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] 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 - 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

\"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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documentation - + @@ -40,7 +40,7 @@
- + @@ -84,7 +84,7 @@

Semi-Supervised Learning Library (sslearn)

-

+

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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