diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 973d168..979d055 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -11,10 +11,11 @@ on: jobs: build: - runs-on: ubuntu-latest + runs-on: ${{ matrix.os }} strategy: fail-fast: false matrix: + os: [ubuntu-latest, macos-latest, windows-latest] python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: @@ -27,7 +28,7 @@ jobs: run: | python -m pip install --upgrade pip python -m pip install flake8 pytest coverage - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + pip install -r requirements.txt - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names @@ -37,7 +38,9 @@ jobs: - name: Test with pytest run: | coverage run -m pytest test + # Only if ubuntu-latest - name: Coverage + if: ${{ matrix.os == 'ubuntu-latest' }} run: | wget https://s3.amazonaws.com/codeclimate/test-reporter/test-reporter-latest-linux-amd64 -O reporter chmod +x reporter diff --git a/CHANGELOG.md b/CHANGELOG.md index 814a60d..63b9fc9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,14 @@ 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] - 2024-05-08 + +### Added +- `feature_fusion` and `probability_fusion` methods for restricted in `sslearn.restricted` module. + +### Fixed +- CoForest random integer is now compatible with Windows. + ## [1.0.4] - 2024-01-31 ### Added @@ -16,7 +24,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed - DeTriTraining now is vectorized and is faster than before. -# [1.0.3.1] - 2023-04-01 +## [1.0.3.1] - 2023-04-01 ### Changed - Hot fix for avoid problems with Pypi diff --git a/README.md b/README.md index e24719d..40d0877 100644 --- a/README.md +++ b/README.md @@ -45,14 +45,12 @@ model.score(X_unlabel, true_label) ## Citing ```bibtex -@software{jose_luis_garrido_labrador_2024_10623889, +@software{garrido2024sslearn, author = {José Luis Garrido-Labrador}, - title = {jlgarridol/sslearn: v1.0.4}, + title = {jlgarridol/sslearn}, month = feb, year = 2024, publisher = {Zenodo}, - version = {1.0.4}, - doi = {10.5281/zenodo.10623889}, - url = {https://doi.org/10.5281/zenodo.10623889} + doi = {10.5281/zenodo.7565221}, } ``` diff --git a/docs/search.js b/docs/search.js index 42844a8..2cb5448 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. This may cause problems when serialising the index.\n",e)},t.Pipeline.load=function(e){var n=new t.Pipeline;return e.forEach(function(e){var i=t.Pipeline.getRegisteredFunction(e);if(!i)throw new Error("Cannot load un-registered function: "+e);n.add(i)}),n},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(e){t.Pipeline.warnIfFunctionNotRegistered(e),this._queue.push(e)},this)},t.Pipeline.prototype.after=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i+1,0,n)},t.Pipeline.prototype.before=function(e,n){t.Pipeline.warnIfFunctionNotRegistered(n);var i=this._queue.indexOf(e);if(-1===i)throw new Error("Cannot find existingFn");this._queue.splice(i,0,n)},t.Pipeline.prototype.remove=function(e){var t=this._queue.indexOf(e);-1!==t&&this._queue.splice(t,1)},t.Pipeline.prototype.run=function(e){for(var t=[],n=e.length,i=this._queue.length,o=0;n>o;o++){for(var r=e[o],s=0;i>s&&(r=this._queue[s](r,o,e),void 0!==r&&null!==r);s++);void 0!==r&&null!==r&&t.push(r)}return t},t.Pipeline.prototype.reset=function(){this._queue=[]},t.Pipeline.prototype.get=function(){return this._queue},t.Pipeline.prototype.toJSON=function(){return this._queue.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})},t.Index=function(){this._fields=[],this._ref="id",this.pipeline=new t.Pipeline,this.documentStore=new t.DocumentStore,this.index={},this.eventEmitter=new t.EventEmitter,this._idfCache={},this.on("add","remove","update",function(){this._idfCache={}}.bind(this))},t.Index.prototype.on=function(){var e=Array.prototype.slice.call(arguments);return this.eventEmitter.addListener.apply(this.eventEmitter,e)},t.Index.prototype.off=function(e,t){return this.eventEmitter.removeListener(e,t)},t.Index.load=function(e){e.version!==t.version&&t.utils.warn("version mismatch: current "+t.version+" importing "+e.version);var n=new this;n._fields=e.fields,n._ref=e.ref,n.documentStore=t.DocumentStore.load(e.documentStore),n.pipeline=t.Pipeline.load(e.pipeline),n.index={};for(var i in e.index)n.index[i]=t.InvertedIndex.load(e.index[i]);return n},t.Index.prototype.addField=function(e){return this._fields.push(e),this.index[e]=new t.InvertedIndex,this},t.Index.prototype.setRef=function(e){return this._ref=e,this},t.Index.prototype.saveDocument=function(e){return this.documentStore=new t.DocumentStore(e),this},t.Index.prototype.addDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.addDoc(i,e),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));this.documentStore.addFieldLength(i,n,o.length);var r={};o.forEach(function(e){e in r?r[e]+=1:r[e]=1},this);for(var s in r){var u=r[s];u=Math.sqrt(u),this.index[n].addToken(s,{ref:i,tf:u})}},this),n&&this.eventEmitter.emit("add",e,this)}},t.Index.prototype.removeDocByRef=function(e){if(e&&this.documentStore.isDocStored()!==!1&&this.documentStore.hasDoc(e)){var t=this.documentStore.getDoc(e);this.removeDoc(t,!1)}},t.Index.prototype.removeDoc=function(e,n){if(e){var n=void 0===n?!0:n,i=e[this._ref];this.documentStore.hasDoc(i)&&(this.documentStore.removeDoc(i),this._fields.forEach(function(n){var o=this.pipeline.run(t.tokenizer(e[n]));o.forEach(function(e){this.index[n].removeToken(e,i)},this)},this),n&&this.eventEmitter.emit("remove",e,this))}},t.Index.prototype.updateDoc=function(e,t){var t=void 0===t?!0:t;this.removeDocByRef(e[this._ref],!1),this.addDoc(e,!1),t&&this.eventEmitter.emit("update",e,this)},t.Index.prototype.idf=function(e,t){var n="@"+t+"/"+e;if(Object.prototype.hasOwnProperty.call(this._idfCache,n))return this._idfCache[n];var i=this.index[t].getDocFreq(e),o=1+Math.log(this.documentStore.length/(i+1));return this._idfCache[n]=o,o},t.Index.prototype.getFields=function(){return this._fields.slice()},t.Index.prototype.search=function(e,n){if(!e)return[];e="string"==typeof e?{any:e}:JSON.parse(JSON.stringify(e));var i=null;null!=n&&(i=JSON.stringify(n));for(var o=new t.Configuration(i,this.getFields()).get(),r={},s=Object.keys(e),u=0;u0&&t.push(e);for(var i in n)"docs"!==i&&"df"!==i&&this.expandToken(e+i,t,n[i]);return t},t.InvertedIndex.prototype.toJSON=function(){return{root:this.root}},t.Configuration=function(e,n){var e=e||"";if(void 0==n||null==n)throw new Error("fields should not be null");this.config={};var i;try{i=JSON.parse(e),this.buildUserConfig(i,n)}catch(o){t.utils.warn("user configuration parse failed, will use default configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();oSemi-Supervised Learning Library (sslearn)\n\n

\n

\n\n

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

\n\n

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

\n\n

Installation

\n\n

Dependencies

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

pip installation

\n\n

It can be installed using Pypi:

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

Code example

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

Citing

\n\n
\n
@software{jose_luis_garrido_labrador_2024_10623889,\n  author       = {Jos\u00e9 Luis Garrido-Labrador},\n  title        = {jlgarridol/sslearn: v1.0.4},\n  month        = feb,\n  year         = 2024,\n  publisher    = {Zenodo},\n  version      = {1.0.4},\n  doi          = {10.5281/zenodo.10623889},\n  url          = {https://doi.org/10.5281/zenodo.10623889}\n}\n
\n
\n"}, "sslearn.base": {"fullname": "sslearn.base", "modulename": "sslearn.base", "kind": "module", "doc": "

Summary of module sslearn.base:

\n\n

Functions

\n\n

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

\n\n

Classes

\n\n

FakedProbaClassifier:

\n\n
\n

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

\n
\n\n

OneVsRestSSLClassifier:

\n\n
\n

Adapted OneVsRestClassifier for SSL datasets

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

Check and divide dataset between labeled and unlabeled data.

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

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

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

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

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

Fit a FakedProbaClassifier.

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

Predict the classes of X.

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

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

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

Adapted OneVsRestClassifier for SSL datasets

\n\n

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

\n\n

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

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

Adapted OneVsRestClassifier for SSL datasets

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

Fit underlying estimators.

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

Predict multi-class targets using underlying estimators.

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

Probability estimates.

\n\n

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

\n\n

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

\n\n

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

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

Summary of module sslearn.datasets:

\n\n

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

\n\n

Functions

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

Read a .csv file

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

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

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

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

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

Save a dataset in the KEEL format

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

Summary of module sslearn.model_selection:

\n\n

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

\n\n

Functions

\n\n

artificial_ssl_dataset:

\n\n
\n

Generate an artificial semi-supervised learning dataset.

\n
\n\n

Classes

\n\n

StratifiedKFoldSS:

\n\n
\n

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

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

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

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

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

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

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

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

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

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

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

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

Fit the model according to the given training data.

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

Calculate the conflict rate of the model.

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

Predict class for X.

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

Predict class probabilities for X.

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

Summary of module sslearn.subview:

\n\n

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

\n\n

Classes

\n\n

SubViewClassifier:

\n\n
\n

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

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

A classifier that uses a subview of the data.

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

Predict class probabilities using the base estimator.

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

A classifier that uses a subview of the data.

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

Predict using the base estimator.

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

Some utility functions

\n\n

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

\n\n

Functions

\n\n

safe_division:

\n\n
\n

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

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

Safely divide two numbers preventing division by zero

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

Calculate the confidence interval of the predictions

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

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

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

Calculate the priori probability of each label

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

Calculate the mode of a list of values

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

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

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

Summary of module sslearn.wrapper:

\n\n

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

\n\n

Self-Training Algorithms

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

Co-Training Algorithms

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

Self Training Classifier with data loader compatible.

\n\n

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

\n\n

Example

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

References

\n\n

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

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

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

\n\n

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

\n\n

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

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

Fits this SelfTrainingClassifier to a dataset.

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

Self-training with Editing.

\n\n

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

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

Example

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

References

\n\n

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

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

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

\n\n
Parameters
\n\n
    \n
  • base_estimator (ClassifierMixin, optional):\nAn estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
    • \n
  • max_iterations (int, optional):\nMaximum number of iterations allowed. Should be greater than or equal to 0., by default 40
    • \n
  • distance (str, optional):\nThe distance metric to use for the graph.\nThe default metric is euclidean, and with p=2 is equivalent to the standard Euclidean metric.\nFor a list of available metrics, see the documentation of DistanceMetric and the metrics listed in sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS.\nNote that the cosine metric uses cosine_distances., by default euclidean
    • \n
  • poolsize (float, optional):\nMax number of unlabel instances candidates to pseudolabel, by default 0.25
    • \n
  • rejection_threshold (float, optional):\nsignificance level, by default 0.1
    • \n
  • graph_neighbors (int, optional):\nNumber of neighbors for each sample., by default 1
    • \n
  • random_state (int, RandomState instance, optional):\ncontrols the randomness of the estimator, by default None
    • \n
  • n_jobs (int, optional):\nThe number of parallel jobs to run for neighbors search. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors, by default None
    • \n
\n", "signature": "(\tbase_estimator=KNeighborsClassifier(n_neighbors=3),\tmax_iterations=40,\tdistance='euclidean',\tpoolsize=0.25,\trejection_threshold=0.05,\tgraph_neighbors=1,\trandom_state=None,\tn_jobs=None)"}, "sslearn.wrapper.Setred.fit": {"fullname": "sslearn.wrapper.Setred.fit", "modulename": "sslearn.wrapper", "qualname": "Setred.fit", "kind": "function", "doc": "

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

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

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

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

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

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

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

\n\n

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

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

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

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

Build a CoTraining classifier from the training set.

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

Predict probability for each possible outcome.

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

Predict the classes of X.

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

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

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

Co-Training by Committee classifier.

\n\n

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

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

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

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

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

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

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

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

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

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

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

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

\n\n

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

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

Methods

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

Example

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

References

\n\n

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

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

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

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

Fit Democratic-Co classifier

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

Predict probability for each possible outcome.

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

Co-Training based on random subspaces

\n\n

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

Co-Training based on random subspaces

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

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

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

Co-Training based on relevant random subspaces

\n\n

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

\n\n

Methods

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

Example

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

References

\n\n

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

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

Co-Training with relevant random subspaces

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

CoForest classifier. Random Forest co-training

\n\n

Ensemble method for CoTraining based on Random Forest.

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

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

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

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

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n

The main process is:

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

Methods

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

References

\n\n

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

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

TriTraining. Trio of classifiers with bootstrapping.

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

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

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

TriTraining with Data Editing.

\n\n

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

\n\n

Methods

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

References

\n\n

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

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

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

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

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

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}}, "docInfo": {"sslearn": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 550}, "sslearn.base": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.base.get_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 117}, "sslearn.base.FakedProbaClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 155}, "sslearn.base.FakedProbaClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 40}, "sslearn.base.FakedProbaClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 68}, "sslearn.base.FakedProbaClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 59}, "sslearn.base.FakedProbaClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 78}, "sslearn.base.OneVsRestSSLClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 37}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 63}, "sslearn.base.OneVsRestSSLClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 80}, "sslearn.base.OneVsRestSSLClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 66}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 162}, "sslearn.datasets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 85}, "sslearn.datasets.read_csv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 53, "bases": 0, "doc": 134}, "sslearn.datasets.read_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 158}, "sslearn.datasets.secure_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 70}, "sslearn.datasets.save_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 97, "bases": 0, "doc": 163}, "sslearn.model_selection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.model_selection.artificial_ssl_dataset": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 329}, "sslearn.model_selection.StratifiedKFoldSS": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 52}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 36, "bases": 0, "doc": 73}, "sslearn.model_selection.StratifiedKFoldSS.split": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 137}, "sslearn.restricted": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 89}, "sslearn.restricted.conflict_rate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 113}, "sslearn.restricted.WhoIsWhoClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 51}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 35, "bases": 0, "doc": 118}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 39, "bases": 0, "doc": 113}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 84}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 92}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 63}, "sslearn.subview": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 58}, "sslearn.subview.SubViewClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 7, "doc": 315}, "sslearn.subview.SubViewClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 64}, "sslearn.subview.SubViewRegressor": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 7, "doc": 315}, "sslearn.subview.SubViewRegressor.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 56}, "sslearn.utils": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 129}, "sslearn.utils.safe_division": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 73}, "sslearn.utils.confidence_interval": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 102}, "sslearn.utils.choice_with_proportion": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 111}, "sslearn.utils.calculate_prior_probability": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 56}, "sslearn.utils.mode": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 80}, "sslearn.utils.check_n_jobs": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 12, "bases": 0, "doc": 64}, "sslearn.wrapper": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 130}, "sslearn.wrapper.SelfTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 332}, "sslearn.wrapper.SelfTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 73, "bases": 0, "doc": 361}, "sslearn.wrapper.SelfTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 85}, "sslearn.wrapper.Setred": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 459}, "sslearn.wrapper.Setred.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 118, "bases": 0, "doc": 265}, "sslearn.wrapper.Setred.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.Setred.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 71}, "sslearn.wrapper.Setred.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 82}, "sslearn.wrapper.CoTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 692}, "sslearn.wrapper.CoTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 92, "bases": 0, "doc": 200}, "sslearn.wrapper.CoTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 81, "bases": 0, "doc": 174}, "sslearn.wrapper.CoTraining.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 33, "bases": 0, "doc": 90}, "sslearn.wrapper.CoTraining.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 33, "bases": 0, "doc": 86}, "sslearn.wrapper.CoTraining.score": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 39, "bases": 0, "doc": 162}, "sslearn.wrapper.CoTrainingByCommittee": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 489}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 68, "bases": 0, "doc": 107}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 71}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 82}, "sslearn.wrapper.CoTrainingByCommittee.score": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 129}, "sslearn.wrapper.DemocraticCoLearning": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 609}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 105, "bases": 0, "doc": 167}, "sslearn.wrapper.DemocraticCoLearning.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 95}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 63}, "sslearn.wrapper.Rasco": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 495}, "sslearn.wrapper.Rasco.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 79, "bases": 0, "doc": 144}, "sslearn.wrapper.Rasco.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.RelRasco": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 386}, "sslearn.wrapper.RelRasco.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 79, "bases": 0, "doc": 169}, "sslearn.wrapper.CoForest": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 477}, "sslearn.wrapper.CoForest.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 95, "bases": 0, "doc": 166}, "sslearn.wrapper.CoForest.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.TriTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 213}, "sslearn.wrapper.TriTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 55, "bases": 0, "doc": 140}, "sslearn.wrapper.TriTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.DeTriTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 198}, "sslearn.wrapper.DeTriTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 94, "bases": 0, "doc": 259}, "sslearn.wrapper.DeTriTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}}, "length": 82, "save": true}, "index": {"qualname": {"root": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 3}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 12}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 15}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 6, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}}, "df": 3}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}}, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.mode": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "fullname": {"root": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 82}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 5}}}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 5, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 12}}}}, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 5}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 12}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 15}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 8}}}}}}}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 6, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}}, "df": 3}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.mode": {"tf": 1}}, "df": 1, "l": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}}, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 39}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 7}}}}}, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "annotation": {"root": {"docs": {}, "df": 0}}, "default_value": {"root": {"docs": {}, "df": 0}}, "signature": {"root": {"0": {"5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 8}, "1": {"0": {"0": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}, "docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "2": {"5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}, "3": {"0": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "9": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 8}, "docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5}, "4": {"0": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}, "7": {"5": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}, "docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "9": {"5": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {"sslearn.base.get_dataset": {"tf": 3.7416573867739413}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 2.8284271247461903}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 4.242640687119285}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 3.7416573867739413}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 4.58257569495584}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 4.898979485566356}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 4.47213595499958}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 4.47213595499958}, "sslearn.datasets.read_csv": {"tf": 6.48074069840786}, "sslearn.datasets.read_keel": {"tf": 7.615773105863909}, "sslearn.datasets.secure_dataset": {"tf": 3.7416573867739413}, "sslearn.datasets.save_keel": {"tf": 8.774964387392123}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 7.681145747868608}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 5.291502622129181}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 4.242640687119285}, "sslearn.restricted.conflict_rate": {"tf": 4.69041575982343}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 5.0990195135927845}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 5.656854249492381}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 4.242640687119285}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 4.242640687119285}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.subview.SubViewRegressor.predict": {"tf": 3.7416573867739413}, "sslearn.utils.safe_division": {"tf": 4.242640687119285}, "sslearn.utils.confidence_interval": {"tf": 5.0990195135927845}, "sslearn.utils.choice_with_proportion": {"tf": 5.0990195135927845}, "sslearn.utils.calculate_prior_probability": {"tf": 3.1622776601683795}, "sslearn.utils.mode": {"tf": 3.1622776601683795}, "sslearn.utils.check_n_jobs": {"tf": 3.1622776601683795}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 7.483314773547883}, "sslearn.wrapper.SelfTraining.fit": {"tf": 4.242640687119285}, "sslearn.wrapper.Setred.__init__": {"tf": 9.433981132056603}, "sslearn.wrapper.Setred.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.Setred.predict": {"tf": 4.47213595499958}, "sslearn.wrapper.Setred.predict_proba": {"tf": 4.47213595499958}, "sslearn.wrapper.CoTraining.__init__": {"tf": 8.366600265340756}, "sslearn.wrapper.CoTraining.fit": {"tf": 8.18535277187245}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 5.291502622129181}, "sslearn.wrapper.CoTraining.predict": {"tf": 5.291502622129181}, "sslearn.wrapper.CoTraining.score": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 7.211102550927978}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 3.7416573867739413}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 3.7416573867739413}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 5.0990195135927845}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 9}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 5.0990195135927845}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 3.7416573867739413}, "sslearn.wrapper.Rasco.__init__": {"tf": 7.810249675906654}, "sslearn.wrapper.Rasco.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.RelRasco.__init__": {"tf": 7.810249675906654}, "sslearn.wrapper.CoForest.__init__": {"tf": 8.48528137423857}, "sslearn.wrapper.CoForest.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.TriTraining.__init__": {"tf": 6.557438524302}, "sslearn.wrapper.TriTraining.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 8.48528137423857}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 4.898979485566356}}, "df": 58, "x": {"2": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}}, "df": 3}, "docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 36}, "y": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 23}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}, "p": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 31}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 10, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 21}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}}}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 8}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "k": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 18}}, "s": {"docs": {"sslearn.wrapper.Setred.fit": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 6}}}, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 4, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1}}}}}}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}}}}}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "h": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}}}}}}}}}}}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}, "bases": {"root": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 7}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 5, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 3}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}, "o": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}}}}}}}}, "doc": {"root": {"0": {"1": {"8": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 2.6457513110645907}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 20}, "1": {"0": {"0": {"7": {"docs": {}, "df": 0, "/": {"1": {"1": {"4": {"3": {"0": {"9": {"1": {"9": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"2": {"5": {"2": {"3": {"1": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}, "1": {"6": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "j": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}, "docs": {}, "df": 0}, "6": {"2": {"3": {"8": {"8": {"9": {"docs": {"sslearn": {"tf": 1.7320508075688772}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"8": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"8": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 14}, "1": {"0": {"9": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}, "docs": {}, "df": 0}, "4": {"5": {"docs": {}, "df": 0, "/": {"2": {"7": {"9": {"9": {"4": {"3": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "5": {"2": {"9": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"1": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"5": {"2": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"1": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}, "7": {"0": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "8": {"6": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "9": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"5": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "6": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "9": {"5": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 2.8284271247461903}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 39}, "2": {"0": {"0": {"4": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}}, "df": 2}, "6": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 1}, "7": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}}, "df": 2}, "docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "1": {"0": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "7": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"4": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "7": {"9": {"9": {"6": {"2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 5}, "3": {"0": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "1": {"1": {"5": {"docs": {}, "df": 0, "/": {"9": {"8": {"1": {"6": {"5": {"8": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}, "5": {"1": {"8": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "9": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}}, "df": 2}, "docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}, "4": {"0": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "2": {"9": {"3": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "6": {"3": {"3": {"7": {"8": {"9": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 1.7320508075688772}}, "df": 1}, "5": {"2": {"8": {"1": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "6": {"3": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "7": {"2": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"4": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "6": {"0": {"2": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "1": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "7": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "7": {"0": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "1": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}, "docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2}, "8": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "9": {"0": {"4": {"7": {"4": {"5": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 3}, "8": {"1": {"6": {"8": {"4": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 19.519221295943137}, "sslearn.base": {"tf": 5.744562646538029}, "sslearn.base.get_dataset": {"tf": 6.708203932499369}, "sslearn.base.FakedProbaClassifier": {"tf": 9.16515138991168}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 3.872983346207417}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 5.744562646538029}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 5.196152422706632}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 5.0990195135927845}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 3.1622776601683795}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 4.358898943540674}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 5.744562646538029}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 5.196152422706632}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 6.244997998398398}, "sslearn.datasets": {"tf": 5.385164807134504}, "sslearn.datasets.read_csv": {"tf": 6.928203230275509}, "sslearn.datasets.read_keel": {"tf": 7.54983443527075}, "sslearn.datasets.secure_dataset": {"tf": 5.830951894845301}, "sslearn.datasets.save_keel": {"tf": 7.3484692283495345}, "sslearn.model_selection": {"tf": 5.744562646538029}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 9.695359714832659}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 3.7416573867739413}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 4.898979485566356}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 7.0710678118654755}, "sslearn.restricted": {"tf": 5.916079783099616}, "sslearn.restricted.conflict_rate": {"tf": 6.244997998398398}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 4}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 5.744562646538029}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 6.244997998398398}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 5.656854249492381}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 5.744562646538029}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 5.196152422706632}, "sslearn.subview": {"tf": 4.69041575982343}, "sslearn.subview.SubViewClassifier": {"tf": 14.422205101855956}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 5.196152422706632}, "sslearn.subview.SubViewRegressor": {"tf": 14.422205101855956}, "sslearn.subview.SubViewRegressor.predict": {"tf": 5.196152422706632}, "sslearn.utils": {"tf": 5.656854249492381}, "sslearn.utils.safe_division": {"tf": 5.830951894845301}, "sslearn.utils.confidence_interval": {"tf": 6.324555320336759}, "sslearn.utils.choice_with_proportion": {"tf": 6.324555320336759}, "sslearn.utils.calculate_prior_probability": {"tf": 5}, "sslearn.utils.mode": {"tf": 5.385164807134504}, "sslearn.utils.check_n_jobs": {"tf": 5.291502622129181}, "sslearn.wrapper": {"tf": 7.810249675906654}, "sslearn.wrapper.SelfTraining": {"tf": 14.52583904633395}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 8.774964387392123}, "sslearn.wrapper.SelfTraining.fit": {"tf": 5.916079783099616}, "sslearn.wrapper.Setred": {"tf": 14.866068747318506}, "sslearn.wrapper.Setred.__init__": {"tf": 7.3484692283495345}, "sslearn.wrapper.Setred.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.Setred.predict": {"tf": 5.0990195135927845}, "sslearn.wrapper.Setred.predict_proba": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTraining": {"tf": 20.174241001832016}, "sslearn.wrapper.CoTraining.__init__": {"tf": 6.708203932499369}, "sslearn.wrapper.CoTraining.fit": {"tf": 7.3484692283495345}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTraining.predict": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTraining.score": {"tf": 7.14142842854285}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 16.186414056238647}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 5.477225575051661}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 6.164414002968976}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 18.027756377319946}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 7.0710678118654755}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 6}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 5.196152422706632}, "sslearn.wrapper.Rasco": {"tf": 16.278820596099706}, "sslearn.wrapper.Rasco.__init__": {"tf": 5.830951894845301}, "sslearn.wrapper.Rasco.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.RelRasco": {"tf": 15.198684153570664}, "sslearn.wrapper.RelRasco.__init__": {"tf": 6.244997998398398}, "sslearn.wrapper.CoForest": {"tf": 16.15549442140351}, "sslearn.wrapper.CoForest.__init__": {"tf": 6.782329983125268}, "sslearn.wrapper.CoForest.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.TriTraining": {"tf": 8.888194417315589}, "sslearn.wrapper.TriTraining.__init__": {"tf": 6.4031242374328485}, "sslearn.wrapper.TriTraining.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.DeTriTraining": {"tf": 7.416198487095663}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 7.14142842854285}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 5.744562646538029}}, "df": 82, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 4, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 11}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 10, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 13}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.subview": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "f": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 21, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 2, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 3}, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}}, "df": 2}}}}, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 27, "s": {"docs": {"sslearn.model_selection": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}}, "df": 4}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}}}}}}}, "m": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 6}}}}}, "b": {"docs": {"sslearn.subview": {"tf": 1.7320508075688772}}, "df": 1, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}}, "df": 3, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}}, "df": 3}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 5, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 14, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 2.23606797749979}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 18}}}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 19}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 5, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 3, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}}, "df": 1}}}}}}}}}}, "y": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 3}}}}}, "k": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 15}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 38}}}, "u": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 7}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}}, "df": 7, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 47}}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 12}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 2}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1}, "c": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 2.449489742783178}}, "df": 1}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 28}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "y": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 6, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 3}}}, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 4}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 41}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}}, "df": 10, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 10, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.get_dataset": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 25, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}, "s": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 25}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2, "h": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 3.3166247903554}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 4.123105625617661}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2.6457513110645907}, "sslearn.restricted": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 3}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 2.449489742783178}, "sslearn.utils.choice_with_proportion": {"tf": 2}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 4.242640687119285}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 4.58257569495584}, "sslearn.wrapper.Setred.__init__": {"tf": 3.3166247903554}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining": {"tf": 4.69041575982343}, "sslearn.wrapper.CoTraining.__init__": {"tf": 3.872983346207417}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 4.47213595499958}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 4.795831523312719}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 4.47213595499958}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 3.1622776601683795}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoForest": {"tf": 4.47213595499958}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 4}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 4.123105625617661}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3.872983346207417}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 75, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 2}, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}, "i": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 4}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}, "m": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 21}, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 8}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 15}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 3}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 5}}}}}, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}}, "o": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 19}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 3.1622776601683795}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 28}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}}}}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 19, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}}, "df": 3}}}}}}, "o": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 3.4641016151377544}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3.1622776601683795}}, "df": 42, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 11}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 32}, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 1.7320508075688772}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 17, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 12}}}}, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.23606797749979}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 36, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.449489742783178}}, "df": 24, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 3}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.449489742783178}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 24}}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 20}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}, "x": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 5, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 3}}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.23606797749979}}, "df": 17, "o": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 3}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 3}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.23606797749979}, "sslearn.subview.SubViewRegressor": {"tf": 2.23606797749979}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}}, "df": 12}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 9}}}, "f": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 32}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1, "d": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}}}}, "a": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 2.449489742783178}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 2.23606797749979}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1.7320508075688772}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 51, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "d": {"docs": {"sslearn.base": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 29}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 6}, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 5}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 2.449489742783178}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining.fit": {"tf": 2}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.score": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 41, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}}}, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 2}}, "df": 12}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 13, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 12, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 7}}, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9, "s": {"docs": {"sslearn.wrapper": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 5}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}}}, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 4}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}, "v": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {"sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 59}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 6, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "i": {"docs": {}, "df": 0, "p": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}, "s": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.23606797749979}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 36, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 7, "s": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 8}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 21, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 15}}}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.calculate_prior_probability": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 8, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}}, "df": 3}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1, "i": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 4, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}}, "df": 1, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}, "p": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}, "d": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "f": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.6457513110645907}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 46, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.datasets": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 4}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 28, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 9}}}, "s": {"docs": {"sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}}, "df": 3}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 4}}}}, "e": {"docs": {}, "df": 0, "b": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 5, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 2.449489742783178}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 38}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.utils": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 6}}}}}}}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 2}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 8}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 11}}}}}, "m": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}, "k": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}}, "df": 2}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}, "x": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "n": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5, "l": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 19}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}}, "df": 7}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 12, "s": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 6}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 2}}}, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}, "f": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.6457513110645907}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.6457513110645907}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted": {"tf": 2.23606797749979}, "sslearn.restricted.conflict_rate": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 2.6457513110645907}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 2}, "sslearn.utils.choice_with_proportion": {"tf": 3.1622776601683795}, "sslearn.utils.calculate_prior_probability": {"tf": 2}, "sslearn.utils.mode": {"tf": 3.3166247903554}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 2}, "sslearn.wrapper.CoTraining.score": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 76}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 19, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 5}}, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}}}, "r": {"docs": {"sslearn.base.get_dataset": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 28, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1, "/": {"1": {"0": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 4, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}}, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 2.449489742783178}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}}, "df": 23}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1, "a": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 3.1622776601683795}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 35, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets": {"tf": 2.23606797749979}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 23, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 14}}}}, "f": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 2.23606797749979}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 3.1622776601683795}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}}, "df": 26}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 12}}}}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}}, "df": 4, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1, "i": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5, "n": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 4, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 5}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 2}}}}}}}}, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 2}}}, "p": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 9}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "s": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}}, "df": 9}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.8284271247461903}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 31}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "s": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}}, "df": 4}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}, "j": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 2, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 4}}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 8}}, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "/": {"2": {"0": {"1": {"1": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "/": {"0": {"4": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}}}}}}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}}}}}}}}}}}}, "g": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 4}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}}}}}}}, "o": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}}, "df": 6}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"sslearn.base.get_dataset": {"tf": 2.8284271247461903}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 2.23606797749979}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 2.23606797749979}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2.23606797749979}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 2}, "sslearn.utils.check_n_jobs": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.predict": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.score": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 51, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 5}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}}, "df": 21, "s": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.utils.safe_division": {"tf": 2}}, "df": 1}}}}}}, "o": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 18, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 2.23606797749979}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.23606797749979}}, "df": 23}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2}, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}, "v": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 10}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2.23606797749979}}, "df": 3, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 1}, ":": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 8}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 4, "/": {"2": {"docs": {}, "df": 0, "+": {"1": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}}}}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "c": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 7, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1}}, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.utils": {"tf": 2}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 10}}}}}}, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {"sslearn.wrapper": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 11, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 6}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 10}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.datasets": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}}, "df": 4}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 4}}, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 5}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1}, "r": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 7}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 4}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils.mode": {"tf": 1.4142135623730951}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 3}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.utils": {"tf": 2}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 4, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 43, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.23606797749979}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 24}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 10}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 13}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}}, "df": 4}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 8}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 2.449489742783178}}, "df": 1}}}}}}}}, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}}, "df": 2}}, "y": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}}}, "b": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 23, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}}, "df": 3}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 19, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 2.449489742783178}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 3}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3}}, "df": 31}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 11}, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 2, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 7, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 17}}}, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 8, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 9}, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 8}, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 19, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 21}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 3}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}}}}, "r": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 10}}}}}, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 5}}}}}, "o": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.7320508075688772}}, "df": 2, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 4}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 5}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 12}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 31, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 3}}, "s": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "k": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "x": {"1": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 5}, "docs": {"sslearn": {"tf": 2.6457513110645907}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 3}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.8284271247461903}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 51, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"sslearn": {"tf": 2.23606797749979}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.8284271247461903}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoForest": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 47, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "u": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 14, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 42}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 6, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 7}}}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 3}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 5}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2.23606797749979}}, "df": 2}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}}}}}}, "f": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 15}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 20, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}}}}}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "z": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}, "v": {"1": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}}, "df": 7, "s": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 15}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 2}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 7}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 2}}, "df": 1}}, "l": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}, "z": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "g": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}}, "df": 2}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "u": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}, "h": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 5}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "d": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 2}, "w": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "a": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 3}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "i": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 5}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 3.1622776601683795}, "sslearn.subview.SubViewRegressor": {"tf": 3.1622776601683795}}, "df": 2}}}}, "k": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 4, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3}, "p": {"docs": {"sslearn.restricted": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}, "y": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": 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)

\n\n

\n

\n\n

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

\n\n

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

\n\n

Installation

\n\n

Dependencies

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

pip installation

\n\n

It can be installed using Pypi:

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

Code example

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

Citing

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

Summary of module sslearn.base:

\n\n

Functions

\n\n

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

\n\n

Classes

\n\n

FakedProbaClassifier:

\n\n
\n

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

\n
\n\n

OneVsRestSSLClassifier:

\n\n
\n

Adapted OneVsRestClassifier for SSL datasets

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

Check and divide dataset between labeled and unlabeled data.

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

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

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

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

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

Fit a FakedProbaClassifier.

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

Predict the classes of X.

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

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

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

Adapted OneVsRestClassifier for SSL datasets

\n\n

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

\n\n

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

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

Adapted OneVsRestClassifier for SSL datasets

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

Fit underlying estimators.

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

Predict multi-class targets using underlying estimators.

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

Probability estimates.

\n\n

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

\n\n

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

\n\n

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

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

Summary of module sslearn.datasets:

\n\n

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

\n\n

Functions

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

Read a .csv file

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

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

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

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

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

Save a dataset in the KEEL format

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

Summary of module sslearn.model_selection:

\n\n

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

\n\n

Functions

\n\n

artificial_ssl_dataset:

\n\n
\n

Generate an artificial semi-supervised learning dataset.

\n
\n\n

Classes

\n\n

StratifiedKFoldSS:

\n\n
\n

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

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

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

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

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

\n\n

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

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

Generate a artificial dataset based on StratifiedKFold method

\n\n
Parameters
\n\n
    \n
  • X (array-like of shape (n_samples, n_features)):\nTraining data, where n_samples is the number of samples\nand n_features is the number of features.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target variable for supervised learning problems.
    • \n
\n\n
Yields
\n\n
    \n
  • X (ndarray):\nThe feature set.
    • \n
  • y (ndarray):\nThe label set, -1 for unlabel instance.
    • \n
  • label (ndarray):\nThe training set indices for split mark as labeled.
    • \n
  • unlabel (ndarray):\nThe training set indices for split mark as unlabeled.
    • \n
\n", "signature": "(self, X, y):", "funcdef": "def"}, "sslearn.restricted": {"fullname": "sslearn.restricted", "modulename": "sslearn.restricted", "kind": "module", "doc": "

Summary of module sslearn.restricted:

\n\n

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

\n\n

Classes

\n\n

WhoIsWhoClassifier:

\n\n
\n

Who is Who Classifier

\n
\n\n

Functions

\n\n

conflict_rate:

\n\n
\n

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

\n
\n\n

combine_predictions:

\n\n
\n

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

\n
\n\n

feature_fusion:

\n\n
\n

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

\n
\n\n

probability_fusion:

\n\n
\n

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

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

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

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

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

\n\n
Parameters
\n\n
    \n
  • y_probas (array-like of shape (n_samples, n_classes)):\nThe class probabilities of the input samples.
    • \n
  • instance_group (array-like of shape (n_samples)):\nThe group. Two instances with the same label are not allowed to be in the same group.
    • \n
  • class_number (int):\nThe number of classes.
    • \n
  • method (str, optional):\nThe method to use to assing class, it can be greedy to first-look or hungarian to use the Hungarian algorithm, by default \"hungarian\"
    • \n
\n\n
Returns
\n\n
    \n
  • array-like of shape (n_samples,): The predicted labels.
    • \n
\n", "signature": "(y_probas, instance_group, class_number, method='hungarian'):", "funcdef": "def"}, "sslearn.restricted.feature_fusion": {"fullname": "sslearn.restricted.feature_fusion", "modulename": "sslearn.restricted", "qualname": "feature_fusion", "kind": "function", "doc": "

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

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

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

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

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

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

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

\n\n
Parameters
\n\n
    \n

  • classifier (ClassifierMixin with predict_proba method):

    • \n

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

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

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

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

Base class for all estimators in scikit-learn.

\n\n
Notes
\n\n

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

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

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

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

Fit the model according to the given training data.

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

Calculate the conflict rate of the model.

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

Predict class for X.

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

Predict class probabilities for X.

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

Summary of module sslearn.subview:

\n\n

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

\n\n

Classes

\n\n

SubViewClassifier:

\n\n
\n

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

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

A classifier that uses a subview of the data.

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

Predict class probabilities using the base estimator.

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

A classifier that uses a subview of the data.

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

Predict using the base estimator.

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

Some utility functions

\n\n

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

\n\n

Functions

\n\n

safe_division:

\n\n
\n

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

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

Safely divide two numbers preventing division by zero

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

Calculate the confidence interval of the predictions

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

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

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

Calculate the priori probability of each label

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

Calculate the mode of a list of values

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

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

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

Summary of module sslearn.wrapper:

\n\n

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

\n\n

Self-Training Algorithms

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

Co-Training Algorithms

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

Self Training Classifier with data loader compatible.

\n\n

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

\n\n

Example

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

References

\n\n

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

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

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

\n\n

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

\n\n

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

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

Fits this SelfTrainingClassifier to a dataset.

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

Self-training with Editing.

\n\n

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

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

Example

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

References

\n\n

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

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

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

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

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

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

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

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

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

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

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

\n\n

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

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

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

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

Build a CoTraining classifier from the training set.

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

Predict probability for each possible outcome.

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

Predict the classes of X.

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

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

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

Co-Training by Committee classifier.

\n\n

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

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

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

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

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

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

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

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

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

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

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

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

\n\n

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

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

Methods

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

Example

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

References

\n\n

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

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

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

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

Fit Democratic-Co classifier

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

Predict probability for each possible outcome.

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

Co-Training based on random subspaces

\n\n

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

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

Co-Training based on random subspaces

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

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

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

Co-Training based on relevant random subspaces

\n\n

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

\n\n

Methods

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

Example

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

References

\n\n

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

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

Co-Training with relevant random subspaces

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

CoForest classifier. Random Forest co-training

\n\n

Ensemble method for CoTraining based on Random Forest.

\n\n

The main process is:

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

Methods

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

Example

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

References

\n\n

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

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

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

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

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

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

TriTraining. Trio of classifiers with bootstrapping.

\n\n

The main process is:

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

Methods

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

References

\n\n

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

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

TriTraining. Trio of classifiers with bootstrapping.

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

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

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

TriTraining with Data Editing.

\n\n

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

\n\n

Methods

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

References

\n\n

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

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

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

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

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

\n\n
Parameters
\n\n
    \n
  • X ({array-like, sparse matrix} of shape (n_samples, n_features)):\nThe training input samples.
    • \n
  • y (array-like of shape (n_samples,)):\nThe target values (class labels), -1 if unlabel.
    • \n
\n\n
Returns
\n\n
    \n
  • self (DeTriTraining):\nFitted estimator.
    • \n
\n", "signature": "(self, X, y, **kwards):", "funcdef": "def"}}, "docInfo": {"sslearn": {"qualname": 0, "fullname": 1, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 500}, "sslearn.base": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.base.get_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 117}, "sslearn.base.FakedProbaClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 155}, "sslearn.base.FakedProbaClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 10, "bases": 0, "doc": 40}, "sslearn.base.FakedProbaClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 68}, "sslearn.base.FakedProbaClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 59}, "sslearn.base.FakedProbaClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 78}, "sslearn.base.OneVsRestSSLClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 3, "doc": 37}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 25, "bases": 0, "doc": 63}, "sslearn.base.OneVsRestSSLClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 29, "bases": 0, "doc": 80}, "sslearn.base.OneVsRestSSLClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 66}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 162}, "sslearn.datasets": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 85}, "sslearn.datasets.read_csv": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 53, "bases": 0, "doc": 134}, "sslearn.datasets.read_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 158}, "sslearn.datasets.secure_dataset": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 70}, "sslearn.datasets.save_keel": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 97, "bases": 0, "doc": 163}, "sslearn.model_selection": {"qualname": 0, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 73}, "sslearn.model_selection.artificial_ssl_dataset": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 74, "bases": 0, "doc": 329}, "sslearn.model_selection.StratifiedKFoldSS": {"qualname": 1, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 52}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"qualname": 3, "fullname": 6, "annotation": 0, "default_value": 0, "signature": 36, "bases": 0, "doc": 73}, "sslearn.model_selection.StratifiedKFoldSS.split": {"qualname": 2, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 137}, "sslearn.restricted": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 182}, "sslearn.restricted.conflict_rate": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 27, "bases": 0, "doc": 113}, "sslearn.restricted.combine_predictions": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 38, "bases": 0, "doc": 148}, "sslearn.restricted.feature_fusion": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 787}, "sslearn.restricted.probability_fusion": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 796}, "sslearn.restricted.WhoIsWhoClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 9, "doc": 51}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 35, "bases": 0, "doc": 118}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 39, "bases": 0, "doc": 113}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 84}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 22, "bases": 0, "doc": 92}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 63}, "sslearn.subview": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 58}, "sslearn.subview.SubViewClassifier": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 7, "doc": 315}, "sslearn.subview.SubViewClassifier.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 64}, "sslearn.subview.SubViewRegressor": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 7, "doc": 315}, "sslearn.subview.SubViewRegressor.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 56}, "sslearn.utils": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 129}, "sslearn.utils.safe_division": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 73}, "sslearn.utils.confidence_interval": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 102}, "sslearn.utils.choice_with_proportion": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 111}, "sslearn.utils.calculate_prior_probability": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 56}, "sslearn.utils.mode": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 11, "bases": 0, "doc": 80}, "sslearn.utils.check_n_jobs": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 12, "bases": 0, "doc": 64}, "sslearn.wrapper": {"qualname": 0, "fullname": 2, "annotation": 0, "default_value": 0, "signature": 0, "bases": 0, "doc": 130}, "sslearn.wrapper.SelfTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 332}, "sslearn.wrapper.SelfTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 73, "bases": 0, "doc": 361}, "sslearn.wrapper.SelfTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 21, "bases": 0, "doc": 85}, "sslearn.wrapper.Setred": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 6, "doc": 459}, "sslearn.wrapper.Setred.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 118, "bases": 0, "doc": 262}, "sslearn.wrapper.Setred.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.Setred.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 71}, "sslearn.wrapper.Setred.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 23, "bases": 0, "doc": 82}, "sslearn.wrapper.CoTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 692}, "sslearn.wrapper.CoTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 92, "bases": 0, "doc": 200}, "sslearn.wrapper.CoTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 81, "bases": 0, "doc": 174}, "sslearn.wrapper.CoTraining.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 33, "bases": 0, "doc": 90}, "sslearn.wrapper.CoTraining.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 33, "bases": 0, "doc": 86}, "sslearn.wrapper.CoTraining.score": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 39, "bases": 0, "doc": 162}, "sslearn.wrapper.CoTrainingByCommittee": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 489}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 68, "bases": 0, "doc": 107}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 71}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 82}, "sslearn.wrapper.CoTrainingByCommittee.score": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 129}, "sslearn.wrapper.DemocraticCoLearning": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 609}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 105, "bases": 0, "doc": 167}, "sslearn.wrapper.DemocraticCoLearning.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 32, "bases": 0, "doc": 95}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 16, "bases": 0, "doc": 63}, "sslearn.wrapper.Rasco": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 495}, "sslearn.wrapper.Rasco.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 79, "bases": 0, "doc": 144}, "sslearn.wrapper.Rasco.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.RelRasco": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 386}, "sslearn.wrapper.RelRasco.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 79, "bases": 0, "doc": 169}, "sslearn.wrapper.CoForest": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 477}, "sslearn.wrapper.CoForest.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 95, "bases": 0, "doc": 166}, "sslearn.wrapper.CoForest.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.TriTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 213}, "sslearn.wrapper.TriTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 55, "bases": 0, "doc": 140}, "sslearn.wrapper.TriTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}, "sslearn.wrapper.DeTriTraining": {"qualname": 1, "fullname": 3, "annotation": 0, "default_value": 0, "signature": 0, "bases": 4, "doc": 198}, "sslearn.wrapper.DeTriTraining.__init__": {"qualname": 3, "fullname": 5, "annotation": 0, "default_value": 0, "signature": 94, "bases": 0, "doc": 259}, "sslearn.wrapper.DeTriTraining.fit": {"qualname": 2, "fullname": 4, "annotation": 0, "default_value": 0, "signature": 28, "bases": 0, "doc": 79}}, "length": 85, "save": true}, "index": {"qualname": {"root": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 3}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 12}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 15, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 2}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 6, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}}, "df": 3}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}}, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.mode": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "fullname": {"root": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 85}}}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 5}}}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 5, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 12}}}}, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 5}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 12}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 15, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 2}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 5}}}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 11}}}}}}}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}}, "df": 3}}}}}, "c": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}}}}, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 6, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}}, "df": 3}}}}}}}, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 1}}}}}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 2}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.mode": {"tf": 1}}, "df": 1, "l": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 5}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}}, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 39}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 7}}}}}, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}, "annotation": {"root": {"docs": {}, "df": 0}}, "default_value": {"root": {"docs": {}, "df": 0}}, "signature": {"root": {"0": {"5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 8}, "1": {"0": {"0": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}, "docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "2": {"5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}, "3": {"0": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "9": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 9}, "docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5}, "4": {"0": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "5": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}, "7": {"5": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}, "docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "9": {"5": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}, "docs": {}, "df": 0}, "docs": {"sslearn.base.get_dataset": {"tf": 3.7416573867739413}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 2.8284271247461903}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 4.242640687119285}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 3.7416573867739413}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 4.58257569495584}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 4.898979485566356}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 4.47213595499958}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 4.47213595499958}, "sslearn.datasets.read_csv": {"tf": 6.48074069840786}, "sslearn.datasets.read_keel": {"tf": 7.615773105863909}, "sslearn.datasets.secure_dataset": {"tf": 3.7416573867739413}, "sslearn.datasets.save_keel": {"tf": 8.774964387392123}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 7.681145747868608}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 5.291502622129181}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 4.242640687119285}, "sslearn.restricted.conflict_rate": {"tf": 4.69041575982343}, "sslearn.restricted.combine_predictions": {"tf": 5.291502622129181}, "sslearn.restricted.feature_fusion": {"tf": 4.69041575982343}, "sslearn.restricted.probability_fusion": {"tf": 4.69041575982343}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 5.0990195135927845}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 5.656854249492381}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 4.242640687119285}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 4.242640687119285}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 3.7416573867739413}, "sslearn.subview.SubViewRegressor.predict": {"tf": 3.7416573867739413}, "sslearn.utils.safe_division": {"tf": 4.242640687119285}, "sslearn.utils.confidence_interval": {"tf": 5.0990195135927845}, "sslearn.utils.choice_with_proportion": {"tf": 5.0990195135927845}, "sslearn.utils.calculate_prior_probability": {"tf": 3.1622776601683795}, "sslearn.utils.mode": {"tf": 3.1622776601683795}, "sslearn.utils.check_n_jobs": {"tf": 3.1622776601683795}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 7.483314773547883}, "sslearn.wrapper.SelfTraining.fit": {"tf": 4.242640687119285}, "sslearn.wrapper.Setred.__init__": {"tf": 9.433981132056603}, "sslearn.wrapper.Setred.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.Setred.predict": {"tf": 4.47213595499958}, "sslearn.wrapper.Setred.predict_proba": {"tf": 4.47213595499958}, "sslearn.wrapper.CoTraining.__init__": {"tf": 8.366600265340756}, "sslearn.wrapper.CoTraining.fit": {"tf": 8.18535277187245}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 5.291502622129181}, "sslearn.wrapper.CoTraining.predict": {"tf": 5.291502622129181}, "sslearn.wrapper.CoTraining.score": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 7.211102550927978}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 3.7416573867739413}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 3.7416573867739413}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 5.0990195135927845}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 9}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 5.0990195135927845}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 3.7416573867739413}, "sslearn.wrapper.Rasco.__init__": {"tf": 7.810249675906654}, "sslearn.wrapper.Rasco.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.RelRasco.__init__": {"tf": 7.810249675906654}, "sslearn.wrapper.CoForest.__init__": {"tf": 8.48528137423857}, "sslearn.wrapper.CoForest.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.TriTraining.__init__": {"tf": 6.557438524302}, "sslearn.wrapper.TriTraining.fit": {"tf": 4.898979485566356}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 8.48528137423857}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 4.898979485566356}}, "df": 61, "x": {"2": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}}, "df": 3}, "docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 38}, "y": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 24}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}}}}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}, "p": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 31}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 10, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 21}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.7320508075688772}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}}}}}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 8}}}}, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 3}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "k": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 18}}, "s": {"docs": {"sslearn.wrapper.Setred.fit": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 6}}}, "h": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}, "n": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1}}}}}}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 2}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}}}}}}}}}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}}, "df": 4}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}, "h": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}}}}}}}}}}}}}}}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}, "bases": {"root": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 7}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}}}}, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 5, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 3}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 6}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 2}}}}}}}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 4}}}}}}}}}}}}}}, "o": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 7}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}}, "w": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}}}}}}}}, "doc": {"root": {"0": {"1": {"8": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 2.23606797749979}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2.8284271247461903}, "sslearn.restricted.probability_fusion": {"tf": 2.8284271247461903}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 22}, "1": {"0": {"0": {"7": {"docs": {}, "df": 0, "/": {"1": {"1": {"4": {"3": {"0": {"9": {"1": {"9": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "9": {"2": {"5": {"2": {"3": {"1": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}, "1": {"6": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "j": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 3}}}, "docs": {}, "df": 0}, "2": {"1": {"8": {"8": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 2}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "4": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}, "8": {"8": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"8": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 16}, "1": {"0": {"9": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "j": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}, "k": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}, "docs": {}, "df": 0}, "4": {"5": {"docs": {}, "df": 0, "/": {"2": {"7": {"9": {"9": {"4": {"3": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "5": {"2": {"9": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "4": {"1": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "8": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"5": {"2": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "6": {"1": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}, "7": {"0": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "8": {"6": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}, "9": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"5": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "6": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "9": {"5": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 2.6457513110645907}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 3.3166247903554}, "sslearn.restricted.probability_fusion": {"tf": 3.3166247903554}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 41}, "2": {"0": {"0": {"4": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}}, "df": 2}, "6": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 1}, "7": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 1}, "8": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}}, "df": 2}, "docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "1": {"0": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}}, "df": 1}, "1": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "7": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "2": {"3": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}, "4": {"docs": {"sslearn": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "7": {"9": {"9": {"6": {"2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 7}, "3": {"0": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}, "1": {"1": {"5": {"docs": {}, "df": 0, "/": {"9": {"8": {"1": {"6": {"5": {"8": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "3": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}, "5": {"1": {"8": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "7": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "9": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}}, "df": 4}, "docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 8}, "4": {"0": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}, "2": {"9": {"3": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "6": {"3": {"3": {"7": {"8": {"9": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "8": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {"sslearn": {"tf": 1}}, "df": 1}, "5": {"2": {"8": {"1": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "6": {"3": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "7": {"2": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "9": {"4": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "6": {"0": {"2": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "1": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}, "7": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "7": {"0": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "1": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "3": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}, "5": {"6": {"5": {"2": {"2": {"1": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}, "docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2}, "8": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "9": {"0": {"4": {"7": {"4": {"5": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "5": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 3}, "8": {"1": {"6": {"8": {"4": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 18.65475810617763}, "sslearn.base": {"tf": 5.744562646538029}, "sslearn.base.get_dataset": {"tf": 6.708203932499369}, "sslearn.base.FakedProbaClassifier": {"tf": 9.16515138991168}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 3.872983346207417}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 5.744562646538029}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 5.196152422706632}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 5.0990195135927845}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 3.1622776601683795}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 4.358898943540674}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 5.744562646538029}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 5.196152422706632}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 6.244997998398398}, "sslearn.datasets": {"tf": 5.385164807134504}, "sslearn.datasets.read_csv": {"tf": 6.928203230275509}, "sslearn.datasets.read_keel": {"tf": 7.54983443527075}, "sslearn.datasets.secure_dataset": {"tf": 5.830951894845301}, "sslearn.datasets.save_keel": {"tf": 7.3484692283495345}, "sslearn.model_selection": {"tf": 5.744562646538029}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 9.695359714832659}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 3.7416573867739413}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 4.898979485566356}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 7.0710678118654755}, "sslearn.restricted": {"tf": 8.18535277187245}, "sslearn.restricted.conflict_rate": {"tf": 6.244997998398398}, "sslearn.restricted.combine_predictions": {"tf": 7}, "sslearn.restricted.feature_fusion": {"tf": 21.400934559032695}, "sslearn.restricted.probability_fusion": {"tf": 21.400934559032695}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 4}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 5.744562646538029}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 6.244997998398398}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 5.656854249492381}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 5.744562646538029}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 5.196152422706632}, "sslearn.subview": {"tf": 4.69041575982343}, "sslearn.subview.SubViewClassifier": {"tf": 14.422205101855956}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 5.196152422706632}, "sslearn.subview.SubViewRegressor": {"tf": 14.422205101855956}, "sslearn.subview.SubViewRegressor.predict": {"tf": 5.196152422706632}, "sslearn.utils": {"tf": 5.656854249492381}, "sslearn.utils.safe_division": {"tf": 5.830951894845301}, "sslearn.utils.confidence_interval": {"tf": 6.324555320336759}, "sslearn.utils.choice_with_proportion": {"tf": 6.324555320336759}, "sslearn.utils.calculate_prior_probability": {"tf": 5}, "sslearn.utils.mode": {"tf": 5.385164807134504}, "sslearn.utils.check_n_jobs": {"tf": 5.291502622129181}, "sslearn.wrapper": {"tf": 7.810249675906654}, "sslearn.wrapper.SelfTraining": {"tf": 14.52583904633395}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 8.774964387392123}, "sslearn.wrapper.SelfTraining.fit": {"tf": 5.916079783099616}, "sslearn.wrapper.Setred": {"tf": 14.866068747318506}, "sslearn.wrapper.Setred.__init__": {"tf": 7.3484692283495345}, "sslearn.wrapper.Setred.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.Setred.predict": {"tf": 5.0990195135927845}, "sslearn.wrapper.Setred.predict_proba": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTraining": {"tf": 20.174241001832016}, "sslearn.wrapper.CoTraining.__init__": {"tf": 6.708203932499369}, "sslearn.wrapper.CoTraining.fit": {"tf": 7.3484692283495345}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTraining.predict": {"tf": 5.656854249492381}, "sslearn.wrapper.CoTraining.score": {"tf": 7.14142842854285}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 16.186414056238647}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 5.477225575051661}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 5.0990195135927845}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 6.164414002968976}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 18.027756377319946}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 7.0710678118654755}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 6}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 5.196152422706632}, "sslearn.wrapper.Rasco": {"tf": 16.278820596099706}, "sslearn.wrapper.Rasco.__init__": {"tf": 5.830951894845301}, "sslearn.wrapper.Rasco.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.RelRasco": {"tf": 15.198684153570664}, "sslearn.wrapper.RelRasco.__init__": {"tf": 6.244997998398398}, "sslearn.wrapper.CoForest": {"tf": 16.15549442140351}, "sslearn.wrapper.CoForest.__init__": {"tf": 6.782329983125268}, "sslearn.wrapper.CoForest.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.TriTraining": {"tf": 8.888194417315589}, "sslearn.wrapper.TriTraining.__init__": {"tf": 6.4031242374328485}, "sslearn.wrapper.TriTraining.fit": {"tf": 5.744562646538029}, "sslearn.wrapper.DeTriTraining": {"tf": 7.416198487095663}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 7.14142842854285}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 5.744562646538029}}, "df": 85, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 6, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 10, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 13}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.subview": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "f": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 21, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 2, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 3}, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}}, "df": 2}}}}, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29, "s": {"docs": {"sslearn.model_selection": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}}, "df": 4}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}}}}}}}, "m": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 6}}}}}, "b": {"docs": {"sslearn.subview": {"tf": 1.7320508075688772}}, "df": 1, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}}, "df": 3, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}}, "df": 3}}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "s": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 5, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 14, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 2.23606797749979}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 20}}}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 4}}}, "p": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 19}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 6, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 3, "k": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}}, "df": 1}}}}}}}}}}, "y": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 3}}}}, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}, "k": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 17}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1}}}, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1.7320508075688772}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 41}}}, "u": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 7}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}}, "df": 7, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 2}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 50}}}}, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2.449489742783178}, "sslearn.restricted.probability_fusion": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 2}}, "f": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1}, "c": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 2.449489742783178}}, "df": 1}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 30}}}}, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}, "y": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 6, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}}}}}, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 3}}}, "y": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "l": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 4}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}, "f": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}}, "df": 2}}}}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1.7320508075688772}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 44}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}}, "df": 12, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 3.3166247903554}, "sslearn.restricted.probability_fusion": {"tf": 3.3166247903554}}, "df": 3, "s": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 2}}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}}, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 10, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}}, "df": 2}, "r": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 2}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.get_dataset": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 3.1622776601683795}, "sslearn.restricted.probability_fusion": {"tf": 3.1622776601683795}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 28, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}, "s": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 28}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3}}}}}}}, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "o": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2, "h": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 3.3166247903554}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 4.123105625617661}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2.6457513110645907}, "sslearn.restricted": {"tf": 3.605551275463989}, "sslearn.restricted.conflict_rate": {"tf": 2.6457513110645907}, "sslearn.restricted.combine_predictions": {"tf": 3.3166247903554}, "sslearn.restricted.feature_fusion": {"tf": 4}, "sslearn.restricted.probability_fusion": {"tf": 4.358898943540674}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 3}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 2.449489742783178}, "sslearn.utils.choice_with_proportion": {"tf": 2}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 4.242640687119285}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 4.58257569495584}, "sslearn.wrapper.Setred.__init__": {"tf": 3.3166247903554}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining": {"tf": 4.69041575982343}, "sslearn.wrapper.CoTraining.__init__": {"tf": 3.872983346207417}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 4.47213595499958}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 4.795831523312719}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 4.47213595499958}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 3.1622776601683795}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoForest": {"tf": 4.47213595499958}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 4}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 4.123105625617661}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3.872983346207417}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 78, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 2}, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}, "i": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 6}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}, "m": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 24}, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 8}}, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 15}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 3}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 5}}}}}, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 7}}}}}}}}, "o": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 2}}, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 19}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 3.1622776601683795}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 28}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}}}}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 19, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}}, "df": 3}}}}}}, "o": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 2.449489742783178}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 3.4641016151377544}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3.1622776601683795}}, "df": 45, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}}, "df": 1}}}}, "w": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 12}}, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}}, "df": 2, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 34}, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 1.7320508075688772}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 18, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 12}}}}, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.23606797749979}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 37, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.449489742783178}}, "df": 28, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.restricted": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2.8284271247461903}, "sslearn.restricted.probability_fusion": {"tf": 2.6457513110645907}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 3}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.449489742783178}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 27}}}}}}}, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 21}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}}}}}}, "x": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 7, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}}, "df": 4}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 5}}}}}}}}, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.23606797749979}}, "df": 20, "o": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 3}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 3}}}, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3}}}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.23606797749979}, "sslearn.subview.SubViewRegressor": {"tf": 2.23606797749979}}, "df": 2}}}}}, "v": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}}, "df": 14}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}}}}}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 9}}}, "f": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 32}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1, "d": {"docs": {"sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 2}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}}}}}}}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}}}}, "a": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 2.449489742783178}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 2}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 2.23606797749979}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1.7320508075688772}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 54, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "d": {"docs": {"sslearn.base": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2.6457513110645907}, "sslearn.restricted.probability_fusion": {"tf": 2.6457513110645907}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 31}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 6}, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 5}}}}}}, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 13}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1.7320508075688772}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 2.449489742783178}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining.fit": {"tf": 2}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.score": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 44, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7}}}}, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 2}}, "df": 13}, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}}, "df": 3}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 3}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 16, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewRegressor": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 8}}, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 10, "s": {"docs": {"sslearn.wrapper": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "p": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 2}}}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}}, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}}, "df": 7}}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}}}, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 4}, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {"sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2, "y": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "i": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "a": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 5}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 62}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 6, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}}}}}}}}}, "t": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3}, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2}}}}, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 4}}}}}}}, "i": {"docs": {}, "df": 0, "p": {"docs": {"sslearn": {"tf": 1.4142135623730951}}, "df": 1}, "s": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.23606797749979}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 38, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 17}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 7, "s": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 9}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}}}, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 23, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 18}}}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.calculate_prior_probability": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 17}}}}}}, "s": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 8, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}, "p": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}}, "df": 3}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils": {"tf": 1}}, "df": 1, "i": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}}, "df": 2}}}}}, "d": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}}, "df": 2, "f": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "\u00e9": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "z": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 2}}}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 4, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "z": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 3}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}}, "df": 1, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}, "p": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}, "f": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}}, "df": 2, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.6457513110645907}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 49, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.datasets": {"tf": 1}}, "df": 1}}}}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 4}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 3}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 31}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 30, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 9}}}, "s": {"docs": {"sslearn.wrapper.SelfTraining.fit": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}}, "df": 3}}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5}}}}, "e": {"docs": {}, "df": 0, "b": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 8, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 2.449489742783178}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.7320508075688772}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 41}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.utils": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 6}}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}}, "df": 3}}}}}, "a": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}}, "df": 1, "d": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 2, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 2}}}, "l": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 8}}}, "s": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 11}}}}}, "m": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 31}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}, "k": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}}, "df": 2}}, "k": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}, "x": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}, "j": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "n": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 5, "l": {"docs": {"sslearn": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 19}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}}, "df": 7}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 2}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 15, "s": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 10}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 12, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 6}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 6, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 3.1622776601683795}, "sslearn.restricted.probability_fusion": {"tf": 3.1622776601683795}}, "df": 5}}, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 2}}}, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}, "v": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}}, "df": 1}}, "f": {"docs": {"sslearn": {"tf": 1}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.6457513110645907}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.6457513110645907}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted": {"tf": 2.6457513110645907}, "sslearn.restricted.conflict_rate": {"tf": 2.23606797749979}, "sslearn.restricted.combine_predictions": {"tf": 2.6457513110645907}, "sslearn.restricted.feature_fusion": {"tf": 3}, "sslearn.restricted.probability_fusion": {"tf": 3}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 2.6457513110645907}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 2}, "sslearn.utils.choice_with_proportion": {"tf": 3.1622776601683795}, "sslearn.utils.calculate_prior_probability": {"tf": 2}, "sslearn.utils.mode": {"tf": 3.3166247903554}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict": {"tf": 2}, "sslearn.wrapper.CoTraining.score": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 79}, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 21, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 5}}, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3, "v": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}}, "df": 1}}}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}}}, "r": {"docs": {"sslearn.base.get_dataset": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 29, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 4, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 2}}}, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "b": {"docs": {}, "df": 0, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 14}}}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 2.449489742783178}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}}, "df": 24}}}}}}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 4}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 2}}}}}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1, "a": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 3.1622776601683795}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}}, "df": 37, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.datasets": {"tf": 2.23606797749979}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 25, "s": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.model_selection": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 14}}}}, "f": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.get_dataset": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 2}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 2.23606797749979}, "sslearn.datasets.save_keel": {"tf": 2.6457513110645907}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 3}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.6457513110645907}}, "df": 27}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1, "d": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 2, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2}, "sslearn.subview.SubViewRegressor": {"tf": 2}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}}}}}}}}}}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}}, "df": 4, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}}, "df": 3}}}}}}}}}}}}}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1, "i": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 5, "n": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 3}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 4, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 5}, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 7, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 4}}}}}}}}, "s": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}, "m": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "t": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}}, "df": 2}}}}, "c": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}}, "df": 1}}}, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "x": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}}, "df": 1}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 12, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3}}}}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}}, "df": 2}}}, "p": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}}, "df": 3}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 9}}}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}}}}, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}, "s": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 29, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}}, "df": 9}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.utils.calculate_prior_probability": {"tf": 1.4142135623730951}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.8284271247461903}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.449489742783178}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 33}}}, "p": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}}}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 3}}}}}, "s": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 1}}}}}}}}}, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}}, "df": 1}}}}}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.7320508075688772}}, "df": 4}}}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}}}, "j": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 4, "o": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 4}}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.check_n_jobs": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 8}}, "s": {"docs": {"sslearn": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "l": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}}, "a": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}, "p": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "/": {"2": {"0": {"1": {"1": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "/": {"0": {"4": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}}}}}}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}}}}}}}}}}}}}}}, "g": {"docs": {}, "df": 0, "t": {"docs": {"sslearn": {"tf": 2.449489742783178}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}}, "df": 6}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"2": {"0": {"2": {"4": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}}}}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {}, "df": 0}, "docs": {"sslearn": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3}}}}, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "b": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}, "e": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base": {"tf": 1}}, "df": 1}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 7, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 15}}}}, "u": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3}}}}}}}}, "o": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}}, "df": 7}}}, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 4}}}}}, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}}}}, "n": {"docs": {"sslearn.base.get_dataset": {"tf": 2.8284271247461903}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 2}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 2.23606797749979}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 2.23606797749979}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2.23606797749979}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 2}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 2}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1.7320508075688772}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 2}, "sslearn.utils.check_n_jobs": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.fit": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTraining.predict": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.score": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 2.449489742783178}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 2.449489742783178}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.7320508075688772}}, "df": 54, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "y": {"docs": {"sslearn": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 5}}, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}}, "df": 22, "s": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 2}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {"sslearn.utils.safe_division": {"tf": 2}}, "df": 1}}}}}}, "o": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4, "t": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 19, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 1.7320508075688772}, "sslearn.datasets.save_keel": {"tf": 2.23606797749979}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2.23606797749979}}, "df": 23}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}}, "df": 2}, "e": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}, "v": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.base.get_dataset": {"tf": 2.23606797749979}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 2}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 12}}}}}}, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 2.23606797749979}}, "df": 3, "s": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 1}, ":": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTraining.fit": {"tf": 1}}, "df": 1}}}}}}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 8}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}}, "df": 4, "/": {"2": {"docs": {}, "df": 0, "+": {"1": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}, "docs": {}, "df": 0}}, "docs": {}, "df": 0}}}}}}}}, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "y": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "c": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.4142135623730951}}, "df": 2, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 8, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 3}, "sslearn.restricted.probability_fusion": {"tf": 3}}, "df": 3}}}, "d": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 2}}}}}}}}, "l": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}}, "df": 1}}, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}, "c": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.utils": {"tf": 2}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 10}}}}}}, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}}, "df": 4}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}}}}, "o": {"docs": {"sslearn.wrapper": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 11, "d": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 3, "s": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 3}}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 4, "s": {"docs": {"sslearn.datasets": {"tf": 1}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.wrapper": {"tf": 1}}, "df": 6}}}}, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 10}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.datasets": {"tf": 1}}, "df": 1}}}}, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 1}}}}}}, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 2}}}}}}}, "f": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}}, "df": 4}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 4}}, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 5}}}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}}, "df": 4, "u": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}}, "df": 2, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}}}}, "t": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "m": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1}}, "df": 1, "s": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}}, "df": 1}, "r": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}, "b": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 9}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 4}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils.mode": {"tf": 1.4142135623730951}}, "df": 1}}}, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 3}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 8, "b": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}}, "df": 3}}}}}}}}}}}}}}}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}}, "df": 4}}}}}}, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.utils": {"tf": 2}, "sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 4, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.choice_with_proportion": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 1}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.4142135623730951}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 46, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 2.23606797749979}, "sslearn.model_selection": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 25}}, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.feature_fusion": {"tf": 2}, "sslearn.restricted.probability_fusion": {"tf": 2.23606797749979}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.subview": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 31, "s": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 10}, "m": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "x": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.utils": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 15}}}}}}}, "c": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}}}, "f": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 2.449489742783178}, "sslearn.subview.SubViewRegressor": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred": {"tf": 1.7320508075688772}}, "df": 4}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.utils.safe_division": {"tf": 1}}, "df": 1}}, "n": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}}, "df": 2}}}, "u": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 2}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 8}}}}, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 2}}}, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 2.449489742783178}}, "df": 1}}}}}}}}, "s": {"docs": {}, "df": 0, "v": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2.449489742783178}, "sslearn.restricted.probability_fusion": {"tf": 2.449489742783178}}, "df": 4}}, "y": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}}}}, "b": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.7320508075688772}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 24, "t": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}}, "f": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 2}}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}}, "df": 3}}, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}, "l": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "s": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.__init__": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 19, "n": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "m": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}}, "d": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 8}}}, "t": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 3}}}, "c": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 3}}}}}, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "y": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1.7320508075688772}, "sslearn.datasets.read_keel": {"tf": 2}, "sslearn.datasets.save_keel": {"tf": 2.449489742783178}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.4142135623730951}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.RelRasco.__init__": {"tf": 2.449489742783178}, "sslearn.wrapper.CoForest.__init__": {"tf": 2.6457513110645907}, "sslearn.wrapper.TriTraining.__init__": {"tf": 2}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 3}}, "df": 34}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 4}}, "o": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 11}, "t": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}}, "df": 2, "p": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}}, "df": 2}}}}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}, "s": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.utils.check_n_jobs": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 2}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 7, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}, "u": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.restricted": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 17}}}, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 9, "d": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 9}, "s": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 8}, "f": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1}}, "df": 2}}}}, "a": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}, "n": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn": {"tf": 1.4142135623730951}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.449489742783178}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.CoForest": {"tf": 2}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 21, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.4142135623730951}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}}, "df": 21}}}}}}, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 3}}}}, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "y": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}}}}}, "s": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 5}}}}, "t": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}}, "df": 1}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.utils": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1}}}}}, "w": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 10}}}}}, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 4}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 6}}, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 5}}}}}, "o": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.7320508075688772}}, "df": 2, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.restricted": {"tf": 1}}, "df": 1}}}}}}}}}}}}}}}}, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 4}}, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 5}}}}, "i": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1.7320508075688772}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}}, "df": 12}}, "t": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.restricted": {"tf": 1.7320508075688772}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 2.6457513110645907}, "sslearn.restricted.probability_fusion": {"tf": 2.449489742783178}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2}, "sslearn.wrapper.Setred": {"tf": 2}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.23606797749979}, "sslearn.wrapper.CoTraining.fit": {"tf": 2}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.23606797749979}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 2.23606797749979}, "sslearn.wrapper.TriTraining": {"tf": 2}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 35, "o": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 3}}, "s": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}, "k": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}}, "df": 1, "h": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}}}}}, "l": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}}}, "x": {"1": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}}, "df": 1}, "2": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 5}, "docs": {"sslearn": {"tf": 2.6457513110645907}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1.7320508075688772}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2.8284271247461903}, "sslearn.restricted.probability_fusion": {"tf": 2.8284271247461903}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 3}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.7320508075688772}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.6457513110645907}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.8284271247461903}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 53, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {"sslearn": {"tf": 2.23606797749979}, "sslearn.base": {"tf": 1}, "sslearn.base.get_dataset": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 2.23606797749979}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 3.4641016151377544}, "sslearn.restricted.probability_fusion": {"tf": 3.4641016151377544}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 2.6457513110645907}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 2.6457513110645907}, "sslearn.wrapper.Setred.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 2.8284271247461903}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.6457513110645907}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2.8284271247461903}, "sslearn.wrapper.CoForest": {"tf": 2.6457513110645907}, "sslearn.wrapper.CoForest.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1.4142135623730951}}, "df": 50, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 1}}}}}, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "k": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}, "s": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}}, "df": 1}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "k": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}, "u": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "n": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 14, "s": {"docs": {"sslearn.base.get_dataset": {"tf": 1}, "sslearn.base.FakedProbaClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.fit": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.7320508075688772}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewClassifier.predict_proba": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.choice_with_proportion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.utils.mode": {"tf": 1}, "sslearn.utils.check_n_jobs": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.Setred.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 45}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}}, "df": 3}}}}}, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 6, "s": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2}}}}}}}, "p": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.base.FakedProbaClassifier.predict": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.predict_proba": {"tf": 1}}, "df": 9}}}}}}}}, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}}, "df": 1}}}}}}}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}, "e": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets": {"tf": 1.4142135623730951}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.7320508075688772}, "sslearn.restricted.probability_fusion": {"tf": 1.7320508075688772}}, "df": 5}, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 5}}}}}, "g": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.datasets.save_keel": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.subview": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "e": {"docs": {}, "df": 0, "x": {"docs": {"sslearn.subview.SubViewClassifier": {"tf": 1.7320508075688772}, "sslearn.subview.SubViewRegressor": {"tf": 1.7320508075688772}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted": {"tf": 2}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 4}}, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}}, "df": 2, "s": {"docs": {"sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 1.7320508075688772}, "sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 3}}}}}}}}}, "u": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.utils.safe_division": {"tf": 1.4142135623730951}}, "df": 1}}}, "p": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}}}}}}, "f": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 12}}}}}}}}, "c": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "d": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper": {"tf": 1}}, "df": 1}}}}}}, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper": {"tf": 1}}, "df": 1}}}}}}}, "l": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 2.23606797749979}}, "df": 2}}}}}, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 3}}}}}}, "j": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}}, "df": 1, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}, "q": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}}, "y": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}}, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn": {"tf": 1}, "sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.restricted": {"tf": 1.4142135623730951}, "sslearn.restricted.conflict_rate": {"tf": 2}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 15}}, "n": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "m": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper": {"tf": 1.4142135623730951}, "sslearn.wrapper.SelfTraining": {"tf": 1}, "sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 2}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 20, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 11}}}}}, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}, "sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 9}}}}}}}}, "m": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.wrapper": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 2.23606797749979}, "sslearn.wrapper.Rasco.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.RelRasco": {"tf": 1.4142135623730951}}, "df": 4}}}, "i": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.__init__": {"tf": 1}, "sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}, "sslearn.wrapper.TriTraining.__init__": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 6}, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}}}, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}, "u": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.save_keel": {"tf": 1.4142135623730951}}, "df": 1}}}, "d": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "\u00ed": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "z": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "z": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 1}}}}}}}}, "i": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.SelfTraining": {"tf": 1}}, "df": 1}}}}}}}}, "z": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 3, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "o": {"docs": {"sslearn": {"tf": 1}}, "df": 1}}}, "g": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}}, "df": 1}}, "r": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.utils": {"tf": 1}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}}, "df": 2}}}, "h": {"docs": {}, "df": 0, "i": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}, "o": {"docs": {}, "df": 0, "u": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 4}}}}, "v": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.base.get_dataset": {"tf": 1.4142135623730951}}, "df": 1}}}}, "r": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1.4142135623730951}}, "df": 1}}}}}}, "a": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "u": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.utils.safe_division": {"tf": 1.7320508075688772}, "sslearn.utils.calculate_prior_probability": {"tf": 1}, "sslearn.wrapper.Setred.predict": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.predict": {"tf": 1}}, "df": 9, "s": {"docs": {"sslearn.base.FakedProbaClassifier.fit": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1}, "sslearn.restricted.feature_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.probability_fusion": {"tf": 1.4142135623730951}, "sslearn.restricted.WhoIsWhoClassifier.fit": {"tf": 1}, "sslearn.subview.SubViewRegressor.predict": {"tf": 1}, "sslearn.utils": {"tf": 1}, "sslearn.utils.confidence_interval": {"tf": 1}, "sslearn.utils.mode": {"tf": 1.4142135623730951}, "sslearn.wrapper.Setred.fit": {"tf": 1}, "sslearn.wrapper.CoTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.fit": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1}, "sslearn.wrapper.Rasco.fit": {"tf": 1}, "sslearn.wrapper.CoForest.fit": {"tf": 1}, "sslearn.wrapper.TriTraining.fit": {"tf": 1}, "sslearn.wrapper.DeTriTraining.fit": {"tf": 1}}, "df": 17}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}}, "df": 3, "a": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}}, "df": 2}}}}}}}, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.model_selection.artificial_ssl_dataset": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS.split": {"tf": 1}}, "df": 2}}}, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.wrapper.RelRasco": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "o": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}, "e": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.subview": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoTraining.__init__": {"tf": 2}, "sslearn.wrapper.CoTraining.fit": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoTraining.predict_proba": {"tf": 1}, "sslearn.wrapper.CoTraining.predict": {"tf": 1}, "sslearn.wrapper.CoTraining.score": {"tf": 1}}, "df": 7}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.DemocraticCoLearning": {"tf": 2}}, "df": 1}}, "l": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}, "h": {"docs": {"sslearn.wrapper.Rasco": {"tf": 1}, "sslearn.wrapper.CoForest": {"tf": 1}}, "df": 2, "a": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.7320508075688772}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}, "sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1.4142135623730951}, "sslearn.restricted": {"tf": 1}, "sslearn.restricted.conflict_rate": {"tf": 1.4142135623730951}, "sslearn.restricted.feature_fusion": {"tf": 2.6457513110645907}, "sslearn.restricted.probability_fusion": {"tf": 2.449489742783178}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.SelfTraining.fit": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 12}}, "s": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}, "sslearn.datasets.read_csv": {"tf": 1}, "sslearn.datasets.read_keel": {"tf": 1}, "sslearn.datasets.secure_dataset": {"tf": 1}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 5}, "n": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 1}, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "h": {"docs": {"sslearn.wrapper.CoTraining.score": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee.score": {"tf": 1}}, "df": 2}}}, "d": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}}, "df": 1}}, "l": {"docs": {}, "df": 0, "f": {"docs": {"sslearn.wrapper.Rasco.__init__": {"tf": 1}, "sslearn.wrapper.RelRasco.__init__": {"tf": 1}}, "df": 2}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.base.FakedProbaClassifier": {"tf": 1.4142135623730951}, "sslearn.base.FakedProbaClassifier.predict_proba": {"tf": 1}}, "df": 2}, "w": {"docs": {"sslearn.wrapper.DeTriTraining.__init__": {"tf": 1}}, "df": 1}}, "t": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "p": {"docs": {}, "df": 0, ":": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "/": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "w": {"docs": {"sslearn.datasets.read_keel": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.restricted.combine_predictions": {"tf": 1.7320508075688772}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1.7320508075688772}}, "df": 2}}}}}}}, "a": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 3}, "m": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.CoForest": {"tf": 1}}, "df": 1}}}}}, "e": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "y": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}}, "df": 2}}}}}}}, "i": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.Setred": {"tf": 1}, "sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 2}}}}}}}}}, "y": {"docs": {}, "df": 0, "p": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1, "o": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.wrapper.TriTraining": {"tf": 1.4142135623730951}}, "df": 1}}}}}}}}}, "i": {"docs": {"sslearn.utils.confidence_interval": {"tf": 1}}, "df": 1, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.SelfTraining.__init__": {"tf": 1}, "sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1.4142135623730951}, "sslearn.wrapper.Rasco": {"tf": 1.4142135623730951}, "sslearn.wrapper.CoForest": {"tf": 1.4142135623730951}}, "df": 5}}}}}}}, "q": {"docs": {"sslearn.wrapper.DemocraticCoLearning.__init__": {"tf": 1}}, "df": 1, "u": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "t": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "n": {"docs": {"sslearn.base.OneVsRestSSLClassifier.predict_proba": {"tf": 1}}, "df": 1}}}}}}, "o": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 2.449489742783178}, "sslearn.restricted.probability_fusion": {"tf": 2.449489742783178}, "sslearn.subview.SubViewClassifier": {"tf": 3.1622776601683795}, "sslearn.subview.SubViewRegressor": {"tf": 3.1622776601683795}}, "df": 4}}}}, "k": {"docs": {"sslearn.model_selection": {"tf": 1}, "sslearn.model_selection.StratifiedKFoldSS": {"tf": 1}, "sslearn.wrapper.SelfTraining.__init__": {"tf": 2.23606797749979}, "sslearn.wrapper.DeTriTraining.__init__": {"tf": 1.4142135623730951}}, "df": 4, "e": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "l": {"docs": {"sslearn.datasets": {"tf": 2}, "sslearn.datasets.read_keel": {"tf": 1.4142135623730951}, "sslearn.datasets.save_keel": {"tf": 1}}, "df": 3}, "p": {"docs": {"sslearn.restricted": {"tf": 1}, "sslearn.restricted.combine_predictions": {"tf": 1}}, "df": 2, "i": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "g": {"docs": {"sslearn.wrapper.CoTraining": {"tf": 1}, "sslearn.wrapper.CoTrainingByCommittee": {"tf": 1}, "sslearn.wrapper.Rasco": {"tf": 1}}, "df": 3}}}}}, "y": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.utils.calculate_prior_probability": {"tf": 1}}, "df": 3, "w": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "d": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}}}}, "p": {"docs": {}, "df": 0, "t": {"docs": {"sslearn.wrapper.DeTriTraining": {"tf": 1}}, "df": 1}}}, "u": {"docs": {}, "df": 0, "n": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "v": {"docs": {}, "df": 0, "a": {"docs": {"sslearn.restricted.feature_fusion": {"tf": 1}, "sslearn.restricted.probability_fusion": {"tf": 1}, "sslearn.restricted.WhoIsWhoClassifier.__init__": {"tf": 1}}, "df": 3}}}}}}}, "w": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier": {"tf": 1}}, "df": 1}}, "d": {"docs": {}, "df": 0, "s": {"docs": {"sslearn.restricted.WhoIsWhoClassifier.predict": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning.fit": {"tf": 1.7320508075688772}, "sslearn.wrapper.CoForest.__init__": {"tf": 1}}, "df": 3}}}}}, "n": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "w": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "d": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "e": {"docs": {"sslearn.wrapper.Setred": {"tf": 1.4142135623730951}, "sslearn.wrapper.TriTraining": {"tf": 1}}, "df": 2}}}}}}}, "e": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "g": {"docs": {}, "df": 0, "h": {"docs": {}, "df": 0, "b": {"docs": {}, "df": 0, "o": {"docs": {}, "df": 0, "r": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "c": {"docs": {}, "df": 0, "l": {"docs": {}, "df": 0, "a": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "s": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "f": {"docs": {}, "df": 0, "i": {"docs": {}, "df": 0, "e": {"docs": {}, "df": 0, "r": {"docs": {"sslearn.wrapper.Setred.__init__": {"tf": 1}, "sslearn.wrapper.DemocraticCoLearning": {"tf": 1.4142135623730951}}, "df": 2}}}}}}}}}}}}}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true}; // mirrored in build-search-index.js (part 1) // Also split on html tags. this is a cheap heuristic, but good enough. diff --git a/docs/sslearn.html b/docs/sslearn.html index d1629d2..ed83203 100644 --- a/docs/sslearn.html +++ b/docs/sslearn.html @@ -134,15 +134,13 @@

Code example

Citing

-
@software{jose_luis_garrido_labrador_2024_10623889,
+
@software{garrido2024sslearn,
   author       = {José Luis Garrido-Labrador},
-  title        = {jlgarridol/sslearn: v1.0.4},
+  title        = {jlgarridol/sslearn},
   month        = feb,
   year         = 2024,
   publisher    = {Zenodo},
-  version      = {1.0.4},
-  doi          = {10.5281/zenodo.10623889},
-  url          = {https://doi.org/10.5281/zenodo.10623889}
+  doi          = {10.5281/zenodo.7565221},
 }
@@ -164,7 +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__='1.0.4.1' +13__version__='1.0.5' 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.html b/docs/sslearn/restricted.html index e39f346..21455c2 100644 --- a/docs/sslearn/restricted.html +++ b/docs/sslearn/restricted.html @@ -63,6 +63,15 @@

API Documentation

  • conflict_rate
    • +
  • + combine_predictions +
    • +
  • + feature_fusion +
    • +
  • + probability_fusion +
  • WhoIsWhoClassifier
      @@ -118,9 +127,25 @@

      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.

      +

      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.

      +
      + +

      feature_fusion:

      + +
      +

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

      +
      + +

      probability_fusion:

      + +
      +

      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.

      @@ -141,201 +166,454 @@

      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 + 14 + 15[combine_predictions](#combine_predictions): + 16> Combine the predictions of a group of instances to keep the restrictions. 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 + 18[feature_fusion](#feature_fusion): + 19> Restricted Set Classification for the instances with pairwise constraints. Combine all instances that have the must-link constraint with the average of their features. + 20 + 21[probability_fusion](#probability_fusion): + 22> 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. + 23 + 24 + 25""" + 26 + 27import numpy as np + 28from sklearn.base import ClassifierMixin, MetaEstimatorMixin, BaseEstimator + 29from scipy.optimize import linear_sum_assignment + 30import warnings + 31import pandas as pd + 32 + 33__all__ = ["conflict_rate", "combine_predictions", "feature_fusion", "probability_fusion", "WhoIsWhoClassifier"] + 34 + 35 + 36def feature_fusion(classifier, X, must_link, cannot_link): + 37 """ + 38 Restricted Set Classification for the instances with pairwise constraints. + 39 Combine all instances that have the must-link constraint with the average of their features. + 40 + 41 Parameters + 42 ---------- + 43 classifier : ClassifierMixin with predict_proba method + 44 X : {array-like, sparse matrix} of shape (n_samples, n_features) + 45 Array representing the data. + 46 must_link : dict of {int: list of int} + 47 Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label. + 48 cannot_link : dict of {int: list of int} + 49 Dictionary with the cannot links, where the value is a list of instances that cannot have the same label. + 50 + 51 Returns + 52 ---------- + 53 y : ndarray of shape (n_samples,) + 54 Array with predicted labels. + 55 + 56 Examples + 57 ---------- + 58 ```python + 59 from sslearn.restricted import feature_fusion + 60 from sklearn.bayes import GaussianNB + 61 import pandas as pd + 62 + 63 dataset = pd.read_csv("dataset.csv") + 64 + 65 must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index') + 66 # must_link = {0: [0, 2], 1: [1, 3]} -> + 67 # instances 0 and 2 must have the same label, + 68 # and instances 1 and 3 must have the same label + 69 + 70 cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index') + 71 # cannot_link = {0: [0, 1], 1: [2, 3]} -> + 72 # instances 0 and 1 cannot have the same label, + 73 # and instances 2 and 3 cannot have the same label + 74 + 75 X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values + 76 X_label = X[y != y.dtype.type(-1)] + 77 y_label = y[y != y.dtype.type(-1)] + 78 X_unlabel = X[y == y.dtype.type(-1)] + 79 + 80 classifier = GaussianNB() + 81 classifier.fit(X_label, y_label) + 82 + 83 y_pred = feature_fusion(classifier, X_unlabel, must_link, cannot_link) + 84 ``` + 85 + 86 References + 87 ---------- + 88 L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).<br> + 89 Semi-supervised classification with pairwise constraints: A case study on animal identification from video.<br> + 90 <i>Information Fusion,</i><br> + 91 104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188) + 92 """ + 93 + 94 X_combined = __combine_features(X, must_link) + 95 y_pred_proba = classifier.predict_proba(X_combined) + 96 + 97 return __restricted_set_classification(y_pred_proba, cannot_link, classifier.classes_) + 98 + 99 +100def probability_fusion(classifier, X, must_link, cannot_link): +101 """ +102 Restricted Set Classification for the instances with pairwise constraints. +103 The class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint. +104 +105 Parameters +106 ---------- +107 classifier : ClassifierMixin with predict_proba method +108 X : {array-like, sparse matrix} of shape (n_samples, n_features) +109 Array representing the data. +110 must_link : dict of {int: list of int} +111 Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label. +112 cannot_link : dict of {int: list of int} +113 Dictionary with the cannot links, where the value is a list of instances that cannot have the same label. +114 +115 Returns +116 ---------- +117 y : ndarray of shape (n_samples,) +118 Array with predicted labels. +119 +120 Examples +121 ---------- +122 ```python +123 from sslearn.restricted import feature_fusion +124 from sklearn.bayes import GaussianNB +125 import pandas as pd 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}) +127 dataset = pd.read_csv("dataset.csv") +128 +129 must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index') +130 # must_link = {0: [0, 2], 1: [1, 3]} -> +131 # instances 0 and 2 must have the same label, +132 # and instances 1 and 3 must have the same label +133 +134 cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index') +135 # cannot_link = {0: [0, 1], 1: [2, 3]} -> +136 # instances 0 and 1 cannot have the same label, +137 # and instances 2 and 3 cannot have the same label +138 +139 X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values +140 X_label = X[y != y.dtype.type(-1)] +141 y_label = y[y != y.dtype.type(-1)] +142 X_unlabel = X[y == y.dtype.type(-1)] +143 +144 classifier = GaussianNB() +145 classifier.fit(X_label, y_label) +146 +147 y_pred = probability_fusion(classifier, X_unlabel, must_link, cannot_link) +148 ``` 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])] +150 References +151 ---------- +152 L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).<br> +153 Semi-supervised classification with pairwise constraints: A case study on animal identification from video.<br> +154 <i>Information Fusion,</i><br> +155 104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188) +156 """ +157 +158 y_probs = classifier.predict_proba(X) +159 classes = classifier.classes_ +160 y_probs_combined, _ = __combine_probabilities(y_probs, must_link, classes) +161 return __restricted_set_classification(y_probs_combined, cannot_link, classes) +162 +163 +164class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin): +165 +166 def __init__(self, base_estimator, method="hungarian", conflict_weighted=True): +167 """ +168 Who is Who Classifier +169 Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017). +170 Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170. +171 +172 Parameters +173 ---------- +174 base_estimator : ClassifierMixin +175 The base estimator to be used for training. +176 method : str, optional +177 The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian" +178 conflict_weighted : bool, default=True +179 Whether to weighted the confusion rate by the number of instances with the same group. +180 """ +181 allowed_methods = ["greedy", "hungarian"] +182 self.base_estimator = base_estimator +183 self.method = method +184 if method not in allowed_methods: +185 raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}") +186 self.conflict_weighted = conflict_weighted 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 +188 +189 def fit(self, X, y, instance_group=None, **kwards): +190 """Fit the model according to the given training data. +191 Parameters +192 ---------- +193 X : {array-like, sparse matrix} of shape (n_samples, n_features) +194 The input samples. +195 y : array-like of shape (n_samples,) +196 The target values. +197 instance_group : array-like of shape (n_samples) +198 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. +199 Returns +200 ------- +201 self : object +202 Returns self. +203 """ +204 self.base_estimator = self.base_estimator.fit(X, y, **kwards) +205 self.classes_ = self.base_estimator.classes_ +206 if instance_group is not None: +207 self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted) +208 else: +209 self.conflict_in_train = None +210 return self +211 +212 def conflict_rate(self, X, instance_group): +213 """Calculate the conflict rate of the model. +214 Parameters +215 ---------- +216 X : {array-like, sparse matrix} of shape (n_samples, n_features) +217 The input samples. +218 instance_group : array-like of shape (n_samples) +219 The group. Two instances with the same label are not allowed to be in the same group. +220 Returns +221 ------- +222 float +223 The conflict rate. +224 """ +225 y_pred = self.base_estimator.predict(X) +226 return conflict_rate(y_pred, instance_group, self.conflict_weighted) +227 +228 def predict(self, X, instance_group): +229 """Predict class for X. +230 Parameters +231 ---------- +232 X : {array-like, sparse matrix} of shape (n_samples, n_features) +233 The input samples. +234 **kwards : array-like of shape (n_samples) +235 The group. Two instances with the same label are not allowed to be in the same group. +236 Returns +237 ------- +238 array-like of shape (n_samples, n_classes) +239 The class probabilities of the input samples. +240 """ +241 +242 y_prob = self.predict_proba(X) +243 +244 y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method) +245 +246 return self.classes_.take(y_predicted) +247 +248 +249 def predict_proba(self, X): +250 """Predict class probabilities for X. +251 Parameters +252 ---------- +253 X : {array-like, sparse matrix} of shape (n_samples, n_features) +254 The input samples. +255 Returns +256 ------- +257 array-like of shape (n_samples, n_classes) +258 The class probabilities of the input samples. +259 """ +260 return self.base_estimator.predict_proba(X) +261 +262 +263def conflict_rate(y_pred, restrictions, weighted=True): +264 """ +265 Computes the conflict rate of a prediction, given a set of restrictions. +266 Parameters +267 ---------- +268 y_pred : array-like of shape (n_samples,) +269 Predicted target values. +270 restrictions : array-like of shape (n_samples,) +271 Restrictions for each sample. If two samples have the same restriction, they cannot have the same y. +272 weighted : bool, default=True +273 Whether to weighted the confusion rate by the number of instances with the same group. +274 Returns +275 ------- +276 conflict rate : float +277 The conflict rate. +278 """ +279 +280 # Check that y_pred and restrictions have the same length +281 if len(y_pred) != len(restrictions): +282 raise ValueError("y_pred and restrictions must have the same length.") +283 +284 restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions}) +285 +286 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()}) +287 if weighted: +288 return conflicted.sum().y_pred / len(y_pred) +289 else: +290 rcount = restricted_df.groupby('restrictions').count() +291 return (conflicted.y_pred / rcount.y_pred).sum() +292 +293def combine_predictions(y_probas, instance_group, class_number, method="hungarian"): +294 """ +295 Combine the predictions of a group of instances to keep the restrictions. +296 +297 Parameters +298 ---------- +299 y_probas : array-like of shape (n_samples, n_classes) +300 The class probabilities of the input samples. +301 instance_group : array-like of shape (n_samples) +302 The group. Two instances with the same label are not allowed to be in the same group. +303 class_number : int +304 The number of classes. +305 method : str, optional +306 The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian" +307 +308 Returns +309 ------- +310 array-like of shape (n_samples,) +311 The predicted labels. +312 """ +313 y_predicted = [] +314 for group in np.unique(instance_group): +315 +316 mask = instance_group == group +317 probas_matrix = y_probas[mask] +318 +319 +320 preds = list(np.argmax(probas_matrix, axis=1)) +321 +322 if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number: +323 y_predicted.extend(preds) +324 if probas_matrix.shape[0] > class_number: +325 warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning) +326 continue +327 +328 if method == "greedy": +329 y = _greedy(probas_matrix) +330 elif method == "hungarian": +331 y = _hungarian(probas_matrix) +332 +333 y_predicted.extend(y) +334 return y_predicted +335 +336def _greedy(probas_matrix): +337 +338 probas = probas_matrix.reshape(probas_matrix.size,) +339 order = probas.argsort()[::-1] +340 +341 y_pred_group = [None for i in range(probas_matrix.shape[0])] +342 +343 instance_to_predict = {i for i in range(probas_matrix.shape[0])} +344 class_predicted = set() +345 for item in order: +346 class_ = item % probas_matrix.shape[0] +347 instance = item // probas_matrix.shape[0] +348 if instance in instance_to_predict and class_ not in class_predicted: +349 y_pred_group[instance] = class_ +350 instance_to_predict.remove(instance) +351 class_predicted.add(class_) +352 +353 return y_pred_group +354 +355 +356def _hungarian(probas_matrix): +357 +358 costs = np.log(probas_matrix) +359 costs[costs == -np.inf] = 0 # if proba is 0, then the cost is 0 +360 _, col_ind = linear_sum_assignment(costs, maximize=True) +361 col_ind = list(col_ind) +362 +363 return col_ind +364 +365def __combine_probabilities(y_probs, objects_in_track, classes): +366 """" +367 Averages the classifier probabilities of the instances in the same track. +368 +369 :param y_probs: classifier probabilities for the instances +370 :param objects_in_track: dictionary with the tracks +371 :param classes: classes used to train the classifier +372 +373 :return: a tuple with the modified y_probs ans the predicted classes +374 """ +375 +376 y_probs_combined = y_probs.copy() +377 +378 for objects in objects_in_track.values(): +379 if len(objects) <= 1: +380 continue +381 means = y_probs_combined[objects, :].mean(axis=0) +382 y_probs_combined[objects, :] = means +383 +384 preds = classes.take(list(np.argmax(y_probs_combined, axis=1))) +385 return y_probs_combined, preds +386 +387def __combine_features(X, objects_in_track): +388 """ +389 Averages the features of the instances in the same track. +390 +391 :param X: feature values of the instances. +392 :param objects_in_track: dictionary with the tracks +393 +394 :return: a modified X with averaged features +395 """ +396 +397 X_combined = X.copy() +398 is_df = isinstance(X, pd.DataFrame) +399 if is_df: +400 X_combined = X.values +401 for objects in objects_in_track.values(): +402 if len(objects) <= 1: +403 continue +404 means = X_combined[objects].mean(axis=0) +405 X_combined[objects] = means +406 return X_combined +407 +408def __restricted_set_classification(y_probs, instances_by_frame, classes): +409 """ +410 Restricted Set Classification for the instances in several frames +411 +412 :param y_probs: the probabilities given by the classifier for the instances +413 :param instances_by_frame: which instances are in each frame +414 :param classes: the classes seen by the classifier +415 +416 :return: the predicted labels +417 """ +418 +419 restricted_pred = [] +420 num_conflicts = 0 +421 for fr, group in instances_by_frame.items(): +422 if len(group) == 0: +423 continue +424 first, last = group[0], group[-1] +425 group_probs = y_probs[first:last + 1] +426 conflict, group_pred = __restricted_set_hungarian(group_probs, classes) +427 restricted_pred.extend(group_pred) +428 num_conflicts += conflict +429 +430 assert len(restricted_pred) == len(y_probs), "The number of predictions is different from the number of instances, check cannot link constraints, all instances must be in a cannot-link group." +431 +432 return restricted_pred +433 +434def __restricted_set_hungarian(probs, classes): +435 """ +436 Restricted Set Classification for a set of objects that have to be of different classes +437 +438 :param probs: the probabilities given by the classifier +439 :param classes: the classes seen by the classifier +440 +441 :return: a tuple with 1) the Hungarian method was used (0 or 1), and 2) the predicted classes +442 """ +443 +444 rows, cols = probs.shape +445 preds = list(np.argmax(probs, axis=1)) +446 +447 if rows > cols or len(preds) == len(set(preds)): +448 # return 0 if rows > cols else 1, classes.take(preds) +449 return 0, classes.take(preds) +450 costs = np.log(probs) +451 +452 try: +453 row_ind, col_ind = linear_sum_assignment(costs, maximize=True) +454 col_ind = list(col_ind) +455 except: # some of the values was -Inf +456 probs += np.nextafter(0, 1) # small double value +457 costs = np.log(probs) +458 row_ind, col_ind = linear_sum_assignment(costs, maximize=True) +459 col_ind = list(col_ind) +460 +461 return 1, classes.take(col_ind) @@ -351,35 +629,35 @@

      Functions

      - 
      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()
+            
      264def conflict_rate(y_pred, restrictions, weighted=True):
+265    """
+266    Computes the conflict rate of a prediction, given a set of restrictions.
+267    Parameters
+268    ----------
+269    y_pred : array-like of shape (n_samples,)
+270        Predicted target values.
+271    restrictions : array-like of shape (n_samples,)
+272        Restrictions for each sample. If two samples have the same restriction, they cannot have the same y.
+273    weighted : bool, default=True
+274        Whether to weighted the confusion rate by the number of instances with the same group.
+275    Returns
+276    -------
+277    conflict rate : float
+278        The conflict rate.
+279    """
+280    
+281    # Check that y_pred and restrictions have the same length
+282    if len(y_pred) != len(restrictions):
+283        raise ValueError("y_pred and restrictions must have the same length.")
+284    
+285    restricted_df = pd.DataFrame({'y_pred': y_pred, 'restrictions': restrictions})
+286
+287    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()})
+288    if weighted:
+289        return conflicted.sum().y_pred / len(y_pred)
+290    else:
+291        rcount = restricted_df.groupby('restrictions').count()
+292        return (conflicted.y_pred / rcount.y_pred).sum()
 
      
 
 
@@ -405,6 +683,364 @@ 
      Returns
      + +
      + +
      + + def + combine_predictions(y_probas, instance_group, class_number, method='hungarian'): + + + +
      + + 
      294def combine_predictions(y_probas, instance_group, class_number, method="hungarian"):
+295    """
+296    Combine the predictions of a group of instances to keep the restrictions.
+297
+298    Parameters
+299    ----------
+300    y_probas : array-like of shape (n_samples, n_classes)
+301        The class probabilities of the input samples.
+302    instance_group : array-like of shape (n_samples)
+303        The group. Two instances with the same label are not allowed to be in the same group.
+304    class_number : int
+305        The number of classes.
+306    method : str, optional
+307        The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+308
+309    Returns
+310    -------
+311    array-like of shape (n_samples,)
+312        The predicted labels.
+313    """
+314    y_predicted = []
+315    for group in np.unique(instance_group):
+316           
+317        mask = instance_group == group
+318        probas_matrix = y_probas[mask]
+319        
+320
+321        preds = list(np.argmax(probas_matrix, axis=1))
+322
+323        if len(preds) == len(set(preds)) or probas_matrix.shape[0] > class_number:
+324            y_predicted.extend(preds)
+325            if probas_matrix.shape[0] > class_number:
+326                warnings.warn("That the number of instances in the group is greater than the number of classes.", UserWarning)
+327            continue
+328
+329        if method == "greedy":
+330            y = _greedy(probas_matrix)
+331        elif method == "hungarian":
+332            y = _hungarian(probas_matrix)
+333        
+334        y_predicted.extend(y)
+335    return y_predicted
+
      + + +

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

      + +
      Parameters
      + +
        +
      • y_probas (array-like of shape (n_samples, n_classes)): +The class probabilities of 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.
        • +
      • class_number (int): +The number of classes.
        • +
      • 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"
        • +
      + +
      Returns
      + +
        +
      • array-like of shape (n_samples,): The predicted labels.
        • +
      +
      + + +
      +
      + +
      + + def + feature_fusion(classifier, X, must_link, cannot_link): + + + +
      + + 
      37def feature_fusion(classifier, X, must_link, cannot_link):
+38    """
+39    Restricted Set Classification for the instances with pairwise constraints. 
+40    Combine all instances that have the must-link constraint with the average of their features.    
+41
+42    Parameters
+43    ----------
+44    classifier : ClassifierMixin with predict_proba method
+45    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+46        Array representing the data.
+47    must_link : dict of {int: list of int}
+48        Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
+49    cannot_link : dict of {int: list of int}
+50        Dictionary with the cannot links, where the value is a list of instances that cannot have the same label.
+51
+52    Returns
+53    ----------
+54    y : ndarray of shape (n_samples,)
+55        Array with predicted labels.
+56
+57    Examples
+58    ----------
+59    ```python
+60    from sslearn.restricted import feature_fusion
+61    from sklearn.bayes import GaussianNB
+62    import pandas as pd
+63
+64    dataset = pd.read_csv("dataset.csv")
+65
+66    must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')
+67    # must_link = {0: [0, 2], 1: [1, 3]} -> 
+68    # instances 0 and 2 must have the same label, 
+69    # and instances 1 and 3 must have the same label
+70
+71    cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')
+72    # cannot_link = {0: [0, 1], 1: [2, 3]} ->
+73    # instances 0 and 1 cannot have the same label, 
+74    # and instances 2 and 3 cannot have the same label
+75
+76    X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values
+77    X_label = X[y != y.dtype.type(-1)]
+78    y_label = y[y != y.dtype.type(-1)]
+79    X_unlabel = X[y == y.dtype.type(-1)]
+80
+81    classifier = GaussianNB()
+82    classifier.fit(X_label, y_label)
+83
+84    y_pred = feature_fusion(classifier, X_unlabel, must_link, cannot_link)
+85    ```
+86
+87    References
+88    ----------
+89    L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).<br>
+90    Semi-supervised classification with pairwise constraints: A case study on animal identification from video.<br>
+91    <i>Information Fusion,</i><br> 
+92    104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188)
+93    """
+94    
+95    X_combined = __combine_features(X, must_link)
+96    y_pred_proba = classifier.predict_proba(X_combined)
+97
+98    return __restricted_set_classification(y_pred_proba, cannot_link, classifier.classes_)
+
      + + +

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

      + +
      Parameters
      + +
        +

      • classifier (ClassifierMixin with predict_proba method):

        • +

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

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

      L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).
      +Semi-supervised classification with pairwise constraints: A case study on animal identification from video.
      +Information Fusion,
      +104, 102188, 10.1016/j.inffus.2023.102188

      +
      + + +
      +
      + +
      + + def + probability_fusion(classifier, X, must_link, cannot_link): + + + +
      + + 
      101def probability_fusion(classifier, X, must_link, cannot_link):
+102    """
+103    Restricted Set Classification for the instances with pairwise constraints. 
+104    The class probability for each instance is defined as the mean of the probabilities reported by the classifier according to the must-link constraint.
+105
+106    Parameters
+107    ----------
+108    classifier : ClassifierMixin with predict_proba method
+109    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+110        Array representing the data.
+111    must_link : dict of {int: list of int}
+112        Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
+113    cannot_link : dict of {int: list of int}
+114        Dictionary with the cannot links, where the value is a list of instances that cannot have the same label.
+115
+116    Returns
+117    ----------
+118    y : ndarray of shape (n_samples,)
+119        Array with predicted labels.
+120
+121    Examples
+122    ----------
+123    ```python
+124    from sslearn.restricted import feature_fusion
+125    from sklearn.bayes import GaussianNB
+126    import pandas as pd
+127
+128    dataset = pd.read_csv("dataset.csv")
+129
+130    must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')
+131    # must_link = {0: [0, 2], 1: [1, 3]} -> 
+132    # instances 0 and 2 must have the same label, 
+133    # and instances 1 and 3 must have the same label
+134
+135    cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')
+136    # cannot_link = {0: [0, 1], 1: [2, 3]} ->
+137    # instances 0 and 1 cannot have the same label, 
+138    # and instances 2 and 3 cannot have the same label
+139
+140    X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values
+141    X_label = X[y != y.dtype.type(-1)]
+142    y_label = y[y != y.dtype.type(-1)]
+143    X_unlabel = X[y == y.dtype.type(-1)]
+144
+145    classifier = GaussianNB()
+146    classifier.fit(X_label, y_label)
+147
+148    y_pred = probability_fusion(classifier, X_unlabel, must_link, cannot_link)
+149    ```
+150
+151    References
+152    ----------
+153    L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).<br>
+154    Semi-supervised classification with pairwise constraints: A case study on animal identification from video.<br>
+155    <i>Information Fusion,</i><br> 
+156    104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188)
+157    """
+158
+159    y_probs = classifier.predict_proba(X)
+160    classes = classifier.classes_
+161    y_probs_combined, _ = __combine_probabilities(y_probs, must_link, classes)
+162    return __restricted_set_classification(y_probs_combined, cannot_link, classes)
+
      + + +

      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.

      + +
      Parameters
      + +
        +

      • classifier (ClassifierMixin with predict_proba method):

        • +

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

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

      L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).
      +Semi-supervised classification with pairwise constraints: A case study on animal identification from video.
      +Information Fusion,
      +104, 102188, 10.1016/j.inffus.2023.102188

      +
      + +
      @@ -417,103 +1053,103 @@
      Returns
      - 
       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)
+            
      165class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
+166
+167    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+168        """
+169        Who is Who Classifier
+170        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+171        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+172
+173        Parameters
+174        ----------
+175        base_estimator : ClassifierMixin
+176            The base estimator to be used for training.
+177        method : str, optional
+178            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+179        conflict_weighted : bool, default=True
+180            Whether to weighted the confusion rate by the number of instances with the same group.
+181        """        
+182        allowed_methods = ["greedy", "hungarian"]
+183        self.base_estimator = base_estimator
+184        self.method = method
+185        if method not in allowed_methods:
+186            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+187        self.conflict_weighted = conflict_weighted
+188
+189
+190    def fit(self, X, y, instance_group=None, **kwards):
+191        """Fit the model according to the given training data.
+192        Parameters
+193        ----------
+194        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+195            The input samples.
+196        y : array-like of shape (n_samples,)
+197            The target values.
+198        instance_group : array-like of shape (n_samples)
+199            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.
+200        Returns
+201        -------
+202        self : object
+203            Returns self.
+204        """
+205        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+206        self.classes_ = self.base_estimator.classes_
+207        if instance_group is not None:
+208            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+209        else:
+210            self.conflict_in_train = None
+211        return self
+212
+213    def conflict_rate(self, X, instance_group):
+214        """Calculate the conflict rate of the model.
+215        Parameters
+216        ----------
+217        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+218            The input samples.
+219        instance_group : array-like of shape (n_samples)
+220            The group. Two instances with the same label are not allowed to be in the same group.
+221        Returns
+222        -------
+223        float
+224            The conflict rate.
+225        """
+226        y_pred = self.base_estimator.predict(X)
+227        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
+228
+229    def predict(self, X, instance_group):
+230        """Predict class for X.
+231        Parameters
+232        ----------
+233        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+234            The input samples.
+235        **kwards : array-like of shape (n_samples)
+236            The group. Two instances with the same label are not allowed to be in the same group.
+237        Returns
+238        -------
+239        array-like of shape (n_samples, n_classes)
+240            The class probabilities of the input samples.
+241        """
+242        
+243        y_prob = self.predict_proba(X)
+244        
+245        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+246
+247        return self.classes_.take(y_predicted)
+248
+249
+250    def predict_proba(self, X):
+251        """Predict class probabilities for X.
+252        Parameters
+253        ----------
+254        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+255            The input samples.
+256        Returns
+257        -------
+258        array-like of shape (n_samples, n_classes)
+259            The class probabilities of the input samples.
+260        """
+261        return self.base_estimator.predict_proba(X)
 
      
 
 
@@ -537,27 +1173,27 @@ 
      Notes
      - 
      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
+            
      167    def __init__(self, base_estimator, method="hungarian", conflict_weighted=True):
+168        """
+169        Who is Who Classifier
+170        Kuncheva, L. I., Rodriguez, J. J., & Jackson, A. S. (2017).
+171        Restricted set classification: Who is there?. <i>Pattern Recognition</i>, 63, 158-170.
+172
+173        Parameters
+174        ----------
+175        base_estimator : ClassifierMixin
+176            The base estimator to be used for training.
+177        method : str, optional
+178            The method to use to assing class, it can be `greedy` to first-look or `hungarian` to use the Hungarian algorithm, by default "hungarian"
+179        conflict_weighted : bool, default=True
+180            Whether to weighted the confusion rate by the number of instances with the same group.
+181        """        
+182        allowed_methods = ["greedy", "hungarian"]
+183        self.base_estimator = base_estimator
+184        self.method = method
+185        if method not in allowed_methods:
+186            raise ValueError(f"method {self.method} not supported, use one of {allowed_methods}")
+187        self.conflict_weighted = conflict_weighted
 
      
 
 
@@ -590,28 +1226,28 @@ 
      Parameters
      - 
      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
+            
      190    def fit(self, X, y, instance_group=None, **kwards):
+191        """Fit the model according to the given training data.
+192        Parameters
+193        ----------
+194        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+195            The input samples.
+196        y : array-like of shape (n_samples,)
+197            The target values.
+198        instance_group : array-like of shape (n_samples)
+199            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.
+200        Returns
+201        -------
+202        self : object
+203            Returns self.
+204        """
+205        self.base_estimator = self.base_estimator.fit(X, y, **kwards)
+206        self.classes_ = self.base_estimator.classes_
+207        if instance_group is not None:
+208            self.conflict_in_train = conflict_rate(self.base_estimator.predict(X), instance_group, self.conflict_weighted)
+209        else:
+210            self.conflict_in_train = None
+211        return self
 
      
 
 
@@ -649,21 +1285,21 @@ 
      Returns
      - 
      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)
+            
      213    def conflict_rate(self, X, instance_group):
+214        """Calculate the conflict rate of the model.
+215        Parameters
+216        ----------
+217        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+218            The input samples.
+219        instance_group : array-like of shape (n_samples)
+220            The group. Two instances with the same label are not allowed to be in the same group.
+221        Returns
+222        -------
+223        float
+224            The conflict rate.
+225        """
+226        y_pred = self.base_estimator.predict(X)
+227        return conflict_rate(y_pred, instance_group, self.conflict_weighted)
 
      
 
 
@@ -698,25 +1334,25 @@ 
      Returns
      - 
       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)
+            
      229    def predict(self, X, instance_group):
+230        """Predict class for X.
+231        Parameters
+232        ----------
+233        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+234            The input samples.
+235        **kwards : array-like of shape (n_samples)
+236            The group. Two instances with the same label are not allowed to be in the same group.
+237        Returns
+238        -------
+239        array-like of shape (n_samples, n_classes)
+240            The class probabilities of the input samples.
+241        """
+242        
+243        y_prob = self.predict_proba(X)
+244        
+245        y_predicted = combine_predictions(y_prob, instance_group, len(self.classes_), self.method)
+246
+247        return self.classes_.take(y_predicted)
 
      
 
 
@@ -751,18 +1387,18 @@ 
      Returns
      - 
      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)
+            
      250    def predict_proba(self, X):
+251        """Predict class probabilities for X.
+252        Parameters
+253        ----------
+254        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+255            The input samples.
+256        Returns
+257        -------
+258        array-like of shape (n_samples, n_classes)
+259            The class probabilities of the input samples.
+260        """
+261        return self.base_estimator.predict_proba(X)
 
      
 
 
diff --git a/docs/sslearn/wrapper.html b/docs/sslearn/wrapper.html
index bb2c4cf..a4c1a02 100644
--- a/docs/sslearn/wrapper.html
+++ b/docs/sslearn/wrapper.html
@@ -719,7 +719,7 @@ 
      Inherited Members
      
       187        Parameters
 188        ----------
 189        base_estimator : ClassifierMixin, optional
-190            An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
+190            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
 191        max_iterations : int, optional
 192            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
 193        distance : str, optional
@@ -730,7 +730,7 @@ 
      Inherited Members
      
       198        poolsize : float, optional
 199            Max number of unlabel instances candidates to pseudolabel, by default 0.25
 200        rejection_threshold : float, optional
-201            significance level, by default 0.1
+201            significance level, by default 0.05
 202        graph_neighbors : int, optional
 203            Number of neighbors for each sample., by default 1
 204        random_state : int, RandomState instance, optional
@@ -974,7 +974,7 @@ 
      References
      
       187        Parameters
 188        ----------
 189        base_estimator : ClassifierMixin, optional
-190            An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
+190            An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
 191        max_iterations : int, optional
 192            Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
 193        distance : str, optional
@@ -985,7 +985,7 @@ 
      References
      
       198        poolsize : float, optional
 199            Max number of unlabel instances candidates to pseudolabel, by default 0.25
 200        rejection_threshold : float, optional
-201            significance level, by default 0.1
+201            significance level, by default 0.05
 202        graph_neighbors : int, optional
 203            Number of neighbors for each sample., by default 1
 204        random_state : int, RandomState instance, optional
@@ -1011,7 +1011,7 @@ 
      Parameters
      
 
 
        
 
      • base_estimator (ClassifierMixin, optional):
-An estimator object implementing fit and predict_proba,, by default DecisionTreeClassifier(), by default KNeighborsClassifier(n_neighbors=3)
        • 
+An estimator object implementing fit and predict_proba, by default KNeighborsClassifier(n_neighbors=3)
 
      • max_iterations (int, optional):
 Maximum number of iterations allowed. Should be greater than or equal to 0., by default 40
        • 
 
      • distance (str, optional):
@@ -1022,7 +1022,7 @@ 
        Parameters
        
 
      • poolsize (float, optional):
 Max number of unlabel instances candidates to pseudolabel, by default 0.25
        • 
 
      • rejection_threshold (float, optional):
-significance level, by default 0.1
        • 
+significance level, by default 0.05
 
      • graph_neighbors (int, optional):
 Number of neighbors for each sample., by default 1
        • 
 
      • random_state (int, RandomState instance, optional):
@@ -2194,181 +2194,180 @@ 
        Inherited Members
        
 1103    Pisa, 2008, pp. 563-572,  [10.1109/ICDMW.2008.27](https://doi.org/10.1109/ICDMW.2008.27)
 1104    """
 1105
-1106
-1107    def __init__(
-1108        self,
-1109        ensemble_estimator=BaggingClassifier(),
-1110        max_iterations=100,
-1111        poolsize=100,
-1112        min_instances_for_class=3,
-1113        random_state=None,
-1114    ):
-1115        """
-1116        Create a committee trained by cotraining based on
-1117        the diversity of classifiers.
-1118
-1119        Parameters
-1120        ----------
-1121        ensemble_estimator : ClassifierMixin, optional
-1122            ensemble method, works without a ensemble as
-1123            self training with pool, by default BaggingClassifier().
-1124        max_iterations : int, optional
-1125            number of iterations of training, -1 if no max iterations, by default 100
-1126        poolsize : int, optional
-1127            max number of unlabeled instances candidates to pseudolabel, by default 100
-1128        random_state : int, RandomState instance, optional
-1129            controls the randomness of the estimator, by default None
+1106    def __init__(
+1107        self,
+1108        ensemble_estimator=BaggingClassifier(),
+1109        max_iterations=100,
+1110        poolsize=100,
+1111        min_instances_for_class=3,
+1112        random_state=None,
+1113    ):
+1114        """
+1115        Create a committee trained by cotraining based on
+1116        the diversity of classifiers.
+1117
+1118        Parameters
+1119        ----------
+1120        ensemble_estimator : ClassifierMixin, optional
+1121            ensemble method, works without a ensemble as
+1122            self training with pool, by default BaggingClassifier().
+1123        max_iterations : int, optional
+1124            number of iterations of training, -1 if no max iterations, by default 100
+1125        poolsize : int, optional
+1126            max number of unlabeled instances candidates to pseudolabel, by default 100
+1127        random_state : int, RandomState instance, optional
+1128            controls the randomness of the estimator, by default None
+1129
 1130
-1131
-1132        """
-1133        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
-1134        self.max_iterations = max_iterations
-1135        self.poolsize = poolsize
-1136        self.random_state = random_state
-1137        self.min_instances_for_class = min_instances_for_class
-1138
-1139    def fit(self, X, y, **kwards):
-1140        """Build a CoTrainingByCommittee classifier from the training set (X, y).
-1141        Parameters
-1142        ----------
-1143        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1144            The training input samples.
-1145        y : array-like of shape (n_samples,)
-1146            The target values (class labels), -1 if unlabel.
-1147        Returns
-1148        -------
-1149        self : CoTrainingByCommittee
-1150            Fitted estimator.
-1151        """
-1152        self.ensemble_estimator = skclone(self.ensemble_estimator)
-1153        random_state = check_random_state(self.random_state)
-1154
-1155        X_label, y_prev, X_unlabel = get_dataset(X, y)
-1156
-1157        is_df = isinstance(X_label, pd.DataFrame)
-1158
-1159        self.label_encoder_ = LabelEncoder()
-1160        y_label = self.label_encoder_.fit_transform(y_prev)
-1161
-1162        self.classes_ = self.label_encoder_.classes_
-1163
-1164        prior = calculate_prior_probability(y_label)
-1165        permutation = random_state.permutation(len(X_unlabel))
-1166
-1167        self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1168
-1169        if X_unlabel.shape[0] == 0:
-1170            return self
-1171
-1172        for _ in range(self.max_iterations):
-1173            if len(permutation) == 0:
-1174                break
-1175            raw_predictions = self.ensemble_estimator.predict_proba(
-1176                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
-1177            )
-1178
-1179            predictions = np.max(raw_predictions, axis=1)
-1180            class_predicted = np.argmax(raw_predictions, axis=1)
-1181
-1182            added = np.zeros(predictions.shape, dtype=bool)
-1183            # First the n (or less) most confidence instances will be selected
-1184            for c in self.ensemble_estimator.classes_:
-1185                condition = class_predicted == c
-1186
-1187                candidates = predictions[condition]
-1188                candidates_bool = np.zeros(predictions.shape, dtype=bool)
-1189                candidates_sub_set = candidates_bool[condition]
-1190
-1191                instances_index_selected = candidates.argsort(kind="mergesort")[
-1192                    -self.min_instances_for_class:
-1193                ]
-1194
-1195                candidates_sub_set[instances_index_selected] = True
-1196                candidates_bool[condition] += candidates_sub_set
-1197
-1198                added[candidates_bool] = True
-1199
-1200            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
-1201            # Pero si se añaden ya en el proceso de proporción no se duplica.
-1202
-1203            # Con esta otra interpretación ignora las n primeras instancias de cada clase
-1204            to_label = choice_with_proportion(
-1205                predictions, class_predicted, prior, extra=self.min_instances_for_class
-1206            )
-1207            added[to_label] = True
-1208
-1209            index = permutation[0: self.poolsize][added]
-1210            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
-1211                [X_label, X_unlabel.iloc[index, :]]
-1212            )
-1213            pseudoy = class_predicted[added]
-1214
-1215            y_label = np.append(y_label, pseudoy)
-1216            permutation = permutation[list(map(lambda x: x not in index, permutation))]
-1217
-1218            self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1219
-1220        return self
-1221
-1222    def predict(self, X):
-1223        """Predict class value for X.
-1224        For a classification model, the predicted class for each sample in X is returned.
-1225        Parameters
-1226        ----------
-1227        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1228            The input samples.
-1229        Returns
-1230        -------
-1231        y : array-like of shape (n_samples,)
-1232            The predicted classes
-1233        """
-1234        check_is_fitted(self.ensemble_estimator)
-1235        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
-1236
-1237    def predict_proba(self, X):
-1238        """Predict class probabilities of the input samples X.
-1239        The predicted class probability depends on the ensemble estimator.
-1240        Parameters
-1241        ----------
-1242        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1243            The input samples.
-1244        Returns
-1245        -------
-1246        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-1247            The predicted classes
-1248        """
-1249        check_is_fitted(self.ensemble_estimator)
-1250        return self.ensemble_estimator.predict_proba(X)
-1251
-1252    def score(self, X, y, sample_weight=None):
-1253        """Return the mean accuracy on the given test data and labels.
-1254        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
-1255        Parameters
-1256        ----------
-1257        X : array-like of shape (n_samples, n_features)
-1258            Test samples.
-1259        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-1260            True labels for X.
-1261        sample_weight : array-like of shape (n_samples,), optional
-1262            Sample weights., by default None
-1263        Returns
-1264        -------
-1265        score: float
-1266            Mean accuracy of self.predict(X) wrt. y.
-1267        """
-1268        try:
-1269            y = self.label_encoder_.transform(y)
-1270        except ValueError:
-1271            if "le_dict_" not in dir(self):
-1272                self.le_dict_ = dict(
-1273                    zip(
-1274                        self.label_encoder_.classes_,
-1275                        self.label_encoder_.transform(self.label_encoder_.classes_),
-1276                    )
-1277                )
-1278            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
-1279
-1280        return self.ensemble_estimator.score(X, y, sample_weight)
+1131        """
+1132        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+1133        self.max_iterations = max_iterations
+1134        self.poolsize = poolsize
+1135        self.random_state = random_state
+1136        self.min_instances_for_class = min_instances_for_class
+1137
+1138    def fit(self, X, y, **kwards):
+1139        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+1140        Parameters
+1141        ----------
+1142        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1143            The training input samples.
+1144        y : array-like of shape (n_samples,)
+1145            The target values (class labels), -1 if unlabel.
+1146        Returns
+1147        -------
+1148        self : CoTrainingByCommittee
+1149            Fitted estimator.
+1150        """
+1151        self.ensemble_estimator = skclone(self.ensemble_estimator)
+1152        random_state = check_random_state(self.random_state)
+1153
+1154        X_label, y_prev, X_unlabel = get_dataset(X, y)
+1155
+1156        is_df = isinstance(X_label, pd.DataFrame)
+1157
+1158        self.label_encoder_ = LabelEncoder()
+1159        y_label = self.label_encoder_.fit_transform(y_prev)
+1160
+1161        self.classes_ = self.label_encoder_.classes_
+1162
+1163        prior = calculate_prior_probability(y_label)
+1164        permutation = random_state.permutation(len(X_unlabel))
+1165
+1166        self.ensemble_estimator.fit(X_label, y_label, **kwards)
+1167
+1168        if X_unlabel.shape[0] == 0:
+1169            return self
+1170
+1171        for _ in range(self.max_iterations):
+1172            if len(permutation) == 0:
+1173                break
+1174            raw_predictions = self.ensemble_estimator.predict_proba(
+1175                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+1176            )
+1177
+1178            predictions = np.max(raw_predictions, axis=1)
+1179            class_predicted = np.argmax(raw_predictions, axis=1)
+1180
+1181            added = np.zeros(predictions.shape, dtype=bool)
+1182            # First the n (or less) most confidence instances will be selected
+1183            for c in self.ensemble_estimator.classes_:
+1184                condition = class_predicted == c
+1185
+1186                candidates = predictions[condition]
+1187                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+1188                candidates_sub_set = candidates_bool[condition]
+1189
+1190                instances_index_selected = candidates.argsort(kind="mergesort")[
+1191                    -self.min_instances_for_class:
+1192                ]
+1193
+1194                candidates_sub_set[instances_index_selected] = True
+1195                candidates_bool[condition] += candidates_sub_set
+1196
+1197                added[candidates_bool] = True
+1198
+1199            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+1200            # Pero si se añaden ya en el proceso de proporción no se duplica.
+1201
+1202            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+1203            to_label = choice_with_proportion(
+1204                predictions, class_predicted, prior, extra=self.min_instances_for_class
+1205            )
+1206            added[to_label] = True
+1207
+1208            index = permutation[0: self.poolsize][added]
+1209            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+1210                [X_label, X_unlabel.iloc[index, :]]
+1211            )
+1212            pseudoy = class_predicted[added]
+1213
+1214            y_label = np.append(y_label, pseudoy)
+1215            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1216
+1217            self.ensemble_estimator.fit(X_label, y_label, **kwards)
+1218
+1219        return self
+1220
+1221    def predict(self, X):
+1222        """Predict class value for X.
+1223        For a classification model, the predicted class for each sample in X is returned.
+1224        Parameters
+1225        ----------
+1226        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1227            The input samples.
+1228        Returns
+1229        -------
+1230        y : array-like of shape (n_samples,)
+1231            The predicted classes
+1232        """
+1233        check_is_fitted(self.ensemble_estimator)
+1234        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+1235
+1236    def predict_proba(self, X):
+1237        """Predict class probabilities of the input samples X.
+1238        The predicted class probability depends on the ensemble estimator.
+1239        Parameters
+1240        ----------
+1241        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1242            The input samples.
+1243        Returns
+1244        -------
+1245        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1246            The predicted classes
+1247        """
+1248        check_is_fitted(self.ensemble_estimator)
+1249        return self.ensemble_estimator.predict_proba(X)
+1250
+1251    def score(self, X, y, sample_weight=None):
+1252        """Return the mean accuracy on the given test data and labels.
+1253        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1254        Parameters
+1255        ----------
+1256        X : array-like of shape (n_samples, n_features)
+1257            Test samples.
+1258        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1259            True labels for X.
+1260        sample_weight : array-like of shape (n_samples,), optional
+1261            Sample weights., by default None
+1262        Returns
+1263        -------
+1264        score: float
+1265            Mean accuracy of self.predict(X) wrt. y.
+1266        """
+1267        try:
+1268            y = self.label_encoder_.transform(y)
+1269        except ValueError:
+1270            if "le_dict_" not in dir(self):
+1271                self.le_dict_ = dict(
+1272                    zip(
+1273                        self.label_encoder_.classes_,
+1274                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1275                    )
+1276                )
+1277            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1278
+1279        return self.ensemble_estimator.score(X, y, sample_weight)
      @@ -2434,37 +2433,37 @@

      References

      - 
      1107    def __init__(
-1108        self,
-1109        ensemble_estimator=BaggingClassifier(),
-1110        max_iterations=100,
-1111        poolsize=100,
-1112        min_instances_for_class=3,
-1113        random_state=None,
-1114    ):
-1115        """
-1116        Create a committee trained by cotraining based on
-1117        the diversity of classifiers.
-1118
-1119        Parameters
-1120        ----------
-1121        ensemble_estimator : ClassifierMixin, optional
-1122            ensemble method, works without a ensemble as
-1123            self training with pool, by default BaggingClassifier().
-1124        max_iterations : int, optional
-1125            number of iterations of training, -1 if no max iterations, by default 100
-1126        poolsize : int, optional
-1127            max number of unlabeled instances candidates to pseudolabel, by default 100
-1128        random_state : int, RandomState instance, optional
-1129            controls the randomness of the estimator, by default None
+            
      1106    def __init__(
+1107        self,
+1108        ensemble_estimator=BaggingClassifier(),
+1109        max_iterations=100,
+1110        poolsize=100,
+1111        min_instances_for_class=3,
+1112        random_state=None,
+1113    ):
+1114        """
+1115        Create a committee trained by cotraining based on
+1116        the diversity of classifiers.
+1117
+1118        Parameters
+1119        ----------
+1120        ensemble_estimator : ClassifierMixin, optional
+1121            ensemble method, works without a ensemble as
+1122            self training with pool, by default BaggingClassifier().
+1123        max_iterations : int, optional
+1124            number of iterations of training, -1 if no max iterations, by default 100
+1125        poolsize : int, optional
+1126            max number of unlabeled instances candidates to pseudolabel, by default 100
+1127        random_state : int, RandomState instance, optional
+1128            controls the randomness of the estimator, by default None
+1129
 1130
-1131
-1132        """
-1133        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
-1134        self.max_iterations = max_iterations
-1135        self.poolsize = poolsize
-1136        self.random_state = random_state
-1137        self.min_instances_for_class = min_instances_for_class
+1131        """
+1132        self.ensemble_estimator = check_classifier(ensemble_estimator, False)
+1133        self.max_iterations = max_iterations
+1134        self.poolsize = poolsize
+1135        self.random_state = random_state
+1136        self.min_instances_for_class = min_instances_for_class
 
      
 
 
@@ -2499,88 +2498,88 @@ 
      Parameters
      - 
      1139    def fit(self, X, y, **kwards):
-1140        """Build a CoTrainingByCommittee classifier from the training set (X, y).
-1141        Parameters
-1142        ----------
-1143        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1144            The training input samples.
-1145        y : array-like of shape (n_samples,)
-1146            The target values (class labels), -1 if unlabel.
-1147        Returns
-1148        -------
-1149        self : CoTrainingByCommittee
-1150            Fitted estimator.
-1151        """
-1152        self.ensemble_estimator = skclone(self.ensemble_estimator)
-1153        random_state = check_random_state(self.random_state)
-1154
-1155        X_label, y_prev, X_unlabel = get_dataset(X, y)
-1156
-1157        is_df = isinstance(X_label, pd.DataFrame)
-1158
-1159        self.label_encoder_ = LabelEncoder()
-1160        y_label = self.label_encoder_.fit_transform(y_prev)
-1161
-1162        self.classes_ = self.label_encoder_.classes_
-1163
-1164        prior = calculate_prior_probability(y_label)
-1165        permutation = random_state.permutation(len(X_unlabel))
-1166
-1167        self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1168
-1169        if X_unlabel.shape[0] == 0:
-1170            return self
-1171
-1172        for _ in range(self.max_iterations):
-1173            if len(permutation) == 0:
-1174                break
-1175            raw_predictions = self.ensemble_estimator.predict_proba(
-1176                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
-1177            )
-1178
-1179            predictions = np.max(raw_predictions, axis=1)
-1180            class_predicted = np.argmax(raw_predictions, axis=1)
-1181
-1182            added = np.zeros(predictions.shape, dtype=bool)
-1183            # First the n (or less) most confidence instances will be selected
-1184            for c in self.ensemble_estimator.classes_:
-1185                condition = class_predicted == c
-1186
-1187                candidates = predictions[condition]
-1188                candidates_bool = np.zeros(predictions.shape, dtype=bool)
-1189                candidates_sub_set = candidates_bool[condition]
-1190
-1191                instances_index_selected = candidates.argsort(kind="mergesort")[
-1192                    -self.min_instances_for_class:
-1193                ]
-1194
-1195                candidates_sub_set[instances_index_selected] = True
-1196                candidates_bool[condition] += candidates_sub_set
-1197
-1198                added[candidates_bool] = True
-1199
-1200            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
-1201            # Pero si se añaden ya en el proceso de proporción no se duplica.
-1202
-1203            # Con esta otra interpretación ignora las n primeras instancias de cada clase
-1204            to_label = choice_with_proportion(
-1205                predictions, class_predicted, prior, extra=self.min_instances_for_class
-1206            )
-1207            added[to_label] = True
-1208
-1209            index = permutation[0: self.poolsize][added]
-1210            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
-1211                [X_label, X_unlabel.iloc[index, :]]
-1212            )
-1213            pseudoy = class_predicted[added]
-1214
-1215            y_label = np.append(y_label, pseudoy)
-1216            permutation = permutation[list(map(lambda x: x not in index, permutation))]
-1217
-1218            self.ensemble_estimator.fit(X_label, y_label, **kwards)
-1219
-1220        return self
+            
      1138    def fit(self, X, y, **kwards):
+1139        """Build a CoTrainingByCommittee classifier from the training set (X, y).
+1140        Parameters
+1141        ----------
+1142        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1143            The training input samples.
+1144        y : array-like of shape (n_samples,)
+1145            The target values (class labels), -1 if unlabel.
+1146        Returns
+1147        -------
+1148        self : CoTrainingByCommittee
+1149            Fitted estimator.
+1150        """
+1151        self.ensemble_estimator = skclone(self.ensemble_estimator)
+1152        random_state = check_random_state(self.random_state)
+1153
+1154        X_label, y_prev, X_unlabel = get_dataset(X, y)
+1155
+1156        is_df = isinstance(X_label, pd.DataFrame)
+1157
+1158        self.label_encoder_ = LabelEncoder()
+1159        y_label = self.label_encoder_.fit_transform(y_prev)
+1160
+1161        self.classes_ = self.label_encoder_.classes_
+1162
+1163        prior = calculate_prior_probability(y_label)
+1164        permutation = random_state.permutation(len(X_unlabel))
+1165
+1166        self.ensemble_estimator.fit(X_label, y_label, **kwards)
+1167
+1168        if X_unlabel.shape[0] == 0:
+1169            return self
+1170
+1171        for _ in range(self.max_iterations):
+1172            if len(permutation) == 0:
+1173                break
+1174            raw_predictions = self.ensemble_estimator.predict_proba(
+1175                X_unlabel[permutation[0: self.poolsize]] if not is_df else X_unlabel.iloc[permutation[0: self.poolsize]]
+1176            )
+1177
+1178            predictions = np.max(raw_predictions, axis=1)
+1179            class_predicted = np.argmax(raw_predictions, axis=1)
+1180
+1181            added = np.zeros(predictions.shape, dtype=bool)
+1182            # First the n (or less) most confidence instances will be selected
+1183            for c in self.ensemble_estimator.classes_:
+1184                condition = class_predicted == c
+1185
+1186                candidates = predictions[condition]
+1187                candidates_bool = np.zeros(predictions.shape, dtype=bool)
+1188                candidates_sub_set = candidates_bool[condition]
+1189
+1190                instances_index_selected = candidates.argsort(kind="mergesort")[
+1191                    -self.min_instances_for_class:
+1192                ]
+1193
+1194                candidates_sub_set[instances_index_selected] = True
+1195                candidates_bool[condition] += candidates_sub_set
+1196
+1197                added[candidates_bool] = True
+1198
+1199            # Bajo esta interpretación se garantiza que al menos existen n elemento de cada clase por iteración
+1200            # Pero si se añaden ya en el proceso de proporción no se duplica.
+1201
+1202            # Con esta otra interpretación ignora las n primeras instancias de cada clase
+1203            to_label = choice_with_proportion(
+1204                predictions, class_predicted, prior, extra=self.min_instances_for_class
+1205            )
+1206            added[to_label] = True
+1207
+1208            index = permutation[0: self.poolsize][added]
+1209            X_label = np.append(X_label, X_unlabel[index], axis=0) if not is_df else pd.concat(
+1210                [X_label, X_unlabel.iloc[index, :]]
+1211            )
+1212            pseudoy = class_predicted[added]
+1213
+1214            y_label = np.append(y_label, pseudoy)
+1215            permutation = permutation[list(map(lambda x: x not in index, permutation))]
+1216
+1217            self.ensemble_estimator.fit(X_label, y_label, **kwards)
+1218
+1219        return self
 
      
 
 
@@ -2616,20 +2615,20 @@ 
      Returns
      - 
      1222    def predict(self, X):
-1223        """Predict class value for X.
-1224        For a classification model, the predicted class for each sample in X is returned.
-1225        Parameters
-1226        ----------
-1227        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1228            The input samples.
-1229        Returns
-1230        -------
-1231        y : array-like of shape (n_samples,)
-1232            The predicted classes
-1233        """
-1234        check_is_fitted(self.ensemble_estimator)
-1235        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
+            
      1221    def predict(self, X):
+1222        """Predict class value for X.
+1223        For a classification model, the predicted class for each sample in X is returned.
+1224        Parameters
+1225        ----------
+1226        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1227            The input samples.
+1228        Returns
+1229        -------
+1230        y : array-like of shape (n_samples,)
+1231            The predicted classes
+1232        """
+1233        check_is_fitted(self.ensemble_estimator)
+1234        return self.label_encoder_.inverse_transform(self.ensemble_estimator.predict(X))
 
      
 
 
@@ -2664,20 +2663,20 @@ 
      Returns
      - 
      1237    def predict_proba(self, X):
-1238        """Predict class probabilities of the input samples X.
-1239        The predicted class probability depends on the ensemble estimator.
-1240        Parameters
-1241        ----------
-1242        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1243            The input samples.
-1244        Returns
-1245        -------
-1246        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
-1247            The predicted classes
-1248        """
-1249        check_is_fitted(self.ensemble_estimator)
-1250        return self.ensemble_estimator.predict_proba(X)
+            
      1236    def predict_proba(self, X):
+1237        """Predict class probabilities of the input samples X.
+1238        The predicted class probability depends on the ensemble estimator.
+1239        Parameters
+1240        ----------
+1241        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1242            The input samples.
+1243        Returns
+1244        -------
+1245        y : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
+1246            The predicted classes
+1247        """
+1248        check_is_fitted(self.ensemble_estimator)
+1249        return self.ensemble_estimator.predict_proba(X)
 
      
 
 
@@ -2712,35 +2711,35 @@ 
      Returns
      - 
      1252    def score(self, X, y, sample_weight=None):
-1253        """Return the mean accuracy on the given test data and labels.
-1254        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
-1255        Parameters
-1256        ----------
-1257        X : array-like of shape (n_samples, n_features)
-1258            Test samples.
-1259        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
-1260            True labels for X.
-1261        sample_weight : array-like of shape (n_samples,), optional
-1262            Sample weights., by default None
-1263        Returns
-1264        -------
-1265        score: float
-1266            Mean accuracy of self.predict(X) wrt. y.
-1267        """
-1268        try:
-1269            y = self.label_encoder_.transform(y)
-1270        except ValueError:
-1271            if "le_dict_" not in dir(self):
-1272                self.le_dict_ = dict(
-1273                    zip(
-1274                        self.label_encoder_.classes_,
-1275                        self.label_encoder_.transform(self.label_encoder_.classes_),
-1276                    )
-1277                )
-1278            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
-1279
-1280        return self.ensemble_estimator.score(X, y, sample_weight)
+            
      1251    def score(self, X, y, sample_weight=None):
+1252        """Return the mean accuracy on the given test data and labels.
+1253        In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
+1254        Parameters
+1255        ----------
+1256        X : array-like of shape (n_samples, n_features)
+1257            Test samples.
+1258        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+1259            True labels for X.
+1260        sample_weight : array-like of shape (n_samples,), optional
+1261            Sample weights., by default None
+1262        Returns
+1263        -------
+1264        score: float
+1265            Mean accuracy of self.predict(X) wrt. y.
+1266        """
+1267        try:
+1268            y = self.label_encoder_.transform(y)
+1269        except ValueError:
+1270            if "le_dict_" not in dir(self):
+1271                self.le_dict_ = dict(
+1272                    zip(
+1273                        self.label_encoder_.classes_,
+1274                        self.label_encoder_.transform(self.label_encoder_.classes_),
+1275                    )
+1276                )
+1277            y = np.array(list(map(lambda x: self.le_dict_.get(x, -1), y)), dtype=y.dtype)
+1278
+1279        return self.ensemble_estimator.score(X, y, sample_weight)
 
      
 
 
@@ -4257,257 +4256,257 @@ 
      Inherited Members
      - 
      1283class CoForest(BaseCoTraining):
-1284    """
-1285    **CoForest classifier. Random Forest co-training**
-1286    ----------------------------
-1287    
-1288    Ensemble method for CoTraining based on Random Forest.
-1289
-1290    The main process is:
-1291    1. Train a committee of classifiers using bootstrap.
-1292    2. While any base classifier is retrained:
-1293        1. Predict the instances from the unlabeled set.
-1294        2. Select the instances with the highest probability.
-1295        3. Label the instances with the highest probability
-1296        4. Add the instances to the labeled set only if the error is not bigger than the previous error.
-1297        5. Retrain the classifier with the new instances.
-1298    3. Combine the probabilities of each classifier.
+            
      1282class CoForest(BaseCoTraining):
+1283    """
+1284    **CoForest classifier. Random Forest co-training**
+1285    ----------------------------
+1286    
+1287    Ensemble method for CoTraining based on Random Forest.
+1288
+1289    The main process is:
+1290    1. Train a committee of classifiers using bootstrap.
+1291    2. While any base classifier is retrained:
+1292        1. Predict the instances from the unlabeled set.
+1293        2. Select the instances with the highest probability.
+1294        3. Label the instances with the highest probability
+1295        4. Add the instances to the labeled set only if the error is not bigger than the previous error.
+1296        5. Retrain the classifier with the new instances.
+1297    3. Combine the probabilities of each classifier.
+1298
 1299
-1300
-1301    **Methods**
-1302    -------
-1303    - `fit`: Fit the model with the labeled instances.
-1304    - `predict` : Predict the class for each instance.
-1305    - `predict_proba`: Predict the probability for each class.
-1306    - `score`: Return the mean accuracy on the given test data and labels.
-1307
-1308    **Example**
-1309    -------
-1310    ```python
-1311    from sklearn.datasets import load_iris
-1312    from sslearn.wrapper import CoForest
-1313    from sslearn.model_selection import artificial_ssl_dataset
-1314
-1315    X, y = load_iris(return_X_y=True)
-1316    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
-1317    coforest = CoForest()
-1318    coforest.fit(X, y)
-1319    coforest.score(X_unlabel, y_unlabel)
-1320    ```
-1321
-1322    **References**
-1323    ----------
-1324    Li, M., & Zhou, Z.-H. (2007).<br>
-1325    Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.<br>
-1326    <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,<br>
-1327    37(6), 1088-1098. [10.1109/tsmca.2007.904745](https://doi.org/10.1109/tsmca.2007.904745)
-1328    """
-1329
-1330    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
-1331        """
-1332        Generate a CoForest classifier.
-1333        A SSL Random Forest adaption for CoTraining. 
-1334
-1335        Parameters
-1336        ----------
-1337        base_estimator : ClassifierMixin, optional
-1338            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1339        n_estimators : int, optional
-1340            The number of base estimators in the ensemble., by default 7
-1341        threshold : float, optional
-1342            The decision threshold. Should be in [0, 1)., by default 0.5
-1343        n_jobs : int, optional
-1344            The number of jobs to run in parallel for both fit and predict., by default None
-1345        bootstrap : bool, optional
-1346            Whether bootstrap samples are used when building estimators., by default True
-1347        random_state : int, RandomState instance, optional
-1348            controls the randomness of the estimator, by default None
-1349        **kwards : dict, optional
-1350            Additional parameters to be passed to base_estimator, by default None.
-1351        """
-1352        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
-1353        self.n_estimators = n_estimators
-1354        self.threshold = threshold
-1355        self.bootstrap = bootstrap
-1356        self._epsilon = sys.float_info.epsilon
-1357        self.n_jobs = n_jobs
-1358        self.random_state = random_state
-1359        self.version = version
-1360        if self.version == "1.0.2":
-1361            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
-1362
-1363    def __bootstraping(self, X, y, r_state):
-1364        # It is necessary to bootstrap the data
-1365        if self.bootstrap and self.version == "1.0.3":
-1366            is_df = isinstance(X, pd.DataFrame)
-1367            columns = None
-1368            if is_df:
-1369                columns = X.columns
-1370                X = X.to_numpy()
-1371            y = y.copy()
-1372            # Get a reprentation of each class
-1373            classes = np.unique(y)
-1374            # Choose at least one sample from each class
-1375            X_label, y_label = [], []
-1376            for c in classes:
-1377                index = np.where(y == c)[0]
-1378                # Choose one sample from each class
-1379                X_label.append(X[index[0], :])
-1380                y_label.append(y[index[0]])
-1381                # Remove the sample from the original data
-1382                X = np.delete(X, index[0], axis=0)
-1383                y = np.delete(y, index[0], axis=0)
-1384            X, y = resample(X, y, random_state=r_state)
-1385            X = np.concatenate((X, np.array(X_label)), axis=0)
-1386            y = np.concatenate((y, np.array(y_label)), axis=0)
-1387            if is_df:
-1388                X = pd.DataFrame(X, columns=columns)
-1389        return X, y
-1390
-1391    def __estimate_error(self, hypothesis, X, y, index):
-1392        if self.version == "1.0.3":
-1393            concomitants = [h for i, h in enumerate(self.hypotheses) if i != index]
-1394            predicted = [h.predict(X) for h in concomitants]
-1395            predicted = np.array(predicted, dtype=y.dtype)
-1396            # Get the majority vote
-1397            predicted, _ = mode(predicted)
-1398            # predicted, _ = st.mode(predicted, axis=1)
-1399            # Get the error rate
-1400            return 1 - accuracy_score(y, predicted)
-1401        else:
-1402            probas = hypothesis.predict_proba(X)
-1403            ei_t = 0
-1404            classes = list(hypothesis.classes_)
-1405            for j in range(y.shape[0]):
-1406                true_y = y[j]
-1407                true_y_index = classes.index(true_y)
-1408                ei_t += 1 - probas[j, true_y_index]
-1409            if ei_t == 0:
-1410                ei_t = self._epsilon
-1411            return ei_t
-1412
-1413    def __confidence(self, h_index, X):
-1414        concomitants = [h for i, h in enumerate(self.hypotheses) if i != h_index]
-1415
-1416        predicted = [h.predict(X) for h in concomitants]
-1417        predicted = np.array(predicted, dtype=predicted[0].dtype)
-1418        # Get the majority vote and the number of votes
-1419        _, counts = mode(predicted)
-1420        # _, counts = st.mode(predicted, axis=1)
-1421        confidences = counts / len(concomitants)
-1422        return confidences
-1423
-1424    def _fit_estimator(self, X, y, i, beginning=False, **kwards):
-1425        estimator = self.base_estimator
-1426        if type(self.base_estimator) == list:
-1427            estimator = skclone(self.hypotheses[i])
-1428
-1429        if "random_state" in estimator.get_params():
-1430            r_state = estimator.random_state
-1431        else:
-1432            r_state = self.random_state
-1433            if r_state is None:
-1434                r_state = np.random.randint(0, 1000)
-1435            r_state += i
-1436        # Only in the beginning
-1437        if beginning:
-1438            X, y = self.__bootstraping(X, y, r_state)
-1439
-1440        return skclone(estimator).fit(X, y, **kwards)
-1441
-1442    def fit(self, X, y, **kwards):
-1443        """Build a CoForest classifier from the training set (X, y).
-1444
-1445        Parameters
-1446        ----------
-1447        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1448            The training input samples.
-1449        y : array-like of shape (n_samples,)
-1450            The target values (class labels), -1 if unlabel.
-1451
-1452        Returns
-1453        -------
-1454        self: CoForest
-1455            Fitted estimator.
-1456        """
-1457        random_state = check_random_state(self.random_state)
-1458        n_jobs = check_n_jobs(self.n_jobs)
-1459
-1460        X_label, y_label, X_unlabel = get_dataset(X, y)
-1461
-1462        is_df = isinstance(X_label, pd.DataFrame)
-1463
-1464        self.classes_ = np.unique(y_label)
-1465
-1466        self.hypotheses = []
-1467        errors = []
-1468        weights = []
-1469        for i in range(self.n_estimators):
-1470            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
-1471            if "random_state" in dir(self.hypotheses[-1]):
-1472                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
-1473            errors.append(0.5)
-1474
-1475        self.hypotheses = Parallel(n_jobs=n_jobs)(
-1476            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
-1477            for i in range(self.n_estimators)
-1478        )
-1479
-1480        for i in range(self.n_estimators):
-1481            # The paper stablishes that the weight of each hypothesis is 0,
-1482            # but it is not possible to do that because it will be impossible increase the training set
-1483            if self.version == "1.0.2":
-1484                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
-1485            else:
-1486                weights.append(self.__confidence(i, X_label).sum())
-1487
-1488        changing = True if X_unlabel.shape[0] > 0 else False
-1489        while changing:
-1490            changing = False
-1491            for i in range(self.n_estimators):
-1492                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
-1493
-1494                ei_t = self.__estimate_error(hi, X_label, y_label, i)
-1495
-1496                if ei_t < ei:
-1497                    random_index_subsample = list(range(X_unlabel.shape[0]))
-1498                    random_index_subsample = random_state.permutation(
-1499                        random_index_subsample
-1500                    )
-1501                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
-1502                    if is_df:
-1503                        Ui_t = X_unlabel.iloc[cond, :]
-1504                    else:
-1505                        Ui_t = X_unlabel[cond, :]
-1506
-1507                    raw_predictions = hi.predict_proba(Ui_t)
-1508                    predictions = np.max(raw_predictions, axis=1)
-1509                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
-1510
-1511                    to_label = predictions > self.threshold
-1512                    wi_t = predictions[to_label].sum()
-1513
-1514                    if ei_t * wi_t < ei * wi:
-1515                        changing = True
-1516                        if is_df:
-1517                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
-1518                        else:
-1519                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
-1520                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
-1521                        hi.fit(
-1522                            x_temp,
-1523                            y_temp,
-1524                            **kwards
-1525                        )
-1526
-1527                    errors[i] = ei_t
-1528                    weights[i] = wi_t
-1529
-1530        self.h_ = self.hypotheses
-1531        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
-1532
-1533        return self
+1300    **Methods**
+1301    -------
+1302    - `fit`: Fit the model with the labeled instances.
+1303    - `predict` : Predict the class for each instance.
+1304    - `predict_proba`: Predict the probability for each class.
+1305    - `score`: Return the mean accuracy on the given test data and labels.
+1306
+1307    **Example**
+1308    -------
+1309    ```python
+1310    from sklearn.datasets import load_iris
+1311    from sslearn.wrapper import CoForest
+1312    from sslearn.model_selection import artificial_ssl_dataset
+1313
+1314    X, y = load_iris(return_X_y=True)
+1315    X, y, X_unlabel, y_unlabel, _, _ = artificial_ssl_dataset(X, y, label_rate=0.1, random_state=0)
+1316    coforest = CoForest()
+1317    coforest.fit(X, y)
+1318    coforest.score(X_unlabel, y_unlabel)
+1319    ```
+1320
+1321    **References**
+1322    ----------
+1323    Li, M., & Zhou, Z.-H. (2007).<br>
+1324    Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples.<br>
+1325    <i>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</i>,<br>
+1326    37(6), 1088-1098. [10.1109/tsmca.2007.904745](https://doi.org/10.1109/tsmca.2007.904745)
+1327    """
+1328
+1329    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1330        """
+1331        Generate a CoForest classifier.
+1332        A SSL Random Forest adaption for CoTraining. 
+1333
+1334        Parameters
+1335        ----------
+1336        base_estimator : ClassifierMixin, optional
+1337            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1338        n_estimators : int, optional
+1339            The number of base estimators in the ensemble., by default 7
+1340        threshold : float, optional
+1341            The decision threshold. Should be in [0, 1)., by default 0.5
+1342        n_jobs : int, optional
+1343            The number of jobs to run in parallel for both fit and predict., by default None
+1344        bootstrap : bool, optional
+1345            Whether bootstrap samples are used when building estimators., by default True
+1346        random_state : int, RandomState instance, optional
+1347            controls the randomness of the estimator, by default None
+1348        **kwards : dict, optional
+1349            Additional parameters to be passed to base_estimator, by default None.
+1350        """
+1351        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1352        self.n_estimators = n_estimators
+1353        self.threshold = threshold
+1354        self.bootstrap = bootstrap
+1355        self._epsilon = sys.float_info.epsilon
+1356        self.n_jobs = n_jobs
+1357        self.random_state = random_state
+1358        self.version = version
+1359        if self.version == "1.0.2":
+1360            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+1361
+1362    def __bootstraping(self, X, y, r_state):
+1363        # It is necessary to bootstrap the data
+1364        if self.bootstrap and self.version == "1.0.3":
+1365            is_df = isinstance(X, pd.DataFrame)
+1366            columns = None
+1367            if is_df:
+1368                columns = X.columns
+1369                X = X.to_numpy()
+1370            y = y.copy()
+1371            # Get a reprentation of each class
+1372            classes = np.unique(y)
+1373            # Choose at least one sample from each class
+1374            X_label, y_label = [], []
+1375            for c in classes:
+1376                index = np.where(y == c)[0]
+1377                # Choose one sample from each class
+1378                X_label.append(X[index[0], :])
+1379                y_label.append(y[index[0]])
+1380                # Remove the sample from the original data
+1381                X = np.delete(X, index[0], axis=0)
+1382                y = np.delete(y, index[0], axis=0)
+1383            X, y = resample(X, y, random_state=r_state)
+1384            X = np.concatenate((X, np.array(X_label)), axis=0)
+1385            y = np.concatenate((y, np.array(y_label)), axis=0)
+1386            if is_df:
+1387                X = pd.DataFrame(X, columns=columns)
+1388        return X, y
+1389
+1390    def __estimate_error(self, hypothesis, X, y, index):
+1391        if self.version == "1.0.3":
+1392            concomitants = [h for i, h in enumerate(self.hypotheses) if i != index]
+1393            predicted = [h.predict(X) for h in concomitants]
+1394            predicted = np.array(predicted, dtype=y.dtype)
+1395            # Get the majority vote
+1396            predicted, _ = mode(predicted)
+1397            # predicted, _ = st.mode(predicted, axis=1)
+1398            # Get the error rate
+1399            return 1 - accuracy_score(y, predicted)
+1400        else:
+1401            probas = hypothesis.predict_proba(X)
+1402            ei_t = 0
+1403            classes = list(hypothesis.classes_)
+1404            for j in range(y.shape[0]):
+1405                true_y = y[j]
+1406                true_y_index = classes.index(true_y)
+1407                ei_t += 1 - probas[j, true_y_index]
+1408            if ei_t == 0:
+1409                ei_t = self._epsilon
+1410            return ei_t
+1411
+1412    def __confidence(self, h_index, X):
+1413        concomitants = [h for i, h in enumerate(self.hypotheses) if i != h_index]
+1414
+1415        predicted = [h.predict(X) for h in concomitants]
+1416        predicted = np.array(predicted, dtype=predicted[0].dtype)
+1417        # Get the majority vote and the number of votes
+1418        _, counts = mode(predicted)
+1419        # _, counts = st.mode(predicted, axis=1)
+1420        confidences = counts / len(concomitants)
+1421        return confidences
+1422
+1423    def _fit_estimator(self, X, y, i, beginning=False, **kwards):
+1424        estimator = self.base_estimator
+1425        if type(self.base_estimator) == list:
+1426            estimator = skclone(self.hypotheses[i])
+1427
+1428        if "random_state" in estimator.get_params():
+1429            r_state = estimator.random_state
+1430        else:
+1431            r_state = self.random_state
+1432            if r_state is None:
+1433                r_state = np.random.randint(0, 1000)
+1434            r_state += i
+1435        # Only in the beginning
+1436        if beginning:
+1437            X, y = self.__bootstraping(X, y, r_state)
+1438
+1439        return skclone(estimator).fit(X, y, **kwards)
+1440
+1441    def fit(self, X, y, **kwards):
+1442        """Build a CoForest classifier from the training set (X, y).
+1443
+1444        Parameters
+1445        ----------
+1446        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1447            The training input samples.
+1448        y : array-like of shape (n_samples,)
+1449            The target values (class labels), -1 if unlabel.
+1450
+1451        Returns
+1452        -------
+1453        self: CoForest
+1454            Fitted estimator.
+1455        """
+1456        random_state = check_random_state(self.random_state)
+1457        n_jobs = check_n_jobs(self.n_jobs)
+1458
+1459        X_label, y_label, X_unlabel = get_dataset(X, y)
+1460
+1461        is_df = isinstance(X_label, pd.DataFrame)
+1462
+1463        self.classes_ = np.unique(y_label)
+1464
+1465        self.hypotheses = []
+1466        errors = []
+1467        weights = []
+1468        for i in range(self.n_estimators):
+1469            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1470            if "random_state" in dir(self.hypotheses[-1]):
+1471                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
+1472            errors.append(0.5)
+1473
+1474        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1475            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1476            for i in range(self.n_estimators)
+1477        )
+1478
+1479        for i in range(self.n_estimators):
+1480            # The paper stablishes that the weight of each hypothesis is 0,
+1481            # but it is not possible to do that because it will be impossible increase the training set
+1482            if self.version == "1.0.2":
+1483                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1484            else:
+1485                weights.append(self.__confidence(i, X_label).sum())
+1486
+1487        changing = True if X_unlabel.shape[0] > 0 else False
+1488        while changing:
+1489            changing = False
+1490            for i in range(self.n_estimators):
+1491                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1492
+1493                ei_t = self.__estimate_error(hi, X_label, y_label, i)
+1494
+1495                if ei_t < ei:
+1496                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1497                    random_index_subsample = random_state.permutation(
+1498                        random_index_subsample
+1499                    )
+1500                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1501                    if is_df:
+1502                        Ui_t = X_unlabel.iloc[cond, :]
+1503                    else:
+1504                        Ui_t = X_unlabel[cond, :]
+1505
+1506                    raw_predictions = hi.predict_proba(Ui_t)
+1507                    predictions = np.max(raw_predictions, axis=1)
+1508                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1509
+1510                    to_label = predictions > self.threshold
+1511                    wi_t = predictions[to_label].sum()
+1512
+1513                    if ei_t * wi_t < ei * wi:
+1514                        changing = True
+1515                        if is_df:
+1516                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1517                        else:
+1518                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1519                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1520                        hi.fit(
+1521                            x_temp,
+1522                            y_temp,
+1523                            **kwards
+1524                        )
+1525
+1526                    errors[i] = ei_t
+1527                    weights[i] = wi_t
+1528
+1529        self.h_ = self.hypotheses
+1530        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1531
+1532        return self
 
      
 
 
@@ -4573,38 +4572,38 @@ 
      References
      - 
      1330    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
-1331        """
-1332        Generate a CoForest classifier.
-1333        A SSL Random Forest adaption for CoTraining. 
-1334
-1335        Parameters
-1336        ----------
-1337        base_estimator : ClassifierMixin, optional
-1338            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
-1339        n_estimators : int, optional
-1340            The number of base estimators in the ensemble., by default 7
-1341        threshold : float, optional
-1342            The decision threshold. Should be in [0, 1)., by default 0.5
-1343        n_jobs : int, optional
-1344            The number of jobs to run in parallel for both fit and predict., by default None
-1345        bootstrap : bool, optional
-1346            Whether bootstrap samples are used when building estimators., by default True
-1347        random_state : int, RandomState instance, optional
-1348            controls the randomness of the estimator, by default None
-1349        **kwards : dict, optional
-1350            Additional parameters to be passed to base_estimator, by default None.
-1351        """
-1352        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
-1353        self.n_estimators = n_estimators
-1354        self.threshold = threshold
-1355        self.bootstrap = bootstrap
-1356        self._epsilon = sys.float_info.epsilon
-1357        self.n_jobs = n_jobs
-1358        self.random_state = random_state
-1359        self.version = version
-1360        if self.version == "1.0.2":
-1361            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
+            
      1329    def __init__(self, base_estimator=DecisionTreeClassifier(), n_estimators=7, threshold=0.75, bootstrap=True, n_jobs=None, random_state=None, version="1.0.3"):
+1330        """
+1331        Generate a CoForest classifier.
+1332        A SSL Random Forest adaption for CoTraining. 
+1333
+1334        Parameters
+1335        ----------
+1336        base_estimator : ClassifierMixin, optional
+1337            An estimator object implementing fit and predict_proba, by default DecisionTreeClassifier()
+1338        n_estimators : int, optional
+1339            The number of base estimators in the ensemble., by default 7
+1340        threshold : float, optional
+1341            The decision threshold. Should be in [0, 1)., by default 0.5
+1342        n_jobs : int, optional
+1343            The number of jobs to run in parallel for both fit and predict., by default None
+1344        bootstrap : bool, optional
+1345            Whether bootstrap samples are used when building estimators., by default True
+1346        random_state : int, RandomState instance, optional
+1347            controls the randomness of the estimator, by default None
+1348        **kwards : dict, optional
+1349            Additional parameters to be passed to base_estimator, by default None.
+1350        """
+1351        self.base_estimator = check_classifier(base_estimator, collection_size=n_estimators)
+1352        self.n_estimators = n_estimators
+1353        self.threshold = threshold
+1354        self.bootstrap = bootstrap
+1355        self._epsilon = sys.float_info.epsilon
+1356        self.n_jobs = n_jobs
+1357        self.random_state = random_state
+1358        self.version = version
+1359        if self.version == "1.0.2":
+1360            warnings.warn("The version 1.0.2 is deprecated. Please use the version 1.0.3", DeprecationWarning)
 
      
 
 
@@ -4644,98 +4643,98 @@ 
      Parameters
      - 
      1442    def fit(self, X, y, **kwards):
-1443        """Build a CoForest classifier from the training set (X, y).
-1444
-1445        Parameters
-1446        ----------
-1447        X : {array-like, sparse matrix} of shape (n_samples, n_features)
-1448            The training input samples.
-1449        y : array-like of shape (n_samples,)
-1450            The target values (class labels), -1 if unlabel.
-1451
-1452        Returns
-1453        -------
-1454        self: CoForest
-1455            Fitted estimator.
-1456        """
-1457        random_state = check_random_state(self.random_state)
-1458        n_jobs = check_n_jobs(self.n_jobs)
-1459
-1460        X_label, y_label, X_unlabel = get_dataset(X, y)
-1461
-1462        is_df = isinstance(X_label, pd.DataFrame)
-1463
-1464        self.classes_ = np.unique(y_label)
-1465
-1466        self.hypotheses = []
-1467        errors = []
-1468        weights = []
-1469        for i in range(self.n_estimators):
-1470            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
-1471            if "random_state" in dir(self.hypotheses[-1]):
-1472                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
-1473            errors.append(0.5)
-1474
-1475        self.hypotheses = Parallel(n_jobs=n_jobs)(
-1476            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
-1477            for i in range(self.n_estimators)
-1478        )
-1479
-1480        for i in range(self.n_estimators):
-1481            # The paper stablishes that the weight of each hypothesis is 0,
-1482            # but it is not possible to do that because it will be impossible increase the training set
-1483            if self.version == "1.0.2":
-1484                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
-1485            else:
-1486                weights.append(self.__confidence(i, X_label).sum())
-1487
-1488        changing = True if X_unlabel.shape[0] > 0 else False
-1489        while changing:
-1490            changing = False
-1491            for i in range(self.n_estimators):
-1492                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
-1493
-1494                ei_t = self.__estimate_error(hi, X_label, y_label, i)
-1495
-1496                if ei_t < ei:
-1497                    random_index_subsample = list(range(X_unlabel.shape[0]))
-1498                    random_index_subsample = random_state.permutation(
-1499                        random_index_subsample
-1500                    )
-1501                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
-1502                    if is_df:
-1503                        Ui_t = X_unlabel.iloc[cond, :]
-1504                    else:
-1505                        Ui_t = X_unlabel[cond, :]
-1506
-1507                    raw_predictions = hi.predict_proba(Ui_t)
-1508                    predictions = np.max(raw_predictions, axis=1)
-1509                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
-1510
-1511                    to_label = predictions > self.threshold
-1512                    wi_t = predictions[to_label].sum()
-1513
-1514                    if ei_t * wi_t < ei * wi:
-1515                        changing = True
-1516                        if is_df:
-1517                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
-1518                        else:
-1519                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
-1520                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
-1521                        hi.fit(
-1522                            x_temp,
-1523                            y_temp,
-1524                            **kwards
-1525                        )
-1526
-1527                    errors[i] = ei_t
-1528                    weights[i] = wi_t
-1529
-1530        self.h_ = self.hypotheses
-1531        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
-1532
-1533        return self
+            
      1441    def fit(self, X, y, **kwards):
+1442        """Build a CoForest classifier from the training set (X, y).
+1443
+1444        Parameters
+1445        ----------
+1446        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+1447            The training input samples.
+1448        y : array-like of shape (n_samples,)
+1449            The target values (class labels), -1 if unlabel.
+1450
+1451        Returns
+1452        -------
+1453        self: CoForest
+1454            Fitted estimator.
+1455        """
+1456        random_state = check_random_state(self.random_state)
+1457        n_jobs = check_n_jobs(self.n_jobs)
+1458
+1459        X_label, y_label, X_unlabel = get_dataset(X, y)
+1460
+1461        is_df = isinstance(X_label, pd.DataFrame)
+1462
+1463        self.classes_ = np.unique(y_label)
+1464
+1465        self.hypotheses = []
+1466        errors = []
+1467        weights = []
+1468        for i in range(self.n_estimators):
+1469            self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
+1470            if "random_state" in dir(self.hypotheses[-1]):
+1471                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
+1472            errors.append(0.5)
+1473
+1474        self.hypotheses = Parallel(n_jobs=n_jobs)(
+1475            delayed(self._fit_estimator)(X_label, y_label, i, beginning=True, **kwards)
+1476            for i in range(self.n_estimators)
+1477        )
+1478
+1479        for i in range(self.n_estimators):
+1480            # The paper stablishes that the weight of each hypothesis is 0,
+1481            # but it is not possible to do that because it will be impossible increase the training set
+1482            if self.version == "1.0.2":
+1483                weights.append(np.max(self.hypotheses[i].predict_proba(X_label), axis=1).sum())  # Version 1.0.2
+1484            else:
+1485                weights.append(self.__confidence(i, X_label).sum())
+1486
+1487        changing = True if X_unlabel.shape[0] > 0 else False
+1488        while changing:
+1489            changing = False
+1490            for i in range(self.n_estimators):
+1491                hi, ei, wi = self.hypotheses[i], errors[i], weights[i]
+1492
+1493                ei_t = self.__estimate_error(hi, X_label, y_label, i)
+1494
+1495                if ei_t < ei:
+1496                    random_index_subsample = list(range(X_unlabel.shape[0]))
+1497                    random_index_subsample = random_state.permutation(
+1498                        random_index_subsample
+1499                    )
+1500                    cond = random_index_subsample[0:int(safe_division(ei * wi, ei_t, self._epsilon))]
+1501                    if is_df:
+1502                        Ui_t = X_unlabel.iloc[cond, :]
+1503                    else:
+1504                        Ui_t = X_unlabel[cond, :]
+1505
+1506                    raw_predictions = hi.predict_proba(Ui_t)
+1507                    predictions = np.max(raw_predictions, axis=1)
+1508                    class_predicted = self.classes_.take(np.argmax(raw_predictions, axis=1), axis=0)
+1509
+1510                    to_label = predictions > self.threshold
+1511                    wi_t = predictions[to_label].sum()
+1512
+1513                    if ei_t * wi_t < ei * wi:
+1514                        changing = True
+1515                        if is_df:
+1516                            x_temp = pd.concat([X_label, Ui_t.iloc[to_label, :]])
+1517                        else:
+1518                            x_temp = np.concatenate((X_label, Ui_t[to_label]))
+1519                        y_temp = np.concatenate((y_label, class_predicted[to_label]))
+1520                        hi.fit(
+1521                            x_temp,
+1522                            y_temp,
+1523                            **kwards
+1524                        )
+1525
+1526                    errors[i] = ei_t
+1527                    weights[i] = wi_t
+1528
+1529        self.h_ = self.hypotheses
+1530        self.columns_ = [list(range(X.shape[1]))] * self.n_estimators
+1531
+1532        return self
 
      
 
 
diff --git a/sslearn/__init__.py b/sslearn/__init__.py
index f6bbaae..a01ad62 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__='1.0.4.1'
+__version__='1.0.5'
 __AUTHOR__="José Luis Garrido-Labrador"  # Author of the package
 __AUTHOR_EMAIL__="jlgarrido@ubu.es"  # Author's email
 __URL__="https://pypi.org/project/sslearn/"
diff --git a/sslearn/restricted.py b/sslearn/restricted.py
index 011be28..5b2a91b 100644
--- a/sslearn/restricted.py
+++ b/sslearn/restricted.py
@@ -11,9 +11,16 @@
 
 [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.
 
+[feature_fusion](#feature_fusion):
+> Restricted Set Classification for the instances with pairwise constraints. Combine all instances that have the must-link constraint with the average of their features.
+
+[probability_fusion](#probability_fusion):
+> 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.
+
 
 """
 
@@ -23,7 +30,136 @@
 import warnings
 import pandas as pd
 
-__all__ = ["conflict_rate", "combine_predictions", "WhoIsWhoClassifier"]
+__all__ = ["conflict_rate", "combine_predictions", "feature_fusion", "probability_fusion", "WhoIsWhoClassifier"]
+ 
+    
+def feature_fusion(classifier, X, must_link, cannot_link):
+    """
+    Restricted Set Classification for the instances with pairwise constraints. 
+    Combine all instances that have the must-link constraint with the average of their features.    
+
+    Parameters
+    ----------
+    classifier : ClassifierMixin with predict_proba method
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Array representing the data.
+    must_link : dict of {int: list of int}
+        Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
+    cannot_link : dict of {int: list of int}
+        Dictionary with the cannot links, where the value is a list of instances that cannot have the same label.
+
+    Returns
+    ----------
+    y : ndarray of shape (n_samples,)
+        Array with predicted labels.
+
+    Examples
+    ----------
+    ```python
+    from sslearn.restricted import feature_fusion
+    from sklearn.bayes import GaussianNB
+    import pandas as pd
+
+    dataset = pd.read_csv("dataset.csv")
+
+    must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')
+    # must_link = {0: [0, 2], 1: [1, 3]} -> 
+    # instances 0 and 2 must have the same label, 
+    # and instances 1 and 3 must have the same label
+
+    cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')
+    # cannot_link = {0: [0, 1], 1: [2, 3]} ->
+    # instances 0 and 1 cannot have the same label, 
+    # and instances 2 and 3 cannot have the same label
+
+    X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values
+    X_label = X[y != y.dtype.type(-1)]
+    y_label = y[y != y.dtype.type(-1)]
+    X_unlabel = X[y == y.dtype.type(-1)]
+
+    classifier = GaussianNB()
+    classifier.fit(X_label, y_label)
+
+    y_pred = feature_fusion(classifier, X_unlabel, must_link, cannot_link)
+    ```
+
+    References
+    ----------
+    L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).
      
+    Semi-supervised classification with pairwise constraints: A case study on animal identification from video.
      
+    Information Fusion,
       
+    104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188)
+    """
+    
+    X_combined = __combine_features(X, must_link)
+    y_pred_proba = classifier.predict_proba(X_combined)
+
+    return __restricted_set_classification(y_pred_proba, cannot_link, classifier.classes_)
+
+
+def probability_fusion(classifier, X, must_link, cannot_link):
+    """
+    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.
+
+    Parameters
+    ----------
+    classifier : ClassifierMixin with predict_proba method
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Array representing the data.
+    must_link : dict of {int: list of int}
+        Dictionary with the must links, where the key is the instance and the value is a list of instances that must have the same label.
+    cannot_link : dict of {int: list of int}
+        Dictionary with the cannot links, where the value is a list of instances that cannot have the same label.
+
+    Returns
+    ----------
+    y : ndarray of shape (n_samples,)
+        Array with predicted labels.
+
+    Examples
+    ----------
+    ```python
+    from sslearn.restricted import feature_fusion
+    from sklearn.bayes import GaussianNB
+    import pandas as pd
+
+    dataset = pd.read_csv("dataset.csv")
+
+    must_link = pd.read_csv("must_link.csv", index_col=0).to_dict(orient='index')
+    # must_link = {0: [0, 2], 1: [1, 3]} -> 
+    # instances 0 and 2 must have the same label, 
+    # and instances 1 and 3 must have the same label
+
+    cannot_link = pd.read_csv("cannot_link.csv", index_col=0).to_dict(orient='index')
+    # cannot_link = {0: [0, 1], 1: [2, 3]} ->
+    # instances 0 and 1 cannot have the same label, 
+    # and instances 2 and 3 cannot have the same label
+
+    X, y = dataset.iloc[:, :-1].values, dataset.iloc[:, -1].values
+    X_label = X[y != y.dtype.type(-1)]
+    y_label = y[y != y.dtype.type(-1)]
+    X_unlabel = X[y == y.dtype.type(-1)]
+
+    classifier = GaussianNB()
+    classifier.fit(X_label, y_label)
+
+    y_pred = probability_fusion(classifier, X_unlabel, must_link, cannot_link)
+    ```
+
+    References
+    ----------
+    L.I. Kuncheva, J.L. Garrido-Labrador, I. Ramos-Pérez, S.L. Hennessey, J.J. Rodríguez (2024).
      
+    Semi-supervised classification with pairwise constraints: A case study on animal identification from video.
      
+    Information Fusion,
       
+    104, 102188, [10.1016/j.inffus.2023.102188](https://doi.org/10.1016/j.inffus.2023.102188)
+    """
+
+    y_probs = classifier.predict_proba(X)
+    classes = classifier.classes_
+    y_probs_combined, _ = __combine_probabilities(y_probs, must_link, classes)
+    return __restricted_set_classification(y_probs_combined, cannot_link, classes)
+
 
 class WhoIsWhoClassifier(BaseEstimator, ClassifierMixin, MetaEstimatorMixin):
 
@@ -155,6 +291,25 @@ def conflict_rate(y_pred, restrictions, weighted=True):
         return (conflicted.y_pred / rcount.y_pred).sum()
 
 def combine_predictions(y_probas, instance_group, class_number, method="hungarian"):
+    """
+    Combine the predictions of a group of instances to keep the restrictions.
+
+    Parameters
+    ----------
+    y_probas : array-like of shape (n_samples, n_classes)
+        The class probabilities of 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.
+    class_number : int
+        The number of classes.
+    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"
+
+    Returns
+    -------
+    array-like of shape (n_samples,)
+        The predicted labels.
+    """
     y_predicted = []
     for group in np.unique(instance_group):
            
@@ -205,4 +360,102 @@ def _hungarian(probas_matrix):
     _, col_ind = linear_sum_assignment(costs, maximize=True)
     col_ind = list(col_ind)
         
-    return col_ind
\ No newline at end of file
+    return col_ind
+
+def __combine_probabilities(y_probs, objects_in_track, classes):
+    """"
+    Averages the classifier probabilities of the instances in the same track.
+    
+    :param y_probs: classifier probabilities for the instances 
+    :param objects_in_track: dictionary with the tracks
+    :param classes: classes used to train the classifier
+    
+    :return: a tuple with the modified y_probs ans the predicted classes
+    """
+    
+    y_probs_combined = y_probs.copy()
+    
+    for objects in objects_in_track.values():
+        if len(objects) <= 1:
+            continue         
+        means = y_probs_combined[objects, :].mean(axis=0)
+        y_probs_combined[objects, :] = means  
+
+    preds = classes.take(list(np.argmax(y_probs_combined, axis=1)))
+    return y_probs_combined, preds
+
+def __combine_features(X, objects_in_track):
+    """
+    Averages the features of the instances in the same track.
+    
+    :param X: feature values of the instances.
+    :param objects_in_track: dictionary with the tracks
+    
+    :return: a modified X with averaged features
+    """
+    
+    X_combined = X.copy()
+    is_df = isinstance(X, pd.DataFrame)
+    if is_df:
+        X_combined = X.values
+    for objects in objects_in_track.values():
+        if len(objects) <= 1:
+            continue
+        means = X_combined[objects].mean(axis=0)
+        X_combined[objects] = means 
+    return X_combined
+
+def __restricted_set_classification(y_probs, instances_by_frame, classes):
+    """
+    Restricted Set Classification for the instances in several frames
+    
+    :param y_probs: the probabilities given by the classifier for the instances
+    :param instances_by_frame: which instances are in each frame
+    :param classes: the classes seen by the classifier
+    
+    :return: the predicted labels
+    """
+
+    restricted_pred = []
+    num_conflicts = 0
+    for fr, group in instances_by_frame.items():
+        if len(group) == 0:
+            continue
+        first, last = group[0], group[-1]
+        group_probs = y_probs[first:last + 1]
+        conflict, group_pred = __restricted_set_hungarian(group_probs, classes)
+        restricted_pred.extend(group_pred)
+        num_conflicts += conflict
+
+    assert len(restricted_pred) == len(y_probs), "The number of predictions is different from the number of instances, check cannot link constraints, all instances must be in a cannot-link group."
+        
+    return restricted_pred
+
+def __restricted_set_hungarian(probs, classes):
+    """
+    Restricted Set Classification for a set of objects that have to be of different classes
+    
+    :param probs: the probabilities given by the classifier
+    :param classes: the classes seen by the classifier
+    
+    :return: a tuple with 1) the Hungarian method was used (0 or 1), and 2) the predicted classes
+    """
+    
+    rows, cols = probs.shape
+    preds = list(np.argmax(probs, axis=1))
+
+    if rows > cols or len(preds) == len(set(preds)):
+        # return 0 if rows > cols else 1, classes.take(preds)
+        return 0, classes.take(preds)
+    costs = np.log(probs)    
+        
+    try:
+        row_ind, col_ind = linear_sum_assignment(costs, maximize=True)
+        col_ind = list(col_ind)
+    except: # some of the values was -Inf
+        probs += np.nextafter(0, 1) # small double value
+        costs = np.log(probs)        
+        row_ind, col_ind = linear_sum_assignment(costs, maximize=True)
+        col_ind = list(col_ind)
+        
+    return 1, classes.take(col_ind)
diff --git a/sslearn/wrapper/_co.py b/sslearn/wrapper/_co.py
index a01d67d..9340378 100644
--- a/sslearn/wrapper/_co.py
+++ b/sslearn/wrapper/_co.py
@@ -1467,7 +1467,7 @@ def fit(self, X, y, **kwards):
         for i in range(self.n_estimators):
             self.hypotheses.append(skclone(self.base_estimator if type(self.base_estimator) is not list else self.base_estimator[i]))
             if "random_state" in dir(self.hypotheses[-1]):
-                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2 ** 32 - 1))
+                self.hypotheses[-1].set_params(random_state=random_state.randint(0, 2147483647))
             errors.append(0.5)
 
         self.hypotheses = Parallel(n_jobs=n_jobs)(
diff --git a/test/test_general.py b/test/test_general.py
index ed94462..2c2fa94 100644
--- a/test/test_general.py
+++ b/test/test_general.py
@@ -15,7 +15,7 @@
 sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), ".."))
 from sslearn.base import FakedProbaClassifier, OneVsRestSSLClassifier
 from sslearn.restricted import (WhoIsWhoClassifier, combine_predictions,
-                                conflict_rate)
+                                conflict_rate, probability_fusion, feature_fusion)
 from sslearn.utils import (calc_number_per_class, calculate_prior_probability,
                            check_n_jobs, choice_with_proportion,
                            confidence_interval, is_int, safe_division)
@@ -129,4 +129,27 @@ def test_WhoIsWhoClassifier(self):
         assert hyp.conflict_in_train == 1
         assert hyp.predict(X, group).tolist() == [0, 1, 0, 2, 1]
 
+    def test_probability_fusion(self):
+        X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
+        y = np.array([0, 1, 0, 1, 2])
+        cannot_link = {0: [0, 1], 1: [2, 3, 4]}
+        must_link = {1: [1, 3], 0: [0, 2], 4: [4]}
+
+        h = GaussianNB()
+        h.fit(X, y)
+
+        probability_fusion(h, X, must_link=must_link, cannot_link=cannot_link)
+
+    def test_feature_fusion(self):
+        X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10]])
+        y = np.array([0, 1, 0, 1, 2])
+        cannot_link = {0: [0, 1], 1: [2, 3, 4]}
+        must_link = {1: [1, 3], 0: [0, 2], 4: [4]}
+
+        h = GaussianNB()
+        h.fit(X, y)
+
+        result = feature_fusion(h, X, must_link=must_link, cannot_link=cannot_link)
+
+