From f7e697f278b5f7f373958f7301895ed35fda2a08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Mon, 20 May 2024 12:07:39 +0200 Subject: [PATCH 1/3] Repaired bug reported in #18. artificial_ssl_dataset now compatible with pandas --- sslearn/model_selection/_split.py | 15 ++ ...ia en conflicto de CROSS-PC 2024-05-14).py | 208 ++++++++++++++++++ sslearn/wrapper/_co.py | 2 - sslearn/wrapper/_self.py | 31 ++- test/test_model_selection.py | 7 + 5 files changed, 259 insertions(+), 4 deletions(-) create mode 100644 sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py diff --git a/sslearn/model_selection/_split.py b/sslearn/model_selection/_split.py index 0463435..0e5cd26 100644 --- a/sslearn/model_selection/_split.py +++ b/sslearn/model_selection/_split.py @@ -1,6 +1,7 @@ import sklearn.model_selection as ms from sklearn.utils import check_random_state import numpy as np +import pandas as pd class StratifiedKFoldSS(): @@ -108,6 +109,16 @@ def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimu "Label rate must be in (0, 1)." assert "test_size" not in kwards and "train_size" not in kwards,\ "Test size and train size are illegal parameters in this method." + + columns = None + is_df = False + if hasattr(X, "iloc"): + is_df = True + columns = X.columns + X = X.values + if hasattr(y, "iloc"): + is_df = True + y = y.values indices = np.arange(len(y)) @@ -135,6 +146,10 @@ def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimu X = np.concatenate((X_label, X_unlabel), axis=0) y = np.concatenate((y_label, y_unlabel), axis=0) + if is_df: + X = pd.DataFrame(X, columns=columns) + y = pd.Series(y) + if indexes: return X, y, X_unlabel, y_unlabel, label, unlabel diff --git a/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py b/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py new file mode 100644 index 0000000..011be28 --- /dev/null +++ b/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py @@ -0,0 +1,208 @@ +"""Summary of module `sslearn.restricted`: + +This module contains classes to train a classifier using the restricted set classification approach. + +## Classes + +[WhoIsWhoClassifier](#WhoIsWhoClassifier): +> Who is Who Classifier + +## Functions + +[conflict_rate](#conflict_rate): +> Compute the conflict rate of a prediction, given a set of restrictions. +[combine_predictions](#combine_predictions): +> Combine the predictions of a group of instances to keep the restrictions. + + +""" + +import numpy as np +from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator +from scipy.optimize import linear_sum_assignment +import warnings +import pandas as pd + +__all__ = ["conflict_rate", "combine_predictions", "WhoIsWhoClassifier"] + +class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): + + def __init__(self, base_estimator, method="hungarian", conflict_weighted=True): + """ + Who is Who Classifier + Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017). + Restricted set classification: Who is there?. Pattern Recognition, 63, 158-170. + + Parameters + ---------- + base_estimator : ClassifierMixin + The base estimator to be used for training. + method : str, optional + The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian" + conflict_weighted : bool, default=True + Whether to weighted the confusion rate by the number of instances with the same group. + """ + allowed_methods = ["greedy", "hungarian"] + self.base_estimator = base_estimator + self.method = method + if method not in allowed_methods: + raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}") + self.conflict_weighted = conflict_weighted + + + def fit(self, X, y, instance_group=None, **kwards): + """Fit the model according to the given training data. + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + y : array-like of shape (n_samples,) + The target values. + instance_group : array-like of shape (n_samples) + The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training. + Returns + ------- + self : object + Returns self. + """ + self.base_estimator = self.base_estimator.fit(X, y, **kwards) + self.classes_ = self.base_estimator.classes_ + if instance_group is not None: + self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted) + else: + self.conflict_in_train = None + return self + + def conflict_rate(self, X, instance_group): + """Calculate the conflict rate of the model. + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + instance_group : array-like of shape (n_samples) + The group. Two instances with the same label are not allowed to be in the same group. + Returns + ------- + float + The conflict rate. + """ + y_pred = self.base_estimator.predict(X) + return conflict_rate(y_pred, instance_group, self.conflict_weighted) + + def predict(self, X, instance_group): + """Predict class for X. + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + **kwards : array-like of shape (n_samples) + The group. Two instances with the same label are not allowed to be in the same group. + Returns + ------- + array-like of shape (n_samples, n_classes) + The class probabilities of the input samples. + """ + + y_prob = self.predict_proba(X) + + y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method) + + return self.classes_.take(y_predicted) + + + def predict_proba(self, X): + """Predict class probabilities for X. + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + The input samples. + Returns + ------- + array-like of shape (n_samples, n_classes) + The class probabilities of the input samples. + """ + return self.base_estimator.predict_proba(X) + + +def conflict_rate(y_pred, restrictions, weighted=True): + """ + Computes the conflict rate of a prediction, given a set of restrictions. + Parameters + ---------- + y_pred : array-like of shape (n_samples,) + Predicted target values. + restrictions : array-like of shape (n_samples,) + Restrictions for each sample. If two samples have the same restriction, they cannot have the same y. + weighted : bool, default=True + Whether to weighted the confusion rate by the number of instances with the same group. + Returns + ------- + conflict rate : float + The conflict rate. + """ + + # Check that y_pred and restrictions have the same length + if len(y_pred) != len(restrictions): + raise ValueError("y_pred and restrictions must have the same length.") + + restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions}) + + conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()}) + if weighted: + return conflicted.sum().y_pred / len(y_pred) + else: + rcount = restricted_df.groupby('restrictions').count() + return (conflicted.y_pred / rcount.y_pred).sum() + +def combine_predictions(y_probas, instance_group, class_number, method="hungarian"): + y_predicted = [] + for group in np.unique(instance_group): + + mask = instance_group == group + probas_matrix = y_probas[mask] + + + preds = list(np.argmax(probas_matrix, axis=1)) + + if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number: + y_predicted.extend(preds) + if probas_matrix.shape[0] > class_number: + warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning) + continue + + if method == "greedy": + y = _greedy(probas_matrix) + elif method == "hungarian": + y = _hungarian(probas_matrix) + + y_predicted.extend(y) + return y_predicted + +def _greedy(probas_matrix): + + probas = probas_matrix.reshape(probas_matrix.size,) + order = probas.argsort()[::-1] + + y_pred_group = [None for i in range(probas_matrix.shape[0])] + + instance_to_predict = {i for i in range(probas_matrix.shape[0])} + class_predicted = set() + for item in order: + class_ = item % probas_matrix.shape[0] + instance = item // probas_matrix.shape[0] + if instance in instance_to_predict and class_ not in class_predicted: + y_pred_group[instance] = class_ + instance_to_predict.remove(instance) + class_predicted.add(class_) + + return y_pred_group + + +def _hungarian(probas_matrix): + + costs = np.log(probas_matrix) + costs[costs == -np.inf] = 0 # if proba is 0, then the cost is 0 + _, col_ind = linear_sum_assignment(costs, maximize=True) + col_ind = list(col_ind) + + return col_ind \ No newline at end of file diff --git a/sslearn/wrapper/_co.py b/sslearn/wrapper/_co.py index 9340378..8af9492 100644 --- a/sslearn/wrapper/_co.py +++ b/sslearn/wrapper/_co.py @@ -109,8 +109,6 @@ def score(self, X, y, sample_weight=None): score : float Mean accuracy of ``self.predict(X)`` w.r.t. `y`. """ - from .metrics import accuracy_score - return accuracy_score(y, self.predict(X), sample_weight=sample_weight) diff --git a/sslearn/wrapper/_self.py b/sslearn/wrapper/_self.py index e18799d..23c00f0 100644 --- a/sslearn/wrapper/_self.py +++ b/sslearn/wrapper/_self.py @@ -1,11 +1,12 @@ import numpy as np import pandas as pd from scipy.stats import norm -from sklearn.base import BaseEstimator, ClassifierMixin +from sklearn.base import BaseEstimator, MetaEstimatorMixin from sklearn.base import clone as skclone from sklearn.neighbors import KNeighborsClassifier, kneighbors_graph from sklearn.semi_supervised import SelfTrainingClassifier from sklearn.utils import check_random_state, resample +from sklearn.metrics import accuracy_score from sslearn.utils import calculate_prior_probability, check_classifier @@ -124,7 +125,7 @@ def fit(self, X, y): return super().fit(X, y_adapted) -class Setred(ClassifierMixin, BaseEstimator): +class Setred(BaseEstimator, MetaEstimatorMixin): """ **Self-training with Editing.** ---------------------------- @@ -365,3 +366,29 @@ def predict_proba(self, X, **kwards): The predicted classes """ return self._base_estimator.predict_proba(X, **kwards) + + def score(self, X, y, sample_weight=None): + """ + Return the mean accuracy on the given test data and labels. + + In multi-label classification, this is the subset accuracy + which is a harsh metric since you require for each sample that + each label set be correctly predicted. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Test samples. + + y : array-like of shape (n_samples,) or (n_samples, n_outputs) + True labels for `X`. + + sample_weight : array-like of shape (n_samples,), default=None + Sample weights. + + Returns + ------- + score : float + Mean accuracy of ``self.predict(X)`` w.r.t. `y`. + """ + return accuracy_score(y, self.predict(X), sample_weight=sample_weight) diff --git a/test/test_model_selection.py b/test/test_model_selection.py index 3ed2cb6..5a1b30f 100644 --- a/test/test_model_selection.py +++ b/test/test_model_selection.py @@ -1,6 +1,7 @@ import os import sys import numpy as np +import pandas as pd import pytest sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..")) @@ -8,6 +9,12 @@ from sslearn.model_selection import (artificial_ssl_dataset, StratifiedKFoldSS) from sklearn.datasets import load_iris +def test_artificial_ssl_dataset_with_pandas(): + X, y = load_iris(return_X_y=True) + X, y, X_unlabel, true_label = artificial_ssl_dataset(pd.DataFrame(X), pd.Series(y), label_rate=0.1) + assert X_unlabel.shape[0] == true_label.shape[0] + assert X_unlabel.shape[0]/X.shape[0] == pytest.approx(0.9) + def test_artificial_ssl_dataset(): X, y = load_iris(return_X_y=True) X, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1) From 42babeb3fb0fa52ddda2568d7610f0f5ea6b9a97 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Mon, 20 May 2024 12:28:49 +0200 Subject: [PATCH 2/3] Repaired bug in --- sslearn/model_selection/_split.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/sslearn/model_selection/_split.py b/sslearn/model_selection/_split.py index 0e5cd26..731caa2 100644 --- a/sslearn/model_selection/_split.py +++ b/sslearn/model_selection/_split.py @@ -138,6 +138,8 @@ def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimu if force_minimum is not None: label = np.concatenate((selected, label)) + y_unlabel_original = y[unlabel] + # Create the label and unlabel sets X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\ X[unlabel], np.array([-1] * len(unlabel)) @@ -151,9 +153,9 @@ def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimu y = pd.Series(y) if indexes: - return X, y, X_unlabel, y_unlabel, label, unlabel + return X, y, X_unlabel, y_unlabel_original, label, unlabel - return X, y, X_unlabel, y_unlabel + return X, y, X_unlabel, y_unlabel_original """ From 0c8698e3121e4dfbfdf56a1346bfdc765473fe7c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Mon, 20 May 2024 12:57:45 +0200 Subject: [PATCH 3/3] Preparing for version 1.0.5.1 --- CHANGELOG.md | 6 + docs/search.js | 2 +- docs/sslearn.html | 3 +- docs/sslearn/model_selection.html | 438 +- ... en conflicto de CROSS-PC 2024-05-14).html | 985 +++ docs/sslearn/wrapper.html | 5859 +++++++++-------- sitemap.xml | 1 + sslearn/__init__.py | 2 +- 8 files changed, 4198 insertions(+), 3098 deletions(-) create mode 100644 docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html diff --git a/CHANGELOG.md b/CHANGELOG.md index 63b9fc9..821e385 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,12 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [1.0.5.1] - 2024-05-20 + +### Fixed + +- Fixed bugs in `artificial_ssl_dataset`, now support again pandas DataFrame and y_unlabeled returns the right values + ## [1.0.5] - 2024-05-08 ### Added diff --git a/docs/search.js b/docs/search.js index 2cb5448..fdb9f22 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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e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oSemi-Supervised Learning Library (sslearn)\n\n

\n

\n\n

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

\n\n

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

\n\n

Installation

\n\n

Dependencies

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

pip installation

\n\n

It can be installed using Pypi:

\n\n
pip install sslearn\n
\n\n

Code example

\n\n
\n
from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
\n
\n\n

Citing

\n\n
\n
@software{garrido2024sslearn,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  doi          = {10.5281/zenodo.7565221},\n}\n
\n
\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

\n\n

Functions

\n\n

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

\n\n

Classes

\n\n

FakedProbaClassifier:

\n\n
\n

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

\n
\n\n

OneVsRestSSLClassifier:

\n\n
\n

Adapted OneVsRestClassifier for SSL datasets

\n
\n"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

Check and divide dataset between labeled and unlabeled data.

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

Fake predict_proba method for classifiers that do not have it. \nWhen predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.

\n\n
Examples
\n\n
\n
from sklearn.svm import SVC\n# SVC does not have predict_proba method\n\nfrom sslearn.base import FakedProbaClassifier\nfaked_svc = FakedProbaClassifier(SVC())\nfaked_svc.fit(X, y)\nfaked_svc.predict_proba(X) # One hot encoding probabilities\n
\n
\n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

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

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

Fit a FakedProbaClassifier.

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

Predict the classes of X.

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

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

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

Adapted OneVsRestClassifier for SSL datasets

\n\n

Prevent use unlabeled data as a independent class in the classifier.

\n\n

For more information of OvR classifier, see the documentation of OneVsRestClassifier.

\n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

Adapted OneVsRestClassifier for SSL datasets

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

Fit underlying estimators.

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

Predict multi-class targets using underlying estimators.

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

Probability estimates.

\n\n

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

\n\n

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

\n\n

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

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

Summary of module sslearn.datasets:

\n\n

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

\n\n

Functions

\n\n
    \n
  1. read_csv : Load a dataset from a CSV file.
    2. \n
  2. read_keel : Load a dataset from a KEEL file.
    3. \n
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. \n
  4. save_keel : Save a dataset in KEEL format.
    5. \n
\n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

Read a .csv file

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

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

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

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

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

Save a dataset in the KEEL format

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

Summary of module sslearn.model_selection:

\n\n

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

\n\n

Functions

\n\n

artificial_ssl_dataset:

\n\n
\n

Generate an artificial semi-supervised learning dataset.

\n
\n\n

Classes

\n\n

StratifiedKFoldSS:

\n\n
\n

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

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

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

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

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

\n\n

Provides label and unlabel indices for each split. Using the StratifiedKFold method from sklearn.\nThe test set is the labeled set and the train set is the unlabeled set.

\n"}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"fullname": "sslearn.model_selection.StratifiedKFoldSS.__init__", "modulename": "sslearn.model_selection", "qualname": "StratifiedKFoldSS.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • n_splits (int, default=5):\nNumber of folds. Must be at least 2.
    • \n
  • shuffle (bool, default=False):\nWhether to shuffle each class's samples before splitting into batches.
    • \n
  • random_state (int or RandomState instance, default=None):\nWhen shuffle is True, random_state affects the ordering of the indices.
    • \n
\n", "signature": "(n_splits=5, shuffle=False, random_state=None)"}, "sslearn.model_selection.StratifiedKFoldSS.split": {"fullname": "sslearn.model_selection.StratifiedKFoldSS.split", "modulename": "sslearn.model_selection", "qualname": "StratifiedKFoldSS.split", "kind": "function", "doc": "

Generate a artificial dataset based on StratifiedKFold method

\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
\n\n
Yields
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • label (ndarray):\nThe training set indices for split mark as labeled.
    • \n
  • unlabel (ndarray):\nThe training set indices for split mark as unlabeled.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

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

\n\n

Classes

\n\n

WhoIsWhoClassifier:

\n\n
\n

Who is Who Classifier

\n
\n\n

Functions

\n\n

conflict_rate:

\n\n
\n

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

\n
\n\n

combine_predictions:

\n\n
\n

Combine the predictions of a group of instances to keep the restrictions.

\n
\n\n

feature_fusion:

\n\n
\n

Restricted Set Classification for the instances with pairwise constraints. Combine all instances that have the must-link constraint with the average of their features.

\n
\n\n

probability_fusion:

\n\n
\n

Restricted Set Classification for the instances with pairwise constraints. The class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint.

\n
\n"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

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

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

Combine the predictions of a group of instances to keep the restrictions.

\n\n
Parameters
\n\n
    \n
  • y_probas (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the 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
  • class_number (int):\nThe number of classes.
    • \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
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples,): The predicted labels.
    • \n
\n", "signature": "(y_probas, instance_group, class_number, method='hungarian'):", "funcdef": "def"}, "sslearn.restricted.feature_fusion": {"fullname": "sslearn.restricted.feature_fusion", "modulename": "sslearn.restricted", "qualname": "feature_fusion", "kind": "function", "doc": "

Restricted Set Classification for the instances with pairwise constraints. \nCombine all instances that have the must-link constraint with the average of their features.

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.

    • \n
  • must_link : dict of {int (list of int}):\nDictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
    • \n
  • cannot_link : dict of {int (list of int}):\nDictionary with the cannot links, where the value is a list of instances that cannot have the same label.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n\n
Examples
\n\n
\n
from sslearn.restricted import feature_fusion\nfrom sklearn.bayes import GaussianNB\nimport pandas as pd\n\ndataset = pd.read_csv("dataset.csv")\n\nmust_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')\n# must_link = {0: [0, 2], 1: [1, 3]} -> \n# instances 0 and 2 must have the same label, \n# and instances 1 and 3 must have the same label\n\ncannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')\n# cannot_link = {0: [0, 1], 1: [2, 3]} ->\n# instances 0 and 1 cannot have the same label, \n# and instances 2 and 3 cannot have the same label\n\nX, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values\nX_label = X[y != y.dtype.type(-1)]\ny_label = y[y != y.dtype.type(-1)]\nX_unlabel = X[y == y.dtype.type(-1)]\n\nclassifier = GaussianNB()\nclassifier.fit(X_label, y_label)\n\ny_pred = feature_fusion(classifier, X_unlabel, must_link, cannot_link)\n
\n
\n\n
References
\n\n

L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-P\u00e9rez, S.L. Hennessey, J.J. Rodr\u00edguez (2024).
\nSemi-supervised classification with pairwise constraints: A case study on animal identification from video.
\nInformation Fusion,
\n104, 102188, 10.1016/j.inffus.2023.102188

\n", "signature": "(classifier, X, must_link, cannot_link):", "funcdef": "def"}, "sslearn.restricted.probability_fusion": {"fullname": "sslearn.restricted.probability_fusion", "modulename": "sslearn.restricted", "qualname": "probability_fusion", "kind": "function", "doc": "

Restricted Set Classification for the instances with pairwise constraints. \nThe class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint.

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.

    • \n
  • must_link : dict of {int (list of int}):\nDictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
    • \n
  • cannot_link : dict of {int (list of int}):\nDictionary with the cannot links, where the value is a list of instances that cannot have the same label.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n\n
Examples
\n\n
\n
from sslearn.restricted import feature_fusion\nfrom sklearn.bayes import GaussianNB\nimport pandas as pd\n\ndataset = pd.read_csv("dataset.csv")\n\nmust_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')\n# must_link = {0: [0, 2], 1: [1, 3]} -> \n# instances 0 and 2 must have the same label, \n# and instances 1 and 3 must have the same label\n\ncannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')\n# cannot_link = {0: [0, 1], 1: [2, 3]} ->\n# instances 0 and 1 cannot have the same label, \n# and instances 2 and 3 cannot have the same label\n\nX, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values\nX_label = X[y != y.dtype.type(-1)]\ny_label = y[y != y.dtype.type(-1)]\nX_unlabel = X[y == y.dtype.type(-1)]\n\nclassifier = GaussianNB()\nclassifier.fit(X_label, y_label)\n\ny_pred = probability_fusion(classifier, X_unlabel, must_link, cannot_link)\n
\n
\n\n
References
\n\n

L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-P\u00e9rez, S.L. Hennessey, J.J. Rodr\u00edguez (2024).
\nSemi-supervised classification with pairwise constraints: A case study on animal identification from video.
\nInformation Fusion,
\n104, 102188, 10.1016/j.inffus.2023.102188

\n", "signature": "(classifier, X, must_link, cannot_link):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

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

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

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

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

Fit the model according to the given training data.

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

Calculate the conflict rate of the model.

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

Predict class for X.

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

Predict class probabilities for X.

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

Summary of module sslearn.subview:

\n\n

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

\n\n

Classes

\n\n

SubViewClassifier:

\n\n
\n

Train a sub-view classifier.\n SubViewRegressor:\n Train a sub-view regressor.

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

A classifier that uses a subview of the data.

\n\n
Example
\n\n
\n
from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
\n
\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities using the base estimator.

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

A classifier that uses a subview of the data.

\n\n
Example
\n\n
\n
from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
\n
\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

Predict using the base estimator.

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

Some utility functions

\n\n

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

\n\n

Functions

\n\n

safe_division:

\n\n
\n

Safely divide two numbers preventing division by zero.\n confidence_interval:\n Calculate the confidence interval of the predictions.\n choice_with_proportion: \n Choice the best predictions according to the proportion of each class.\n calculate_prior_probability:\n Calculate the priori probability of each label.\n mode:\n Calculate the mode of a list of values.\n check_n_jobs:\n Check n_jobs parameter according to the scikit-learn convention.\n check_classifier:\n Check if the classifier is a ClassifierMixin or a list of ClassifierMixin.

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

Safely divide two numbers preventing division by zero

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

Calculate the confidence interval of the predictions

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

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

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

Calculate the priori probability of each label

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

Calculate the mode of a list of values

\n\n
Parameters
\n\n
    \n
  • y (array-like of shape (n_samples, n_estimators)):\narray of values
    • \n
\n\n
Returns
\n\n
    \n
  • mode (array-like of shape (n_samples,)):\narray of mode of each label
    • \n
  • count (array-like of shape (n_samples,)):\narray of count of the mode of each label
    • \n
\n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

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

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

Summary of module sslearn.wrapper:

\n\n

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

\n\n

Self-Training Algorithms

\n\n
    \n
  • SelfTraining: \nSelf-training algorithm.
    • \n
  • Setred:\nSelf-training with redundancy reduction.
    • \n
\n\n

Co-Training Algorithms

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

Self Training Classifier with data loader compatible.

\n\n

Is the same SelfTrainingClassifier from sklearn but with sslearn data loader compatible.\nFor more information, see the sklearn documentation.

\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import SelfTraining\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = SelfTraining()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

David Yarowsky. (1995).
\nUnsupervised word sense disambiguation rivaling supervised methods.
\nIn Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95).
\nAssociation for Computational Linguistics,
\nStroudsburg, PA, USA, 189-196.
\n10.3115/981658.981684

\n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

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

\n\n

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

\n\n

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

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

Fits this SelfTrainingClassifier to a dataset.

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

Self-training with Editing.

\n\n

Create a SETRED classifier. It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.\nThe main process are:

\n\n
    \n
  1. Train a classifier with the labeled data.
    2. \n
  2. Create a pool of unlabeled data and select the most confident predictions.
    3. \n
  3. Repeat until the maximum number of iterations is reached:\na. Select the most confident predictions from the unlabeled data.\nb. Calculate the neighborhood graph of the labeled data and the selected instances from the unlabeled data.\nc. Calculate the significance level of the selected instances.\nd. Reject the instances that are not significant according their position in the neighborhood graph.\ne. Add the selected instances to the labeled data and retrains the classifier.\nf. Add new instances to the pool of unlabeled data.
    4. \n
  4. Return the classifier trained with the labeled data.
    5. \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import Setred\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = Setred()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Li, Ming, and Zhi-Hua Zhou. (2005)
\nSETRED: Self-training with editing,
\nin Advances in Knowledge Discovery and Data Mining.
\nPacific-Asia Conference on Knowledge Discovery and Data Mining
\nLNAI 3518, Springer, Berlin, Heidelberg,
\n10.1007/11430919_71

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the cosine metric uses cosine_distances., by default euclidean
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.05
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

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

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

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

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

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

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

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

\n\n

The main process is:

\n\n
    \n
  1. Train each classifier with the labeled instances and their respective view.
    2. \n
  2. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances that have the same prediction and the predictions are above the threshold.
      3. \n
    3. Label the instances with the highest probability, keeping the balance of the classes.
      4. \n
    4. Retrain the classifier with the new instances.
      5. \n
    3. \n
  3. Combine the probabilities of each classifier.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.wrapper import CoTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTraining(DecisionTreeClassifier())\nX1 = X[:, [0, 1]]\nX2 = X[:, [2, 3]]\ncotraining.fit(X1, y, X2) \n# or\ncotraining.fit(X, y, features=[[0, 1], [2, 3]])\n# or\ncotraining = CoTraining(DecisionTreeClassifier(), force_second_view=False)\ncotraining.fit(X, y)\n
\n
\n\n

References

\n\n

Avrim Blum and Tom Mitchell. (1998).
\nCombining labeled and unlabeled data with co-training
\nin Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).
\nAssociation for Computing Machinery, New York, NY, USA, 92-100.
\n10.1145/279943.279962

\n\n

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. (2011).
\nMulti-Class Co-Training Learning for Object and Scene Recognition,
\npp. 67-70 in. Nara, Japan.
\nhttp://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • \n
  • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • \n
  • max_iterations (int, optional):\nThe number of iterations, by default 30
    • \n
  • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • \n
  • threshold (float, optional):\nThe threshold for label instances, by default 0.5
    • \n
  • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

Build a CoTraining classifier from the training set.

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

Predict probability for each possible outcome.

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

Predict the classes of X.

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

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

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

Co-Training by Committee classifier.

\n\n

Create a committee trained by co-training based on the diversity of the classifiers

\n\n

The main process is:

\n\n
    \n
  1. Train a committee of classifiers.
    2. \n
  2. Create a pool of unlabeled instances.
    3. \n
  3. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances with the highest probability.
      3. \n
    3. Label the instances with the highest probability, keeping the balance of the classes but ensuring that at least n instances of each class are added.
      4. \n
    4. Retrain the classifier with the new instances.
      5. \n
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoTrainingByCommittee\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTrainingByCommittee()\ncotraining.fit(X, y)\ncotraining.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

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

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

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

\n\n
Parameters
\n\n
    \n
  • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
    • \n
  • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
    • \n
  • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

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

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

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

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

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

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

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

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

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

\n\n

A iterative algorithm that uses a ensemble of classifiers to label instances.\nThe main process is:

\n\n
    \n
  1. Train each classifier with the labeled instances.
    2. \n
  2. While any classifier is retrained:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Calculate the confidence interval for each classifier for define weights.
      3. \n
    3. Calculate the weighted vote for each instance.
      4. \n
    4. Calculate the majority vote for each instance.
      5. \n
    5. Select the instances to label if majority vote is the same as weighted vote.
      6. \n
    6. Select the instances to retrain the classifier, if only_mislabeled is False then select all instances, else select only mislabeled instances for each classifier.
      7. \n
    7. Retrain the classifier with the new instances if the error rate is lower than the previous iteration.
      8. \n
    3. \n
  3. Ignore the classifiers with confidence interval lower than 0.5.
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sslearn.wrapper import DemocraticCoLearning\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ndcl = DemocraticCoLearning(base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)])\ndcl.fit(X, y)\ndcl.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Y. Zhou and S. Goldman, (2004)
\nDemocratic co-learning,
\nin 16th IEEE International Conference on Tools with Artificial Intelligence,
\npp. 594-602, 10.1109/ICTAI.2004.48.

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
    • \n
  • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
    • \n
  • alpha (float, optional):\nconfidence level, by default 0.95
    • \n
  • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
Raises
\n\n
    \n
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • \n
\n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

Fit Democratic-Co classifier

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

Predict probability for each possible outcome.

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

Co-Training based on random subspaces

\n\n

Generate a set of random subspaces and train a classifier for each subspace.

\n\n

The main process is:

\n\n
    \n
  1. Generate a set of random subspaces.
    2. \n
  2. Train a classifier for each subspace.
    3. \n
  3. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set for each classifier.
      2. \n
    2. Calculate the average of the predictions.
      3. \n
    3. Select the instances with the highest probability.
      4. \n
    4. Label the instances with the highest probability, keeping the balance of the classes.
      5. \n
    5. Retrain the classifier with the new instances.
      6. \n
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import Rasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrasco = Rasco()\nrasco.fit(X, y)\nrasco.score(X_unlabel, y_unlabel) \n
\n
\n\n

References

\n\n

Wang, J., Luo, S. W., & Zeng, X. H. (2008).
\nA random subspace method for co-training,
\nin 2008 IEEE International Joint Conference on Neural Networks
\nIEEE World Congress on Computational Intelligence
\n(pp. 195-200). IEEE. 10.1109/IJCNN.2008.4633789

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

Co-Training based on random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

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

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

Co-Training based on relevant random subspaces

\n\n

Is a variation of sslearn.wrapper.Rasco that uses the mutual information of each feature to select the random subspaces.\nThe process of training is the same as Rasco.

\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import RelRasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrelrasco = RelRasco()\nrelrasco.fit(X, y)\nrelrasco.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Yaslan, Y., & Cataltepe, Z. (2010).
\nCo-training with relevant random subspaces.
\nNeurocomputing, 73(10-12), 1652-1661.
\n10.1016/j.neucom.2010.01.018

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

Co-Training with relevant random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

CoForest classifier. Random Forest co-training

\n\n

Ensemble method for CoTraining based on Random Forest.

\n\n

The main process is:

\n\n
    \n
  1. Train a committee of classifiers using bootstrap.
    2. \n
  2. While any base classifier is retrained:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances with the highest probability.
      3. \n
    3. Label the instances with the highest probability
      4. \n
    4. Add the instances to the labeled set only if the error is not bigger than the previous error.
      5. \n
    5. Retrain the classifier with the new instances.
      6. \n
    3. \n
  3. Combine the probabilities of each classifier.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoForest\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncoforest = CoForest()\ncoforest.fit(X, y)\ncoforest.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

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

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
    • \n
  • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
    • \n
  • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

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

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n

The main process is:

\n\n
    \n
  1. Generate three classifiers using bootstrapping.
    2. \n
  2. Iterate until convergence:\n
      \n
    1. Calculate the error between two hypotheses.
      2. \n
    2. If the error is less than the previous error, generate a dataset with the instances where both hypotheses agree.
      3. \n
    3. Retrain the classifiers with the new dataset and the original labeled dataset.
      4. \n
    3. \n
  3. Combine the predictions of the three classifiers.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

References

\n\n

Zhi-Hua Zhou and Ming Li,
\nTri-training: exploiting unlabeled data using three classifiers,
\nin IEEE Transactions on Knowledge and Data Engineering,
\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,
\n10.1109/TKDE.2005.186

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

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

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

TriTraining with Data Editing.

\n\n

It is a variation of the TriTraining, the main difference is that the instances are depurated in each iteration.\nIt means that the instances with their neighbors that have the same class are kept, the rest are removed.\nAt the end of the iterations, the instances are clustered and the class is assigned to the cluster centroid.

\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

References

\n\n

Deng C., Guo M.Z. (2006)
\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm,
\nin Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006.
\nLecture Notes in Computer Science, vol 4293. Springer, Berlin, Heidelberg.
\n10.1007/11925231_61

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

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

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

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

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}}, "docInfo": {"sslearn": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 500}, "sslearn.base": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.base.get_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 117}, "sslearn.base.FakedProbaClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 155}, "sslearn.base.FakedProbaClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 40}, "sslearn.base.FakedProbaClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 68}, "sslearn.base.FakedProbaClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 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Semi-Supervised Learning Library (sslearn)

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

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

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Installation

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Dependencies

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

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It can be installed using Pypi:

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pip install sslearn\n
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Code example

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from sslearn.wrapper import TriTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sklearn.datasets import load_iris\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, true_label = artificial_ssl_dataset(X, y, label_rate=0.1)\n\nmodel = TriTraining().fit(X, y)\nmodel.score(X_unlabel, true_label)\n
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Citing

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@software{garrido2024sslearn,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  doi          = {10.5281/zenodo.7565221},\n}\n
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\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

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Functions

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

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Classes

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FakedProbaClassifier:

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

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OneVsRestSSLClassifier:

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Adapted OneVsRestClassifier for SSL datasets

\n
\n"}, "sslearn.base.get_dataset": {"fullname": "sslearn.base.get_dataset", "modulename": "sslearn.base", "qualname": "get_dataset", "kind": "function", "doc": "

Check and divide dataset between labeled and unlabeled data.

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

Fake predict_proba method for classifiers that do not have it. \nWhen predict_proba is called, it will use one hot encoding to fake the probabilities if base_estimator does not have predict_proba method.

\n\n
Examples
\n\n
\n
from sklearn.svm import SVC\n# SVC does not have predict_proba method\n\nfrom sslearn.base import FakedProbaClassifier\nfaked_svc = FakedProbaClassifier(SVC())\nfaked_svc.fit(X, y)\nfaked_svc.predict_proba(X) # One hot encoding probabilities\n
\n
\n", "bases": "sklearn.base.MetaEstimatorMixin, sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator"}, "sslearn.base.FakedProbaClassifier.__init__": {"fullname": "sslearn.base.FakedProbaClassifier.__init__", "modulename": "sslearn.base", "qualname": "FakedProbaClassifier.__init__", "kind": "function", "doc": "

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

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

Fit a FakedProbaClassifier.

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

Predict the classes of X.

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

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

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

Adapted OneVsRestClassifier for SSL datasets

\n\n

Prevent use unlabeled data as a independent class in the classifier.

\n\n

For more information of OvR classifier, see the documentation of OneVsRestClassifier.

\n", "bases": "sklearn.multiclass.OneVsRestClassifier"}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"fullname": "sslearn.base.OneVsRestSSLClassifier.__init__", "modulename": "sslearn.base", "qualname": "OneVsRestSSLClassifier.__init__", "kind": "function", "doc": "

Adapted OneVsRestClassifier for SSL datasets

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

Fit underlying estimators.

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

Predict multi-class targets using underlying estimators.

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

Probability estimates.

\n\n

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

\n\n

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

\n\n

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

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

Summary of module sslearn.datasets:

\n\n

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

\n\n

Functions

\n\n
    \n
  1. read_csv : Load a dataset from a CSV file.
    2. \n
  2. read_keel : Load a dataset from a KEEL file.
    3. \n
  3. secure_dataset : Secure the dataset by converting it into a secure format.
    4. \n
  4. save_keel : Save a dataset in KEEL format.
    5. \n
\n"}, "sslearn.datasets.read_csv": {"fullname": "sslearn.datasets.read_csv", "modulename": "sslearn.datasets", "qualname": "read_csv", "kind": "function", "doc": "

Read a .csv file

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

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

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

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

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

Save a dataset in the KEEL format

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

Summary of module sslearn.model_selection:

\n\n

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

\n\n

Functions

\n\n

artificial_ssl_dataset:

\n\n
\n

Generate an artificial semi-supervised learning dataset.

\n
\n\n

Classes

\n\n

StratifiedKFoldSS:

\n\n
\n

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

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

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

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

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

\n\n

Provides label and unlabel indices for each split. Using the StratifiedKFold method from sklearn.\nThe test set is the labeled set and the train set is the unlabeled set.

\n"}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"fullname": "sslearn.model_selection.StratifiedKFoldSS.__init__", "modulename": "sslearn.model_selection", "qualname": "StratifiedKFoldSS.__init__", "kind": "function", "doc": "
Parameters
\n\n
    \n
  • n_splits (int, default=5):\nNumber of folds. Must be at least 2.
    • \n
  • shuffle (bool, default=False):\nWhether to shuffle each class's samples before splitting into batches.
    • \n
  • random_state (int or RandomState instance, default=None):\nWhen shuffle is True, random_state affects the ordering of the indices.
    • \n
\n", "signature": "(n_splits=5, shuffle=False, random_state=None)"}, "sslearn.model_selection.StratifiedKFoldSS.split": {"fullname": "sslearn.model_selection.StratifiedKFoldSS.split", "modulename": "sslearn.model_selection", "qualname": "StratifiedKFoldSS.split", "kind": "function", "doc": "

Generate a artificial dataset based on StratifiedKFold method

\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
\n\n
Yields
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • label (ndarray):\nThe training set indices for split mark as labeled.
    • \n
  • unlabel (ndarray):\nThe training set indices for split mark as unlabeled.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

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

\n\n

Classes

\n\n

WhoIsWhoClassifier:

\n\n
\n

Who is Who Classifier

\n
\n\n

Functions

\n\n

conflict_rate:

\n\n
\n

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

\n
\n"}, "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).conflict_rate": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).conflict_rate", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "qualname": "conflict_rate", "kind": "function", "doc": "

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

\n\n
Parameters
\n\n
    \n
  • y_pred (array-like of shape (n_samples,)):\nPredicted target values.
    • \n
  • restrictions (array-like of shape (n_samples,)):\nRestrictions for each sample. If two samples have the same restriction, they cannot have the same y.
    • \n
  • weighted (bool, default=True):\nWhether to weighted the confusion rate by the number of instances with the same group.
    • \n
\n\n
Returns
\n\n
    \n
  • conflict rate (float):\nThe conflict rate.
    • \n
\n", "signature": "(y_pred, restrictions, weighted=True):", "funcdef": "def"}, "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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 (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.__init__": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.__init__", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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 (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.fit": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.fit", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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 (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.conflict_rate": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.conflict_rate", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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 (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.predict": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.predict", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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 (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.predict_proba": {"fullname": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14).WhoIsWhoClassifier.predict_proba", "modulename": "sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)", "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": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

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

\n\n

Classes

\n\n

WhoIsWhoClassifier:

\n\n
\n

Who is Who Classifier

\n
\n\n

Functions

\n\n

conflict_rate:

\n\n
\n

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

\n
\n\n

combine_predictions:

\n\n
\n

Combine the predictions of a group of instances to keep the restrictions.

\n
\n\n

feature_fusion:

\n\n
\n

Restricted Set Classification for the instances with pairwise constraints. Combine all instances that have the must-link constraint with the average of their features.

\n
\n\n

probability_fusion:

\n\n
\n

Restricted Set Classification for the instances with pairwise constraints. The class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint.

\n
\n"}, "sslearn.restricted.conflict_rate": {"fullname": "sslearn.restricted.conflict_rate", "modulename": "sslearn.restricted", "qualname": "conflict_rate", "kind": "function", "doc": "

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

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

Combine the predictions of a group of instances to keep the restrictions.

\n\n
Parameters
\n\n
    \n
  • y_probas (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the 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
  • class_number (int):\nThe number of classes.
    • \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
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples,): The predicted labels.
    • \n
\n", "signature": "(y_probas, instance_group, class_number, method='hungarian'):", "funcdef": "def"}, "sslearn.restricted.feature_fusion": {"fullname": "sslearn.restricted.feature_fusion", "modulename": "sslearn.restricted", "qualname": "feature_fusion", "kind": "function", "doc": "

Restricted Set Classification for the instances with pairwise constraints. \nCombine all instances that have the must-link constraint with the average of their features.

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.

    • \n
  • must_link : dict of {int (list of int}):\nDictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
    • \n
  • cannot_link : dict of {int (list of int}):\nDictionary with the cannot links, where the value is a list of instances that cannot have the same label.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n\n
Examples
\n\n
\n
from sslearn.restricted import feature_fusion\nfrom sklearn.bayes import GaussianNB\nimport pandas as pd\n\ndataset = pd.read_csv("dataset.csv")\n\nmust_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')\n# must_link = {0: [0, 2], 1: [1, 3]} -> \n# instances 0 and 2 must have the same label, \n# and instances 1 and 3 must have the same label\n\ncannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')\n# cannot_link = {0: [0, 1], 1: [2, 3]} ->\n# instances 0 and 1 cannot have the same label, \n# and instances 2 and 3 cannot have the same label\n\nX, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values\nX_label = X[y != y.dtype.type(-1)]\ny_label = y[y != y.dtype.type(-1)]\nX_unlabel = X[y == y.dtype.type(-1)]\n\nclassifier = GaussianNB()\nclassifier.fit(X_label, y_label)\n\ny_pred = feature_fusion(classifier, X_unlabel, must_link, cannot_link)\n
\n
\n\n
References
\n\n

L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-P\u00e9rez, S.L. Hennessey, J.J. Rodr\u00edguez (2024).
\nSemi-supervised classification with pairwise constraints: A case study on animal identification from video.
\nInformation Fusion,
\n104, 102188, 10.1016/j.inffus.2023.102188

\n", "signature": "(classifier, X, must_link, cannot_link):", "funcdef": "def"}, "sslearn.restricted.probability_fusion": {"fullname": "sslearn.restricted.probability_fusion", "modulename": "sslearn.restricted", "qualname": "probability_fusion", "kind": "function", "doc": "

Restricted Set Classification for the instances with pairwise constraints. \nThe class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint.

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.

    • \n
  • must_link : dict of {int (list of int}):\nDictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
    • \n
  • cannot_link : dict of {int (list of int}):\nDictionary with the cannot links, where the value is a list of instances that cannot have the same label.
    • \n
\n\n
Returns
\n\n
    \n
  • y (ndarray of shape (n_samples,)):\nArray with predicted labels.
    • \n
\n\n
Examples
\n\n
\n
from sslearn.restricted import feature_fusion\nfrom sklearn.bayes import GaussianNB\nimport pandas as pd\n\ndataset = pd.read_csv("dataset.csv")\n\nmust_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')\n# must_link = {0: [0, 2], 1: [1, 3]} -> \n# instances 0 and 2 must have the same label, \n# and instances 1 and 3 must have the same label\n\ncannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')\n# cannot_link = {0: [0, 1], 1: [2, 3]} ->\n# instances 0 and 1 cannot have the same label, \n# and instances 2 and 3 cannot have the same label\n\nX, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values\nX_label = X[y != y.dtype.type(-1)]\ny_label = y[y != y.dtype.type(-1)]\nX_unlabel = X[y == y.dtype.type(-1)]\n\nclassifier = GaussianNB()\nclassifier.fit(X_label, y_label)\n\ny_pred = probability_fusion(classifier, X_unlabel, must_link, cannot_link)\n
\n
\n\n
References
\n\n

L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-P\u00e9rez, S.L. Hennessey, J.J. Rodr\u00edguez (2024).
\nSemi-supervised classification with pairwise constraints: A case study on animal identification from video.
\nInformation Fusion,
\n104, 102188, 10.1016/j.inffus.2023.102188

\n", "signature": "(classifier, X, must_link, cannot_link):", "funcdef": "def"}, "sslearn.restricted.WhoIsWhoClassifier": {"fullname": "sslearn.restricted.WhoIsWhoClassifier", "modulename": "sslearn.restricted", "qualname": "WhoIsWhoClassifier", "kind": "class", "doc": "

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

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

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

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

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

Fit the model according to the given training data.

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

Calculate the conflict rate of the model.

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

Predict class for X.

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

Predict class probabilities for X.

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

Summary of module sslearn.subview:

\n\n

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

\n\n

Classes

\n\n

SubViewClassifier:

\n\n
\n

Train a sub-view classifier.\n SubViewRegressor:\n Train a sub-view regressor.

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

A classifier that uses a subview of the data.

\n\n
Example
\n\n
\n
from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
\n
\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.ClassifierMixin"}, "sslearn.subview.SubViewClassifier.predict_proba": {"fullname": "sslearn.subview.SubViewClassifier.predict_proba", "modulename": "sslearn.subview", "qualname": "SubViewClassifier.predict_proba", "kind": "function", "doc": "

Predict class probabilities using the base estimator.

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

A classifier that uses a subview of the data.

\n\n
Example
\n\n
\n
from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.subview import SubViewClassifier\n\n# Mode 'include' will include all columns that contain `string`\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal", mode="include")\nclf.fit(X, y)\n\n# Mode 'regex' will include all columns that match the regex\nclf = SubViewClassifier(DecisionTreeClassifier(), "sepal.*", mode="regex")\nclf.fit(X, y)\n\n# Mode 'index' will include the columns at the index, useful for numpy arrays\nclf = SubViewClassifier(DecisionTreeClassifier(), [0, 1], mode="index")\nclf.fit(X, y)\n
\n
\n", "bases": "sslearn.subview._subview.SubView, sklearn.base.RegressorMixin"}, "sslearn.subview.SubViewRegressor.predict": {"fullname": "sslearn.subview.SubViewRegressor.predict", "modulename": "sslearn.subview", "qualname": "SubViewRegressor.predict", "kind": "function", "doc": "

Predict using the base estimator.

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

Some utility functions

\n\n

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

\n\n

Functions

\n\n

safe_division:

\n\n
\n

Safely divide two numbers preventing division by zero.\n confidence_interval:\n Calculate the confidence interval of the predictions.\n choice_with_proportion: \n Choice the best predictions according to the proportion of each class.\n calculate_prior_probability:\n Calculate the priori probability of each label.\n mode:\n Calculate the mode of a list of values.\n check_n_jobs:\n Check n_jobs parameter according to the scikit-learn convention.\n check_classifier:\n Check if the classifier is a ClassifierMixin or a list of ClassifierMixin.

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

Safely divide two numbers preventing division by zero

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

Calculate the confidence interval of the predictions

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

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

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

Calculate the priori probability of each label

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

Calculate the mode of a list of values

\n\n
Parameters
\n\n
    \n
  • y (array-like of shape (n_samples, n_estimators)):\narray of values
    • \n
\n\n
Returns
\n\n
    \n
  • mode (array-like of shape (n_samples,)):\narray of mode of each label
    • \n
  • count (array-like of shape (n_samples,)):\narray of count of the mode of each label
    • \n
\n", "signature": "(y):", "funcdef": "def"}, "sslearn.utils.check_n_jobs": {"fullname": "sslearn.utils.check_n_jobs", "modulename": "sslearn.utils", "qualname": "check_n_jobs", "kind": "function", "doc": "

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

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

Summary of module sslearn.wrapper:

\n\n

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

\n\n

Self-Training Algorithms

\n\n
    \n
  • SelfTraining: \nSelf-training algorithm.
    • \n
  • Setred:\nSelf-training with redundancy reduction.
    • \n
\n\n

Co-Training Algorithms

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

Self Training Classifier with data loader compatible.

\n\n

Is the same SelfTrainingClassifier from sklearn but with sslearn data loader compatible.\nFor more information, see the sklearn documentation.

\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import SelfTraining\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = SelfTraining()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

David Yarowsky. (1995).
\nUnsupervised word sense disambiguation rivaling supervised methods.
\nIn Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95).
\nAssociation for Computational Linguistics,
\nStroudsburg, PA, USA, 189-196.
\n10.3115/981658.981684

\n", "bases": "sklearn.semi_supervised._self_training.SelfTrainingClassifier"}, "sslearn.wrapper.SelfTraining.__init__": {"fullname": "sslearn.wrapper.SelfTraining.__init__", "modulename": "sslearn.wrapper", "qualname": "SelfTraining.__init__", "kind": "function", "doc": "

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

\n\n

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

\n\n

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

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

Fits this SelfTrainingClassifier to a dataset.

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

Self-training with Editing.

\n\n

Create a SETRED classifier. It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.\nThe main process are:

\n\n
    \n
  1. Train a classifier with the labeled data.
    2. \n
  2. Create a pool of unlabeled data and select the most confident predictions.
    3. \n
  3. Repeat until the maximum number of iterations is reached:\na. Select the most confident predictions from the unlabeled data.\nb. Calculate the neighborhood graph of the labeled data and the selected instances from the unlabeled data.\nc. Calculate the significance level of the selected instances.\nd. Reject the instances that are not significant according their position in the neighborhood graph.\ne. Add the selected instances to the labeled data and retrains the classifier.\nf. Add new instances to the pool of unlabeled data.
    4. \n
  4. Return the classifier trained with the labeled data.
    5. \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.model_selection import artificial_ssl_dataset\nfrom sslearn.wrapper import Setred\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\n\nclf = Setred()\nclf.fit(X, y)\nclf.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Li, Ming, and Zhi-Hua Zhou. (2005)
\nSETRED: Self-training with editing,
\nin Advances in Knowledge Discovery and Data Mining.
\nPacific-Asia Conference on Knowledge Discovery and Data Mining
\nLNAI 3518, Springer, Berlin, Heidelberg,
\n10.1007/11430919_71

\n", "bases": "sklearn.base.BaseEstimator, sklearn.base.MetaEstimatorMixin"}, "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 KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the cosine metric uses cosine_distances., by default euclidean
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.05
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

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

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

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

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

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

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

Return the mean accuracy on the given test data and labels.

\n\n

In 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
\n\n
Returns
\n\n
    \n
  • score (float):\nMean accuracy of self.predict(X) w.r.t. y.
    • \n
\n", "signature": "(self, X, y, sample_weight=None):", "funcdef": "def"}, "sslearn.wrapper.CoTraining": {"fullname": "sslearn.wrapper.CoTraining", "modulename": "sslearn.wrapper", "qualname": "CoTraining", "kind": "class", "doc": "

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

\n\n

The main process is:

\n\n
    \n
  1. Train each classifier with the labeled instances and their respective view.
    2. \n
  2. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances that have the same prediction and the predictions are above the threshold.
      3. \n
    3. Label the instances with the highest probability, keeping the balance of the classes.
      4. \n
    4. Retrain the classifier with the new instances.
      5. \n
    3. \n
  3. Combine the probabilities of each classifier.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sslearn.wrapper import CoTraining\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTraining(DecisionTreeClassifier())\nX1 = X[:, [0, 1]]\nX2 = X[:, [2, 3]]\ncotraining.fit(X1, y, X2) \n# or\ncotraining.fit(X, y, features=[[0, 1], [2, 3]])\n# or\ncotraining = CoTraining(DecisionTreeClassifier(), force_second_view=False)\ncotraining.fit(X, y)\n
\n
\n\n

References

\n\n

Avrim Blum and Tom Mitchell. (1998).
\nCombining labeled and unlabeled data with co-training
\nin Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98).
\nAssociation for Computing Machinery, New York, NY, USA, 92-100.
\n10.1145/279943.279962

\n\n

Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. (2011).
\nMulti-Class Co-Training Learning for Object and Scene Recognition,
\npp. 67-70 in. Nara, Japan.
\nhttp://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
    • \n
  • second_base_estimator (ClassifierMixin, optional):\nThe classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
    • \n
  • max_iterations (int, optional):\nThe number of iterations, by default 30
    • \n
  • poolsize (int, optional):\nThe size of the pool of unlabeled samples from which the classifier can choose, by default 75
    • \n
  • threshold (float, optional):\nThe threshold for label instances, by default 0.5
    • \n
  • force_second_view (bool, optional):\nThe second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tsecond_base_estimator=None,\tmax_iterations=30,\tpoolsize=75,\tthreshold=0.5,\tforce_second_view=True,\trandom_state=None)"}, "sslearn.wrapper.CoTraining.fit": {"fullname": "sslearn.wrapper.CoTraining.fit", "modulename": "sslearn.wrapper", "qualname": "CoTraining.fit", "kind": "function", "doc": "

Build a CoTraining classifier from the training set.

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

Predict probability for each possible outcome.

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

Predict the classes of X.

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

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

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

Co-Training by Committee classifier.

\n\n

Create a committee trained by co-training based on the diversity of the classifiers

\n\n

The main process is:

\n\n
    \n
  1. Train a committee of classifiers.
    2. \n
  2. Create a pool of unlabeled instances.
    3. \n
  3. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances with the highest probability.
      3. \n
    3. Label the instances with the highest probability, keeping the balance of the classes but ensuring that at least n instances of each class are added.
      4. \n
    4. Retrain the classifier with the new instances.
      5. \n
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoTrainingByCommittee\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncotraining = CoTrainingByCommittee()\ncotraining.fit(X, y)\ncotraining.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

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

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

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

\n\n
Parameters
\n\n
    \n
  • ensemble_estimator (ClassifierMixin, optional):\nensemble method, works without a ensemble as\nself training with pool, by default BaggingClassifier().
    • \n
  • max_iterations (int, optional):\nnumber of iterations of training, -1 if no max iterations, by default 100
    • \n
  • poolsize (int, optional):\nmax number of unlabeled instances candidates to pseudolabel, by default 100
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tensemble_estimator=BaggingClassifier(),\tmax_iterations=100,\tpoolsize=100,\tmin_instances_for_class=3,\trandom_state=None)"}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"fullname": "sslearn.wrapper.CoTrainingByCommittee.fit", "modulename": "sslearn.wrapper", "qualname": "CoTrainingByCommittee.fit", "kind": "function", "doc": "

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

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

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

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

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

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

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

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

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

\n\n

A iterative algorithm that uses a ensemble of classifiers to label instances.\nThe main process is:

\n\n
    \n
  1. Train each classifier with the labeled instances.
    2. \n
  2. While any classifier is retrained:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Calculate the confidence interval for each classifier for define weights.
      3. \n
    3. Calculate the weighted vote for each instance.
      4. \n
    4. Calculate the majority vote for each instance.
      5. \n
    5. Select the instances to label if majority vote is the same as weighted vote.
      6. \n
    6. Select the instances to retrain the classifier, if only_mislabeled is False then select all instances, else select only mislabeled instances for each classifier.
      7. \n
    7. Retrain the classifier with the new instances if the error rate is lower than the previous iteration.
      8. \n
    3. \n
  3. Ignore the classifiers with confidence interval lower than 0.5.
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sslearn.wrapper import DemocraticCoLearning\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ndcl = DemocraticCoLearning(base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)])\ndcl.fit(X, y)\ndcl.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Y. Zhou and S. Goldman, (2004)
\nDemocratic co-learning,
\nin 16th IEEE International Conference on Tools with Artificial Intelligence,
\npp. 594-602, 10.1109/ICTAI.2004.48.

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator ({ClassifierMixin, list}, optional):\nAn estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nnumber of base_estimators to use. None if base_estimator is a list, by default None
    • \n
  • expand_only_mislabeled (bool, optional):\nexpand only mislabeled instances by itself, by default True
    • \n
  • alpha (float, optional):\nconfidence level, by default 0.95
    • \n
  • q_exp (int, optional):\nexponent for the estimation for error rate, by default 2
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n\n
Raises
\n\n
    \n
  • AttributeError: If n_estimators is None and base_estimator is not a list
    • \n
\n", "signature": "(\tbase_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)],\tn_estimators=None,\texpand_only_mislabeled=True,\talpha=0.95,\tq_exp=2,\trandom_state=None)"}, "sslearn.wrapper.DemocraticCoLearning.fit": {"fullname": "sslearn.wrapper.DemocraticCoLearning.fit", "modulename": "sslearn.wrapper", "qualname": "DemocraticCoLearning.fit", "kind": "function", "doc": "

Fit Democratic-Co classifier

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

Predict probability for each possible outcome.

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

Co-Training based on random subspaces

\n\n

Generate a set of random subspaces and train a classifier for each subspace.

\n\n

The main process is:

\n\n
    \n
  1. Generate a set of random subspaces.
    2. \n
  2. Train a classifier for each subspace.
    3. \n
  3. While max iterations is not reached or any instance is unlabeled:\n
      \n
    1. Predict the instances from the unlabeled set for each classifier.
      2. \n
    2. Calculate the average of the predictions.
      3. \n
    3. Select the instances with the highest probability.
      4. \n
    4. Label the instances with the highest probability, keeping the balance of the classes.
      5. \n
    5. Retrain the classifier with the new instances.
      6. \n
    4. \n
  4. Combine the probabilities of each classifier.
    5. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import Rasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrasco = Rasco()\nrasco.fit(X, y)\nrasco.score(X_unlabel, y_unlabel) \n
\n
\n\n

References

\n\n

Wang, J., Luo, S. W., & Zeng, X. H. (2008).
\nA random subspace method for co-training,
\nin 2008 IEEE International Joint Conference on Neural Networks
\nIEEE World Congress on Computational Intelligence
\n(pp. 195-200). IEEE. 10.1109/IJCNN.2008.4633789

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

Co-Training based on random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Rasco.fit": {"fullname": "sslearn.wrapper.Rasco.fit", "modulename": "sslearn.wrapper", "qualname": "Rasco.fit", "kind": "function", "doc": "

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

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

Co-Training based on relevant random subspaces

\n\n

Is a variation of sslearn.wrapper.Rasco that uses the mutual information of each feature to select the random subspaces.\nThe process of training is the same as Rasco.

\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import RelRasco\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\nrelrasco = RelRasco()\nrelrasco.fit(X, y)\nrelrasco.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

Yaslan, Y., & Cataltepe, Z. (2010).
\nCo-training with relevant random subspaces.
\nNeurocomputing, 73(10-12), 1652-1661.
\n10.1016/j.neucom.2010.01.018

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

Co-Training with relevant random subspaces

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0.\nIf is -1 then will be infinite iterations until U be empty, by default 10
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 30
    • \n
  • subspace_size (int, optional):\nThe number of features for each subspace. If it is None will be the half of the features size., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel. -1 means using all processors., by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tmax_iterations=10,\tn_estimators=30,\tsubspace_size=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.CoForest": {"fullname": "sslearn.wrapper.CoForest", "modulename": "sslearn.wrapper", "qualname": "CoForest", "kind": "class", "doc": "

CoForest classifier. Random Forest co-training

\n\n

Ensemble method for CoTraining based on Random Forest.

\n\n

The main process is:

\n\n
    \n
  1. Train a committee of classifiers using bootstrap.
    2. \n
  2. While any base classifier is retrained:\n
      \n
    1. Predict the instances from the unlabeled set.
      2. \n
    2. Select the instances with the highest probability.
      3. \n
    3. Label the instances with the highest probability
      4. \n
    4. Add the instances to the labeled set only if the error is not bigger than the previous error.
      5. \n
    5. Retrain the classifier with the new instances.
      6. \n
    3. \n
  3. Combine the probabilities of each classifier.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

Example

\n\n
\n
from sklearn.datasets import load_iris\nfrom sslearn.wrapper import CoForest\nfrom sslearn.model_selection import artificial_ssl_dataset\n\nX, y = load_iris(return_X_y=True)\nX, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)\ncoforest = CoForest()\ncoforest.fit(X, y)\ncoforest.score(X_unlabel, y_unlabel)\n
\n
\n\n

References

\n\n

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

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_estimators (int, optional):\nThe number of base estimators in the ensemble., by default 7
    • \n
  • threshold (float, optional):\nThe decision threshold. Should be in [0, 1)., by default 0.5
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict., by default None
    • \n
  • bootstrap (bool, optional):\nWhether bootstrap samples are used when building estimators., by default True
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • **kwards (dict, optional):\nAdditional parameters to be passed to base_estimator, by default None.
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_estimators=7,\tthreshold=0.75,\tbootstrap=True,\tn_jobs=None,\trandom_state=None,\tversion='1.0.3')"}, "sslearn.wrapper.CoForest.fit": {"fullname": "sslearn.wrapper.CoForest.fit", "modulename": "sslearn.wrapper", "qualname": "CoForest.fit", "kind": "function", "doc": "

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

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n

The main process is:

\n\n
    \n
  1. Generate three classifiers using bootstrapping.
    2. \n
  2. Iterate until convergence:\n
      \n
    1. Calculate the error between two hypotheses.
      2. \n
    2. If the error is less than the previous error, generate a dataset with the instances where both hypotheses agree.
      3. \n
    3. Retrain the classifiers with the new dataset and the original labeled dataset.
      4. \n
    3. \n
  3. Combine the predictions of the three classifiers.
    4. \n
\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

References

\n\n

Zhi-Hua Zhou and Ming Li,
\nTri-training: exploiting unlabeled data using three classifiers,
\nin IEEE Transactions on Knowledge and Data Engineering,
\nvol. 17, no. 11, pp. 1529-1541, Nov. 2005,
\n10.1109/TKDE.2005.186

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
    • \n
  • n_samples (int, optional):\nNumber of samples to generate.\nIf left to None this is automatically set to the first dimension of the arrays., by default None
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of jobs to run in parallel for both fit and predict.\nNone means 1 unless in a joblib.parallel_backend context.\n-1 means using all processors., by default None
    • \n
\n", "signature": "(\tbase_estimator=DecisionTreeClassifier(),\tn_samples=None,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.TriTraining.fit": {"fullname": "sslearn.wrapper.TriTraining.fit", "modulename": "sslearn.wrapper", "qualname": "TriTraining.fit", "kind": "function", "doc": "

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

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

TriTraining with Data Editing.

\n\n

It is a variation of the TriTraining, the main difference is that the instances are depurated in each iteration.\nIt means that the instances with their neighbors that have the same class are kept, the rest are removed.\nAt the end of the iterations, the instances are clustered and the class is assigned to the cluster centroid.

\n\n

Methods

\n\n
    \n
  • fit: Fit the model with the labeled instances.
    • \n
  • predict : Predict the class for each instance.
    • \n
  • predict_proba: Predict the probability for each class.
    • \n
  • score: Return the mean accuracy on the given test data and labels.
    • \n
\n\n

References

\n\n

Deng C., Guo M.Z. (2006)
\nTri-training and Data Editing Based Semi-supervised Clustering Algorithm,
\nin Gelbukh A., Reyes-Garcia C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006.
\nLecture Notes in Computer Science, vol 4293. Springer, Berlin, Heidelberg.
\n10.1007/11925231_61

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

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

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

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

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}}, "docInfo": {"sslearn": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 500}, "sslearn.base": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.base.get_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 117}, "sslearn.base.FakedProbaClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 155}, "sslearn.base.FakedProbaClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 40}, "sslearn.base.FakedProbaClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 68}, "sslearn.base.FakedProbaClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 59}, "sslearn.base.FakedProbaClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 78}, "sslearn.base.OneVsRestSSLClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 37}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 63}, "sslearn.base.OneVsRestSSLClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 80}, "sslearn.base.OneVsRestSSLClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 66}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 162}, "sslearn.datasets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 85}, "sslearn.datasets.read_csv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 53, "bases": 0, "doc": 134}, "sslearn.datasets.read_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 158}, "sslearn.datasets.secure_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 70}, "sslearn.datasets.save_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 97, "bases": 0, "doc": 163}, "sslearn.model_selection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.model_selection.artificial_ssl_dataset": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 329}, 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true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough. diff --git a/docs/sslearn.html b/docs/sslearn.html index ed83203..774ed5f 100644 --- a/docs/sslearn.html +++ b/docs/sslearn.html @@ -61,6 +61,7 @@

Submodules

  • base
  • datasets
  • model_selection
    • +
  • restricted (Copia en conflicto de CROSS-PC 2024-05-14)
  • restricted
  • subview
  • utils
    • @@ -162,7 +163,7 @@

    Citing

    10 __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models." 11 12 -13__version__='1.0.5' +13__version__='1.0.5.1' 14__AUTHOR__="José Luis Garrido-Labrador" # Author of the package 15__AUTHOR_EMAIL__="jlgarrido@ubu.es" # Author's email 16__URL__="https://pypi.org/project/sslearn/" diff --git a/docs/sslearn/model_selection.html b/docs/sslearn/model_selection.html index 4d7d2fb..48e0f9a 100644 --- a/docs/sslearn/model_selection.html +++ b/docs/sslearn/model_selection.html @@ -153,110 +153,126 @@

    Classes

    - 
     70def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimum=None, indexes=False, **kwards):
- 71    """Create an artificial Semi-supervised dataset from a supervised dataset.
- 72
- 73    Parameters
- 74    ----------
- 75    X : array-like of shape (n_samples, n_features)
- 76        Training data, where n_samples is the number of samples
- 77        and n_features is the number of features.
- 78    y : array-like of shape (n_samples,)
- 79        The target variable for supervised learning problems.
- 80    label_rate : float, optional
- 81        Proportion between labeled instances and unlabel instances, by default 0.1
- 82    random_state : int or RandomState, optional
- 83        Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
- 84    force_minimum: int, optional
- 85        Force a minimum of instances of each class, by default None
- 86    indexes: bool, optional
- 87        If True, return the indexes of the labeled and unlabeled instances, by default False
- 88    shuffle: bool, default=True
- 89        Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
- 90    stratify: array-like, default=None
- 91        If not None, data is split in a stratified fashion, using this as the class labels.
- 92
- 93    Returns
- 94    -------
- 95    X : ndarray
- 96        The feature set.
- 97    y : ndarray
- 98        The label set, -1 for unlabel instance.
- 99    X_unlabel: ndarray
-100        The feature set for each y mark as unlabel
-101    y_unlabel: ndarray
-102        The true label for each y in the same order.
-103    label: ndarray (optional)
-104        The training set indexes for split mark as labeled.
-105    unlabel: ndarray (optional)
-106        The training set indexes for split mark as unlabeled.
-107    """
-108    assert (label_rate > 0) and (label_rate < 1),\
-109        "Label rate must be in (0, 1)."
-110    assert "test_size" not in kwards and "train_size" not in kwards,\
-111        "Test size and train size are illegal parameters in this method."
-112
-113    indices = np.arange(len(y))
-114
-115    if force_minimum is not None:
-116        try:
-117            selected = __random_select_n_instances(y, force_minimum, random_state)
-118        except ValueError:
-119            raise ValueError("The number of instances of each class is less than force_minimum.")
-120
-121        # Remove selected instances from indices
-122        indices = np.delete(indices, selected, axis=0)    
+            
     71def artificial_ssl_dataset(X, y, label_rate=0.1, random_state=None, force_minimum=None, indexes=False, **kwards):
+ 72    """Create an artificial Semi-supervised dataset from a supervised dataset.
+ 73
+ 74    Parameters
+ 75    ----------
+ 76    X : array-like of shape (n_samples, n_features)
+ 77        Training data, where n_samples is the number of samples
+ 78        and n_features is the number of features.
+ 79    y : array-like of shape (n_samples,)
+ 80        The target variable for supervised learning problems.
+ 81    label_rate : float, optional
+ 82        Proportion between labeled instances and unlabel instances, by default 0.1
+ 83    random_state : int or RandomState, optional
+ 84        Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls, by default None
+ 85    force_minimum: int, optional
+ 86        Force a minimum of instances of each class, by default None
+ 87    indexes: bool, optional
+ 88        If True, return the indexes of the labeled and unlabeled instances, by default False
+ 89    shuffle: bool, default=True
+ 90        Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.
+ 91    stratify: array-like, default=None
+ 92        If not None, data is split in a stratified fashion, using this as the class labels.
+ 93
+ 94    Returns
+ 95    -------
+ 96    X : ndarray
+ 97        The feature set.
+ 98    y : ndarray
+ 99        The label set, -1 for unlabel instance.
+100    X_unlabel: ndarray
+101        The feature set for each y mark as unlabel
+102    y_unlabel: ndarray
+103        The true label for each y in the same order.
+104    label: ndarray (optional)
+105        The training set indexes for split mark as labeled.
+106    unlabel: ndarray (optional)
+107        The training set indexes for split mark as unlabeled.
+108    """
+109    assert (label_rate > 0) and (label_rate < 1),\
+110        "Label rate must be in (0, 1)."
+111    assert "test_size" not in kwards and "train_size" not in kwards,\
+112        "Test size and train size are illegal parameters in this method."
+113    
+114    columns = None
+115    is_df = False
+116    if hasattr(X, "iloc"):
+117        is_df = True
+118        columns = X.columns
+119        X = X.values
+120    if hasattr(y, "iloc"):
+121        is_df = True
+122        y = y.values
 123
-124    # Train test split with indexes
-125    label, unlabel = ms.train_test_split(indices, train_size=label_rate,
-126                                         random_state=random_state, **kwards)
-127
-128    if force_minimum is not None:
-129        label = np.concatenate((selected, label))
-130    
-131    # Create the label and unlabel sets
-132    X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
-133        X[unlabel], np.array([-1] * len(unlabel))
+124    indices = np.arange(len(y))
+125
+126    if force_minimum is not None:
+127        try:
+128            selected = __random_select_n_instances(y, force_minimum, random_state)
+129        except ValueError:
+130            raise ValueError("The number of instances of each class is less than force_minimum.")
+131
+132        # Remove selected instances from indices
+133        indices = np.delete(indices, selected, axis=0)    
 134
-135    # Create the artificial dataset
-136    X = np.concatenate((X_label, X_unlabel), axis=0)
-137    y = np.concatenate((y_label, y_unlabel), axis=0)
+135    # Train test split with indexes
+136    label, unlabel = ms.train_test_split(indices, train_size=label_rate,
+137                                         random_state=random_state, **kwards)
 138
-139    if indexes:
-140        return X, y, X_unlabel, y_unlabel, label, unlabel
-141
-142    return X, y, X_unlabel, y_unlabel
+139    if force_minimum is not None:
+140        label = np.concatenate((selected, label))
+141    
+142    y_unlabel_original = y[unlabel]
 143
-144
-145    """    
-146    if force_minimum is not None:
-147        try:
-148            selected = __random_select_n_instances(y, force_minimum, random_state)
-149        except ValueError:
-150            raise ValueError("The number of instances of each class is less than force_minimum.")
-151        X_selected = X[selected]
-152        y_selected = y[selected]
-153
-154        # Remove selected instances from X and y
-155        X = np.delete(X, selected, axis=0)
-156        y = np.delete(y, selected, axis=0)
-157    
-158    X_label, X_unlabel, y_label, true_label = \
-159        ms.train_test_split(X, y,
-160                            train_size=label_rate,
-161                            random_state=random_state, **kwards)
-162    X = np.concatenate((X_label, X_unlabel), axis=0)
-163    y = np.concatenate((y_label, np.array([-1] * len(true_label))), axis=0)
-164
-165    if force_minimum is not None:
-166        X = np.concatenate((X, X_selected), axis=0)
-167        y = np.concatenate((y, y_selected), axis=0)
-168    
-169    if indexes:
-170        return X, y, X_unlabel, true_label, X_label, X_unlabel
-171
-172    return X, y, X_unlabel, true_label
-173    """
+144    # Create the label and unlabel sets
+145    X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
+146        X[unlabel], np.array([-1] * len(unlabel))
+147
+148    # Create the artificial dataset
+149    X = np.concatenate((X_label, X_unlabel), axis=0)
+150    y = np.concatenate((y_label, y_unlabel), axis=0)
+151
+152    if is_df:
+153        X = pd.DataFrame(X, columns=columns)
+154        y = pd.Series(y)
+155
+156    if indexes:
+157        return X, y, X_unlabel, y_unlabel_original, label, unlabel
+158
+159    return X, y, X_unlabel, y_unlabel_original
+160
+161
+162    """    
+163    if force_minimum is not None:
+164        try:
+165            selected = __random_select_n_instances(y, force_minimum, random_state)
+166        except ValueError:
+167            raise ValueError("The number of instances of each class is less than force_minimum.")
+168        X_selected = X[selected]
+169        y_selected = y[selected]
+170
+171        # Remove selected instances from X and y
+172        X = np.delete(X, selected, axis=0)
+173        y = np.delete(y, selected, axis=0)
+174    
+175    X_label, X_unlabel, y_label, true_label = \
+176        ms.train_test_split(X, y,
+177                            train_size=label_rate,
+178                            random_state=random_state, **kwards)
+179    X = np.concatenate((X_label, X_unlabel), axis=0)
+180    y = np.concatenate((y_label, np.array([-1] * len(true_label))), axis=0)
+181
+182    if force_minimum is not None:
+183        X = np.concatenate((X, X_selected), axis=0)
+184        y = np.concatenate((y, y_selected), axis=0)
+185    
+186    if indexes:
+187        return X, y, X_unlabel, true_label, X_label, X_unlabel
+188
+189    return X, y, X_unlabel, true_label
+190    """
 
    
 
 
@@ -315,67 +331,67 @@ 
    Returns
    - 
     7class StratifiedKFoldSS():
- 8    """
- 9    Stratified K-Folds cross-validator for semi-supervised learning.
-10    
-11    Provides label and unlabel indices for each split. Using the `StratifiedKFold` method from `sklearn`.
-12    The `test` set is the labeled set and the `train` set is the unlabeled set.
-13    """
-14
+            
     8class StratifiedKFoldSS():
+ 9    """
+10    Stratified K-Folds cross-validator for semi-supervised learning.
+11    
+12    Provides label and unlabel indices for each split. Using the `StratifiedKFold` method from `sklearn`.
+13    The `test` set is the labeled set and the `train` set is the unlabeled set.
+14    """
 15
-16    def __init__(self, n_splits=5, shuffle=False, random_state=None):
-17        """
-18        Parameters
-19        ----------
-20        n_splits : int, default=5
-21            Number of folds. Must be at least 2.
-22        shuffle : bool, default=False
-23            Whether to shuffle each class's samples before splitting into batches.
-24        random_state : int or RandomState instance, default=None
-25            When shuffle is True, random_state affects the ordering of the indices.
-26            
-27        """
-28
-29        self.K = ms.StratifiedKFold(n_splits=n_splits, shuffle=shuffle,
-30                                    random_state=random_state)
-31        self.n_splits = n_splits
-32        self.shuffle = shuffle
-33        self.random_state = random_state
-34
-35    def split(self, X, y):
-36        """Generate a artificial dataset based on StratifiedKFold method
-37
-38        Parameters
-39        ----------
-40        X : array-like of shape (n_samples, n_features)
-41            Training data, where n_samples is the number of samples
-42            and n_features is the number of features.
-43        y : array-like of shape (n_samples,)
-44            The target variable for supervised learning problems.
-45
-46        Yields
-47        -------
-48        X : ndarray
-49            The feature set.
-50        y : ndarray
-51            The label set, -1 for unlabel instance.
-52        label : ndarray
-53            The training set indices for split mark as labeled.
-54        unlabel : ndarray
-55            The training set indices for split mark as unlabeled.
-56        """
-57        for train, test in self.K.split(X, y):
-58            # Inverse train and test because train is big dataset
-59            label = test
-60            unlabel = train
-61
-62            X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
-63                X[unlabel], np.array([-1] * len(unlabel))
-64            X_ = np.concatenate((X_label, X_unlabel), axis=0)
-65            y_ = np.concatenate((y_label, y_unlabel), axis=0)
-66
-67            yield X_, y_, label, unlabel
+16
+17    def __init__(self, n_splits=5, shuffle=False, random_state=None):
+18        """
+19        Parameters
+20        ----------
+21        n_splits : int, default=5
+22            Number of folds. Must be at least 2.
+23        shuffle : bool, default=False
+24            Whether to shuffle each class's samples before splitting into batches.
+25        random_state : int or RandomState instance, default=None
+26            When shuffle is True, random_state affects the ordering of the indices.
+27            
+28        """
+29
+30        self.K = ms.StratifiedKFold(n_splits=n_splits, shuffle=shuffle,
+31                                    random_state=random_state)
+32        self.n_splits = n_splits
+33        self.shuffle = shuffle
+34        self.random_state = random_state
+35
+36    def split(self, X, y):
+37        """Generate a artificial dataset based on StratifiedKFold method
+38
+39        Parameters
+40        ----------
+41        X : array-like of shape (n_samples, n_features)
+42            Training data, where n_samples is the number of samples
+43            and n_features is the number of features.
+44        y : array-like of shape (n_samples,)
+45            The target variable for supervised learning problems.
+46
+47        Yields
+48        -------
+49        X : ndarray
+50            The feature set.
+51        y : ndarray
+52            The label set, -1 for unlabel instance.
+53        label : ndarray
+54            The training set indices for split mark as labeled.
+55        unlabel : ndarray
+56            The training set indices for split mark as unlabeled.
+57        """
+58        for train, test in self.K.split(X, y):
+59            # Inverse train and test because train is big dataset
+60            label = test
+61            unlabel = train
+62
+63            X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
+64                X[unlabel], np.array([-1] * len(unlabel))
+65            X_ = np.concatenate((X_label, X_unlabel), axis=0)
+66            y_ = np.concatenate((y_label, y_unlabel), axis=0)
+67
+68            yield X_, y_, label, unlabel
 
    
 
 
@@ -396,24 +412,24 @@ 
    Returns
    - 
    16    def __init__(self, n_splits=5, shuffle=False, random_state=None):
-17        """
-18        Parameters
-19        ----------
-20        n_splits : int, default=5
-21            Number of folds. Must be at least 2.
-22        shuffle : bool, default=False
-23            Whether to shuffle each class's samples before splitting into batches.
-24        random_state : int or RandomState instance, default=None
-25            When shuffle is True, random_state affects the ordering of the indices.
-26            
-27        """
-28
-29        self.K = ms.StratifiedKFold(n_splits=n_splits, shuffle=shuffle,
-30                                    random_state=random_state)
-31        self.n_splits = n_splits
-32        self.shuffle = shuffle
-33        self.random_state = random_state
+            
    17    def __init__(self, n_splits=5, shuffle=False, random_state=None):
+18        """
+19        Parameters
+20        ----------
+21        n_splits : int, default=5
+22            Number of folds. Must be at least 2.
+23        shuffle : bool, default=False
+24            Whether to shuffle each class's samples before splitting into batches.
+25        random_state : int or RandomState instance, default=None
+26            When shuffle is True, random_state affects the ordering of the indices.
+27            
+28        """
+29
+30        self.K = ms.StratifiedKFold(n_splits=n_splits, shuffle=shuffle,
+31                                    random_state=random_state)
+32        self.n_splits = n_splits
+33        self.shuffle = shuffle
+34        self.random_state = random_state
 
    
 
 
@@ -442,39 +458,39 @@ 
    Returns
    - 
    35    def split(self, X, y):
-36        """Generate a artificial dataset based on StratifiedKFold method
-37
-38        Parameters
-39        ----------
-40        X : array-like of shape (n_samples, n_features)
-41            Training data, where n_samples is the number of samples
-42            and n_features is the number of features.
-43        y : array-like of shape (n_samples,)
-44            The target variable for supervised learning problems.
-45
-46        Yields
-47        -------
-48        X : ndarray
-49            The feature set.
-50        y : ndarray
-51            The label set, -1 for unlabel instance.
-52        label : ndarray
-53            The training set indices for split mark as labeled.
-54        unlabel : ndarray
-55            The training set indices for split mark as unlabeled.
-56        """
-57        for train, test in self.K.split(X, y):
-58            # Inverse train and test because train is big dataset
-59            label = test
-60            unlabel = train
-61
-62            X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
-63                X[unlabel], np.array([-1] * len(unlabel))
-64            X_ = np.concatenate((X_label, X_unlabel), axis=0)
-65            y_ = np.concatenate((y_label, y_unlabel), axis=0)
-66
-67            yield X_, y_, label, unlabel
+            
    36    def split(self, X, y):
+37        """Generate a artificial dataset based on StratifiedKFold method
+38
+39        Parameters
+40        ----------
+41        X : array-like of shape (n_samples, n_features)
+42            Training data, where n_samples is the number of samples
+43            and n_features is the number of features.
+44        y : array-like of shape (n_samples,)
+45            The target variable for supervised learning problems.
+46
+47        Yields
+48        -------
+49        X : ndarray
+50            The feature set.
+51        y : ndarray
+52            The label set, -1 for unlabel instance.
+53        label : ndarray
+54            The training set indices for split mark as labeled.
+55        unlabel : ndarray
+56            The training set indices for split mark as unlabeled.
+57        """
+58        for train, test in self.K.split(X, y):
+59            # Inverse train and test because train is big dataset
+60            label = test
+61            unlabel = train
+62
+63            X_label, y_label, X_unlabel, y_unlabel = X[label], y[label],\
+64                X[unlabel], np.array([-1] * len(unlabel))
+65            X_ = np.concatenate((X_label, X_unlabel), axis=0)
+66            y_ = np.concatenate((y_label, y_unlabel), axis=0)
+67
+68            yield X_, y_, label, unlabel
 
    
 
 
diff --git a/docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html b/docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html
new file mode 100644
index 0000000..07783fd
--- /dev/null
+++ b/docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html	
@@ -0,0 +1,985 @@
+
+
+
+    
+    
+    
+    sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14) API documentation
+            
+
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+    
+    
    
+            
    
+                    
    
+sslearn.restricted (Copia en conflicto de CROSS-PC 2024-05-14)    
    
+
+                        
    Summary of module sslearn.restricted:
    
+
+
    This module contains classes to train a classifier using the restricted set classification approach.
    
+
+
    Classes
    
+
+
    WhoIsWhoClassifier:
    
+
+
    
+  
    Who is Who Classifier
    
+
    
+
+
    Functions
    
+
+
    conflict_rate: 
    
+
+
    
+  
    Compute the conflict rate of a prediction, given a set of restrictions.
+  combine_predictions: 
+  Combine the predictions of a group of instances to keep the restrictions.
    
+
    
+
    
+
+                        
+
+                        
+
+                        
      1"""Summary of module `sslearn.restricted`:
+  2
+  3This module contains classes to train a classifier using the restricted set classification approach.
+  4
+  5## Classes
+  6
+  7[WhoIsWhoClassifier](#WhoIsWhoClassifier):
+  8> Who is Who Classifier
+  9
+ 10## Functions
+ 11
+ 12[conflict_rate](#conflict_rate): 
+ 13> Compute the conflict rate of a prediction, given a set of restrictions.
+ 14[combine_predictions](#combine_predictions): 
+ 15> Combine the predictions of a group of instances to keep the restrictions.
+ 16
+ 17
+ 18"""
+ 19
+ 20import numpy as np
+ 21from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator
+ 22from scipy.optimize import linear_sum_assignment
+ 23import warnings
+ 24import pandas as pd
+ 25
+ 26__all__ = ["conflict_rate", "combine_predictions", "WhoIsWhoClassifier"]
+ 27
+ 28class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ 29
+ 30    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+ 31        """
+ 32        Who is Who Classifier
+ 33        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+ 34        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+ 35
+ 36        Parameters
+ 37        ----------
+ 38        base_estimator : ClassifierMixin
+ 39            The base estimator to be used for training.
+ 40        method : str, optional
+ 41            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+ 42        conflict_weighted : bool, default=True
+ 43            Whether to weighted the confusion rate by the number of instances with the same group.
+ 44        """        
+ 45        allowed_methods = ["greedy", "hungarian"]
+ 46        self.base_estimator = base_estimator
+ 47        self.method = method
+ 48        if method not in allowed_methods:
+ 49            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+ 50        self.conflict_weighted = conflict_weighted
+ 51
+ 52
+ 53    def fit(self, X, y, instance_group=None, **kwards):
+ 54        """Fit the model according to the given training data.
+ 55        Parameters
+ 56        ----------
+ 57        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 58            The input samples.
+ 59        y : array-like of shape (n_samples,)
+ 60            The target values.
+ 61        instance_group : array-like of shape (n_samples)
+ 62            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+ 63        Returns
+ 64        -------
+ 65        self : object
+ 66            Returns self.
+ 67        """
+ 68        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+ 69        self.classes_ = self.base_estimator.classes_
+ 70        if instance_group is not None:
+ 71            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+ 72        else:
+ 73            self.conflict_in_train = None
+ 74        return self
+ 75
+ 76    def conflict_rate(self, X, instance_group):
+ 77        """Calculate the conflict rate of the model.
+ 78        Parameters
+ 79        ----------
+ 80        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 81            The input samples.
+ 82        instance_group : array-like of shape (n_samples)
+ 83            The group. Two instances with the same label are not allowed to be in the same group.
+ 84        Returns
+ 85        -------
+ 86        float
+ 87            The conflict rate.
+ 88        """
+ 89        y_pred = self.base_estimator.predict(X)
+ 90        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+ 91
+ 92    def predict(self, X, instance_group):
+ 93        """Predict class for X.
+ 94        Parameters
+ 95        ----------
+ 96        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 97            The input samples.
+ 98        **kwards : array-like of shape (n_samples)
+ 99            The group. Two instances with the same label are not allowed to be in the same group.
+100        Returns
+101        -------
+102        array-like of shape (n_samples, n_classes)
+103            The class probabilities of the input samples.
+104        """
+105        
+106        y_prob = self.predict_proba(X)
+107        
+108        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+109
+110        return self.classes_.take(y_predicted)
+111
+112
+113    def predict_proba(self, X):
+114        """Predict class probabilities for X.
+115        Parameters
+116        ----------
+117        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+118            The input samples.
+119        Returns
+120        -------
+121        array-like of shape (n_samples, n_classes)
+122            The class probabilities of the input samples.
+123        """
+124        return self.base_estimator.predict_proba(X)
+125
+126
+127def conflict_rate(y_pred, restrictions, weighted=True):
+128    """
+129    Computes the conflict rate of a prediction, given a set of restrictions.
+130    Parameters
+131    ----------
+132    y_pred : array-like of shape (n_samples,)
+133        Predicted target values.
+134    restrictions : array-like of shape (n_samples,)
+135        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+136    weighted : bool, default=True
+137        Whether to weighted the confusion rate by the number of instances with the same group.
+138    Returns
+139    -------
+140    conflict rate : float
+141        The conflict rate.
+142    """
+143    
+144    # Check that y_pred and restrictions have the same length
+145    if len(y_pred) != len(restrictions):
+146        raise ValueError("y_pred and restrictions must have the same length.")
+147    
+148    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+149
+150    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
+151    if weighted:
+152        return conflicted.sum().y_pred / len(y_pred)
+153    else:
+154        rcount = restricted_df.groupby('restrictions').count()
+155        return (conflicted.y_pred / rcount.y_pred).sum()
+156
+157def combine_predictions(y_probas, instance_group, class_number, method="hungarian"):
+158    y_predicted = []
+159    for group in np.unique(instance_group):
+160           
+161        mask = instance_group == group
+162        probas_matrix = y_probas[mask]
+163        
+164
+165        preds = list(np.argmax(probas_matrix, axis=1))
+166
+167        if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number:
+168            y_predicted.extend(preds)
+169            if probas_matrix.shape[0] > class_number:
+170                warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning)
+171            continue
+172
+173        if method == "greedy":
+174            y = _greedy(probas_matrix)
+175        elif method == "hungarian":
+176            y = _hungarian(probas_matrix)
+177        
+178        y_predicted.extend(y)
+179    return y_predicted
+180
+181def _greedy(probas_matrix):        
+182
+183    probas = probas_matrix.reshape(probas_matrix.size,)
+184    order = probas.argsort()[::-1]
+185
+186    y_pred_group = [None for i in range(probas_matrix.shape[0])]
+187
+188    instance_to_predict = {i for i in range(probas_matrix.shape[0])}
+189    class_predicted = set()
+190    for item in order:
+191        class_ = item % probas_matrix.shape[0]
+192        instance = item // probas_matrix.shape[0]
+193        if instance in instance_to_predict and class_ not in class_predicted:
+194            y_pred_group[instance] = class_
+195            instance_to_predict.remove(instance)
+196            class_predicted.add(class_)
+197            
+198    return y_pred_group
+199        
+200
+201def _hungarian(probas_matrix):
+202    
+203    costs = np.log(probas_matrix)
+204    costs[costs == -np.inf] = 0  # if proba is 0, then the cost is 0
+205    _, col_ind = linear_sum_assignment(costs, maximize=True)
+206    col_ind = list(col_ind)
+207        
+208    return col_ind
+
    
+
+
+            
    
+                
    
+                            
+
    
+            
+        def
+        conflict_rate(y_pred, restrictions, weighted=True):
+
+                
+
+    
    
+    
+            
    128def conflict_rate(y_pred, restrictions, weighted=True):
+129    """
+130    Computes the conflict rate of a prediction, given a set of restrictions.
+131    Parameters
+132    ----------
+133    y_pred : array-like of shape (n_samples,)
+134        Predicted target values.
+135    restrictions : array-like of shape (n_samples,)
+136        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+137    weighted : bool, default=True
+138        Whether to weighted the confusion rate by the number of instances with the same group.
+139    Returns
+140    -------
+141    conflict rate : float
+142        The conflict rate.
+143    """
+144    
+145    # Check that y_pred and restrictions have the same length
+146    if len(y_pred) != len(restrictions):
+147        raise ValueError("y_pred and restrictions must have the same length.")
+148    
+149    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+150
+151    conflicted = restricted_df.groupby('restrictions').agg({'y_pred': lambda x: np.unique(x, return_counts=True)[1][np.unique(x, return_counts=True)[1]>1].sum()})
+152    if weighted:
+153        return conflicted.sum().y_pred / len(y_pred)
+154    else:
+155        rcount = restricted_df.groupby('restrictions').count()
+156        return (conflicted.y_pred / rcount.y_pred).sum()
+
    
+
+
+            
    Computes the conflict rate of a prediction, given a set of restrictions.
    
+
+
    Parameters
    
+
+
      
+
    • y_pred (array-like of shape (n_samples,)):
+Predicted target values.
      • 
+
    • restrictions (array-like of shape (n_samples,)):
+Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
      • 
+
    • weighted (bool, default=True):
+Whether to weighted the confusion rate by the number of instances with the same group.
      • 
+
    
+
+
    Returns
    
+
+
      
+
    • conflict rate (float):
+The conflict rate.
      • 
+
    
+
    
+
+
+                
    
+                
    
+                            
+
    
+            
+    class
+    WhoIsWhoClassifier(sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin, sklearn.base.MetaEstimatorMixin):
+
+                
+
+    
    
+    
+            
     29class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+ 30
+ 31    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+ 32        """
+ 33        Who is Who Classifier
+ 34        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+ 35        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+ 36
+ 37        Parameters
+ 38        ----------
+ 39        base_estimator : ClassifierMixin
+ 40            The base estimator to be used for training.
+ 41        method : str, optional
+ 42            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+ 43        conflict_weighted : bool, default=True
+ 44            Whether to weighted the confusion rate by the number of instances with the same group.
+ 45        """        
+ 46        allowed_methods = ["greedy", "hungarian"]
+ 47        self.base_estimator = base_estimator
+ 48        self.method = method
+ 49        if method not in allowed_methods:
+ 50            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+ 51        self.conflict_weighted = conflict_weighted
+ 52
+ 53
+ 54    def fit(self, X, y, instance_group=None, **kwards):
+ 55        """Fit the model according to the given training data.
+ 56        Parameters
+ 57        ----------
+ 58        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 59            The input samples.
+ 60        y : array-like of shape (n_samples,)
+ 61            The target values.
+ 62        instance_group : array-like of shape (n_samples)
+ 63            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+ 64        Returns
+ 65        -------
+ 66        self : object
+ 67            Returns self.
+ 68        """
+ 69        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+ 70        self.classes_ = self.base_estimator.classes_
+ 71        if instance_group is not None:
+ 72            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+ 73        else:
+ 74            self.conflict_in_train = None
+ 75        return self
+ 76
+ 77    def conflict_rate(self, X, instance_group):
+ 78        """Calculate the conflict rate of the model.
+ 79        Parameters
+ 80        ----------
+ 81        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 82            The input samples.
+ 83        instance_group : array-like of shape (n_samples)
+ 84            The group. Two instances with the same label are not allowed to be in the same group.
+ 85        Returns
+ 86        -------
+ 87        float
+ 88            The conflict rate.
+ 89        """
+ 90        y_pred = self.base_estimator.predict(X)
+ 91        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+ 92
+ 93    def predict(self, X, instance_group):
+ 94        """Predict class for X.
+ 95        Parameters
+ 96        ----------
+ 97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 98            The input samples.
+ 99        **kwards : array-like of shape (n_samples)
+100            The group. Two instances with the same label are not allowed to be in the same group.
+101        Returns
+102        -------
+103        array-like of shape (n_samples, n_classes)
+104            The class probabilities of the input samples.
+105        """
+106        
+107        y_prob = self.predict_proba(X)
+108        
+109        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+110
+111        return self.classes_.take(y_predicted)
+112
+113
+114    def predict_proba(self, X):
+115        """Predict class probabilities for X.
+116        Parameters
+117        ----------
+118        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+119            The input samples.
+120        Returns
+121        -------
+122        array-like of shape (n_samples, n_classes)
+123            The class probabilities of the input samples.
+124        """
+125        return self.base_estimator.predict_proba(X)
+
    
+
+
+            
    Base class for all estimators in scikit-learn.
    
+
+
    Notes
    
+
+
    All estimators should specify all the parameters that can be set
+at the class level in their __init__ as explicit keyword
+arguments (no *args or **kwargs).
    
+
    
+
+
+                            
    
+                                        
+
    
+            
+        WhoIsWhoClassifier(base_estimator, method='hungarian', conflict_weighted=True)
+
+                
+
+    
    
+    
+            
    31    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+32        """
+33        Who is Who Classifier
+34        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+35        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+36
+37        Parameters
+38        ----------
+39        base_estimator : ClassifierMixin
+40            The base estimator to be used for training.
+41        method : str, optional
+42            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+43        conflict_weighted : bool, default=True
+44            Whether to weighted the confusion rate by the number of instances with the same group.
+45        """        
+46        allowed_methods = ["greedy", "hungarian"]
+47        self.base_estimator = base_estimator
+48        self.method = method
+49        if method not in allowed_methods:
+50            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+51        self.conflict_weighted = conflict_weighted
+
    
+
+
+            
    Who is Who Classifier
+Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+Restricted set classification: Who is there?. Pattern Recognition, 63, 158-170.
    
+
+
    Parameters
    
+
+
      
+
    • base_estimator (ClassifierMixin):
+The base estimator to be used for training.
      • 
+
    • method (str, optional):
+The method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default "hungarian"
      • 
+
    • conflict_weighted (bool, default=True):
+Whether to weighted the confusion rate by the number of instances with the same group.
      • 
+
    
+
    
+
+
+                            
    
+                            
    
+                                        
+
    
+            
+        def
+        fit(self, X, y, instance_group=None, **kwards):
+
+                
+
+    
    
+    
+            
    54    def fit(self, X, y, instance_group=None, **kwards):
+55        """Fit the model according to the given training data.
+56        Parameters
+57        ----------
+58        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+59            The input samples.
+60        y : array-like of shape (n_samples,)
+61            The target values.
+62        instance_group : array-like of shape (n_samples)
+63            The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
+64        Returns
+65        -------
+66        self : object
+67            Returns self.
+68        """
+69        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+70        self.classes_ = self.base_estimator.classes_
+71        if instance_group is not None:
+72            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+73        else:
+74            self.conflict_in_train = None
+75        return self
+
    
+
+
+            
    Fit the model according to the given training data.
    
+
+
    Parameters
    
+
+
      
+
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
+The input samples.
      • 
+
    • y (array-like of shape (n_samples,)):
+The target values.
      • 
+
    • instance_group (array-like of shape (n_samples)):
+The group. Two instances with the same label are not allowed to be in the same group. If None, group restriction will not be used in training.
      • 
+
    
+
+
    Returns
    
+
+
      
+
    • self (object):
+Returns self.
      • 
+
    
+
    
+
+
+                            
    
+                            
    
+                                        
+
    
+            
+        def
+        conflict_rate(self, X, instance_group):
+
+                
+
+    
    
+    
+            
    77    def conflict_rate(self, X, instance_group):
+78        """Calculate the conflict rate of the model.
+79        Parameters
+80        ----------
+81        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+82            The input samples.
+83        instance_group : array-like of shape (n_samples)
+84            The group. Two instances with the same label are not allowed to be in the same group.
+85        Returns
+86        -------
+87        float
+88            The conflict rate.
+89        """
+90        y_pred = self.base_estimator.predict(X)
+91        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+
    
+
+
+            
    Calculate the conflict rate of the model.
    
+
+
    Parameters
    
+
+
      
+
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
+The input samples.
      • 
+
    • instance_group (array-like of shape (n_samples)):
+The group. Two instances with the same label are not allowed to be in the same group.
      • 
+
    
+
+
    Returns
    
+
+
      
+
    • float: The conflict rate.
      • 
+
    
+
    
+
+
+                            
    
+                            
    
+                                        
+
    
+            
+        def
+        predict(self, X, instance_group):
+
+                
+
+    
    
+    
+            
     93    def predict(self, X, instance_group):
+ 94        """Predict class for X.
+ 95        Parameters
+ 96        ----------
+ 97        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+ 98            The input samples.
+ 99        **kwards : array-like of shape (n_samples)
+100            The group. Two instances with the same label are not allowed to be in the same group.
+101        Returns
+102        -------
+103        array-like of shape (n_samples, n_classes)
+104            The class probabilities of the input samples.
+105        """
+106        
+107        y_prob = self.predict_proba(X)
+108        
+109        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+110
+111        return self.classes_.take(y_predicted)
+
    
+
+
+            
    Predict class for X.
    
+
+
    Parameters
    
+
+
      
+
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
+The input samples.
      • 
+
    • **kwards (array-like of shape (n_samples)):
+The group. Two instances with the same label are not allowed to be in the same group.
      • 
+
    
+
+
    Returns
    
+
+
      
+
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • 
+
    
+
    
+
+
+                            
    
+                            
    
+                                        
+
    
+            
+        def
+        predict_proba(self, X):
+
+                
+
+    
    
+    
+            
    114    def predict_proba(self, X):
+115        """Predict class probabilities for X.
+116        Parameters
+117        ----------
+118        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+119            The input samples.
+120        Returns
+121        -------
+122        array-like of shape (n_samples, n_classes)
+123            The class probabilities of the input samples.
+124        """
+125        return self.base_estimator.predict_proba(X)
+
    
+
+
+            
    Predict class probabilities for X.
    
+
+
    Parameters
    
+
+
      
+
    • X ({array-like, sparse matrix} of shape (n_samples, n_features)):
+The input samples.
      • 
+
    
+
+
    Returns
    
+
+
      
+
    • array-like of shape (n_samples, n_classes): The class probabilities of the input samples.
      • 
+
    
+
    
+
+
+                            
    
+                            
    
+                                
    Inherited Members
    
+                                
    
+                                    
    sklearn.base.BaseEstimator
    
+                                
    get_params
    
+                
    set_params
    
+
+            
    
+            
    sklearn.base.ClassifierMixin
    
+                                
    score
    
+
+            
    
+                                
    
+                            
    
+                
    
+    
    
+
+
\ No newline at end of file
diff --git a/docs/sslearn/wrapper.html b/docs/sslearn/wrapper.html
index a4c1a02..023ba08 100644
--- a/docs/sslearn/wrapper.html
+++ b/docs/sslearn/wrapper.html
@@ -87,6 +87,9 @@ 
    API Documentation
    
                         
  • 
                                 predict_proba
                         
    • 
+                        
  • 
+                                score
+                        
    • 
                 
 
             
@@ -316,116 +319,116 @@ 
    Co-Training Algorithms
    - 
     16class SelfTraining(SelfTrainingClassifier):
- 17    """
- 18    **Self Training Classifier with data loader compatible.**
- 19    ----------------------------
- 20
- 21    Is the same `SelfTrainingClassifier` from sklearn but with `sslearn` data loader compatible.
- 22    For more information, see the [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html).
- 23
- 24    **Example**
- 25    -----------
- 26    ```python
- 27    from sklearn.datasets import load_iris
- 28    from sslearn.model_selection import artificial_ssl_dataset
- 29    from sslearn.wrapper import SelfTraining
- 30
- 31    X, y = load_iris(return_X_y=True)
- 32    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
- 33
- 34    clf = SelfTraining()
- 35    clf.fit(X, y)
- 36    clf.score(X_unlabel, y_unlabel)
- 37    ```
- 38
- 39    **References**
- 40    --------------
- 41    David Yarowsky. (1995). <br>
- 42    Unsupervised word sense disambiguation rivaling supervised methods.<br>
- 43    In <i>Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95).</i><br>
- 44    Association for Computational Linguistics,<br>
- 45    Stroudsburg, PA, USA, 189-196. <br>
- 46    [10.3115/981658.981684](https://doi.org/10.3115/981658.981684)
- 47    """
- 48
- 49    _estimator_type = "classifier"
- 50
- 51    def __init__(self,
- 52                 base_estimator,
- 53                 threshold=0.75,
- 54                 criterion='threshold',
- 55                 k_best=10,
- 56                 max_iter=10,
- 57                 verbose=False):
- 58        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
- 59
- 60        This class allows a given supervised classifier to function as a
- 61        semi-supervised classifier, allowing it to learn from unlabeled data. It
- 62        does this by iteratively predicting pseudo-labels for the unlabeled data
- 63        and adding them to the training set.
- 64
- 65        The classifier will continue iterating until either max_iter is reached, or
- 66        no pseudo-labels were added to the training set in the previous iteration.
- 67
- 68        Parameters
- 69        ----------
- 70        base_estimator : estimator object
- 71            An estimator object implementing ``fit`` and ``predict_proba``.
- 72            Invoking the ``fit`` method will fit a clone of the passed estimator,
- 73            which will be stored in the ``base_estimator_`` attribute.
- 74
- 75        threshold : float, default=0.75
- 76            The decision threshold for use with `criterion='threshold'`.
- 77            Should be in [0, 1). When using the 'threshold' criterion, a
- 78            :ref:`well calibrated classifier <calibration>` should be used.
- 79
- 80        criterion : {'threshold', 'k_best'}, default='threshold'
- 81            The selection criterion used to select which labels to add to the
- 82            training set. If 'threshold', pseudo-labels with prediction
- 83            probabilities above `threshold` are added to the dataset. If 'k_best',
- 84            the `k_best` pseudo-labels with highest prediction probabilities are
- 85            added to the dataset. When using the 'threshold' criterion, a
- 86            :ref:`well calibrated classifier <calibration>` should be used.
- 87
- 88        k_best : int, default=10
- 89            The amount of samples to add in each iteration. Only used when
- 90            `criterion` is k_best'.
- 91
- 92        max_iter : int or None, default=10
- 93            Maximum number of iterations allowed. Should be greater than or equal
- 94            to 0. If it is ``None``, the classifier will continue to predict labels
- 95            until no new pseudo-labels are added, or all unlabeled samples have
- 96            been labeled.
- 97
- 98        verbose : bool, default=False
- 99            Enable verbose output.
-100        """
-101        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
-102
-103    def fit(self, X, y):
-104        """
-105        Fits this ``SelfTrainingClassifier`` to a dataset.
-106
-107        Parameters
-108        ----------
-109        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-110            Array representing the data.
-111
-112        y : {array-like, sparse matrix} of shape (n_samples,)
-113            Array representing the labels. Unlabeled samples should have the
-114            label -1.
-115
-116        Returns
-117        -------
-118        self : SelfTrainingClassifier
-119            Returns an instance of self.
-120        """
-121        y_adapted = y.copy()
-122        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
-123            y_adapted = y_adapted.astype(object)
-124            y_adapted[y_adapted == '-1'] = -1
-125        return super().fit(X, y_adapted)
+            
     17class SelfTraining(SelfTrainingClassifier):
+ 18    """
+ 19    **Self Training Classifier with data loader compatible.**
+ 20    ----------------------------
+ 21
+ 22    Is the same `SelfTrainingClassifier` from sklearn but with `sslearn` data loader compatible.
+ 23    For more information, see the [sklearn documentation](https://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.SelfTrainingClassifier.html).
+ 24
+ 25    **Example**
+ 26    -----------
+ 27    ```python
+ 28    from sklearn.datasets import load_iris
+ 29    from sslearn.model_selection import artificial_ssl_dataset
+ 30    from sslearn.wrapper import SelfTraining
+ 31
+ 32    X, y = load_iris(return_X_y=True)
+ 33    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+ 34
+ 35    clf = SelfTraining()
+ 36    clf.fit(X, y)
+ 37    clf.score(X_unlabel, y_unlabel)
+ 38    ```
+ 39
+ 40    **References**
+ 41    --------------
+ 42    David Yarowsky. (1995). <br>
+ 43    Unsupervised word sense disambiguation rivaling supervised methods.<br>
+ 44    In <i>Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL '95).</i><br>
+ 45    Association for Computational Linguistics,<br>
+ 46    Stroudsburg, PA, USA, 189-196. <br>
+ 47    [10.3115/981658.981684](https://doi.org/10.3115/981658.981684)
+ 48    """
+ 49
+ 50    _estimator_type = "classifier"
+ 51
+ 52    def __init__(self,
+ 53                 base_estimator,
+ 54                 threshold=0.75,
+ 55                 criterion='threshold',
+ 56                 k_best=10,
+ 57                 max_iter=10,
+ 58                 verbose=False):
+ 59        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
+ 60
+ 61        This class allows a given supervised classifier to function as a
+ 62        semi-supervised classifier, allowing it to learn from unlabeled data. It
+ 63        does this by iteratively predicting pseudo-labels for the unlabeled data
+ 64        and adding them to the training set.
+ 65
+ 66        The classifier will continue iterating until either max_iter is reached, or
+ 67        no pseudo-labels were added to the training set in the previous iteration.
+ 68
+ 69        Parameters
+ 70        ----------
+ 71        base_estimator : estimator object
+ 72            An estimator object implementing ``fit`` and ``predict_proba``.
+ 73            Invoking the ``fit`` method will fit a clone of the passed estimator,
+ 74            which will be stored in the ``base_estimator_`` attribute.
+ 75
+ 76        threshold : float, default=0.75
+ 77            The decision threshold for use with `criterion='threshold'`.
+ 78            Should be in [0, 1). When using the 'threshold' criterion, a
+ 79            :ref:`well calibrated classifier <calibration>` should be used.
+ 80
+ 81        criterion : {'threshold', 'k_best'}, default='threshold'
+ 82            The selection criterion used to select which labels to add to the
+ 83            training set. If 'threshold', pseudo-labels with prediction
+ 84            probabilities above `threshold` are added to the dataset. If 'k_best',
+ 85            the `k_best` pseudo-labels with highest prediction probabilities are
+ 86            added to the dataset. When using the 'threshold' criterion, a
+ 87            :ref:`well calibrated classifier <calibration>` should be used.
+ 88
+ 89        k_best : int, default=10
+ 90            The amount of samples to add in each iteration. Only used when
+ 91            `criterion` is k_best'.
+ 92
+ 93        max_iter : int or None, default=10
+ 94            Maximum number of iterations allowed. Should be greater than or equal
+ 95            to 0. If it is ``None``, the classifier will continue to predict labels
+ 96            until no new pseudo-labels are added, or all unlabeled samples have
+ 97            been labeled.
+ 98
+ 99        verbose : bool, default=False
+100            Enable verbose output.
+101        """
+102        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
+103
+104    def fit(self, X, y):
+105        """
+106        Fits this ``SelfTrainingClassifier`` to a dataset.
+107
+108        Parameters
+109        ----------
+110        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+111            Array representing the data.
+112
+113        y : {array-like, sparse matrix} of shape (n_samples,)
+114            Array representing the labels. Unlabeled samples should have the
+115            label -1.
+116
+117        Returns
+118        -------
+119        self : SelfTrainingClassifier
+120            Returns an instance of self.
+121        """
+122        y_adapted = y.copy()
+123        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
+124            y_adapted = y_adapted.astype(object)
+125            y_adapted[y_adapted == '-1'] = -1
+126        return super().fit(X, y_adapted)
 
    
 
 
@@ -471,57 +474,57 @@ 
    References
    - 
     51    def __init__(self,
- 52                 base_estimator,
- 53                 threshold=0.75,
- 54                 criterion='threshold',
- 55                 k_best=10,
- 56                 max_iter=10,
- 57                 verbose=False):
- 58        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
- 59
- 60        This class allows a given supervised classifier to function as a
- 61        semi-supervised classifier, allowing it to learn from unlabeled data. It
- 62        does this by iteratively predicting pseudo-labels for the unlabeled data
- 63        and adding them to the training set.
- 64
- 65        The classifier will continue iterating until either max_iter is reached, or
- 66        no pseudo-labels were added to the training set in the previous iteration.
- 67
- 68        Parameters
- 69        ----------
- 70        base_estimator : estimator object
- 71            An estimator object implementing ``fit`` and ``predict_proba``.
- 72            Invoking the ``fit`` method will fit a clone of the passed estimator,
- 73            which will be stored in the ``base_estimator_`` attribute.
- 74
- 75        threshold : float, default=0.75
- 76            The decision threshold for use with `criterion='threshold'`.
- 77            Should be in [0, 1). When using the 'threshold' criterion, a
- 78            :ref:`well calibrated classifier <calibration>` should be used.
- 79
- 80        criterion : {'threshold', 'k_best'}, default='threshold'
- 81            The selection criterion used to select which labels to add to the
- 82            training set. If 'threshold', pseudo-labels with prediction
- 83            probabilities above `threshold` are added to the dataset. If 'k_best',
- 84            the `k_best` pseudo-labels with highest prediction probabilities are
- 85            added to the dataset. When using the 'threshold' criterion, a
- 86            :ref:`well calibrated classifier <calibration>` should be used.
- 87
- 88        k_best : int, default=10
- 89            The amount of samples to add in each iteration. Only used when
- 90            `criterion` is k_best'.
- 91
- 92        max_iter : int or None, default=10
- 93            Maximum number of iterations allowed. Should be greater than or equal
- 94            to 0. If it is ``None``, the classifier will continue to predict labels
- 95            until no new pseudo-labels are added, or all unlabeled samples have
- 96            been labeled.
- 97
- 98        verbose : bool, default=False
- 99            Enable verbose output.
-100        """
-101        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
+            
     52    def __init__(self,
+ 53                 base_estimator,
+ 54                 threshold=0.75,
+ 55                 criterion='threshold',
+ 56                 k_best=10,
+ 57                 max_iter=10,
+ 58                 verbose=False):
+ 59        """Self-training. Adaptation of SelfTrainingClassifier from sklearn with data loader compatible.
+ 60
+ 61        This class allows a given supervised classifier to function as a
+ 62        semi-supervised classifier, allowing it to learn from unlabeled data. It
+ 63        does this by iteratively predicting pseudo-labels for the unlabeled data
+ 64        and adding them to the training set.
+ 65
+ 66        The classifier will continue iterating until either max_iter is reached, or
+ 67        no pseudo-labels were added to the training set in the previous iteration.
+ 68
+ 69        Parameters
+ 70        ----------
+ 71        base_estimator : estimator object
+ 72            An estimator object implementing ``fit`` and ``predict_proba``.
+ 73            Invoking the ``fit`` method will fit a clone of the passed estimator,
+ 74            which will be stored in the ``base_estimator_`` attribute.
+ 75
+ 76        threshold : float, default=0.75
+ 77            The decision threshold for use with `criterion='threshold'`.
+ 78            Should be in [0, 1). When using the 'threshold' criterion, a
+ 79            :ref:`well calibrated classifier <calibration>` should be used.
+ 80
+ 81        criterion : {'threshold', 'k_best'}, default='threshold'
+ 82            The selection criterion used to select which labels to add to the
+ 83            training set. If 'threshold', pseudo-labels with prediction
+ 84            probabilities above `threshold` are added to the dataset. If 'k_best',
+ 85            the `k_best` pseudo-labels with highest prediction probabilities are
+ 86            added to the dataset. When using the 'threshold' criterion, a
+ 87            :ref:`well calibrated classifier <calibration>` should be used.
+ 88
+ 89        k_best : int, default=10
+ 90            The amount of samples to add in each iteration. Only used when
+ 91            `criterion` is k_best'.
+ 92
+ 93        max_iter : int or None, default=10
+ 94            Maximum number of iterations allowed. Should be greater than or equal
+ 95            to 0. If it is ``None``, the classifier will continue to predict labels
+ 96            until no new pseudo-labels are added, or all unlabeled samples have
+ 97            been labeled.
+ 98
+ 99        verbose : bool, default=False
+100            Enable verbose output.
+101        """
+102        super().__init__(base_estimator, threshold, criterion, k_best, max_iter, verbose)
 
    
 
 
@@ -579,29 +582,29 @@ 
    Parameters
    - 
    103    def fit(self, X, y):
-104        """
-105        Fits this ``SelfTrainingClassifier`` to a dataset.
-106
-107        Parameters
-108        ----------
-109        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-110            Array representing the data.
-111
-112        y : {array-like, sparse matrix} of shape (n_samples,)
-113            Array representing the labels. Unlabeled samples should have the
-114            label -1.
-115
-116        Returns
-117        -------
-118        self : SelfTrainingClassifier
-119            Returns an instance of self.
-120        """
-121        y_adapted = y.copy()
-122        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
-123            y_adapted = y_adapted.astype(object)
-124            y_adapted[y_adapted == '-1'] = -1
-125        return super().fit(X, y_adapted)
+            
    104    def fit(self, X, y):
+105        """
+106        Fits this ``SelfTrainingClassifier`` to a dataset.
+107
+108        Parameters
+109        ----------
+110        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+111            Array representing the data.
+112
+113        y : {array-like, sparse matrix} of shape (n_samples,)
+114            Array representing the labels. Unlabeled samples should have the
+115            label -1.
+116
+117        Returns
+118        -------
+119        self : SelfTrainingClassifier
+120            Returns an instance of self.
+121        """
+122        y_adapted = y.copy()
+123        if y_adapted.dtype.type is str or y_adapted.dtype.type is np.str_:
+124            y_adapted = y_adapted.astype(object)
+125            y_adapted[y_adapted == '-1'] = -1
+126        return super().fit(X, y_adapted)
 
    
 
 
@@ -651,253 +654,279 @@ 
    Inherited Members
    
 
    
             
     class
-    Setred(sklearn.base.ClassifierMixin, sklearn.base.BaseEstimator):
+    Setred(sklearn.base.BaseEstimator, sklearn.base.MetaEstimatorMixin):
 
                 
 
     
    
     
-            
    128class Setred(ClassifierMixin, BaseEstimator):
-129    """
-130    **Self-training with Editing.**
-131    ----------------------------
-132
-133    Create a SETRED classifier. It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
-134    The main process are:
-135    1. Train a classifier with the labeled data.
-136    2. Create a pool of unlabeled data and select the most confident predictions.
-137    3. Repeat until the maximum number of iterations is reached:
-138        a. Select the most confident predictions from the unlabeled data.
-139        b. Calculate the neighborhood graph of the labeled data and the selected instances from the unlabeled data.
-140        c. Calculate the significance level of the selected instances.
-141        d. Reject the instances that are not significant according their position in the neighborhood graph.
-142        e. Add the selected instances to the labeled data and retrains the classifier.
-143        f. Add new instances to the pool of unlabeled data.
-144    4. Return the classifier trained with the labeled data.
-145
-146    **Example**
-147    -----------
-148    ```python
-149    from sklearn.datasets import load_iris
-150    from sslearn.model_selection import artificial_ssl_dataset
-151    from sslearn.wrapper import Setred
-152
-153    X, y = load_iris(return_X_y=True)
-154    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-155
-156    clf = Setred()
-157    clf.fit(X, y)
-158    clf.score(X_unlabel, y_unlabel)
-159    ```
-160
-161    **References**
-162    ----------
-163    Li, Ming, and Zhi-Hua Zhou. (2005)<br>
-164    SETRED: Self-training with editing,<br>
-165    in <i>Advances in Knowledge Discovery and Data Mining.</i> <br>
-166    Pacific-Asia Conference on Knowledge Discovery and Data Mining <br>
-167    LNAI 3518, Springer, Berlin, Heidelberg, <br>
-168    [10.1007/11430919_71](https://doi.org/10.1007/11430919_71)
-169
-170    """
-171
-172    def __init__(
-173        self,
-174        base_estimator=KNeighborsClassifier(n_neighbors=3),
-175        max_iterations=40,
-176        distance="euclidean",
-177        poolsize=0.25,
-178        rejection_threshold=0.05,
-179        graph_neighbors=1,
-180        random_state=None,
-181        n_jobs=None,
-182    ):
-183        """
-184        Create a SETRED classifier.
-185        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
-186        
-187        Parameters
-188        ----------
-189        base_estimator : ClassifierMixin, optional
-190            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
-191        max_iterations : int, optional
-192            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
-193        distance : str, optional
-194            The distance metric to use for the graph.
-195            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
-196            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
-197            Note that the `cosine` metric uses cosine_distances., by default `euclidean`
-198        poolsize : float, optional
-199            Max number of unlabel instances candidates to pseudolabel, by default 0.25
-200        rejection_threshold : float, optional
-201            significance level, by default 0.05
-202        graph_neighbors : int, optional
-203            Number of neighbors for each sample., by default 1
-204        random_state : int, RandomState instance, optional
-205            controls the randomness of the estimator, by default None
-206        n_jobs : int, optional
-207            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
-208        """
-209        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
-210        self.max_iterations = max_iterations
-211        self.poolsize = poolsize
-212        self.distance = distance
-213        self.rejection_threshold = rejection_threshold
-214        self.graph_neighbors = graph_neighbors
-215        self.random_state = random_state
-216        self.n_jobs = n_jobs
-217
-218    def __create_neighborhood(self, X):
-219        # kneighbors_graph(X, 1, metric=self.distance, n_jobs=self.n_jobs).toarray()
-220        return kneighbors_graph(
-221            X, self.graph_neighbors, metric=self.distance, n_jobs=self.n_jobs, mode="distance"
-222        ).toarray()
-223
-224    def fit(self, X, y, **kwars):
-225        """Build a Setred classifier from the training set (X, y).
-226
-227        Parameters
-228        ----------
-229        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-230            The training input samples.
-231        y : array-like of shape (n_samples,)
-232            The target values (class labels), -1 if unlabeled.
-233
-234        Returns
-235        -------
-236        self: Setred
-237            Fitted estimator.
-238        """        
-239        random_state = check_random_state(self.random_state)
-240
-241        X_label, y_label, X_unlabel = get_dataset(X, y)
-242
-243        is_df = isinstance(X_label, pd.DataFrame)
-244
-245        self.classes_ = np.unique(y_label)
-246
-247        each_iteration_candidates = X_label.shape[0]
-248
-249        pool = int(len(X_unlabel) * self.poolsize)
-250        self._base_estimator = skclone(self.base_estimator)
-251
-252        self._base_estimator.fit(X_label, y_label, **kwars)
-253
-254        y_probabilities = calculate_prior_probability(
-255            y_label
-256        )  # Should probabilities change every iteration or may it keep with the first L?
-257
-258        sort_idx = np.argsort(list(y_probabilities.keys()))
-259
-260        if X_unlabel.shape[0] == 0:
-261            return self
-262
-263        for _ in range(self.max_iterations):
-264            U_ = resample(
-265                X_unlabel, replace=False, n_samples=pool, random_state=random_state
-266            )
-267
-268            if is_df:
-269                U_ = pd.DataFrame(U_, columns=X_label.columns)
-270
-271            raw_predictions = self._base_estimator.predict_proba(U_)
-272            predictions = np.max(raw_predictions, axis=1)
-273            class_predicted = np.argmax(raw_predictions, axis=1)
-274            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
-275            indexes = predictions.argsort()[-each_iteration_candidates:]
-276
-277            if is_df:
-278                L_ = U_.iloc[indexes]
-279            else:
-280                L_ = U_[indexes]
-281            y_ = np.array(
-282                list(
-283                    map(
-284                        lambda x: self._base_estimator.classes_[x],
-285                        class_predicted[indexes],
-286                    )
-287                )
-288            )
-289
-290            if is_df:
-291                pre_L = pd.concat([X_label, L_])
-292            else:
-293                pre_L = np.concatenate((X_label, L_), axis=0)
-294
-295            weights = self.__create_neighborhood(pre_L)
-296            #  Keep only weights for L_
-297            weights = weights[-L_.shape[0]:, :]
-298
-299            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
-300            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
-301            #  Must weights be the inverse of distance?
-302            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
-303
-304            weights_sum = weights.sum(axis=1)
-305            weights_square_sum = (weights ** 2).sum(axis=1)
-306
-307            iid_random = random_state.binomial(
-308                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
-309            )
-310            ji = (iid_random * weights).sum(axis=1)
-311
-312            mu_h0 = p_wrong * weights_sum
-313            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
-314            
-315            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
-316            # z_score = (ji - mu_h0) / sigma_h0
-317            
-318            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
-319            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
-320
-321            if is_df:
-322                L_filtered = L_.iloc[to_add, :]
-323            else:
-324                L_filtered = L_[to_add, :]
-325            y_filtered = y_[to_add]
-326            
-327            if is_df:
-328                X_label = pd.concat([X_label, L_filtered])
-329            else:
-330                X_label = np.concatenate((X_label, L_filtered), axis=0)
-331            y_label = np.concatenate((y_label, y_filtered), axis=0)
-332
-333            #  Remove the instances from the unlabeled set.
-334            to_delete = indexes[to_add]
-335            if is_df:
-336                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
-337            else:
-338                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
-339
-340        return self
-341
-342    def predict(self, X, **kwards):
-343        """Predict class value for X.
-344        For a classification model, the predicted class for each sample in X is returned.
-345        Parameters
-346        ----------
-347        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-348            The input samples.
-349        Returns
-350        -------
-351        y : array-like of shape (n_samples,)
-352            The predicted classes
-353        """
-354        return self._base_estimator.predict(X, **kwards)
-355
-356    def predict_proba(self, X, **kwards):
-357        """Predict class probabilities of the input samples X.
-358        The predicted class probability depends on the ensemble estimator.
-359        Parameters
-360        ----------
-361        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-362            The input samples.
-363        Returns
-364        -------
-365        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-366            The predicted classes
-367        """
-368        return self._base_estimator.predict_proba(X, **kwards)
+            
    129class Setred(BaseEstimator, MetaEstimatorMixin):
+130    """
+131    **Self-training with Editing.**
+132    ----------------------------
+133
+134    Create a SETRED classifier. It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
+135    The main process are:
+136    1. Train a classifier with the labeled data.
+137    2. Create a pool of unlabeled data and select the most confident predictions.
+138    3. Repeat until the maximum number of iterations is reached:
+139        a. Select the most confident predictions from the unlabeled data.
+140        b. Calculate the neighborhood graph of the labeled data and the selected instances from the unlabeled data.
+141        c. Calculate the significance level of the selected instances.
+142        d. Reject the instances that are not significant according their position in the neighborhood graph.
+143        e. Add the selected instances to the labeled data and retrains the classifier.
+144        f. Add new instances to the pool of unlabeled data.
+145    4. Return the classifier trained with the labeled data.
+146
+147    **Example**
+148    -----------
+149    ```python
+150    from sklearn.datasets import load_iris
+151    from sslearn.model_selection import artificial_ssl_dataset
+152    from sslearn.wrapper import Setred
+153
+154    X, y = load_iris(return_X_y=True)
+155    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+156
+157    clf = Setred()
+158    clf.fit(X, y)
+159    clf.score(X_unlabel, y_unlabel)
+160    ```
+161
+162    **References**
+163    ----------
+164    Li, Ming, and Zhi-Hua Zhou. (2005)<br>
+165    SETRED: Self-training with editing,<br>
+166    in <i>Advances in Knowledge Discovery and Data Mining.</i> <br>
+167    Pacific-Asia Conference on Knowledge Discovery and Data Mining <br>
+168    LNAI 3518, Springer, Berlin, Heidelberg, <br>
+169    [10.1007/11430919_71](https://doi.org/10.1007/11430919_71)
+170
+171    """
+172
+173    def __init__(
+174        self,
+175        base_estimator=KNeighborsClassifier(n_neighbors=3),
+176        max_iterations=40,
+177        distance="euclidean",
+178        poolsize=0.25,
+179        rejection_threshold=0.05,
+180        graph_neighbors=1,
+181        random_state=None,
+182        n_jobs=None,
+183    ):
+184        """
+185        Create a SETRED classifier.
+186        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
+187        
+188        Parameters
+189        ----------
+190        base_estimator : ClassifierMixin, optional
+191            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
+192        max_iterations : int, optional
+193            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
+194        distance : str, optional
+195            The distance metric to use for the graph.
+196            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
+197            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
+198            Note that the `cosine` metric uses cosine_distances., by default `euclidean`
+199        poolsize : float, optional
+200            Max number of unlabel instances candidates to pseudolabel, by default 0.25
+201        rejection_threshold : float, optional
+202            significance level, by default 0.05
+203        graph_neighbors : int, optional
+204            Number of neighbors for each sample., by default 1
+205        random_state : int, RandomState instance, optional
+206            controls the randomness of the estimator, by default None
+207        n_jobs : int, optional
+208            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
+209        """
+210        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
+211        self.max_iterations = max_iterations
+212        self.poolsize = poolsize
+213        self.distance = distance
+214        self.rejection_threshold = rejection_threshold
+215        self.graph_neighbors = graph_neighbors
+216        self.random_state = random_state
+217        self.n_jobs = n_jobs
+218
+219    def __create_neighborhood(self, X):
+220        # kneighbors_graph(X, 1, metric=self.distance, n_jobs=self.n_jobs).toarray()
+221        return kneighbors_graph(
+222            X, self.graph_neighbors, metric=self.distance, n_jobs=self.n_jobs, mode="distance"
+223        ).toarray()
+224
+225    def fit(self, X, y, **kwars):
+226        """Build a Setred classifier from the training set (X, y).
+227
+228        Parameters
+229        ----------
+230        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+231            The training input samples.
+232        y : array-like of shape (n_samples,)
+233            The target values (class labels), -1 if unlabeled.
+234
+235        Returns
+236        -------
+237        self: Setred
+238            Fitted estimator.
+239        """        
+240        random_state = check_random_state(self.random_state)
+241
+242        X_label, y_label, X_unlabel = get_dataset(X, y)
+243
+244        is_df = isinstance(X_label, pd.DataFrame)
+245
+246        self.classes_ = np.unique(y_label)
+247
+248        each_iteration_candidates = X_label.shape[0]
+249
+250        pool = int(len(X_unlabel) * self.poolsize)
+251        self._base_estimator = skclone(self.base_estimator)
+252
+253        self._base_estimator.fit(X_label, y_label, **kwars)
+254
+255        y_probabilities = calculate_prior_probability(
+256            y_label
+257        )  # Should probabilities change every iteration or may it keep with the first L?
+258
+259        sort_idx = np.argsort(list(y_probabilities.keys()))
+260
+261        if X_unlabel.shape[0] == 0:
+262            return self
+263
+264        for _ in range(self.max_iterations):
+265            U_ = resample(
+266                X_unlabel, replace=False, n_samples=pool, random_state=random_state
+267            )
+268
+269            if is_df:
+270                U_ = pd.DataFrame(U_, columns=X_label.columns)
+271
+272            raw_predictions = self._base_estimator.predict_proba(U_)
+273            predictions = np.max(raw_predictions, axis=1)
+274            class_predicted = np.argmax(raw_predictions, axis=1)
+275            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
+276            indexes = predictions.argsort()[-each_iteration_candidates:]
+277
+278            if is_df:
+279                L_ = U_.iloc[indexes]
+280            else:
+281                L_ = U_[indexes]
+282            y_ = np.array(
+283                list(
+284                    map(
+285                        lambda x: self._base_estimator.classes_[x],
+286                        class_predicted[indexes],
+287                    )
+288                )
+289            )
+290
+291            if is_df:
+292                pre_L = pd.concat([X_label, L_])
+293            else:
+294                pre_L = np.concatenate((X_label, L_), axis=0)
+295
+296            weights = self.__create_neighborhood(pre_L)
+297            #  Keep only weights for L_
+298            weights = weights[-L_.shape[0]:, :]
+299
+300            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
+301            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
+302            #  Must weights be the inverse of distance?
+303            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
+304
+305            weights_sum = weights.sum(axis=1)
+306            weights_square_sum = (weights ** 2).sum(axis=1)
+307
+308            iid_random = random_state.binomial(
+309                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
+310            )
+311            ji = (iid_random * weights).sum(axis=1)
+312
+313            mu_h0 = p_wrong * weights_sum
+314            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
+315            
+316            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
+317            # z_score = (ji - mu_h0) / sigma_h0
+318            
+319            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
+320            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
+321
+322            if is_df:
+323                L_filtered = L_.iloc[to_add, :]
+324            else:
+325                L_filtered = L_[to_add, :]
+326            y_filtered = y_[to_add]
+327            
+328            if is_df:
+329                X_label = pd.concat([X_label, L_filtered])
+330            else:
+331                X_label = np.concatenate((X_label, L_filtered), axis=0)
+332            y_label = np.concatenate((y_label, y_filtered), axis=0)
+333
+334            #  Remove the instances from the unlabeled set.
+335            to_delete = indexes[to_add]
+336            if is_df:
+337                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
+338            else:
+339                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
+340
+341        return self
+342
+343    def predict(self, X, **kwards):
+344        """Predict class value for X.
+345        For a classification model, the predicted class for each sample in X is returned.
+346        Parameters
+347        ----------
+348        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+349            The input samples.
+350        Returns
+351        -------
+352        y : array-like of shape (n_samples,)
+353            The predicted classes
+354        """
+355        return self._base_estimator.predict(X, **kwards)
+356
+357    def predict_proba(self, X, **kwards):
+358        """Predict class probabilities of the input samples X.
+359        The predicted class probability depends on the ensemble estimator.
+360        Parameters
+361        ----------
+362        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+363            The input samples.
+364        Returns
+365        -------
+366        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+367            The predicted classes
+368        """
+369        return self._base_estimator.predict_proba(X, **kwards)
+370    
+371    def score(self, X, y, sample_weight=None):
+372        """
+373        Return the mean accuracy on the given test data and labels.
+374
+375        In multi-label classification, this is the subset accuracy
+376        which is a harsh metric since you require for each sample that
+377        each label set be correctly predicted.
+378
+379        Parameters
+380        ----------
+381        X : array-like of shape (n_samples, n_features)
+382            Test samples.
+383
+384        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+385            True labels for `X`.
+386
+387        sample_weight : array-like of shape (n_samples,), default=None
+388            Sample weights.
+389
+390        Returns
+391        -------
+392        score : float
+393            Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
+394        """
+395        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
 
    
 
 
@@ -956,51 +985,51 @@ 
    References
    
 
     
    
     
-            
    172    def __init__(
-173        self,
-174        base_estimator=KNeighborsClassifier(n_neighbors=3),
-175        max_iterations=40,
-176        distance="euclidean",
-177        poolsize=0.25,
-178        rejection_threshold=0.05,
-179        graph_neighbors=1,
-180        random_state=None,
-181        n_jobs=None,
-182    ):
-183        """
-184        Create a SETRED classifier.
-185        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
-186        
-187        Parameters
-188        ----------
-189        base_estimator : ClassifierMixin, optional
-190            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
-191        max_iterations : int, optional
-192            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
-193        distance : str, optional
-194            The distance metric to use for the graph.
-195            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
-196            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
-197            Note that the `cosine` metric uses cosine_distances., by default `euclidean`
-198        poolsize : float, optional
-199            Max number of unlabel instances candidates to pseudolabel, by default 0.25
-200        rejection_threshold : float, optional
-201            significance level, by default 0.05
-202        graph_neighbors : int, optional
-203            Number of neighbors for each sample., by default 1
-204        random_state : int, RandomState instance, optional
-205            controls the randomness of the estimator, by default None
-206        n_jobs : int, optional
-207            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
-208        """
-209        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
-210        self.max_iterations = max_iterations
-211        self.poolsize = poolsize
-212        self.distance = distance
-213        self.rejection_threshold = rejection_threshold
-214        self.graph_neighbors = graph_neighbors
-215        self.random_state = random_state
-216        self.n_jobs = n_jobs
+            
    173    def __init__(
+174        self,
+175        base_estimator=KNeighborsClassifier(n_neighbors=3),
+176        max_iterations=40,
+177        distance="euclidean",
+178        poolsize=0.25,
+179        rejection_threshold=0.05,
+180        graph_neighbors=1,
+181        random_state=None,
+182        n_jobs=None,
+183    ):
+184        """
+185        Create a SETRED classifier.
+186        It is a self-training algorithm that uses a rejection mechanism to avoid adding noisy samples to the training set.
+187        
+188        Parameters
+189        ----------
+190        base_estimator : ClassifierMixin, optional
+191            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
+192        max_iterations : int, optional
+193            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
+194        distance : str, optional
+195            The distance metric to use for the graph.
+196            The default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.
+197            For a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.
+198            Note that the `cosine` metric uses cosine_distances., by default `euclidean`
+199        poolsize : float, optional
+200            Max number of unlabel instances candidates to pseudolabel, by default 0.25
+201        rejection_threshold : float, optional
+202            significance level, by default 0.05
+203        graph_neighbors : int, optional
+204            Number of neighbors for each sample., by default 1
+205        random_state : int, RandomState instance, optional
+206            controls the randomness of the estimator, by default None
+207        n_jobs : int, optional
+208            The number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
+209        """
+210        self.base_estimator = check_classifier(base_estimator, can_be_list=False)
+211        self.max_iterations = max_iterations
+212        self.poolsize = poolsize
+213        self.distance = distance
+214        self.rejection_threshold = rejection_threshold
+215        self.graph_neighbors = graph_neighbors
+216        self.random_state = random_state
+217        self.n_jobs = n_jobs
 
    
 
 
@@ -1045,123 +1074,123 @@ 
    Parameters
    
 
     
    
     
-            
    224    def fit(self, X, y, **kwars):
-225        """Build a Setred classifier from the training set (X, y).
-226
-227        Parameters
-228        ----------
-229        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-230            The training input samples.
-231        y : array-like of shape (n_samples,)
-232            The target values (class labels), -1 if unlabeled.
-233
-234        Returns
-235        -------
-236        self: Setred
-237            Fitted estimator.
-238        """        
-239        random_state = check_random_state(self.random_state)
-240
-241        X_label, y_label, X_unlabel = get_dataset(X, y)
-242
-243        is_df = isinstance(X_label, pd.DataFrame)
-244
-245        self.classes_ = np.unique(y_label)
-246
-247        each_iteration_candidates = X_label.shape[0]
-248
-249        pool = int(len(X_unlabel) * self.poolsize)
-250        self._base_estimator = skclone(self.base_estimator)
-251
-252        self._base_estimator.fit(X_label, y_label, **kwars)
-253
-254        y_probabilities = calculate_prior_probability(
-255            y_label
-256        )  # Should probabilities change every iteration or may it keep with the first L?
-257
-258        sort_idx = np.argsort(list(y_probabilities.keys()))
-259
-260        if X_unlabel.shape[0] == 0:
-261            return self
-262
-263        for _ in range(self.max_iterations):
-264            U_ = resample(
-265                X_unlabel, replace=False, n_samples=pool, random_state=random_state
-266            )
-267
-268            if is_df:
-269                U_ = pd.DataFrame(U_, columns=X_label.columns)
-270
-271            raw_predictions = self._base_estimator.predict_proba(U_)
-272            predictions = np.max(raw_predictions, axis=1)
-273            class_predicted = np.argmax(raw_predictions, axis=1)
-274            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
-275            indexes = predictions.argsort()[-each_iteration_candidates:]
-276
-277            if is_df:
-278                L_ = U_.iloc[indexes]
-279            else:
-280                L_ = U_[indexes]
-281            y_ = np.array(
-282                list(
-283                    map(
-284                        lambda x: self._base_estimator.classes_[x],
-285                        class_predicted[indexes],
-286                    )
-287                )
-288            )
-289
-290            if is_df:
-291                pre_L = pd.concat([X_label, L_])
-292            else:
-293                pre_L = np.concatenate((X_label, L_), axis=0)
-294
-295            weights = self.__create_neighborhood(pre_L)
-296            #  Keep only weights for L_
-297            weights = weights[-L_.shape[0]:, :]
-298
-299            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
-300            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
-301            #  Must weights be the inverse of distance?
-302            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
-303
-304            weights_sum = weights.sum(axis=1)
-305            weights_square_sum = (weights ** 2).sum(axis=1)
-306
-307            iid_random = random_state.binomial(
-308                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
-309            )
-310            ji = (iid_random * weights).sum(axis=1)
-311
-312            mu_h0 = p_wrong * weights_sum
-313            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
-314            
-315            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
-316            # z_score = (ji - mu_h0) / sigma_h0
-317            
-318            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
-319            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
-320
-321            if is_df:
-322                L_filtered = L_.iloc[to_add, :]
-323            else:
-324                L_filtered = L_[to_add, :]
-325            y_filtered = y_[to_add]
-326            
-327            if is_df:
-328                X_label = pd.concat([X_label, L_filtered])
-329            else:
-330                X_label = np.concatenate((X_label, L_filtered), axis=0)
-331            y_label = np.concatenate((y_label, y_filtered), axis=0)
-332
-333            #  Remove the instances from the unlabeled set.
-334            to_delete = indexes[to_add]
-335            if is_df:
-336                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
-337            else:
-338                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
-339
-340        return self
+            
    225    def fit(self, X, y, **kwars):
+226        """Build a Setred classifier from the training set (X, y).
+227
+228        Parameters
+229        ----------
+230        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+231            The training input samples.
+232        y : array-like of shape (n_samples,)
+233            The target values (class labels), -1 if unlabeled.
+234
+235        Returns
+236        -------
+237        self: Setred
+238            Fitted estimator.
+239        """        
+240        random_state = check_random_state(self.random_state)
+241
+242        X_label, y_label, X_unlabel = get_dataset(X, y)
+243
+244        is_df = isinstance(X_label, pd.DataFrame)
+245
+246        self.classes_ = np.unique(y_label)
+247
+248        each_iteration_candidates = X_label.shape[0]
+249
+250        pool = int(len(X_unlabel) * self.poolsize)
+251        self._base_estimator = skclone(self.base_estimator)
+252
+253        self._base_estimator.fit(X_label, y_label, **kwars)
+254
+255        y_probabilities = calculate_prior_probability(
+256            y_label
+257        )  # Should probabilities change every iteration or may it keep with the first L?
+258
+259        sort_idx = np.argsort(list(y_probabilities.keys()))
+260
+261        if X_unlabel.shape[0] == 0:
+262            return self
+263
+264        for _ in range(self.max_iterations):
+265            U_ = resample(
+266                X_unlabel, replace=False, n_samples=pool, random_state=random_state
+267            )
+268
+269            if is_df:
+270                U_ = pd.DataFrame(U_, columns=X_label.columns)
+271
+272            raw_predictions = self._base_estimator.predict_proba(U_)
+273            predictions = np.max(raw_predictions, axis=1)
+274            class_predicted = np.argmax(raw_predictions, axis=1)
+275            # Unless a better understanding is given, only the size of L will be used as maximal size of the candidate set.
+276            indexes = predictions.argsort()[-each_iteration_candidates:]
+277
+278            if is_df:
+279                L_ = U_.iloc[indexes]
+280            else:
+281                L_ = U_[indexes]
+282            y_ = np.array(
+283                list(
+284                    map(
+285                        lambda x: self._base_estimator.classes_[x],
+286                        class_predicted[indexes],
+287                    )
+288                )
+289            )
+290
+291            if is_df:
+292                pre_L = pd.concat([X_label, L_])
+293            else:
+294                pre_L = np.concatenate((X_label, L_), axis=0)
+295
+296            weights = self.__create_neighborhood(pre_L)
+297            #  Keep only weights for L_
+298            weights = weights[-L_.shape[0]:, :]
+299
+300            idx = np.searchsorted(np.array(list(y_probabilities.keys())), y_, sorter=sort_idx)
+301            p_wrong = 1 - np.asarray(np.array(list(y_probabilities.values())))[sort_idx][idx]
+302            #  Must weights be the inverse of distance?
+303            weights = np.divide(1, weights, out=np.zeros_like(weights), where=weights != 0)
+304
+305            weights_sum = weights.sum(axis=1)
+306            weights_square_sum = (weights ** 2).sum(axis=1)
+307
+308            iid_random = random_state.binomial(
+309                1, np.repeat(p_wrong, weights.shape[1]).reshape(weights.shape)
+310            )
+311            ji = (iid_random * weights).sum(axis=1)
+312
+313            mu_h0 = p_wrong * weights_sum
+314            sigma_h0 = np.sqrt((1 - p_wrong) * p_wrong * weights_square_sum)
+315            
+316            z_score = np.divide((ji - mu_h0), sigma_h0, out=np.zeros_like(sigma_h0), where=sigma_h0 != 0)
+317            # z_score = (ji - mu_h0) / sigma_h0
+318            
+319            oi = norm.sf(abs(z_score), mu_h0, sigma_h0)
+320            to_add = (oi < self.rejection_threshold) & (z_score < mu_h0)
+321
+322            if is_df:
+323                L_filtered = L_.iloc[to_add, :]
+324            else:
+325                L_filtered = L_[to_add, :]
+326            y_filtered = y_[to_add]
+327            
+328            if is_df:
+329                X_label = pd.concat([X_label, L_filtered])
+330            else:
+331                X_label = np.concatenate((X_label, L_filtered), axis=0)
+332            y_label = np.concatenate((y_label, y_filtered), axis=0)
+333
+334            #  Remove the instances from the unlabeled set.
+335            to_delete = indexes[to_add]
+336            if is_df:
+337                X_unlabel = X_unlabel.drop(index=X_unlabel.index[to_delete])
+338            else:
+339                X_unlabel = np.delete(X_unlabel, to_delete, axis=0)
+340
+341        return self
 
    
 
 
@@ -1197,19 +1226,19 @@ 
    Returns
    
 
     
    
     
-            
    342    def predict(self, X, **kwards):
-343        """Predict class value for X.
-344        For a classification model, the predicted class for each sample in X is returned.
-345        Parameters
-346        ----------
-347        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-348            The input samples.
-349        Returns
-350        -------
-351        y : array-like of shape (n_samples,)
-352            The predicted classes
-353        """
-354        return self._base_estimator.predict(X, **kwards)
+            
    343    def predict(self, X, **kwards):
+344        """Predict class value for X.
+345        For a classification model, the predicted class for each sample in X is returned.
+346        Parameters
+347        ----------
+348        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+349            The input samples.
+350        Returns
+351        -------
+352        y : array-like of shape (n_samples,)
+353            The predicted classes
+354        """
+355        return self._base_estimator.predict(X, **kwards)
 
    
 
 
@@ -1244,19 +1273,19 @@ 
    Returns
    
 
     
    
     
-            
    356    def predict_proba(self, X, **kwards):
-357        """Predict class probabilities of the input samples X.
-358        The predicted class probability depends on the ensemble estimator.
-359        Parameters
-360        ----------
-361        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-362            The input samples.
-363        Returns
-364        -------
-365        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-366            The predicted classes
-367        """
-368        return self._base_estimator.predict_proba(X, **kwards)
+            
    357    def predict_proba(self, X, **kwards):
+358        """Predict class probabilities of the input samples X.
+359        The predicted class probability depends on the ensemble estimator.
+360        Parameters
+361        ----------
+362        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+363            The input samples.
+364        Returns
+365        -------
+366        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+367            The predicted classes
+368        """
+369        return self._base_estimator.predict_proba(X, **kwards)
 
    
 
 
@@ -1279,15 +1308,77 @@ 
    Returns
    
 
    
 
 
+
    +
    + +
    + + def + score(self, X, y, sample_weight=None): + + + +
    + + 
    371    def score(self, X, y, sample_weight=None):
+372        """
+373        Return the mean accuracy on the given test data and labels.
+374
+375        In multi-label classification, this is the subset accuracy
+376        which is a harsh metric since you require for each sample that
+377        each label set be correctly predicted.
+378
+379        Parameters
+380        ----------
+381        X : array-like of shape (n_samples, n_features)
+382            Test samples.
+383
+384        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+385            True labels for `X`.
+386
+387        sample_weight : array-like of shape (n_samples,), default=None
+388            Sample weights.
+389
+390        Returns
+391        -------
+392        score : float
+393            Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
+394        """
+395        return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
+
    + + +

    Return the mean accuracy on the given test data and labels.

    + +

    In multi-label classification, this is the subset accuracy +which is a harsh metric since you require for each sample that +each label set be correctly predicted.

    + +
    Parameters
    + +
      +
    • X (array-like of shape (n_samples, n_features)): +Test samples.
      • +
    • y (array-like of shape (n_samples,) or (n_samples, n_outputs)): +True labels for X.
      • +
    • sample_weight (array-like of shape (n_samples,), default=None): +Sample weights.
      • +
    + +
    Returns
    + +
      +
    • score (float): +Mean accuracy of self.predict(X) w.r.t. y.
      • +
    +
    + +
    Inherited Members
    -
    sklearn.base.ClassifierMixin
    -
    score
    - -
    -
    sklearn.base.BaseEstimator
    +
    sklearn.base.BaseEstimator
    get_params
    set_params
    @@ -1306,318 +1397,318 @@
    Inherited Members
    - 
    453class CoTraining(BaseCoTraining):
-454    """
-455    **CoTraining classifier. Multi-view learning algorithm that uses two classifiers to label instances.**
-456    --------------------------------------------
-457
-458    The main process is:
-459    1. Train each classifier with the labeled instances and their respective view.
-460    2. While max iterations is not reached or any instance is unlabeled:
-461        1. Predict the instances from the unlabeled set.
-462        2. Select the instances that have the same prediction and the predictions are above the threshold.
-463        3. Label the instances with the highest probability, keeping the balance of the classes.
-464        4. Retrain the classifier with the new instances.
-465    3. Combine the probabilities of each classifier.
-466
-467    **Methods**
-468    -------
-469    - `fit`: Fit the model with the labeled instances.
-470    - `predict` : Predict the class for each instance.
-471    - `predict_proba`: Predict the probability for each class.
-472    - `score`: Return the mean accuracy on the given test data and labels.
-473
-474    **Example**
-475    -------
-476    ```python
-477    from sklearn.datasets import load_iris
-478    from sklearn.tree import DecisionTreeClassifier
-479    from sslearn.wrapper import CoTraining
-480    from sslearn.model_selection import artificial_ssl_dataset
-481
-482    X, y = load_iris(return_X_y=True)
-483    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-484    cotraining = CoTraining(DecisionTreeClassifier())
-485    X1 = X[:, [0, 1]]
-486    X2 = X[:, [2, 3]]
-487    cotraining.fit(X1, y, X2) 
+            
    451class CoTraining(BaseCoTraining):
+452    """
+453    **CoTraining classifier. Multi-view learning algorithm that uses two classifiers to label instances.**
+454    --------------------------------------------
+455
+456    The main process is:
+457    1. Train each classifier with the labeled instances and their respective view.
+458    2. While max iterations is not reached or any instance is unlabeled:
+459        1. Predict the instances from the unlabeled set.
+460        2. Select the instances that have the same prediction and the predictions are above the threshold.
+461        3. Label the instances with the highest probability, keeping the balance of the classes.
+462        4. Retrain the classifier with the new instances.
+463    3. Combine the probabilities of each classifier.
+464
+465    **Methods**
+466    -------
+467    - `fit`: Fit the model with the labeled instances.
+468    - `predict` : Predict the class for each instance.
+469    - `predict_proba`: Predict the probability for each class.
+470    - `score`: Return the mean accuracy on the given test data and labels.
+471
+472    **Example**
+473    -------
+474    ```python
+475    from sklearn.datasets import load_iris
+476    from sklearn.tree import DecisionTreeClassifier
+477    from sslearn.wrapper import CoTraining
+478    from sslearn.model_selection import artificial_ssl_dataset
+479
+480    X, y = load_iris(return_X_y=True)
+481    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+482    cotraining = CoTraining(DecisionTreeClassifier())
+483    X1 = X[:, [0, 1]]
+484    X2 = X[:, [2, 3]]
+485    cotraining.fit(X1, y, X2) 
+486    # or
+487    cotraining.fit(X, y, features=[[0, 1], [2, 3]])
 488    # or
-489    cotraining.fit(X, y, features=[[0, 1], [2, 3]])
-490    # or
-491    cotraining = CoTraining(DecisionTreeClassifier(), force_second_view=False)
-492    cotraining.fit(X, y)
-493    ``` 
-494
-495    **References**
-496    ----------
-497    Avrim Blum and Tom Mitchell. (1998).<br>
-498    Combining labeled and unlabeled data with co-training<br>
-499    in <i>Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98)</i>.<br>
-500    Association for Computing Machinery, New York, NY, USA, 92-100.<br>
-501    [10.1145/279943.279962](https://doi.org/10.1145/279943.279962)
-502
-503    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. (2011). <br>
-504    Multi-Class Co-Training Learning for Object and Scene Recognition,<br>
-505    pp. 67-70 in. Nara, Japan. <br>
-506    [http://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf](http://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf)<br>
-507    """
-508
-509    def __init__(
-510        self,
-511        base_estimator=DecisionTreeClassifier(),
-512        second_base_estimator=None,
-513        max_iterations=30,
-514        poolsize=75,
-515        threshold=0.5,
-516        force_second_view=True,
-517        random_state=None
-518    ):
-519        """
-520        Create a CoTraining classifier. 
-521        Multi-view learning algorithm that uses two classifiers to label instances.
-522
-523        Parameters
-524        ----------
-525        base_estimator : ClassifierMixin, optional
-526            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
-527        second_base_estimator : ClassifierMixin, optional
-528            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
-529        max_iterations : int, optional
-530            The number of iterations, by default 30
-531        poolsize : int, optional
-532            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
-533        threshold : float, optional
-534            The threshold for label instances, by default 0.5
-535        force_second_view : bool, optional
-536            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
-537        random_state : int, RandomState instance, optional
-538            controls the randomness of the estimator, by default None
-539
-540        """
-541        self.base_estimator = check_classifier(base_estimator, False)
-542        if second_base_estimator is not None:
-543            second_base_estimator = check_classifier(second_base_estimator, False)
-544        self.second_base_estimator = second_base_estimator
-545        self.max_iterations = max_iterations
-546        self.poolsize = poolsize
-547        self.threshold = threshold
-548        self.force_second_view = force_second_view
-549        self.random_state = random_state
-550
-551    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
-552        """
-553        Build a CoTraining classifier from the training set.
-554
-555        Parameters
-556        ----------
-557        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-558            Array representing the data.
-559        y : array-like of shape (n_samples,)
-560            The target values (class labels), -1 if unlabeled.
-561        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-562            Array representing the data from another view, not compatible with `features`, by default None
-563        features : {list, tuple}, optional
-564            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
-565        number_per_class : {dict}, optional
-566            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
-567
-568        Returns
-569        -------
-570        self: CoTraining
-571            Fitted estimator.
-572        """
-573        rs = check_random_state(self.random_state)
+489    cotraining = CoTraining(DecisionTreeClassifier(), force_second_view=False)
+490    cotraining.fit(X, y)
+491    ``` 
+492
+493    **References**
+494    ----------
+495    Avrim Blum and Tom Mitchell. (1998).<br>
+496    Combining labeled and unlabeled data with co-training<br>
+497    in <i>Proceedings of the eleventh annual conference on Computational learning theory (COLT' 98)</i>.<br>
+498    Association for Computing Machinery, New York, NY, USA, 92-100.<br>
+499    [10.1145/279943.279962](https://doi.org/10.1145/279943.279962)
+500
+501    Han, Xian-Hua, Yen-wei Chen, and Xiang Ruan. (2011). <br>
+502    Multi-Class Co-Training Learning for Object and Scene Recognition,<br>
+503    pp. 67-70 in. Nara, Japan. <br>
+504    [http://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf](http://www.mva-org.jp/Proceedings/2011CD/papers/04-08.pdf)<br>
+505    """
+506
+507    def __init__(
+508        self,
+509        base_estimator=DecisionTreeClassifier(),
+510        second_base_estimator=None,
+511        max_iterations=30,
+512        poolsize=75,
+513        threshold=0.5,
+514        force_second_view=True,
+515        random_state=None
+516    ):
+517        """
+518        Create a CoTraining classifier. 
+519        Multi-view learning algorithm that uses two classifiers to label instances.
+520
+521        Parameters
+522        ----------
+523        base_estimator : ClassifierMixin, optional
+524            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
+525        second_base_estimator : ClassifierMixin, optional
+526            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
+527        max_iterations : int, optional
+528            The number of iterations, by default 30
+529        poolsize : int, optional
+530            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
+531        threshold : float, optional
+532            The threshold for label instances, by default 0.5
+533        force_second_view : bool, optional
+534            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
+535        random_state : int, RandomState instance, optional
+536            controls the randomness of the estimator, by default None
+537
+538        """
+539        self.base_estimator = check_classifier(base_estimator, False)
+540        if second_base_estimator is not None:
+541            second_base_estimator = check_classifier(second_base_estimator, False)
+542        self.second_base_estimator = second_base_estimator
+543        self.max_iterations = max_iterations
+544        self.poolsize = poolsize
+545        self.threshold = threshold
+546        self.force_second_view = force_second_view
+547        self.random_state = random_state
+548
+549    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
+550        """
+551        Build a CoTraining classifier from the training set.
+552
+553        Parameters
+554        ----------
+555        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+556            Array representing the data.
+557        y : array-like of shape (n_samples,)
+558            The target values (class labels), -1 if unlabeled.
+559        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+560            Array representing the data from another view, not compatible with `features`, by default None
+561        features : {list, tuple}, optional
+562            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
+563        number_per_class : {dict}, optional
+564            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
+565
+566        Returns
+567        -------
+568        self: CoTraining
+569            Fitted estimator.
+570        """
+571        rs = check_random_state(self.random_state)
+572
+573        X_label, y_label, X_unlabel = get_dataset(X, y)
 574
-575        X_label, y_label, X_unlabel = get_dataset(X, y)
+575        is_df = isinstance(X_label, pd.DataFrame)
 576
-577        is_df = isinstance(X_label, pd.DataFrame)
-578
-579        if X2 is not None:
-580            X2_label, _, X2_unlabel = get_dataset(X2, y)
-581        elif features is not None:
-582            if is_df:
-583                X2_label = X_label.iloc[:, features[1]]
-584                X2_unlabel = X_unlabel.iloc[:, features[1]]
-585                X_label = X_label.iloc[:, features[0]]
-586                X_unlabel = X_unlabel.iloc[:, features[0]]
-587            else:
-588                X2_label = X_label[:, features[1]]
-589                X2_unlabel = X_unlabel[:, features[1]]
-590                X_label = X_label[:, features[0]]
-591                X_unlabel = X_unlabel[:, features[0]]
-592            self.columns_ = features
-593        elif self.force_second_view:
-594            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
-595        else:
-596            self.columns_ = [list(range(X.shape[1]))] * 2
-597            X2_label = X_label.copy()
-598            X2_unlabel = X_unlabel.copy()
-599
-600        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
-601            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
-602
-603        self.h = [
-604            skclone(self.base_estimator),
-605            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
-606        ]
-607        assert (
-608            X2 is None or features is None
-609        ), "The list of features and X2 cannot be defined at the same time"
-610
-611        self.classes_ = np.unique(y_label)
-612        if number_per_class is None:
-613            number_per_class = calc_number_per_class(y_label)
-614
-615        if X_unlabel.shape[0] < self.poolsize:
-616            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+577        if X2 is not None:
+578            X2_label, _, X2_unlabel = get_dataset(X2, y)
+579        elif features is not None:
+580            if is_df:
+581                X2_label = X_label.iloc[:, features[1]]
+582                X2_unlabel = X_unlabel.iloc[:, features[1]]
+583                X_label = X_label.iloc[:, features[0]]
+584                X_unlabel = X_unlabel.iloc[:, features[0]]
+585            else:
+586                X2_label = X_label[:, features[1]]
+587                X2_unlabel = X_unlabel[:, features[1]]
+588                X_label = X_label[:, features[0]]
+589                X_unlabel = X_unlabel[:, features[0]]
+590            self.columns_ = features
+591        elif self.force_second_view:
+592            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
+593        else:
+594            self.columns_ = [list(range(X.shape[1]))] * 2
+595            X2_label = X_label.copy()
+596            X2_unlabel = X_unlabel.copy()
+597
+598        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
+599            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
+600
+601        self.h = [
+602            skclone(self.base_estimator),
+603            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
+604        ]
+605        assert (
+606            X2 is None or features is None
+607        ), "The list of features and X2 cannot be defined at the same time"
+608
+609        self.classes_ = np.unique(y_label)
+610        if number_per_class is None:
+611            number_per_class = calc_number_per_class(y_label)
+612
+613        if X_unlabel.shape[0] < self.poolsize:
+614            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+615
+616        permutation = rs.permutation(len(X_unlabel))
 617
-618        permutation = rs.permutation(len(X_unlabel))
-619
-620        self.h[0].fit(X_label, y_label)
-621        self.h[1].fit(X2_label, y_label)
-622
-623        it = 0
-624        while it < self.max_iterations and any(permutation):
-625            it += 1
-626
-627            get_index = permutation[:self.poolsize]
-628            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
-629            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
-630
-631            predictions1 = np.max(y1_prob, axis=1)
-632            class_predicted1 = np.argmax(y1_prob, axis=1)
-633
-634            predictions2 = np.max(y2_prob, axis=1)
-635            class_predicted2 = np.argmax(y2_prob, axis=1)
-636
-637            # If two classifier select same instance and bring different predictions then the instance is not labeled
-638            candidates1 = predictions1 > self.threshold
-639            candidates2 = predictions2 > self.threshold
-640            aggreement = class_predicted1 == class_predicted2
-641
-642            full_candidates = candidates1 ^ candidates2
-643            medium_candidates = candidates1 & candidates2 & aggreement
-644            true_candidates1 = full_candidates & candidates1
-645            true_candidates2 = full_candidates & candidates2
-646
-647            # Fill probas and candidate classes.
-648            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
-649            y_class = class_predicted1.copy()
-650
-651            temp_probas1 = predictions1[true_candidates1]
-652            temp_probas2 = predictions2[true_candidates2]
-653            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+618        self.h[0].fit(X_label, y_label)
+619        self.h[1].fit(X2_label, y_label)
+620
+621        it = 0
+622        while it < self.max_iterations and any(permutation):
+623            it += 1
+624
+625            get_index = permutation[:self.poolsize]
+626            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
+627            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
+628
+629            predictions1 = np.max(y1_prob, axis=1)
+630            class_predicted1 = np.argmax(y1_prob, axis=1)
+631
+632            predictions2 = np.max(y2_prob, axis=1)
+633            class_predicted2 = np.argmax(y2_prob, axis=1)
+634
+635            # If two classifier select same instance and bring different predictions then the instance is not labeled
+636            candidates1 = predictions1 > self.threshold
+637            candidates2 = predictions2 > self.threshold
+638            aggreement = class_predicted1 == class_predicted2
+639
+640            full_candidates = candidates1 ^ candidates2
+641            medium_candidates = candidates1 & candidates2 & aggreement
+642            true_candidates1 = full_candidates & candidates1
+643            true_candidates2 = full_candidates & candidates2
+644
+645            # Fill probas and candidate classes.
+646            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
+647            y_class = class_predicted1.copy()
+648
+649            temp_probas1 = predictions1[true_candidates1]
+650            temp_probas2 = predictions2[true_candidates2]
+651            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+652
+653            temp_classes2 = class_predicted2[true_candidates2]
 654
-655            temp_classes2 = class_predicted2[true_candidates2]
-656
-657            y_probas[true_candidates1] = temp_probas1
-658            y_probas[true_candidates2] = temp_probas2
-659            y_probas[medium_candidates] = temp_probasB
-660            y_class[true_candidates2] = temp_classes2
-661
-662            # Select the best candidates
-663            final_instances = list()
-664            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
-665            for c in self.classes_:
-666                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
-667
-668            # Fill the new labeled instances
-669            pseudoy = y_class[final_instances]
-670            y_label = np.append(y_label, pseudoy)
-671
-672            index = permutation[0: self.poolsize][final_instances]
-673            if is_df:
-674                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
-675                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
-676            else:
-677                X_label = np.append(X_label, X_unlabel[index], axis=0)
-678                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+655            y_probas[true_candidates1] = temp_probas1
+656            y_probas[true_candidates2] = temp_probas2
+657            y_probas[medium_candidates] = temp_probasB
+658            y_class[true_candidates2] = temp_classes2
+659
+660            # Select the best candidates
+661            final_instances = list()
+662            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
+663            for c in self.classes_:
+664                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
+665
+666            # Fill the new labeled instances
+667            pseudoy = y_class[final_instances]
+668            y_label = np.append(y_label, pseudoy)
+669
+670            index = permutation[0: self.poolsize][final_instances]
+671            if is_df:
+672                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
+673                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
+674            else:
+675                X_label = np.append(X_label, X_unlabel[index], axis=0)
+676                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+677
+678            permutation = permutation[list(map(lambda x: x not in index, permutation))]
 679
-680            permutation = permutation[list(map(lambda x: x not in index, permutation))]
-681
-682            # Poolsize increments in order double of max instances candidates:
-683            self.poolsize += sum(number_per_class.values()) * 2
-684
-685            self.h[0].fit(X_label, y_label)
-686            self.h[1].fit(X2_label, y_label)
+680            # Poolsize increments in order double of max instances candidates:
+681            self.poolsize += sum(number_per_class.values()) * 2
+682
+683            self.h[0].fit(X_label, y_label)
+684            self.h[1].fit(X2_label, y_label)
+685
+686        self.h_ = self.h
 687
-688        self.h_ = self.h
+688        return self
 689
-690        return self
-691
-692    def predict_proba(self, X, X2=None, **kwards):
-693        """Predict probability for each possible outcome.
-694
-695        Parameters
-696        ----------
-697        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-698            Array representing the data.
-699        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-700            Array representing the data from another view, by default None
-701        Returns
-702        -------
-703        class probabilities: ndarray of shape (n_samples, n_classes)
-704            Array with prediction probabilities.
-705        """
-706        if "columns_" in dir(self):
-707            return super().predict_proba(X, **kwards)
-708        elif "h_" in dir(self):
-709            ys = []
-710            ys.append(self.h_[0].predict_proba(X, **kwards))
-711            ys.append(self.h_[1].predict_proba(X2, **kwards))
-712            y = sum(ys) / len(ys)
-713            return y
-714        else:
-715            raise NotFittedError("Classifier not fitted")
-716
-717    def predict(self, X, X2=None, **kwards):
-718        """Predict the classes of X.
-719        Parameters
-720        ----------
-721        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-722            Array representing the data.
-723        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-724            Array representing the data from another view, by default None
-725
-726        Returns
-727        -------
-728        y : ndarray of shape (n_samples,)
-729            Array with predicted labels.
-730        """
-731        if "columns_" in dir(self):
-732            result = super().predict(X, **kwards)
-733        else:
-734            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
-735            result = self.classes_.take(
-736                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
-737            )
-738        return result
-739
-740    def score(self, X, y, sample_weight=None, **kwards):
-741        """
-742        Return the mean accuracy on the given test data and labels.
-743        In multi-label classification, this is the subset accuracy
-744        which is a harsh metric since you require for each sample that
-745        each label set be correctly predicted.
-746        Parameters
-747        ----------
-748        X : array-like of shape (n_samples, n_features)
-749            Test samples.
-750        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-751            True labels for `X`.
-752        sample_weight : array-like of shape (n_samples,), default=None
-753            Sample weights.
-754        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-755            Array representing the data from another view, by default None
-756        Returns
-757        -------
-758        score : float
-759            Mean accuracy of ``self.predict(X)`` wrt. `y`.
-760        """
-761        if "X2" in kwards:
-762            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
-763        else:
-764            return super().score(X, y, sample_weight=sample_weight)
+690    def predict_proba(self, X, X2=None, **kwards):
+691        """Predict probability for each possible outcome.
+692
+693        Parameters
+694        ----------
+695        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+696            Array representing the data.
+697        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+698            Array representing the data from another view, by default None
+699        Returns
+700        -------
+701        class probabilities: ndarray of shape (n_samples, n_classes)
+702            Array with prediction probabilities.
+703        """
+704        if "columns_" in dir(self):
+705            return super().predict_proba(X, **kwards)
+706        elif "h_" in dir(self):
+707            ys = []
+708            ys.append(self.h_[0].predict_proba(X, **kwards))
+709            ys.append(self.h_[1].predict_proba(X2, **kwards))
+710            y = sum(ys) / len(ys)
+711            return y
+712        else:
+713            raise NotFittedError("Classifier not fitted")
+714
+715    def predict(self, X, X2=None, **kwards):
+716        """Predict the classes of X.
+717        Parameters
+718        ----------
+719        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+720            Array representing the data.
+721        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+722            Array representing the data from another view, by default None
+723
+724        Returns
+725        -------
+726        y : ndarray of shape (n_samples,)
+727            Array with predicted labels.
+728        """
+729        if "columns_" in dir(self):
+730            result = super().predict(X, **kwards)
+731        else:
+732            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
+733            result = self.classes_.take(
+734                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
+735            )
+736        return result
+737
+738    def score(self, X, y, sample_weight=None, **kwards):
+739        """
+740        Return the mean accuracy on the given test data and labels.
+741        In multi-label classification, this is the subset accuracy
+742        which is a harsh metric since you require for each sample that
+743        each label set be correctly predicted.
+744        Parameters
+745        ----------
+746        X : array-like of shape (n_samples, n_features)
+747            Test samples.
+748        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+749            True labels for `X`.
+750        sample_weight : array-like of shape (n_samples,), default=None
+751            Sample weights.
+752        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+753            Array representing the data from another view, by default None
+754        Returns
+755        -------
+756        score : float
+757            Mean accuracy of ``self.predict(X)`` wrt. `y`.
+758        """
+759        if "X2" in kwards:
+760            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
+761        else:
+762            return super().score(X, y, sample_weight=sample_weight)
 
    
 
 
@@ -1693,47 +1784,47 @@ 
    References
    - 
    509    def __init__(
-510        self,
-511        base_estimator=DecisionTreeClassifier(),
-512        second_base_estimator=None,
-513        max_iterations=30,
-514        poolsize=75,
-515        threshold=0.5,
-516        force_second_view=True,
-517        random_state=None
-518    ):
-519        """
-520        Create a CoTraining classifier. 
-521        Multi-view learning algorithm that uses two classifiers to label instances.
-522
-523        Parameters
-524        ----------
-525        base_estimator : ClassifierMixin, optional
-526            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
-527        second_base_estimator : ClassifierMixin, optional
-528            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
-529        max_iterations : int, optional
-530            The number of iterations, by default 30
-531        poolsize : int, optional
-532            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
-533        threshold : float, optional
-534            The threshold for label instances, by default 0.5
-535        force_second_view : bool, optional
-536            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
-537        random_state : int, RandomState instance, optional
-538            controls the randomness of the estimator, by default None
-539
-540        """
-541        self.base_estimator = check_classifier(base_estimator, False)
-542        if second_base_estimator is not None:
-543            second_base_estimator = check_classifier(second_base_estimator, False)
-544        self.second_base_estimator = second_base_estimator
-545        self.max_iterations = max_iterations
-546        self.poolsize = poolsize
-547        self.threshold = threshold
-548        self.force_second_view = force_second_view
-549        self.random_state = random_state
+            
    507    def __init__(
+508        self,
+509        base_estimator=DecisionTreeClassifier(),
+510        second_base_estimator=None,
+511        max_iterations=30,
+512        poolsize=75,
+513        threshold=0.5,
+514        force_second_view=True,
+515        random_state=None
+516    ):
+517        """
+518        Create a CoTraining classifier. 
+519        Multi-view learning algorithm that uses two classifiers to label instances.
+520
+521        Parameters
+522        ----------
+523        base_estimator : ClassifierMixin, optional
+524            The classifier that will be used in the cotraining algorithm on the feature set, by default DecisionTreeClassifier()
+525        second_base_estimator : ClassifierMixin, optional
+526            The classifier that will be used in the cotraining algorithm on another feature set, if none are a clone of base_estimator, by default None
+527        max_iterations : int, optional
+528            The number of iterations, by default 30
+529        poolsize : int, optional
+530            The size of the pool of unlabeled samples from which the classifier can choose, by default 75
+531        threshold : float, optional
+532            The threshold for label instances, by default 0.5
+533        force_second_view : bool, optional
+534            The second classifier needs a different view of the data. If False then a second view will be same as the first, by default True
+535        random_state : int, RandomState instance, optional
+536            controls the randomness of the estimator, by default None
+537
+538        """
+539        self.base_estimator = check_classifier(base_estimator, False)
+540        if second_base_estimator is not None:
+541            second_base_estimator = check_classifier(second_base_estimator, False)
+542        self.second_base_estimator = second_base_estimator
+543        self.max_iterations = max_iterations
+544        self.poolsize = poolsize
+545        self.threshold = threshold
+546        self.force_second_view = force_second_view
+547        self.random_state = random_state
 
    
 
 
@@ -1773,146 +1864,146 @@ 
    Parameters
    - 
    551    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
-552        """
-553        Build a CoTraining classifier from the training set.
-554
-555        Parameters
-556        ----------
-557        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-558            Array representing the data.
-559        y : array-like of shape (n_samples,)
-560            The target values (class labels), -1 if unlabeled.
-561        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-562            Array representing the data from another view, not compatible with `features`, by default None
-563        features : {list, tuple}, optional
-564            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
-565        number_per_class : {dict}, optional
-566            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
-567
-568        Returns
-569        -------
-570        self: CoTraining
-571            Fitted estimator.
-572        """
-573        rs = check_random_state(self.random_state)
+            
    549    def fit(self, X, y, X2=None, features: list = None, number_per_class: dict = None, **kwards):
+550        """
+551        Build a CoTraining classifier from the training set.
+552
+553        Parameters
+554        ----------
+555        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+556            Array representing the data.
+557        y : array-like of shape (n_samples,)
+558            The target values (class labels), -1 if unlabeled.
+559        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+560            Array representing the data from another view, not compatible with `features`, by default None
+561        features : {list, tuple}, optional
+562            list or tuple of two arrays with `feature` index for each subspace view, not compatible with `X2`, by default None
+563        number_per_class : {dict}, optional
+564            dict of class name:integer with the max ammount of instances to label in this class in each iteration, by default None
+565
+566        Returns
+567        -------
+568        self: CoTraining
+569            Fitted estimator.
+570        """
+571        rs = check_random_state(self.random_state)
+572
+573        X_label, y_label, X_unlabel = get_dataset(X, y)
 574
-575        X_label, y_label, X_unlabel = get_dataset(X, y)
+575        is_df = isinstance(X_label, pd.DataFrame)
 576
-577        is_df = isinstance(X_label, pd.DataFrame)
-578
-579        if X2 is not None:
-580            X2_label, _, X2_unlabel = get_dataset(X2, y)
-581        elif features is not None:
-582            if is_df:
-583                X2_label = X_label.iloc[:, features[1]]
-584                X2_unlabel = X_unlabel.iloc[:, features[1]]
-585                X_label = X_label.iloc[:, features[0]]
-586                X_unlabel = X_unlabel.iloc[:, features[0]]
-587            else:
-588                X2_label = X_label[:, features[1]]
-589                X2_unlabel = X_unlabel[:, features[1]]
-590                X_label = X_label[:, features[0]]
-591                X_unlabel = X_unlabel[:, features[0]]
-592            self.columns_ = features
-593        elif self.force_second_view:
-594            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
-595        else:
-596            self.columns_ = [list(range(X.shape[1]))] * 2
-597            X2_label = X_label.copy()
-598            X2_unlabel = X_unlabel.copy()
-599
-600        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
-601            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
-602
-603        self.h = [
-604            skclone(self.base_estimator),
-605            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
-606        ]
-607        assert (
-608            X2 is None or features is None
-609        ), "The list of features and X2 cannot be defined at the same time"
-610
-611        self.classes_ = np.unique(y_label)
-612        if number_per_class is None:
-613            number_per_class = calc_number_per_class(y_label)
-614
-615        if X_unlabel.shape[0] < self.poolsize:
-616            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+577        if X2 is not None:
+578            X2_label, _, X2_unlabel = get_dataset(X2, y)
+579        elif features is not None:
+580            if is_df:
+581                X2_label = X_label.iloc[:, features[1]]
+582                X2_unlabel = X_unlabel.iloc[:, features[1]]
+583                X_label = X_label.iloc[:, features[0]]
+584                X_unlabel = X_unlabel.iloc[:, features[0]]
+585            else:
+586                X2_label = X_label[:, features[1]]
+587                X2_unlabel = X_unlabel[:, features[1]]
+588                X_label = X_label[:, features[0]]
+589                X_unlabel = X_unlabel[:, features[0]]
+590            self.columns_ = features
+591        elif self.force_second_view:
+592            raise AttributeError("Either X2 or features must be defined. CoTraining need another view to train the second classifier")
+593        else:
+594            self.columns_ = [list(range(X.shape[1]))] * 2
+595            X2_label = X_label.copy()
+596            X2_unlabel = X_unlabel.copy()
+597
+598        if is_df and X2_label is not None and not isinstance(X2_label, pd.DataFrame):
+599            raise AttributeError("X and X2 must be both pandas DataFrame or numpy arrays")
+600
+601        self.h = [
+602            skclone(self.base_estimator),
+603            skclone(self.base_estimator) if self.second_base_estimator is None else skclone(self.second_base_estimator)
+604        ]
+605        assert (
+606            X2 is None or features is None
+607        ), "The list of features and X2 cannot be defined at the same time"
+608
+609        self.classes_ = np.unique(y_label)
+610        if number_per_class is None:
+611            number_per_class = calc_number_per_class(y_label)
+612
+613        if X_unlabel.shape[0] < self.poolsize:
+614            warnings.warn(f"Poolsize ({self.poolsize}) is bigger than U ({X_unlabel.shape[0]})")
+615
+616        permutation = rs.permutation(len(X_unlabel))
 617
-618        permutation = rs.permutation(len(X_unlabel))
-619
-620        self.h[0].fit(X_label, y_label)
-621        self.h[1].fit(X2_label, y_label)
-622
-623        it = 0
-624        while it < self.max_iterations and any(permutation):
-625            it += 1
-626
-627            get_index = permutation[:self.poolsize]
-628            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
-629            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
-630
-631            predictions1 = np.max(y1_prob, axis=1)
-632            class_predicted1 = np.argmax(y1_prob, axis=1)
-633
-634            predictions2 = np.max(y2_prob, axis=1)
-635            class_predicted2 = np.argmax(y2_prob, axis=1)
-636
-637            # If two classifier select same instance and bring different predictions then the instance is not labeled
-638            candidates1 = predictions1 > self.threshold
-639            candidates2 = predictions2 > self.threshold
-640            aggreement = class_predicted1 == class_predicted2
-641
-642            full_candidates = candidates1 ^ candidates2
-643            medium_candidates = candidates1 & candidates2 & aggreement
-644            true_candidates1 = full_candidates & candidates1
-645            true_candidates2 = full_candidates & candidates2
-646
-647            # Fill probas and candidate classes.
-648            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
-649            y_class = class_predicted1.copy()
-650
-651            temp_probas1 = predictions1[true_candidates1]
-652            temp_probas2 = predictions2[true_candidates2]
-653            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+618        self.h[0].fit(X_label, y_label)
+619        self.h[1].fit(X2_label, y_label)
+620
+621        it = 0
+622        while it < self.max_iterations and any(permutation):
+623            it += 1
+624
+625            get_index = permutation[:self.poolsize]
+626            y1_prob = self.h[0].predict_proba(X_unlabel[get_index] if not is_df else X_unlabel.iloc[get_index, :])
+627            y2_prob = self.h[1].predict_proba(X2_unlabel[get_index] if not is_df else X2_unlabel.iloc[get_index, :])
+628
+629            predictions1 = np.max(y1_prob, axis=1)
+630            class_predicted1 = np.argmax(y1_prob, axis=1)
+631
+632            predictions2 = np.max(y2_prob, axis=1)
+633            class_predicted2 = np.argmax(y2_prob, axis=1)
+634
+635            # If two classifier select same instance and bring different predictions then the instance is not labeled
+636            candidates1 = predictions1 > self.threshold
+637            candidates2 = predictions2 > self.threshold
+638            aggreement = class_predicted1 == class_predicted2
+639
+640            full_candidates = candidates1 ^ candidates2
+641            medium_candidates = candidates1 & candidates2 & aggreement
+642            true_candidates1 = full_candidates & candidates1
+643            true_candidates2 = full_candidates & candidates2
+644
+645            # Fill probas and candidate classes.
+646            y_probas = np.zeros(predictions1.shape, dtype=predictions1.dtype)
+647            y_class = class_predicted1.copy()
+648
+649            temp_probas1 = predictions1[true_candidates1]
+650            temp_probas2 = predictions2[true_candidates2]
+651            temp_probasB = (predictions1[medium_candidates]+predictions2[medium_candidates])/2
+652
+653            temp_classes2 = class_predicted2[true_candidates2]
 654
-655            temp_classes2 = class_predicted2[true_candidates2]
-656
-657            y_probas[true_candidates1] = temp_probas1
-658            y_probas[true_candidates2] = temp_probas2
-659            y_probas[medium_candidates] = temp_probasB
-660            y_class[true_candidates2] = temp_classes2
-661
-662            # Select the best candidates
-663            final_instances = list()
-664            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
-665            for c in self.classes_:
-666                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
-667
-668            # Fill the new labeled instances
-669            pseudoy = y_class[final_instances]
-670            y_label = np.append(y_label, pseudoy)
-671
-672            index = permutation[0: self.poolsize][final_instances]
-673            if is_df:
-674                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
-675                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
-676            else:
-677                X_label = np.append(X_label, X_unlabel[index], axis=0)
-678                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+655            y_probas[true_candidates1] = temp_probas1
+656            y_probas[true_candidates2] = temp_probas2
+657            y_probas[medium_candidates] = temp_probasB
+658            y_class[true_candidates2] = temp_classes2
+659
+660            # Select the best candidates
+661            final_instances = list()
+662            best_candidates = np.argsort(y_probas, kind="mergesort")[::-1]
+663            for c in self.classes_:
+664                final_instances += list(best_candidates[y_class[best_candidates] == c])[:number_per_class[c]]
+665
+666            # Fill the new labeled instances
+667            pseudoy = y_class[final_instances]
+668            y_label = np.append(y_label, pseudoy)
+669
+670            index = permutation[0: self.poolsize][final_instances]
+671            if is_df:
+672                X_label = pd.concat([X_label, X_unlabel.iloc[index, :]])
+673                X2_label = pd.concat([X2_label, X2_unlabel.iloc[index, :]])
+674            else:
+675                X_label = np.append(X_label, X_unlabel[index], axis=0)
+676                X2_label = np.append(X2_label, X2_unlabel[index], axis=0)
+677
+678            permutation = permutation[list(map(lambda x: x not in index, permutation))]
 679
-680            permutation = permutation[list(map(lambda x: x not in index, permutation))]
-681
-682            # Poolsize increments in order double of max instances candidates:
-683            self.poolsize += sum(number_per_class.values()) * 2
-684
-685            self.h[0].fit(X_label, y_label)
-686            self.h[1].fit(X2_label, y_label)
+680            # Poolsize increments in order double of max instances candidates:
+681            self.poolsize += sum(number_per_class.values()) * 2
+682
+683            self.h[0].fit(X_label, y_label)
+684            self.h[1].fit(X2_label, y_label)
+685
+686        self.h_ = self.h
 687
-688        self.h_ = self.h
-689
-690        return self
+688        return self
 
    
 
 
@@ -1954,30 +2045,30 @@ 
    Returns
    - 
    692    def predict_proba(self, X, X2=None, **kwards):
-693        """Predict probability for each possible outcome.
-694
-695        Parameters
-696        ----------
-697        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-698            Array representing the data.
-699        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-700            Array representing the data from another view, by default None
-701        Returns
-702        -------
-703        class probabilities: ndarray of shape (n_samples, n_classes)
-704            Array with prediction probabilities.
-705        """
-706        if "columns_" in dir(self):
-707            return super().predict_proba(X, **kwards)
-708        elif "h_" in dir(self):
-709            ys = []
-710            ys.append(self.h_[0].predict_proba(X, **kwards))
-711            ys.append(self.h_[1].predict_proba(X2, **kwards))
-712            y = sum(ys) / len(ys)
-713            return y
-714        else:
-715            raise NotFittedError("Classifier not fitted")
+            
    690    def predict_proba(self, X, X2=None, **kwards):
+691        """Predict probability for each possible outcome.
+692
+693        Parameters
+694        ----------
+695        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+696            Array representing the data.
+697        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+698            Array representing the data from another view, by default None
+699        Returns
+700        -------
+701        class probabilities: ndarray of shape (n_samples, n_classes)
+702            Array with prediction probabilities.
+703        """
+704        if "columns_" in dir(self):
+705            return super().predict_proba(X, **kwards)
+706        elif "h_" in dir(self):
+707            ys = []
+708            ys.append(self.h_[0].predict_proba(X, **kwards))
+709            ys.append(self.h_[1].predict_proba(X2, **kwards))
+710            y = sum(ys) / len(ys)
+711            return y
+712        else:
+713            raise NotFittedError("Classifier not fitted")
 
    
 
 
@@ -2013,28 +2104,28 @@ 
    Returns
    - 
    717    def predict(self, X, X2=None, **kwards):
-718        """Predict the classes of X.
-719        Parameters
-720        ----------
-721        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-722            Array representing the data.
-723        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-724            Array representing the data from another view, by default None
-725
-726        Returns
-727        -------
-728        y : ndarray of shape (n_samples,)
-729            Array with predicted labels.
-730        """
-731        if "columns_" in dir(self):
-732            result = super().predict(X, **kwards)
-733        else:
-734            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
-735            result = self.classes_.take(
-736                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
-737            )
-738        return result
+            
    715    def predict(self, X, X2=None, **kwards):
+716        """Predict the classes of X.
+717        Parameters
+718        ----------
+719        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+720            Array representing the data.
+721        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+722            Array representing the data from another view, by default None
+723
+724        Returns
+725        -------
+726        y : ndarray of shape (n_samples,)
+727            Array with predicted labels.
+728        """
+729        if "columns_" in dir(self):
+730            result = super().predict(X, **kwards)
+731        else:
+732            predicted_probabilitiy = self.predict_proba(X, X2, **kwards)
+733            result = self.classes_.take(
+734                (np.argmax(predicted_probabilitiy, axis=1)), axis=0
+735            )
+736        return result
 
    
 
 
@@ -2070,31 +2161,31 @@ 
    Returns
    - 
    740    def score(self, X, y, sample_weight=None, **kwards):
-741        """
-742        Return the mean accuracy on the given test data and labels.
-743        In multi-label classification, this is the subset accuracy
-744        which is a harsh metric since you require for each sample that
-745        each label set be correctly predicted.
-746        Parameters
-747        ----------
-748        X : array-like of shape (n_samples, n_features)
-749            Test samples.
-750        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-751            True labels for `X`.
-752        sample_weight : array-like of shape (n_samples,), default=None
-753            Sample weights.
-754        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
-755            Array representing the data from another view, by default None
-756        Returns
-757        -------
-758        score : float
-759            Mean accuracy of ``self.predict(X)`` wrt. `y`.
-760        """
-761        if "X2" in kwards:
-762            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
-763        else:
-764            return super().score(X, y, sample_weight=sample_weight)
+            
    738    def score(self, X, y, sample_weight=None, **kwards):
+739        """
+740        Return the mean accuracy on the given test data and labels.
+741        In multi-label classification, this is the subset accuracy
+742        which is a harsh metric since you require for each sample that
+743        each label set be correctly predicted.
+744        Parameters
+745        ----------
+746        X : array-like of shape (n_samples, n_features)
+747            Test samples.
+748        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+749            True labels for `X`.
+750        sample_weight : array-like of shape (n_samples,), default=None
+751            Sample weights.
+752        X2 : {array-like, sparse matrix} of shape (n_samples, n_features), optional
+753            Array representing the data from another view, by default None
+754        Returns
+755        -------
+756        score : float
+757            Mean accuracy of ``self.predict(X)`` wrt. `y`.
+758        """
+759        if "X2" in kwards:
+760            return accuracy_score(y, self.predict(X, kwards["X2"]), sample_weight=sample_weight)
+761        else:
+762            return super().score(X, y, sample_weight=sample_weight)
 
    
 
 
@@ -2148,226 +2239,226 @@ 
    Inherited Members
    - 
    1060class CoTrainingByCommittee(BaseCoTraining):
-1061    """
-1062    **Co-Training by Committee classifier.**
-1063    --------------------------------------------
+            
    1058class CoTrainingByCommittee(BaseCoTraining):
+1059    """
+1060    **Co-Training by Committee classifier.**
+1061    --------------------------------------------
+1062
+1063    Create a committee trained by co-training based on the diversity of the classifiers
 1064
-1065    Create a committee trained by co-training based on the diversity of the classifiers
-1066
-1067    The main process is:
-1068    1. Train a committee of classifiers.
-1069    2. Create a pool of unlabeled instances.
-1070    3. While max iterations is not reached or any instance is unlabeled:
-1071        1. Predict the instances from the unlabeled set.
-1072        2. Select the instances with the highest probability.
-1073        3. Label the instances with the highest probability, keeping the balance of the classes but ensuring that at least n instances of each class are added.
-1074        4. Retrain the classifier with the new instances.
-1075    4. Combine the probabilities of each classifier.
-1076
-1077    **Methods**
-1078    -------
-1079    - `fit`: Fit the model with the labeled instances.
-1080    - `predict` : Predict the class for each instance.
-1081    - `predict_proba`: Predict the probability for each class.
-1082    - `score`: Return the mean accuracy on the given test data and labels.
-1083
-1084    **Example**
-1085    -------
-1086    ```python
-1087    from sklearn.datasets import load_iris
-1088    from sslearn.wrapper import CoTrainingByCommittee
-1089    from sslearn.model_selection import artificial_ssl_dataset
-1090
-1091    X, y = load_iris(return_X_y=True)
-1092    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-1093    cotraining = CoTrainingByCommittee()
-1094    cotraining.fit(X, y)
-1095    cotraining.score(X_unlabel, y_unlabel)
-1096    ```
-1097
-1098    **References**
-1099    ----------
-1100    M. F. A. Hady and F. Schwenker,<br>
-1101    Co-training by Committee: A New Semi-supervised Learning Framework,<br>
-1102    in <i>2008 IEEE International Conference on Data Mining Workshops</i>,<br>
-1103    Pisa, 2008, pp. 563-572,  [10.1109/ICDMW.2008.27](https://doi.org/10.1109/ICDMW.2008.27)
-1104    """
-1105
-1106    def __init__(
-1107        self,
-1108        ensemble_estimator=BaggingClassifier(),
-1109        max_iterations=100,
-1110        poolsize=100,
-1111        min_instances_for_class=3,
-1112        random_state=None,
-1113    ):
-1114        """
-1115        Create a committee trained by cotraining based on
-1116        the diversity of classifiers.
-1117
-1118        Parameters
-1119        ----------
-1120        ensemble_estimator : ClassifierMixin, optional
-1121            ensemble method, works without a ensemble as
-1122            self training with pool, by default BaggingClassifier().
-1123        max_iterations : int, optional
-1124            number of iterations of training, -1 if no max iterations, by default 100
-1125        poolsize : int, optional
-1126            max number of unlabeled instances candidates to pseudolabel, by default 100
-1127        random_state : int, RandomState instance, optional
-1128            controls the randomness of the estimator, by default None
-1129
-1130
-1131        """
-1132        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
-1133        self.max_iterations = max_iterations
-1134        self.poolsize = poolsize
-1135        self.random_state = random_state
-1136        self.min_instances_for_class = min_instances_for_class
-1137
-1138    def fit(self, X, y, **kwards):
-1139        """Build a CoTrainingByCommittee classifier from the training set (X, y).
-1140        Parameters
-1141        ----------
-1142        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1143            The training input samples.
-1144        y : array-like of shape (n_samples,)
-1145            The target values (class labels), -1 if unlabel.
-1146        Returns
-1147        -------
-1148        self : CoTrainingByCommittee
-1149            Fitted estimator.
-1150        """
-1151        self.ensemble_estimator = skclone(self.ensemble_estimator)
-1152        random_state = check_random_state(self.random_state)
+1065    The main process is:
+1066    1. Train a committee of classifiers.
+1067    2. Create a pool of unlabeled instances.
+1068    3. While max iterations is not reached or any instance is unlabeled:
+1069        1. Predict the instances from the unlabeled set.
+1070        2. Select the instances with the highest probability.
+1071        3. Label the instances with the highest probability, keeping the balance of the classes but ensuring that at least n instances of each class are added.
+1072        4. Retrain the classifier with the new instances.
+1073    4. Combine the probabilities of each classifier.
+1074
+1075    **Methods**
+1076    -------
+1077    - `fit`: Fit the model with the labeled instances.
+1078    - `predict` : Predict the class for each instance.
+1079    - `predict_proba`: Predict the probability for each class.
+1080    - `score`: Return the mean accuracy on the given test data and labels.
+1081
+1082    **Example**
+1083    -------
+1084    ```python
+1085    from sklearn.datasets import load_iris
+1086    from sslearn.wrapper import CoTrainingByCommittee
+1087    from sslearn.model_selection import artificial_ssl_dataset
+1088
+1089    X, y = load_iris(return_X_y=True)
+1090    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+1091    cotraining = CoTrainingByCommittee()
+1092    cotraining.fit(X, y)
+1093    cotraining.score(X_unlabel, y_unlabel)
+1094    ```
+1095
+1096    **References**
+1097    ----------
+1098    M. F. A. Hady and F. Schwenker,<br>
+1099    Co-training by Committee: A New Semi-supervised Learning Framework,<br>
+1100    in <i>2008 IEEE International Conference on Data Mining Workshops</i>,<br>
+1101    Pisa, 2008, pp. 563-572,  [10.1109/ICDMW.2008.27](https://doi.org/10.1109/ICDMW.2008.27)
+1102    """
+1103
+1104    def __init__(
+1105        self,
+1106        ensemble_estimator=BaggingClassifier(),
+1107        max_iterations=100,
+1108        poolsize=100,
+1109        min_instances_for_class=3,
+1110        random_state=None,
+1111    ):
+1112        """
+1113        Create a committee trained by cotraining based on
+1114        the diversity of classifiers.
+1115
+1116        Parameters
+1117        ----------
+1118        ensemble_estimator : ClassifierMixin, optional
+1119            ensemble method, works without a ensemble as
+1120            self training with pool, by default BaggingClassifier().
+1121        max_iterations : int, optional
+1122            number of iterations of training, -1 if no max iterations, by default 100
+1123        poolsize : int, optional
+1124            max number of unlabeled instances candidates to pseudolabel, by default 100
+1125        random_state : int, RandomState instance, optional
+1126            controls the randomness of the estimator, by default None
+1127
+1128
+1129        """
+1130        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+1131        self.max_iterations = max_iterations
+1132        self.poolsize = poolsize
+1133        self.random_state = random_state
+1134        self.min_instances_for_class = min_instances_for_class
+1135
+1136    def fit(self, X, y, **kwards):
+1137        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+1138        Parameters
+1139        ----------
+1140        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1141            The training input samples.
+1142        y : array-like of shape (n_samples,)
+1143            The target values (class labels), -1 if unlabel.
+1144        Returns
+1145        -------
+1146        self : CoTrainingByCommittee
+1147            Fitted estimator.
+1148        """
+1149        self.ensemble_estimator = skclone(self.ensemble_estimator)
+1150        random_state = check_random_state(self.random_state)
+1151
+1152        X_label, y_prev, X_unlabel = get_dataset(X, y)
 1153
-1154        X_label, y_prev, X_unlabel = get_dataset(X, y)
+1154        is_df = isinstance(X_label, pd.DataFrame)
 1155
-1156        is_df = isinstance(X_label, pd.DataFrame)
-1157
-1158        self.label_encoder_ = LabelEncoder()
-1159        y_label = self.label_encoder_.fit_transform(y_prev)
+1156        self.label_encoder_ = LabelEncoder()
+1157        y_label = self.label_encoder_.fit_transform(y_prev)
+1158
+1159        self.classes_ = self.label_encoder_.classes_
 1160
-1161        self.classes_ = self.label_encoder_.classes_
-1162
-1163        prior = calculate_prior_probability(y_label)
-1164        permutation = random_state.permutation(len(X_unlabel))
+1161        prior = calculate_prior_probability(y_label)
+1162        permutation = random_state.permutation(len(X_unlabel))
+1163
+1164        self.ensemble_estimator.fit(X_label, y_label, **kwards)
 1165
-1166        self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1167
-1168        if X_unlabel.shape[0] == 0:
-1169            return self
-1170
-1171        for _ in range(self.max_iterations):
-1172            if len(permutation) == 0:
-1173                break
-1174            raw_predictions = self.ensemble_estimator.predict_proba(
-1175                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
-1176            )
-1177
-1178            predictions = np.max(raw_predictions, axis=1)
-1179            class_predicted = np.argmax(raw_predictions, axis=1)
-1180
-1181            added = np.zeros(predictions.shape, dtype=bool)
-1182            # First the n (or less) most confidence instances will be selected
-1183            for c in self.ensemble_estimator.classes_:
-1184                condition = class_predicted == c
-1185
-1186                candidates = predictions[condition]
-1187                candidates_bool = np.zeros(predictions.shape, dtype=bool)
-1188                candidates_sub_set = candidates_bool[condition]
-1189
-1190                instances_index_selected = candidates.argsort(kind="mergesort")[
-1191                    -self.min_instances_for_class:
-1192                ]
-1193
-1194                candidates_sub_set[instances_index_selected] = True
-1195                candidates_bool[condition] += candidates_sub_set
+1166        if X_unlabel.shape[0] == 0:
+1167            return self
+1168
+1169        for _ in range(self.max_iterations):
+1170            if len(permutation) == 0:
+1171                break
+1172            raw_predictions = self.ensemble_estimator.predict_proba(
+1173                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+1174            )
+1175
+1176            predictions = np.max(raw_predictions, axis=1)
+1177            class_predicted = np.argmax(raw_predictions, axis=1)
+1178
+1179            added = np.zeros(predictions.shape, dtype=bool)
+1180            # First the n (or less) most confidence instances will be selected
+1181            for c in self.ensemble_estimator.classes_:
+1182                condition = class_predicted == c
+1183
+1184                candidates = predictions[condition]
+1185                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+1186                candidates_sub_set = candidates_bool[condition]
+1187
+1188                instances_index_selected = candidates.argsort(kind="mergesort")[
+1189                    -self.min_instances_for_class:
+1190                ]
+1191
+1192                candidates_sub_set[instances_index_selected] = True
+1193                candidates_bool[condition] += candidates_sub_set
+1194
+1195                added[candidates_bool] = True
 1196
-1197                added[candidates_bool] = True
-1198
-1199            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
-1200            # Pero si se añaden ya en el proceso de proporción no se duplica.
-1201
-1202            # Con esta otra interpretación ignora las n primeras instancias de cada clase
-1203            to_label = choice_with_proportion(
-1204                predictions, class_predicted, prior, extra=self.min_instances_for_class
-1205            )
-1206            added[to_label] = True
-1207
-1208            index = permutation[0: self.poolsize][added]
-1209            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
-1210                [X_label, X_unlabel.iloc[index, :]]
-1211            )
-1212            pseudoy = class_predicted[added]
-1213
-1214            y_label = np.append(y_label, pseudoy)
-1215            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1197            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+1198            # Pero si se añaden ya en el proceso de proporción no se duplica.
+1199
+1200            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+1201            to_label = choice_with_proportion(
+1202                predictions, class_predicted, prior, extra=self.min_instances_for_class
+1203            )
+1204            added[to_label] = True
+1205
+1206            index = permutation[0: self.poolsize][added]
+1207            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+1208                [X_label, X_unlabel.iloc[index, :]]
+1209            )
+1210            pseudoy = class_predicted[added]
+1211
+1212            y_label = np.append(y_label, pseudoy)
+1213            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1214
+1215            self.ensemble_estimator.fit(X_label, y_label, **kwards)
 1216
-1217            self.ensemble_estimator.fit(X_label, y_label, **kwards)
+1217        return self
 1218
-1219        return self
-1220
-1221    def predict(self, X):
-1222        """Predict class value for X.
-1223        For a classification model, the predicted class for each sample in X is returned.
-1224        Parameters
-1225        ----------
-1226        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1227            The input samples.
-1228        Returns
-1229        -------
-1230        y : array-like of shape (n_samples,)
-1231            The predicted classes
-1232        """
-1233        check_is_fitted(self.ensemble_estimator)
-1234        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
-1235
-1236    def predict_proba(self, X):
-1237        """Predict class probabilities of the input samples X.
-1238        The predicted class probability depends on the ensemble estimator.
-1239        Parameters
-1240        ----------
-1241        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1242            The input samples.
-1243        Returns
-1244        -------
-1245        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-1246            The predicted classes
-1247        """
-1248        check_is_fitted(self.ensemble_estimator)
-1249        return self.ensemble_estimator.predict_proba(X)
-1250
-1251    def score(self, X, y, sample_weight=None):
-1252        """Return the mean accuracy on the given test data and labels.
-1253        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
-1254        Parameters
-1255        ----------
-1256        X : array-like of shape (n_samples, n_features)
-1257            Test samples.
-1258        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-1259            True labels for X.
-1260        sample_weight : array-like of shape (n_samples,), optional
-1261            Sample weights., by default None
-1262        Returns
-1263        -------
-1264        score: float
-1265            Mean accuracy of self.predict(X) wrt. y.
-1266        """
-1267        try:
-1268            y = self.label_encoder_.transform(y)
-1269        except ValueError:
-1270            if "le_dict_" not in dir(self):
-1271                self.le_dict_ = dict(
-1272                    zip(
-1273                        self.label_encoder_.classes_,
-1274                        self.label_encoder_.transform(self.label_encoder_.classes_),
-1275                    )
-1276                )
-1277            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
-1278
-1279        return self.ensemble_estimator.score(X, y, sample_weight)
+1219    def predict(self, X):
+1220        """Predict class value for X.
+1221        For a classification model, the predicted class for each sample in X is returned.
+1222        Parameters
+1223        ----------
+1224        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1225            The input samples.
+1226        Returns
+1227        -------
+1228        y : array-like of shape (n_samples,)
+1229            The predicted classes
+1230        """
+1231        check_is_fitted(self.ensemble_estimator)
+1232        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+1233
+1234    def predict_proba(self, X):
+1235        """Predict class probabilities of the input samples X.
+1236        The predicted class probability depends on the ensemble estimator.
+1237        Parameters
+1238        ----------
+1239        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1240            The input samples.
+1241        Returns
+1242        -------
+1243        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1244            The predicted classes
+1245        """
+1246        check_is_fitted(self.ensemble_estimator)
+1247        return self.ensemble_estimator.predict_proba(X)
+1248
+1249    def score(self, X, y, sample_weight=None):
+1250        """Return the mean accuracy on the given test data and labels.
+1251        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1252        Parameters
+1253        ----------
+1254        X : array-like of shape (n_samples, n_features)
+1255            Test samples.
+1256        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1257            True labels for X.
+1258        sample_weight : array-like of shape (n_samples,), optional
+1259            Sample weights., by default None
+1260        Returns
+1261        -------
+1262        score: float
+1263            Mean accuracy of self.predict(X) wrt. y.
+1264        """
+1265        try:
+1266            y = self.label_encoder_.transform(y)
+1267        except ValueError:
+1268            if "le_dict_" not in dir(self):
+1269                self.le_dict_ = dict(
+1270                    zip(
+1271                        self.label_encoder_.classes_,
+1272                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1273                    )
+1274                )
+1275            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1276
+1277        return self.ensemble_estimator.score(X, y, sample_weight)
 
    
 
 
@@ -2433,37 +2524,37 @@ 
    References
    - 
    1106    def __init__(
-1107        self,
-1108        ensemble_estimator=BaggingClassifier(),
-1109        max_iterations=100,
-1110        poolsize=100,
-1111        min_instances_for_class=3,
-1112        random_state=None,
-1113    ):
-1114        """
-1115        Create a committee trained by cotraining based on
-1116        the diversity of classifiers.
-1117
-1118        Parameters
-1119        ----------
-1120        ensemble_estimator : ClassifierMixin, optional
-1121            ensemble method, works without a ensemble as
-1122            self training with pool, by default BaggingClassifier().
-1123        max_iterations : int, optional
-1124            number of iterations of training, -1 if no max iterations, by default 100
-1125        poolsize : int, optional
-1126            max number of unlabeled instances candidates to pseudolabel, by default 100
-1127        random_state : int, RandomState instance, optional
-1128            controls the randomness of the estimator, by default None
-1129
-1130
-1131        """
-1132        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
-1133        self.max_iterations = max_iterations
-1134        self.poolsize = poolsize
-1135        self.random_state = random_state
-1136        self.min_instances_for_class = min_instances_for_class
+            
    1104    def __init__(
+1105        self,
+1106        ensemble_estimator=BaggingClassifier(),
+1107        max_iterations=100,
+1108        poolsize=100,
+1109        min_instances_for_class=3,
+1110        random_state=None,
+1111    ):
+1112        """
+1113        Create a committee trained by cotraining based on
+1114        the diversity of classifiers.
+1115
+1116        Parameters
+1117        ----------
+1118        ensemble_estimator : ClassifierMixin, optional
+1119            ensemble method, works without a ensemble as
+1120            self training with pool, by default BaggingClassifier().
+1121        max_iterations : int, optional
+1122            number of iterations of training, -1 if no max iterations, by default 100
+1123        poolsize : int, optional
+1124            max number of unlabeled instances candidates to pseudolabel, by default 100
+1125        random_state : int, RandomState instance, optional
+1126            controls the randomness of the estimator, by default None
+1127
+1128
+1129        """
+1130        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+1131        self.max_iterations = max_iterations
+1132        self.poolsize = poolsize
+1133        self.random_state = random_state
+1134        self.min_instances_for_class = min_instances_for_class
 
    
 
 
@@ -2498,88 +2589,88 @@ 
    Parameters
    - 
    1138    def fit(self, X, y, **kwards):
-1139        """Build a CoTrainingByCommittee classifier from the training set (X, y).
-1140        Parameters
-1141        ----------
-1142        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1143            The training input samples.
-1144        y : array-like of shape (n_samples,)
-1145            The target values (class labels), -1 if unlabel.
-1146        Returns
-1147        -------
-1148        self : CoTrainingByCommittee
-1149            Fitted estimator.
-1150        """
-1151        self.ensemble_estimator = skclone(self.ensemble_estimator)
-1152        random_state = check_random_state(self.random_state)
+            
    1136    def fit(self, X, y, **kwards):
+1137        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+1138        Parameters
+1139        ----------
+1140        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1141            The training input samples.
+1142        y : array-like of shape (n_samples,)
+1143            The target values (class labels), -1 if unlabel.
+1144        Returns
+1145        -------
+1146        self : CoTrainingByCommittee
+1147            Fitted estimator.
+1148        """
+1149        self.ensemble_estimator = skclone(self.ensemble_estimator)
+1150        random_state = check_random_state(self.random_state)
+1151
+1152        X_label, y_prev, X_unlabel = get_dataset(X, y)
 1153
-1154        X_label, y_prev, X_unlabel = get_dataset(X, y)
+1154        is_df = isinstance(X_label, pd.DataFrame)
 1155
-1156        is_df = isinstance(X_label, pd.DataFrame)
-1157
-1158        self.label_encoder_ = LabelEncoder()
-1159        y_label = self.label_encoder_.fit_transform(y_prev)
+1156        self.label_encoder_ = LabelEncoder()
+1157        y_label = self.label_encoder_.fit_transform(y_prev)
+1158
+1159        self.classes_ = self.label_encoder_.classes_
 1160
-1161        self.classes_ = self.label_encoder_.classes_
-1162
-1163        prior = calculate_prior_probability(y_label)
-1164        permutation = random_state.permutation(len(X_unlabel))
+1161        prior = calculate_prior_probability(y_label)
+1162        permutation = random_state.permutation(len(X_unlabel))
+1163
+1164        self.ensemble_estimator.fit(X_label, y_label, **kwards)
 1165
-1166        self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1167
-1168        if X_unlabel.shape[0] == 0:
-1169            return self
-1170
-1171        for _ in range(self.max_iterations):
-1172            if len(permutation) == 0:
-1173                break
-1174            raw_predictions = self.ensemble_estimator.predict_proba(
-1175                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
-1176            )
-1177
-1178            predictions = np.max(raw_predictions, axis=1)
-1179            class_predicted = np.argmax(raw_predictions, axis=1)
-1180
-1181            added = np.zeros(predictions.shape, dtype=bool)
-1182            # First the n (or less) most confidence instances will be selected
-1183            for c in self.ensemble_estimator.classes_:
-1184                condition = class_predicted == c
-1185
-1186                candidates = predictions[condition]
-1187                candidates_bool = np.zeros(predictions.shape, dtype=bool)
-1188                candidates_sub_set = candidates_bool[condition]
-1189
-1190                instances_index_selected = candidates.argsort(kind="mergesort")[
-1191                    -self.min_instances_for_class:
-1192                ]
-1193
-1194                candidates_sub_set[instances_index_selected] = True
-1195                candidates_bool[condition] += candidates_sub_set
+1166        if X_unlabel.shape[0] == 0:
+1167            return self
+1168
+1169        for _ in range(self.max_iterations):
+1170            if len(permutation) == 0:
+1171                break
+1172            raw_predictions = self.ensemble_estimator.predict_proba(
+1173                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+1174            )
+1175
+1176            predictions = np.max(raw_predictions, axis=1)
+1177            class_predicted = np.argmax(raw_predictions, axis=1)
+1178
+1179            added = np.zeros(predictions.shape, dtype=bool)
+1180            # First the n (or less) most confidence instances will be selected
+1181            for c in self.ensemble_estimator.classes_:
+1182                condition = class_predicted == c
+1183
+1184                candidates = predictions[condition]
+1185                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+1186                candidates_sub_set = candidates_bool[condition]
+1187
+1188                instances_index_selected = candidates.argsort(kind="mergesort")[
+1189                    -self.min_instances_for_class:
+1190                ]
+1191
+1192                candidates_sub_set[instances_index_selected] = True
+1193                candidates_bool[condition] += candidates_sub_set
+1194
+1195                added[candidates_bool] = True
 1196
-1197                added[candidates_bool] = True
-1198
-1199            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
-1200            # Pero si se añaden ya en el proceso de proporción no se duplica.
-1201
-1202            # Con esta otra interpretación ignora las n primeras instancias de cada clase
-1203            to_label = choice_with_proportion(
-1204                predictions, class_predicted, prior, extra=self.min_instances_for_class
-1205            )
-1206            added[to_label] = True
-1207
-1208            index = permutation[0: self.poolsize][added]
-1209            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
-1210                [X_label, X_unlabel.iloc[index, :]]
-1211            )
-1212            pseudoy = class_predicted[added]
-1213
-1214            y_label = np.append(y_label, pseudoy)
-1215            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1197            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+1198            # Pero si se añaden ya en el proceso de proporción no se duplica.
+1199
+1200            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+1201            to_label = choice_with_proportion(
+1202                predictions, class_predicted, prior, extra=self.min_instances_for_class
+1203            )
+1204            added[to_label] = True
+1205
+1206            index = permutation[0: self.poolsize][added]
+1207            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+1208                [X_label, X_unlabel.iloc[index, :]]
+1209            )
+1210            pseudoy = class_predicted[added]
+1211
+1212            y_label = np.append(y_label, pseudoy)
+1213            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1214
+1215            self.ensemble_estimator.fit(X_label, y_label, **kwards)
 1216
-1217            self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1218
-1219        return self
+1217        return self
 
    
 
 
@@ -2615,20 +2706,20 @@ 
    Returns
    - 
    1221    def predict(self, X):
-1222        """Predict class value for X.
-1223        For a classification model, the predicted class for each sample in X is returned.
-1224        Parameters
-1225        ----------
-1226        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1227            The input samples.
-1228        Returns
-1229        -------
-1230        y : array-like of shape (n_samples,)
-1231            The predicted classes
-1232        """
-1233        check_is_fitted(self.ensemble_estimator)
-1234        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+            
    1219    def predict(self, X):
+1220        """Predict class value for X.
+1221        For a classification model, the predicted class for each sample in X is returned.
+1222        Parameters
+1223        ----------
+1224        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1225            The input samples.
+1226        Returns
+1227        -------
+1228        y : array-like of shape (n_samples,)
+1229            The predicted classes
+1230        """
+1231        check_is_fitted(self.ensemble_estimator)
+1232        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
 
    
 
 
@@ -2663,20 +2754,20 @@ 
    Returns
    - 
    1236    def predict_proba(self, X):
-1237        """Predict class probabilities of the input samples X.
-1238        The predicted class probability depends on the ensemble estimator.
-1239        Parameters
-1240        ----------
-1241        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1242            The input samples.
-1243        Returns
-1244        -------
-1245        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-1246            The predicted classes
-1247        """
-1248        check_is_fitted(self.ensemble_estimator)
-1249        return self.ensemble_estimator.predict_proba(X)
+            
    1234    def predict_proba(self, X):
+1235        """Predict class probabilities of the input samples X.
+1236        The predicted class probability depends on the ensemble estimator.
+1237        Parameters
+1238        ----------
+1239        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1240            The input samples.
+1241        Returns
+1242        -------
+1243        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1244            The predicted classes
+1245        """
+1246        check_is_fitted(self.ensemble_estimator)
+1247        return self.ensemble_estimator.predict_proba(X)
 
    
 
 
@@ -2711,35 +2802,35 @@ 
    Returns
    - 
    1251    def score(self, X, y, sample_weight=None):
-1252        """Return the mean accuracy on the given test data and labels.
-1253        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
-1254        Parameters
-1255        ----------
-1256        X : array-like of shape (n_samples, n_features)
-1257            Test samples.
-1258        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-1259            True labels for X.
-1260        sample_weight : array-like of shape (n_samples,), optional
-1261            Sample weights., by default None
-1262        Returns
-1263        -------
-1264        score: float
-1265            Mean accuracy of self.predict(X) wrt. y.
-1266        """
-1267        try:
-1268            y = self.label_encoder_.transform(y)
-1269        except ValueError:
-1270            if "le_dict_" not in dir(self):
-1271                self.le_dict_ = dict(
-1272                    zip(
-1273                        self.label_encoder_.classes_,
-1274                        self.label_encoder_.transform(self.label_encoder_.classes_),
-1275                    )
-1276                )
-1277            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
-1278
-1279        return self.ensemble_estimator.score(X, y, sample_weight)
+            
    1249    def score(self, X, y, sample_weight=None):
+1250        """Return the mean accuracy on the given test data and labels.
+1251        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1252        Parameters
+1253        ----------
+1254        X : array-like of shape (n_samples, n_features)
+1255            Test samples.
+1256        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1257            True labels for X.
+1258        sample_weight : array-like of shape (n_samples,), optional
+1259            Sample weights., by default None
+1260        Returns
+1261        -------
+1262        score: float
+1263            Mean accuracy of self.predict(X) wrt. y.
+1264        """
+1265        try:
+1266            y = self.label_encoder_.transform(y)
+1267        except ValueError:
+1268            if "le_dict_" not in dir(self):
+1269                self.le_dict_ = dict(
+1270                    zip(
+1271                        self.label_encoder_.classes_,
+1272                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1273                    )
+1274                )
+1275            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1276
+1277        return self.ensemble_estimator.score(X, y, sample_weight)
 
    
 
 
@@ -2789,339 +2880,339 @@ 
    Inherited Members
    - 
    118class DemocraticCoLearning(BaseCoTraining):
-119    """
-120    **Democratic Co-learning. Ensemble of classifiers of different types.**
-121    --------------------------------------------
-122
-123    A iterative algorithm that uses a ensemble of classifiers to label instances.
-124    The main process is:
-125    1. Train each classifier with the labeled instances.
-126    2. While any classifier is retrained:
-127        1. Predict the instances from the unlabeled set.
-128        2. Calculate the confidence interval for each classifier for define weights.
-129        3. Calculate the weighted vote for each instance.
-130        4. Calculate the majority vote for each instance.
-131        5. Select the instances to label if majority vote is the same as weighted vote.
-132        6. Select the instances to retrain the classifier, if `only_mislabeled` is False then select all instances, else select only mislabeled instances for each classifier.
-133        7. Retrain the classifier with the new instances if the error rate is lower than the previous iteration.
-134    3. Ignore the classifiers with confidence interval lower than 0.5.
-135    4. Combine the probabilities of each classifier.
-136
-137    **Methods**
-138    -------
-139    - `fit`: Fit the model with the labeled instances.
-140    - `predict` : Predict the class for each instance.
-141    - `predict_proba`: Predict the probability for each class.
-142    - `score`: Return the mean accuracy on the given test data and labels.
-143    
-144    
-145    **Example**
-146    -------
-147    ```python
-148    from sklearn.datasets import load_iris
-149    from sklearn.tree import DecisionTreeClassifier
-150    from sklearn.naive_bayes import GaussianNB
-151    from sklearn.neighbors import KNeighborsClassifier
-152    from sslearn.wrapper import DemocraticCoLearning
-153    from sslearn.model_selection import artificial_ssl_dataset
-154
-155    X, y = load_iris(return_X_y=True)
-156    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-157    dcl = DemocraticCoLearning(base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)])
-158    dcl.fit(X, y)
-159    dcl.score(X_unlabel, y_unlabel)
-160    ``` 
-161
-162    **References**
-163    ----------
-164    Y. Zhou and S. Goldman, (2004) <br>
-165    Democratic co-learning, <br>
-166    in <i>16th IEEE International Conference on Tools with Artificial Intelligence</i>,<br>
-167    pp. 594-602, [10.1109/ICTAI.2004.48](https://doi.org/10.1109/ICTAI.2004.48).
-168    """
-169
-170    def __init__(
-171        self,
-172        base_estimator=[
-173            DecisionTreeClassifier(),
-174            GaussianNB(),
-175            KNeighborsClassifier(n_neighbors=3),
-176        ],
-177        n_estimators=None,
-178        expand_only_mislabeled=True,
-179        alpha=0.95,
-180        q_exp=2,
-181        random_state=None
-182    ):
-183        """
-184        Democratic Co-learning. Ensemble of classifiers of different types.
-185
-186        Parameters
-187        ----------
-188        base_estimator : {ClassifierMixin, list}, optional
-189            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
-190        n_estimators : int, optional
-191            number of base_estimators to use. None if base_estimator is a list, by default None
-192        expand_only_mislabeled : bool, optional
-193            expand only mislabeled instances by itself, by default True
-194        alpha : float, optional
-195            confidence level, by default 0.95
-196        q_exp : int, optional
-197            exponent for the estimation for error rate, by default 2
-198        random_state : int, RandomState instance, optional
-199            controls the randomness of the estimator, by default None
-200        Raises
-201        ------
-202        AttributeError
-203            If n_estimators is None and base_estimator is not a list
-204        """
-205
-206        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
-207            estimators = list()
-208            random_available = True
-209            rand = check_random_state(random_state)
-210            if "random_state" not in dir(base_estimator):
-211                warnings.warn(
-212                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
-213                    ConvergenceWarning,
-214                )
-215                random_available = False
-216            for i in range(n_estimators):
-217                estimators.append(skclone(base_estimator))
-218                if random_available:
-219                    estimators[i].random_state = rand.randint(0, 1e5)
-220            self.base_estimator = estimators
-221
-222        elif isinstance(base_estimator, list):
-223            self.base_estimator = base_estimator
-224        else:
-225            raise AttributeError(
-226                "If `n_estimators` is None then `base_estimator` must be a `list`."
-227            )
-228        self.base_estimator = check_classifier(self.base_estimator)
-229        self.n_estimators = len(self.base_estimator)
-230        self.one_hot = OneHotEncoder(sparse_output=False)
-231        self.expand_only_mislabeled = expand_only_mislabeled
-232
-233        self.alpha = alpha
-234        self.q_exp = q_exp
-235        self.random_state = random_state
-236
-237    def __weighted_y(self, predictions, weights):
-238        y_complete = np.sum(
-239            [
-240                self.one_hot.transform(p.reshape(-1, 1)) * wi
-241                for p, wi in zip(predictions, weights)
-242            ],
-243            0,
-244        )
-245        y_zeros = np.zeros(y_complete.shape)
-246        y_zeros[np.arange(y_complete.shape[0]), y_complete.argmax(1)] = 1
+            
    116class DemocraticCoLearning(BaseCoTraining):
+117    """
+118    **Democratic Co-learning. Ensemble of classifiers of different types.**
+119    --------------------------------------------
+120
+121    A iterative algorithm that uses a ensemble of classifiers to label instances.
+122    The main process is:
+123    1. Train each classifier with the labeled instances.
+124    2. While any classifier is retrained:
+125        1. Predict the instances from the unlabeled set.
+126        2. Calculate the confidence interval for each classifier for define weights.
+127        3. Calculate the weighted vote for each instance.
+128        4. Calculate the majority vote for each instance.
+129        5. Select the instances to label if majority vote is the same as weighted vote.
+130        6. Select the instances to retrain the classifier, if `only_mislabeled` is False then select all instances, else select only mislabeled instances for each classifier.
+131        7. Retrain the classifier with the new instances if the error rate is lower than the previous iteration.
+132    3. Ignore the classifiers with confidence interval lower than 0.5.
+133    4. Combine the probabilities of each classifier.
+134
+135    **Methods**
+136    -------
+137    - `fit`: Fit the model with the labeled instances.
+138    - `predict` : Predict the class for each instance.
+139    - `predict_proba`: Predict the probability for each class.
+140    - `score`: Return the mean accuracy on the given test data and labels.
+141    
+142    
+143    **Example**
+144    -------
+145    ```python
+146    from sklearn.datasets import load_iris
+147    from sklearn.tree import DecisionTreeClassifier
+148    from sklearn.naive_bayes import GaussianNB
+149    from sklearn.neighbors import KNeighborsClassifier
+150    from sslearn.wrapper import DemocraticCoLearning
+151    from sslearn.model_selection import artificial_ssl_dataset
+152
+153    X, y = load_iris(return_X_y=True)
+154    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+155    dcl = DemocraticCoLearning(base_estimator=[DecisionTreeClassifier(), GaussianNB(), KNeighborsClassifier(n_neighbors=3)])
+156    dcl.fit(X, y)
+157    dcl.score(X_unlabel, y_unlabel)
+158    ``` 
+159
+160    **References**
+161    ----------
+162    Y. Zhou and S. Goldman, (2004) <br>
+163    Democratic co-learning, <br>
+164    in <i>16th IEEE International Conference on Tools with Artificial Intelligence</i>,<br>
+165    pp. 594-602, [10.1109/ICTAI.2004.48](https://doi.org/10.1109/ICTAI.2004.48).
+166    """
+167
+168    def __init__(
+169        self,
+170        base_estimator=[
+171            DecisionTreeClassifier(),
+172            GaussianNB(),
+173            KNeighborsClassifier(n_neighbors=3),
+174        ],
+175        n_estimators=None,
+176        expand_only_mislabeled=True,
+177        alpha=0.95,
+178        q_exp=2,
+179        random_state=None
+180    ):
+181        """
+182        Democratic Co-learning. Ensemble of classifiers of different types.
+183
+184        Parameters
+185        ----------
+186        base_estimator : {ClassifierMixin, list}, optional
+187            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
+188        n_estimators : int, optional
+189            number of base_estimators to use. None if base_estimator is a list, by default None
+190        expand_only_mislabeled : bool, optional
+191            expand only mislabeled instances by itself, by default True
+192        alpha : float, optional
+193            confidence level, by default 0.95
+194        q_exp : int, optional
+195            exponent for the estimation for error rate, by default 2
+196        random_state : int, RandomState instance, optional
+197            controls the randomness of the estimator, by default None
+198        Raises
+199        ------
+200        AttributeError
+201            If n_estimators is None and base_estimator is not a list
+202        """
+203
+204        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
+205            estimators = list()
+206            random_available = True
+207            rand = check_random_state(random_state)
+208            if "random_state" not in dir(base_estimator):
+209                warnings.warn(
+210                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
+211                    ConvergenceWarning,
+212                )
+213                random_available = False
+214            for i in range(n_estimators):
+215                estimators.append(skclone(base_estimator))
+216                if random_available:
+217                    estimators[i].random_state = rand.randint(0, 1e5)
+218            self.base_estimator = estimators
+219
+220        elif isinstance(base_estimator, list):
+221            self.base_estimator = base_estimator
+222        else:
+223            raise AttributeError(
+224                "If `n_estimators` is None then `base_estimator` must be a `list`."
+225            )
+226        self.base_estimator = check_classifier(self.base_estimator)
+227        self.n_estimators = len(self.base_estimator)
+228        self.one_hot = OneHotEncoder(sparse_output=False)
+229        self.expand_only_mislabeled = expand_only_mislabeled
+230
+231        self.alpha = alpha
+232        self.q_exp = q_exp
+233        self.random_state = random_state
+234
+235    def __weighted_y(self, predictions, weights):
+236        y_complete = np.sum(
+237            [
+238                self.one_hot.transform(p.reshape(-1, 1)) * wi
+239                for p, wi in zip(predictions, weights)
+240            ],
+241            0,
+242        )
+243        y_zeros = np.zeros(y_complete.shape)
+244        y_zeros[np.arange(y_complete.shape[0]), y_complete.argmax(1)] = 1
+245
+246        return self.one_hot.inverse_transform(y_zeros).flatten()
 247
-248        return self.one_hot.inverse_transform(y_zeros).flatten()
-249
-250    def __calcule_last_confidences(self, X, y):
-251        """Calculate the confidence of each learner
-252
-253        Parameters
-254        ----------
-255        X : array-like
-256            Set of instances
-257        y : array-like
-258            Set of classes for each instance
-259        """
-260        w = []
-261        w = [sum(confidence_interval(X, H, y, self.alpha)) / 2 for H in self.h_]
-262        self.confidences_ = w
-263
-264    def fit(self, X, y, estimator_kwards=None):
-265        """Fit Democratic-Co classifier
-266
-267        Parameters
-268        ----------
-269        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-270            The training input samples.
-271        y : array-like of shape (n_samples,)
-272            The target values (class labels), -1 if unlabel.
-273        estimator_kwards : {list, dict}, optional
-274            list of kwards for each estimator or kwards for all estimators, by default None
-275
-276        Returns
-277        -------
-278        self : DemocraticCoLearning
-279            fitted classifier
-280        """
+248    def __calcule_last_confidences(self, X, y):
+249        """Calculate the confidence of each learner
+250
+251        Parameters
+252        ----------
+253        X : array-like
+254            Set of instances
+255        y : array-like
+256            Set of classes for each instance
+257        """
+258        w = []
+259        w = [sum(confidence_interval(X, H, y, self.alpha)) / 2 for H in self.h_]
+260        self.confidences_ = w
+261
+262    def fit(self, X, y, estimator_kwards=None):
+263        """Fit Democratic-Co classifier
+264
+265        Parameters
+266        ----------
+267        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+268            The training input samples.
+269        y : array-like of shape (n_samples,)
+270            The target values (class labels), -1 if unlabel.
+271        estimator_kwards : {list, dict}, optional
+272            list of kwards for each estimator or kwards for all estimators, by default None
+273
+274        Returns
+275        -------
+276        self : DemocraticCoLearning
+277            fitted classifier
+278        """
+279
+280        X_label, y_label, X_unlabel = get_dataset(X, y)
 281
-282        X_label, y_label, X_unlabel = get_dataset(X, y)
+282        is_df = isinstance(X_label, pd.DataFrame)
 283
-284        is_df = isinstance(X_label, pd.DataFrame)
-285
-286        self.classes_ = np.unique(y_label)
-287        self.encoder = LabelEncoder().fit(y_label)
-288        y_label = self.encoder.transform(y_label)
+284        self.classes_ = np.unique(y_label)
+285        self.encoder = LabelEncoder().fit(y_label)
+286        y_label = self.encoder.transform(y_label)
+287
+288        self.one_hot.fit(y_label.reshape(-1, 1))
 289
-290        self.one_hot.fit(y_label.reshape(-1, 1))
-291
-292        L = [X_label] * self.n_estimators
-293        Ly = [y_label] * self.n_estimators
-294        # This variable prevents duplicate instances.
-295        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
-296        e = [0] * self.n_estimators
-297
-298        if estimator_kwards is None:
-299            estimator_kwards = [{}] * self.n_estimators
-300
-301        changed = True
-302        iteration = 0
-303        while changed:
-304            changed = False
-305            iteration_dict = {}
-306            iteration += 1
-307
-308            for i in range(self.n_estimators):
-309                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
-310            if X_unlabel.shape[0] == 0:
-311                break
-312            # Majority Vote
-313            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
-314            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
-315            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
-316            #     0
-317            # ].flatten()  # K in pseudocode
-318
-319            L_ = [[]] * self.n_estimators
-320            Ly_ = [[]] * self.n_estimators
-321
-322            # Calculate confidence interval
-323            conf_interval = [
-324                confidence_interval(
-325                    X_label,
-326                    H,
-327                    y_label,
-328                    self.alpha
-329                )
-330                for H in self.base_estimator
-331            ]
-332
-333            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
-334            iteration_dict["weights"] = {
-335                "cl" + str(i): (l, h, w)
-336                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
-337            }
-338            # weighted vote
-339            weighted_class = self.__weighted_y(predictions, weights)
-340
-341            # If `weighted_class` is equal as `majority_class` then
-342            # the sum of classifier's weights of max voted class
-343            # is greater than the max of sum of classifier's weights
-344            # from another classes.
-345
-346            candidates = weighted_class == majority_class
-347            candidates_bool = list()
-348
-349            if not self.expand_only_mislabeled:
-350                all_same_list = list()
-351                for i in range(1, self.n_estimators):
-352                    all_same_list.append(predictions[i] == predictions[i - 1])
-353                all_same = np.logical_and(*all_same_list)
-354            # new_instances = []
-355            for i in range(self.n_estimators):
-356
-357                mispredictions = predictions[i] != weighted_class
-358                # An instance from U are added to Li' only if:
-359                #   It is a misprediction for i
-360                #   It is a candidate (weighted_class are same majority_class)
-361                #   It hasn't been added yet in Li
+290        L = [X_label] * self.n_estimators
+291        Ly = [y_label] * self.n_estimators
+292        # This variable prevents duplicate instances.
+293        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
+294        e = [0] * self.n_estimators
+295
+296        if estimator_kwards is None:
+297            estimator_kwards = [{}] * self.n_estimators
+298
+299        changed = True
+300        iteration = 0
+301        while changed:
+302            changed = False
+303            iteration_dict = {}
+304            iteration += 1
+305
+306            for i in range(self.n_estimators):
+307                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
+308            if X_unlabel.shape[0] == 0:
+309                break
+310            # Majority Vote
+311            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
+312            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
+313            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
+314            #     0
+315            # ].flatten()  # K in pseudocode
+316
+317            L_ = [[]] * self.n_estimators
+318            Ly_ = [[]] * self.n_estimators
+319
+320            # Calculate confidence interval
+321            conf_interval = [
+322                confidence_interval(
+323                    X_label,
+324                    H,
+325                    y_label,
+326                    self.alpha
+327                )
+328                for H in self.base_estimator
+329            ]
+330
+331            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
+332            iteration_dict["weights"] = {
+333                "cl" + str(i): (l, h, w)
+334                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
+335            }
+336            # weighted vote
+337            weighted_class = self.__weighted_y(predictions, weights)
+338
+339            # If `weighted_class` is equal as `majority_class` then
+340            # the sum of classifier's weights of max voted class
+341            # is greater than the max of sum of classifier's weights
+342            # from another classes.
+343
+344            candidates = weighted_class == majority_class
+345            candidates_bool = list()
+346
+347            if not self.expand_only_mislabeled:
+348                all_same_list = list()
+349                for i in range(1, self.n_estimators):
+350                    all_same_list.append(predictions[i] == predictions[i - 1])
+351                all_same = np.logical_and(*all_same_list)
+352            # new_instances = []
+353            for i in range(self.n_estimators):
+354
+355                mispredictions = predictions[i] != weighted_class
+356                # An instance from U are added to Li' only if:
+357                #   It is a misprediction for i
+358                #   It is a candidate (weighted_class are same majority_class)
+359                #   It hasn't been added yet in Li
+360
+361                candidates_temp = np.logical_and(mispredictions, candidates)
 362
-363                candidates_temp = np.logical_and(mispredictions, candidates)
-364
-365                if not self.expand_only_mislabeled:
-366                    candidates_temp = np.logical_or(candidates_temp, all_same)
+363                if not self.expand_only_mislabeled:
+364                    candidates_temp = np.logical_or(candidates_temp, all_same)
+365
+366                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
 367
-368                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
-369
-370                candidates_bool.append(to_add)
-371                if is_df:
-372                    L_[i] = X_unlabel.iloc[to_add, :]
-373                else:
-374                    L_[i] = X_unlabel[to_add, :]
-375                Ly_[i] = weighted_class[to_add]
-376
-377            new_conf_interval = [
-378                confidence_interval(L[i], H, Ly[i], self.alpha)
-379                for i, H in enumerate(self.base_estimator)
-380            ]
-381            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
-382            for i, _ in enumerate(self.base_estimator):
-383                if len(L_[i]) > 0:
-384
-385                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
-386                    e_i = e_factor * len(L_[i])
-387                    # |Li|+|L'i| == |Li U L'i| because of to_add
-388                    q_i = (len(L[i]) + len(L_[i])) * (
-389                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
-390                    ) ** self.q_exp
-391                    if q_i <= qi:
-392                        continue
-393                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
-394                    if is_df:
-395                        L[i] = pd.concat([L[i], L_[i]])
-396                    else:
-397                        L[i] = np.concatenate((L[i], np.array(L_[i])))
-398                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
-399
-400                    e[i] = e[i] + e_i
-401                    changed = True
-402
-403        self.h_ = self.base_estimator
-404        self.__calcule_last_confidences(X_label, y_label)
-405
-406        # Ignore hypothesis
-407        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
-408        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+368                candidates_bool.append(to_add)
+369                if is_df:
+370                    L_[i] = X_unlabel.iloc[to_add, :]
+371                else:
+372                    L_[i] = X_unlabel[to_add, :]
+373                Ly_[i] = weighted_class[to_add]
+374
+375            new_conf_interval = [
+376                confidence_interval(L[i], H, Ly[i], self.alpha)
+377                for i, H in enumerate(self.base_estimator)
+378            ]
+379            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
+380            for i, _ in enumerate(self.base_estimator):
+381                if len(L_[i]) > 0:
+382
+383                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
+384                    e_i = e_factor * len(L_[i])
+385                    # |Li|+|L'i| == |Li U L'i| because of to_add
+386                    q_i = (len(L[i]) + len(L_[i])) * (
+387                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
+388                    ) ** self.q_exp
+389                    if q_i <= qi:
+390                        continue
+391                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
+392                    if is_df:
+393                        L[i] = pd.concat([L[i], L_[i]])
+394                    else:
+395                        L[i] = np.concatenate((L[i], np.array(L_[i])))
+396                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
+397
+398                    e[i] = e[i] + e_i
+399                    changed = True
+400
+401        self.h_ = self.base_estimator
+402        self.__calcule_last_confidences(X_label, y_label)
+403
+404        # Ignore hypothesis
+405        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
+406        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+407
+408        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
 409
-410        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+410        return self
 411
-412        return self
+412    def __combine_probabilities(self, X):
 413
-414    def __combine_probabilities(self, X):
-415
-416        n_instances = X.shape[0]  # uppercase X as it will be an np.array
-417        sizes = np.zeros((n_instances, len(self.classes_)), dtype=int)
-418        C = np.zeros((n_instances, len(self.classes_)), dtype=float)
-419        Cavg = np.zeros((n_instances, len(self.classes_)), dtype=float)
-420
-421        for w, H in zip(self.confidences_, self.h_):
-422            cj = H.predict(X)
-423            factor = self.one_hot.transform(cj.reshape(-1, 1)).astype(int)
-424            C += w * factor
-425            sizes += factor
-426
-427        Cavg[sizes == 0] = 0.5  # «voting power» of 0.5 for small groups
-428        ne = (sizes != 0)  # non empty groups
-429        Cavg[ne] = (sizes[ne] + 0.5) / (sizes[ne] + 1) * C[ne] / sizes[ne]
+414        n_instances = X.shape[0]  # uppercase X as it will be an np.array
+415        sizes = np.zeros((n_instances, len(self.classes_)), dtype=int)
+416        C = np.zeros((n_instances, len(self.classes_)), dtype=float)
+417        Cavg = np.zeros((n_instances, len(self.classes_)), dtype=float)
+418
+419        for w, H in zip(self.confidences_, self.h_):
+420            cj = H.predict(X)
+421            factor = self.one_hot.transform(cj.reshape(-1, 1)).astype(int)
+422            C += w * factor
+423            sizes += factor
+424
+425        Cavg[sizes == 0] = 0.5  # «voting power» of 0.5 for small groups
+426        ne = (sizes != 0)  # non empty groups
+427        Cavg[ne] = (sizes[ne] + 0.5) / (sizes[ne] + 1) * C[ne] / sizes[ne]
+428
+429        return softmax(Cavg, axis=1)
 430
-431        return softmax(Cavg, axis=1)
-432
-433    def predict_proba(self, X):
-434        """Predict probability for each possible outcome.
-435
-436        Parameters
-437        ----------
-438        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-439            Array representing the data.
-440        Returns
-441        -------
-442        class probabilities: ndarray of shape (n_samples, n_classes)
-443            Array with prediction probabilities.
-444        """
-445        if "h_" in dir(self):
-446            if len(X) == 1:
-447                X = [X]
-448            return self.__combine_probabilities(X)
-449        else:
-450            raise NotFittedError("Classifier not fitted")
+431    def predict_proba(self, X):
+432        """Predict probability for each possible outcome.
+433
+434        Parameters
+435        ----------
+436        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+437            Array representing the data.
+438        Returns
+439        -------
+440        class probabilities: ndarray of shape (n_samples, n_classes)
+441            Array with prediction probabilities.
+442        """
+443        if "h_" in dir(self):
+444            if len(X) == 1:
+445                X = [X]
+446            return self.__combine_probabilities(X)
+447        else:
+448            raise NotFittedError("Classifier not fitted")
 
    
 
 
@@ -3192,72 +3283,72 @@ 
    References
    - 
    170    def __init__(
-171        self,
-172        base_estimator=[
-173            DecisionTreeClassifier(),
-174            GaussianNB(),
-175            KNeighborsClassifier(n_neighbors=3),
-176        ],
-177        n_estimators=None,
-178        expand_only_mislabeled=True,
-179        alpha=0.95,
-180        q_exp=2,
-181        random_state=None
-182    ):
-183        """
-184        Democratic Co-learning. Ensemble of classifiers of different types.
-185
-186        Parameters
-187        ----------
-188        base_estimator : {ClassifierMixin, list}, optional
-189            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
-190        n_estimators : int, optional
-191            number of base_estimators to use. None if base_estimator is a list, by default None
-192        expand_only_mislabeled : bool, optional
-193            expand only mislabeled instances by itself, by default True
-194        alpha : float, optional
-195            confidence level, by default 0.95
-196        q_exp : int, optional
-197            exponent for the estimation for error rate, by default 2
-198        random_state : int, RandomState instance, optional
-199            controls the randomness of the estimator, by default None
-200        Raises
-201        ------
-202        AttributeError
-203            If n_estimators is None and base_estimator is not a list
-204        """
-205
-206        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
-207            estimators = list()
-208            random_available = True
-209            rand = check_random_state(random_state)
-210            if "random_state" not in dir(base_estimator):
-211                warnings.warn(
-212                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
-213                    ConvergenceWarning,
-214                )
-215                random_available = False
-216            for i in range(n_estimators):
-217                estimators.append(skclone(base_estimator))
-218                if random_available:
-219                    estimators[i].random_state = rand.randint(0, 1e5)
-220            self.base_estimator = estimators
-221
-222        elif isinstance(base_estimator, list):
-223            self.base_estimator = base_estimator
-224        else:
-225            raise AttributeError(
-226                "If `n_estimators` is None then `base_estimator` must be a `list`."
-227            )
-228        self.base_estimator = check_classifier(self.base_estimator)
-229        self.n_estimators = len(self.base_estimator)
-230        self.one_hot = OneHotEncoder(sparse_output=False)
-231        self.expand_only_mislabeled = expand_only_mislabeled
-232
-233        self.alpha = alpha
-234        self.q_exp = q_exp
-235        self.random_state = random_state
+            
    168    def __init__(
+169        self,
+170        base_estimator=[
+171            DecisionTreeClassifier(),
+172            GaussianNB(),
+173            KNeighborsClassifier(n_neighbors=3),
+174        ],
+175        n_estimators=None,
+176        expand_only_mislabeled=True,
+177        alpha=0.95,
+178        q_exp=2,
+179        random_state=None
+180    ):
+181        """
+182        Democratic Co-learning. Ensemble of classifiers of different types.
+183
+184        Parameters
+185        ----------
+186        base_estimator : {ClassifierMixin, list}, optional
+187            An estimator object implementing fit and predict_proba or a list of ClassifierMixin, by default DecisionTreeClassifier()
+188        n_estimators : int, optional
+189            number of base_estimators to use. None if base_estimator is a list, by default None
+190        expand_only_mislabeled : bool, optional
+191            expand only mislabeled instances by itself, by default True
+192        alpha : float, optional
+193            confidence level, by default 0.95
+194        q_exp : int, optional
+195            exponent for the estimation for error rate, by default 2
+196        random_state : int, RandomState instance, optional
+197            controls the randomness of the estimator, by default None
+198        Raises
+199        ------
+200        AttributeError
+201            If n_estimators is None and base_estimator is not a list
+202        """
+203
+204        if isinstance(base_estimator, ClassifierMixin) and n_estimators is not None:
+205            estimators = list()
+206            random_available = True
+207            rand = check_random_state(random_state)
+208            if "random_state" not in dir(base_estimator):
+209                warnings.warn(
+210                    "The classifier will not be able to converge correctly, there is not enough diversity among the estimators (learners should be different).",
+211                    ConvergenceWarning,
+212                )
+213                random_available = False
+214            for i in range(n_estimators):
+215                estimators.append(skclone(base_estimator))
+216                if random_available:
+217                    estimators[i].random_state = rand.randint(0, 1e5)
+218            self.base_estimator = estimators
+219
+220        elif isinstance(base_estimator, list):
+221            self.base_estimator = base_estimator
+222        else:
+223            raise AttributeError(
+224                "If `n_estimators` is None then `base_estimator` must be a `list`."
+225            )
+226        self.base_estimator = check_classifier(self.base_estimator)
+227        self.n_estimators = len(self.base_estimator)
+228        self.one_hot = OneHotEncoder(sparse_output=False)
+229        self.expand_only_mislabeled = expand_only_mislabeled
+230
+231        self.alpha = alpha
+232        self.q_exp = q_exp
+233        self.random_state = random_state
 
    
 
 
@@ -3300,155 +3391,155 @@ 
    Raises
    - 
    264    def fit(self, X, y, estimator_kwards=None):
-265        """Fit Democratic-Co classifier
-266
-267        Parameters
-268        ----------
-269        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-270            The training input samples.
-271        y : array-like of shape (n_samples,)
-272            The target values (class labels), -1 if unlabel.
-273        estimator_kwards : {list, dict}, optional
-274            list of kwards for each estimator or kwards for all estimators, by default None
-275
-276        Returns
-277        -------
-278        self : DemocraticCoLearning
-279            fitted classifier
-280        """
+            
    262    def fit(self, X, y, estimator_kwards=None):
+263        """Fit Democratic-Co classifier
+264
+265        Parameters
+266        ----------
+267        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+268            The training input samples.
+269        y : array-like of shape (n_samples,)
+270            The target values (class labels), -1 if unlabel.
+271        estimator_kwards : {list, dict}, optional
+272            list of kwards for each estimator or kwards for all estimators, by default None
+273
+274        Returns
+275        -------
+276        self : DemocraticCoLearning
+277            fitted classifier
+278        """
+279
+280        X_label, y_label, X_unlabel = get_dataset(X, y)
 281
-282        X_label, y_label, X_unlabel = get_dataset(X, y)
+282        is_df = isinstance(X_label, pd.DataFrame)
 283
-284        is_df = isinstance(X_label, pd.DataFrame)
-285
-286        self.classes_ = np.unique(y_label)
-287        self.encoder = LabelEncoder().fit(y_label)
-288        y_label = self.encoder.transform(y_label)
+284        self.classes_ = np.unique(y_label)
+285        self.encoder = LabelEncoder().fit(y_label)
+286        y_label = self.encoder.transform(y_label)
+287
+288        self.one_hot.fit(y_label.reshape(-1, 1))
 289
-290        self.one_hot.fit(y_label.reshape(-1, 1))
-291
-292        L = [X_label] * self.n_estimators
-293        Ly = [y_label] * self.n_estimators
-294        # This variable prevents duplicate instances.
-295        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
-296        e = [0] * self.n_estimators
-297
-298        if estimator_kwards is None:
-299            estimator_kwards = [{}] * self.n_estimators
-300
-301        changed = True
-302        iteration = 0
-303        while changed:
-304            changed = False
-305            iteration_dict = {}
-306            iteration += 1
-307
-308            for i in range(self.n_estimators):
-309                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
-310            if X_unlabel.shape[0] == 0:
-311                break
-312            # Majority Vote
-313            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
-314            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
-315            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
-316            #     0
-317            # ].flatten()  # K in pseudocode
-318
-319            L_ = [[]] * self.n_estimators
-320            Ly_ = [[]] * self.n_estimators
-321
-322            # Calculate confidence interval
-323            conf_interval = [
-324                confidence_interval(
-325                    X_label,
-326                    H,
-327                    y_label,
-328                    self.alpha
-329                )
-330                for H in self.base_estimator
-331            ]
-332
-333            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
-334            iteration_dict["weights"] = {
-335                "cl" + str(i): (l, h, w)
-336                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
-337            }
-338            # weighted vote
-339            weighted_class = self.__weighted_y(predictions, weights)
-340
-341            # If `weighted_class` is equal as `majority_class` then
-342            # the sum of classifier's weights of max voted class
-343            # is greater than the max of sum of classifier's weights
-344            # from another classes.
-345
-346            candidates = weighted_class == majority_class
-347            candidates_bool = list()
-348
-349            if not self.expand_only_mislabeled:
-350                all_same_list = list()
-351                for i in range(1, self.n_estimators):
-352                    all_same_list.append(predictions[i] == predictions[i - 1])
-353                all_same = np.logical_and(*all_same_list)
-354            # new_instances = []
-355            for i in range(self.n_estimators):
-356
-357                mispredictions = predictions[i] != weighted_class
-358                # An instance from U are added to Li' only if:
-359                #   It is a misprediction for i
-360                #   It is a candidate (weighted_class are same majority_class)
-361                #   It hasn't been added yet in Li
+290        L = [X_label] * self.n_estimators
+291        Ly = [y_label] * self.n_estimators
+292        # This variable prevents duplicate instances.
+293        L_added = [np.zeros(X_unlabel.shape[0]).astype(bool)] * self.n_estimators
+294        e = [0] * self.n_estimators
+295
+296        if estimator_kwards is None:
+297            estimator_kwards = [{}] * self.n_estimators
+298
+299        changed = True
+300        iteration = 0
+301        while changed:
+302            changed = False
+303            iteration_dict = {}
+304            iteration += 1
+305
+306            for i in range(self.n_estimators):
+307                self.base_estimator[i].fit(L[i], Ly[i], **estimator_kwards[i])
+308            if X_unlabel.shape[0] == 0:
+309                break
+310            # Majority Vote
+311            predictions = [H.predict(X_unlabel) for H in self.base_estimator]
+312            majority_class = mode(np.array(predictions, dtype=predictions[0].dtype))[0]
+313            # majority_class = st.mode(np.array(predictions, dtype=predictions[0].dtype), axis=0, keepdims=True)[
+314            #     0
+315            # ].flatten()  # K in pseudocode
+316
+317            L_ = [[]] * self.n_estimators
+318            Ly_ = [[]] * self.n_estimators
+319
+320            # Calculate confidence interval
+321            conf_interval = [
+322                confidence_interval(
+323                    X_label,
+324                    H,
+325                    y_label,
+326                    self.alpha
+327                )
+328                for H in self.base_estimator
+329            ]
+330
+331            weights = [(li + hi) / 2 for (li, hi) in conf_interval]
+332            iteration_dict["weights"] = {
+333                "cl" + str(i): (l, h, w)
+334                for i, ((l, h), w) in enumerate(zip(conf_interval, weights))
+335            }
+336            # weighted vote
+337            weighted_class = self.__weighted_y(predictions, weights)
+338
+339            # If `weighted_class` is equal as `majority_class` then
+340            # the sum of classifier's weights of max voted class
+341            # is greater than the max of sum of classifier's weights
+342            # from another classes.
+343
+344            candidates = weighted_class == majority_class
+345            candidates_bool = list()
+346
+347            if not self.expand_only_mislabeled:
+348                all_same_list = list()
+349                for i in range(1, self.n_estimators):
+350                    all_same_list.append(predictions[i] == predictions[i - 1])
+351                all_same = np.logical_and(*all_same_list)
+352            # new_instances = []
+353            for i in range(self.n_estimators):
+354
+355                mispredictions = predictions[i] != weighted_class
+356                # An instance from U are added to Li' only if:
+357                #   It is a misprediction for i
+358                #   It is a candidate (weighted_class are same majority_class)
+359                #   It hasn't been added yet in Li
+360
+361                candidates_temp = np.logical_and(mispredictions, candidates)
 362
-363                candidates_temp = np.logical_and(mispredictions, candidates)
-364
-365                if not self.expand_only_mislabeled:
-366                    candidates_temp = np.logical_or(candidates_temp, all_same)
+363                if not self.expand_only_mislabeled:
+364                    candidates_temp = np.logical_or(candidates_temp, all_same)
+365
+366                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
 367
-368                to_add = np.logical_and(np.logical_not(L_added[i]), candidates_temp)
-369
-370                candidates_bool.append(to_add)
-371                if is_df:
-372                    L_[i] = X_unlabel.iloc[to_add, :]
-373                else:
-374                    L_[i] = X_unlabel[to_add, :]
-375                Ly_[i] = weighted_class[to_add]
-376
-377            new_conf_interval = [
-378                confidence_interval(L[i], H, Ly[i], self.alpha)
-379                for i, H in enumerate(self.base_estimator)
-380            ]
-381            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
-382            for i, _ in enumerate(self.base_estimator):
-383                if len(L_[i]) > 0:
-384
-385                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
-386                    e_i = e_factor * len(L_[i])
-387                    # |Li|+|L'i| == |Li U L'i| because of to_add
-388                    q_i = (len(L[i]) + len(L_[i])) * (
-389                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
-390                    ) ** self.q_exp
-391                    if q_i <= qi:
-392                        continue
-393                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
-394                    if is_df:
-395                        L[i] = pd.concat([L[i], L_[i]])
-396                    else:
-397                        L[i] = np.concatenate((L[i], np.array(L_[i])))
-398                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
-399
-400                    e[i] = e[i] + e_i
-401                    changed = True
-402
-403        self.h_ = self.base_estimator
-404        self.__calcule_last_confidences(X_label, y_label)
-405
-406        # Ignore hypothesis
-407        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
-408        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+368                candidates_bool.append(to_add)
+369                if is_df:
+370                    L_[i] = X_unlabel.iloc[to_add, :]
+371                else:
+372                    L_[i] = X_unlabel[to_add, :]
+373                Ly_[i] = weighted_class[to_add]
+374
+375            new_conf_interval = [
+376                confidence_interval(L[i], H, Ly[i], self.alpha)
+377                for i, H in enumerate(self.base_estimator)
+378            ]
+379            e_factor = 1 - sum([l_ for l_, _ in new_conf_interval]) / self.n_estimators
+380            for i, _ in enumerate(self.base_estimator):
+381                if len(L_[i]) > 0:
+382
+383                    qi = len(L[i]) * ((1 - 2 * (e[i] / len(L[i]))) ** 2)
+384                    e_i = e_factor * len(L_[i])
+385                    # |Li|+|L'i| == |Li U L'i| because of to_add
+386                    q_i = (len(L[i]) + len(L_[i])) * (
+387                        1 - 2 * (e[i] + e_i) / (len(L[i]) + len(L_[i]))
+388                    ) ** self.q_exp
+389                    if q_i <= qi:
+390                        continue
+391                    L_added[i] = np.logical_or(L_added[i], candidates_bool[i])
+392                    if is_df:
+393                        L[i] = pd.concat([L[i], L_[i]])
+394                    else:
+395                        L[i] = np.concatenate((L[i], np.array(L_[i])))
+396                    Ly[i] = np.concatenate((Ly[i], np.array(Ly_[i])))
+397
+398                    e[i] = e[i] + e_i
+399                    changed = True
+400
+401        self.h_ = self.base_estimator
+402        self.__calcule_last_confidences(X_label, y_label)
+403
+404        # Ignore hypothesis
+405        self.h_ = [H for w, H in zip(self.confidences_, self.h_) if w > 0.5]
+406        self.confidences_ = [w for w in self.confidences_ if w > 0.5]
+407
+408        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
 409
-410        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
-411
-412        return self
+410        return self
 
    
 
 
@@ -3486,24 +3577,24 @@ 
    Returns
    - 
    433    def predict_proba(self, X):
-434        """Predict probability for each possible outcome.
-435
-436        Parameters
-437        ----------
-438        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-439            Array representing the data.
-440        Returns
-441        -------
-442        class probabilities: ndarray of shape (n_samples, n_classes)
-443            Array with prediction probabilities.
-444        """
-445        if "h_" in dir(self):
-446            if len(X) == 1:
-447                X = [X]
-448            return self.__combine_probabilities(X)
-449        else:
-450            raise NotFittedError("Classifier not fitted")
+            
    431    def predict_proba(self, X):
+432        """Predict probability for each possible outcome.
+433
+434        Parameters
+435        ----------
+436        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+437            Array representing the data.
+438        Returns
+439        -------
+440        class probabilities: ndarray of shape (n_samples, n_classes)
+441            Array with prediction probabilities.
+442        """
+443        if "h_" in dir(self):
+444            if len(X) == 1:
+445                X = [X]
+446            return self.__combine_probabilities(X)
+447        else:
+448            raise NotFittedError("Classifier not fitted")
 
    
 
 
@@ -3556,190 +3647,190 @@ 
    Inherited Members
    - 
    767class Rasco(BaseCoTraining):
-768    """
-769    **Co-Training based on random subspaces**
-770    --------------------------------------------
+            
    765class Rasco(BaseCoTraining):
+766    """
+767    **Co-Training based on random subspaces**
+768    --------------------------------------------
+769
+770    Generate a set of random subspaces and train a classifier for each subspace.
 771
-772    Generate a set of random subspaces and train a classifier for each subspace.
-773
-774    The main process is:
-775    1. Generate a set of random subspaces.
-776    2. Train a classifier for each subspace.
-777    3. While max iterations is not reached or any instance is unlabeled:
-778        1. Predict the instances from the unlabeled set for each classifier.
-779        2. Calculate the average of the predictions.
-780        3. Select the instances with the highest probability.
-781        4. Label the instances with the highest probability, keeping the balance of the classes.
-782        5. Retrain the classifier with the new instances.
-783    4. Combine the probabilities of each classifier.
-784
-785    **Methods**
-786    -------
-787    - `fit`: Fit the model with the labeled instances.
-788    - `predict` : Predict the class for each instance.
-789    - `predict_proba`: Predict the probability for each class.
-790    - `score`: Return the mean accuracy on the given test data and labels.
-791
-792    **Example**
-793    -------
-794    ```python
-795    from sklearn.datasets import load_iris
-796    from sslearn.wrapper import Rasco
-797    from sslearn.model_selection import artificial_ssl_dataset
-798
-799    X, y = load_iris(return_X_y=True)
-800    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-801    rasco = Rasco()
-802    rasco.fit(X, y)
-803    rasco.score(X_unlabel, y_unlabel) 
-804    ```    
-805
-806    **References**
-807    ----------
-808    Wang, J., Luo, S. W., & Zeng, X. H. (2008).<br>
-809    A random subspace method for co-training,<br>
-810    in <i>2008 IEEE International Joint Conference on Neural Networks</i><br>
-811    IEEE World Congress on Computational Intelligence<br>
-812    (pp. 195-200). IEEE. [10.1109/IJCNN.2008.4633789](https://doi.org/10.1109/IJCNN.2008.4633789)
-813    """
-814
-815
-816    def __init__(
-817        self,
-818        base_estimator=DecisionTreeClassifier(),
-819        max_iterations=10,
-820        n_estimators=30,
-821        subspace_size=None,
-822        random_state=None,
-823        n_jobs=None,
-824    ):
-825        """
-826        Co-Training based on random subspaces
-827
-828        Parameters
-829        ----------
-830        base_estimator : ClassifierMixin, optional
-831            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-832        max_iterations : int, optional
-833            Maximum number of iterations allowed. Should be greater than or equal to 0.
-834            If is -1 then will be infinite iterations until U be empty, by default 10
-835        n_estimators : int, optional
-836            The number of base estimators in the ensemble., by default 30
-837        subspace_size : int, optional
-838            The number of features for each subspace. If it is None will be the half of the features size., by default None
-839        random_state : int, RandomState instance, optional
-840            controls the randomness of the estimator, by default None
-841        """
-842        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
-843        self.max_iterations = max_iterations  # J in paper
-844        self.n_estimators = n_estimators  # K in paper
-845        self.subspace_size = subspace_size  # m in paper
-846        self.n_jobs = check_n_jobs(n_jobs)
+772    The main process is:
+773    1. Generate a set of random subspaces.
+774    2. Train a classifier for each subspace.
+775    3. While max iterations is not reached or any instance is unlabeled:
+776        1. Predict the instances from the unlabeled set for each classifier.
+777        2. Calculate the average of the predictions.
+778        3. Select the instances with the highest probability.
+779        4. Label the instances with the highest probability, keeping the balance of the classes.
+780        5. Retrain the classifier with the new instances.
+781    4. Combine the probabilities of each classifier.
+782
+783    **Methods**
+784    -------
+785    - `fit`: Fit the model with the labeled instances.
+786    - `predict` : Predict the class for each instance.
+787    - `predict_proba`: Predict the probability for each class.
+788    - `score`: Return the mean accuracy on the given test data and labels.
+789
+790    **Example**
+791    -------
+792    ```python
+793    from sklearn.datasets import load_iris
+794    from sslearn.wrapper import Rasco
+795    from sslearn.model_selection import artificial_ssl_dataset
+796
+797    X, y = load_iris(return_X_y=True)
+798    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+799    rasco = Rasco()
+800    rasco.fit(X, y)
+801    rasco.score(X_unlabel, y_unlabel) 
+802    ```    
+803
+804    **References**
+805    ----------
+806    Wang, J., Luo, S. W., & Zeng, X. H. (2008).<br>
+807    A random subspace method for co-training,<br>
+808    in <i>2008 IEEE International Joint Conference on Neural Networks</i><br>
+809    IEEE World Congress on Computational Intelligence<br>
+810    (pp. 195-200). IEEE. [10.1109/IJCNN.2008.4633789](https://doi.org/10.1109/IJCNN.2008.4633789)
+811    """
+812
+813
+814    def __init__(
+815        self,
+816        base_estimator=DecisionTreeClassifier(),
+817        max_iterations=10,
+818        n_estimators=30,
+819        subspace_size=None,
+820        random_state=None,
+821        n_jobs=None,
+822    ):
+823        """
+824        Co-Training based on random subspaces
+825
+826        Parameters
+827        ----------
+828        base_estimator : ClassifierMixin, optional
+829            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+830        max_iterations : int, optional
+831            Maximum number of iterations allowed. Should be greater than or equal to 0.
+832            If is -1 then will be infinite iterations until U be empty, by default 10
+833        n_estimators : int, optional
+834            The number of base estimators in the ensemble., by default 30
+835        subspace_size : int, optional
+836            The number of features for each subspace. If it is None will be the half of the features size., by default None
+837        random_state : int, RandomState instance, optional
+838            controls the randomness of the estimator, by default None
+839        """
+840        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
+841        self.max_iterations = max_iterations  # J in paper
+842        self.n_estimators = n_estimators  # K in paper
+843        self.subspace_size = subspace_size  # m in paper
+844        self.n_jobs = check_n_jobs(n_jobs)
+845
+846        self.random_state = random_state
 847
-848        self.random_state = random_state
-849
-850    def _generate_random_subspaces(self, X, y=None, random_state=None):
-851        """Generate the random subspaces
-852
-853        Parameters
-854        ----------
-855        X : array like
-856            Labeled dataset
-857        y : array like, optional
-858            Target for each X, not needed on Rasco, by default None
-859
-860        Returns
-861        -------
-862        subspaces : list
-863            List of index of features
-864        """
-865        random_state = check_random_state(random_state)
-866        features = list(range(X.shape[1]))
-867        idxs = []
-868        for _ in range(self.n_estimators):
-869            idxs.append(random_state.permutation(features)[: self.subspace_size])
-870        return idxs
-871
-872    def _fit_estimator(self, X, y, i, **kwards):
-873        estimator = self.base_estimator
-874        if type(self.base_estimator) == list:
-875            estimator = skclone(self.base_estimator[i])
-876        return skclone(estimator).fit(X, y, **kwards)
-877
-878    def fit(self, X, y, **kwards):
-879        """Build a Rasco classifier from the training set (X, y).
-880
-881        Parameters
-882        ----------
-883        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-884            The training input samples.
-885        y : array-like of shape (n_samples,)
-886            The target values (class labels), -1 if unlabel.
-887
-888        Returns
-889        -------
-890        self: Rasco
-891            Fitted estimator.
-892        """
-893        X_label, y_label, X_unlabel = get_dataset(X, y)
-894        self.classes_ = np.unique(y_label)
+848    def _generate_random_subspaces(self, X, y=None, random_state=None):
+849        """Generate the random subspaces
+850
+851        Parameters
+852        ----------
+853        X : array like
+854            Labeled dataset
+855        y : array like, optional
+856            Target for each X, not needed on Rasco, by default None
+857
+858        Returns
+859        -------
+860        subspaces : list
+861            List of index of features
+862        """
+863        random_state = check_random_state(random_state)
+864        features = list(range(X.shape[1]))
+865        idxs = []
+866        for _ in range(self.n_estimators):
+867            idxs.append(random_state.permutation(features)[: self.subspace_size])
+868        return idxs
+869
+870    def _fit_estimator(self, X, y, i, **kwards):
+871        estimator = self.base_estimator
+872        if type(self.base_estimator) == list:
+873            estimator = skclone(self.base_estimator[i])
+874        return skclone(estimator).fit(X, y, **kwards)
+875
+876    def fit(self, X, y, **kwards):
+877        """Build a Rasco classifier from the training set (X, y).
+878
+879        Parameters
+880        ----------
+881        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+882            The training input samples.
+883        y : array-like of shape (n_samples,)
+884            The target values (class labels), -1 if unlabel.
+885
+886        Returns
+887        -------
+888        self: Rasco
+889            Fitted estimator.
+890        """
+891        X_label, y_label, X_unlabel = get_dataset(X, y)
+892        self.classes_ = np.unique(y_label)
+893
+894        is_df = isinstance(X_label, pd.DataFrame)
 895
-896        is_df = isinstance(X_label, pd.DataFrame)
+896        random_state = check_random_state(self.random_state)
 897
-898        random_state = check_random_state(self.random_state)
-899
-900        self.classes_ = np.unique(y_label)
-901        number_per_class = calc_number_per_class(y_label)
-902
-903        if self.subspace_size is None:
-904            self.subspace_size = int(X.shape[1] / 2)
-905        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
-906
-907        cfs = Parallel(n_jobs=self.n_jobs)(
-908            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
-909            for i in range(self.n_estimators)
-910        )
-911
-912        it = 0
-913        while True:
-914            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
-915                X_unlabel
-916            ) == 0:
-917                break
-918
-919            raw_predicions = []
-920            for i in range(self.n_estimators):
-921                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
-922                raw_predicions.append(rp)
-923            raw_predicions = sum(raw_predicions) / self.n_estimators
-924            predictions = np.max(raw_predicions, axis=1)
-925            class_predicted = np.argmax(raw_predicions, axis=1)
-926            pseudoy = self.classes_.take(class_predicted, axis=0)
-927
-928            final_instances = list()
-929            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
-930            for c in self.classes_:
-931                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
-932
-933            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
-934            yj = pseudoy[final_instances]
-935
-936            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
-937            y_label = np.append(y_label, yj)
-938            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
-939
-940            cfs = Parallel(n_jobs=self.n_jobs)(
-941                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
-942                for i in range(self.n_estimators)
-943            )
+898        self.classes_ = np.unique(y_label)
+899        number_per_class = calc_number_per_class(y_label)
+900
+901        if self.subspace_size is None:
+902            self.subspace_size = int(X.shape[1] / 2)
+903        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
+904
+905        cfs = Parallel(n_jobs=self.n_jobs)(
+906            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+907            for i in range(self.n_estimators)
+908        )
+909
+910        it = 0
+911        while True:
+912            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
+913                X_unlabel
+914            ) == 0:
+915                break
+916
+917            raw_predicions = []
+918            for i in range(self.n_estimators):
+919                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
+920                raw_predicions.append(rp)
+921            raw_predicions = sum(raw_predicions) / self.n_estimators
+922            predictions = np.max(raw_predicions, axis=1)
+923            class_predicted = np.argmax(raw_predicions, axis=1)
+924            pseudoy = self.classes_.take(class_predicted, axis=0)
+925
+926            final_instances = list()
+927            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
+928            for c in self.classes_:
+929                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
+930
+931            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
+932            yj = pseudoy[final_instances]
+933
+934            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
+935            y_label = np.append(y_label, yj)
+936            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
+937
+938            cfs = Parallel(n_jobs=self.n_jobs)(
+939                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+940                for i in range(self.n_estimators)
+941            )
+942
+943            it += 1
 944
-945            it += 1
-946
-947        self.h_ = cfs
-948        self.columns_ = idxs
-949
-950        return self
+945        self.h_ = cfs
+946        self.columns_ = idxs
+947
+948        return self
 
    
 
 
@@ -3807,39 +3898,39 @@ 
    References
    - 
    816    def __init__(
-817        self,
-818        base_estimator=DecisionTreeClassifier(),
-819        max_iterations=10,
-820        n_estimators=30,
-821        subspace_size=None,
-822        random_state=None,
-823        n_jobs=None,
-824    ):
-825        """
-826        Co-Training based on random subspaces
-827
-828        Parameters
-829        ----------
-830        base_estimator : ClassifierMixin, optional
-831            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-832        max_iterations : int, optional
-833            Maximum number of iterations allowed. Should be greater than or equal to 0.
-834            If is -1 then will be infinite iterations until U be empty, by default 10
-835        n_estimators : int, optional
-836            The number of base estimators in the ensemble., by default 30
-837        subspace_size : int, optional
-838            The number of features for each subspace. If it is None will be the half of the features size., by default None
-839        random_state : int, RandomState instance, optional
-840            controls the randomness of the estimator, by default None
-841        """
-842        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
-843        self.max_iterations = max_iterations  # J in paper
-844        self.n_estimators = n_estimators  # K in paper
-845        self.subspace_size = subspace_size  # m in paper
-846        self.n_jobs = check_n_jobs(n_jobs)
-847
-848        self.random_state = random_state
+            
    814    def __init__(
+815        self,
+816        base_estimator=DecisionTreeClassifier(),
+817        max_iterations=10,
+818        n_estimators=30,
+819        subspace_size=None,
+820        random_state=None,
+821        n_jobs=None,
+822    ):
+823        """
+824        Co-Training based on random subspaces
+825
+826        Parameters
+827        ----------
+828        base_estimator : ClassifierMixin, optional
+829            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+830        max_iterations : int, optional
+831            Maximum number of iterations allowed. Should be greater than or equal to 0.
+832            If is -1 then will be infinite iterations until U be empty, by default 10
+833        n_estimators : int, optional
+834            The number of base estimators in the ensemble., by default 30
+835        subspace_size : int, optional
+836            The number of features for each subspace. If it is None will be the half of the features size., by default None
+837        random_state : int, RandomState instance, optional
+838            controls the randomness of the estimator, by default None
+839        """
+840        self.base_estimator = check_classifier(base_estimator, True, n_estimators)  # C in paper
+841        self.max_iterations = max_iterations  # J in paper
+842        self.n_estimators = n_estimators  # K in paper
+843        self.subspace_size = subspace_size  # m in paper
+844        self.n_jobs = check_n_jobs(n_jobs)
+845
+846        self.random_state = random_state
 
    
 
 
@@ -3875,79 +3966,79 @@ 
    Parameters
    - 
    878    def fit(self, X, y, **kwards):
-879        """Build a Rasco classifier from the training set (X, y).
-880
-881        Parameters
-882        ----------
-883        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-884            The training input samples.
-885        y : array-like of shape (n_samples,)
-886            The target values (class labels), -1 if unlabel.
-887
-888        Returns
-889        -------
-890        self: Rasco
-891            Fitted estimator.
-892        """
-893        X_label, y_label, X_unlabel = get_dataset(X, y)
-894        self.classes_ = np.unique(y_label)
+            
    876    def fit(self, X, y, **kwards):
+877        """Build a Rasco classifier from the training set (X, y).
+878
+879        Parameters
+880        ----------
+881        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+882            The training input samples.
+883        y : array-like of shape (n_samples,)
+884            The target values (class labels), -1 if unlabel.
+885
+886        Returns
+887        -------
+888        self: Rasco
+889            Fitted estimator.
+890        """
+891        X_label, y_label, X_unlabel = get_dataset(X, y)
+892        self.classes_ = np.unique(y_label)
+893
+894        is_df = isinstance(X_label, pd.DataFrame)
 895
-896        is_df = isinstance(X_label, pd.DataFrame)
+896        random_state = check_random_state(self.random_state)
 897
-898        random_state = check_random_state(self.random_state)
-899
-900        self.classes_ = np.unique(y_label)
-901        number_per_class = calc_number_per_class(y_label)
-902
-903        if self.subspace_size is None:
-904            self.subspace_size = int(X.shape[1] / 2)
-905        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
-906
-907        cfs = Parallel(n_jobs=self.n_jobs)(
-908            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
-909            for i in range(self.n_estimators)
-910        )
-911
-912        it = 0
-913        while True:
-914            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
-915                X_unlabel
-916            ) == 0:
-917                break
-918
-919            raw_predicions = []
-920            for i in range(self.n_estimators):
-921                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
-922                raw_predicions.append(rp)
-923            raw_predicions = sum(raw_predicions) / self.n_estimators
-924            predictions = np.max(raw_predicions, axis=1)
-925            class_predicted = np.argmax(raw_predicions, axis=1)
-926            pseudoy = self.classes_.take(class_predicted, axis=0)
-927
-928            final_instances = list()
-929            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
-930            for c in self.classes_:
-931                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
-932
-933            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
-934            yj = pseudoy[final_instances]
-935
-936            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
-937            y_label = np.append(y_label, yj)
-938            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
-939
-940            cfs = Parallel(n_jobs=self.n_jobs)(
-941                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
-942                for i in range(self.n_estimators)
-943            )
+898        self.classes_ = np.unique(y_label)
+899        number_per_class = calc_number_per_class(y_label)
+900
+901        if self.subspace_size is None:
+902            self.subspace_size = int(X.shape[1] / 2)
+903        idxs = self._generate_random_subspaces(X_label, y_label, random_state)
+904
+905        cfs = Parallel(n_jobs=self.n_jobs)(
+906            delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+907            for i in range(self.n_estimators)
+908        )
+909
+910        it = 0
+911        while True:
+912            if (self.max_iterations != -1 and it >= self.max_iterations) or len(
+913                X_unlabel
+914            ) == 0:
+915                break
+916
+917            raw_predicions = []
+918            for i in range(self.n_estimators):
+919                rp = cfs[i].predict_proba(X_unlabel[:, idxs[i]] if not is_df else X_unlabel.iloc[:, idxs[i]])
+920                raw_predicions.append(rp)
+921            raw_predicions = sum(raw_predicions) / self.n_estimators
+922            predictions = np.max(raw_predicions, axis=1)
+923            class_predicted = np.argmax(raw_predicions, axis=1)
+924            pseudoy = self.classes_.take(class_predicted, axis=0)
+925
+926            final_instances = list()
+927            best_candidates = np.argsort(predictions, kind="mergesort")[::-1]
+928            for c in self.classes_:
+929                final_instances += list(best_candidates[pseudoy[best_candidates] == c])[:number_per_class[c]]
+930
+931            Lj = X_unlabel[final_instances] if not is_df else X_unlabel.iloc[final_instances]
+932            yj = pseudoy[final_instances]
+933
+934            X_label = np.append(X_label, Lj, axis=0) if not is_df else pd.concat([X_label, Lj])
+935            y_label = np.append(y_label, yj)
+936            X_unlabel = np.delete(X_unlabel, final_instances, axis=0) if not is_df else X_unlabel.drop(index=X_unlabel.index[final_instances])
+937
+938            cfs = Parallel(n_jobs=self.n_jobs)(
+939                delayed(self._fit_estimator)(X_label[:, idxs[i]] if not is_df else X_label.iloc[:, idxs[i]], y_label, i, **kwards)
+940                for i in range(self.n_estimators)
+941            )
+942
+943            it += 1
 944
-945            it += 1
-946
-947        self.h_ = cfs
-948        self.columns_ = idxs
-949
-950        return self
+945        self.h_ = cfs
+946        self.columns_ = idxs
+947
+948        return self
 
    
 
 
@@ -4003,110 +4094,110 @@ 
    Inherited Members
    - 
     953class RelRasco(Rasco):
- 954    """
- 955    **Co-Training based on relevant random subspaces**
- 956    --------------------------------------------
- 957
- 958    Is a variation of `sslearn.wrapper.Rasco` that uses the mutual information of each feature to select the random subspaces.
- 959    The process of training is the same as Rasco.
- 960
- 961    **Methods**
- 962    -------
- 963    - `fit`: Fit the model with the labeled instances.
- 964    - `predict` : Predict the class for each instance.
- 965    - `predict_proba`: Predict the probability for each class.
- 966    - `score`: Return the mean accuracy on the given test data and labels.
- 967
- 968    **Example**
- 969    -------
- 970    ```python
- 971    from sklearn.datasets import load_iris
- 972    from sslearn.wrapper import RelRasco
- 973    from sslearn.model_selection import artificial_ssl_dataset
- 974
- 975    X, y = load_iris(return_X_y=True)
- 976    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
- 977    relrasco = RelRasco()
- 978    relrasco.fit(X, y)
- 979    relrasco.score(X_unlabel, y_unlabel)
- 980    ```
- 981
- 982    **References**
- 983    ----------
- 984    Yaslan, Y., & Cataltepe, Z. (2010).<br>
- 985    Co-training with relevant random subspaces.<br>
- 986    <i>Neurocomputing</i>, 73(10-12), 1652-1661.<br>
- 987    [10.1016/j.neucom.2010.01.018](https://doi.org/10.1016/j.neucom.2010.01.018)
- 988    """
- 989
- 990    def __init__(
- 991        self,
- 992        base_estimator=DecisionTreeClassifier(),
- 993        max_iterations=10,
- 994        n_estimators=30,
- 995        subspace_size=None,
- 996        random_state=None,
- 997        n_jobs=None,
- 998    ):
- 999        """
-1000        Co-Training with relevant random subspaces
-1001
-1002        Parameters
-1003        ----------
-1004        base_estimator : ClassifierMixin, optional
-1005            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1006        max_iterations : int, optional
-1007            Maximum number of iterations allowed. Should be greater than or equal to 0.
-1008            If is -1 then will be infinite iterations until U be empty, by default 10
-1009        n_estimators : int, optional
-1010            The number of base estimators in the ensemble., by default 30
-1011        subspace_size : int, optional
-1012            The number of features for each subspace. If it is None will be the half of the features size., by default None
-1013        random_state : int, RandomState instance, optional
-1014            controls the randomness of the estimator, by default None
-1015        n_jobs : int, optional
-1016            The number of jobs to run in parallel. -1 means using all processors., by default None
-1017
-1018        """
-1019        super().__init__(
-1020            base_estimator,
-1021            max_iterations,
-1022            n_estimators,
-1023            subspace_size,
-1024            random_state,
-1025            n_jobs,
-1026        )
-1027
-1028    def _generate_random_subspaces(self, X, y, random_state=None):
-1029        """Generate the relevant random subspcaes
-1030
-1031        Parameters
-1032        ----------
-1033        X : array like
-1034            Labeled dataset
-1035        y : array like, optional
-1036            Target for each X, only needed on Rel-Rasco, by default None
-1037
-1038        Returns
-1039        -------
-1040        subspaces: list
-1041            List of index of features
-1042        """
-1043        random_state = check_random_state(random_state)
-1044        relevance = mutual_info_classif(X, y, random_state=random_state)
-1045        idxs = []
-1046        for _ in range(self.n_estimators):
-1047            subspace = []
-1048            for __ in range(self.subspace_size):
-1049                f1 = random_state.randint(0, X.shape[1])
-1050                f2 = random_state.randint(0, X.shape[1])
-1051                if relevance[f1] > relevance[f2]:
-1052                    subspace.append(f1)
-1053                else:
-1054                    subspace.append(f2)
-1055            idxs.append(subspace)
-1056        return idxs
+            
     951class RelRasco(Rasco):
+ 952    """
+ 953    **Co-Training based on relevant random subspaces**
+ 954    --------------------------------------------
+ 955
+ 956    Is a variation of `sslearn.wrapper.Rasco` that uses the mutual information of each feature to select the random subspaces.
+ 957    The process of training is the same as Rasco.
+ 958
+ 959    **Methods**
+ 960    -------
+ 961    - `fit`: Fit the model with the labeled instances.
+ 962    - `predict` : Predict the class for each instance.
+ 963    - `predict_proba`: Predict the probability for each class.
+ 964    - `score`: Return the mean accuracy on the given test data and labels.
+ 965
+ 966    **Example**
+ 967    -------
+ 968    ```python
+ 969    from sklearn.datasets import load_iris
+ 970    from sslearn.wrapper import RelRasco
+ 971    from sslearn.model_selection import artificial_ssl_dataset
+ 972
+ 973    X, y = load_iris(return_X_y=True)
+ 974    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+ 975    relrasco = RelRasco()
+ 976    relrasco.fit(X, y)
+ 977    relrasco.score(X_unlabel, y_unlabel)
+ 978    ```
+ 979
+ 980    **References**
+ 981    ----------
+ 982    Yaslan, Y., & Cataltepe, Z. (2010).<br>
+ 983    Co-training with relevant random subspaces.<br>
+ 984    <i>Neurocomputing</i>, 73(10-12), 1652-1661.<br>
+ 985    [10.1016/j.neucom.2010.01.018](https://doi.org/10.1016/j.neucom.2010.01.018)
+ 986    """
+ 987
+ 988    def __init__(
+ 989        self,
+ 990        base_estimator=DecisionTreeClassifier(),
+ 991        max_iterations=10,
+ 992        n_estimators=30,
+ 993        subspace_size=None,
+ 994        random_state=None,
+ 995        n_jobs=None,
+ 996    ):
+ 997        """
+ 998        Co-Training with relevant random subspaces
+ 999
+1000        Parameters
+1001        ----------
+1002        base_estimator : ClassifierMixin, optional
+1003            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1004        max_iterations : int, optional
+1005            Maximum number of iterations allowed. Should be greater than or equal to 0.
+1006            If is -1 then will be infinite iterations until U be empty, by default 10
+1007        n_estimators : int, optional
+1008            The number of base estimators in the ensemble., by default 30
+1009        subspace_size : int, optional
+1010            The number of features for each subspace. If it is None will be the half of the features size., by default None
+1011        random_state : int, RandomState instance, optional
+1012            controls the randomness of the estimator, by default None
+1013        n_jobs : int, optional
+1014            The number of jobs to run in parallel. -1 means using all processors., by default None
+1015
+1016        """
+1017        super().__init__(
+1018            base_estimator,
+1019            max_iterations,
+1020            n_estimators,
+1021            subspace_size,
+1022            random_state,
+1023            n_jobs,
+1024        )
+1025
+1026    def _generate_random_subspaces(self, X, y, random_state=None):
+1027        """Generate the relevant random subspcaes
+1028
+1029        Parameters
+1030        ----------
+1031        X : array like
+1032            Labeled dataset
+1033        y : array like, optional
+1034            Target for each X, only needed on Rel-Rasco, by default None
+1035
+1036        Returns
+1037        -------
+1038        subspaces: list
+1039            List of index of features
+1040        """
+1041        random_state = check_random_state(random_state)
+1042        relevance = mutual_info_classif(X, y, random_state=random_state)
+1043        idxs = []
+1044        for _ in range(self.n_estimators):
+1045            subspace = []
+1046            for __ in range(self.subspace_size):
+1047                f1 = random_state.randint(0, X.shape[1])
+1048                f2 = random_state.randint(0, X.shape[1])
+1049                if relevance[f1] > relevance[f2]:
+1050                    subspace.append(f1)
+1051                else:
+1052                    subspace.append(f2)
+1053            idxs.append(subspace)
+1054        return idxs
 
    
 
 
@@ -4158,43 +4249,43 @@ 
    References
    - 
     990    def __init__(
- 991        self,
- 992        base_estimator=DecisionTreeClassifier(),
- 993        max_iterations=10,
- 994        n_estimators=30,
- 995        subspace_size=None,
- 996        random_state=None,
- 997        n_jobs=None,
- 998    ):
- 999        """
-1000        Co-Training with relevant random subspaces
-1001
-1002        Parameters
-1003        ----------
-1004        base_estimator : ClassifierMixin, optional
-1005            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1006        max_iterations : int, optional
-1007            Maximum number of iterations allowed. Should be greater than or equal to 0.
-1008            If is -1 then will be infinite iterations until U be empty, by default 10
-1009        n_estimators : int, optional
-1010            The number of base estimators in the ensemble., by default 30
-1011        subspace_size : int, optional
-1012            The number of features for each subspace. If it is None will be the half of the features size., by default None
-1013        random_state : int, RandomState instance, optional
-1014            controls the randomness of the estimator, by default None
-1015        n_jobs : int, optional
-1016            The number of jobs to run in parallel. -1 means using all processors., by default None
-1017
-1018        """
-1019        super().__init__(
-1020            base_estimator,
-1021            max_iterations,
-1022            n_estimators,
-1023            subspace_size,
-1024            random_state,
-1025            n_jobs,
-1026        )
+            
     988    def __init__(
+ 989        self,
+ 990        base_estimator=DecisionTreeClassifier(),
+ 991        max_iterations=10,
+ 992        n_estimators=30,
+ 993        subspace_size=None,
+ 994        random_state=None,
+ 995        n_jobs=None,
+ 996    ):
+ 997        """
+ 998        Co-Training with relevant random subspaces
+ 999
+1000        Parameters
+1001        ----------
+1002        base_estimator : ClassifierMixin, optional
+1003            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1004        max_iterations : int, optional
+1005            Maximum number of iterations allowed. Should be greater than or equal to 0.
+1006            If is -1 then will be infinite iterations until U be empty, by default 10
+1007        n_estimators : int, optional
+1008            The number of base estimators in the ensemble., by default 30
+1009        subspace_size : int, optional
+1010            The number of features for each subspace. If it is None will be the half of the features size., by default None
+1011        random_state : int, RandomState instance, optional
+1012            controls the randomness of the estimator, by default None
+1013        n_jobs : int, optional
+1014            The number of jobs to run in parallel. -1 means using all processors., by default None
+1015
+1016        """
+1017        super().__init__(
+1018            base_estimator,
+1019            max_iterations,
+1020            n_estimators,
+1021            subspace_size,
+1022            random_state,
+1023            n_jobs,
+1024        )
 
    
 
 
@@ -4256,257 +4347,257 @@ 
    Inherited Members
    - 
    1282class CoForest(BaseCoTraining):
-1283    """
-1284    **CoForest classifier. Random Forest co-training**
-1285    ----------------------------
-1286    
-1287    Ensemble method for CoTraining based on Random Forest.
-1288
-1289    The main process is:
-1290    1. Train a committee of classifiers using bootstrap.
-1291    2. While any base classifier is retrained:
-1292        1. Predict the instances from the unlabeled set.
-1293        2. Select the instances with the highest probability.
-1294        3. Label the instances with the highest probability
-1295        4. Add the instances to the labeled set only if the error is not bigger than the previous error.
-1296        5. Retrain the classifier with the new instances.
-1297    3. Combine the probabilities of each classifier.
-1298
-1299
-1300    **Methods**
-1301    -------
-1302    - `fit`: Fit the model with the labeled instances.
-1303    - `predict` : Predict the class for each instance.
-1304    - `predict_proba`: Predict the probability for each class.
-1305    - `score`: Return the mean accuracy on the given test data and labels.
-1306
-1307    **Example**
-1308    -------
-1309    ```python
-1310    from sklearn.datasets import load_iris
-1311    from sslearn.wrapper import CoForest
-1312    from sslearn.model_selection import artificial_ssl_dataset
-1313
-1314    X, y = load_iris(return_X_y=True)
-1315    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-1316    coforest = CoForest()
-1317    coforest.fit(X, y)
-1318    coforest.score(X_unlabel, y_unlabel)
-1319    ```
-1320
-1321    **References**
-1322    ----------
-1323    Li, M., & Zhou, Z.-H. (2007).<br>
-1324    Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.<br>
-1325    <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,<br>
-1326    37(6), 1088-1098. [10.1109/tsmca.2007.904745](https://doi.org/10.1109/tsmca.2007.904745)
-1327    """
-1328
-1329    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
-1330        """
-1331        Generate a CoForest classifier.
-1332        A SSL Random Forest adaption for CoTraining. 
-1333
-1334        Parameters
-1335        ----------
-1336        base_estimator : ClassifierMixin, optional
-1337            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1338        n_estimators : int, optional
-1339            The number of base estimators in the ensemble., by default 7
-1340        threshold : float, optional
-1341            The decision threshold. Should be in [0, 1)., by default 0.5
-1342        n_jobs : int, optional
-1343            The number of jobs to run in parallel for both fit and predict., by default None
-1344        bootstrap : bool, optional
-1345            Whether bootstrap samples are used when building estimators., by default True
-1346        random_state : int, RandomState instance, optional
-1347            controls the randomness of the estimator, by default None
-1348        **kwards : dict, optional
-1349            Additional parameters to be passed to base_estimator, by default None.
-1350        """
-1351        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
-1352        self.n_estimators = n_estimators
-1353        self.threshold = threshold
-1354        self.bootstrap = bootstrap
-1355        self._epsilon = sys.float_info.epsilon
-1356        self.n_jobs = n_jobs
-1357        self.random_state = random_state
-1358        self.version = version
-1359        if self.version == "1.0.2":
-1360            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
-1361
-1362    def __bootstraping(self, X, y, r_state):
-1363        # It is necessary to bootstrap the data
-1364        if self.bootstrap and self.version == "1.0.3":
-1365            is_df = isinstance(X, pd.DataFrame)
-1366            columns = None
-1367            if is_df:
-1368                columns = X.columns
-1369                X = X.to_numpy()
-1370            y = y.copy()
-1371            # Get a reprentation of each class
-1372            classes = np.unique(y)
-1373            # Choose at least one sample from each class
-1374            X_label, y_label = [], []
-1375            for c in classes:
-1376                index = np.where(y == c)[0]
-1377                # Choose one sample from each class
-1378                X_label.append(X[index[0], :])
-1379                y_label.append(y[index[0]])
-1380                # Remove the sample from the original data
-1381                X = np.delete(X, index[0], axis=0)
-1382                y = np.delete(y, index[0], axis=0)
-1383            X, y = resample(X, y, random_state=r_state)
-1384            X = np.concatenate((X, np.array(X_label)), axis=0)
-1385            y = np.concatenate((y, np.array(y_label)), axis=0)
-1386            if is_df:
-1387                X = pd.DataFrame(X, columns=columns)
-1388        return X, y
-1389
-1390    def __estimate_error(self, hypothesis, X, y, index):
-1391        if self.version == "1.0.3":
-1392            concomitants = [h for i, h in enumerate(self.hypotheses) if i != index]
-1393            predicted = [h.predict(X) for h in concomitants]
-1394            predicted = np.array(predicted, dtype=y.dtype)
-1395            # Get the majority vote
-1396            predicted, _ = mode(predicted)
-1397            # predicted, _ = st.mode(predicted, axis=1)
-1398            # Get the error rate
-1399            return 1 - accuracy_score(y, predicted)
-1400        else:
-1401            probas = hypothesis.predict_proba(X)
-1402            ei_t = 0
-1403            classes = list(hypothesis.classes_)
-1404            for j in range(y.shape[0]):
-1405                true_y = y[j]
-1406                true_y_index = classes.index(true_y)
-1407                ei_t += 1 - probas[j, true_y_index]
-1408            if ei_t == 0:
-1409                ei_t = self._epsilon
-1410            return ei_t
-1411
-1412    def __confidence(self, h_index, X):
-1413        concomitants = [h for i, h in enumerate(self.hypotheses) if i != h_index]
-1414
-1415        predicted = [h.predict(X) for h in concomitants]
-1416        predicted = np.array(predicted, dtype=predicted[0].dtype)
-1417        # Get the majority vote and the number of votes
-1418        _, counts = mode(predicted)
-1419        # _, counts = st.mode(predicted, axis=1)
-1420        confidences = counts / len(concomitants)
-1421        return confidences
-1422
-1423    def _fit_estimator(self, X, y, i, beginning=False, **kwards):
-1424        estimator = self.base_estimator
-1425        if type(self.base_estimator) == list:
-1426            estimator = skclone(self.hypotheses[i])
-1427
-1428        if "random_state" in estimator.get_params():
-1429            r_state = estimator.random_state
-1430        else:
-1431            r_state = self.random_state
-1432            if r_state is None:
-1433                r_state = np.random.randint(0, 1000)
-1434            r_state += i
-1435        # Only in the beginning
-1436        if beginning:
-1437            X, y = self.__bootstraping(X, y, r_state)
+            
    1280class CoForest(BaseCoTraining):
+1281    """
+1282    **CoForest classifier. Random Forest co-training**
+1283    ----------------------------
+1284    
+1285    Ensemble method for CoTraining based on Random Forest.
+1286
+1287    The main process is:
+1288    1. Train a committee of classifiers using bootstrap.
+1289    2. While any base classifier is retrained:
+1290        1. Predict the instances from the unlabeled set.
+1291        2. Select the instances with the highest probability.
+1292        3. Label the instances with the highest probability
+1293        4. Add the instances to the labeled set only if the error is not bigger than the previous error.
+1294        5. Retrain the classifier with the new instances.
+1295    3. Combine the probabilities of each classifier.
+1296
+1297
+1298    **Methods**
+1299    -------
+1300    - `fit`: Fit the model with the labeled instances.
+1301    - `predict` : Predict the class for each instance.
+1302    - `predict_proba`: Predict the probability for each class.
+1303    - `score`: Return the mean accuracy on the given test data and labels.
+1304
+1305    **Example**
+1306    -------
+1307    ```python
+1308    from sklearn.datasets import load_iris
+1309    from sslearn.wrapper import CoForest
+1310    from sslearn.model_selection import artificial_ssl_dataset
+1311
+1312    X, y = load_iris(return_X_y=True)
+1313    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+1314    coforest = CoForest()
+1315    coforest.fit(X, y)
+1316    coforest.score(X_unlabel, y_unlabel)
+1317    ```
+1318
+1319    **References**
+1320    ----------
+1321    Li, M., & Zhou, Z.-H. (2007).<br>
+1322    Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.<br>
+1323    <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,<br>
+1324    37(6), 1088-1098. [10.1109/tsmca.2007.904745](https://doi.org/10.1109/tsmca.2007.904745)
+1325    """
+1326
+1327    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1328        """
+1329        Generate a CoForest classifier.
+1330        A SSL Random Forest adaption for CoTraining. 
+1331
+1332        Parameters
+1333        ----------
+1334        base_estimator : ClassifierMixin, optional
+1335            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1336        n_estimators : int, optional
+1337            The number of base estimators in the ensemble., by default 7
+1338        threshold : float, optional
+1339            The decision threshold. Should be in [0, 1)., by default 0.5
+1340        n_jobs : int, optional
+1341            The number of jobs to run in parallel for both fit and predict., by default None
+1342        bootstrap : bool, optional
+1343            Whether bootstrap samples are used when building estimators., by default True
+1344        random_state : int, RandomState instance, optional
+1345            controls the randomness of the estimator, by default None
+1346        **kwards : dict, optional
+1347            Additional parameters to be passed to base_estimator, by default None.
+1348        """
+1349        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1350        self.n_estimators = n_estimators
+1351        self.threshold = threshold
+1352        self.bootstrap = bootstrap
+1353        self._epsilon = sys.float_info.epsilon
+1354        self.n_jobs = n_jobs
+1355        self.random_state = random_state
+1356        self.version = version
+1357        if self.version == "1.0.2":
+1358            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+1359
+1360    def __bootstraping(self, X, y, r_state):
+1361        # It is necessary to bootstrap the data
+1362        if self.bootstrap and self.version == "1.0.3":
+1363            is_df = isinstance(X, pd.DataFrame)
+1364            columns = None
+1365            if is_df:
+1366                columns = X.columns
+1367                X = X.to_numpy()
+1368            y = y.copy()
+1369            # Get a reprentation of each class
+1370            classes = np.unique(y)
+1371            # Choose at least one sample from each class
+1372            X_label, y_label = [], []
+1373            for c in classes:
+1374                index = np.where(y == c)[0]
+1375                # Choose one sample from each class
+1376                X_label.append(X[index[0], :])
+1377                y_label.append(y[index[0]])
+1378                # Remove the sample from the original data
+1379                X = np.delete(X, index[0], axis=0)
+1380                y = np.delete(y, index[0], axis=0)
+1381            X, y = resample(X, y, random_state=r_state)
+1382            X = np.concatenate((X, np.array(X_label)), axis=0)
+1383            y = np.concatenate((y, np.array(y_label)), axis=0)
+1384            if is_df:
+1385                X = pd.DataFrame(X, columns=columns)
+1386        return X, y
+1387
+1388    def __estimate_error(self, hypothesis, X, y, index):
+1389        if self.version == "1.0.3":
+1390            concomitants = [h for i, h in enumerate(self.hypotheses) if i != index]
+1391            predicted = [h.predict(X) for h in concomitants]
+1392            predicted = np.array(predicted, dtype=y.dtype)
+1393            # Get the majority vote
+1394            predicted, _ = mode(predicted)
+1395            # predicted, _ = st.mode(predicted, axis=1)
+1396            # Get the error rate
+1397            return 1 - accuracy_score(y, predicted)
+1398        else:
+1399            probas = hypothesis.predict_proba(X)
+1400            ei_t = 0
+1401            classes = list(hypothesis.classes_)
+1402            for j in range(y.shape[0]):
+1403                true_y = y[j]
+1404                true_y_index = classes.index(true_y)
+1405                ei_t += 1 - probas[j, true_y_index]
+1406            if ei_t == 0:
+1407                ei_t = self._epsilon
+1408            return ei_t
+1409
+1410    def __confidence(self, h_index, X):
+1411        concomitants = [h for i, h in enumerate(self.hypotheses) if i != h_index]
+1412
+1413        predicted = [h.predict(X) for h in concomitants]
+1414        predicted = np.array(predicted, dtype=predicted[0].dtype)
+1415        # Get the majority vote and the number of votes
+1416        _, counts = mode(predicted)
+1417        # _, counts = st.mode(predicted, axis=1)
+1418        confidences = counts / len(concomitants)
+1419        return confidences
+1420
+1421    def _fit_estimator(self, X, y, i, beginning=False, **kwards):
+1422        estimator = self.base_estimator
+1423        if type(self.base_estimator) == list:
+1424            estimator = skclone(self.hypotheses[i])
+1425
+1426        if "random_state" in estimator.get_params():
+1427            r_state = estimator.random_state
+1428        else:
+1429            r_state = self.random_state
+1430            if r_state is None:
+1431                r_state = np.random.randint(0, 1000)
+1432            r_state += i
+1433        # Only in the beginning
+1434        if beginning:
+1435            X, y = self.__bootstraping(X, y, r_state)
+1436
+1437        return skclone(estimator).fit(X, y, **kwards)
 1438
-1439        return skclone(estimator).fit(X, y, **kwards)
-1440
-1441    def fit(self, X, y, **kwards):
-1442        """Build a CoForest classifier from the training set (X, y).
-1443
-1444        Parameters
-1445        ----------
-1446        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1447            The training input samples.
-1448        y : array-like of shape (n_samples,)
-1449            The target values (class labels), -1 if unlabel.
-1450
-1451        Returns
-1452        -------
-1453        self: CoForest
-1454            Fitted estimator.
-1455        """
-1456        random_state = check_random_state(self.random_state)
-1457        n_jobs = check_n_jobs(self.n_jobs)
+1439    def fit(self, X, y, **kwards):
+1440        """Build a CoForest classifier from the training set (X, y).
+1441
+1442        Parameters
+1443        ----------
+1444        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1445            The training input samples.
+1446        y : array-like of shape (n_samples,)
+1447            The target values (class labels), -1 if unlabel.
+1448
+1449        Returns
+1450        -------
+1451        self: CoForest
+1452            Fitted estimator.
+1453        """
+1454        random_state = check_random_state(self.random_state)
+1455        n_jobs = check_n_jobs(self.n_jobs)
+1456
+1457        X_label, y_label, X_unlabel = get_dataset(X, y)
 1458
-1459        X_label, y_label, X_unlabel = get_dataset(X, y)
+1459        is_df = isinstance(X_label, pd.DataFrame)
 1460
-1461        is_df = isinstance(X_label, pd.DataFrame)
+1461        self.classes_ = np.unique(y_label)
 1462
-1463        self.classes_ = np.unique(y_label)
-1464
-1465        self.hypotheses = []
-1466        errors = []
-1467        weights = []
-1468        for i in range(self.n_estimators):
-1469            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
-1470            if "random_state" in dir(self.hypotheses[-1]):
-1471                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
-1472            errors.append(0.5)
-1473
-1474        self.hypotheses = Parallel(n_jobs=n_jobs)(
-1475            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
-1476            for i in range(self.n_estimators)
-1477        )
-1478
-1479        for i in range(self.n_estimators):
-1480            # The paper stablishes that the weight of each hypothesis is 0,
-1481            # but it is not possible to do that because it will be impossible increase the training set
-1482            if self.version == "1.0.2":
-1483                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
-1484            else:
-1485                weights.append(self.__confidence(i, X_label).sum())
-1486
-1487        changing = True if X_unlabel.shape[0] > 0 else False
-1488        while changing:
-1489            changing = False
-1490            for i in range(self.n_estimators):
-1491                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1463        self.hypotheses = []
+1464        errors = []
+1465        weights = []
+1466        for i in range(self.n_estimators):
+1467            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1468            if "random_state" in dir(self.hypotheses[-1]):
+1469                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
+1470            errors.append(0.5)
+1471
+1472        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1473            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1474            for i in range(self.n_estimators)
+1475        )
+1476
+1477        for i in range(self.n_estimators):
+1478            # The paper stablishes that the weight of each hypothesis is 0,
+1479            # but it is not possible to do that because it will be impossible increase the training set
+1480            if self.version == "1.0.2":
+1481                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1482            else:
+1483                weights.append(self.__confidence(i, X_label).sum())
+1484
+1485        changing = True if X_unlabel.shape[0] > 0 else False
+1486        while changing:
+1487            changing = False
+1488            for i in range(self.n_estimators):
+1489                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1490
+1491                ei_t = self.__estimate_error(hi, X_label, y_label, i)
 1492
-1493                ei_t = self.__estimate_error(hi, X_label, y_label, i)
-1494
-1495                if ei_t < ei:
-1496                    random_index_subsample = list(range(X_unlabel.shape[0]))
-1497                    random_index_subsample = random_state.permutation(
-1498                        random_index_subsample
-1499                    )
-1500                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
-1501                    if is_df:
-1502                        Ui_t = X_unlabel.iloc[cond, :]
-1503                    else:
-1504                        Ui_t = X_unlabel[cond, :]
-1505
-1506                    raw_predictions = hi.predict_proba(Ui_t)
-1507                    predictions = np.max(raw_predictions, axis=1)
-1508                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
-1509
-1510                    to_label = predictions > self.threshold
-1511                    wi_t = predictions[to_label].sum()
-1512
-1513                    if ei_t * wi_t < ei * wi:
-1514                        changing = True
-1515                        if is_df:
-1516                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
-1517                        else:
-1518                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
-1519                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
-1520                        hi.fit(
-1521                            x_temp,
-1522                            y_temp,
-1523                            **kwards
-1524                        )
-1525
-1526                    errors[i] = ei_t
-1527                    weights[i] = wi_t
-1528
-1529        self.h_ = self.hypotheses
-1530        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
-1531
-1532        return self
+1493                if ei_t < ei:
+1494                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1495                    random_index_subsample = random_state.permutation(
+1496                        random_index_subsample
+1497                    )
+1498                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1499                    if is_df:
+1500                        Ui_t = X_unlabel.iloc[cond, :]
+1501                    else:
+1502                        Ui_t = X_unlabel[cond, :]
+1503
+1504                    raw_predictions = hi.predict_proba(Ui_t)
+1505                    predictions = np.max(raw_predictions, axis=1)
+1506                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1507
+1508                    to_label = predictions > self.threshold
+1509                    wi_t = predictions[to_label].sum()
+1510
+1511                    if ei_t * wi_t < ei * wi:
+1512                        changing = True
+1513                        if is_df:
+1514                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1515                        else:
+1516                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1517                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1518                        hi.fit(
+1519                            x_temp,
+1520                            y_temp,
+1521                            **kwards
+1522                        )
+1523
+1524                    errors[i] = ei_t
+1525                    weights[i] = wi_t
+1526
+1527        self.h_ = self.hypotheses
+1528        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1529
+1530        return self
 
    
 
 
@@ -4572,38 +4663,38 @@ 
    References
    - 
    1329    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
-1330        """
-1331        Generate a CoForest classifier.
-1332        A SSL Random Forest adaption for CoTraining. 
-1333
-1334        Parameters
-1335        ----------
-1336        base_estimator : ClassifierMixin, optional
-1337            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1338        n_estimators : int, optional
-1339            The number of base estimators in the ensemble., by default 7
-1340        threshold : float, optional
-1341            The decision threshold. Should be in [0, 1)., by default 0.5
-1342        n_jobs : int, optional
-1343            The number of jobs to run in parallel for both fit and predict., by default None
-1344        bootstrap : bool, optional
-1345            Whether bootstrap samples are used when building estimators., by default True
-1346        random_state : int, RandomState instance, optional
-1347            controls the randomness of the estimator, by default None
-1348        **kwards : dict, optional
-1349            Additional parameters to be passed to base_estimator, by default None.
-1350        """
-1351        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
-1352        self.n_estimators = n_estimators
-1353        self.threshold = threshold
-1354        self.bootstrap = bootstrap
-1355        self._epsilon = sys.float_info.epsilon
-1356        self.n_jobs = n_jobs
-1357        self.random_state = random_state
-1358        self.version = version
-1359        if self.version == "1.0.2":
-1360            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+            
    1327    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1328        """
+1329        Generate a CoForest classifier.
+1330        A SSL Random Forest adaption for CoTraining. 
+1331
+1332        Parameters
+1333        ----------
+1334        base_estimator : ClassifierMixin, optional
+1335            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1336        n_estimators : int, optional
+1337            The number of base estimators in the ensemble., by default 7
+1338        threshold : float, optional
+1339            The decision threshold. Should be in [0, 1)., by default 0.5
+1340        n_jobs : int, optional
+1341            The number of jobs to run in parallel for both fit and predict., by default None
+1342        bootstrap : bool, optional
+1343            Whether bootstrap samples are used when building estimators., by default True
+1344        random_state : int, RandomState instance, optional
+1345            controls the randomness of the estimator, by default None
+1346        **kwards : dict, optional
+1347            Additional parameters to be passed to base_estimator, by default None.
+1348        """
+1349        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1350        self.n_estimators = n_estimators
+1351        self.threshold = threshold
+1352        self.bootstrap = bootstrap
+1353        self._epsilon = sys.float_info.epsilon
+1354        self.n_jobs = n_jobs
+1355        self.random_state = random_state
+1356        self.version = version
+1357        if self.version == "1.0.2":
+1358            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
 
    
 
 
@@ -4643,98 +4734,98 @@ 
    Parameters
    - 
    1441    def fit(self, X, y, **kwards):
-1442        """Build a CoForest classifier from the training set (X, y).
-1443
-1444        Parameters
-1445        ----------
-1446        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1447            The training input samples.
-1448        y : array-like of shape (n_samples,)
-1449            The target values (class labels), -1 if unlabel.
-1450
-1451        Returns
-1452        -------
-1453        self: CoForest
-1454            Fitted estimator.
-1455        """
-1456        random_state = check_random_state(self.random_state)
-1457        n_jobs = check_n_jobs(self.n_jobs)
+            
    1439    def fit(self, X, y, **kwards):
+1440        """Build a CoForest classifier from the training set (X, y).
+1441
+1442        Parameters
+1443        ----------
+1444        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1445            The training input samples.
+1446        y : array-like of shape (n_samples,)
+1447            The target values (class labels), -1 if unlabel.
+1448
+1449        Returns
+1450        -------
+1451        self: CoForest
+1452            Fitted estimator.
+1453        """
+1454        random_state = check_random_state(self.random_state)
+1455        n_jobs = check_n_jobs(self.n_jobs)
+1456
+1457        X_label, y_label, X_unlabel = get_dataset(X, y)
 1458
-1459        X_label, y_label, X_unlabel = get_dataset(X, y)
+1459        is_df = isinstance(X_label, pd.DataFrame)
 1460
-1461        is_df = isinstance(X_label, pd.DataFrame)
+1461        self.classes_ = np.unique(y_label)
 1462
-1463        self.classes_ = np.unique(y_label)
-1464
-1465        self.hypotheses = []
-1466        errors = []
-1467        weights = []
-1468        for i in range(self.n_estimators):
-1469            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
-1470            if "random_state" in dir(self.hypotheses[-1]):
-1471                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
-1472            errors.append(0.5)
-1473
-1474        self.hypotheses = Parallel(n_jobs=n_jobs)(
-1475            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
-1476            for i in range(self.n_estimators)
-1477        )
-1478
-1479        for i in range(self.n_estimators):
-1480            # The paper stablishes that the weight of each hypothesis is 0,
-1481            # but it is not possible to do that because it will be impossible increase the training set
-1482            if self.version == "1.0.2":
-1483                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
-1484            else:
-1485                weights.append(self.__confidence(i, X_label).sum())
-1486
-1487        changing = True if X_unlabel.shape[0] > 0 else False
-1488        while changing:
-1489            changing = False
-1490            for i in range(self.n_estimators):
-1491                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1463        self.hypotheses = []
+1464        errors = []
+1465        weights = []
+1466        for i in range(self.n_estimators):
+1467            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1468            if "random_state" in dir(self.hypotheses[-1]):
+1469                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
+1470            errors.append(0.5)
+1471
+1472        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1473            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1474            for i in range(self.n_estimators)
+1475        )
+1476
+1477        for i in range(self.n_estimators):
+1478            # The paper stablishes that the weight of each hypothesis is 0,
+1479            # but it is not possible to do that because it will be impossible increase the training set
+1480            if self.version == "1.0.2":
+1481                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1482            else:
+1483                weights.append(self.__confidence(i, X_label).sum())
+1484
+1485        changing = True if X_unlabel.shape[0] > 0 else False
+1486        while changing:
+1487            changing = False
+1488            for i in range(self.n_estimators):
+1489                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1490
+1491                ei_t = self.__estimate_error(hi, X_label, y_label, i)
 1492
-1493                ei_t = self.__estimate_error(hi, X_label, y_label, i)
-1494
-1495                if ei_t < ei:
-1496                    random_index_subsample = list(range(X_unlabel.shape[0]))
-1497                    random_index_subsample = random_state.permutation(
-1498                        random_index_subsample
-1499                    )
-1500                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
-1501                    if is_df:
-1502                        Ui_t = X_unlabel.iloc[cond, :]
-1503                    else:
-1504                        Ui_t = X_unlabel[cond, :]
-1505
-1506                    raw_predictions = hi.predict_proba(Ui_t)
-1507                    predictions = np.max(raw_predictions, axis=1)
-1508                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
-1509
-1510                    to_label = predictions > self.threshold
-1511                    wi_t = predictions[to_label].sum()
-1512
-1513                    if ei_t * wi_t < ei * wi:
-1514                        changing = True
-1515                        if is_df:
-1516                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
-1517                        else:
-1518                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
-1519                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
-1520                        hi.fit(
-1521                            x_temp,
-1522                            y_temp,
-1523                            **kwards
-1524                        )
-1525
-1526                    errors[i] = ei_t
-1527                    weights[i] = wi_t
-1528
-1529        self.h_ = self.hypotheses
-1530        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
-1531
-1532        return self
+1493                if ei_t < ei:
+1494                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1495                    random_index_subsample = random_state.permutation(
+1496                        random_index_subsample
+1497                    )
+1498                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1499                    if is_df:
+1500                        Ui_t = X_unlabel.iloc[cond, :]
+1501                    else:
+1502                        Ui_t = X_unlabel[cond, :]
+1503
+1504                    raw_predictions = hi.predict_proba(Ui_t)
+1505                    predictions = np.max(raw_predictions, axis=1)
+1506                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1507
+1508                    to_label = predictions > self.threshold
+1509                    wi_t = predictions[to_label].sum()
+1510
+1511                    if ei_t * wi_t < ei * wi:
+1512                        changing = True
+1513                        if is_df:
+1514                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1515                        else:
+1516                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1517                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1518                        hi.fit(
+1519                            x_temp,
+1520                            y_temp,
+1521                            **kwards
+1522                        )
+1523
+1524                    errors[i] = ei_t
+1525                    weights[i] = wi_t
+1526
+1527        self.h_ = self.hypotheses
+1528        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1529
+1530        return self
 
    
 
 
diff --git a/sitemap.xml b/sitemap.xml
index 6e7fb7f..f78ea1c 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -5,6 +5,7 @@
 https://pdoc.dev/docs/
 https://pdoc.dev/docs/sslearn.html
 https://pdoc.dev/docs/sslearn/subview.html
+https://pdoc.dev/docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html
 https://pdoc.dev/docs/sslearn/model_selection.html
 https://pdoc.dev/docs/sslearn/base.html
 https://pdoc.dev/docs/sslearn/datasets.html
diff --git a/sslearn/__init__.py b/sslearn/__init__.py
index a01ad62..17fa7b9 100644
--- a/sslearn/__init__.py
+++ b/sslearn/__init__.py
@@ -10,7 +10,7 @@
     __doc__ = "Semi-Supervised Learning (SSL) is a Python package that provides tools to train and evaluate semi-supervised learning models."
 
 
-__version__='1.0.5'
+__version__='1.0.5.1'
 __AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
 __AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
 __URL__="https://pypi.org/project/sslearn/"