
      \n
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
      • \n
    \n\n
    Returns
    \n\n
      \n
    • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
      • \n
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    Contents

    @@ -86,11 +91,11 @@

    -

    Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version

    +

    Code Climate maintainability Code Climate coverage GitHub Workflow Status PyPI - Version Static Badge

    The sslearn library is a Python package for machine learning over Semi-supervised datasets. It is an extension of scikit-learn.

    -

    Installation
    +

    Installation

    Dependencies

    @@ -111,7 +116,7 @@

    pip installation

    pip install sslearn
    -
    Code example
    +

    Code example

    from sslearn.wrapper import TriTraining
@@ -126,19 +131,19 @@ 
    Code example
    -
    Citing
    +

    Citing

    -
    @software{jose_luis_garrido_labrador_2024_10623889,
-  author       = {José Luis Garrido-Labrador},
-  title        = {jlgarridol/sslearn: v1.0.4},
-  month        = feb,
-  year         = 2024,
-  publisher    = {Zenodo},
-  version      = {1.0.4},
-  doi          = {10.5281/zenodo.10623889},
-  url          = {https://doi.org/10.5281/zenodo.10623889}
-}
+
    @software{jose_luis_garrido_labrador_2024_10623889,
+  author       = {José Luis Garrido-Labrador},
+  title        = {jlgarridol/sslearn: v1.0.4},
+  month        = feb,
+  year         = 2024,
+  publisher    = {Zenodo},
+  version      = {1.0.4},
+  doi          = {10.5281/zenodo.10623889},
+  url          = {https://doi.org/10.5281/zenodo.10623889}
+}
    diff --git a/docs/sslearn/base.html b/docs/sslearn/base.html index 471347f..d6e74ac 100644 --- a/docs/sslearn/base.html +++ b/docs/sslearn/base.html @@ -52,13 +52,17 @@

    Contents

    API Documentation

      +
    • + get_dataset +
    • FakedProbaClassifier
      • -
    • - get_dataset -
    • OneVsRestSSLClassifier
      • @@ -126,20 +118,24 @@

      Summary of module sslearn.base:

      -
      Functions
      +

      Functions

      get_dataset(X, y): Check and divide dataset between labeled and unlabeled data.

      -
      Classes
      +

      Classes

      + +

      FakedProbaClassifier:

      -

      FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator): - Create a classifier that fakes predict_proba method if it does not exist.

      +
      +

      Create a classifier that fakes predict_proba method if it does not exist.

      +
      -

      OneVsRestSSLClassifier(OneVsRestClassifier): - Adapted OneVsRestClassifier for SSL datasets

      +

      OneVsRestSSLClassifier:

      -

      All doc

      +
      +

      Adapted OneVsRestClassifier for SSL datasets

      +
      @@ -149,21 +145,21 @@

      All doc

        1"""
   2Summary of module `sslearn.base`:
   3
-  4Functions
-  5---------
-  6get_dataset(X, y):
-  7    Check and divide dataset between labeled and unlabeled data.
-  8
-  9Classes
- 10-------
- 11FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
- 12    Create a classifier that fakes predict_proba method if it does not exist.
- 13
- 14OneVsRestSSLClassifier(OneVsRestClassifier):
- 15    Adapted OneVsRestClassifier for SSL datasets
- 16
- 17All doc
- 18----
+  4## Functions
+  5
+  6
+  7get_dataset(X, y):
+  8    Check and divide dataset between labeled and unlabeled data.
+  9
+ 10## Classes
+ 11
+ 12
+ 13[FakedProbaClassifier](#FakedProbaClassifier):
+ 14>  Create a classifier that fakes predict_proba method if it does not exist.
+ 15
+ 16[OneVsRestSSLClassifier](#OneVsRestSSLClassifier):
+ 17> Adapted OneVsRestClassifier for SSL datasets
+ 18
  19"""
  20
  21import array
@@ -187,12 +183,12 @@ 
      All doc
      
        39from sklearn.ensemble._base import _set_random_states
  40from sklearn.utils import check_random_state
  41
- 42__all__ = ["FakedProbaClassifier", "get_dataset", "OneVsRestSSLClassifier"]
+ 42__all__ = ["get_dataset", "FakedProbaClassifier", "OneVsRestSSLClassifier"]
  43
  44
  45
  46def get_dataset(X, y):
- 47    """Check and divide dataset between labeled and unlabeled data.
+ 47    """Check and divide dataset between labeled and unlabeled data.
  48
  49    Parameters
  50    ----------
@@ -232,14 +228,14 @@ 
      All doc
      
        84    return X_label, y_label, X_unlabel
  85
  86
- 87class BaseEnsemble(ABC, MetaEstimatorMixin):
+ 87class BaseEnsemble(ABC, MetaEstimatorMixin, BaseEstimator):
  88
  89    @abstractmethod
  90    def predict_proba(self, X):
  91        pass
  92
  93    def predict(self, X):
- 94        """Predict the classes of X.
+ 94        """Predict the classes of X.
  95        Parameters
  96        ----------
  97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
@@ -261,201 +257,301 @@ 
      All doc
      
       113
 114
 115class FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
-116
-117    def __init__(self, base_estimator):
-118        """Create a classifier that fakes predict_proba method if it does not exist.
+116    """
+117    Fake predict_proba method for classifiers that do not have it. 
+118    When predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.
 119
-120        Parameters
-121        ----------
-122        base_estimator : ClassifierMixin
-123            A classifier that implements fit and predict methods.
-124        """
-125        self.base_estimator = base_estimator
-126
-127    def fit(self, X, y):
-128        """Fit a FakedProbaClassifier.
-129
-130        Parameters
-131        ----------
-132        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-133            The input samples.
-134        y : {array-like, sparse matrix} of shape (n_samples,)
-135            The target values.
-136
-137        Returns
-138        -------
-139        self : FakedProbaClassifier
-140            Returns self.
-141        """
-142        self.classes_ = np.unique(y)
-143        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
-144        self.base_estimator.fit(X, y)
-145        return self
-146
-147    def predict(self, X):
-148        """Predict the classes of X.
-149
-150        Parameters
-151        ----------
-152        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-153            Array representing the data.
-154
-155        Returns
-156        -------
-157        y : ndarray of shape (n_samples,)
-158            Array with predicted labels.
-159        """
-160        return self.base_estimator.predict(X)
-161
-162    def predict_proba(self, X):
-163        """Predict the probabilities of each class for X. 
-164        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+120    Examples
+121    --------
+122    ```python
+123    from sklearn.svm import SVC
+124    # SVC does not have predict_proba method
+125
+126    from sslearn.base import FakedProbaClassifier
+127    faked_svc = FakedProbaClassifier(SVC())
+128    faked_svc.fit(X, y)
+129    faked_svc.predict_proba(X) # One hot encoding probabilities
+130    ```
+131    """
+132
+133    def __init__(self, base_estimator):
+134        """Create a classifier that fakes predict_proba method if it does not exist.
+135
+136        Parameters
+137        ----------
+138        base_estimator : ClassifierMixin
+139            A classifier that implements fit and predict methods.
+140        """
+141        self.base_estimator = base_estimator
+142
+143    def fit(self, X, y):
+144        """Fit a FakedProbaClassifier.
+145
+146        Parameters
+147        ----------
+148        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+149            The input samples.
+150        y : {array-like, sparse matrix} of shape (n_samples,)
+151            The target values.
+152
+153        Returns
+154        -------
+155        self : FakedProbaClassifier
+156            Returns self.
+157        """
+158        self.classes_ = np.unique(y)
+159        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+160        self.base_estimator.fit(X, y)
+161        return self
+162
+163    def predict(self, X):
+164        """Predict the classes of X.
 165
 166        Parameters
 167        ----------
 168        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-169
-170        Returns
-171        -------
-172        y : ndarray of shape (n_samples, n_classes)
-173            Array with predicted probabilities.
-174        """
-175        if "predict_proba" in dir(self.base_estimator):
-176            return self.base_estimator.predict_proba(X)
-177        else:
-178            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
-179
-180
-181def _fit_binary_ssl(estimator, X, y_label, size, classes=None, **fit_params):
-182    # unique_y = np.unique(y_label)
-183    # X = np.concatenate((X_label, X_unlabel), axis=0)
-184    y = np.concatenate((y_label, np.array([y_label.dtype.type(-1)] * size)))
-185    unique_y = np.unique(y_label)
-186    if len(unique_y) == 1:
-187        if classes is not None:
-188            if y_label[0] == -1:
-189                c = 0
-190            else:
-191                c = y_label[0]
-192            warnings.warn(
-193                "Label %s is present in all training examples." % str(classes[c])
-194            )
-195        estimator = _ConstantPredictor().fit(None, unique_y)
-196    else:
-197        estimator = skclone(estimator)
-198        estimator.fit(X, y, **fit_params)
-199    return estimator
-200
-201def _predict_binary_ssl(estimator, X, **predict_params):
-202    """Make predictions using a single binary estimator."""
-203    try:
-204        score = np.ravel(estimator.decision_function(X, **predict_params))
-205    except (AttributeError, NotImplementedError):
-206        # probabilities of the positive class
-207        score = estimator.predict_proba(X, **predict_params)[:, 1]
-208    return score
-209
-210
-211class OneVsRestSSLClassifier(OneVsRestClassifier):
-212
-213    def __init__(self, estimator, *, n_jobs=None):
-214        """Adapted OneVsRestClassifier for SSL datasets
-215
-216        Parameters
-217        ----------
-218        estimator : {ClassifierMixin, list},
-219            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
-220        n_jobs : n_jobs : int, optional
-221            The number of jobs to run in parallel. -1 means using all processors., by default None
-222        """
-223        super().__init__(estimator, n_jobs=n_jobs)
-224
-225    def fit(self, X, y, **fit_params):
-226        #
-227        y_label = y[y != y.dtype.type(-1)]
-228        size = len(y) - len(y_label)
+169            Array representing the data.
+170
+171        Returns
+172        -------
+173        y : ndarray of shape (n_samples,)
+174            Array with predicted labels.
+175        """
+176        return self.base_estimator.predict(X)
+177
+178    def predict_proba(self, X):
+179        """Predict the probabilities of each class for X. 
+180        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+181
+182        Parameters
+183        ----------
+184        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+185
+186        Returns
+187        -------
+188        y : ndarray of shape (n_samples, n_classes)
+189            Array with predicted probabilities.
+190        """
+191        if "predict_proba" in dir(self.base_estimator):
+192            return self.base_estimator.predict_proba(X)
+193        else:
+194            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+195
+196
+197def _fit_binary_ssl(estimator, X, y_label, size, classes=None, **fit_params):
+198    # unique_y = np.unique(y_label)
+199    # X = np.concatenate((X_label, X_unlabel), axis=0)
+200    y = np.concatenate((y_label, np.array([y_label.dtype.type(-1)] * size)))
+201    unique_y = np.unique(y_label)
+202    if len(unique_y) == 1:
+203        if classes is not None:
+204            if y_label[0] == -1:
+205                c = 0
+206            else:
+207                c = y_label[0]
+208            warnings.warn(
+209                "Label %s is present in all training examples." % str(classes[c])
+210            )
+211        estimator = _ConstantPredictor().fit(None, unique_y)
+212    else:
+213        estimator = skclone(estimator)
+214        estimator.fit(X, y, **fit_params)
+215    return estimator
+216
+217def _predict_binary_ssl(estimator, X, **predict_params):
+218    """Make predictions using a single binary estimator."""
+219    try:
+220        score = np.ravel(estimator.decision_function(X, **predict_params))
+221    except (AttributeError, NotImplementedError):
+222        # probabilities of the positive class
+223        score = estimator.predict_proba(X, **predict_params)[:, 1]
+224    return score
+225
+226
+227class OneVsRestSSLClassifier(OneVsRestClassifier):
+228    """Adapted OneVsRestClassifier for SSL datasets
 229
-230        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
-231        Y = self.label_binarizer_.fit_transform(y_label)
-232        Y = Y.tocsc()
-233        self.classes_ = self.label_binarizer_.classes_
-234        columns = (col.toarray().ravel() for col in Y.T)
-235
-236        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
-237        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
-238        for e in estimators:
-239            _set_random_states(e, rs)
-240
-241        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
-242            delayed(_fit_binary_ssl)(
-243                estimators[i],
-244                X,
-245                column,
-246                size,
-247                classes=[
-248                    "not %s" % self.label_binarizer_.classes_[i],
-249                    self.label_binarizer_.classes_[i],
-250                ],
-251                **fit_params
-252            )
-253            for i, column in enumerate(columns)
-254        )
-255
-256        if hasattr(self.estimators_[0], "n_features_in_"):
-257            self.n_features_in_ = self.estimators_[0].n_features_in_
-258        if hasattr(self.estimators_[0], "feature_names_in_"):
-259            self.feature_names_in_ = self.estimators_[0].feature_names_in_
-260
-261        return self
+230    Prevent use unlabeled data as a independent class in the classifier.
+231
+232    For more information of OvR classifier, see the documentation of [OneVsRestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html).
+233    """
+234
+235    def __init__(self, estimator, *, n_jobs=None):
+236        """Adapted OneVsRestClassifier for SSL datasets
+237
+238        Parameters
+239        ----------
+240        estimator : {ClassifierMixin, list},
+241            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+242        n_jobs : n_jobs : int, optional
+243            The number of jobs to run in parallel. -1 means using all processors., by default None
+244        """
+245        super().__init__(estimator, n_jobs=n_jobs)
+246
+247    def fit(self, X, y, **fit_params):
+248        #
+249        y_label = y[y != y.dtype.type(-1)]
+250        size = len(y) - len(y_label)
+251
+252        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+253        Y = self.label_binarizer_.fit_transform(y_label)
+254        Y = Y.tocsc()
+255        self.classes_ = self.label_binarizer_.classes_
+256        columns = (col.toarray().ravel() for col in Y.T)
+257
+258        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+259        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+260        for e in estimators:
+261            _set_random_states(e, rs)
 262
-263    def predict(self, X, **kwards):
-264        check_is_fitted(self)
-265
-266        n_samples = _num_samples(X)
-267        if self.label_binarizer_.y_type_ == "multiclass":
-268            maxima = np.empty(n_samples, dtype=float)
-269            maxima.fill(-np.inf)
-270            argmaxima = np.zeros(n_samples, dtype=int)
-271            for i, e in enumerate(self.estimators_):
-272                pred = _predict_binary_ssl(e, X, **kwards)
-273                np.maximum(maxima, pred, out=maxima)
-274                argmaxima[maxima == pred] = i
-275            return self.classes_[argmaxima]
-276        else:
-277            if (hasattr(self.estimators_[0], "decision_function") and
-278                    is_classifier(self.estimators_[0])):
-279                thresh = 0
-280            else:
-281                thresh = .5
-282            indices = array.array('i')
-283            indptr = array.array('i', [0])
-284            for e in self.estimators_:
-285                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
-286                indptr.append(len(indices))
-287            data = np.ones(len(indices), dtype=int)
-288            indicator = sp.csc_matrix((data, indices, indptr),
-289                                      shape=(n_samples, len(self.estimators_)))
-290            return self.label_binarizer_.inverse_transform(indicator)
-291
-292    def predict_proba(self, X, **kwards):
-293        check_is_fitted(self)
-294        # Y[i, j] gives the probability that sample i has the label j.
-295        # In the multi-label case, these are not disjoint.
-296        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
-297
-298        if len(self.estimators_) == 1:
-299            # Only one estimator, but we still want to return probabilities
-300            # for two classes.
-301            Y = np.concatenate(((1 - Y), Y), axis=1)
-302
-303        if not self.multilabel_:
-304            # Then, probabilities should be normalized to 1.
-305            Y /= np.sum(Y, axis=1)[:, np.newaxis]
-306        return Y
+263        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+264            delayed(_fit_binary_ssl)(
+265                estimators[i],
+266                X,
+267                column,
+268                size,
+269                classes=[
+270                    "not %s" % self.label_binarizer_.classes_[i],
+271                    self.label_binarizer_.classes_[i],
+272                ],
+273                **fit_params
+274            )
+275            for i, column in enumerate(columns)
+276        )
+277
+278        if hasattr(self.estimators_[0], "n_features_in_"):
+279            self.n_features_in_ = self.estimators_[0].n_features_in_
+280        if hasattr(self.estimators_[0], "feature_names_in_"):
+281            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+282
+283        return self
+284
+285    def predict(self, X, **kwards):
+286        check_is_fitted(self)
+287
+288        n_samples = _num_samples(X)
+289        if self.label_binarizer_.y_type_ == "multiclass":
+290            maxima = np.empty(n_samples, dtype=float)
+291            maxima.fill(-np.inf)
+292            argmaxima = np.zeros(n_samples, dtype=int)
+293            for i, e in enumerate(self.estimators_):
+294                pred = _predict_binary_ssl(e, X, **kwards)
+295                np.maximum(maxima, pred, out=maxima)
+296                argmaxima[maxima == pred] = i
+297            return self.classes_[argmaxima]
+298        else:
+299            if (hasattr(self.estimators_[0], "decision_function") and
+300                    is_classifier(self.estimators_[0])):
+301                thresh = 0
+302            else:
+303                thresh = .5
+304            indices = array.array('i')
+305            indptr = array.array('i', [0])
+306            for e in self.estimators_:
+307                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+308                indptr.append(len(indices))
+309            data = np.ones(len(indices), dtype=int)
+310            indicator = sp.csc_matrix((data, indices, indptr),
+311                                      shape=(n_samples, len(self.estimators_)))
+312            return self.label_binarizer_.inverse_transform(indicator)
+313
+314    def predict_proba(self, X, **kwards):
+315        check_is_fitted(self)
+316        # Y[i, j] gives the probability that sample i has the label j.
+317        # In the multi-label case, these are not disjoint.
+318        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+319
+320        if len(self.estimators_) == 1:
+321            # Only one estimator, but we still want to return probabilities
+322            # for two classes.
+323            Y = np.concatenate(((1 - Y), Y), axis=1)
+324
+325        if not self.multilabel_:
+326            # Then, probabilities should be normalized to 1.
+327            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+328        return Y
      +
      + +
      + + def + get_dataset(X, y): + + + +
      + + 
      47def get_dataset(X, y):
+48    """Check and divide dataset between labeled and unlabeled data.
+49
+50    Parameters
+51    ----------
+52    X : ndarray or DataFrame of shape (n_samples, n_features)
+53        Features matrix.
+54    y : ndarray of shape (n_samples,)
+55        Target vector.
+56
+57    Returns
+58    -------
+59    X_label : ndarray or DataFrame of shape (n_label, n_features)
+60        Labeled features matrix.
+61    y_label : ndarray or Serie of shape (n_label,)
+62        Labeled target vector.
+63    X_unlabel : ndarray or Serie DataFrame of shape (n_unlabel, n_features)
+64        Unlabeled features matrix.
+65    """
+66
+67    is_df = False
+68    if isinstance(X, pd.DataFrame):
+69        is_df = True
+70        columns = X.columns
+71
+72    X = check_array(X)
+73    y = check_array(y, ensure_2d=False, dtype=y.dtype.type)
+74    
+75    X_label = X[y != y.dtype.type(-1)]
+76    y_label = y[y != y.dtype.type(-1)]
+77    X_unlabel = X[y == y.dtype.type(-1)]
+78
+79    X_label, y_label = check_X_y(X_label, y_label)
+80
+81    if is_df:
+82        X_label = pd.DataFrame(X_label, columns=columns)
+83        X_unlabel = pd.DataFrame(X_unlabel, columns=columns)
+84
+85    return X_label, y_label, X_unlabel
+
      + + +

      Check and divide dataset between labeled and unlabeled data.

      + +
      Parameters
      + +
        +
      • X (ndarray or DataFrame of shape (n_samples, n_features)): +Features matrix.
        • +
      • y (ndarray of shape (n_samples,)): +Target vector.
        • +
      + +
      Returns
      + +
        +
      • X_label (ndarray or DataFrame of shape (n_label, n_features)): +Labeled features matrix.
        • +
      • y_label (ndarray or Serie of shape (n_label,)): +Labeled target vector.
        • +
      • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)): +Unlabeled features matrix.
        • +
      +
      + + +
      @@ -468,73 +564,103 @@

      All doc

      116class FakedProbaClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
-117
-118    def __init__(self, base_estimator):
-119        """Create a classifier that fakes predict_proba method if it does not exist.
+117    """
+118    Fake predict_proba method for classifiers that do not have it. 
+119    When predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.
 120
-121        Parameters
-122        ----------
-123        base_estimator : ClassifierMixin
-124            A classifier that implements fit and predict methods.
-125        """
-126        self.base_estimator = base_estimator
-127
-128    def fit(self, X, y):
-129        """Fit a FakedProbaClassifier.
-130
-131        Parameters
-132        ----------
-133        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-134            The input samples.
-135        y : {array-like, sparse matrix} of shape (n_samples,)
-136            The target values.
-137
-138        Returns
-139        -------
-140        self : FakedProbaClassifier
-141            Returns self.
-142        """
-143        self.classes_ = np.unique(y)
-144        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
-145        self.base_estimator.fit(X, y)
-146        return self
-147
-148    def predict(self, X):
-149        """Predict the classes of X.
-150
-151        Parameters
-152        ----------
-153        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-154            Array representing the data.
-155
-156        Returns
-157        -------
-158        y : ndarray of shape (n_samples,)
-159            Array with predicted labels.
-160        """
-161        return self.base_estimator.predict(X)
-162
-163    def predict_proba(self, X):
-164        """Predict the probabilities of each class for X. 
-165        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+121    Examples
+122    --------
+123    ```python
+124    from sklearn.svm import SVC
+125    # SVC does not have predict_proba method
+126
+127    from sslearn.base import FakedProbaClassifier
+128    faked_svc = FakedProbaClassifier(SVC())
+129    faked_svc.fit(X, y)
+130    faked_svc.predict_proba(X) # One hot encoding probabilities
+131    ```
+132    """
+133
+134    def __init__(self, base_estimator):
+135        """Create a classifier that fakes predict_proba method if it does not exist.
+136
+137        Parameters
+138        ----------
+139        base_estimator : ClassifierMixin
+140            A classifier that implements fit and predict methods.
+141        """
+142        self.base_estimator = base_estimator
+143
+144    def fit(self, X, y):
+145        """Fit a FakedProbaClassifier.
+146
+147        Parameters
+148        ----------
+149        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+150            The input samples.
+151        y : {array-like, sparse matrix} of shape (n_samples,)
+152            The target values.
+153
+154        Returns
+155        -------
+156        self : FakedProbaClassifier
+157            Returns self.
+158        """
+159        self.classes_ = np.unique(y)
+160        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+161        self.base_estimator.fit(X, y)
+162        return self
+163
+164    def predict(self, X):
+165        """Predict the classes of X.
 166
 167        Parameters
 168        ----------
 169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-170
-171        Returns
-172        -------
-173        y : ndarray of shape (n_samples, n_classes)
-174            Array with predicted probabilities.
-175        """
-176        if "predict_proba" in dir(self.base_estimator):
-177            return self.base_estimator.predict_proba(X)
-178        else:
-179            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+170            Array representing the data.
+171
+172        Returns
+173        -------
+174        y : ndarray of shape (n_samples,)
+175            Array with predicted labels.
+176        """
+177        return self.base_estimator.predict(X)
+178
+179    def predict_proba(self, X):
+180        """Predict the probabilities of each class for X. 
+181        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+182
+183        Parameters
+184        ----------
+185        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+186
+187        Returns
+188        -------
+189        y : ndarray of shape (n_samples, n_classes)
+190            Array with predicted probabilities.
+191        """
+192        if "predict_proba" in dir(self.base_estimator):
+193            return self.base_estimator.predict_proba(X)
+194        else:
+195            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
      -

      Mixin class for all classifiers in scikit-learn.

      +

      Fake predict_proba method for classifiers that do not have it. +When predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.

      + +
      Examples
      + +
      +
      from sklearn.svm import SVC
+# SVC does not have predict_proba method
+
+from sslearn.base import FakedProbaClassifier
+faked_svc = FakedProbaClassifier(SVC())
+faked_svc.fit(X, y)
+faked_svc.predict_proba(X) # One hot encoding probabilities
+
      +
      @@ -548,15 +674,15 @@

      All doc

      - 
      118    def __init__(self, base_estimator):
-119        """Create a classifier that fakes predict_proba method if it does not exist.
-120
-121        Parameters
-122        ----------
-123        base_estimator : ClassifierMixin
-124            A classifier that implements fit and predict methods.
-125        """
-126        self.base_estimator = base_estimator
+            
      134    def __init__(self, base_estimator):
+135        """Create a classifier that fakes predict_proba method if it does not exist.
+136
+137        Parameters
+138        ----------
+139        base_estimator : ClassifierMixin
+140            A classifier that implements fit and predict methods.
+141        """
+142        self.base_estimator = base_estimator
 
      
 
 
@@ -583,25 +709,25 @@ 
      Parameters
      - 
      128    def fit(self, X, y):
-129        """Fit a FakedProbaClassifier.
-130
-131        Parameters
-132        ----------
-133        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-134            The input samples.
-135        y : {array-like, sparse matrix} of shape (n_samples,)
-136            The target values.
-137
-138        Returns
-139        -------
-140        self : FakedProbaClassifier
-141            Returns self.
-142        """
-143        self.classes_ = np.unique(y)
-144        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
-145        self.base_estimator.fit(X, y)
-146        return self
+            
      144    def fit(self, X, y):
+145        """Fit a FakedProbaClassifier.
+146
+147        Parameters
+148        ----------
+149        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+150            The input samples.
+151        y : {array-like, sparse matrix} of shape (n_samples,)
+152            The target values.
+153
+154        Returns
+155        -------
+156        self : FakedProbaClassifier
+157            Returns self.
+158        """
+159        self.classes_ = np.unique(y)
+160        self.one_hot = OneHotEncoder().fit(y.reshape(-1, 1))
+161        self.base_estimator.fit(X, y)
+162        return self
 
      
 
 
@@ -637,20 +763,20 @@ 
      Returns
      - 
      148    def predict(self, X):
-149        """Predict the classes of X.
-150
-151        Parameters
-152        ----------
-153        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-154            Array representing the data.
-155
-156        Returns
-157        -------
-158        y : ndarray of shape (n_samples,)
-159            Array with predicted labels.
-160        """
-161        return self.base_estimator.predict(X)
+            
      164    def predict(self, X):
+165        """Predict the classes of X.
+166
+167        Parameters
+168        ----------
+169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+170            Array representing the data.
+171
+172        Returns
+173        -------
+174        y : ndarray of shape (n_samples,)
+175            Array with predicted labels.
+176        """
+177        return self.base_estimator.predict(X)
 
      
 
 
@@ -684,23 +810,23 @@ 
      Returns
      - 
      163    def predict_proba(self, X):
-164        """Predict the probabilities of each class for X. 
-165        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
-166
-167        Parameters
-168        ----------
-169        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-170
-171        Returns
-172        -------
-173        y : ndarray of shape (n_samples, n_classes)
-174            Array with predicted probabilities.
-175        """
-176        if "predict_proba" in dir(self.base_estimator):
-177            return self.base_estimator.predict_proba(X)
-178        else:
-179            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
+            
      179    def predict_proba(self, X):
+180        """Predict the probabilities of each class for X. 
+181        If the base estimator does not have a predict_proba method, it will be faked using one hot encoding.
+182
+183        Parameters
+184        ----------
+185        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+186
+187        Returns
+188        -------
+189        y : ndarray of shape (n_samples, n_classes)
+190            Array with predicted probabilities.
+191        """
+192        if "predict_proba" in dir(self.base_estimator):
+193            return self.base_estimator.predict_proba(X)
+194        else:
+195            return self.one_hot.transform(self.base_estimator.predict(X).reshape(-1, 1)).toarray()
 
      
 
 
@@ -722,58 +848,6 @@ 
      Returns
      -
    -
    -
    - - def - set_score_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - -
    Inherited Members
    @@ -786,91 +860,9 @@
    Inherited Members
    get_params
    set_params
    -
    -
    sklearn.utils._metadata_requests._MetadataRequester
    -
    get_metadata_routing
    -
    - -
    - -
    - - def - get_dataset(X, y): - - - -
    - - 
    47def get_dataset(X, y):
-48    """Check and divide dataset between labeled and unlabeled data.
-49
-50    Parameters
-51    ----------
-52    X : ndarray or DataFrame of shape (n_samples, n_features)
-53        Features matrix.
-54    y : ndarray of shape (n_samples,)
-55        Target vector.
-56
-57    Returns
-58    -------
-59    X_label : ndarray or DataFrame of shape (n_label, n_features)
-60        Labeled features matrix.
-61    y_label : ndarray or Serie of shape (n_label,)
-62        Labeled target vector.
-63    X_unlabel : ndarray or Serie DataFrame of shape (n_unlabel, n_features)
-64        Unlabeled features matrix.
-65    """
-66
-67    is_df = False
-68    if isinstance(X, pd.DataFrame):
-69        is_df = True
-70        columns = X.columns
-71
-72    X = check_array(X)
-73    y = check_array(y, ensure_2d=False, dtype=y.dtype.type)
-74    
-75    X_label = X[y != y.dtype.type(-1)]
-76    y_label = y[y != y.dtype.type(-1)]
-77    X_unlabel = X[y == y.dtype.type(-1)]
-78
-79    X_label, y_label = check_X_y(X_label, y_label)
-80
-81    if is_df:
-82        X_label = pd.DataFrame(X_label, columns=columns)
-83        X_unlabel = pd.DataFrame(X_unlabel, columns=columns)
-84
-85    return X_label, y_label, X_unlabel
-
    - - -

    Check and divide dataset between labeled and unlabeled data.

    - -
    Parameters
    - -
      -
    • X (ndarray or DataFrame of shape (n_samples, n_features)): -Features matrix.
      • -
    • y (ndarray of shape (n_samples,)): -Target vector.
      • -
    - -
    Returns
    - -
      -
    • X_label (ndarray or DataFrame of shape (n_label, n_features)): -Labeled features matrix.
      • -
    • y_label (ndarray or Serie of shape (n_label,)): -Labeled target vector.
      • -
    • X_unlabel (ndarray or Serie DataFrame of shape (n_unlabel, n_features)): -Unlabeled features matrix.
      • -
    -
    - -
    @@ -883,210 +875,116 @@
    Returns
    - 
    212class OneVsRestSSLClassifier(OneVsRestClassifier):
-213
-214    def __init__(self, estimator, *, n_jobs=None):
-215        """Adapted OneVsRestClassifier for SSL datasets
-216
-217        Parameters
-218        ----------
-219        estimator : {ClassifierMixin, list},
-220            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
-221        n_jobs : n_jobs : int, optional
-222            The number of jobs to run in parallel. -1 means using all processors., by default None
-223        """
-224        super().__init__(estimator, n_jobs=n_jobs)
-225
-226    def fit(self, X, y, **fit_params):
-227        #
-228        y_label = y[y != y.dtype.type(-1)]
-229        size = len(y) - len(y_label)
+            
    228class OneVsRestSSLClassifier(OneVsRestClassifier):
+229    """Adapted OneVsRestClassifier for SSL datasets
 230
-231        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
-232        Y = self.label_binarizer_.fit_transform(y_label)
-233        Y = Y.tocsc()
-234        self.classes_ = self.label_binarizer_.classes_
-235        columns = (col.toarray().ravel() for col in Y.T)
-236
-237        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
-238        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
-239        for e in estimators:
-240            _set_random_states(e, rs)
-241
-242        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
-243            delayed(_fit_binary_ssl)(
-244                estimators[i],
-245                X,
-246                column,
-247                size,
-248                classes=[
-249                    "not %s" % self.label_binarizer_.classes_[i],
-250                    self.label_binarizer_.classes_[i],
-251                ],
-252                **fit_params
-253            )
-254            for i, column in enumerate(columns)
-255        )
-256
-257        if hasattr(self.estimators_[0], "n_features_in_"):
-258            self.n_features_in_ = self.estimators_[0].n_features_in_
-259        if hasattr(self.estimators_[0], "feature_names_in_"):
-260            self.feature_names_in_ = self.estimators_[0].feature_names_in_
-261
-262        return self
+231    Prevent use unlabeled data as a independent class in the classifier.
+232
+233    For more information of OvR classifier, see the documentation of [OneVsRestClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html).
+234    """
+235
+236    def __init__(self, estimator, *, n_jobs=None):
+237        """Adapted OneVsRestClassifier for SSL datasets
+238
+239        Parameters
+240        ----------
+241        estimator : {ClassifierMixin, list},
+242            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+243        n_jobs : n_jobs : int, optional
+244            The number of jobs to run in parallel. -1 means using all processors., by default None
+245        """
+246        super().__init__(estimator, n_jobs=n_jobs)
+247
+248    def fit(self, X, y, **fit_params):
+249        #
+250        y_label = y[y != y.dtype.type(-1)]
+251        size = len(y) - len(y_label)
+252
+253        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+254        Y = self.label_binarizer_.fit_transform(y_label)
+255        Y = Y.tocsc()
+256        self.classes_ = self.label_binarizer_.classes_
+257        columns = (col.toarray().ravel() for col in Y.T)
+258
+259        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+260        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+261        for e in estimators:
+262            _set_random_states(e, rs)
 263
-264    def predict(self, X, **kwards):
-265        check_is_fitted(self)
-266
-267        n_samples = _num_samples(X)
-268        if self.label_binarizer_.y_type_ == "multiclass":
-269            maxima = np.empty(n_samples, dtype=float)
-270            maxima.fill(-np.inf)
-271            argmaxima = np.zeros(n_samples, dtype=int)
-272            for i, e in enumerate(self.estimators_):
-273                pred = _predict_binary_ssl(e, X, **kwards)
-274                np.maximum(maxima, pred, out=maxima)
-275                argmaxima[maxima == pred] = i
-276            return self.classes_[argmaxima]
-277        else:
-278            if (hasattr(self.estimators_[0], "decision_function") and
-279                    is_classifier(self.estimators_[0])):
-280                thresh = 0
-281            else:
-282                thresh = .5
-283            indices = array.array('i')
-284            indptr = array.array('i', [0])
-285            for e in self.estimators_:
-286                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
-287                indptr.append(len(indices))
-288            data = np.ones(len(indices), dtype=int)
-289            indicator = sp.csc_matrix((data, indices, indptr),
-290                                      shape=(n_samples, len(self.estimators_)))
-291            return self.label_binarizer_.inverse_transform(indicator)
-292
-293    def predict_proba(self, X, **kwards):
-294        check_is_fitted(self)
-295        # Y[i, j] gives the probability that sample i has the label j.
-296        # In the multi-label case, these are not disjoint.
-297        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
-298
-299        if len(self.estimators_) == 1:
-300            # Only one estimator, but we still want to return probabilities
-301            # for two classes.
-302            Y = np.concatenate(((1 - Y), Y), axis=1)
-303
-304        if not self.multilabel_:
-305            # Then, probabilities should be normalized to 1.
-306            Y /= np.sum(Y, axis=1)[:, np.newaxis]
-307        return Y
+264        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+265            delayed(_fit_binary_ssl)(
+266                estimators[i],
+267                X,
+268                column,
+269                size,
+270                classes=[
+271                    "not %s" % self.label_binarizer_.classes_[i],
+272                    self.label_binarizer_.classes_[i],
+273                ],
+274                **fit_params
+275            )
+276            for i, column in enumerate(columns)
+277        )
+278
+279        if hasattr(self.estimators_[0], "n_features_in_"):
+280            self.n_features_in_ = self.estimators_[0].n_features_in_
+281        if hasattr(self.estimators_[0], "feature_names_in_"):
+282            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+283
+284        return self
+285
+286    def predict(self, X, **kwards):
+287        check_is_fitted(self)
+288
+289        n_samples = _num_samples(X)
+290        if self.label_binarizer_.y_type_ == "multiclass":
+291            maxima = np.empty(n_samples, dtype=float)
+292            maxima.fill(-np.inf)
+293            argmaxima = np.zeros(n_samples, dtype=int)
+294            for i, e in enumerate(self.estimators_):
+295                pred = _predict_binary_ssl(e, X, **kwards)
+296                np.maximum(maxima, pred, out=maxima)
+297                argmaxima[maxima == pred] = i
+298            return self.classes_[argmaxima]
+299        else:
+300            if (hasattr(self.estimators_[0], "decision_function") and
+301                    is_classifier(self.estimators_[0])):
+302                thresh = 0
+303            else:
+304                thresh = .5
+305            indices = array.array('i')
+306            indptr = array.array('i', [0])
+307            for e in self.estimators_:
+308                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+309                indptr.append(len(indices))
+310            data = np.ones(len(indices), dtype=int)
+311            indicator = sp.csc_matrix((data, indices, indptr),
+312                                      shape=(n_samples, len(self.estimators_)))
+313            return self.label_binarizer_.inverse_transform(indicator)
+314
+315    def predict_proba(self, X, **kwards):
+316        check_is_fitted(self)
+317        # Y[i, j] gives the probability that sample i has the label j.
+318        # In the multi-label case, these are not disjoint.
+319        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+320
+321        if len(self.estimators_) == 1:
+322            # Only one estimator, but we still want to return probabilities
+323            # for two classes.
+324            Y = np.concatenate(((1 - Y), Y), axis=1)
+325
+326        if not self.multilabel_:
+327            # Then, probabilities should be normalized to 1.
+328            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+329        return Y
 
    
 
 
-            
    One-vs-the-rest (OvR) multiclass strategy.
    
-
-
    Also known as one-vs-all, this strategy consists in fitting one classifier
-per class. For each classifier, the class is fitted against all the other
-classes. In addition to its computational efficiency (only n_classes
-classifiers are needed), one advantage of this approach is its
-interpretability. Since each class is represented by one and one classifier
-only, it is possible to gain knowledge about the class by inspecting its
-corresponding classifier. This is the most commonly used strategy for
-multiclass classification and is a fair default choice.
    
-
-
    OneVsRestClassifier can also be used for multilabel classification. To use
-this feature, provide an indicator matrix for the target y when calling
-.fit. In other words, the target labels should be formatted as a 2D
-binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j
-in sample i. This estimator uses the binary relevance method to perform
-multilabel classification, which involves training one binary classifier
-independently for each label.
    
-
-
    Read more in the :ref:User Guide <ovr_classification>.
    
-
-
    Parameters
    
-
-
      
-
    • estimator (estimator object):
-A regressor or a classifier that implements :term:fit.
-When a classifier is passed, :term:decision_function will be used
-in priority and it will fallback to :term:predict_proba if it is not
-available.
-When a regressor is passed, :term:predict is used.
      • 
-
    • n_jobs (int, default=None):
-The number of jobs to use for the computation: the n_classes
-one-vs-rest problems are computed in parallel.
      
-
-
      None means 1 unless in a joblib.parallel_backend context.
--1 means using all processors. See :term:Glossary <n_jobs>
-for more details.
      
-
-
      Changed in version 0.20:
-n_jobs default changed from 1 to None
      • 
-
    • verbose (int, default=0):
-The verbosity level, if non zero, progress messages are printed.
-Below 50, the output is sent to stderr. Otherwise, the output is sent
-to stdout. The frequency of the messages increases with the verbosity
-level, reporting all iterations at 10. See joblib.Parallel for
-more details.
      
-
-
      New in version 1.1.
      • 
-
    
-
-
    Attributes
    
-
-
      
-
    • estimators_ (list of n_classes estimators):
-Estimators used for predictions.
      • 
-
    • classes_ (array, shape = [n_classes]):
-Class labels.
      • 
-
    • n_classes_ (int):
-Number of classes.
      • 
-
    • label_binarizer_ (LabelBinarizer object):
-Object used to transform multiclass labels to binary labels and
-vice-versa.
      • 
-
    • multilabel_ (boolean):
-Whether a OneVsRestClassifier is a multilabel classifier.
      • 
-
    • n_features_in_ (int):
-Number of features seen during :term:fit. Only defined if the
-underlying estimator exposes such an attribute when fit.
      
-
-
      New in version 0.24.
      • 
-
    • feature_names_in_ (ndarray of shape (n_features_in_,)):
-Names of features seen during :term:fit. Only defined if the
-underlying estimator exposes such an attribute when fit.
      
-
-
      New in version 1.0.
      • 
-
    
-
-
    See Also
    
-
-
    OneVsOneClassifier:  One-vs-one multiclass strategy. 
    
-OutputCodeClassifier:  (Error-Correcting) Output-Code multiclass strategy. 
    
-sklearn.multioutput.MultiOutputClassifier:  Alternate way of extending an
-estimator for multilabel classification. 
    
-sklearn.preprocessing.MultiLabelBinarizer:  Transform iterable of iterables
-to binary indicator matrix.  
    
+            
    Adapted OneVsRestClassifier for SSL datasets
    
 
-
    Examples
    
+
    Prevent use unlabeled data as a independent class in the classifier.
    
 
-
    
-
    >>> import numpy as np
->>> from sklearn.multiclass import OneVsRestClassifier
->>> from sklearn.svm import SVC
->>> X = np.array([
-...     [10, 10],
-...     [8, 10],
-...     [-5, 5.5],
-...     [-5.4, 5.5],
-...     [-20, -20],
-...     [-15, -20]
-... ])
->>> y = np.array([0, 0, 1, 1, 2, 2])
->>> clf = OneVsRestClassifier(SVC()).fit(X, y)
->>> clf.predict([[-19, -20], [9, 9], [-5, 5]])
-array([2, 0, 1])
-
    
-
    
+
    For more information of OvR classifier, see the documentation of OneVsRestClassifier.
    
 
    
 
 
@@ -1100,17 +998,17 @@ 
    Examples
    
 
     
    
     
-            
    214    def __init__(self, estimator, *, n_jobs=None):
-215        """Adapted OneVsRestClassifier for SSL datasets
-216
-217        Parameters
-218        ----------
-219        estimator : {ClassifierMixin, list},
-220            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
-221        n_jobs : n_jobs : int, optional
-222            The number of jobs to run in parallel. -1 means using all processors., by default None
-223        """
-224        super().__init__(estimator, n_jobs=n_jobs)
+            
    236    def __init__(self, estimator, *, n_jobs=None):
+237        """Adapted OneVsRestClassifier for SSL datasets
+238
+239        Parameters
+240        ----------
+241        estimator : {ClassifierMixin, list},
+242            An estimator object implementing fit and predict_proba or a list of ClassifierMixin
+243        n_jobs : n_jobs : int, optional
+244            The number of jobs to run in parallel. -1 means using all processors., by default None
+245        """
+246        super().__init__(estimator, n_jobs=n_jobs)
 
    
 
 
@@ -1139,43 +1037,43 @@ 
    Parameters
    
 
     
    
     
-            
    226    def fit(self, X, y, **fit_params):
-227        #
-228        y_label = y[y != y.dtype.type(-1)]
-229        size = len(y) - len(y_label)
-230
-231        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
-232        Y = self.label_binarizer_.fit_transform(y_label)
-233        Y = Y.tocsc()
-234        self.classes_ = self.label_binarizer_.classes_
-235        columns = (col.toarray().ravel() for col in Y.T)
-236
-237        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
-238        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
-239        for e in estimators:
-240            _set_random_states(e, rs)
-241
-242        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
-243            delayed(_fit_binary_ssl)(
-244                estimators[i],
-245                X,
-246                column,
-247                size,
-248                classes=[
-249                    "not %s" % self.label_binarizer_.classes_[i],
-250                    self.label_binarizer_.classes_[i],
-251                ],
-252                **fit_params
-253            )
-254            for i, column in enumerate(columns)
-255        )
-256
-257        if hasattr(self.estimators_[0], "n_features_in_"):
-258            self.n_features_in_ = self.estimators_[0].n_features_in_
-259        if hasattr(self.estimators_[0], "feature_names_in_"):
-260            self.feature_names_in_ = self.estimators_[0].feature_names_in_
-261
-262        return self
+            
    248    def fit(self, X, y, **fit_params):
+249        #
+250        y_label = y[y != y.dtype.type(-1)]
+251        size = len(y) - len(y_label)
+252
+253        self.label_binarizer_ = LabelBinarizer(sparse_output=True)
+254        Y = self.label_binarizer_.fit_transform(y_label)
+255        Y = Y.tocsc()
+256        self.classes_ = self.label_binarizer_.classes_
+257        columns = (col.toarray().ravel() for col in Y.T)
+258
+259        estimators = [skclone(self.estimator) for _ in range(len(self.classes_))]
+260        rs = check_random_state(estimators[0].get_params(deep=False).get("random_state", None))
+261        for e in estimators:
+262            _set_random_states(e, rs)
+263
+264        self.estimators_ = Parallel(n_jobs=self.n_jobs)(
+265            delayed(_fit_binary_ssl)(
+266                estimators[i],
+267                X,
+268                column,
+269                size,
+270                classes=[
+271                    "not %s" % self.label_binarizer_.classes_[i],
+272                    self.label_binarizer_.classes_[i],
+273                ],
+274                **fit_params
+275            )
+276            for i, column in enumerate(columns)
+277        )
+278
+279        if hasattr(self.estimators_[0], "n_features_in_"):
+280            self.n_features_in_ = self.estimators_[0].n_features_in_
+281        if hasattr(self.estimators_[0], "feature_names_in_"):
+282            self.feature_names_in_ = self.estimators_[0].feature_names_in_
+283
+284        return self
 
    
 
 
@@ -1212,34 +1110,34 @@ 
    Returns
    
 
     
    
     
-            
    264    def predict(self, X, **kwards):
-265        check_is_fitted(self)
-266
-267        n_samples = _num_samples(X)
-268        if self.label_binarizer_.y_type_ == "multiclass":
-269            maxima = np.empty(n_samples, dtype=float)
-270            maxima.fill(-np.inf)
-271            argmaxima = np.zeros(n_samples, dtype=int)
-272            for i, e in enumerate(self.estimators_):
-273                pred = _predict_binary_ssl(e, X, **kwards)
-274                np.maximum(maxima, pred, out=maxima)
-275                argmaxima[maxima == pred] = i
-276            return self.classes_[argmaxima]
-277        else:
-278            if (hasattr(self.estimators_[0], "decision_function") and
-279                    is_classifier(self.estimators_[0])):
-280                thresh = 0
-281            else:
-282                thresh = .5
-283            indices = array.array('i')
-284            indptr = array.array('i', [0])
-285            for e in self.estimators_:
-286                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
-287                indptr.append(len(indices))
-288            data = np.ones(len(indices), dtype=int)
-289            indicator = sp.csc_matrix((data, indices, indptr),
-290                                      shape=(n_samples, len(self.estimators_)))
-291            return self.label_binarizer_.inverse_transform(indicator)
+            
    286    def predict(self, X, **kwards):
+287        check_is_fitted(self)
+288
+289        n_samples = _num_samples(X)
+290        if self.label_binarizer_.y_type_ == "multiclass":
+291            maxima = np.empty(n_samples, dtype=float)
+292            maxima.fill(-np.inf)
+293            argmaxima = np.zeros(n_samples, dtype=int)
+294            for i, e in enumerate(self.estimators_):
+295                pred = _predict_binary_ssl(e, X, **kwards)
+296                np.maximum(maxima, pred, out=maxima)
+297                argmaxima[maxima == pred] = i
+298            return self.classes_[argmaxima]
+299        else:
+300            if (hasattr(self.estimators_[0], "decision_function") and
+301                    is_classifier(self.estimators_[0])):
+302                thresh = 0
+303            else:
+304                thresh = .5
+305            indices = array.array('i')
+306            indptr = array.array('i', [0])
+307            for e in self.estimators_:
+308                indices.extend(np.where(_predict_binary_ssl(e, X, **kwards) > thresh)[0])
+309                indptr.append(len(indices))
+310            data = np.ones(len(indices), dtype=int)
+311            indicator = sp.csc_matrix((data, indices, indptr),
+312                                      shape=(n_samples, len(self.estimators_)))
+313            return self.label_binarizer_.inverse_transform(indicator)
 
    
 
 
@@ -1273,21 +1171,21 @@ 
    Returns
    
 
     
    
     
-            
    293    def predict_proba(self, X, **kwards):
-294        check_is_fitted(self)
-295        # Y[i, j] gives the probability that sample i has the label j.
-296        # In the multi-label case, these are not disjoint.
-297        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
-298
-299        if len(self.estimators_) == 1:
-300            # Only one estimator, but we still want to return probabilities
-301            # for two classes.
-302            Y = np.concatenate(((1 - Y), Y), axis=1)
-303
-304        if not self.multilabel_:
-305            # Then, probabilities should be normalized to 1.
-306            Y /= np.sum(Y, axis=1)[:, np.newaxis]
-307        return Y
+            
    315    def predict_proba(self, X, **kwards):
+316        check_is_fitted(self)
+317        # Y[i, j] gives the probability that sample i has the label j.
+318        # In the multi-label case, these are not disjoint.
+319        Y = np.array([e.predict_proba(X, **kwards)[:, 1] for e in self.estimators_]).T
+320
+321        if len(self.estimators_) == 1:
+322            # Only one estimator, but we still want to return probabilities
+323            # for two classes.
+324            Y = np.concatenate(((1 - Y), Y), axis=1)
+325
+326        if not self.multilabel_:
+327            # Then, probabilities should be normalized to 1.
+328            Y /= np.sum(Y, axis=1)[:, np.newaxis]
+329        return Y
 
    
 
 
@@ -1320,110 +1218,6 @@ 
    Returns
    
 
    
 
 
-
    -
    -
    - - def - set_partial_fit_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - - -
    -
    -
    - - def - set_score_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - -
    Inherited Members
    @@ -1443,10 +1237,6 @@
    Inherited Members
    get_params
    set_params
    -
    -
    sklearn.utils._metadata_requests._MetadataRequester
    -
    get_metadata_routing
    -
    diff --git a/docs/sslearn/datasets.html b/docs/sslearn/datasets.html index 8bcfd1f..ee56f59 100644 --- a/docs/sslearn/datasets.html +++ b/docs/sslearn/datasets.html @@ -52,7 +52,7 @@

    Contents

    @@ -92,7 +92,7 @@

    This module contains functions to load and save datasets in different formats.

    -

    Functions
    +

    Functions

    1. read_csv : Load a dataset from a CSV file.
      2. @@ -100,8 +100,6 @@
      Functions
    2. secure_dataset : Secure the dataset by converting it into a secure format.
    3. save_keel : Save a dataset in KEEL format.
    - -

    All doc

    @@ -113,22 +111,21 @@

    All doc

    3 4This module contains functions to load and save datasets in different formats. 5 - 6Functions - 7--------- + 6## Functions + 7 81. read_csv : Load a dataset from a CSV file. 92. read_keel : Load a dataset from a KEEL file. 103. secure_dataset : Secure the dataset by converting it into a secure format. 114. save_keel : Save a dataset in KEEL format. 12 -13All doc -14------- -15""" -16 -17from ._loader import read_csv, read_keel -18from ._writer import save_keel -19from ._preprocess import secure_dataset -20 -21__all__ = ["read_csv", "read_keel", "secure_dataset", "save_keel"] +13 +14""" +15 +16from ._loader import read_csv, read_keel +17from ._writer import save_keel +18from ._preprocess import secure_dataset +19 +20__all__ = ["read_csv", "read_keel", "secure_dataset", "save_keel"] @@ -145,7 +142,7 @@

    All doc

     94def read_csv(path, format="pandas", secure=False, target_col=-1, **kwards):
- 95    """Read a .csv file
+ 95    """Read a .csv file
  96
  97    Parameters
  98    ----------
@@ -221,7 +218,7 @@ 
    Returns
    
     
    14def read_keel(path, format="pandas", secure=False, target_col=None, encoding="utf-8", **kwards):
-15    """Read a .dat file from KEEL (http://www.keel.es/)
+15    """Read a .dat file from KEEL (http://www.keel.es/)
 16
 17    Parameters
 18    ----------
@@ -340,7 +337,7 @@ 
    Returns
    
     
     2def secure_dataset(X, y):
- 3    """It guarantees that the dataset has not  `-1` as valid class, in order to make it semi-supervised after
+ 3    """It guarantees that the dataset has not  `-1` as valid class, in order to make it semi-supervised after
  4
  5    Parameters
  6    ----------
@@ -397,7 +394,7 @@ 
    Returns
    
     
     8def save_keel(X, y, route, name=None, attribute_name=None, target_name="Class",  classification=True, unlabeled=True, force_targets=None):
- 9    """Save a dataset in the KEEL format
+ 9    """Save a dataset in the KEEL format
 10
 11    Parameters
 12    ----------
diff --git a/docs/sslearn/model_selection.html b/docs/sslearn/model_selection.html
index bdb747e..177f988 100644
--- a/docs/sslearn/model_selection.html
+++ b/docs/sslearn/model_selection.html
@@ -52,7 +52,8 @@
 
             
    Contents
    
             
 
 
@@ -83,15 +84,21 @@ 
    
 
 
    This module contains functions to split datasets into training and testing sets.
    
 
-
    Functions
    
+
    Functions
    
 
-
    artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset.
    
+
    artificial_ssl_dataset:
    
 
-
    Classes
    
+
    
+  
    Generate an artificial semi-supervised learning dataset.
    
+
    
 
-
    StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning.
    
+
    Classes
    
 
-
    All doc
    
+
    StratifiedKFoldSS:
    
+
+
    
+  
    Stratified K-Folds cross-validator for semi-supervised learning.
    
+
    @@ -103,21 +110,22 @@

    All doc

    3 4This module contains functions to split datasets into training and testing sets. 5 - 6Functions - 7--------- - 8artificial_ssl_dataset : Generate an artificial semi-supervised learning dataset. - 9 -10Classes -11------- -12StratifiedKFoldSS : Stratified K-Folds cross-validator for semi-supervised learning. -13 -14All doc -15---- -16""" -17 -18from ._split import * -19 -20__all__ = ['StratifiedKFoldSS', 'artificial_ssl_dataset'] + 6## Functions + 7 + 8[artificial_ssl_dataset](#artificial_ssl_dataset): + 9> Generate an artificial semi-supervised learning dataset. +10 +11## Classes +12 +13[StratifiedKFoldSS](#StratifiedKFoldSS): +14> Stratified K-Folds cross-validator for semi-supervised learning. +15 +16 +17""" +18 +19from ._split import * +20 +21__all__ = ['artificial_ssl_dataset', 'StratifiedKFoldSS'] @@ -134,7 +142,7 @@

    All doc

     65def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimum=None, indexes=False, **kwards):
- 66    """Create an artificial Semi-supervised dataset from a supervised dataset.
+ 66    """Create an artificial Semi-supervised dataset from a supervised dataset.
  67
  68    Parameters
  69    ----------
@@ -208,7 +216,7 @@ 
    All doc
    
     137    return X, y, X_unlabel, y_unlabel
 138
 139
-140    """    
+140    """    
 141    if force_minimum is not None:
 142        try:
 143            selected = __random_select_n_instances(y, force_minimum, random_state)
diff --git a/docs/sslearn/restricted.html b/docs/sslearn/restricted.html
index a7e0b00..e39f346 100644
--- a/docs/sslearn/restricted.html
+++ b/docs/sslearn/restricted.html
@@ -52,13 +52,17 @@
 
             
    Contents
    
             
 
 
 
             
    API Documentation
    
                 
 
 
@@ -113,16 +105,23 @@ 
    
 
 
    This module contains classes to train a classifier using the restricted set classification approach.
    
 
-
    Classes
    
+
    Classes
    
+
+
    WhoIsWhoClassifier:
    
 
-
    WhoIsWhoClassifier : Who is Who Classifier
    
+
    
+  
    Who is Who Classifier
    
+
    
 
-
    Functions
    
+
    Functions
    
 
-
    conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions.
-combine_predictions : Combine the predictions of a group of instances to keep the restrictions.
    
+
    conflict_rate: 
    
 
-
    All doc
    
+
    
+  
    Compute the conflict rate of a prediction, given a set of restrictions.
+  combine_predictions: 
+  Combine the predictions of a group of instances to keep the restrictions.
    
+
    @@ -133,212 +132,280 @@

    All doc

    2 3This module contains classes to train a classifier using the restricted set classification approach. 4 - 5Classes - 6------- - 7WhoIsWhoClassifier : Who is Who Classifier - 8 - 9Functions - 10--------- - 11conflict_rate : Compute the conflict rate of a prediction, given a set of restrictions. - 12combine_predictions : Combine the predictions of a group of instances to keep the restrictions. - 13 - 14All doc - 15------- - 16""" + 5## Classes + 6 + 7[WhoIsWhoClassifier](#WhoIsWhoClassifier): + 8> Who is Who Classifier + 9 + 10## Functions + 11 + 12[conflict_rate](#conflict_rate): + 13> Compute the conflict rate of a prediction, given a set of restrictions. + 14[combine_predictions](#combine_predictions): + 15> Combine the predictions of a group of instances to keep the restrictions. + 16 17 - 18import numpy as np - 19from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator - 20from scipy.optimize import linear_sum_assignment - 21import warnings - 22import pandas as pd - 23 - 24__all__ = ["WhoIsWhoClassifier", "conflict_rate", "combine_predictions"] + 18""" + 19 + 20import numpy as np + 21from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator + 22from scipy.optimize import linear_sum_assignment + 23import warnings + 24import pandas as pd 25 - 26class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): + 26__all__ = ["conflict_rate", "combine_predictions", "WhoIsWhoClassifier"] 27 - 28 def __init__(self, base_estimator, method="hungarian", conflict_weighted=True): - 29 """ - 30 Who is Who Classifier - 31 Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017). - 32 Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170. - 33 - 34 Parameters - 35 ---------- - 36 base_estimator : ClassifierMixin - 37 The base estimator to be used for training. - 38 method : str, optional - 39 The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian" - 40 conflict_weighted : bool, default=True - 41 Whether to weighted the confusion rate by the number of instances with the same group. - 42 """ - 43 allowed_methods = ["greedy", "hungarian"] - 44 self.base_estimator = base_estimator - 45 self.method = method - 46 if method not in allowed_methods: - 47 raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}") - 48 self.conflict_weighted = conflict_weighted - 49 - 50 - 51 def fit(self, X, y, instance_group=None, **kwards): - 52 """Fit the model according to the given training data. - 53 Parameters - 54 ---------- - 55 X : {array-like, sparse matrix} of shape (n_samples, n_features) - 56 The input samples. - 57 y : array-like of shape (n_samples,) - 58 The target values. - 59 instance_group : array-like of shape (n_samples) - 60 The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training. - 61 Returns - 62 ------- - 63 self : object - 64 Returns self. - 65 """ - 66 self.base_estimator = self.base_estimator.fit(X, y, **kwards) - 67 self.classes_ = self.base_estimator.classes_ - 68 if instance_group is not None: - 69 self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted) - 70 else: - 71 self.conflict_in_train = None - 72 return self - 73 - 74 def conflict_rate(self, X, instance_group): - 75 """Calculate the conflict rate of the model. - 76 Parameters - 77 ---------- - 78 X : {array-like, sparse matrix} of shape (n_samples, n_features) - 79 The input samples. - 80 instance_group : array-like of shape (n_samples) - 81 The group. Two instances with the same label are not allowed to be in the same group. - 82 Returns - 83 ------- - 84 float - 85 The conflict rate. - 86 """ - 87 y_pred = self.base_estimator.predict(X) - 88 return conflict_rate(y_pred, instance_group, self.conflict_weighted) - 89 - 90 def predict(self, X, instance_group): - 91 """Predict class for X. - 92 Parameters - 93 ---------- - 94 X : {array-like, sparse matrix} of shape (n_samples, n_features) - 95 The input samples. - 96 **kwards : array-like of shape (n_samples) - 97 The group. Two instances with the same label are not allowed to be in the same group. - 98 Returns - 99 ------- -100 array-like of shape (n_samples, n_classes) -101 The class probabilities of the input samples. -102 """ -103 -104 y_prob = self.predict_proba(X) + 28class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): + 29 + 30 def __init__(self, base_estimator, method="hungarian", conflict_weighted=True): + 31 """ + 32 Who is Who Classifier + 33 Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017). + 34 Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170. + 35 + 36 Parameters + 37 ---------- + 38 base_estimator : ClassifierMixin + 39 The base estimator to be used for training. + 40 method : str, optional + 41 The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian" + 42 conflict_weighted : bool, default=True + 43 Whether to weighted the confusion rate by the number of instances with the same group. + 44 """ + 45 allowed_methods = ["greedy", "hungarian"] + 46 self.base_estimator = base_estimator + 47 self.method = method + 48 if method not in allowed_methods: + 49 raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}") + 50 self.conflict_weighted = conflict_weighted + 51 + 52 + 53 def fit(self, X, y, instance_group=None, **kwards): + 54 """Fit the model according to the given training data. + 55 Parameters + 56 ---------- + 57 X : {array-like, sparse matrix} of shape (n_samples, n_features) + 58 The input samples. + 59 y : array-like of shape (n_samples,) + 60 The target values. + 61 instance_group : array-like of shape (n_samples) + 62 The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training. + 63 Returns + 64 ------- + 65 self : object + 66 Returns self. + 67 """ + 68 self.base_estimator = self.base_estimator.fit(X, y, **kwards) + 69 self.classes_ = self.base_estimator.classes_ + 70 if instance_group is not None: + 71 self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted) + 72 else: + 73 self.conflict_in_train = None + 74 return self + 75 + 76 def conflict_rate(self, X, instance_group): + 77 """Calculate the conflict rate of the model. + 78 Parameters + 79 ---------- + 80 X : {array-like, sparse matrix} of shape (n_samples, n_features) + 81 The input samples. + 82 instance_group : array-like of shape (n_samples) + 83 The group. Two instances with the same label are not allowed to be in the same group. + 84 Returns + 85 ------- + 86 float + 87 The conflict rate. + 88 """ + 89 y_pred = self.base_estimator.predict(X) + 90 return conflict_rate(y_pred, instance_group, self.conflict_weighted) + 91 + 92 def predict(self, X, instance_group): + 93 """Predict class for X. + 94 Parameters + 95 ---------- + 96 X : {array-like, sparse matrix} of shape (n_samples, n_features) + 97 The input samples. + 98 **kwards : array-like of shape (n_samples) + 99 The group. Two instances with the same label are not allowed to be in the same group. +100 Returns +101 ------- +102 array-like of shape (n_samples, n_classes) +103 The class probabilities of the input samples. +104 """ 105 -106 y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method) -107 -108 return self.classes_.take(y_predicted) +106 y_prob = self.predict_proba(X) +107 +108 y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method) 109 -110 -111 def predict_proba(self, X): -112 """Predict class probabilities for X. -113 Parameters -114 ---------- -115 X : {array-like, sparse matrix} of shape (n_samples, n_features) -116 The input samples. -117 Returns -118 ------- -119 array-like of shape (n_samples, n_classes) -120 The class probabilities of the input samples. -121 """ -122 return self.base_estimator.predict_proba(X) -123 -124 -125def conflict_rate(y_pred, restrictions, weighted=True): -126 """ -127 Computes the conflict rate of a prediction, given a set of restrictions. -128 Parameters -129 ---------- -130 y_pred : array-like of shape (n_samples,) -131 Predicted target values. -132 restrictions : array-like of shape (n_samples,) -133 Restrictions for each sample. If two samples have the same restriction, they cannot have the same y. -134 weighted : bool, default=True -135 Whether to weighted the confusion rate by the number of instances with the same group. -136 Returns -137 ------- -138 conflict rate : float -139 The conflict rate. -140 """ -141 -142 # Check that y_pred and restrictions have the same length -143 if len(y_pred) != len(restrictions): -144 raise ValueError("y_pred and restrictions must have the same length.") -145 -146 restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions}) -147 -148 conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()}) -149 if weighted: -150 return conflicted.sum().y_pred / len(y_pred) -151 else: -152 rcount = restricted_df.groupby('restrictions').count() -153 return (conflicted.y_pred / rcount.y_pred).sum() -154 -155def combine_predictions(y_probas, instance_group, class_number, method="hungarian"): -156 y_predicted = [] -157 for group in np.unique(instance_group): -158 -159 mask = instance_group == group -160 probas_matrix = y_probas[mask] -161 -162 -163 preds = list(np.argmax(probas_matrix, axis=1)) +110 return self.classes_.take(y_predicted) +111 +112 +113 def predict_proba(self, X): +114 """Predict class probabilities for X. +115 Parameters +116 ---------- +117 X : {array-like, sparse matrix} of shape (n_samples, n_features) +118 The input samples. +119 Returns +120 ------- +121 array-like of shape (n_samples, n_classes) +122 The class probabilities of the input samples. +123 """ +124 return self.base_estimator.predict_proba(X) +125 +126 +127def conflict_rate(y_pred, restrictions, weighted=True): +128 """ +129 Computes the conflict rate of a prediction, given a set of restrictions. +130 Parameters +131 ---------- +132 y_pred : array-like of shape (n_samples,) +133 Predicted target values. +134 restrictions : array-like of shape (n_samples,) +135 Restrictions for each sample. If two samples have the same restriction, they cannot have the same y. +136 weighted : bool, default=True +137 Whether to weighted the confusion rate by the number of instances with the same group. +138 Returns +139 ------- +140 conflict rate : float +141 The conflict rate. +142 """ +143 +144 # Check that y_pred and restrictions have the same length +145 if len(y_pred) != len(restrictions): +146 raise ValueError("y_pred and restrictions must have the same length.") +147 +148 restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions}) +149 +150 conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()}) +151 if weighted: +152 return conflicted.sum().y_pred / len(y_pred) +153 else: +154 rcount = restricted_df.groupby('restrictions').count() +155 return (conflicted.y_pred / rcount.y_pred).sum() +156 +157def combine_predictions(y_probas, instance_group, class_number, method="hungarian"): +158 y_predicted = [] +159 for group in np.unique(instance_group): +160 +161 mask = instance_group == group +162 probas_matrix = y_probas[mask] +163 164 -165 if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number: -166 y_predicted.extend(preds) -167 if probas_matrix.shape[0] > class_number: -168 warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning) -169 continue -170 -171 if method == "greedy": -172 y = _greedy(probas_matrix) -173 elif method == "hungarian": -174 y = _hungarian(probas_matrix) -175 -176 y_predicted.extend(y) -177 return y_predicted -178 -179def _greedy(probas_matrix): +165 preds = list(np.argmax(probas_matrix, axis=1)) +166 +167 if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number: +168 y_predicted.extend(preds) +169 if probas_matrix.shape[0] > class_number: +170 warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning) +171 continue +172 +173 if method == "greedy": +174 y = _greedy(probas_matrix) +175 elif method == "hungarian": +176 y = _hungarian(probas_matrix) +177 +178 y_predicted.extend(y) +179 return y_predicted 180 -181 probas = probas_matrix.reshape(probas_matrix.size,) -182 order = probas.argsort()[::-1] -183 -184 y_pred_group = [None for i in range(probas_matrix.shape[0])] +181def _greedy(probas_matrix): +182 +183 probas = probas_matrix.reshape(probas_matrix.size,) +184 order = probas.argsort()[::-1] 185 -186 instance_to_predict = {i for i in range(probas_matrix.shape[0])} -187 class_predicted = set() -188 for item in order: -189 class_ = item % probas_matrix.shape[0] -190 instance = item // probas_matrix.shape[0] -191 if instance in instance_to_predict and class_ not in class_predicted: -192 y_pred_group[instance] = class_ -193 instance_to_predict.remove(instance) -194 class_predicted.add(class_) -195 -196 return y_pred_group -197 -198 -199def _hungarian(probas_matrix): -200 -201 costs = np.log(probas_matrix) -202 costs[costs == -np.inf] = 0 # if proba is 0, then the cost is 0 -203 _, col_ind = linear_sum_assignment(costs, maximize=True) -204 col_ind = list(col_ind) -205 -206 return col_ind +186 y_pred_group = [None for i in range(probas_matrix.shape[0])] +187 +188 instance_to_predict = {i for i in range(probas_matrix.shape[0])} +189 class_predicted = set() +190 for item in order: +191 class_ = item % probas_matrix.shape[0] +192 instance = item // probas_matrix.shape[0] +193 if instance in instance_to_predict and class_ not in class_predicted: +194 y_pred_group[instance] = class_ +195 instance_to_predict.remove(instance) +196 class_predicted.add(class_) +197 +198 return y_pred_group +199 +200 +201def _hungarian(probas_matrix): +202 +203 costs = np.log(probas_matrix) +204 costs[costs == -np.inf] = 0 # if proba is 0, then the cost is 0 +205 _, col_ind = linear_sum_assignment(costs, maximize=True) +206 col_ind = list(col_ind) +207 +208 return col_ind
    +
    + +
    + + def + conflict_rate(y_pred, restrictions, weighted=True): + + + +
    + + 
    128def conflict_rate(y_pred, restrictions, weighted=True):
+129    """
+130    Computes the conflict rate of a prediction, given a set of restrictions.
+131    Parameters
+132    ----------
+133    y_pred : array-like of shape (n_samples,)
+134        Predicted target values.
+135    restrictions : array-like of shape (n_samples,)
+136        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+137    weighted : bool, default=True
+138        Whether to weighted the confusion rate by the number of instances with the same group.
+139    Returns
+140    -------
+141    conflict rate : float
+142        The conflict rate.
+143    """
+144    
+145    # Check that y_pred and restrictions have the same length
+146    if len(y_pred) != len(restrictions):
+147        raise ValueError("y_pred and restrictions must have the same length.")
+148    
+149    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+150
+151    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
+152    if weighted:
+153        return conflicted.sum().y_pred / len(y_pred)
+154    else:
+155        rcount = restricted_df.groupby('restrictions').count()
+156        return (conflicted.y_pred / rcount.y_pred).sum()
+
    + + +

    Computes the conflict rate of a prediction, given a set of restrictions.

    + +
    Parameters
    + +
      +
    • y_pred (array-like of shape (n_samples,)): +Predicted target values.
      • +
    • restrictions (array-like of shape (n_samples,)): +Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • +
    • weighted (bool, default=True): +Whether to weighted the confusion rate by the number of instances with the same group.
      • +
    + +
    Returns
    + +
      +
    • conflict rate (float): +The conflict rate.
      • +
    +
    + + +
    @@ -350,103 +417,103 @@

    All doc

    - 
     27class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
- 28
- 29    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
- 30        """
- 31        Who is Who Classifier
- 32        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
- 33        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
- 34
- 35        Parameters
- 36        ----------
- 37        base_estimator : ClassifierMixin
- 38            The base estimator to be used for training.
- 39        method : str, optional
- 40            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
- 41        conflict_weighted : bool, default=True
- 42            Whether to weighted the confusion rate by the number of instances with the same group.
- 43        """        
- 44        allowed_methods = ["greedy", "hungarian"]
- 45        self.base_estimator = base_estimator
- 46        self.method = method
- 47        if method not in allowed_methods:
- 48            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
- 49        self.conflict_weighted = conflict_weighted
- 50
- 51
- 52    def fit(self, X, y, instance_group=None, **kwards):
- 53        """Fit the model according to the given training data.
- 54        Parameters
- 55        ----------
- 56        X : {array-like, sparse matrix} of shape (n_samples, n_features)
- 57            The input samples.
- 58        y : array-like of shape (n_samples,)
- 59            The target values.
- 60        instance_group : array-like of shape (n_samples)
- 61            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
- 62        Returns
- 63        -------
- 64        self : object
- 65            Returns self.
- 66        """
- 67        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
- 68        self.classes_ = self.base_estimator.classes_
- 69        if instance_group is not None:
- 70            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
- 71        else:
- 72            self.conflict_in_train = None
- 73        return self
- 74
- 75    def conflict_rate(self, X, instance_group):
- 76        """Calculate the conflict rate of the model.
- 77        Parameters
- 78        ----------
- 79        X : {array-like, sparse matrix} of shape (n_samples, n_features)
- 80            The input samples.
- 81        instance_group : array-like of shape (n_samples)
- 82            The group. Two instances with the same label are not allowed to be in the same group.
- 83        Returns
- 84        -------
- 85        float
- 86            The conflict rate.
- 87        """
- 88        y_pred = self.base_estimator.predict(X)
- 89        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
- 90
- 91    def predict(self, X, instance_group):
- 92        """Predict class for X.
- 93        Parameters
- 94        ----------
- 95        X : {array-like, sparse matrix} of shape (n_samples, n_features)
- 96            The input samples.
- 97        **kwards : array-like of shape (n_samples)
- 98            The group. Two instances with the same label are not allowed to be in the same group.
- 99        Returns
-100        -------
-101        array-like of shape (n_samples, n_classes)
-102            The class probabilities of the input samples.
-103        """
-104        
-105        y_prob = self.predict_proba(X)
+            
     29class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ 30
+ 31    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+ 32        """
+ 33        Who is Who Classifier
+ 34        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+ 35        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+ 36
+ 37        Parameters
+ 38        ----------
+ 39        base_estimator : ClassifierMixin
+ 40            The base estimator to be used for training.
+ 41        method : str, optional
+ 42            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+ 43        conflict_weighted : bool, default=True
+ 44            Whether to weighted the confusion rate by the number of instances with the same group.
+ 45        """        
+ 46        allowed_methods = ["greedy", "hungarian"]
+ 47        self.base_estimator = base_estimator
+ 48        self.method = method
+ 49        if method not in allowed_methods:
+ 50            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+ 51        self.conflict_weighted = conflict_weighted
+ 52
+ 53
+ 54    def fit(self, X, y, instance_group=None, **kwards):
+ 55        """Fit the model according to the given training data.
+ 56        Parameters
+ 57        ----------
+ 58        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 59            The input samples.
+ 60        y : array-like of shape (n_samples,)
+ 61            The target values.
+ 62        instance_group : array-like of shape (n_samples)
+ 63            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+ 64        Returns
+ 65        -------
+ 66        self : object
+ 67            Returns self.
+ 68        """
+ 69        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+ 70        self.classes_ = self.base_estimator.classes_
+ 71        if instance_group is not None:
+ 72            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+ 73        else:
+ 74            self.conflict_in_train = None
+ 75        return self
+ 76
+ 77    def conflict_rate(self, X, instance_group):
+ 78        """Calculate the conflict rate of the model.
+ 79        Parameters
+ 80        ----------
+ 81        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 82            The input samples.
+ 83        instance_group : array-like of shape (n_samples)
+ 84            The group. Two instances with the same label are not allowed to be in the same group.
+ 85        Returns
+ 86        -------
+ 87        float
+ 88            The conflict rate.
+ 89        """
+ 90        y_pred = self.base_estimator.predict(X)
+ 91        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+ 92
+ 93    def predict(self, X, instance_group):
+ 94        """Predict class for X.
+ 95        Parameters
+ 96        ----------
+ 97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 98            The input samples.
+ 99        **kwards : array-like of shape (n_samples)
+100            The group. Two instances with the same label are not allowed to be in the same group.
+101        Returns
+102        -------
+103        array-like of shape (n_samples, n_classes)
+104            The class probabilities of the input samples.
+105        """
 106        
-107        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
-108
-109        return self.classes_.take(y_predicted)
+107        y_prob = self.predict_proba(X)
+108        
+109        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
 110
-111
-112    def predict_proba(self, X):
-113        """Predict class probabilities for X.
-114        Parameters
-115        ----------
-116        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-117            The input samples.
-118        Returns
-119        -------
-120        array-like of shape (n_samples, n_classes)
-121            The class probabilities of the input samples.
-122        """
-123        return self.base_estimator.predict_proba(X)
+111        return self.classes_.take(y_predicted)
+112
+113
+114    def predict_proba(self, X):
+115        """Predict class probabilities for X.
+116        Parameters
+117        ----------
+118        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+119            The input samples.
+120        Returns
+121        -------
+122        array-like of shape (n_samples, n_classes)
+123            The class probabilities of the input samples.
+124        """
+125        return self.base_estimator.predict_proba(X)
 
    
 
 
@@ -470,27 +537,27 @@ 
    Notes
    - 
    29    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
-30        """
-31        Who is Who Classifier
-32        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
-33        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
-34
-35        Parameters
-36        ----------
-37        base_estimator : ClassifierMixin
-38            The base estimator to be used for training.
-39        method : str, optional
-40            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
-41        conflict_weighted : bool, default=True
-42            Whether to weighted the confusion rate by the number of instances with the same group.
-43        """        
-44        allowed_methods = ["greedy", "hungarian"]
-45        self.base_estimator = base_estimator
-46        self.method = method
-47        if method not in allowed_methods:
-48            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
-49        self.conflict_weighted = conflict_weighted
+            
    31    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+32        """
+33        Who is Who Classifier
+34        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+35        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+36
+37        Parameters
+38        ----------
+39        base_estimator : ClassifierMixin
+40            The base estimator to be used for training.
+41        method : str, optional
+42            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+43        conflict_weighted : bool, default=True
+44            Whether to weighted the confusion rate by the number of instances with the same group.
+45        """        
+46        allowed_methods = ["greedy", "hungarian"]
+47        self.base_estimator = base_estimator
+48        self.method = method
+49        if method not in allowed_methods:
+50            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+51        self.conflict_weighted = conflict_weighted
 
    
 
 
@@ -523,28 +590,28 @@ 
    Parameters
    - 
    52    def fit(self, X, y, instance_group=None, **kwards):
-53        """Fit the model according to the given training data.
-54        Parameters
-55        ----------
-56        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-57            The input samples.
-58        y : array-like of shape (n_samples,)
-59            The target values.
-60        instance_group : array-like of shape (n_samples)
-61            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
-62        Returns
-63        -------
-64        self : object
-65            Returns self.
-66        """
-67        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
-68        self.classes_ = self.base_estimator.classes_
-69        if instance_group is not None:
-70            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
-71        else:
-72            self.conflict_in_train = None
-73        return self
+            
    54    def fit(self, X, y, instance_group=None, **kwards):
+55        """Fit the model according to the given training data.
+56        Parameters
+57        ----------
+58        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+59            The input samples.
+60        y : array-like of shape (n_samples,)
+61            The target values.
+62        instance_group : array-like of shape (n_samples)
+63            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+64        Returns
+65        -------
+66        self : object
+67            Returns self.
+68        """
+69        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+70        self.classes_ = self.base_estimator.classes_
+71        if instance_group is not None:
+72            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+73        else:
+74            self.conflict_in_train = None
+75        return self
 
    
 
 
@@ -582,21 +649,21 @@ 
    Returns
    - 
    75    def conflict_rate(self, X, instance_group):
-76        """Calculate the conflict rate of the model.
-77        Parameters
-78        ----------
-79        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-80            The input samples.
-81        instance_group : array-like of shape (n_samples)
-82            The group. Two instances with the same label are not allowed to be in the same group.
-83        Returns
-84        -------
-85        float
-86            The conflict rate.
-87        """
-88        y_pred = self.base_estimator.predict(X)
-89        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+            
    77    def conflict_rate(self, X, instance_group):
+78        """Calculate the conflict rate of the model.
+79        Parameters
+80        ----------
+81        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+82            The input samples.
+83        instance_group : array-like of shape (n_samples)
+84            The group. Two instances with the same label are not allowed to be in the same group.
+85        Returns
+86        -------
+87        float
+88            The conflict rate.
+89        """
+90        y_pred = self.base_estimator.predict(X)
+91        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
 
    
 
 
@@ -631,25 +698,25 @@ 
    Returns
    - 
     91    def predict(self, X, instance_group):
- 92        """Predict class for X.
- 93        Parameters
- 94        ----------
- 95        X : {array-like, sparse matrix} of shape (n_samples, n_features)
- 96            The input samples.
- 97        **kwards : array-like of shape (n_samples)
- 98            The group. Two instances with the same label are not allowed to be in the same group.
- 99        Returns
-100        -------
-101        array-like of shape (n_samples, n_classes)
-102            The class probabilities of the input samples.
-103        """
-104        
-105        y_prob = self.predict_proba(X)
+            
     93    def predict(self, X, instance_group):
+ 94        """Predict class for X.
+ 95        Parameters
+ 96        ----------
+ 97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 98            The input samples.
+ 99        **kwards : array-like of shape (n_samples)
+100            The group. Two instances with the same label are not allowed to be in the same group.
+101        Returns
+102        -------
+103        array-like of shape (n_samples, n_classes)
+104            The class probabilities of the input samples.
+105        """
 106        
-107        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
-108
-109        return self.classes_.take(y_predicted)
+107        y_prob = self.predict_proba(X)
+108        
+109        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+110
+111        return self.classes_.take(y_predicted)
 
    
 
 
@@ -684,18 +751,18 @@ 
    Returns
    - 
    112    def predict_proba(self, X):
-113        """Predict class probabilities for X.
-114        Parameters
-115        ----------
-116        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-117            The input samples.
-118        Returns
-119        -------
-120        array-like of shape (n_samples, n_classes)
-121            The class probabilities of the input samples.
-122        """
-123        return self.base_estimator.predict_proba(X)
+            
    114    def predict_proba(self, X):
+115        """Predict class probabilities for X.
+116        Parameters
+117        ----------
+118        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+119            The input samples.
+120        Returns
+121        -------
+122        array-like of shape (n_samples, n_classes)
+123            The class probabilities of the input samples.
+124        """
+125        return self.base_estimator.predict_proba(X)
 
    
 
 
@@ -716,162 +783,6 @@ 
    Returns
    - -
    -
    - - def - set_fit_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - - -
    -
    -
    - - def - set_predict_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - - -
    -
    -
    - - def - set_score_request(unknown): - - -
    - - -

    A descriptor for request methods.

    - -

    New in version 1.3.

    - -
    Parameters
    - -
      -
    • name (str): -The name of the method for which the request function should be -created, e.g. "fit" would create a set_fit_request function.
      • -
    • keys (list of str): -A list of strings which are accepted parameters by the created -function, e.g. ["sample_weight"] if the corresponding method -accepts it as a metadata.
      • -
    • validate_keys (bool, default=True): -Whether to check if the requested parameters fit the actual parameters -of the method.
      • -
    - -
    Notes
    - -

    This class is a descriptor 1 and uses PEP-362 to set the signature of -the returned function 2.

    - -
    References
    - -
    -
    -
      -
    1. -

      https://docs.python.org/3/howto/descriptor.html ↩

      -
      2. - -
    2. -

      https://www.python.org/dev/peps/pep-0362/ ↩

      -
      3. -
    -
    -
    - -
    Inherited Members
    @@ -880,10 +791,6 @@
    Inherited Members
    get_params
    set_params
    -
    -
    sklearn.utils._metadata_requests._MetadataRequester
    -
    get_metadata_routing
    -
    sklearn.base.ClassifierMixin
    score
    @@ -892,72 +799,6 @@
    Inherited Members
    -
    - -
    - - def - conflict_rate(y_pred, restrictions, weighted=True): - - - -
    - - 
    126def conflict_rate(y_pred, restrictions, weighted=True):
-127    """
-128    Computes the conflict rate of a prediction, given a set of restrictions.
-129    Parameters
-130    ----------
-131    y_pred : array-like of shape (n_samples,)
-132        Predicted target values.
-133    restrictions : array-like of shape (n_samples,)
-134        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
-135    weighted : bool, default=True
-136        Whether to weighted the confusion rate by the number of instances with the same group.
-137    Returns
-138    -------
-139    conflict rate : float
-140        The conflict rate.
-141    """
-142    
-143    # Check that y_pred and restrictions have the same length
-144    if len(y_pred) != len(restrictions):
-145        raise ValueError("y_pred and restrictions must have the same length.")
-146    
-147    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
-148
-149    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
-150    if weighted:
-151        return conflicted.sum().y_pred / len(y_pred)
-152    else:
-153        rcount = restricted_df.groupby('restrictions').count()
-154        return (conflicted.y_pred / rcount.y_pred).sum()
-
    - - -

    Computes the conflict rate of a prediction, given a set of restrictions.

    - -
    Parameters
    - -
      -
    • y_pred (array-like of shape (n_samples,)): -Predicted target values.
      • -
    • restrictions (array-like of shape (n_samples,)): -Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • -
    • weighted (bool, default=True): -Whether to weighted the confusion rate by the number of instances with the same group.
      • -
    - -
    Returns
    - -
      -
    • conflict rate (float): -The conflict rate.
      • -
    -
    - - -