From 67d6c179ede5c0326601eddfa591998c11fb8ff3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Mon, 27 May 2024 09:39:56 +0200 Subject: [PATCH 1/2] Remove conflicts --- ...ia en conflicto de CROSS-PC 2024-05-14).py | 208 ------------------ 1 file changed, 208 deletions(-) delete mode 100644 sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py 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 deleted file mode 100644 index 011be28..0000000 --- a/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).py +++ /dev/null @@ -1,208 +0,0 @@ -"""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 From 646051f6939207a8c6636ff603ed2e6124155ed6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jos=C3=A9=20Luis=20Garrido-Labrador?= Date: Mon, 27 May 2024 09:52:14 +0200 Subject: [PATCH 2/2] 1.0.5.2 --- CHANGELOG.md | 6 + docs/search.js | 2 +- docs/sslearn.html | 3 +- ... en conflicto de CROSS-PC 2024-05-14).html | 985 ------------------ sitemap.xml | 1 - sslearn/__init__.py | 2 +- 6 files changed, 9 insertions(+), 990 deletions(-) delete mode 100644 docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html diff --git a/CHANGELOG.md b/CHANGELOG.md index 821e385..2c2195d 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). +## [1.0.5.2] - 2024-05-27 + +### HotFix + +- Remove some files that are not necessary in the package. + ## [1.0.5.1] - 2024-05-20 ### Fixed diff --git a/docs/search.js b/docs/search.js index fdb9f22..494c8de 100644 --- a/docs/search.js +++ b/docs/search.js @@ -1,6 +1,6 @@ window.pdocSearch = (function(){ /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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\n

\n\n

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

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    \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

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It can be installed using Pypi:

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

Code example

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\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

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\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:

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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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\n

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

\n
\n\n

OneVsRestSSLClassifier:

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\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.

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Examples
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\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.

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Parameters
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    \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.

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Parameters
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    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nArray representing the data.
    • \n
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Returns
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    \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": 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true}; + /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"sslearn": {"fullname": "sslearn", "modulename": "sslearn", "kind": "module", "doc": "

Semi-Supervised Learning Library (sslearn)

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\n

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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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    \n
  • 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

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\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.

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Parameters
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    \n
  • X (ndarray or DataFrame of shape (n_samples, n_features)):\nFeatures matrix.
    • \n
  • y (ndarray of shape (n_samples,)):\nTarget vector.
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Returns
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  • X_label (ndarray or DataFrame of shape (n_label, n_features)):\nLabeled features matrix.
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  • y_label (ndarray or Serie of shape (n_label,)):\nLabeled target vector.
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  • 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.

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Examples
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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.

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Parameters
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    \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.

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Parameters
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    \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
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Returns
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    \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.

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

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

    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__='1.0.5.1' +13__version__='1.0.5.2' 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/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 deleted file mode 100644 index 07783fd..0000000 --- a/docs/sslearn/restricted (Copia en conflicto de CROSS-PC 2024-05-14).html +++ /dev/null @@ -1,985 +0,0 @@ - - - - - - - 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/sitemap.xml b/sitemap.xml index f78ea1c..6e7fb7f 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -5,7 +5,6 @@ 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 17fa7b9..d3fd80c 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__='1.0.5.1' +__version__='1.0.5.2' __AUTHOR__="José Luis Garrido-Labrador" # Author of the package __AUTHOR_EMAIL__="jlgarrido@ubu.es" # Author's email __URL__="https://pypi.org/project/sslearn/"