last updates before submission

.github/workflows/ci.yml

@@ -1,108 +0,0 @@
-name: CI
-
-on:
-  pull_request:
-  push:
-    branches:
-      - master
-      - devel
-    tags:
-      - "[0-9]+.[0-9]+.[0-9]+"
-
-jobs:
-
-  # take out unit tests
-  test:
-    name: Unit tests (Python ${{ matrix.python-version }})
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version:
-        - "3.11"
-    env:
-      QUAPY_TESTS_OMIT_LARGE_DATASETS: True
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip setuptools wheel
-        python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@main"
-        python -m pip install -e .[bayes,tests]
-    - name: Test with unittest
-      run: python -m unittest
-
-  # build and push documentation to gh-pages (only if pushed to the master branch)
-  docs:
-    name: Documentation
-    runs-on: ubuntu-latest
-    if: github.ref == 'refs/heads/master'
-    steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python
-      uses: actions/setup-python@v5
-      with:
-        python-version: 3.11
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip setuptools wheel "jax[cpu]"
-        python -m pip install "qunfold @ git+https://github.com/mirkobunse/qunfold@main"
-        python -m pip install -e .[neural,docs]
-    - name: Build documentation
-      run: sphinx-build -M html docs/source docs/build
-    - name: Publish documentation
-      run: |
-        git clone ${{ github.server_url }}/${{ github.repository }}.git --branch gh-pages --single-branch __gh-pages/
-        cp -r docs/build/html/* __gh-pages/
-        cd __gh-pages/
-        git config --local user.email "action@github.com"
-        git config --local user.name "GitHub Action"
-        git add .
-        git commit -am "Documentation based on ${{ github.sha }}" || true
-    - name: Push changes
-      uses: ad-m/github-push-action@master
-      with:
-        branch: gh-pages
-        directory: __gh-pages/
-        github_token: ${{ secrets.GITHUB_TOKEN }}
-
-  release:
-    name: Build & Publish Release
-    runs-on: ubuntu-latest
-    if: startsWith(github.ref, 'refs/tags/')
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install build dependencies
-        run: |
-          python -m pip install --upgrade pip build twine
-      - name: Build package
-        run: python -m build
-      - name: Publish to TestPyPI
-        uses: pypa/gh-action-pypi-publish@release/v1
-        with:
-          user: __token__
-           # use these for TESTs!
-#          password: ${{ secrets.TEST_PYPI_API_TOKEN }}
-#          repository_url: https://test.pypi.org/legacy/
-          password: ${{ secrets.PYPI_API_TOKEN }}
-          repository_url: https://upload.pypi.org/legacy/
-      - name: Create GitHub Release
-        id: create_release
-        uses: actions/create-release@v1
-        with:
-          tag_name: ${{ github.ref_name }}
-          release_name: Release ${{ github.ref_name }}
-          body: |
-            Changes in this release: 
-            - see commit history for details
-          draft: false
-          prerelease: false
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/pylint.yml

@@ -0,0 +1,23 @@
+name: Pylint
+
+on: [push]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10"]
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v3
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pylint
+    - name: Analysing the code with pylint
+      run: |
+        pylint $(git ls-files '*.py')

.gitignore

@@ -69,12 +69,8 @@ instance/
 # Scrapy stuff:
 .scrapy
 
-# vscode config:
-.vscode/
-
 # Sphinx documentation
-docs/_build/doctest
-docs/_build/doctrees
+docs/_build/
 
 # PyBuilder
 target/
@@ -89,11 +85,6 @@ ipython_config.py
 # pyenv
 .python-version
 
-# poetry
-poetry.toml
-pyproject.toml 
-poetry.lock
-
 # pipenv
 #   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
 #   However, in case of collaboration, if having platform-specific dependencies or dependencies
@@ -152,8 +143,7 @@ LeQua2022
 MultiLabel
 NewMethods
 Ordinal
-Retrieval
-eDiscovery
+Archived/eDiscovery
 poster-cikm
 slides-cikm
 slides-short-cikm
@@ -162,10 +152,4 @@ svm_perf_quantification/svm_struct
 svm_perf_quantification/svm_light
 TweetSentQuant
 
-
-
-
-
-
 *.png
-.idea

CHANGE_LOG.txt

@@ -1,118 +1,7 @@
-Change Log 0.2.1
------------------
-
-- Improved documentation of confidence regions. Added QuaPy logo :')
-
-- Added Bayesian KDEy and Bayesian MAPLS quantifiers.
-
-- Added temperature calibration utilities for Bayesian confidence-aware methods.
-
-- Added compositional CLR and ILR transformations.
-
-- Extended KDEy with Aitchison/ILR kernels, shrinkage, and improved numerical stability.
-
-- Added image-embedding-based datasets including CIFAR10, CIFAR100, CIFAR100coarse, VSHN, FashionMNIST, MNIST.
-
-- Added TemperatureScalingFromLogits for calibrating pretrained logits.
-
-- Added DirichletProtocol for prevalence sampling from Dirichlet priors.
-
-- Added ReadMe method by Daniel Hopkins and Gary King.
-
-- Internal index in LabelledCollection is now "lazy", and is only constructed if required.
-
-- Improved unit testing and separated integration tests.
-
-- Added RLLS (Regularized Learning for Domain Adaptation under Label Shifts) method.
-
-- Added visualization tools for 3-class problems in the simplex, see also the new example no.19
-
-- Deep code revision and improved codebase
-
-- Added EDx/EDy from quantificationlib (thanks to Pablo and Juanjo!)
-
-
-
-Change Log 0.2.0
------------------
-
-- Base code Refactor:
-    - Removing coupling between LabelledCollection and quantification methods; the fit interface changes:
-        def fit(data:LabelledCollection): -> def fit(X, y):
-    - Adding function "predict" (function "quantify" is still present as an alias, for the nostalgic)
-    - Aggregative methods's behavior in terms of fit_classifier and how to treat the val_split is now
-        indicated exclusively at construction time, and it is no longer possible to indicate it at fit time.
-        This is because, in v<=0.1.9, one could create a method (e.g., ACC) and then indicate:
-        my_acc.fit(tr_data, fit_classifier=False, val_split=val_data)
-        in which case the first argument is unused, and this was ambiguous with
-        my_acc.fit(the_data, fit_classifier=False)
-        in which case the_data is to be used for validation purposes. However, the val_split could be set as a fraction
-        indicating only part of the_data must be used for validation, and the rest wasted... it was certainly confusing.
-    - This change imposes a versioning constrain with qunfold, which now must be >= 0.1.6
-
-- EMQ has been modified, so that the representation function "classify" now only provides posterior
-    probabilities and, if required, these are recalibrated (e.g., by "bcts") during the aggregation function.
-    - A new parameter "on_calib_error" is passed to the constructor, which informs of the policy to follow
-        in case the abstention's calibration functions failed (which happens sometimes). Options include:
-            - 'raise': raises a RuntimeException (default)
-            - 'backup': reruns by silently avoiding calibration
-    - Parameter "recalib" has been renamed "calib"
-
-- Added aggregative bootstrap for deriving confidence regions (confidence intervals, ellipses in the simplex, or
-    ellipses in the CLR space). This method is efficient as it leverages the two-phases of the aggregative quantifiers.
-    This method applies resampling only to the aggregation phase, thus avoiding to train many quantifiers, or
-    classify multiple times the instances of a sample. See:
-    - quapy/method/confidence.py (new)
-    - the new example no. 16.confidence_regions.py
-
-- BayesianCC moved to confidence.py, where methods having to do with confidence intervals belong.
-
-- Improved documentation of qp.plot module.
-
-
 Change Log 0.1.9
 ----------------
 
-- Added LeQua 2024 datasets and normalized match distance to qp.error
-
-- Improved data loaders for UCI binary and UCI multiclass datasets (thanks to Lorenzo Volpi!); these datasets
-    can be loaded with standardised covariates (default)
-
-- Added a default classifier for aggregative quantifiers, which now can be instantiated without specifying
-    the classifier. The default classifier can be accessed in qp.environ['DEFAULT_CLS'] and is assigned to
-    sklearn.linear_model.LogisticRegression(max_iter=3000). If the classifier is not specified, then a clone
-    of said classifier is returned. E.g.:
-    > pacc = PACC()
-    is equivalent to:
-    > pacc = PACC(classifier=LogisticRegression(max_iter=3000))
-
-- Improved error loging in model selection. In v0.1.8 only Status.INVALID was reported; in v0.1.9 it is
-    now accompanied by a textual description of the error
-
-- The number of parallel workers can now be set via an environment variable by running, e.g.:
-    > N_JOBS=10 python3 your_script.py
-    which has the same effect as writing the following code at the beginning of your_script.py:
-    > import quapy as qp
-    > qp.environ["N_JOBS"] = 10
-
-- Some examples have been added to the ./examples/ dir, which now contains numbered examples from basics (0)
-    to advanced topics (higher numbers)
-
-- Moved the wiki documents to the ./docs/ folder so that they become editable via PR for the community
-
-- Added Composable methods from Mirko Bunse's qunfold library! (thanks to Mirko Bunse!)
-
-- Added Continuous Integration with GitHub Actions (thanks to Mirko Bunse!)
-
-- Added Bayesian CC method (thanks to Pawel Czyz!). The method is described in detail in the paper
-    Ziegler, Albert, and Paweł Czyż. "Bayesian Quantification with Black-Box Estimators."
-    arXiv preprint arXiv:2302.09159 (2023).
-
-- Removed binary UCI datasets {acute.a, acute.b, balance.2} from the list qp.data.datasets.UCI_BINARY_DATASETS
-    (the datasets are still loadable from the fetch_UCIBinaryLabelledCollection and fetch_UCIBinaryDataset
-    functions, though). The reason is that these datasets tend to yield results (for all methods) that are
-    one or two orders of magnitude greater than for other datasets, and this has a disproportionate impact in
-    methods average (I suspect there is something wrong in those datasets).
+<...>
 
 
 Change Log 0.1.8
@@ -168,7 +57,6 @@ Change Log 0.1.8
 
 - New API documentation template.
 
-
 Change Log 0.1.7
 ----------------
 
@@ -222,7 +110,7 @@ Change Log 0.1.7
 - hyperparameters yielding to inconsistent runs raise a ValueError exception, while hyperparameter combinations
     yielding to internal errors of surrogate functions are reported and skipped, without stopping the grid search.
 
-- DistributionMatching methods added. This is a general framework for distribution matching methods that caters for
+- DistributionMatching methods added. This is a general framework for distribution matching methods that catters for
     multiclass quantification. That is to say, one could get a multiclass variant of the (originally binary) HDy
     method aligned with the Firat's formulation.
 
@@ -247,3 +135,4 @@ Change Log 0.1.7
     any instance of BaseQuantifier), and a subclass of it called OneVsAllAggregative which implements the
     classify / aggregate interface. Both are instances of OneVsAll. There is a method getOneVsAll that returns the
     best instance based on the type of quantifier.
+

README.md

@@ -1,7 +1,5 @@
 # QuaPy
 
-## version 0.2.1
-
 QuaPy is an open source framework for quantification (a.k.a. supervised prevalence estimation, or learning to quantify)
 written in Python.
 
@@ -15,8 +13,9 @@ for facilitating the analysis and interpretation of the experimental results.
 
 ### Last updates:
 
-* Version 0.2.1 is released! major changes can be consulted [here](CHANGE_LOG.txt).
-* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/index.html)
+* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
+* A detailed documentation is now available [here](https://hlt-isti.github.io/QuaPy/)
+* The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
 
 ### Installation
 
@@ -26,7 +25,7 @@ pip install quapy
 
 ### Cite QuaPy
 
-If you find QuaPy useful (and we hope you will), please consider citing the original paper in your research:
+If you find QuaPy useful (and we hope you will), plese consider citing the original paper in your research:
 
 ```
 @inproceedings{moreo2021quapy,
@@ -47,18 +46,19 @@ of the test set.
 
 ```python
 import quapy as qp
+from sklearn.linear_model import LogisticRegression
 
-training, test = qp.datasets.fetch_UCIBinaryDataset("yeast").train_test
+dataset = qp.datasets.fetch_twitter('semeval16')
 
 # create an "Adjusted Classify & Count" quantifier
-model = qp.method.aggregative.ACC()
-Xtr, ytr = training.Xy
-model.fit(Xtr, ytr)
+model = qp.method.aggregative.ACC(LogisticRegression())
+model.fit(dataset.training)
 
-estim_prevalence = model.predict(test.X)
-true_prevalence = test.prevalence()
+estim_prevalence = model.quantify(dataset.test.instances)
+true_prevalence  = dataset.test.prevalence()
 
 error = qp.error.mae(true_prevalence, estim_prevalence)
+
 print(f'Mean Absolute Error (MAE)={error:.3f}')
 ```
 
@@ -69,21 +69,19 @@ class prevalence of the training set. For this reason, any quantification model
 should be tested across many samples, even ones characterized by class prevalence 
 values different or very different from those found in the training set.
 QuaPy implements sampling procedures and evaluation protocols that automate this workflow.
-See the [documentation](https://hlt-isti.github.io/QuaPy/manuals.html) for detailed examples.
+See the [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki) for detailed examples.
 
 ## Features
 
 * Implementation of many popular quantification methods (Classify-&-Count and its variants, Expectation Maximization,
 quantification methods based on structured output learning, HDy, QuaNet, quantification ensembles, among others).
-* Support for uncertainty quantification via bootstrap-based and Bayesian methods, including confidence intervals and simplex-aware confidence regions.
 * Versatile functionality for performing evaluation based on sampling generation protocols (e.g., APP, NPP, etc.).
 * Implementation of most commonly used evaluation metrics (e.g., AE, RAE, NAE, NRAE, SE, KLD, NKLD, etc.).
 * Datasets frequently used in quantification (textual and numeric), including:
     * 32 UCI Machine Learning datasets.
     * 11 Twitter quantification-by-sentiment datasets.
     * 3 product reviews quantification-by-sentiment datasets. 
-    * 4 tasks from LeQua 2022 competition and 4 tasks from LeQua 2024 competition
-    * IFCB for Plancton quantification 
+    * 4 tasks from LeQua competition (_new in v0.1.7!_)
 * Native support for binary and single-label multiclass quantification scenarios.
 * Model selection functionality that minimizes quantification-oriented loss functions.
 * Visualization tools for analysing the experimental results.
@@ -104,23 +102,19 @@ In case you want to contribute improvements to quapy, please generate pull reque
   
 ## Documentation
 
-Check out the [developer API documentation here](https://hlt-isti.github.io/QuaPy/index.html). 
+The [developer API documentation](https://hlt-isti.github.io/QuaPy/build/html/modules.html) is available [here](https://hlt-isti.github.io/QuaPy/build/html/index.html). 
 
-Check out the [Manuals](https://hlt-isti.github.io/QuaPy/manuals.html), in which many code examples
+Check out our [Wiki](https://github.com/HLT-ISTI/QuaPy/wiki), in which many examples
 are provided:
 
-* [Datasets](https://hlt-isti.github.io/QuaPy/manuals/datasets.html)
-* [Evaluation](https://hlt-isti.github.io/QuaPy/manuals/evaluation.html)
-* [Protocols](https://hlt-isti.github.io/QuaPy/manuals/protocols.html)
-* [Methods](https://hlt-isti.github.io/QuaPy/manuals/methods.html)
-* [SVMperf](https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html)
-* [Model Selection](https://hlt-isti.github.io/QuaPy/manuals/model-selection.html)
-* [Plotting](https://hlt-isti.github.io/QuaPy/manuals/plotting.html)
+* [Datasets](https://github.com/HLT-ISTI/QuaPy/wiki/Datasets)
+* [Evaluation](https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation)
+* [Protocols](https://github.com/HLT-ISTI/QuaPy/wiki/Protocols)
+* [Methods](https://github.com/HLT-ISTI/QuaPy/wiki/Methods)
+* [SVMperf](https://github.com/HLT-ISTI/QuaPy/wiki/ExplicitLossMinimization)
+* [Model Selection](https://github.com/HLT-ISTI/QuaPy/wiki/Model-Selection)
+* [Plotting](https://github.com/HLT-ISTI/QuaPy/wiki/Plotting)
 
 ## Acknowledgments:
 
-<img src="docs/source/SoBigData.png" alt="SoBigData++" width="250"/>
-
-This work has been supported by the QuaDaSh project 
-_"Finanziato dall’Unione europea---Next Generation EU, 
-Missione 4 Componente 2 CUP B53D23026250001"_.
+<img src="SoBigData.png" alt="SoBigData++" width="250"/>

Retrieval/classifier_kfcv_accuracy.py

@@ -0,0 +1,84 @@
+import itertools
+import os.path
+import pickle
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.svm import LinearSVC
+
+import quapy as qp
+from Retrieval.commons import RetrievedSamples, load_sample
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
+from quapy.data.base import LabelledCollection
+
+from os.path import join
+from tqdm import tqdm
+
+from result_table.src.table import Table
+
+"""
+ 
+"""
+
+data_home = 'data'
+
+datasets = ['continent', 'gender', 'years_category', 'relative_pageviews_category', 'num_sitelinks_category']
+
+param_grid = {'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]}
+
+classifiers = [
+    ('LR', LogisticRegression(max_iter=5000), param_grid),
+    ('SVM', LinearSVC(), param_grid)
+]
+
+def benchmark_name(class_name):
+    return class_name.replace('_', '\_')
+
+table = Table(name=f'accuracy', benchmarks=[benchmark_name(d) for d in datasets])
+table.format.show_std = False
+table.format.stat_test = None
+table.format.lower_is_better = False
+table.format.color = False
+table.format.remove_zero = True
+table.format.style = 'rules'
+
+for class_name, (cls_name, cls, grid) in itertools.product(datasets, classifiers):
+
+    train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json')  # <-------- fixed classifier
+
+    texts, labels = load_sample(train_data_path, class_name=class_name)
+
+    tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
+    Xtr = tfidf.fit_transform(texts)
+    print(f'Xtr shape={Xtr.shape}')
+
+    print('training classifier...', end='')
+    classifier = GridSearchCV(
+        cls,
+        param_grid=grid,
+        n_jobs=-1,
+        cv=5,
+        verbose=10
+    )
+    classifier.fit(Xtr, labels)
+    classifier_acc = classifier.best_score_
+    classifier_acc_per_fold = classifier.cv_results_['mean_test_score'][classifier.best_index_]
+
+    print(f'[done] best-params={classifier.best_params_} got {classifier_acc:.4f} score, per fold {classifier_acc_per_fold}')
+
+    table.add(benchmark=benchmark_name(class_name), method=cls_name, v=classifier_acc_per_fold)
+
+    Table.LatexPDF(f'./latex/classifier_Acc.pdf', tables=[table])
+
+
+
+
+
+
+
+

Retrieval/commons.py

@@ -0,0 +1,153 @@
+import pandas as pd
+import numpy as np
+from glob import glob
+from os.path import join
+
+import quapy.functional as F
+
+
+Ks = [50, 100, 500, 1000]
+
+CLASS_NAMES = ['continent', 'gender', 'years_category'] # ['relative_pageviews_category', 'num_sitelinks_category']:
+
+DATA_SIZES = ['10K', '50K', '100K', '500K', '1M', 'FULL']
+
+protected_group = {
+    'gender': 'Female',
+    'continent': 'Africa',
+    'years_category': 'Pre-1900s',
+}
+
+
+def load_sample(path, class_name):
+    """
+    Loads a sample json as a dataframe and returns text and labels for
+    the given class_name
+
+    :param path: path to a json file
+    :param class_name: string representing the target class
+    :return: texts, labels for class_name
+    """
+    df = pd.read_json(path)
+    text = df.text.values
+    labels = df[class_name].values
+    return text, labels
+
+
+def binarize_labels(labels, positive_class=None):
+    if positive_class is not None:
+        protected_labels = labels==positive_class
+        labels[protected_labels] = 1
+        labels[~protected_labels] = 0
+        labels = labels.astype(int)
+    return labels
+
+
+class RetrievedSamples:
+    def __init__(self,
+                 class_home: str,
+                 test_rankings_path: str,
+                 test_query_prevs_path: str,
+                 vectorizer,
+                 class_name,
+                 positive_class=None,
+                 classes=None,
+                 ):
+        self.class_home = class_home
+        self.test_rankings_df = pd.read_json(test_rankings_path)
+        self.test_query_prevs_df = pd.read_json(test_query_prevs_path)
+        self.vectorizer = vectorizer
+        self.class_name = class_name
+        self.positive_class = positive_class
+        self.classes = classes
+
+    def get_text_label_score(self, df, filter_rank=1000):
+        df = df[df['rank']<filter_rank]
+
+        class_name = self.class_name
+        vectorizer = self.vectorizer
+        filter_classes = self.classes
+
+        text = df.text.values
+        labels = df[class_name].values
+        rel_score = df.score.values
+
+        labels = binarize_labels(labels, self.positive_class)
+
+        if filter_classes is not None:
+            idx = np.isin(labels, filter_classes)
+            text = text[idx]
+            labels = labels[idx]
+            rel_score = rel_score[idx]
+
+        if vectorizer is not None:
+            text = vectorizer.transform(text)
+
+        order = np.argsort(-rel_score)
+        return text[order], labels[order], rel_score[order]
+
+    def __call__(self):
+        tests_df = self.test_rankings_df
+        class_name = self.class_name
+
+        for file in self._list_queries():
+
+            # loads the training sample
+            train_df = pd.read_json(file)
+            if len(train_df) == 0:
+                print('empty dataframe: ', file)
+            else:
+                Xtr, ytr, score_tr = self.get_text_label_score(train_df)
+
+                # loads the test sample
+                query_id = self._get_query_id_from_path(file)
+                sel_df = tests_df[tests_df.qid == query_id]
+                Xte, yte, score_te = self.get_text_label_score(sel_df)
+
+                # gets the prevalence of all judged relevant documents for the query
+                df = self.test_query_prevs_df
+                q_rel_prevs = df.loc[df.id == query_id][class_name+'_proportions'].values[0]
+
+                if self.positive_class is not None:
+                    if self.positive_class not in q_rel_prevs:
+                        print(f'positive class {self.positive_class} not found in the query; skipping')
+                        continue
+                    q_rel_prevs = F.as_binary_prevalence(q_rel_prevs[self.positive_class])
+                else:
+                    q_rel_prevs = np.asarray([q_rel_prevs.get(class_i, 0.) for class_i in self.classes])
+
+                yield (Xtr, ytr, score_tr), (Xte, yte, score_te), q_rel_prevs
+
+    def _list_queries(self):
+        return sorted(glob(join(self.class_home, 'training_Query*200SPLIT.json')))
+
+    # def _get_test_sample(self, query_id, max_lines=-1):
+    #     df = self.test_rankings_df
+    #     sel_df = df[df.qid==int(query_id)]
+    #     return get_text_label_score(sel_df)
+        # texts = sel_df.text.values
+        # try:
+        #     labels = sel_df[self.class_name].values
+        # except KeyError as e:
+        #     print(f'error: key {self.class_name} not found in test rankings')
+        #     raise e
+        # if max_lines > 0 and len(texts) > max_lines:
+        #     ranks = sel_df.rank.values
+        #     idx = np.argsort(ranks)[:max_lines]
+        #     texts = np.asarray(texts)[idx]
+        #     labels = np.asarray(labels)[idx]
+        # return texts, labels
+
+    def total(self):
+        return len(self._list_queries())
+
+    def _get_query_id_from_path(self, path):
+        prefix = 'training_Query-'
+        posfix = 'Sample-200SPLIT'
+        qid = path
+        qid = qid[:qid.index(posfix)]
+        qid = qid[qid.index(prefix) + len(prefix):]
+        qid = int(qid)
+        return qid
+
+

Retrieval/deprecated_code/fifth.py

@@ -0,0 +1,182 @@
+from collections import defaultdict
+
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.svm import LinearSVC
+
+import quapy as qp
+import quapy.functional as F
+from Retrieval.commons import RetrievedSamples, load_txt_sample, load_json_sample
+from Retrieval.tabular import Table
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
+from quapy.protocol import AbstractProtocol
+from quapy.data.base import LabelledCollection
+
+from glob import glob
+from os.path import join
+from tqdm import tqdm
+
+"""
+In this fifth experiment, we have pairs of (Li,Ui) with Li a training set and Ui a test set as 
+in the fourth experiment, and the fairness group are defined upon geographic info as in the fourth case.
+As in the fourth, the data Li and Ui have been drawn by retrieving query-related documents from
+a pool of the same size. Unlike the fourth experiment, here the training queries are
+
+Por ahora 1000 en tr y 100 en test
+Parece que ahora hay muy poco shift  
+"""
+
+
+def cls(classifier_trained=None):
+    if classifier_trained is None:
+        # return LinearSVC()
+        return LogisticRegression()
+    else:
+        return classifier_trained
+
+
+def methods(classifier_trained=None):
+    yield ('CC', ClassifyAndCount(cls(classifier_trained)))
+    yield ('PCC', PCC(cls(classifier_trained)))
+    yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
+    yield ('PACC', PACC(cls(classifier_trained), val_split=5, n_jobs=-1))
+    yield ('EMQ', EMQ(cls(classifier_trained), exact_train_prev=True))
+    yield ('EMQh', EMQ(cls(classifier_trained), exact_train_prev=False))
+    # yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
+    # yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
+    # yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
+    # yield ('EMQ-VS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='vs'))
+    # yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
+    # yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
+    # yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
+    # yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
+    # yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
+    # yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
+    yield ('KDE07', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.07))
+    # yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
+    yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
+
+
+def train_classifier():
+    tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
+    training = LabelledCollection.load(train_path, loader_func=load_json_sample, class_name=CLASS_NAME)
+
+    if REDUCE_TR > 0 and len(training) > REDUCE_TR:
+        print('Reducing the number of documents in the training to', REDUCE_TR)
+        training = training.sampling(REDUCE_TR, *training.prevalence())
+
+    Xtr, ytr = training.Xy
+    Xtr = tfidf.fit_transform(Xtr)
+    print('L orig shape = ', Xtr.shape)
+
+    training = LabelledCollection(Xtr, ytr)
+
+    print('training classifier')
+    classifier_trained = LogisticRegression()
+    classifier_trained = GridSearchCV(classifier_trained,
+                                      param_grid={'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]},
+                                      n_jobs=-1, cv=5)
+    classifier_trained.fit(Xtr, ytr)
+    classifier_trained = classifier_trained.best_estimator_
+    trained = True
+    print('[Done!]')
+
+    classes = training.classes_
+
+    print('training classes:', classes)
+    print('training prevalence:', training.prevalence())
+
+    return tfidf, classifier_trained
+
+
+def reduceAtK(data: LabelledCollection, k):
+    X, y = data.Xy
+    X = X[:k]
+    y = y[:k]
+    return LabelledCollection(X, y, classes=data.classes_)
+
+
+RANK_AT_K = -1
+REDUCE_TR = 50000
+qp.environ['SAMPLE_SIZE'] = RANK_AT_K
+
+
+def scape_latex(string):
+    return string.replace('_', '\_')
+
+
+Ks = [10, 50, 100, 250, 500, 1000, 2000]
+# Ks = [500]
+
+for CLASS_NAME in ['gender_category'] : #'years_category']: #['continent', 'first_letter_category']: #, 'gender', 'gender_category', 'occupations', 'source_countries', 'source_subcont_regions', 'years_category', 'relative_pageviews_category']:
+
+    data_path = './' + CLASS_NAME
+
+    if CLASS_NAME in ['years_category', 'continent', 'gender_category']:
+        train_path = join(data_path, 'train500PerGroup.json')
+    else:
+        train_path = join(data_path, 'train3000samples.json')
+
+    tfidf, classifier_trained = qp.util.pickled_resource(f'classifier_{CLASS_NAME}.pkl', train_classifier)
+    trained=True
+
+    experiment_prot = RetrievedSamples(data_path,
+                               load_fn=load_json_sample,
+                               vectorizer=tfidf,
+                               max_train_lines=None,
+                               max_test_lines=RANK_AT_K, classes=classifier_trained.classes_, class_name=CLASS_NAME)
+
+    method_names = [name for name, *other in methods()]
+    benchmarks = [f'{scape_latex(CLASS_NAME)}@{k}' for k in Ks]
+    table_mae = Table(benchmarks, method_names, color_mode='global')
+    table_mrae = Table(benchmarks, method_names, color_mode='global')
+
+    for method_name, quantifier in methods(classifier_trained):
+        # print('Starting with method=', method_name)
+
+        mae_errors = {k:[] for k in Ks}
+        mrae_errors = {k:[] for k in Ks}
+
+        pbar = tqdm(experiment_prot(), total=49)
+        for train, test in pbar:
+            if train is not None:
+                try:
+                    if trained and method_name!='MLPE':
+                        quantifier.fit(train, val_split=train, fit_classifier=False)
+                    else:
+                        quantifier.fit(train)
+
+                    for k in Ks:
+                        test_k = reduceAtK(test, k)
+                        estim_prev = quantifier.quantify(test_k.instances)
+
+                        mae_errors[k].append(qp.error.mae(test_k.prevalence(), estim_prev))
+                        mrae_errors[k].append(qp.error.mrae(test_k.prevalence(), estim_prev, eps=(1./(2*k))))
+
+                except Exception as e:
+                    print(f'wow, something happened here! skipping; {e}')
+            else:
+                print('skipping one!')
+
+            # pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
+            pbar.set_description(f'{method_name}')
+
+        for k in Ks:
+
+            table_mae.add(benchmark=f'{scape_latex(CLASS_NAME)}@{k}', method=method_name, values=mae_errors[k])
+            table_mrae.add(benchmark=f'{scape_latex(CLASS_NAME)}@{k}', method=method_name, values=mrae_errors[k])
+
+    table_mae.latexPDF('./latex', f'table_{CLASS_NAME}_mae.tex')
+    table_mrae.latexPDF('./latex', f'table_{CLASS_NAME}_mrae.tex')
+
+
+
+
+
+
+
+

Retrieval/deprecated_code/fourth.py

@@ -0,0 +1,161 @@
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.svm import LinearSVC
+
+import quapy as qp
+import quapy.functional as F
+from Retrieval.commons import RetrievedSamples, load_txt_sample
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
+from quapy.protocol import AbstractProtocol
+from quapy.data.base import LabelledCollection
+
+from glob import glob
+from os.path import join
+from tqdm import tqdm
+
+"""
+In this fourth experiment, we have pairs of (Li,Ui) with Li a training set and Ui a test set as 
+in the third experiment, and the fairness group are defined upon geographic info as in the third case.
+The difference here is that the data Li and Ui have been drawn by retrieving query-related documents from
+a pool of the same size.
+
+Por ahora 1000 en tr y 100 en test
+Parece que ahora hay muy poco shift  
+"""
+
+def cls(classifier_trained=None):
+    if classifier_trained is None:
+        # return LinearSVC()
+        return LogisticRegression()
+    else:
+        return classifier_trained
+
+
+def methods(classifier_trained=None):
+    yield ('CC', ClassifyAndCount(cls(classifier_trained)))
+    yield ('PACC', PACC(cls(classifier_trained), val_split=5, n_jobs=-1))
+    yield ('EMQ', EMQ(cls(classifier_trained), exact_train_prev=True))
+    yield ('EMQh', EMQ(cls(classifier_trained), exact_train_prev=False))
+    yield ('EMQ-BCTS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='bcts'))
+    yield ('EMQ-TS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='ts'))
+    yield ('EMQ-NBVS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='nbvs'))
+    # yield ('EMQ-VS', EMQ(cls(classifier_trained), exact_train_prev=False, recalib='vs'))
+    yield ('PCC', PCC(cls(classifier_trained)))
+    yield ('ACC', ACC(cls(classifier_trained), val_split=5, n_jobs=-1))
+    yield ('KDE001', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.001))
+    yield ('KDE005', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.005)) # <-- wow!
+    yield ('KDE01', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.01))
+    yield ('KDE02', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.02))
+    yield ('KDE03', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.03))
+    yield ('KDE05', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.05))
+    yield ('KDE07', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.07))
+    yield ('KDE10', KDEyML(cls(classifier_trained), val_split=5, n_jobs=-1, bandwidth=0.10))
+    yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
+
+
+def train_classifier():
+    tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
+    training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
+
+    if REDUCE_TR > 0:
+        print('Reducing the number of documents in the training to', REDUCE_TR)
+        training = training.sampling(REDUCE_TR, *training.prevalence())
+
+    Xtr, ytr = training.Xy
+    Xtr = tfidf.fit_transform(Xtr)
+    print('L orig shape = ', Xtr.shape)
+
+    training = LabelledCollection(Xtr, ytr)
+
+    print('training classifier')
+    classifier_trained = LogisticRegression()
+    classifier_trained = GridSearchCV(classifier_trained,
+                                      param_grid={'C': np.logspace(-3, 3, 7), 'class_weight': ['balanced', None]},
+                                      n_jobs=-1, cv=5)
+    classifier_trained.fit(Xtr, ytr)
+    classifier_trained = classifier_trained.best_estimator_
+    trained = True
+    print('[Done!]')
+
+    classes = training.classes_
+
+    print('training classes:', classes)
+    print('training prevalence:', training.prevalence())
+
+    return tfidf, classifier_trained
+
+
+
+RANK_AT_K = 1000
+REDUCE_TR = 50000
+qp.environ['SAMPLE_SIZE'] = RANK_AT_K
+
+data_path = './50_50_split_trec'
+train_path = join(data_path, 'train_50_50_continent.txt')
+
+tfidf, classifier_trained = qp.util.pickled_resource('classifier.pkl', train_classifier)
+trained=True
+
+experiment_prot = RetrievedSamples(data_path,
+                           load_fn=load_txt_sample,
+                           vectorizer=tfidf,
+                           max_train_lines=None,
+                           max_test_lines=RANK_AT_K, classes=classifier_trained.classes_)
+
+result_mae_dict = {}
+result_mrae_dict = {}
+for method_name, quantifier in methods(classifier_trained):
+    # print('Starting with method=', method_name)
+
+    mae_errors = []
+    mrae_errors = []
+    pbar = tqdm(experiment_prot(), total=49)
+    for train, test in pbar:
+        if train is not None:
+            try:
+
+                # print(train.prevalence())
+                # print(test.prevalence())
+                if trained and method_name!='MLPE':
+                    quantifier.fit(train, val_split=train, fit_classifier=False)
+                else:
+                    quantifier.fit(train)
+                estim_prev = quantifier.quantify(test.instances)
+
+                mae = qp.error.mae(test.prevalence(), estim_prev)
+                mae_errors.append(mae)
+
+                mrae = qp.error.mrae(test.prevalence(), estim_prev)
+                mrae_errors.append(mrae)
+
+                # print()
+                # print('Training prevalence:', F.strprev(train.prevalence()), 'shape', train.X.shape)
+                # print('Test prevalence:', F.strprev(test.prevalence()), 'shape', test.X.shape)
+                # print('Estim prevalence:', F.strprev(estim_prev))
+
+            except Exception as e:
+                print(f'wow, something happened here! skipping; {e}')
+        else:
+            print('skipping one!')
+
+        pbar.set_description(f'{method_name}\tmae={np.mean(mae_errors):.4f}\tmrae={np.mean(mrae_errors):.4f}')
+    print()
+    result_mae_dict[method_name] = np.mean(mae_errors)
+    result_mrae_dict[method_name] = np.mean(mrae_errors)
+
+print('Results\n'+('-'*100))
+for method_name in result_mae_dict.keys():
+    MAE = result_mae_dict[method_name]
+    MRAE = result_mrae_dict[method_name]
+    print(f'{method_name}\t{MAE=:.5f}\t{MRAE=:.5f}')
+
+
+
+
+
+
+

Retrieval/deprecated_code/preliminary_.py

@@ -0,0 +1,98 @@
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+import quapy as qp
+import quapy.functional as F
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC
+from quapy.protocol import AbstractProtocol
+from quapy.data.base import LabelledCollection
+
+from glob import glob
+from os.path import join
+
+"""
+This was the very first experiment. 1 big training set and many test rankings produced according to some queries.
+The quantification methods did not seem to work. The more sophisticated the method is, the worse it performed.
+This is a clear indication that the PPS assumptions do not hold.
+Actually, while the training set could be some iid sample from a distribution L and every test set
+is a iid sample from a distribution U, it is pretty clear that P(X|Y) is different, since the test set
+are biased towards a query term whereas the training set is not.  
+"""
+
+def methods():
+    yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
+    yield ('CC', ClassifyAndCount(LogisticRegression(n_jobs=-1)))
+    yield ('ACC', ACC(LogisticRegression(n_jobs=-1)))
+    yield ('PCC', PCC(LogisticRegression(n_jobs=-1)))
+    yield ('PACC', PACC(LogisticRegression(n_jobs=-1)))
+    yield ('EMQ', EMQ(LogisticRegression(n_jobs=-1)))
+
+
+def load_txt_sample(path, verbose=False):
+    if verbose:
+        print(f'loading {path}...', end='')
+    df = pd.read_csv(path, sep='\t')
+    if verbose:
+        print('[done]')
+    X = df['text']
+    y = df['first_letter_category']
+
+    return X, y
+
+class RetrievedSamples(AbstractProtocol):
+
+    def __init__(self, path_dir: str, load_fn, vectorizer, classes):
+        self.path_dir = path_dir
+        self.load_fn = load_fn
+        self.vectorizer = vectorizer
+        self.classes = classes
+
+    def __call__(self):
+        for file in glob(join(self.path_dir, 'test_data_*.txt')):
+            X, y = self.load_fn(file)
+            if len(X)!=qp.environ['SAMPLE_SIZE']:
+                print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
+            # assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'
+            X = self.vectorizer.transform(X)
+            sample = LabelledCollection(X, y, classes=self.classes)
+            yield sample.Xp
+
+
+qp.environ['SAMPLE_SIZE']=100
+
+data_path = './data'
+train_path = join(data_path, 'train_data.txt')
+
+
+tfidf = TfidfVectorizer(sublinear_tf=True, min_df=5)
+
+training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True)
+
+# training = training.sampling(1000)
+
+Xtr, ytr = training.Xy
+Xtr = tfidf.fit_transform(Xtr)
+print('Xtr shape = ', Xtr.shape)
+
+training = LabelledCollection(Xtr, ytr)
+classes = training.classes_
+
+test_prot = RetrievedSamples(data_path, load_fn=load_txt_sample, vectorizer=tfidf, classes=classes)
+
+print('Training prevalence:', F.strprev(training.prevalence()))
+for X, p in test_prot():
+    print('Test prevalence:', F.strprev(p))
+
+for method_name, quantifier in methods():
+    print('training ', method_name)
+    quantifier.fit(training)
+    print('[done]')
+
+    report = qp.evaluation.evaluation_report(quantifier, test_prot, error_metrics=['mae', 'mrae'], verbose=True)
+
+    print(report.mean())
+
+
+

Retrieval/deprecated_code/second.py

@@ -0,0 +1,131 @@
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+import quapy as qp
+import quapy.functional as F
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC
+from quapy.protocol import AbstractProtocol
+from quapy.data.base import LabelledCollection
+
+from glob import glob
+from os.path import join
+from tqdm import tqdm
+
+"""
+In this second experiment, we have pairs of (Li,Ui) with Li a training set and Ui a test set.
+Both elements in the pair are *retrieved according to the same query*. This is a way to impose
+the same type of bias that was present in the test, to the training set. Let's see...  
+"""
+
+def methods():
+    yield ('PACC', PACC(LogisticRegression(), val_split=5, n_jobs=-1))
+    yield ('CC', ClassifyAndCount(LogisticRegression()))
+    yield ('EMQ', EMQ(LogisticRegression()))
+    yield ('PCC', PCC(LogisticRegression()))
+    yield ('ACC', ACC(LogisticRegression(), val_split=5, n_jobs=-1))
+    yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
+
+
+def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
+    if verbose:
+        print(f'loading {path}...', end='')
+    df = pd.read_csv(path, sep='\t')
+    if verbose:
+        print('[done]')
+    X = df['text'].values
+    y = df['first_letter_category'].values
+
+    if parse_columns:
+        rank = df['rank'].values
+        scores = df['score'].values
+        order = np.argsort(rank)
+        X = X[order]
+        y = y[order]
+        rank = rank[order]
+        scores = scores[order]
+
+    if max_lines is not None:
+        X = X[:max_lines]
+        y = y[:max_lines]
+
+    return X, y
+
+
+class RetrievedSamples(AbstractProtocol):
+
+    def __init__(self, path_dir: str, load_fn, vectorizer, classes, max_train_lines=None, max_test_lines=None):
+        self.path_dir = path_dir
+        self.load_fn = load_fn
+        self.vectorizer = vectorizer
+        self.classes = classes
+        self.max_train_lines = max_train_lines
+        self.max_test_lines = max_test_lines
+
+    def __call__(self):
+        for file in glob(join(self.path_dir, 'test_rankings_*.txt')):
+
+            X, y = self.load_fn(file.replace('test_', 'training_'), parse_columns=True, max_lines=self.max_train_lines)
+            X = self.vectorizer.transform(X)
+            train_sample = LabelledCollection(X, y, classes=self.classes)
+
+            X, y = self.load_fn(file, parse_columns=True, max_lines=self.max_test_lines)
+            if len(X)!=qp.environ['SAMPLE_SIZE']:
+                print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
+            # assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'
+            X = self.vectorizer.transform(X)
+            test_sample = LabelledCollection(X, y, classes=self.classes)
+
+            yield train_sample, test_sample
+
+
+RANK_AT_K = 500
+REDUCE_TR = 50000
+qp.environ['SAMPLE_SIZE'] = RANK_AT_K
+
+data_path = './newCollection'
+train_path = join(data_path, 'train_data.txt')
+
+tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
+
+training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
+if REDUCE_TR>0:
+    print('Reducing the number of documents in the training to', REDUCE_TR)
+    training = training.sampling(REDUCE_TR)
+
+Xtr, ytr = training.Xy
+Xtr = tfidf.fit_transform(Xtr)
+print('L orig shape = ', Xtr.shape)
+
+training = LabelledCollection(Xtr, ytr)
+classes = training.classes_
+
+experiment_prot = RetrievedSamples(data_path,
+                                   load_fn=load_txt_sample,
+                                   vectorizer=tfidf,
+                                   classes=classes,
+                                   max_train_lines=RANK_AT_K,
+                                   max_test_lines=RANK_AT_K)
+
+for method_name, quantifier in methods():
+    print('Starting with method=', method_name)
+
+    errors = []
+    pbar = tqdm(experiment_prot(), total=49)
+    for train, test in pbar:
+        # print('Training prevalence:', F.strprev(training.prevalence()), 'shape', train.X.shape)
+        # print('Test prevalence:', F.strprev(test.prevalence()), 'shape', test.X.shape)
+
+        quantifier.fit(train)
+        estim_prev = quantifier.quantify(test.instances)
+        mae = qp.error.mae(test.prevalence(), estim_prev)
+        errors.append(mae)
+
+        pbar.set_description(f'mae={np.mean(errors):.4f}')
+    print()
+
+
+
+

Retrieval/deprecated_code/third.py

@@ -0,0 +1,155 @@
+import numpy as np
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+
+import quapy as qp
+import quapy.functional as F
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC
+from quapy.protocol import AbstractProtocol
+from quapy.data.base import LabelledCollection
+
+from glob import glob
+from os.path import join
+from tqdm import tqdm
+
+"""
+In this third experiment, we have pairs of (Li,Ui) with Li a training set and Ui a test set as 
+in the second experiment, but in this case the fairness group are defined upon geographic info.  
+"""
+
+def methods():
+    yield ('CC', ClassifyAndCount(LogisticRegression()))
+    yield ('PACC', PACC(LogisticRegression(), val_split=5, n_jobs=-1))
+    yield ('EMQ', EMQ(LogisticRegression()))
+    yield ('PCC', PCC(LogisticRegression()))
+    yield ('ACC', ACC(LogisticRegression(), val_split=5, n_jobs=-1))
+    yield ('MLPE', MaximumLikelihoodPrevalenceEstimation())
+
+
+def load_txt_sample(path, parse_columns, verbose=False, max_lines=None):
+    # print('reading', path)
+    if verbose:
+        print(f'loading {path}...', end='')
+    df = pd.read_csv(path, sep='\t')
+    if verbose:
+        print('[done]')
+    X = df['text'].values
+    y = df['continent'].values
+
+    if parse_columns:
+        rank = df['rank'].values
+        scores = df['score'].values
+        rank = rank[y != 'Antarctica']
+        scores = scores[y != 'Antarctica']
+
+    X = X[y!='Antarctica']
+    y = y[y!='Antarctica']
+
+    if parse_columns:
+        order = np.argsort(rank)
+        X = X[order]
+        y = y[order]
+        rank = rank[order]
+        scores = scores[order]
+
+    if max_lines is not None:
+        X = X[:max_lines]
+        y = y[:max_lines]
+
+    return X, y
+
+
+class RetrievedSamples(AbstractProtocol):
+
+    def __init__(self, path_dir: str, load_fn, vectorizer, max_train_lines=None, max_test_lines=None):
+        self.path_dir = path_dir
+        self.load_fn = load_fn
+        self.vectorizer = vectorizer
+        self.max_train_lines = max_train_lines
+        self.max_test_lines = max_test_lines
+
+    def __call__(self):
+        for file in glob(join(self.path_dir, 'test_rankings_*.txt')):
+
+            X, y = self.load_fn(file.replace('test_', 'training_'), parse_columns=True, max_lines=self.max_train_lines)
+            X = self.vectorizer.transform(X)
+            train_sample = LabelledCollection(X, y)
+
+            X, y = self.load_fn(file, parse_columns=True, max_lines=self.max_test_lines)
+            if len(X)!=qp.environ['SAMPLE_SIZE']:
+                print(f'[warning]: file {file} contains {len(X)} instances (expected: {qp.environ["SAMPLE_SIZE"]})')
+            # assert len(X) == qp.environ['SAMPLE_SIZE'], f'unexpected sample size for file {file}, found {len(X)}'
+            X = self.vectorizer.transform(X)
+            try:
+                test_sample = LabelledCollection(X, y, classes=train_sample.classes_)
+            except ValueError as e:
+                print(f'file {file} caused error {e}')
+                yield None, None
+
+            # print('train #classes:', train_sample.n_classes, train_sample.prevalence())
+            # print('test  #classes:', test_sample.n_classes, test_sample.prevalence())
+
+            yield train_sample, test_sample
+
+
+RANK_AT_K = 100
+REDUCE_TR = 50000
+qp.environ['SAMPLE_SIZE'] = RANK_AT_K
+
+data_path = './newCollectionGeo'
+train_path = join(data_path, 'train_data_continent.txt')
+
+tfidf = TfidfVectorizer(sublinear_tf=True, min_df=10)
+
+training = LabelledCollection.load(train_path, loader_func=load_txt_sample, verbose=True, parse_columns=False)
+
+if REDUCE_TR>0:
+    print('Reducing the number of documents in the training to', REDUCE_TR)
+    training = training.sampling(REDUCE_TR)
+
+Xtr, ytr = training.Xy
+Xtr = tfidf.fit_transform(Xtr)
+print('L orig shape = ', Xtr.shape)
+
+training = LabelledCollection(Xtr, ytr)
+classes = training.classes_
+
+print('training classes:', classes)
+print('training prevalence:', training.prevalence())
+
+experiment_prot = RetrievedSamples(data_path,
+                                   load_fn=load_txt_sample,
+                                   vectorizer=tfidf,
+                                   max_train_lines=None,
+                                   max_test_lines=RANK_AT_K)
+
+for method_name, quantifier in methods():
+    print('Starting with method=', method_name)
+
+    errors = []
+    pbar = tqdm(experiment_prot(), total=49)
+    for train, test in pbar:
+        if train is not None:
+            try:
+                # print('Training prevalence:', F.strprev(training.prevalence()), 'shape', train.X.shape)
+                # print('Test prevalence:', F.strprev(test.prevalence()), 'shape', test.X.shape)
+
+                # print(train.prevalence())
+                # print(test.prevalence())
+                quantifier.fit(train)
+                estim_prev = quantifier.quantify(test.instances)
+                mae = qp.error.mae(test.prevalence(), estim_prev)
+                errors.append(mae)
+            except Exception as e:
+                print(f'wow, something happened here! skipping; {e}')
+        else:
+            print('skipping one!')
+
+        pbar.set_description(f'mae={np.mean(errors):.4f}')
+    print()
+
+
+
+

Retrieval/experiments.py

@@ -0,0 +1,299 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV, cross_val_predict
+from sklearn.base import clone
+
+import quapy as qp
+from Retrieval.commons import *
+from Retrieval.methods import *
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
+from quapy.data.base import LabelledCollection
+
+from os.path import join
+from tqdm import tqdm
+
+from result_table.src.table import Table
+
+"""
+In this sixth experiment, we have a collection C of >6M documents.
+We split C in two equally-sized pools TrPool, TePool
+
+I have randomly split the collection in 50% train and 50% split. In each split we have approx. 3.25 million documents. 
+
+We have 5 categories we can evaluate over: Continent, Years_Category, Num_Site_Links, Relative Pageviews and Gender. 
+
+From the training set I have created smaller subsets for each category:
+100K, 500K, 1M and FULL (3.25M) 
+
+For each category and subset, I have created a training set called: "classifier_training.json". This is the "base" training set for the classifier. In this set we have 500 documents per group in a category. (For example: Male 500, Female 500, Unknown 500).  Let me know if you think we need more. 
+
+To "bias" the quantifier towards a query, I have executed the queries (97) on the different training sets and retrieved the 200 most relevant documents per group. 
+For example: (Male 200, Female 200, Unknown 200) 
+Sometimes this is infeasible, we should probably discuss this at some point. 
+
+ You can find the results for every query in a file named: 
+
+"training_Query-[QID]Sample-200SPLIT.json" 
+
+Test: 
+To evaluate our approach, I have executed the queries on the test split. You can find the results for all 97 queries up till k=1000 in this file. 
+ testRanking_Results.json 
+  
+"""
+
+
+def methods(classifier, class_name=None, binarize=False):
+
+    kde_param = {
+        'continent': 0.01,
+        'gender': 0.03,
+        'years_category':0.03
+    }
+
+    yield ('NaiveQuery', Naive())
+    yield ('CC', ClassifyAndCount(classifier))
+    yield ('PACC', PACC(classifier, val_split=5, n_jobs=-1))
+    yield ('KDEy-ML', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=kde_param.get(class_name, 0.01)))
+    if binarize:
+        yield ('M3b', M3rND_ModelB(classifier))
+        yield ('M3b+', M3rND_ModelB(classifier))
+        yield ('M3d', M3rND_ModelD(classifier))
+        yield ('M3d+', M3rND_ModelD(classifier))
+
+
+def train_classifier_fn(train_path):
+    """
+    Trains a classifier. To do so, it loads the training set, transforms it into a tfidf representation.
+    The classifier is Logistic Regression, with hyperparameters C (range [0.001, 0.01, ..., 1000]) and
+    class_weight (range {'balanced', None}) optimized via 5FCV.
+
+    :return: the tfidf-vectorizer and the classifier trained
+    """
+    texts, labels = load_sample(train_path, class_name=class_name)
+
+    if BINARIZE:
+        labels = binarize_labels(labels, positive_class=protected_group[class_name])
+
+    tfidf = TfidfVectorizer(sublinear_tf=True, min_df=3)
+    Xtr = tfidf.fit_transform(texts)
+    print(f'Xtr shape={Xtr.shape}')
+
+    print('training classifier...', end='')
+    classifier = LogisticRegression(max_iter=5000)
+    modsel = GridSearchCV(
+        classifier,
+        param_grid={'C': np.logspace(-4, 4, 9), 'class_weight': ['balanced', None]},
+        n_jobs=-1,
+        cv=5
+    )
+    modsel.fit(Xtr, labels)
+    classifier = modsel.best_estimator_
+    classifier_acc = modsel.best_score_
+    best_params = modsel.best_params_
+    print(f'[done] best-params={best_params} got {classifier_acc:.4f} score')
+
+    print('generating cross-val predictions for M3')
+    predictions = cross_val_predict(clone(classifier), Xtr, labels, cv=10, n_jobs=-1, verbose=10)
+    conf_matrix = confusion_matrix(labels, predictions, labels=classifier.classes_)
+
+    training = LabelledCollection(Xtr, labels)
+    print('training classes:', training.classes_)
+    print('training prevalence:', training.prevalence())
+
+    return tfidf, classifier, conf_matrix
+
+
+def reduceAtK(data: LabelledCollection, k):
+    # if k > len(data):
+    #     print(f'[warning] {k=}>{len(data)=}')
+    X, y = data.Xy
+    X = X[:k]
+    y = y[:k]
+    return LabelledCollection(X, y, classes=data.classes_)
+
+
+def benchmark_name(class_name, k=None):
+    scape_class_name = class_name.replace('_', '\_')
+    if k is None:
+        return scape_class_name
+    else:
+        return f'{scape_class_name}@{k}'
+
+
+def run_experiment():
+
+    results = {
+        'mae': {k: [] for k in Ks},
+        'mrae': {k: [] for k in Ks},
+        'rKL_error': [],
+        'rND_error': []
+    }
+
+    pbar = tqdm(experiment_prot(), total=experiment_prot.total())
+    for train, test, q_rel_prevs in pbar:
+        Xtr, ytr, score_tr = train
+        Xte, yte, score_te = test
+
+        train_col = LabelledCollection(Xtr, ytr, classes=classifier.classes_)
+
+        if not method_name.startswith('Naive') and not method_name.startswith('M3'):
+            method.fit(train_col, val_split=train_col, fit_classifier=False)
+        elif method_name == 'Naive':
+            method.fit(train_col)
+
+        test_col = LabelledCollection(Xte, yte, classes=classifier.classes_)
+        rKL_estim, rKL_true = [], []
+        rND_estim, rND_true = [], []
+        for k in Ks:
+            test_k = reduceAtK(test_col, k)
+            if method_name == 'NaiveQuery':
+                train_k = reduceAtK(train_col, k)
+                method.fit(train_k)
+
+            estim_prev = method.quantify(test_k.instances)
+
+            # epsilon value for prevalence smoothing
+            eps=(1. / (2. * k))
+
+            # error metrics
+            test_k_prev = test_k.prevalence()
+            mae = qp.error.mae(test_k_prev, estim_prev)
+            mrae = qp.error.mrae(test_k_prev, estim_prev, eps=eps)
+            rKL_at_k_estim = qp.error.kld(estim_prev, q_rel_prevs, eps=eps)
+            rKL_at_k_true  = qp.error.kld(test_k_prev, q_rel_prevs, eps=eps)
+
+            if BINARIZE:
+                # [1] is the index of the minority or historically disadvantaged group
+                rND_at_k_estim = np.abs(estim_prev[1] - q_rel_prevs[1])
+                rND_at_k_true = np.abs(test_k_prev[1] - q_rel_prevs[1])
+
+            # collect results
+            results['mae'][k].append(mae)
+            results['mrae'][k].append(mrae)
+            rKL_estim.append(rKL_at_k_estim)
+            rKL_true.append(rKL_at_k_true)
+            if BINARIZE:
+                rND_estim.append(rND_at_k_estim)
+                rND_true.append(rND_at_k_true)
+
+
+        # aggregate fairness metrics
+        def aggregate(rMs, Ks, Z=1):
+            return (1 / Z) * sum((1. / np.log2(k)) * v for v, k in zip(rMs, Ks))
+
+        Z = sum((1. / np.log2(k)) for k in Ks)
+        rKL_estim = aggregate(rKL_estim, Ks, Z)
+        rKL_true  = aggregate(rKL_true, Ks, Z)
+        rKL_error = np.abs(rKL_true-rKL_estim)
+        results['rKL_error'].append(rKL_error)
+
+        if BINARIZE:
+            rND_estim = aggregate(rND_estim, Ks, Z)
+            rND_true = aggregate(rND_true, Ks, Z)
+
+            if isinstance(method, AbstractM3rND):
+                if method_name.endswith('+'):
+                    # learns the correction parameters from the query-specific training data
+                    conf_matrix_ = method.get_confusion_matrix(*train_col.Xy)
+                else:
+                    # learns the correction parameters from the training data used to train the classifier
+                    conf_matrix_ = conf_matrix.copy()
+                rND_estim = method.fair_measure_correction(rND_estim, conf_matrix_)
+
+            rND_error = np.abs(rND_true - rND_estim)
+            results['rND_error'].append(rND_error)
+
+        pbar.set_description(f'{method_name}')
+
+    return results
+
+
+data_home = 'data'
+
+if __name__ == '__main__':
+
+    # final tables only contain the information for the data size 10K, each row is a class name and each colum
+    # the corresponding rND (for binary) or rKL (for multiclass) score
+    tables_RND, tables_DKL = [], []
+    tables_final = []
+    for class_mode in ['multiclass', 'binary']:
+        BINARIZE = (class_mode=='binary')
+        method_names = [name for name, *other in methods(None, binarize=BINARIZE)]
+
+        table_final = Table(name=f'rND' if BINARIZE else f'rKL', benchmarks=[benchmark_name(c) for c in CLASS_NAMES], methods=method_names)
+        table_final.format.mean_macro = False
+        tables_final.append(table_final)
+        for class_name in CLASS_NAMES:
+            tables_mae, tables_mrae = [], []
+
+            benchmarks_size =[benchmark_name(class_name, s) for s in DATA_SIZES]
+            table_DKL = Table(name=f'rKL-{class_name}', benchmarks=benchmarks_size, methods=method_names)
+            table_RND = Table(name=f'rND-{class_name}', benchmarks=benchmarks_size, methods=method_names)
+
+            for data_size in DATA_SIZES:
+                print(class_name, class_mode, data_size)
+                benchmarks_k = [benchmark_name(class_name, k) for k in Ks]
+                # table_mae = Table(name=f'{class_name}-{data_size}-mae', benchmarks=benchmarks_k, methods=method_names)
+                table_mrae = Table(name=f'{class_name}-{data_size}-mrae', benchmarks=benchmarks_k, methods=method_names)
+
+                # tables_mae.append(table_mae)
+                tables_mrae.append(table_mrae)
+
+                # sets all paths
+                class_home = join(data_home, class_name, data_size)
+                train_data_path = join(data_home, class_name, 'FULL', 'classifier_training.json') # <----- fixed classifier
+                classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}_{class_mode}.pkl')
+                test_rankings_path = join(data_home, 'testRanking_Results.json')
+                test_query_prevs_path = join(data_home, 'prevelance_vectors_judged_docs.json')
+                results_home = join('results', class_name, class_mode, data_size)
+                positive_class = protected_group[class_name] if BINARIZE else None
+
+                # instantiates the classifier (trains it the first time, loads it in the subsequent executions)
+                tfidf, classifier, conf_matrix \
+                    = qp.util.pickled_resource(classifier_path, train_classifier_fn, train_data_path)
+
+                experiment_prot = RetrievedSamples(
+                    class_home,
+                    test_rankings_path,
+                    test_query_prevs_path,
+                    vectorizer=tfidf,
+                    class_name=class_name,
+                    positive_class=positive_class,
+                    classes=classifier.classes_
+                )
+
+                for method_name, method in methods(classifier, class_name, BINARIZE):
+
+                    results_path = join(results_home, method_name + '.pkl')
+                    results = qp.util.pickled_resource(results_path, run_experiment)
+
+                    # compose the tables
+                    for k in Ks:
+                        # table_mae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mae'][k])
+                        table_mrae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mrae'][k])
+                    table_DKL.add(benchmark=benchmark_name(class_name, data_size), method=method_name, v=results['rKL_error'])
+                    if BINARIZE:
+                        table_RND.add(benchmark=benchmark_name(class_name, data_size), method=method_name, v=results['rND_error'])
+
+                    if data_size=='10K':
+                        value = results['rND_error'] if BINARIZE else results['rKL_error']
+                        table_final.add(benchmark=benchmark_name(class_name), method=method_name, v=value)
+
+            tables = ([table_RND] + tables_mrae) if BINARIZE else ([table_DKL] + tables_mrae)
+            Table.LatexPDF(f'./latex/{class_mode}/{class_name}.pdf', tables=tables)
+
+            if BINARIZE:
+                tables_RND.append(table_RND)
+            else:
+                tables_DKL.append(table_DKL)
+
+    Table.LatexPDF(f'./latex/global/main.pdf', tables=tables_RND+tables_DKL, dedicated_pages=False)
+    Table.LatexPDF(f'./latex/final/main.pdf', tables=tables_final, dedicated_pages=False)
+
+
+
+
+
+
+

Retrieval/kdey_bandwidth_selection_queries.py

@@ -0,0 +1,88 @@
+import os.path
+import pickle
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import GridSearchCV
+from sklearn.svm import LinearSVC
+
+import quapy as qp
+from Retrieval.commons import RetrievedSamples, load_sample
+from method.non_aggregative import MaximumLikelihoodPrevalenceEstimation as Naive
+from quapy.method.aggregative import ClassifyAndCount, EMQ, ACC, PCC, PACC, KDEyML
+from quapy.data.base import LabelledCollection
+from experiments import benchmark_name, reduceAtK, run_experiment
+
+from os.path import join
+from tqdm import tqdm
+
+from result_table.src.table import Table
+
+
+
+def methods(classifier):
+    for i, bandwidth in enumerate(np.linspace(0.01, 0.1, 10)):
+        yield (f'KDE{str(i).zfill(2)}', KDEyML(classifier, val_split=5, n_jobs=-1, bandwidth=bandwidth))
+
+
+if __name__ == '__main__':
+    data_home = 'data-modsel'
+
+    Ks = [5, 10, 25, 50, 75, 100, 250, 500, 750, 1000]
+
+    method_names = [m for m, *_ in methods(None)]
+
+    class_mode = 'multiclass'
+
+    dir_names={
+        'gender': '100K_GENDER_TREC21_QUERIES/100K-NEW-QUERIES',
+        'continent': '100K_CONT_TREC21_QUERIES/100K-NEW-QUERIES',
+        'years_category': '100K_YEARS_TREC21_QUERIES/100K-NEW-QUERIES'
+    }
+
+    for class_name in ['gender', 'continent', 'years_category']:
+
+        tables_mrae = []
+
+        benchmarks = [benchmark_name(class_name, k) for k in Ks]
+
+        for data_size in ['100K']:
+
+            table_mrae = Table(name=f'{class_name}-{data_size}-mrae', benchmarks=benchmarks, methods=method_names)
+            tables_mrae.append(table_mrae)
+
+            class_home = join(data_home, dir_names[class_name])
+            classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}_{class_mode}.pkl')
+            test_rankings_path = join(data_home, 'testRanking-TREC21-Queries_Results.json')
+            test_query_prevs_path = join('data', 'prevelance_vectors_judged_docs.json')
+            results_home = join('results', 'modsel', class_name, data_size)
+
+            tfidf, classifier, conf_matrix = pickle.load(open(classifier_path, 'rb'))
+
+            experiment_prot = RetrievedSamples(
+                class_home,
+                test_rankings_path,
+                test_query_prevs_path,
+                vectorizer=tfidf,
+                class_name=class_name,
+                classes=classifier.classes_
+            )
+            for method_name, quantifier in methods(classifier):
+
+                results_path = join(results_home, method_name + '.pkl')
+                results = qp.util.pickled_resource(results_path, run_experiment)
+
+                for k in Ks:
+                    table_mrae.add(benchmark=benchmark_name(class_name, k), method=method_name, v=results['mrae'][k])
+
+                Table.LatexPDF(f'./latex/modsel/{class_name}.pdf', tables=tables_mrae)
+
+
+
+
+
+
+

Retrieval/methods.py

@@ -0,0 +1,88 @@
+
+"""
+This file implements some of the methods presented in the FAccT'22 paper by
+Ghazimatin, Kleindessner, Russell, Abedjan, and Golebiowski,
+Measuring Fairness of Rankings under Noisy Sensitive Information.
+
+In particular, it implements two variants of a method relying on M3=rND:
+one in which the assumed graphical model is P(Â,A,S) = P(Â|A)*P(S|A) (called "b")
+and another in which the assumed graphical model is P(Â,A,S) = P(Â|A)*P(S|Â) (called "d")
+"""
+
+import numpy as np
+from abc import ABC, abstractmethod
+from sklearn.metrics import confusion_matrix
+
+from quapy.method.aggregative import CC
+
+
+class AbstractM3rND(ABC):
+    def __init__(self, classifier):
+        self.quantifier = CC(classifier)
+
+    def proxy_labels(self, instances):
+        return self.quantifier.classify(instances)
+
+    def quantify(self, instances):
+        return self.quantifier.quantify(instances)
+
+    @abstractmethod
+    def fair_measure_correction(self, rND_estim: float, conf_matrix: np.ndarray):
+        ...
+
+    def get_confusion_matrix(self, X, y, additive_smoothing=0.5):
+        """
+        Some confusion matrices may contain 0 values for certain classes, and this causes
+        instabilities in the correction. If requested, applies additive smoothing. Default
+        is adding half a count.
+
+        :param X: array-like with the covariates
+        :param y: array-like with the true labels
+        :param additive_smoothing: float, default 0.5
+        :return: the confusion matrix C with entries Cij=P(Y=i,Ŷ=j)
+        """
+        proxy_labels = self.proxy_labels(X)
+        true_labels = y
+        labels = self.quantifier.classes_
+        conf_matrix = confusion_matrix(true_labels, proxy_labels, labels=labels)
+        if additive_smoothing > 0:
+            conf_matrix = conf_matrix.astype(float) + additive_smoothing
+        return conf_matrix
+
+
+class M3rND_ModelB(AbstractM3rND):
+    def __init__(self, classifier):
+        super().__init__(classifier)
+
+    def fair_measure_correction(self, rND_estim: float, conf_matrix: np.ndarray):
+        # conf_matrix contains values Cij=P(Y=i,Ŷ=j)
+        # truecond_matrix contains values Cij=P(Ŷ=j|Y=i) (truecond stands for "conditioned on true labels")
+        truecond_matrix = conf_matrix / conf_matrix.sum(axis=1, keepdims=True)
+        p = truecond_matrix[0, 1]  # P(hat{A}=1|A=0)
+        q = truecond_matrix[1, 0]  # P(hat{A}=0|A=1)
+        den = (1 - p - q)
+        if den != 0:
+            corr = 1./den
+            rND_estim = rND_estim * corr
+        return rND_estim
+
+
+class M3rND_ModelD(AbstractM3rND):
+    def __init__(self, classifier):
+        super().__init__(classifier)
+
+    def fair_measure_correction(self, rND_estim: float, conf_matrix: np.ndarray):
+        # conf_matrix contains values Cij=P(Y=i,Ŷ=j)
+        # truecond_matrix contains values Cij=P(Ŷ=j|Y=i) (truecond stands for "conditioned on true labels")
+        truecond_matrix = conf_matrix / conf_matrix.sum(axis=1, keepdims=True)
+        prev_A = conf_matrix.sum(axis=1)
+        beta = prev_A[1]  # P(A)
+        p = truecond_matrix[0, 1]  # P(hat{A}=1|A=0)
+        q = truecond_matrix[1, 0]  # P(hat{A}=0|A=1)
+        x = (1 - q) * beta + p * (1 - beta)
+        y = q * beta + (1 - p) * (1 - beta)
+        if x != 0 and y != 0:
+            corr = ((((1 - q) * beta) / x) - (q * beta / y))
+            rND_estim = rND_estim * corr
+        return rND_estim
+

Retrieval/plot_mrae_xaxis_k.py

@@ -0,0 +1,124 @@
+import itertools
+import os.path
+import pickle
+import numpy as np
+from Retrieval.experiments import methods
+from Retrieval.commons import CLASS_NAMES, Ks, DATA_SIZES
+from os.path import join
+import matplotlib.pyplot as plt
+
+
+
+data_home = 'data'
+class_mode = 'multiclass'
+
+method_names = [name for name, *other in methods(None, 'continent')]
+
+all_results = {}
+
+class_name_label = {
+    'continent': 'Geographic Location',
+    'gender': 'Gender',
+    'years_category': 'Age of Topic'
+}
+
+
+# loads all MRAE results, and returns a dictionary containing the values, which is indexed by:
+# class_name -> data_size -> method_name -> k -> stat -> float
+# where stat is "mean", "std", "max"
+def load_all_results():
+
+    for class_name in CLASS_NAMES:
+
+        all_results[class_name] = {}
+
+        for data_size in DATA_SIZES:
+
+            all_results[class_name][data_size] = {}
+
+            results_home = join('results', class_name, class_mode, data_size)
+
+            all_results[class_name][data_size] = {}
+
+            for method_name in method_names:
+                results_path = join(results_home, method_name + '.pkl')
+                try:
+                    results = pickle.load(open(results_path, 'rb'))
+                except Exception as e:
+                    print(f'missing result {results}', e)
+
+                all_results[class_name][data_size][method_name] = {}
+                for k in Ks:
+                    all_results[class_name][data_size][method_name][k] = {}
+                    values = results['mrae']
+                    all_results[class_name][data_size][method_name][k]['mean'] = np.mean(values[k])
+                    all_results[class_name][data_size][method_name][k]['std'] = np.std(values[k])
+                    all_results[class_name][data_size][method_name][k]['max'] = np.max(values[k])
+
+    return all_results
+
+
+results = load_all_results()
+
+# generates the class-independent, size-independent plots for y-axis=MRAE in which:
+# - the x-axis displays the Ks
+
+for class_name in CLASS_NAMES:
+    for data_size in DATA_SIZES[:1]:
+
+        log = class_name=='gender'
+
+        fig, ax = plt.subplots()
+
+        max_means = []
+        markers = itertools.cycle(['o', 's', '^', 'D', 'v', '*', '+'])
+        for method_name in method_names:
+            # class_name -> data_size -> method_name -> k -> stat -> float
+            means = [
+                results[class_name][data_size][method_name][k]['mean'] for k in Ks
+            ]
+            stds = [
+                results[class_name][data_size][method_name][k]['std'] for k in Ks
+            ]
+            # max_mean = np.max([
+            #         results[class_name][data_size][method_name][k]['max'] for k in Ks
+            # ])
+            max_means.append(max(means))
+
+            means = np.asarray(means)
+            stds = np.asarray(stds)
+
+            method_name = method_name.replace('NaiveQuery', 'Naive@$k$')
+            method_name = method_name.replace('KDEy-ML', 'KDEy')
+            marker = next(markers)
+            line = ax.plot(Ks, means, 'o-', label=method_name, color=None, linewidth=3, markersize=10, marker=marker)
+            color = line[-1].get_color()
+            if log:
+                ax.set_yscale('log')
+            # ax.fill_between(Ks, means - stds, means + stds, alpha=0.3, color=color)
+
+        ax.grid(True, which='both', axis='y', color='gray', linestyle='--', linewidth=0.3)
+        ax.set_xlabel('k')
+        ax.set_ylabel('RAE' + (' (log scale)' if log else ''))
+        data_size_label = '$\mathcal{L}_{10\mathrm{K}}$'
+        ax.set_title(f'{class_name_label[class_name]} from {data_size_label}')
+        ax.set_ylim([0, max(max_means)*1.05])
+
+        if class_name == 'years_category':
+            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+        os.makedirs(f'plots/var_k/{class_name}', exist_ok=True)
+        plotpath = f'plots/var_k/{class_name}/{data_size}_mrae.pdf'
+        print(f'saving plot in {plotpath}')
+        plt.savefig(plotpath, bbox_inches='tight')
+
+
+
+
+
+
+
+
+
+
+

Retrieval/plot_mrae_xaxis_size.py

@@ -0,0 +1,88 @@
+import itertools
+import os.path
+from Retrieval.experiments import methods
+from Retrieval.commons import CLASS_NAMES, Ks, DATA_SIZES
+import matplotlib.pyplot as plt
+
+from Retrieval.plot_mrae_xaxis_k import load_all_results
+
+data_home = 'data'
+class_mode = 'multiclass'
+
+method_names = [name for name, *other in methods(None)]
+
+all_results = {}
+
+class_name_label = {
+    'continent': 'Geographic Location',
+    'gender': 'Gender',
+    'years_category': 'Age of Topic'
+}
+
+# loads all MRAE results, and returns a dictionary containing the values, which is indexed by:
+# class_name -> data_size -> method_name -> k -> stat -> float
+results = load_all_results()
+
+# generates the class-independent, size-independent plots for y-axis=MRAE in which:
+# - the x-axis displays the Ks
+
+# X_DATA_SIZES = [int(x.replace('K', '000').replace('M', '000000').replace('FULL', '3250000')) for x in DATA_SIZES]
+X_DATA_SIZES = [x.replace('FULL', '3.25M') for x in DATA_SIZES]
+
+for class_name in CLASS_NAMES:
+    for k in [100]: #Ks:
+
+        log = class_name=='gender'
+
+        fig, ax = plt.subplots()
+
+        max_means = []
+        markers = itertools.cycle(['o', 's', '^', 'D', 'v', '*', '+'])
+        for method_name in method_names:
+            # class_name -> data_size -> method_name -> k -> stat -> float
+            means = [
+                results[class_name][data_size][method_name][k]['mean'] for data_size in DATA_SIZES
+            ]
+            stds = [
+                results[class_name][data_size][method_name][k]['std'] for data_size in DATA_SIZES
+            ]
+            # max_mean = np.max([
+            #         results[class_name][data_size][method_name][k]['max'] for data_size in DATA_SIZE
+            # ])
+
+            max_means.append(max(means))
+
+            style = 'o-' if method_name != 'CC' else '--'
+            method_name = method_name.replace('NaiveQuery', 'Naive@$k$')
+            method_name = method_name.replace('KDEy-ML', 'KDEy')
+            marker=next(markers)
+            line = ax.plot(X_DATA_SIZES, means, style, label=method_name, color=None, linewidth=3, markersize=10, marker=marker)
+            color = line[-1].get_color()
+            if log:
+                ax.set_yscale('log')
+            # ax.fill_between(Ks, means - stds, means + stds, alpha=0.3, color=color)
+
+        ax.grid(True, which='both', axis='y', color='gray', linestyle='--', linewidth=0.3)
+        ax.set_xlabel('training pool size')
+        ax.set_ylabel('RAE' + (' (log scale)' if log else ''))
+        ax.set_title(f'{class_name_label[class_name]} at exposure {k=}')
+        ax.set_ylim([0, max(max_means)*1.05])
+
+        if class_name == 'years_category':
+            ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+        os.makedirs(f'plots/var_size/{class_name}', exist_ok=True)
+        plotpath = f'plots/var_size/{class_name}/{k}_mrae.pdf'
+        print(f'saving plot in {plotpath}')
+        plt.savefig(plotpath, bbox_inches='tight')
+
+
+
+
+
+
+
+
+
+
+

Retrieval/relscore_distribution.py

@@ -0,0 +1,93 @@
+import os.path
+import pickle
+from itertools import zip_longest
+from commons import RetrievedSamples, load_sample, DATA_SIZES
+from os.path import join
+from tqdm import tqdm
+import numpy as np
+import matplotlib.pyplot as plt
+
+"""
+Plots the distribution of (predicted) relevance score for the test samples and for the training samples wrt:
+- training pool size (10K, 50K, 100K, 500K, 1M, FULL)
+- rank  
+"""
+
+
+data_home = 'data'
+
+up_to = 250
+
+for class_name in ['continent']: # 'num_sitelinks_category', 'relative_pageviews_category', 'years_category', 'continent', 'gender']:
+    test_added = False
+    Mtrs, Mtes, source = [], [], []
+    for data_size in DATA_SIZES:
+
+        class_home = join(data_home, class_name, data_size)
+        classifier_path = join('classifiers', 'FULL', f'classifier_{class_name}.pkl')
+        test_rankings_path = join(data_home, 'testRanking_Results.json')
+        test_query_prevs_path = join(data_home, 'prevelance_vectors_judged_docs.json')
+
+        _, classifier = pickle.load(open(classifier_path, 'rb'))
+
+        experiment_prot = RetrievedSamples(
+            class_home,
+            test_rankings_path,
+            test_query_prevs_path,
+            vectorizer=None,
+            class_name=class_name,
+            classes=classifier.classes_
+        )
+
+        Mtr = []
+        Mte = []
+        pbar = tqdm(experiment_prot(), total=experiment_prot.total())
+        for train, test, *_ in pbar:
+            Xtr, ytr, score_tr = train
+            Xte, yte, score_te = test
+            if len(score_tr) >= up_to:
+                Mtr.append(score_tr)
+                Mte.append(score_te)
+
+        Mtrs.append(Mtr)
+        if not test_added:
+            Mtes.append(Mte)
+            test_added = True
+        source.append(data_size)
+
+    fig, ax = plt.subplots()
+    # train_source = ['train-'+s for s in source]
+    train_source = ['$L_{'+s.replace('FULL', '3.25M').replace('K','\mathrm{K}').replace('M','\mathrm{M}')+'}$' for s in source]
+    # Ms = list(zip(Mtrs, train_source))+list(zip(Mtes, ['test']))
+    Ms = list(zip(Mtrs, train_source)) + list(zip(Mtes, ['$U_{(3.25\mathrm{M})}$']))
+
+
+    for M, source in Ms:
+        M = np.asarray(list(zip_longest(*M, fillvalue=np.nan))).T
+
+        num_rep, num_docs = M.shape
+
+        mean_values = np.nanmean(M, axis=0)
+        n_filled = np.count_nonzero(~np.isnan(M), axis=0)
+        std_errors = np.nanstd(M, axis=0) / np.sqrt(n_filled)
+
+        line = ax.plot(range(num_docs), mean_values, '-', label=source, color=None)
+        color = line[-1].get_color()
+        ax.fill_between(range(num_docs), mean_values - std_errors, mean_values + std_errors, alpha=0.3, color=color)
+
+
+    ax.set_xlabel('rank ($k$)')
+    ax.set_ylabel('predicted relevance score')
+    ax.set_title(class_name.replace('continent', 'Geographic Location'))
+    ax.set_xlim((0,up_to))
+
+    ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+
+    # plt.show()
+    os.makedirs('plots', exist_ok=True)
+    plotpath = f'plots/{class_name}_rel_distrbution_2.pdf'
+    print(f'saving plot in {plotpath}')
+    plt.savefig(plotpath, bbox_inches='tight')
+
+
+

Retrieval/tmp.py

@@ -0,0 +1,16 @@
+import pandas as pd
+
+from os.path import join
+
+from quapy.data import LabelledCollection
+
+data_home = 'data'
+CLASS_NAME = 'continent'
+datasize = '100K'
+
+file_path = join(data_home, 'prevelance_vectors_judged_docs.json')
+
+df = pd.read_json(file_path)
+
+pd.set_option('display.max_columns', None)
+print(df)

TODO.txt

@@ -1,24 +1,95 @@
-Solve the warnings issue; right now there is a warning ignore in method/__init__.py:
+ensembles seem to be broken; they have an internal model selection which takes the parameters, but since quapy now
+    works with protocols it would need to know the validation set in order to pass something like
+    "protocol: APP(val, etc.)"
+sample_size should not be mandatory when qp.environ['SAMPLE_SIZE'] has been specified
+clean all the cumbersome methods that have to be implemented for new quantifiers (e.g., n_classes_ prop, etc.)
+make truly parallel the GridSearchQ
+make more examples in the "examples" directory
+merge with master, because I had to fix some problems with QuaNet due to an issue notified via GitHub!
+added cross_val_predict in qp.model_selection (i.e., a cross_val_predict for quantification) --would be nice to have
+    it parallelized
 
-Add 'platt' to calib options in EMQ?
+check the OneVsAll module(s)
 
-Allow n_prevpoints in APP to be specified by a user-defined grid?
+check the set_params de neural.py, because the separation of estimator__<param> is not implemented; see also
+    __check_params_colision
+
+HDy can be customized so that the number of bins is specified, instead of explored within the fit method
+
+Packaging:
+==========================================
+Document methods with paper references
+unit-tests
+clean wiki_examples!
+
+Refactor:
+==========================================
+Unify ThresholdOptimization methods, as an extension of PACC (and not ACC), the fit methods are almost identical and
+    use a prob classifier (take into account that PACC uses pcc internally, whereas the threshold methods use cc
+    instead). The fit method of ACC and PACC has a block for estimating the validation estimates that should be unified
+    as well...
+Refactor protocols. APP and NPP related functionalities are duplicated in functional, LabelledCollection, and evaluation
+
+
+New features:
+==========================================
+Add "measures for evaluating ordinal"?
+Add datasets for topic.
+Do we want to cover cross-lingual quantification natively in QuaPy, or does it make more sense as an application on top?
+
+Current issues:
+==========================================
+Revise the class structure of quantification methods and the methods they inherit... There is some confusion regarding
+    methods isbinary, isprobabilistic, and the like. The attribute "learner_" in aggregative quantifiers is also
+    confusing, since there is a getter and a setter.
+Remove the "deep" in get_params. There is no real compatibility with scikit-learn as for now.
+SVMperf-based learners do not remove temp files in __del__?
+In binary quantification (hp, kindle, imdb) we used F1 in the minority class (which in kindle and hp happens to be the
+negative class). This is not covered in this new implementation, in which the binary case is not treated as such, but as
+an instance of single-label with 2 labels. Check
+Add automatic reindex of class labels in LabelledCollection (currently, class indexes should be ordered and with no gaps)
+OVR I believe is currently tied to aggregative methods. We should provide a general interface also for general quantifiers
+Currently, being "binary" only adds one checker; we should figure out how to impose the check to be automatically performed
+Add random seed management to support replicability (see temp_seed in util.py).
+GridSearchQ is not trully parallelized. It only parallelizes on the predictions.
+In the context of a quantifier (e.g., QuaNet or CC), the parameters of the learner should be prefixed with "estimator__",
+    in QuaNet this is resolved with a __check_params_colision, but this should be improved. It might be cumbersome to
+    impose the "estimator__" prefix for, e.g., quantifiers like CC though... This should be changed everywhere...
+QuaNet needs refactoring. The base quantifiers ACC and PACC receive val_data with instances already transformed. This
+    issue is due to a bad design.
+
+Improvements:
+==========================================
+Explore the hyperparameter "number of bins" in HDy
+Rename EMQ to SLD ?
+Parallelize the kFCV in ACC and PACC?
+Parallelize model selection trainings
+We might want to think of (improving and) adding the class Tabular (it is defined and used on branch tweetsent). A more
+    recent version is in the project ql4facct. This class is meant to generate latex tables from results (highligting
+    best results, computing statistical tests, colouring cells, producing rankings, producing averages, etc.). Trying
+    to generate tables is typically a bad idea, but in this specific case we do have pretty good control of what an
+    experiment looks like. (Do we want to abstract experimental results? this could be useful not only for tables but
+    also for plots).
+Add proper logging system. Currently we use print
+It might be good to simplify the number of methods that have to be implemented for any new Quantifier. At the moment,
+    there are many functions like get_params, set_params, and, specially, @property classes_, which are cumbersome to
+    implement for quick experiments. A possible solution is to impose get_params and set_params only in cases in which
+    the model extends some "ModelSelectable" interface only. The classes_ should have a default implementation.
+
+Checks:
+==========================================
+How many times is the system of equations for ACC and PACC not solved? How many times is it clipped? Do they sum up
+    to one always?
+Re-check how hyperparameters from the quantifier and hyperparameters from the classifier (in aggregative quantifiers)
+    is handled. In scikit-learn the hyperparameters from a wrapper method are indicated directly whereas the hyperparams
+    from the internal learner are prefixed with "estimator__". In QuaPy, combinations having to do with the classifier
+    can be computed at the begining, and then in an internal loop the hyperparams of the quantifier can be explored,
+    passing fit_learner=False.
+Re-check Ensembles. As for now, they are strongly tied to aggregative quantifiers.
+Re-think the environment variables. Maybe add new ones (like, for example, parameters for the plots)
+Do we want to wrap prevalences (currently simple np.ndarray) as a class? This might be convenient for some interfaces
+    (e.g., for specifying artificial prevalences in samplings, for printing them -- currently supported through
+    F.strprev(), etc.). This might however add some overload, and prevent/difficult post processing with numpy.
+Would be nice to get a better integration with sklearn.
 
-Add the fix suggested by Alexander?
-"For a more general application, I would maybe first establish a per-class threshold value of plausible prevalence
-based on the number of actual positives and the required sample size; e.g., for sample_size=100 and actual
-positives [10, 100, 500] -> [0.1, 1.0, 1.0], meaning that class 0 can be sampled at most at 0.1 prevalence, while
-the others can be sampled up to 1. prevalence. Then, when a prevalence value is requested, e.g., [0.33, 0.33, 0.33],
-we may either clip each value and normalize (as you suggest for the extreme case, e.g., [0.1, 0.33, 0.33]/sum) or
-scale each value by per-class thresholds, i.e., [0.33*0.1, 0.33*1, 0.33*1]/sum."
-- This affects LabelledCollection
-- This functionality should be accessible via sampling protocols and evaluation functions
 
-- [TODO] document confidence in manuals
-- [TODO] add ensemble methods SC-MQ, MC-SQ, MC-MQ
-- [TODO] add HistNetQ
-- [TODO] add CDE-iteration and Bayes-CDE methods
-- [TODO] add Friedman's method and DeBias
-- [TODO] check ignore warning stuff
-    check https://docs.python.org/3/library/warnings.html#temporarily-suppressing-warnings
-- [TODO] nmd and md are not selectable from qp.evaluation.evaluate as a string

docs/.gitignore

@@ -1 +0,0 @@
-build/

docs/Makefile

@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

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-                  
-  <h1>Source code for quapy.classification.calibration</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
-
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">clone</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">cross_val_predict</span><span class="p">,</span> <span class="n">train_test_split</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelEncoder</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils.validation</span><span class="w"> </span><span class="kn">import</span> <span class="n">check_X_y</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-
-
-<span class="c1"># Wrappers of calibration defined by Alexandari et al. in paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;</span>
-<span class="c1"># requires &quot;pip install abstension&quot;</span>
-<span class="c1"># see https://github.com/kundajelab/abstention</span>
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">_require_abstention_calibration</span><span class="p">():</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">from</span><span class="w"> </span><span class="nn">abstention.calibration</span><span class="w"> </span><span class="kn">import</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
-    <span class="k">except</span> <span class="ne">ImportError</span> <span class="k">as</span> <span class="n">exc</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span>
-            <span class="s2">&quot;Calibration methods in quapy.classification.calibration require the optional &quot;</span>
-            <span class="s2">&quot;&#39;abstention&#39; package.&quot;</span>
-        <span class="p">)</span> <span class="kn">from</span><span class="w"> </span><span class="nn">exc</span>
-    <span class="k">return</span> <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">VectorScaling</span>
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifier">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifier">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifier</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Abstract class for (re)calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari, A., Kundaje, A., &amp; Shrikumar, A. (2020, November). Maximum likelihood with bias-corrected calibration</span>
-<span class="sd">    is hard-to-beat at label shift adaptation. In International Conference on Machine Learning (pp. 222-232). PMLR.</span>
-<span class="sd">    &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">pass</span></div>
-
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">RecalibratedProbabilisticClassifierBase</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">RecalibratedProbabilisticClassifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies a (re)calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_.</span>
-
-
-<span class="sd">    :param classifier: a scikit-learn probabilistic classifier</span>
-<span class="sd">    :param calibrator: the calibration object (an instance of abstention.calibration.CalibratorFactory)</span>
-<span class="sd">    :param val_split: indicate an integer k for performing kFCV to obtain the posterior probabilities, or a float p</span>
-<span class="sd">        in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
-<span class="sd">        training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
-<span class="sd">        training set afterwards. Default value is 5.</span>
-<span class="sd">    :param n_jobs: indicate the number of parallel workers (only when val_split is an integer); default=None</span>
-<span class="sd">    :param verbose: whether or not to display information in the standard output</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">calibrator</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">calibrator</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the calibration for the probabilistic classifier.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)` with the class labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the number of folds must be &gt; 2&#39;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_cv</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="mi">0</span> <span class="o">&lt;</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">):</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;wrong value for val_split: the proportion of validation documents must be in (0,1)&#39;</span><span class="p">)</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_tr_val</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_cv">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_cv">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit_cv</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the calibration in a cross-validation manner, i.e., it generates posterior probabilities for all</span>
-<span class="sd">        training instances via cross-validation, and then retrains the classifier on all training instances.</span>
-<span class="sd">        The posterior probabilities thus generated are used for calibrating the outputs of the classifier.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)` with the class labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">posteriors</span> <span class="o">=</span> <span class="n">cross_val_predict</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;predict_proba&#39;</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">y</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.fit_tr_val">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.fit_tr_val">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit_tr_val</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the calibration in a train/val-split manner, i.e.t, it partitions the training instances into a</span>
-<span class="sd">        training and a validation set, and then uses the training samples to learn classifier which is then used</span>
-<span class="sd">        to generate posterior probabilities for the held-out validation data. These posteriors are used to calibrate</span>
-<span class="sd">        the classifier. The classifier is not retrained on the whole dataset.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)` with the class labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">Xtr</span><span class="p">,</span> <span class="n">Xva</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">yva</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">val_split</span><span class="p">,</span> <span class="n">stratify</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">)</span>
-        <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">Xva</span><span class="p">)</span>
-        <span class="n">nclasses</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">yva</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">nclasses</span><span class="p">)[</span><span class="n">yva</span><span class="p">],</span> <span class="n">posterior_supplied</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Predicts class labels for the data instances in `X`</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
-<span class="sd">        :return: array-like of shape `(n_samples,)` with the class label predictions</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="RecalibratedProbabilisticClassifierBase.predict_proba">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.RecalibratedProbabilisticClassifierBase.predict_proba">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Generates posterior probabilities for the data instances in `X`</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the data instances</span>
-<span class="sd">        :return: array-like of shape `(n_samples, n_classes)` with posterior probabilities</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span></div>
-
-
-    <span class="nd">@property</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the classes on which the classifier has been trained on</span>
-
-<span class="sd">        :return: array-like of shape `(n_classes)`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span></div>
-
-
-
-<div class="viewcode-block" id="NBVSCalibration">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.NBVSCalibration">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">NBVSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies the No-Bias Vector Scaling (NBVS) calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
-
-<span class="sd">    :param classifier: a scikit-learn probabilistic classifier</span>
-<span class="sd">    :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
-<span class="sd">        in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
-<span class="sd">        training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
-<span class="sd">        training set afterwards. Default value is 5.</span>
-<span class="sd">    :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
-<span class="sd">    :param verbose: whether or not to display information in the standard output</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="n">NoBiasVectorScaling</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">NoBiasVectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
-
-
-
-<div class="viewcode-block" id="BCTSCalibration">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.BCTSCalibration">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">BCTSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies the Bias-Corrected Temperature Scaling (BCTS) calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
-
-<span class="sd">    :param classifier: a scikit-learn probabilistic classifier</span>
-<span class="sd">    :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
-<span class="sd">        in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
-<span class="sd">        training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
-<span class="sd">        training set afterwards. Default value is 5.</span>
-<span class="sd">    :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
-<span class="sd">    :param verbose: whether or not to display information in the standard output</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">,</span> <span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
-
-
-
-<div class="viewcode-block" id="TSCalibration">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TSCalibration">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">TSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies the Temperature Scaling (TS) calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
-
-<span class="sd">    :param classifier: a scikit-learn probabilistic classifier</span>
-<span class="sd">    :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
-<span class="sd">        in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
-<span class="sd">        training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
-<span class="sd">        training set afterwards. Default value is 5.</span>
-<span class="sd">    :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
-<span class="sd">    :param verbose: whether or not to display information in the standard output</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
-
-
-
-<div class="viewcode-block" id="VSCalibration">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.VSCalibration">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">VSCalibration</span><span class="p">(</span><span class="n">RecalibratedProbabilisticClassifierBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies the Vector Scaling (VS) calibration method from `abstention.calibration`, as defined in</span>
-<span class="sd">    `Alexandari et al. paper &lt;http://proceedings.mlr.press/v119/alexandari20a.html&gt;`_:</span>
-
-<span class="sd">    :param classifier: a scikit-learn probabilistic classifier</span>
-<span class="sd">    :param val_split: indicate an integer k for performing kFCV to obtain the posterior prevalences, or a float p</span>
-<span class="sd">        in (0,1) to indicate that the posteriors are obtained in a stratified validation split containing p% of the</span>
-<span class="sd">        training instances (the rest is used for training). In any case, the classifier is retrained in the whole</span>
-<span class="sd">        training set afterwards. Default value is 5.</span>
-<span class="sd">    :param n_jobs: indicate the number of parallel workers (only when val_split is an integer)</span>
-<span class="sd">    :param verbose: whether or not to display information in the standard output</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">VectorScaling</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">VectorScaling</span><span class="p">(</span><span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">val_split</span> <span class="o">=</span> <span class="n">val_split</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span></div>
-
-
-
-<div class="viewcode-block" id="TemperatureScalingFromLogits">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">TemperatureScalingFromLogits</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Calibrates a matrix of logits by learning a temperature-scaling mapping</span>
-<span class="sd">    with the calibration methods from `abstention.calibration`.</span>
-
-<span class="sd">    This estimator is useful when the inputs are already logits produced by a</span>
-<span class="sd">    pretrained classifier, and the goal is to transform them directly into</span>
-<span class="sd">    calibrated posterior probabilities without retraining the underlying model.</span>
-
-<span class="sd">    :param bias_corrected: if True, uses Bias-Corrected Temperature Scaling</span>
-<span class="sd">        (BCTS); otherwise, uses standard Temperature Scaling (TS)</span>
-<span class="sd">    :param verbose: whether the underlying calibrator should display progress</span>
-<span class="sd">        information</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bias_corrected</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="o">=</span> <span class="n">bias_corrected</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
-
-<div class="viewcode-block" id="TemperatureScalingFromLogits.fit">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the logits calibrator.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_classes)` containing</span>
-<span class="sd">            logits</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)` containing class labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">check_X_y</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
-        <span class="n">y_enc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">classes_</span>
-
-        <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">logits_dim</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">n_classes</span> <span class="o">!=</span> <span class="n">logits_dim</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s1">&#39;mismatch between the number of classes (</span><span class="si">{</span><span class="n">n_classes</span><span class="si">}</span><span class="s1">) and the &#39;</span>
-                <span class="sa">f</span><span class="s1">&#39;dimensionality of the logits (</span><span class="si">{</span><span class="n">logits_dim</span><span class="si">}</span><span class="s1">)&#39;</span>
-            <span class="p">)</span>
-
-        <span class="n">_</span><span class="p">,</span> <span class="n">TempScaling</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">_require_abstention_calibration</span><span class="p">()</span>
-        <span class="n">calibrator</span> <span class="o">=</span> <span class="n">TempScaling</span><span class="p">(</span>
-            <span class="n">verbose</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">,</span>
-            <span class="n">bias_positions</span><span class="o">=</span><span class="s1">&#39;all&#39;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias_corrected</span> <span class="k">else</span> <span class="p">[],</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibrator_</span> <span class="o">=</span> <span class="n">calibrator</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span> <span class="o">=</span> <span class="n">calibrator</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)[</span><span class="n">y_enc</span><span class="p">])</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="TemperatureScalingFromLogits.predict_proba">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict_proba">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Converts logits into calibrated posterior probabilities.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_classes)` containing</span>
-<span class="sd">            logits</span>
-<span class="sd">        :return: array-like of shape `(n_samples, n_classes)` with calibrated</span>
-<span class="sd">            posterior probabilities</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">calibration_function_</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TemperatureScalingFromLogits.predict">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.calibration.TemperatureScalingFromLogits.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Predicts class labels after calibration.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_classes)` containing</span>
-<span class="sd">            logits</span>
-<span class="sd">        :return: array-like of shape `(n_samples,)` with class label</span>
-<span class="sd">            predictions</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">label_encoder_</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span></div>
-</div>
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-  <h1>Source code for quapy.classification.methods</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">TruncatedSVD</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LogisticRegression</span>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">LowRankLogisticRegression</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    An example of a classification method (i.e., an object that implements `fit`, `predict`, and `predict_proba`)</span>
-<span class="sd">    that also generates embedded inputs (i.e., that implements `transform`), as those required for</span>
-<span class="sd">    :class:`quapy.method.neural.QuaNet`. This is a mock method to allow for easily instantiating</span>
-<span class="sd">    :class:`quapy.method.neural.QuaNet` on array-like real-valued instances.</span>
-<span class="sd">    The transformation consists of applying :class:`sklearn.decomposition.TruncatedSVD`</span>
-<span class="sd">    while classification is performed using :class:`sklearn.linear_model.LogisticRegression` on the low-rank space.</span>
-
-<span class="sd">    :param n_components: the number of principal components to retain</span>
-<span class="sd">    :param kwargs: parameters for the</span>
-<span class="sd">        `Logistic Regression &lt;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html&gt;`__ classifier</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_components</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">n_components</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
-<div class="viewcode-block" id="LowRankLogisticRegression.get_params">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.get_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Get hyper-parameters for this estimator.</span>
-
-<span class="sd">        :return: a dictionary with parameter names mapped to their values</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;n_components&#39;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">}</span>
-        <span class="n">params</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>
-        <span class="k">return</span> <span class="n">params</span></div>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression.set_params">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.set_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Set the parameters of this estimator.</span>
-
-<span class="sd">        :param parameters: a `**kwargs` dictionary with the estimator parameters for</span>
-<span class="sd">            `Logistic Regression &lt;https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html&gt;`__</span>
-<span class="sd">            and eventually also `n_components` for `TruncatedSVD`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">params_</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
-        <span class="k">if</span> <span class="s1">&#39;n_components&#39;</span> <span class="ow">in</span> <span class="n">params_</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span> <span class="o">=</span> <span class="n">params_</span><span class="p">[</span><span class="s1">&#39;n_components&#39;</span><span class="p">]</span>
-            <span class="k">del</span> <span class="n">params_</span><span class="p">[</span><span class="s1">&#39;n_components&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params_</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression.fit">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fit the model according to the given training data. The fit consists of</span>
-<span class="sd">        fitting `TruncatedSVD` and then `LogisticRegression` on the low-rank representation.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` with the instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples, n_classes)` with the class labels</span>
-<span class="sd">        :return: `self`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">nF</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="n">nF</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="o">=</span> <span class="n">TruncatedSVD</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">n_components</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">classes_</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression.predict">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Predicts labels for the instances `X` embedded into the low-rank space.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
-<span class="sd">        :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
-<span class="sd">            instances in `X`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression.predict_proba">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.predict_proba">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Predicts posterior probabilities for the instances `X` embedded into the low-rank space.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
-<span class="sd">        :return: array-like of shape `(n_samples, n_classes)` with the posterior probabilities</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="LowRankLogisticRegression.transform">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.LowRankLogisticRegression.transform">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the low-rank approximation of `X` with `n_components` dimensions, or `X` unaltered if</span>
-<span class="sd">        `n_components` &gt;= `X.shape[1]`.</span>
-<span class="sd">        </span>
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` instances to embed</span>
-<span class="sd">        :return: array-like of shape `(n_samples, n_components)` with the embedded instances</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">X</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="MockClassifierFromPosteriors">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">MockClassifierFromPosteriors</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Mock classifier that bypasses classifier training when the input instances</span>
-<span class="sd">    are already posterior probabilities produced by a pretrained probabilistic</span>
-<span class="sd">    classifier.</span>
-
-<span class="sd">    :param X: arrays of shape `(n_samples, n_classes)` are interpreted as posterior probabilities</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="MockClassifierFromPosteriors.fit">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="MockClassifierFromPosteriors.predict">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="MockClassifierFromPosteriors.predict_proba">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.methods.MockClassifierFromPosteriors.predict_proba">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">X</span></div>
-</div>
-
-</pre></div>
-
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-  <h1>Source code for quapy.classification.svmperf</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">subprocess</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">tempfile</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">os</span><span class="w"> </span><span class="kn">import</span> <span class="n">remove</span><span class="p">,</span> <span class="n">makedirs</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">os.path</span><span class="w"> </span><span class="kn">import</span> <span class="n">join</span><span class="p">,</span> <span class="n">exists</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">subprocess</span><span class="w"> </span><span class="kn">import</span> <span class="n">PIPE</span><span class="p">,</span> <span class="n">STDOUT</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.datasets</span><span class="w"> </span><span class="kn">import</span> <span class="n">dump_svmlight_file</span>
-
-
-<div class="viewcode-block" id="SVMperf">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">SVMperf</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">,</span> <span class="n">ClassifierMixin</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;A wrapper for the `SVM-perf package &lt;https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html&gt;`__ by Thorsten Joachims.</span>
-<span class="sd">    When using losses for quantification, the source code has to be patched. See</span>
-<span class="sd">    the `installation documentation &lt;https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses&gt;`__</span>
-<span class="sd">    for further details.</span>
-
-<span class="sd">    References:</span>
-
-<span class="sd">        * `Esuli et al.2015 &lt;https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0&gt;`__</span>
-<span class="sd">        * `Barranquero et al.2015 &lt;https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X&gt;`__</span>
-
-<span class="sd">    :param svmperf_base: path to directory containing the binary files `svm_perf_learn` and `svm_perf_classify`</span>
-<span class="sd">    :param C: trade-off between training error and margin (default 0.01)</span>
-<span class="sd">    :param verbose: set to True to print svm-perf std outputs</span>
-<span class="sd">    :param loss: the loss to optimize for. Available losses are &quot;01&quot;, &quot;f1&quot;, &quot;kld&quot;, &quot;nkld&quot;, &quot;q&quot;, &quot;qacc&quot;, &quot;qf1&quot;, &quot;qgm&quot;, &quot;mae&quot;, &quot;mrae&quot;.</span>
-<span class="sd">    :param host_folder: directory where to store the trained model; set to None (default) for using a tmp directory</span>
-<span class="sd">        (temporal directories are automatically deleted)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># losses with their respective codes in svm_perf implementation</span>
-    <span class="n">valid_losses</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;01&#39;</span><span class="p">:</span><span class="mi">0</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">:</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;kld&#39;</span><span class="p">:</span><span class="mi">12</span><span class="p">,</span> <span class="s1">&#39;nkld&#39;</span><span class="p">:</span><span class="mi">13</span><span class="p">,</span> <span class="s1">&#39;q&#39;</span><span class="p">:</span><span class="mi">22</span><span class="p">,</span> <span class="s1">&#39;qacc&#39;</span><span class="p">:</span><span class="mi">23</span><span class="p">,</span> <span class="s1">&#39;qf1&#39;</span><span class="p">:</span><span class="mi">24</span><span class="p">,</span> <span class="s1">&#39;qgm&#39;</span><span class="p">:</span><span class="mi">25</span><span class="p">,</span> <span class="s1">&#39;mae&#39;</span><span class="p">:</span><span class="mi">26</span><span class="p">,</span> <span class="s1">&#39;mrae&#39;</span><span class="p">:</span><span class="mi">27</span><span class="p">}</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">svmperf_base</span><span class="p">,</span> <span class="n">C</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">&#39;01&#39;</span><span class="p">,</span> <span class="n">host_folder</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="n">svmperf_base</span><span class="p">),</span> \
-            <span class="p">(</span><span class="sa">f</span><span class="s1">&#39;path </span><span class="si">{</span><span class="n">svmperf_base</span><span class="si">}</span><span class="s1"> does not seem to point to a valid path;&#39;</span>
-             <span class="sa">f</span><span class="s1">&#39;did you install svm-perf? &#39;</span>
-             <span class="sa">f</span><span class="s1">&#39;see instructions in https://hlt-isti.github.io/QuaPy/manuals/explicit-loss-minimization.html&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span> <span class="o">=</span> <span class="n">svmperf_base</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">C</span> <span class="o">=</span> <span class="n">C</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="o">=</span> <span class="n">loss</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="o">=</span> <span class="n">host_folder</span>
-
-<div class="viewcode-block" id="SVMperf.fit">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Trains the SVM for the multivariate performance loss</span>
-
-<span class="sd">        :param X: training instances</span>
-<span class="sd">        :param y: a binary vector of labels</span>
-<span class="sd">        :return: `self`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss</span> <span class="ow">in</span> <span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">,</span> \
-            <span class="sa">f</span><span class="s1">&#39;unsupported loss </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="si">}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="nb">list</span><span class="p">(</span><span class="n">SVMperf</span><span class="o">.</span><span class="n">valid_losses</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="si">}</span><span class="s1">&#39;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">&#39;svm_perf_learn&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_base</span><span class="p">,</span> <span class="s1">&#39;svm_perf_classify&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span> <span class="o">=</span> <span class="s1">&#39;-w 3 -l &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">valid_losses</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">loss</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span> <span class="o">=</span> <span class="s1">&#39;-c &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">C</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes_</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-
-        <span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
-        <span class="c1"># this would allow to run parallel instances of predict</span>
-        <span class="n">random_code</span> <span class="o">=</span> <span class="s1">&#39;svmperfprocess&#39;</span><span class="o">+</span><span class="s1">&#39;-&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">tempfile</span><span class="o">.</span><span class="n">gettempdir</span><span class="p">(),</span> <span class="n">random_code</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">host_folder</span><span class="p">,</span> <span class="s1">&#39;.&#39;</span> <span class="o">+</span> <span class="n">random_code</span><span class="p">)</span>
-        <span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">&#39;model-&#39;</span><span class="o">+</span><span class="n">random_code</span><span class="p">)</span>
-        <span class="n">traindat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;train-</span><span class="si">{</span><span class="n">random_code</span><span class="si">}</span><span class="s1">.dat&#39;</span><span class="p">)</span>
-
-        <span class="n">dump_svmlight_file</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">traindat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">cmd</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_learn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">c_cmd</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_cmd</span><span class="p">,</span> <span class="n">traindat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">])</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">PIPE</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">):</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stderr</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
-        <span class="n">remove</span><span class="p">(</span><span class="n">traindat</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="SVMperf.predict">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Predicts labels for the instances `X`</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` instances to classify</span>
-<span class="sd">        :return: a `numpy` array of length `n` containing the label predictions, where `n` is the number of</span>
-<span class="sd">            instances in `X`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">confidence_scores</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">predictions</span> <span class="o">=</span> <span class="p">(</span><span class="n">confidence_scores</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="o">*</span> <span class="mi">1</span>
-        <span class="k">return</span> <span class="n">predictions</span></div>
-
-
-<div class="viewcode-block" id="SVMperf.decision_function">
-<a class="viewcode-back" href="../../../quapy.classification.html#quapy.classification.svmperf.SVMperf.decision_function">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">decision_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Evaluate the decision function for the samples in `X`.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)` containing the instances to classify</span>
-<span class="sd">        :param y: unused</span>
-<span class="sd">        :return: array-like of shape `(n_samples,)` containing the decision scores of the instances</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;tmpdir&#39;</span><span class="p">),</span> <span class="s1">&#39;predict called before fit&#39;</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;model directory corrupted&#39;</span>
-        <span class="k">assert</span> <span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">),</span> <span class="s1">&#39;model not found&#39;</span>
-        <span class="k">if</span> <span class="n">y</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-
-        <span class="c1"># in order to allow for parallel runs of predict, a random code is assigned</span>
-        <span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
-        <span class="n">random_code</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-        <span class="n">predictions_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">&#39;predictions&#39;</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">&#39;.dat&#39;</span><span class="p">)</span>
-        <span class="n">testdat</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="s1">&#39;test&#39;</span> <span class="o">+</span> <span class="n">random_code</span> <span class="o">+</span> <span class="s1">&#39;.dat&#39;</span><span class="p">)</span>
-        <span class="n">dump_svmlight_file</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">testdat</span><span class="p">,</span> <span class="n">zero_based</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">cmd</span> <span class="o">=</span> <span class="s1">&#39; &#39;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">svmperf_classify</span><span class="p">,</span> <span class="n">testdat</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions_path</span><span class="p">])</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[Running] </span><span class="si">{</span><span class="n">cmd</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">subprocess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">cmd</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span> <span class="n">stdout</span><span class="o">=</span><span class="n">PIPE</span><span class="p">,</span> <span class="n">stderr</span><span class="o">=</span><span class="n">STDOUT</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">stdout</span><span class="o">.</span><span class="n">decode</span><span class="p">(</span><span class="s1">&#39;utf-8&#39;</span><span class="p">))</span>
-
-        <span class="n">scores</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">loadtxt</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
-        <span class="n">remove</span><span class="p">(</span><span class="n">testdat</span><span class="p">)</span>
-        <span class="n">remove</span><span class="p">(</span><span class="n">predictions_path</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">scores</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;tmpdir&#39;</span><span class="p">):</span>
-            <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tmpdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-</pre></div>
-
-                </article>
-              
-              
-              
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-              
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-                  
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-          <footer class="bd-footer-content">
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docs/build/html/_modules/quapy/data/_ifcb.html

@@ -1,165 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../../../">
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=92fd9be5" />
-      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
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-            QuaPy: A Python-based open-source framework for quantification
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-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
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-              <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../modules.html">quapy</a></li>
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-    </nav>
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-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../../index.html">QuaPy: A Python-based open-source framework for quantification</a>
-      </nav>
-
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
-          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
-      <li class="breadcrumb-item active">quapy.data._ifcb</li>
-      <li class="wy-breadcrumbs-aside">
-      </li>
-  </ul>
-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.data._ifcb</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span>
-
-<div class="viewcode-block" id="IFCBTrainSamplesFromDir">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTrainSamplesFromDir">[docs]</a>
-<span class="k">class</span> <span class="nc">IFCBTrainSamplesFromDir</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">classes</span><span class="p">:</span> <span class="nb">list</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="n">classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">path_dir</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">filename</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">&#39;.csv&#39;</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">:</span>
-            <span class="n">s</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span><span class="n">sample</span><span class="p">))</span>
-            <span class="c1"># all columns but the first where we get the class</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-            <span class="n">y</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-            <span class="k">yield</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span>
-
-<div class="viewcode-block" id="IFCBTrainSamplesFromDir.total">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTrainSamplesFromDir.total">[docs]</a>
-    <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the total number of samples that the protocol generates.</span>
-
-<span class="sd">        :return: The number of training samples to generate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="IFCBTestSamples">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTestSamples">[docs]</a>
-<span class="k">class</span> <span class="nc">IFCBTestSamples</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">test_prevalences_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">test_prevalences_path</span><span class="p">))</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">test_sample</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
-            <span class="c1">#Load the sample from disk</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span><span class="n">test_sample</span><span class="p">[</span><span class="s1">&#39;sample&#39;</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;.csv&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
-            <span class="n">prevalences</span> <span class="o">=</span> <span class="n">test_sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-            <span class="k">yield</span> <span class="n">X</span><span class="p">,</span> <span class="n">prevalences</span>
-
-<div class="viewcode-block" id="IFCBTestSamples.total">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._ifcb.IFCBTestSamples.total">[docs]</a>
-    <span class="k">def</span> <span class="nf">total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the total number of samples that the protocol generates.</span>
-
-<span class="sd">        :return: The number of test samples to generate.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">test_prevalences</span><span class="o">.</span><span class="n">index</span><span class="p">)</span></div>
-</div>
-
-</pre></div>
-
-           </div>
-          </div>
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docs/build/html/_modules/quapy/data/_lequa2022.html

@@ -1,307 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" data-content_root="../../../">
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-          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
-      <li class="breadcrumb-item active">quapy.data._lequa2022</li>
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-      </li>
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-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.data._lequa2022</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">AbstractProtocol</span>
-
-<span class="n">DEV_SAMPLES</span> <span class="o">=</span> <span class="mi">1000</span>
-<span class="n">TEST_SAMPLES</span> <span class="o">=</span> <span class="mi">5000</span>
-
-<span class="n">ERROR_TOL</span> <span class="o">=</span> <span class="mf">1E-3</span>
-
-
-<div class="viewcode-block" id="load_category_map">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_category_map">[docs]</a>
-<span class="k">def</span> <span class="nf">load_category_map</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-    <span class="n">cat2code</span> <span class="o">=</span> <span class="p">{}</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
-        <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">fin</span><span class="p">:</span>
-            <span class="n">category</span><span class="p">,</span> <span class="n">code</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-            <span class="n">cat2code</span><span class="p">[</span><span class="n">category</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">code</span><span class="p">)</span>
-    <span class="n">code2cat</span> <span class="o">=</span> <span class="p">[</span><span class="n">cat</span> <span class="k">for</span> <span class="n">cat</span><span class="p">,</span> <span class="n">code</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">cat2code</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
-    <span class="k">return</span> <span class="n">cat2code</span><span class="p">,</span> <span class="n">code2cat</span></div>
-
-
-
-<div class="viewcode-block" id="load_raw_documents">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_raw_documents">[docs]</a>
-<span class="k">def</span> <span class="nf">load_raw_documents</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-    <span class="n">documents</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s2">&quot;text&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-    <span class="n">labels</span> <span class="o">=</span> <span class="kc">None</span>
-    <span class="k">if</span> <span class="s2">&quot;label&quot;</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-        <span class="n">labels</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">documents</span><span class="p">,</span> <span class="n">labels</span></div>
-
-
-
-<div class="viewcode-block" id="load_vector_documents">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.load_vector_documents">[docs]</a>
-<span class="k">def</span> <span class="nf">load_vector_documents</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-    <span class="n">D</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">labelled</span> <span class="o">=</span> <span class="n">D</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">301</span>
-    <span class="k">if</span> <span class="n">labelled</span><span class="p">:</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">D</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">:],</span> <span class="n">D</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">D</span><span class="p">,</span> <span class="kc">None</span>
-    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-
-<div class="viewcode-block" id="SamplesFromDir">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.SamplesFromDir">[docs]</a>
-<span class="k">class</span> <span class="nc">SamplesFromDir</span><span class="p">(</span><span class="n">AbstractProtocol</span><span class="p">):</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">ground_truth_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">load_fn</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span> <span class="o">=</span> <span class="n">path_dir</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">load_fn</span> <span class="o">=</span> <span class="n">load_fn</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">true_prevs</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">ground_truth_path</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="nb">id</span><span class="p">,</span> <span class="n">prevalence</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">true_prevs</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
-            <span class="n">sample</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">load_fn</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">path_dir</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="nb">id</span><span class="si">}</span><span class="s1">.txt&#39;</span><span class="p">))</span>
-            <span class="k">yield</span> <span class="n">sample</span><span class="p">,</span> <span class="n">prevalence</span></div>
-
-
-
-<div class="viewcode-block" id="ResultSubmission">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission">[docs]</a>
-<span class="k">class</span> <span class="nc">ResultSubmission</span><span class="p">:</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="kc">None</span>
-
-    <span class="k">def</span> <span class="nf">__init_df</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">categories</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">categories</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">or</span> <span class="n">categories</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">&#39;wrong format for categories: an int (&gt;=2) was expected&#39;</span><span class="p">)</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">categories</span><span class="p">)))</span>
-        <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">set_names</span><span class="p">(</span><span class="s1">&#39;id&#39;</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">n_categories</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ResultSubmission.add">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.add">[docs]</a>
-    <span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">prevalence_values</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_id</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: expected int for sample_sample, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">sample_id</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: expected np.ndarray for prevalence_values, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">__init_df</span><span class="p">(</span><span class="n">categories</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">prevalence_values</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">sample_id</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: prevalence values for &quot;</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">&quot; already added&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">prevalence_values</span><span class="o">.</span><span class="n">ndim</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">prevalence_values</span><span class="o">.</span><span class="n">size</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_categories</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: wrong shape found for prevalence vector </span><span class="si">{</span><span class="n">prevalence_values</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">(</span><span class="n">prevalence_values</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">or</span> <span class="p">(</span><span class="n">prevalence_values</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: prevalence values out of range [0,1] for &quot;</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">prevalence_values</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">ERROR_TOL</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error: prevalence values do not sum up to one for &quot;</span><span class="si">{</span><span class="n">sample_id</span><span class="si">}</span><span class="s1">&quot;&#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;(error tolerance </span><span class="si">{</span><span class="n">ERROR_TOL</span><span class="si">}</span><span class="s1">)&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample_id</span><span class="p">]</span> <span class="o">=</span> <span class="n">prevalence_values</span></div>
-
-
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ResultSubmission.load">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.load">[docs]</a>
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s1">&#39;ResultSubmission&#39;</span><span class="p">:</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_file_format</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="n">r</span> <span class="o">=</span> <span class="n">ResultSubmission</span><span class="p">()</span>
-        <span class="n">r</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
-        <span class="k">return</span> <span class="n">r</span></div>
-
-
-<div class="viewcode-block" id="ResultSubmission.dump">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.dump">[docs]</a>
-    <span class="k">def</span> <span class="nf">dump</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_dataframe_format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">path</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ResultSubmission.prevalence">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.prevalence">[docs]</a>
-    <span class="k">def</span> <span class="nf">prevalence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
-        <span class="n">sel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">sample_id</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">sel</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
-            <span class="k">return</span> <span class="kc">None</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">sel</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="ResultSubmission.iterrows">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.iterrows">[docs]</a>
-    <span class="k">def</span> <span class="nf">iterrows</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">df</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
-            <span class="n">prevalence</span> <span class="o">=</span> <span class="n">row</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-            <span class="k">yield</span> <span class="n">index</span><span class="p">,</span> <span class="n">prevalence</span></div>
-
-
-<div class="viewcode-block" id="ResultSubmission.check_file_format">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.check_file_format">[docs]</a>
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">check_file_format</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
-        <span class="k">try</span><span class="p">:</span>
-            <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the file </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1"> does not seem to be a valid csv file. &#39;</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="n">e</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">ResultSubmission</span><span class="o">.</span><span class="n">check_dataframe_format</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ResultSubmission.check_dataframe_format">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data._lequa2022.ResultSubmission.check_dataframe_format">[docs]</a>
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span> <span class="nf">check_dataframe_format</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">df</span><span class="p">,</span> <span class="n">path</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
-        <span class="n">hint_path</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>  <span class="c1"># if given, show the data path in the error message</span>
-        <span class="k">if</span> <span class="n">path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">hint_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39; in </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">&#39;</span>
-
-        <span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="s1">&#39;id&#39;</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;wrong header</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">, &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;the format of the header should be &quot;id,0,...,n-1&quot;, &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;where n is the number of categories&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">ci</span><span class="p">)</span> <span class="k">for</span> <span class="n">ci</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">]</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">))):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;wrong header</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">, category ids should be 0,1,2,...,n-1, &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;where n is the number of categories&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: results file is empty&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">!=</span> <span class="n">DEV_SAMPLES</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">!=</span> <span class="n">TEST_SAMPLES</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;wrong number of prevalence values found</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">; &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;expected </span><span class="si">{</span><span class="n">DEV_SAMPLES</span><span class="si">}</span><span class="s1"> for development sets and &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">TEST_SAMPLES</span><span class="si">}</span><span class="s1"> for test sets; found </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-        <span class="n">ids</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-        <span class="n">expected_ids</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)))</span>
-        <span class="k">if</span> <span class="n">ids</span> <span class="o">!=</span> <span class="n">expected_ids</span><span class="p">:</span>
-            <span class="n">missing</span> <span class="o">=</span> <span class="n">expected_ids</span> <span class="o">-</span> <span class="n">ids</span>
-            <span class="k">if</span> <span class="n">missing</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;there are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1"> missing ids</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">sorted</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-            <span class="n">unexpected</span> <span class="o">=</span> <span class="n">ids</span> <span class="o">-</span> <span class="n">expected_ids</span>
-            <span class="k">if</span> <span class="n">unexpected</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;there are </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">missing</span><span class="p">)</span><span class="si">}</span><span class="s1"> unexpected ids</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="nb">sorted</span><span class="p">(</span><span class="n">unexpected</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">category_id</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-            <span class="k">if</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">category_id</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">()</span> <span class="ow">or</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">category_id</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error</span><span class="si">{</span><span class="n">hint_path</span><span class="si">}</span><span class="s1"> column &quot;</span><span class="si">{</span><span class="n">category_id</span><span class="si">}</span><span class="s1">&quot; contains values out of range [0,1]&#39;</span><span class="p">)</span>
-
-        <span class="n">prevs</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">values</span>
-        <span class="n">round_errors</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">prevs</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">ERROR_TOL</span>
-        <span class="k">if</span> <span class="n">round_errors</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;warning: prevalence values in rows with id </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">round_errors</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span><span class="si">}</span><span class="s1"> &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;do not sum up to 1 (error tolerance </span><span class="si">{</span><span class="n">ERROR_TOL</span><span class="si">}</span><span class="s1">), &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;probably due to some rounding errors.&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">df</span></div>
-</div>
-
-
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
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docs/build/html/_modules/quapy/data/preprocessing.html

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-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-  <h1>Source code for quapy.data.preprocessing</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">spmatrix</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">TfidfVectorizer</span><span class="p">,</span> <span class="n">CountVectorizer</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">Dataset</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">map_parallel</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
-
-
-<div class="viewcode-block" id="instance_transformation">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.instance_transformation">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">transformer</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transforms a :class:`quapy.data.base.Dataset` applying the `fit_transform` and `transform` functions</span>
-<span class="sd">    of a (sklearn&#39;s) transformer.</span>
-
-<span class="sd">    :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are</span>
-<span class="sd">        lists of str</span>
-<span class="sd">    :param transformer: TransformerMixin implementing `fit_transform` and `transform` functions</span>
-<span class="sd">    :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
-<span class="sd">    :return: a new :class:`quapy.data.base.Dataset` with transformed instances (if inplace=False) or a reference to the</span>
-<span class="sd">        current Dataset (if inplace=True) where the instances have been transformed</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">training_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="o">*</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
-    <span class="n">test_transformed</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">orig_name</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span>
-
-    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">&#39;vocabulary_&#39;</span><span class="p">):</span>
-            <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
-        <span class="k">return</span> <span class="n">dataset</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_transformed</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">vocab</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">transformer</span><span class="p">,</span> <span class="s1">&#39;vocabulary_&#39;</span><span class="p">):</span>
-            <span class="n">vocab</span> <span class="o">=</span> <span class="n">transformer</span><span class="o">.</span><span class="n">vocabulary_</span>
-        <span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">vocabulary</span><span class="o">=</span><span class="n">vocab</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="n">orig_name</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="text2tfidf">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.text2tfidf">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of</span>
-<span class="sd">    tfidf weighted sparse vectors</span>
-
-<span class="sd">    :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are</span>
-<span class="sd">        lists of str</span>
-<span class="sd">    :param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary (default 3)</span>
-<span class="sd">    :param sublinear_tf: whether or not to apply the log scalling to the tf counters (default True)</span>
-<span class="sd">    :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
-<span class="sd">    :param kwargs: the rest of parameters of the transformation (as for sklearn&#39;s</span>
-<span class="sd">        `TfidfVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html&gt;`_)</span>
-<span class="sd">    :return: a new :class:`quapy.data.base.Dataset` in `csr_matrix` format (if inplace=False) or a reference to the</span>
-<span class="sd">        current Dataset (if inplace=True) where the instances are stored in a `csr_matrix` of real-valued tfidf scores</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-
-    <span class="n">vectorizer</span> <span class="o">=</span> <span class="n">TfidfVectorizer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="n">sublinear_tf</span><span class="o">=</span><span class="n">sublinear_tf</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">instance_transformation</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">vectorizer</span><span class="p">,</span> <span class="n">inplace</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="reduce_columns">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.reduce_columns">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">reduce_columns</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of</span>
-<span class="sd">    `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present</span>
-<span class="sd">    in at least `min_df` instances in the training set</span>
-
-<span class="sd">    :param dataset: a :class:`quapy.data.base.Dataset` in which instances are represented in sparse format (any</span>
-<span class="sd">        subtype of scipy.sparse.spmatrix)</span>
-<span class="sd">    :param min_df: integer, minimum number of instances below which the columns are removed</span>
-<span class="sd">    :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
-<span class="sd">    :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current</span>
-<span class="sd">        :class:`quapy.data.base.Dataset` (inplace=True) where the dimensions corresponding to infrequent terms</span>
-<span class="sd">        in the training set have been removed</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">spmatrix</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s1">&#39;unaligned vector spaces&#39;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">filter_by_occurrences</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">W</span><span class="p">):</span>
-        <span class="n">column_prevalence</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">((</span><span class="n">X</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-        <span class="n">take_columns</span> <span class="o">=</span> <span class="n">column_prevalence</span> <span class="o">&gt;=</span> <span class="n">min_df</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
-        <span class="n">W</span> <span class="o">=</span> <span class="n">W</span><span class="p">[:,</span> <span class="n">take_columns</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">W</span>
-
-    <span class="n">Xtr</span><span class="p">,</span> <span class="n">Xte</span> <span class="o">=</span> <span class="n">filter_by_occurrences</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">Xtr</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">Xte</span>
-        <span class="k">return</span> <span class="n">dataset</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">Xtr</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">Xte</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="standardize">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.standardize">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">standardize</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.</span>
-<span class="sd">    Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the</span>
-<span class="sd">    standard deviation.</span>
-
-<span class="sd">    :param dataset: a :class:`quapy.data.base.Dataset` object</span>
-<span class="sd">    :param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new</span>
-<span class="sd">        :class:`quapy.data.base.Dataset` is to be returned</span>
-<span class="sd">    :return: an instance of :class:`quapy.data.base.Dataset`</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">s</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
-    <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">train_test</span>
-    <span class="n">std_train_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">std_test_X</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_train_X</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">std_test_X</span>
-        <span class="k">return</span> <span class="n">dataset</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_train_X</span><span class="p">,</span> <span class="n">train</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">train</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">std_test_X</span><span class="p">,</span> <span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">name</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="index">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.index">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.</span>
-<span class="sd">    To index a document means to replace each different token by a unique numerical index.</span>
-<span class="sd">    Rare words (i.e., words occurring less than `min_df` times) are replaced by a special token `UNK`</span>
-
-<span class="sd">    :param dataset: a :class:`quapy.data.base.Dataset` object where the instances of training and test documents</span>
-<span class="sd">        are lists of str</span>
-<span class="sd">    :param min_df: minimum number of occurrences below which the term is replaced by a `UNK` index</span>
-<span class="sd">    :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)</span>
-<span class="sd">    :param kwargs: the rest of parameters of the transformation (as for sklearn&#39;s</span>
-<span class="sd">        `CountVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html&gt;_`)</span>
-<span class="sd">    :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current</span>
-<span class="sd">        :class:`quapy.data.base.Dataset` (inplace=True) consisting of lists of integer values representing indices.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-    <span class="n">__check_type</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
-
-    <span class="n">indexer</span> <span class="o">=</span> <span class="n">IndexTransformer</span><span class="p">(</span><span class="n">min_df</span><span class="o">=</span><span class="n">min_df</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-    <span class="n">training_index</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-    <span class="n">test_index</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-    <span class="n">training_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
-    <span class="n">test_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">inplace</span><span class="p">:</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary</span> <span class="o">=</span> <span class="n">indexer</span><span class="o">.</span><span class="n">vocabulary_</span>
-        <span class="k">return</span> <span class="n">dataset</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">training</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">training_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">test</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">test_index</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="o">.</span><span class="n">copy</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">training</span><span class="p">,</span> <span class="n">test</span><span class="p">,</span> <span class="n">indexer</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
-
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">__check_type</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">element_type</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">container_type</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">,</span> <span class="n">container_type</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;unexpected type of container (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span>
-    <span class="k">if</span> <span class="n">element_type</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">container</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">element_type</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;unexpected type of element (expected </span><span class="si">{</span><span class="n">container_type</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">container</span><span class="p">)</span><span class="si">}</span><span class="s1">)&#39;</span>
-
-
-<div class="viewcode-block" id="IndexTransformer">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">IndexTransformer</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This class implements a sklearn&#39;s-style transformer that indexes text as numerical ids for the tokens it</span>
-<span class="sd">    contains, and that would be generated by sklearn&#39;s</span>
-<span class="sd">    `CountVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html&gt;`_</span>
-
-<span class="sd">    :param kwargs: keyworded arguments from</span>
-<span class="sd">        `CountVectorizer &lt;https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html&gt;`_</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vect</span> <span class="o">=</span> <span class="n">CountVectorizer</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1"># a valid index is assigned after fit</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="o">-</span><span class="mi">2</span>  <span class="c1"># a valid index is assigned after fit</span>
-
-<div class="viewcode-block" id="IndexTransformer.fit">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the transformer, i.e., decides on the vocabulary, given a list of strings.</span>
-
-<span class="sd">        :param X: a list of strings</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">build_analyzer</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vect</span><span class="o">.</span><span class="n">vocabulary_</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;UNK_TOKEN&#39;</span><span class="p">],</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;UNK_INDEX&#39;</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pad</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PAD_TOKEN&#39;</span><span class="p">],</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;PAD_INDEX&#39;</span><span class="p">])</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="IndexTransformer.transform">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.transform">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Transforms the strings in `X` as lists of numerical ids</span>
-
-<span class="sd">        :param X: a list of strings</span>
-<span class="sd">        :param n_jobs: the number of parallel workers to carry out this task</span>
-<span class="sd">        :return: a `np.ndarray` of numerical ids</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># given the number of tasks and the number of jobs, generates the slices for the parallel processes</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;transform called before fit&#39;</span>
-        <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_index</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
-
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">documents</span><span class="p">):</span>
-        <span class="n">vocab</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="k">return</span> <span class="p">[[</span><span class="n">vocab</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">word</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">unk</span><span class="p">)</span> <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">analyzer</span><span class="p">(</span><span class="n">doc</span><span class="p">)]</span> <span class="k">for</span> <span class="n">doc</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">documents</span><span class="p">,</span> <span class="s1">&#39;indexing&#39;</span><span class="p">)]</span>
-
-<div class="viewcode-block" id="IndexTransformer.fit_transform">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.fit_transform">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Fits the transform on `X` and transforms it.</span>
-
-<span class="sd">        :param X: a list of strings</span>
-<span class="sd">        :param n_jobs: the number of parallel workers to carry out this task</span>
-<span class="sd">        :return: a `np.ndarray` of numerical ids</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="IndexTransformer.vocabulary_size">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.vocabulary_size">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">vocabulary_size</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Gets the length of the vocabulary according to which the document tokens have been indexed</span>
-
-<span class="sd">        :return: integer</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="IndexTransformer.add_word">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.preprocessing.IndexTransformer.add_word">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">add_word</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">word</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nogaps</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.</span>
-<span class="sd">        Useful to define special tokens for codifying unknown words, or padding tokens.</span>
-
-<span class="sd">        :param word: string, surface form of the token</span>
-<span class="sd">        :param id: integer, numerical value to assign to the token (leave as None for indicating the next valid id,</span>
-<span class="sd">            default)</span>
-<span class="sd">        :param nogaps: if set to True (default) asserts that the id indicated leads to no numerical gaps with</span>
-<span class="sd">            precedent ids stored so far</span>
-<span class="sd">        :return: integer, the numerical id for the new token</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="n">word</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;word </span><span class="si">{</span><span class="n">word</span><span class="si">}</span><span class="s1"> already in dictionary&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="c1"># add the word with the next id</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">id2word</span> <span class="o">=</span> <span class="p">{</span><span class="n">id_</span><span class="p">:</span><span class="n">word_</span> <span class="k">for</span> <span class="n">word_</span><span class="p">,</span> <span class="n">id_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-            <span class="k">if</span> <span class="nb">id</span> <span class="ow">in</span> <span class="n">id2word</span><span class="p">:</span>
-                <span class="n">old_word</span> <span class="o">=</span> <span class="n">id2word</span><span class="p">[</span><span class="nb">id</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">id</span>
-                <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">old_word</span><span class="p">]</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">old_word</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">nogaps</span><span class="p">:</span>
-                <span class="k">if</span> <span class="nb">id</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span><span class="p">:</span>
-                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;word </span><span class="si">{</span><span class="n">word</span><span class="si">}</span><span class="s1"> added with id </span><span class="si">{</span><span class="nb">id</span><span class="si">}</span><span class="s1">, while the current vocabulary size &#39;</span>
-                                     <span class="sa">f</span><span class="s1">&#39;is of </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">()</span><span class="si">}</span><span class="s1">, and id gaps are not allowed&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">vocabulary_</span><span class="p">[</span><span class="n">word</span><span class="p">]</span></div>
-</div>
-
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-                  
-  <h1>Source code for quapy.data.reader</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.sparse</span><span class="w"> </span><span class="kn">import</span> <span class="n">dok_matrix</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
-
-
-<div class="viewcode-block" id="from_text">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_text">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">from_text</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">class2int</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Reads a labelled colletion of documents.</span>
-<span class="sd">    File fomart &lt;0 or 1&gt;\t&lt;document&gt;\n</span>
-
-<span class="sd">    :param path: path to the labelled collection</span>
-<span class="sd">    :param encoding: the text encoding used to open the file</span>
-<span class="sd">    :param verbose: if &gt;0 (default) shows some progress information in standard output</span>
-<span class="sd">    :return: a list of sentences, and a list of labels</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">all_sentences</span><span class="p">,</span> <span class="n">all_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">verbose</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
-        <span class="n">file</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="sa">f</span><span class="s1">&#39;loading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()</span>
-    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">file</span><span class="p">:</span>
-        <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">line</span><span class="p">:</span>
-            <span class="k">try</span><span class="p">:</span>
-                <span class="n">label</span><span class="p">,</span> <span class="n">sentence</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">&#39;</span><span class="p">)</span>
-                <span class="n">sentence</span> <span class="o">=</span> <span class="n">sentence</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span>
-                <span class="k">if</span> <span class="n">class2int</span><span class="p">:</span>
-                    <span class="n">label</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-                <span class="k">if</span> <span class="n">sentence</span><span class="p">:</span>
-                    <span class="n">all_sentences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
-                    <span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">label</span><span class="p">)</span>
-            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
-                <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;format error in </span><span class="si">{</span><span class="n">line</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">all_sentences</span><span class="p">,</span> <span class="n">all_labels</span></div>
-
-
-
-<div class="viewcode-block" id="from_sparse">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_sparse">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">from_sparse</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Reads a labelled collection of real-valued instances expressed in sparse format</span>
-<span class="sd">    File format &lt;-1 or 0 or 1&gt;[\s col(int):val(float)]\n</span>
-
-<span class="sd">    :param path: path to the labelled collection</span>
-<span class="sd">    :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">):</span>
-        <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">col_val</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;:&#39;</span><span class="p">)</span>
-        <span class="n">col</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">col</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">float</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">col</span><span class="p">,</span> <span class="n">val</span>
-
-    <span class="n">all_documents</span><span class="p">,</span> <span class="n">all_labels</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="n">max_col</span> <span class="o">=</span> <span class="mi">0</span>
-    <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="sa">f</span><span class="s1">&#39;loading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">):</span>
-        <span class="n">parts</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">parts</span><span class="p">:</span>
-            <span class="n">all_labels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parts</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-            <span class="n">cols</span><span class="p">,</span> <span class="n">vals</span> <span class="o">=</span> <span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="p">[</span><span class="n">split_col_val</span><span class="p">(</span><span class="n">col_val</span><span class="p">)</span> <span class="k">for</span> <span class="n">col_val</span> <span class="ow">in</span> <span class="n">parts</span><span class="p">[</span><span class="mi">1</span><span class="p">:]])</span>
-            <span class="n">cols</span><span class="p">,</span> <span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">cols</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
-            <span class="n">max_col</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_col</span><span class="p">,</span> <span class="n">cols</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
-            <span class="n">all_documents</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">cols</span><span class="p">,</span> <span class="n">vals</span><span class="p">))</span>
-    <span class="n">n_docs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">all_labels</span><span class="p">)</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">dok_matrix</span><span class="p">((</span><span class="n">n_docs</span><span class="p">,</span> <span class="n">max_col</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">cols</span><span class="p">,</span> <span class="n">vals</span><span class="p">)</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">all_documents</span><span class="p">),</span> <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">all_documents</span><span class="p">),</span>
-                                <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;\-- filling matrix of shape </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">):</span>
-        <span class="n">X</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">cols</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">tocsr</span><span class="p">()</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">all_labels</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
-    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-
-<div class="viewcode-block" id="from_csv">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.from_csv">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">from_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf-8&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Reads a csv file in which columns are separated by &#39;,&#39;.</span>
-<span class="sd">    File format &lt;label&gt;,&lt;feat1&gt;,&lt;feat2&gt;,...,&lt;featn&gt;\n</span>
-
-<span class="sd">    :param path: path to the csv file</span>
-<span class="sd">    :param encoding: the text encoding used to open the file</span>
-<span class="sd">    :return: a np.ndarray for the labels and a ndarray (float) for the covariates</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;rt&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="n">encoding</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="sa">f</span><span class="s1">&#39;reading </span><span class="si">{</span><span class="n">path</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">):</span>
-        <span class="n">yi</span><span class="p">,</span> <span class="o">*</span><span class="n">xi</span> <span class="o">=</span> <span class="n">instance</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
-        <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">float</span><span class="p">,</span><span class="n">xi</span><span class="p">)))</span>
-        <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">yi</span><span class="p">)</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span></div>
-
-
-
-<div class="viewcode-block" id="reindex_labels">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.reindex_labels">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">reindex_labels</span><span class="p">(</span><span class="n">y</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.</span>
-<span class="sd">    E.g.:</span>
-
-<span class="sd">    &gt;&gt;&gt; reindex_labels([&#39;B&#39;, &#39;B&#39;, &#39;A&#39;, &#39;C&#39;])</span>
-<span class="sd">    &gt;&gt;&gt; (array([1, 1, 0, 2]), array([&#39;A&#39;, &#39;B&#39;, &#39;C&#39;], dtype=&#39;&lt;U1&#39;))</span>
-
-<span class="sd">    :param y: the list or array of original labels</span>
-<span class="sd">    :return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">classnames</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span>
-    <span class="n">label2index</span> <span class="o">=</span> <span class="p">{</span><span class="n">label</span><span class="p">:</span> <span class="n">index</span> <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">label</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">classnames</span><span class="p">)}</span>
-    <span class="n">indexed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-    <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">classnames</span><span class="p">:</span>
-        <span class="n">indexed</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">label</span><span class="p">]</span> <span class="o">=</span> <span class="n">label2index</span><span class="p">[</span><span class="n">label</span><span class="p">]</span>
-    <span class="k">return</span> <span class="n">indexed</span><span class="p">,</span> <span class="n">classnames</span></div>
-
-
-
-<div class="viewcode-block" id="binarize">
-<a class="viewcode-back" href="../../../quapy.data.html#quapy.data.reader.binarize">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">binarize</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">pos_class</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:</span>
-
-<span class="sd">    &gt;&gt;&gt; binarize([1, 2, 3, 1, 1, 0], pos_class=2)</span>
-<span class="sd">    &gt;&gt;&gt; array([0, 1, 0, 0, 0, 0])</span>
-
-<span class="sd">    :param y: array-like of labels</span>
-<span class="sd">    :param pos_class: integer, the positive class</span>
-<span class="sd">    :return: a binary np.ndarray, in which values 1 corresponds to positions in whcih `y` had `pos_class` labels, and</span>
-<span class="sd">        0 otherwise</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-    <span class="n">ybin</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)</span>
-    <span class="n">ybin</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">pos_class</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="k">return</span> <span class="n">ybin</span></div>
-
-
-</pre></div>
-
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-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.evaluation</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">,</span> <span class="n">IterateProtocol</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
-
-
-<div class="viewcode-block" id="prediction">
-<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.prediction">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">prediction</span><span class="p">(</span>
-        <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
-        <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
-        <span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-        <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Uses a quantification model to generate predictions for the samples generated via a specific protocol.</span>
-<span class="sd">    This function is central to all evaluation processes, and is endowed with an optimization to speed-up the</span>
-<span class="sd">    prediction of protocols that generate samples from a large collection. The optimization applies to aggregative</span>
-<span class="sd">    quantifiers only, and to OnLabelledCollectionProtocol protocols, and comes down to generating the classification</span>
-<span class="sd">    predictions once and for all, and then generating samples over the classification predictions (instead of over</span>
-<span class="sd">    the raw instances), so that the classifier prediction is never called again. This behaviour is obtained by</span>
-<span class="sd">    setting `aggr_speedup` to &#39;auto&#39; or True, and is only carried out if the overall process is convenient in terms</span>
-<span class="sd">    of computations (e.g., if the number of classification predictions needed for the original collection exceed the</span>
-<span class="sd">    number of classification predictions needed for all samples, then the optimization is not undertaken).</span>
-
-<span class="sd">    :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
-<span class="sd">    :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
-<span class="sd">        :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
-<span class="sd">        in charge of generating the samples for which the model has to issue class prevalence predictions.</span>
-<span class="sd">    :param aggr_speedup: whether or not to apply the speed-up. Set to &quot;force&quot; for applying it even if the number of</span>
-<span class="sd">        instances in the original collection on which the protocol acts is larger than the number of instances</span>
-<span class="sd">        in the samples to be generated. Set to True or &quot;auto&quot; (default) for letting QuaPy decide whether it is</span>
-<span class="sd">        convenient or not. Set to False to deactivate.</span>
-<span class="sd">    :param verbose: boolean, show or not information in stdout</span>
-<span class="sd">    :return: a tuple `(true_prevs, estim_prevs)` in which each element in the tuple is an array of shape</span>
-<span class="sd">        `(n_samples, n_classes)` containing the true, or predicted, prevalence values for each sample</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">assert</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;force&#39;</span><span class="p">],</span> <span class="s1">&#39;invalid value for aggr_speedup&#39;</span>
-
-    <span class="n">sout</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="kc">None</span>
-
-    <span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">if</span> <span class="n">aggr_speedup</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span> <span class="s1">&#39;force&#39;</span><span class="p">]:</span>
-        <span class="c1"># checks whether the prediction can be made more efficiently; this check consists in verifying if the model is</span>
-        <span class="c1"># of type aggregative, if the protocol is based on LabelledCollection, and if the total number of documents to</span>
-        <span class="c1"># classify using the protocol would exceed the number of test documents in the original collection</span>
-        <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeQuantifier</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">aggr_speedup</span> <span class="o">==</span> <span class="s1">&#39;force&#39;</span><span class="p">:</span>
-                <span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
-                <span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;forcing aggregative speedup&#39;</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="s1">&#39;sample_size&#39;</span><span class="p">):</span>
-                <span class="n">nD</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">())</span>
-                <span class="n">samplesD</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">()</span> <span class="o">*</span> <span class="n">protocol</span><span class="o">.</span><span class="n">sample_size</span>
-                <span class="k">if</span> <span class="n">nD</span> <span class="o">&lt;</span> <span class="n">samplesD</span><span class="p">:</span>
-                    <span class="n">apply_optimization</span> <span class="o">=</span> <span class="kc">True</span>
-                    <span class="n">sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;speeding up the prediction for the aggregative quantifier, &#39;</span>
-                         <span class="sa">f</span><span class="s1">&#39;total classifications </span><span class="si">{</span><span class="n">nD</span><span class="si">}</span><span class="s1"> instead of </span><span class="si">{</span><span class="n">samplesD</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">apply_optimization</span><span class="p">:</span>
-        <span class="n">pre_classified</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classify</span><span class="p">(</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-        <span class="n">protocol_with_predictions</span> <span class="o">=</span> <span class="n">protocol</span><span class="o">.</span><span class="n">on_preclassified_instances</span><span class="p">(</span><span class="n">pre_classified</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">aggregate</span><span class="p">,</span> <span class="n">protocol_with_predictions</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">__prediction_helper</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">verbose</span><span class="p">)</span></div>
-
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">__prediction_helper</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">,</span> <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">sample_instances</span><span class="p">,</span> <span class="n">sample_prev</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">protocol</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">protocol</span><span class="o">.</span><span class="n">total</span><span class="p">(),</span> <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;predicting&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">verbose</span> <span class="k">else</span> <span class="n">protocol</span><span class="p">():</span>
-        <span class="n">estim_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">quantification_fn</span><span class="p">(</span><span class="n">sample_instances</span><span class="p">))</span>
-        <span class="n">true_prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample_prev</span><span class="p">)</span>
-
-    <span class="n">true_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">)</span>
-    <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">estim_prevs</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span>
-
-
-<div class="viewcode-block" id="evaluation_report">
-<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluation_report">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">evaluation_report</span><span class="p">(</span><span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
-                      <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
-                      <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span><span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">,</span>
-                      <span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-                      <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generates a report (a pandas&#39; DataFrame) containing information of the evaluation of the model as according</span>
-<span class="sd">    to a specific protocol and in terms of one or more evaluation metrics (errors).</span>
-
-
-<span class="sd">    :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
-<span class="sd">    :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
-<span class="sd">        :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the protocol</span>
-<span class="sd">        in charge of generating the samples in which the model is evaluated.</span>
-<span class="sd">    :param error_metrics: a string, or list of strings, representing the name(s) of an error function in `qp.error`</span>
-<span class="sd">        (e.g., &#39;mae&#39;, the default value), or a callable function, or a list of callable functions, implementing</span>
-<span class="sd">        the error function itself.</span>
-<span class="sd">    :param aggr_speedup: whether or not to apply the speed-up. Set to &quot;force&quot; for applying it even if the number of</span>
-<span class="sd">        instances in the original collection on which the protocol acts is larger than the number of instances</span>
-<span class="sd">        in the samples to be generated. Set to True or &quot;auto&quot; (default) for letting QuaPy decide whether it is</span>
-<span class="sd">        convenient or not. Set to False to deactivate.</span>
-<span class="sd">    :param verbose: boolean, show or not information in stdout</span>
-<span class="sd">    :return: a pandas&#39; DataFrame containing the columns &#39;true-prev&#39; (the true prevalence of each sample),</span>
-<span class="sd">        &#39;estim-prev&#39; (the prevalence estimated by the model for each sample), and as many columns as error metrics</span>
-<span class="sd">        have been indicated, each displaying the score in terms of that metric for every sample.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">)</span></div>
-
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">_prevalence_report</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">,</span> <span class="n">error_metrics</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]]</span> <span class="o">=</span> <span class="s1">&#39;mae&#39;</span><span class="p">):</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metrics</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="n">error_metrics</span> <span class="o">=</span> <span class="p">[</span><span class="n">error_metrics</span><span class="p">]</span>
-
-    <span class="n">error_funcs</span> <span class="o">=</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span> <span class="k">else</span> <span class="n">e</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_metrics</span><span class="p">]</span>
-    <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">hasattr</span><span class="p">(</span><span class="n">e</span><span class="p">,</span> <span class="s1">&#39;__call__&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">),</span> <span class="s1">&#39;invalid error functions&#39;</span>
-    <span class="n">error_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">e</span><span class="o">.</span><span class="vm">__name__</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">error_funcs</span><span class="p">]</span>
-
-    <span class="n">row_entries</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">):</span>
-        <span class="n">series</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;true-prev&#39;</span><span class="p">:</span> <span class="n">true_prev</span><span class="p">,</span> <span class="s1">&#39;estim-prev&#39;</span><span class="p">:</span> <span class="n">estim_prev</span><span class="p">}</span>
-        <span class="k">for</span> <span class="n">error_name</span><span class="p">,</span> <span class="n">error_metric</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">error_names</span><span class="p">,</span> <span class="n">error_funcs</span><span class="p">):</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prev</span><span class="p">,</span> <span class="n">estim_prev</span><span class="p">)</span>
-            <span class="n">series</span><span class="p">[</span><span class="n">error_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">score</span>
-        <span class="n">row_entries</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">series</span><span class="p">)</span>
-
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_records</span><span class="p">(</span><span class="n">row_entries</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">df</span>
-
-
-<div class="viewcode-block" id="evaluate">
-<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">evaluate</span><span class="p">(</span>
-        <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
-        <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
-        <span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
-        <span class="n">aggr_speedup</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;auto&#39;</span><span class="p">,</span>
-        <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Evaluates a quantification model according to a specific sample generation protocol and in terms of one</span>
-<span class="sd">    evaluation metric (error).</span>
-
-<span class="sd">    :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
-<span class="sd">    :param protocol: :class:`quapy.protocol.AbstractProtocol`; if this object is also instance of</span>
-<span class="sd">        :class:`quapy.protocol.OnLabelledCollectionProtocol`, then the aggregation speed-up can be run. This is the</span>
-<span class="sd">        protocol in charge of generating the samples in which the model is evaluated.</span>
-<span class="sd">    :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
-<span class="sd">        (e.g., &#39;mae&#39;), or a callable function implementing the error function itself.</span>
-<span class="sd">    :param aggr_speedup: whether or not to apply the speed-up. Set to &quot;force&quot; for applying it even if the number of</span>
-<span class="sd">        instances in the original collection on which the protocol acts is larger than the number of instances</span>
-<span class="sd">        in the samples to be generated. Set to True or &quot;auto&quot; (default) for letting QuaPy decide whether it is</span>
-<span class="sd">        convenient or not. Set to False to deactivate.</span>
-<span class="sd">    :param verbose: boolean, show or not information in stdout</span>
-<span class="sd">    :return: if the error metric is not averaged (e.g., &#39;ae&#39;, &#39;rae&#39;), returns an array of shape `(n_samples,)` with</span>
-<span class="sd">        the error scores for each sample; if the error metric is averaged (e.g., &#39;mae&#39;, &#39;mrae&#39;) then returns</span>
-<span class="sd">        a single float</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error_metric</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-        <span class="n">error_metric</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error_metric</span><span class="p">)</span>
-    <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">prediction</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="n">aggr_speedup</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">error_metric</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="evaluate_on_samples">
-<a class="viewcode-back" href="../../quapy.html#quapy.evaluation.evaluate_on_samples">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">evaluate_on_samples</span><span class="p">(</span>
-        <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
-        <span class="n">samples</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">],</span>
-        <span class="n">error_metric</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">],</span>
-        <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Evaluates a quantification model on a given set of samples and in terms of one evaluation metric (error).</span>
-
-<span class="sd">    :param model: a quantifier, instance of :class:`quapy.method.base.BaseQuantifier`</span>
-<span class="sd">    :param samples: a list of samples on which the quantifier is to be evaluated</span>
-<span class="sd">    :param error_metric: a string representing the name(s) of an error function in `qp.error`</span>
-<span class="sd">        (e.g., &#39;mae&#39;), or a callable function implementing the error function itself.</span>
-<span class="sd">    :param verbose: boolean, show or not information in stdout</span>
-<span class="sd">    :return: if the error metric is not averaged (e.g., &#39;ae&#39;, &#39;rae&#39;), returns an array of shape `(n_samples,)` with</span>
-<span class="sd">        the error scores for each sample; if the error metric is averaged (e.g., &#39;mae&#39;, &#39;mrae&#39;) then returns</span>
-<span class="sd">        a single float</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">return</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">IterateProtocol</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="n">error_metric</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="n">verbose</span><span class="p">)</span></div>
-
-
-
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-
-
-</pre></div>
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-  <h1>Source code for quapy.method._kdey</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">numbers</span><span class="w"> </span><span class="kn">import</span> <span class="n">Real</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.neighbors</span><span class="w"> </span><span class="kn">import</span> <span class="n">KernelDensity</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">AggregativeSoftQuantifier</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.special</span><span class="w"> </span><span class="kn">import</span> <span class="n">logsumexp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics.pairwise</span><span class="w"> </span><span class="kn">import</span> <span class="n">rbf_kernel</span>
-
-
-<div class="viewcode-block" id="KDEBase">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">KDEBase</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Common ancestor for KDE-based methods. Implements some common routines.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">BANDWIDTH_METHOD</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;scott&#39;</span><span class="p">,</span> <span class="s1">&#39;silverman&#39;</span><span class="p">]</span>
-    <span class="n">KERNELS</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">]</span>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">_check_bandwidth</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Checks that the bandwidth parameter is correct</span>
-
-<span class="sd">        :param bandwidth: either a string (see BANDWIDTH_METHOD) or a float</span>
-<span class="sd">        :return: the bandwidth if the check is passed, or raises an exception for invalid values</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="n">bandwidth</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span> <span class="ow">or</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;invalid bandwidth, valid ones are </span><span class="si">{</span><span class="n">KDEBase</span><span class="o">.</span><span class="n">BANDWIDTH_METHOD</span><span class="si">}</span><span class="s1"> or float values&#39;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
-            <span class="n">bandwidth</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">bandwidth</span>
-
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">_check_kernel</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">KERNELS</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">kernel</span><span class="si">=}</span><span class="s1">&#39;</span>
-        <span class="k">return</span> <span class="n">kernel</span>
-
-<div class="viewcode-block" id="KDEBase.get_kde_function">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_kde_function">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">get_kde_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Wraps the KDE function from scikit-learn.</span>
-
-<span class="sd">        :param X: data for which the density function is to be estimated</span>
-<span class="sd">        :param bandwidth: the bandwidth of the kernel</span>
-<span class="sd">        :param kernel: the kernel family</span>
-<span class="sd">        :return: a scikit-learn&#39;s KernelDensity object</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
-        <span class="n">bandwidth</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">effective_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">KernelDensity</span><span class="p">(</span><span class="n">bandwidth</span><span class="o">=</span><span class="n">bandwidth</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.pdf">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.pdf">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">pdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kde</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Wraps the density evalution of scikit-learn&#39;s KDE. Scikit-learn returns log-scores (s), so this</span>
-<span class="sd">        function returns :math:`e^{s}`</span>
-
-<span class="sd">        :param kde: a previously fit KDE function</span>
-<span class="sd">        :param X: the data for which the density is to be estimated</span>
-<span class="sd">        :param kernel: the kernel family</span>
-<span class="sd">        :return: np.ndarray with the densities</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
-        <span class="n">log_density</span> <span class="o">=</span> <span class="n">kde</span><span class="o">.</span><span class="n">score_samples</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">log_densities</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">log_density</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_density</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.get_mixture_components">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.get_mixture_components">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">get_mixture_components</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns an array containing the mixture components, i.e., the KDE functions for each class.</span>
-
-<span class="sd">        :param X: the data containing the covariates</span>
-<span class="sd">        :param y: the class labels</span>
-<span class="sd">        :param n_classes: integer, the number of classes</span>
-<span class="sd">        :param bandwidth: float, the bandwidth of the kernel</span>
-<span class="sd">        :param kernel: the kernel family</span>
-<span class="sd">        :return: a list of KernelDensity objects, each fitted with the corresponding class-specific covariates</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">class_cond_X</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="n">classes</span><span class="p">:</span>
-            <span class="n">selX</span> <span class="o">=</span> <span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">]</span>
-            <span class="k">if</span> <span class="n">selX</span><span class="o">.</span><span class="n">size</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;empty class </span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-            <span class="n">class_cond_X</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selX</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">X_cond_yi</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">X_cond_yi</span> <span class="ow">in</span> <span class="n">class_cond_X</span><span class="p">]</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.transform_posteriors">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.transform_posteriors">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">transform_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">}:</span>
-            <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">shrink_posteriors</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;aitchison&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">kernel</span> <span class="o">==</span> <span class="s1">&#39;ilr&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">X</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.shrink_posteriors">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.shrink_posteriors">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">shrink_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;shrinkage&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">shrinkage</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">X</span>
-        <span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">n_classes</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">uniform</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">X</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
-        <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="n">X</span> <span class="o">+</span> <span class="n">shrinkage</span> <span class="o">*</span> <span class="n">uniform</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.effective_bandwidth">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.effective_bandwidth">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">effective_bandwidth</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">):</span>
-        <span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;shrinkage&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">shrinkage</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">kernel</span> <span class="ow">in</span> <span class="p">{</span><span class="s1">&#39;aitchison&#39;</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">}</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
-            <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">shrinkage</span><span class="p">)</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">bandwidth</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.clr_transform">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.clr_transform">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">clr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;clr&#39;</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">clr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">CLRtransformation</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">clr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="KDEBase.ilr_transform">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEBase.ilr_transform">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">ilr_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;ilr&#39;</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">ilr</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">ILRtransformation</span><span class="p">()</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">ilr</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="KDEyML">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">KDEyML</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Kernel Density Estimation model for quantification (KDEy) relying on the Kullback-Leibler divergence (KLD) as</span>
-<span class="sd">    the divergence measure to be minimized. This method was first proposed in the paper</span>
-<span class="sd">    `Kernel Density Estimation for Multiclass Quantification &lt;https://link.springer.com/article/10.1007/s10994-024-06726-5&gt;`_ (`arXiv &lt;https://arxiv.org/abs/2401.00490&gt;`_), in which</span>
-<span class="sd">    the authors show that minimizing the distribution mathing criterion for KLD is akin to performing</span>
-<span class="sd">    maximum likelihood (ML).</span>
-
-<span class="sd">    The distribution matching optimization problem comes down to solving:</span>
-
-<span class="sd">    :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
-
-<span class="sd">    where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
-<span class="sd">    :math:`\\alpha` defined by</span>
-
-<span class="sd">    :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
-
-<span class="sd">    where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
-<span class="sd">    KDE function that uses the datapoints in X as the kernel centers.</span>
-
-<span class="sd">    In KDEy-ML, the divergence is taken to be the Kullback-Leibler Divergence. This is equivalent to solving:</span>
-<span class="sd">    :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} -</span>
-<span class="sd">    \\mathbb{E}_{q_{\\widetilde{U}}} \\left[ \\log \\boldsymbol{p}_{\\alpha}(\\widetilde{x}) \\right]`</span>
-
-<span class="sd">    which corresponds to the maximum likelihood estimate.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-<span class="sd">    :param bandwidth: float, the bandwidth of the Kernel</span>
-<span class="sd">    :param kernel: kernel of KDE, valid ones are in KDEBase.KERNELS</span>
-<span class="sd">    :param shrinkage: amount of shrinkage towards the uniform distribution to apply before</span>
-<span class="sd">        Aitchison/ILR transformations. Must be in ``[0,1)``.</span>
-<span class="sd">    :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">,</span> <span class="n">shrinkage</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_check_kernel</span><span class="p">(</span><span class="n">kernel</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">shrinkage</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">&#39;shrinkage must be in [0,1)&#39;</span>
-        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">&#39;gaussian&#39;</span> <span class="ow">or</span> <span class="n">shrinkage</span> <span class="o">==</span> <span class="mi">0</span><span class="p">,</span> \
-            <span class="s1">&#39;shrinkage is only supported for Aitchison/ILR kernels&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shrinkage</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">shrinkage</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-
-<div class="viewcode-block" id="KDEyML.aggregation_fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregation_fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span>
-            <span class="n">classif_predictions</span><span class="p">,</span>
-            <span class="n">labels</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="KDEyML.aggregate">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyML.aggregate">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Searches for the mixture model parameter (the sought prevalence values) that maximizes the likelihood</span>
-<span class="sd">        of the data (i.e., that minimizes the negative log-likelihood)</span>
-
-<span class="sd">        :param posteriors: instances in the sample converted into posterior probabilities</span>
-<span class="sd">        :return: a vector of class prevalence estimates</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">):</span>
-            <span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-12</span>
-            <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
-            <span class="k">if</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">!=</span> <span class="s1">&#39;gaussian&#39;</span> <span class="ow">and</span> <span class="n">n_classes</span> <span class="o">&gt;=</span> <span class="mi">20</span><span class="p">)</span> <span class="ow">or</span> <span class="n">n_classes</span> <span class="o">&gt;=</span> <span class="mi">30</span><span class="p">:</span>
-                <span class="n">test_log_densities</span> <span class="o">=</span> <span class="p">[</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="n">log_densities</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-                    <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span>
-                <span class="p">]</span>
-
-                <span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
-                    <span class="n">prev</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">smooth</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">eps</span><span class="o">=</span><span class="n">epsilon</span><span class="p">)</span>
-                    <span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">logsumexp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">prev</span><span class="p">)[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">test_log_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">test_densities</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_i</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
-
-                <span class="k">def</span><span class="w"> </span><span class="nf">neg_loglikelihood</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
-                    <span class="n">test_mixture_likelihood</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">test_densities</span>
-                    <span class="n">test_loglikelihood</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">test_mixture_likelihood</span> <span class="o">+</span> <span class="n">epsilon</span><span class="p">)</span>
-                    <span class="k">return</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">test_loglikelihood</span><span class="p">)</span>
-
-            <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">neg_loglikelihood</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="KDEyHD">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">KDEyHD</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">,</span> <span class="n">KDEBase</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Kernel Density Estimation model for quantification (KDEy) relying on the squared Hellinger Disntace (HD) as</span>
-<span class="sd">    the divergence measure to be minimized. This method was first proposed in the paper</span>
-<span class="sd">    `Kernel Density Estimation for Multiclass Quantification &lt;https://link.springer.com/article/10.1007/s10994-024-06726-5&gt;`_ (`arXiv &lt;https://arxiv.org/abs/2401.00490&gt;`_), in which</span>
-<span class="sd">    the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
-
-<span class="sd">    The distribution matching optimization problem comes down to solving:</span>
-
-<span class="sd">    :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
-
-<span class="sd">    where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
-<span class="sd">    :math:`\\alpha` defined by</span>
-
-<span class="sd">    :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
-
-<span class="sd">    where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
-<span class="sd">    KDE function that uses the datapoints in X as the kernel centers.</span>
-
-<span class="sd">    In KDEy-HD, the divergence is taken to be the squared Hellinger Distance, an f-divergence with corresponding</span>
-<span class="sd">    f-generator function given by:</span>
-
-<span class="sd">    :math:`f(u)=(\\sqrt{u}-1)^2`</span>
-
-<span class="sd">    The authors proposed a Monte Carlo solution that relies on importance sampling:</span>
-
-<span class="sd">    :math:`\\hat{D}_f(p||q)= \\frac{1}{t} \\sum_{i=1}^t f\\left(\\frac{p(x_i)}{q(x_i)}\\right) \\frac{q(x_i)}{r(x_i)}`</span>
-
-<span class="sd">    where the datapoints (trials) :math:`x_1,\\ldots,x_t\\sim_{\\mathrm{iid}} r` with :math:`r`  the</span>
-<span class="sd">    uniform distribution.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-<span class="sd">    :param bandwidth: float, the bandwidth of the Kernel</span>
-<span class="sd">    :param random_state: a seed to be set before fitting any base quantifier (default None)</span>
-<span class="sd">    :param montecarlo_trials: number of Monte Carlo trials (default 10000)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span>
-                 <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">montecarlo_trials</span><span class="o">=</span><span class="mi">10000</span><span class="p">):</span>
-
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span> <span class="o">=</span> <span class="n">montecarlo_trials</span>
-
-<div class="viewcode-block" id="KDEyHD.aggregation_fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregation_fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_mixture_components</span><span class="p">(</span>
-            <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span>
-        <span class="p">)</span>
-
-        <span class="n">N</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">montecarlo_trials</span>
-        <span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">kde_i</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">N</span><span class="o">//</span><span class="n">n</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">rs</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_i</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
-            <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">kde_j</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span> <span class="k">for</span> <span class="n">kde_j</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">]</span>
-        <span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>  <span class="c1"># equiv. to (uniform @ self.reference_classwise_densities)</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="KDEyHD.aggregate">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyHD.aggregate">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="c1"># we retain all n*N examples (sampled from a mixture with uniform parameter), and then</span>
-        <span class="c1"># apply importance sampling (IS). In this version we compute D(p_alpha||q) with IS</span>
-        <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mix_densities</span><span class="p">)</span>
-
-        <span class="n">test_kde</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_kde_function</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-        <span class="n">test_densities</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pdf</span><span class="p">(</span><span class="n">test_kde</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_samples</span><span class="p">,</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">f_squared_hellinger</span><span class="p">(</span><span class="n">u</span><span class="p">):</span>
-            <span class="k">return</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">u</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-        
-        <span class="c1"># todo: this will fail when self.divergence is a callable, and is not the right place to do it anyway</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span> <span class="o">==</span> <span class="s1">&#39;hd&#39;</span><span class="p">:</span>
-            <span class="n">f</span> <span class="o">=</span> <span class="n">f_squared_hellinger</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;only squared HD is currently implemented&#39;</span><span class="p">)</span>
-
-        <span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-10</span>
-        <span class="n">qs</span> <span class="o">=</span> <span class="n">test_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
-        <span class="n">rs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_density</span> <span class="o">+</span> <span class="n">epsilon</span>
-        <span class="n">iw</span> <span class="o">=</span> <span class="n">qs</span><span class="o">/</span><span class="n">rs</span>  <span class="c1">#importance weights</span>
-        <span class="n">p_class</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">reference_classwise_densities</span> <span class="o">+</span> <span class="n">epsilon</span>
-        <span class="n">fracs</span> <span class="o">=</span> <span class="n">p_class</span><span class="o">/</span><span class="n">qs</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
-            <span class="c1"># ps / qs = (prev @ p_class) / qs = prev @ (p_class / qs) = prev @ fracs</span>
-            <span class="n">ps_div_qs</span> <span class="o">=</span> <span class="n">prev</span> <span class="o">@</span> <span class="n">fracs</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span> <span class="n">f</span><span class="p">(</span><span class="n">ps_div_qs</span><span class="p">)</span> <span class="o">*</span> <span class="n">iw</span> <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="KDEyCS">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">KDEyCS</span><span class="p">(</span><span class="n">AggregativeSoftQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Kernel Density Estimation model for quantification (KDEy) relying on the Cauchy-Schwarz divergence (CS) as</span>
-<span class="sd">    the divergence measure to be minimized. This method was first proposed in the paper</span>
-<span class="sd">    `Kernel Density Estimation for Multiclass Quantification &lt;https://link.springer.com/article/10.1007/s10994-024-06726-5&gt;`_ (`arXiv &lt;https://arxiv.org/abs/2401.00490&gt;`_), in which</span>
-<span class="sd">    the authors proposed a Monte Carlo approach for minimizing the divergence.</span>
-
-<span class="sd">    The distribution matching optimization problem comes down to solving:</span>
-
-<span class="sd">    :math:`\\hat{\\alpha} = \\arg\\min_{\\alpha\\in\\Delta^{n-1}} \\mathcal{D}(\\boldsymbol{p}_{\\alpha}||q_{\\widetilde{U}})`</span>
-
-<span class="sd">    where :math:`p_{\\alpha}` is the mixture of class-specific KDEs with mixture parameter (hence class prevalence)</span>
-<span class="sd">    :math:`\\alpha` defined by</span>
-
-<span class="sd">    :math:`\\boldsymbol{p}_{\\alpha}(\\widetilde{x}) = \\sum_{i=1}^n \\alpha_i p_{\\widetilde{L}_i}(\\widetilde{x})`</span>
-
-<span class="sd">    where :math:`p_X(\\boldsymbol{x}) = \\frac{1}{|X|} \\sum_{x_i\\in X} K\\left(\\frac{x-x_i}{h}\\right)` is the</span>
-<span class="sd">    KDE function that uses the datapoints in X as the kernel centers.</span>
-
-<span class="sd">    In KDEy-CS, the divergence is taken to be the Cauchy-Schwarz divergence given by:</span>
-
-<span class="sd">    :math:`\\mathcal{D}_{\\mathrm{CS}}(p||q)=-\\log\\left(\\frac{\\int p(x)q(x)dx}{\\sqrt{\\int p(x)^2dx \\int q(x)^2dx}}\\right)`</span>
-
-<span class="sd">    The authors showed that this distribution matching admits a closed-form solution</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-<span class="sd">    :param bandwidth: float, the bandwidth of the Kernel</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">bandwidth</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span> <span class="o">=</span> <span class="n">KDEBase</span><span class="o">.</span><span class="n">_check_bandwidth</span><span class="p">(</span><span class="n">bandwidth</span><span class="p">,</span> <span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="KDEyCS.gram_matrix_mix_sum">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.gram_matrix_mix_sum">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">gram_matrix_mix_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="c1"># this adapts the output of the rbf_kernel function (pairwise evaluations of Gaussian kernels k(x,y))</span>
-        <span class="c1"># to contain pairwise evaluations of N(x|mu,Sigma1+Sigma2) with mu=y and Sigma1 and Sigma2 are </span>
-        <span class="c1"># two &quot;scalar matrices&quot; (h^2)*I each, so Sigma1+Sigma2 has scalar 2(h^2) (h is the bandwidth)</span>
-        <span class="n">h</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bandwidth</span>
-        <span class="n">variance</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">h</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">nD</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">gamma</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">variance</span><span class="p">)</span>
-        <span class="n">norm_factor</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(((</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span><span class="o">**</span><span class="n">nD</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">variance</span><span class="o">**</span><span class="p">(</span><span class="n">nD</span><span class="p">)))</span>
-        <span class="n">gram</span> <span class="o">=</span> <span class="n">norm_factor</span> <span class="o">*</span> <span class="n">rbf_kernel</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">gram</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span></div>
-
-
-<div class="viewcode-block" id="KDEyCS.aggregation_fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregation_fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
-
-        <span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span>
-
-        <span class="c1"># counts_inv keeps track of the relative weight of each datapoint within its class</span>
-        <span class="c1"># (i.e., the weight in its KDE model)</span>
-        <span class="n">counts_inv</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">counts_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">n</span><span class="p">)))</span>
-
-        <span class="c1"># tr_tr_sums corresponds to symbol \overline{B} in the paper</span>
-        <span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="n">n</span><span class="p">,</span><span class="n">n</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-                <span class="k">if</span> <span class="n">i</span> <span class="o">&gt;</span> <span class="n">j</span><span class="p">:</span>
-                    <span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">]</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">block</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">P</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">i</span><span class="p">],</span> <span class="n">P</span><span class="p">[</span><span class="n">y</span> <span class="o">==</span> <span class="n">j</span><span class="p">]</span> <span class="k">if</span> <span class="n">i</span><span class="o">!=</span><span class="n">j</span> <span class="k">else</span> <span class="kc">None</span><span class="p">)</span>
-                    <span class="n">tr_tr_sums</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">block</span>
-
-        <span class="c1"># keep track of these data structures for the test phase</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span> <span class="o">=</span> <span class="n">P</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span> <span class="o">=</span> <span class="n">y</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="n">tr_tr_sums</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span> <span class="o">=</span> <span class="n">counts_inv</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-
-<div class="viewcode-block" id="KDEyCS.aggregate">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._kdey.KDEyCS.aggregate">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="n">Ptr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">Ptr</span>
-        <span class="n">Pte</span> <span class="o">=</span> <span class="n">posteriors</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ytr</span>
-        <span class="n">tr_tr_sums</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tr_tr_sums</span>
-
-        <span class="n">M</span><span class="p">,</span> <span class="n">nD</span> <span class="o">=</span> <span class="n">Pte</span><span class="o">.</span><span class="n">shape</span>
-        <span class="n">Minv</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">M</span><span class="p">)</span> <span class="c1"># t in the paper</span>
-        <span class="n">n</span> <span class="o">=</span> <span class="n">Ptr</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-
-        <span class="c1"># becomes a constant that does not affect the optimization, no need to compute it</span>
-        <span class="c1"># partC = 0.5*np.log(self.gram_matrix_mix_sum(Pte) * Kinv * Kinv)</span>
-
-        <span class="c1"># tr_te_sums corresponds to \overline{a}*(1/Li)*(1/M) in the paper (note the constants</span>
-        <span class="c1"># are already aggregated to tr_te_sums, so these multiplications are not carried out</span>
-        <span class="c1"># at each iteration of the optimization phase)</span>
-        <span class="n">tr_te_sums</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">n</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
-            <span class="n">tr_te_sums</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gram_matrix_mix_sum</span><span class="p">(</span><span class="n">Ptr</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">i</span><span class="p">],</span> <span class="n">Pte</span><span class="p">)</span> 
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">divergence</span><span class="p">(</span><span class="n">alpha</span><span class="p">):</span>
-            <span class="c1"># called \overline{r} in the paper</span>
-            <span class="n">alpha_ratio</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">counts_inv</span>
-
-            <span class="c1"># recall that tr_te_sums already accounts for the constant terms (1/Li)*(1/M)</span>
-            <span class="n">partA</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">((</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_te_sums</span><span class="p">)</span> <span class="o">*</span> <span class="n">Minv</span><span class="p">)</span>
-            <span class="n">partB</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">alpha_ratio</span> <span class="o">@</span> <span class="n">tr_tr_sums</span> <span class="o">@</span> <span class="n">alpha_ratio</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">partA</span> <span class="o">+</span> <span class="n">partB</span> <span class="c1">#+ partC</span>
-
-        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">optim_minimize</span><span class="p">(</span><span class="n">divergence</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span></div>
-</div>
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-  <h1>Source code for quapy.method._neural</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">random</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">torch</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn</span><span class="w"> </span><span class="kn">import</span> <span class="n">MSELoss</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">torch.nn.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">relu</span>
-
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">UPP</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">EarlyStop</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
-
-
-<div class="viewcode-block" id="QuaNetTrainer">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetTrainer</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Implementation of `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_, a neural network for</span>
-<span class="sd">    quantification. This implementation uses `PyTorch &lt;https://pytorch.org/&gt;`_ and can take advantage of GPU</span>
-<span class="sd">    for speeding-up the training phase.</span>
-
-<span class="sd">    Example:</span>
-
-<span class="sd">    &gt;&gt;&gt; import quapy as qp</span>
-<span class="sd">    &gt;&gt;&gt; from quapy.method.meta import QuaNet</span>
-<span class="sd">    &gt;&gt;&gt; from quapy.classification.neural import NeuralClassifierTrainer, CNNnet</span>
-<span class="sd">    &gt;&gt;&gt;</span>
-<span class="sd">    &gt;&gt;&gt; # use samples of 100 elements</span>
-<span class="sd">    &gt;&gt;&gt; qp.environ[&#39;SAMPLE_SIZE&#39;] = 100</span>
-<span class="sd">    &gt;&gt;&gt;</span>
-<span class="sd">    &gt;&gt;&gt; # load the Kindle dataset as text, and convert words to numerical indexes</span>
-<span class="sd">    &gt;&gt;&gt; dataset = qp.datasets.fetch_reviews(&#39;kindle&#39;, pickle=True)</span>
-<span class="sd">    &gt;&gt;&gt; qp.train.preprocessing.index(dataset, min_df=5, inplace=True)</span>
-<span class="sd">    &gt;&gt;&gt;</span>
-<span class="sd">    &gt;&gt;&gt; # the text classifier is a CNN trained by NeuralClassifierTrainer</span>
-<span class="sd">    &gt;&gt;&gt; cnn = CNNnet(dataset.vocabulary_size, dataset.n_classes)</span>
-<span class="sd">    &gt;&gt;&gt; classifier = NeuralClassifierTrainer(cnn, device=&#39;cuda&#39;)</span>
-<span class="sd">    &gt;&gt;&gt;</span>
-<span class="sd">    &gt;&gt;&gt; # train QuaNet (QuaNet is an alias to QuaNetTrainer)</span>
-<span class="sd">    &gt;&gt;&gt; model = QuaNet(classifier, qp.environ[&#39;SAMPLE_SIZE&#39;], device=&#39;cuda&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; model.fit(*dataset.training.Xy)</span>
-<span class="sd">    &gt;&gt;&gt; estim_prevalence = model.predict(dataset.test.instances)</span>
-
-<span class="sd">    :param classifier: an object implementing `fit` (i.e., that can be trained on labelled data),</span>
-<span class="sd">        `predict_proba` (i.e., that can generate posterior probabilities of unlabelled examples) and</span>
-<span class="sd">        `transform` (i.e., that can generate embedded representations of the unlabelled instances).</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param sample_size: integer, the sample size; default is None, meaning that the sample size should be</span>
-<span class="sd">        taken from qp.environ[&quot;SAMPLE_SIZE&quot;]</span>
-<span class="sd">    :param n_epochs: integer, maximum number of training epochs</span>
-<span class="sd">    :param tr_iter_per_poch: integer, number of training iterations before considering an epoch complete</span>
-<span class="sd">    :param va_iter_per_poch: integer, number of validation iterations to perform after each epoch</span>
-<span class="sd">    :param lr: float, the learning rate</span>
-<span class="sd">    :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cells</span>
-<span class="sd">    :param lstm_nlayers: integer, number of LSTM layers</span>
-<span class="sd">    :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the</span>
-<span class="sd">        quantification embedding</span>
-<span class="sd">    :param bidirectional: boolean, indicates whether the LSTM is bidirectional or not</span>
-<span class="sd">    :param qdrop_p: float, dropout probability</span>
-<span class="sd">    :param patience: integer, number of epochs showing no improvement in the validation set before stopping the</span>
-<span class="sd">        training phase (early stopping)</span>
-<span class="sd">    :param checkpointdir: string, a path where to store models&#39; checkpoints</span>
-<span class="sd">    :param checkpointname: string (optional), the name of the model&#39;s checkpoint</span>
-<span class="sd">    :param device: string, indicate &quot;cpu&quot; or &quot;cuda&quot;</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">classifier</span><span class="p">,</span>
-                 <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">sample_size</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">n_epochs</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                 <span class="n">tr_iter_per_poch</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
-                 <span class="n">va_iter_per_poch</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
-                 <span class="n">lr</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">,</span>
-                 <span class="n">lstm_hidden_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
-                 <span class="n">lstm_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">ff_layers</span><span class="o">=</span><span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
-                 <span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">qdrop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-                 <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
-                 <span class="n">checkpointdir</span><span class="o">=</span><span class="s1">&#39;../checkpoint&#39;</span><span class="p">,</span>
-                 <span class="n">checkpointname</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">):</span>
-
-        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="s1">&#39;transform&#39;</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;the classifier </span><span class="si">{</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> does not seem to be able to produce document embeddings &#39;</span> \
-                <span class="sa">f</span><span class="s1">&#39;since it does not implement the method &quot;transform&quot;&#39;</span>
-        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="s1">&#39;predict_proba&#39;</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;the classifier </span><span class="si">{</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> does not seem to be able to produce posterior probabilities &#39;</span> \
-                <span class="sa">f</span><span class="s1">&#39;since it does not implement the method &quot;predict_proba&quot;&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span> <span class="o">=</span> <span class="n">classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_classifier</span> <span class="o">=</span> <span class="n">fit_classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span> <span class="o">=</span> <span class="n">n_epochs</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tr_iter</span> <span class="o">=</span> <span class="n">tr_iter_per_poch</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">va_iter</span> <span class="o">=</span> <span class="n">va_iter_per_poch</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;lstm_hidden_size&#39;</span><span class="p">:</span> <span class="n">lstm_hidden_size</span><span class="p">,</span>
-            <span class="s1">&#39;lstm_nlayers&#39;</span><span class="p">:</span> <span class="n">lstm_nlayers</span><span class="p">,</span>
-            <span class="s1">&#39;ff_layers&#39;</span><span class="p">:</span> <span class="n">ff_layers</span><span class="p">,</span>
-            <span class="s1">&#39;bidirectional&#39;</span><span class="p">:</span> <span class="n">bidirectional</span><span class="p">,</span>
-            <span class="s1">&#39;qdrop_p&#39;</span><span class="p">:</span> <span class="n">qdrop_p</span>
-        <span class="p">}</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
-        <span class="k">if</span> <span class="n">checkpointname</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">local_random</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
-            <span class="n">random_code</span> <span class="o">=</span> <span class="s1">&#39;-&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">local_random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">))</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
-            <span class="n">checkpointname</span> <span class="o">=</span> <span class="s1">&#39;QuaNet-&#39;</span><span class="o">+</span><span class="n">random_code</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span> <span class="o">=</span> <span class="n">checkpointdir</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">checkpointname</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">())</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="QuaNetTrainer.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Trains QuaNet.</span>
-
-<span class="sd">        :param X: the training instances on which to train QuaNet. If `fit_classifier=True`, the data will be split in</span>
-<span class="sd">            40/40/20 for training the classifier, training QuaNet, and validating QuaNet, respectively. If</span>
-<span class="sd">            `fit_classifier=False`, the data will be split in 66/34 for training QuaNet and validating it, respectively.</span>
-<span class="sd">        :param y: the labels of X</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">classes_</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit_classifier</span><span class="p">:</span>
-            <span class="n">classifier_data</span><span class="p">,</span> <span class="n">unused_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
-            <span class="n">train_data</span><span class="p">,</span> <span class="n">valid_data</span> <span class="o">=</span> <span class="n">unused_data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.66</span><span class="p">)</span>  <span class="c1"># 0.66 split of 60% makes 40% and 20%</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">classifier_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">classifier_data</span> <span class="o">=</span> <span class="kc">None</span>
-            <span class="n">train_data</span><span class="p">,</span> <span class="n">valid_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.66</span><span class="p">)</span>
-
-        <span class="c1"># estimate the hard and soft stats tpr and fpr of the classifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tr_prev</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-
-        <span class="c1"># compute the posterior probabilities of the instances</span>
-        <span class="n">valid_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">valid_data</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-        <span class="n">train_posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-        <span class="c1"># turn instances&#39; original representations into embeddings</span>
-        <span class="n">valid_data_embed</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">valid_data</span><span class="o">.</span><span class="n">instances</span><span class="p">),</span> <span class="n">valid_data</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
-        <span class="n">train_data_embed</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">train_data</span><span class="o">.</span><span class="n">instances</span><span class="p">),</span> <span class="n">train_data</span><span class="o">.</span><span class="n">labels</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;cc&#39;</span><span class="p">:</span> <span class="n">CC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
-            <span class="s1">&#39;acc&#39;</span><span class="p">:</span> <span class="n">ACC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
-            <span class="s1">&#39;pcc&#39;</span><span class="p">:</span> <span class="n">PCC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
-            <span class="s1">&#39;pacc&#39;</span><span class="p">:</span> <span class="n">PACC</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">),</span>
-        <span class="p">}</span>
-        <span class="k">if</span> <span class="n">classifier_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">[</span><span class="s1">&#39;emq&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">valid_data</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="p">{</span>
-            <span class="s1">&#39;tr-loss&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
-            <span class="s1">&#39;va-loss&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
-            <span class="s1">&#39;tr-mae&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
-            <span class="s1">&#39;va-mae&#39;</span><span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span>
-        <span class="p">}</span>
-
-        <span class="n">nQ</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="p">)</span>
-        <span class="n">nC</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span> <span class="o">=</span> <span class="n">QuaNetModule</span><span class="p">(</span>
-            <span class="n">doc_embedding_size</span><span class="o">=</span><span class="n">train_data_embed</span><span class="o">.</span><span class="n">instances</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-            <span class="n">n_classes</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span>
-            <span class="n">stats_size</span><span class="o">=</span><span class="n">nQ</span><span class="o">*</span><span class="n">nC</span><span class="p">,</span>
-            <span class="n">order_by</span><span class="o">=</span><span class="mi">0</span> <span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">binary</span> <span class="k">else</span> <span class="kc">None</span><span class="p">,</span>
-            <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">optim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">Adam</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr</span><span class="p">)</span>
-        <span class="n">early_stop</span> <span class="o">=</span> <span class="n">EarlyStop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">checkpoint</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span>
-
-        <span class="k">for</span> <span class="n">epoch_i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_epochs</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_epoch</span><span class="p">(</span><span class="n">train_data_embed</span><span class="p">,</span> <span class="n">train_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tr_iter</span><span class="p">,</span> <span class="n">epoch_i</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_epoch</span><span class="p">(</span><span class="n">valid_data_embed</span><span class="p">,</span> <span class="n">valid_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">va_iter</span><span class="p">,</span> <span class="n">epoch_i</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-            <span class="n">early_stop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;va-loss&#39;</span><span class="p">],</span> <span class="n">epoch_i</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
-                <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">checkpoint</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">early_stop</span><span class="o">.</span><span class="n">STOP</span><span class="p">:</span>
-                <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
-                    <span class="sa">f</span><span class="s1">&#39;training ended by patience exhausted; loading best model parameters in </span><span class="si">{</span><span class="n">checkpoint</span><span class="si">}</span><span class="s1"> &#39;</span>
-                    <span class="sa">f</span><span class="s1">&#39;for epoch </span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">best_epoch</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">))</span>
-                <span class="k">break</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_get_aggregative_estims</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">):</span>
-        <span class="n">label_predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">posteriors</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">prevs_estim</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">quantifier</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantifiers</span><span class="o">.</span><span class="n">values</span><span class="p">():</span>
-            <span class="n">predictions</span> <span class="o">=</span> <span class="n">posteriors</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">)</span> <span class="k">else</span> <span class="n">label_predictions</span>
-            <span class="n">prevs_estim</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">quantifier</span><span class="o">.</span><span class="n">aggregate</span><span class="p">(</span><span class="n">predictions</span><span class="p">))</span>
-
-        <span class="c1"># there is no real need for adding static estims like the TPR or FPR from training since those are constant</span>
-
-        <span class="k">return</span> <span class="n">prevs_estim</span>
-
-<div class="viewcode-block" id="QuaNetTrainer.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">posteriors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">posteriors</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
-        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-            <span class="n">prevalence</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">embeddings</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">):</span>
-                <span class="n">prevalence</span> <span class="o">=</span> <span class="n">prevalence</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
-            <span class="n">prevalence</span> <span class="o">=</span> <span class="n">prevalence</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">prevalence</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_epoch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">posteriors</span><span class="p">,</span> <span class="n">iterations</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">early_stop</span><span class="p">,</span> <span class="n">train</span><span class="p">):</span>
-        <span class="n">mse_loss</span> <span class="o">=</span> <span class="n">MSELoss</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">train</span><span class="p">)</span>
-        <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">mae_errors</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">sampler</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span>
-            <span class="n">data</span><span class="p">,</span>
-            <span class="n">sample_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_size</span><span class="p">,</span>
-            <span class="n">repeats</span><span class="o">=</span><span class="n">iterations</span><span class="p">,</span>
-            <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span> <span class="k">if</span> <span class="n">train</span> <span class="k">else</span> <span class="mi">0</span>  <span class="c1"># different samples during train, same samples during validation</span>
-        <span class="p">)</span>
-        <span class="n">pbar</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">sampler</span><span class="o">.</span><span class="n">samples_parameters</span><span class="p">(),</span> <span class="n">total</span><span class="o">=</span><span class="n">sampler</span><span class="o">.</span><span class="n">total</span><span class="p">())</span>
-        <span class="k">for</span> <span class="n">it</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pbar</span><span class="p">):</span>
-            <span class="n">sample_data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
-            <span class="n">sample_posteriors</span> <span class="o">=</span> <span class="n">posteriors</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
-            <span class="n">quant_estims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_aggregative_estims</span><span class="p">(</span><span class="n">sample_posteriors</span><span class="p">)</span>
-            <span class="n">ptrue</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span><span class="n">sample_data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">train</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
-                <span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">sample_data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">sample_posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
-                <span class="n">loss</span> <span class="o">=</span> <span class="n">mse_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
-                <span class="n">mae</span> <span class="o">=</span> <span class="n">mae_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
-                <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-                    <span class="n">phat</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">sample_data</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span> <span class="n">sample_posteriors</span><span class="p">,</span> <span class="n">quant_estims</span><span class="p">)</span>
-                    <span class="n">loss</span> <span class="o">=</span> <span class="n">mse_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
-                    <span class="n">mae</span> <span class="o">=</span> <span class="n">mae_loss</span><span class="p">(</span><span class="n">phat</span><span class="p">,</span> <span class="n">ptrue</span><span class="p">)</span>
-
-            <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-            <span class="n">mae_errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mae</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
-
-            <span class="n">mse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span>
-            <span class="n">mae</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">mae_errors</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">train</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;tr-loss&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;tr-mae&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mae</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;va-loss&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mse</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s1">&#39;va-mae&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mae</span>
-
-            <span class="k">if</span> <span class="n">train</span><span class="p">:</span>
-                <span class="n">pbar</span><span class="o">.</span><span class="n">set_description</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[QuaNet] &#39;</span>
-                                     <span class="sa">f</span><span class="s1">&#39;epoch=</span><span class="si">{</span><span class="n">epoch</span><span class="si">}</span><span class="s1"> [it=</span><span class="si">{</span><span class="n">it</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">iterations</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1">&#39;</span>
-                                     <span class="sa">f</span><span class="s1">&#39;tr-mseloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr-loss&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> tr-maeloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;tr-mae&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="se">\t</span><span class="s1">&#39;</span>
-                                     <span class="sa">f</span><span class="s1">&#39;val-mseloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;va-loss&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> val-maeloss=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="p">[</span><span class="s2">&quot;va-mae&quot;</span><span class="p">]</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> &#39;</span>
-                                     <span class="sa">f</span><span class="s1">&#39;patience=</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">patience</span><span class="si">}</span><span class="s1">/</span><span class="si">{</span><span class="n">early_stop</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="QuaNetTrainer.get_params">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.get_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-        <span class="n">classifier_params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">get_params</span><span class="p">()</span>
-        <span class="n">classifier_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__&#39;</span><span class="o">+</span><span class="n">k</span><span class="p">:</span><span class="n">v</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">classifier_params</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
-        <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">classifier_params</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">}</span></div>
-
-
-<div class="viewcode-block" id="QuaNetTrainer.set_params">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.set_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
-        <span class="n">learner_params</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">parameters</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">quanet_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">val</span>
-            <span class="k">elif</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;classifier__&#39;</span><span class="p">):</span>
-                <span class="n">learner_params</span><span class="p">[</span><span class="n">key</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s1">&#39;classifier__&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">)]</span> <span class="o">=</span> <span class="n">val</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;unknown parameter &#39;</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">learner_params</span><span class="p">)</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">__check_params_colision</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quanet_params</span><span class="p">,</span> <span class="n">learner_params</span><span class="p">):</span>
-        <span class="n">quanet_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">quanet_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="n">learner_keys</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">learner_params</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-        <span class="n">intersection</span> <span class="o">=</span> <span class="n">quanet_keys</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="n">learner_keys</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">intersection</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the use of parameters </span><span class="si">{</span><span class="n">intersection</span><span class="si">}</span><span class="s1"> is ambiguous sine those can refer to &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;the parameters of QuaNet or the learner </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Removes the checkpoint</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpoint</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="QuaNetTrainer.clean_checkpoint_dir">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetTrainer.clean_checkpoint_dir">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">clean_checkpoint_dir</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Removes anything contained in the checkpoint directory</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="kn">import</span><span class="w"> </span><span class="nn">shutil</span>
-        <span class="n">shutil</span><span class="o">.</span><span class="n">rmtree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">checkpointdir</span><span class="p">,</span> <span class="n">ignore_errors</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-    <span class="nd">@property</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">classes_</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span></div>
-
-
-
-<div class="viewcode-block" id="mae_loss">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.mae_loss">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">mae_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Torch-like wrapper for the Mean Absolute Error</span>
-
-<span class="sd">    :param output: predictions</span>
-<span class="sd">    :param target: ground truth values</span>
-<span class="sd">    :return: mean absolute error loss</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">output</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span></div>
-
-
-
-<div class="viewcode-block" id="QuaNetModule">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">QuaNetModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Implements the `QuaNet &lt;https://dl.acm.org/doi/abs/10.1145/3269206.3269287&gt;`_ forward pass.</span>
-<span class="sd">    See :class:`QuaNetTrainer` for training QuaNet.</span>
-
-<span class="sd">    :param doc_embedding_size: integer, the dimensionality of the document embeddings</span>
-<span class="sd">    :param n_classes: integer, number of classes</span>
-<span class="sd">    :param stats_size: integer, number of statistics estimated by simple quantification methods</span>
-<span class="sd">    :param lstm_hidden_size: integer, hidden dimensionality of the LSTM cell</span>
-<span class="sd">    :param lstm_nlayers: integer, number of LSTM layers</span>
-<span class="sd">    :param ff_layers: list of integers, dimensions of the densely-connected FF layers on top of the</span>
-<span class="sd">        quantification embedding</span>
-<span class="sd">    :param bidirectional: boolean, whether or not to use bidirectional LSTM</span>
-<span class="sd">    :param qdrop_p: float, dropout probability</span>
-<span class="sd">    :param order_by: integer, class for which the document embeddings are to be sorted</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">doc_embedding_size</span><span class="p">,</span>
-                 <span class="n">n_classes</span><span class="p">,</span>
-                 <span class="n">stats_size</span><span class="p">,</span>
-                 <span class="n">lstm_hidden_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
-                 <span class="n">lstm_nlayers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">ff_layers</span><span class="o">=</span><span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
-                 <span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">qdrop_p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
-                 <span class="n">order_by</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_classes</span> <span class="o">=</span> <span class="n">n_classes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">order_by</span> <span class="o">=</span> <span class="n">order_by</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">lstm_hidden_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">=</span> <span class="n">lstm_nlayers</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="o">=</span> <span class="n">bidirectional</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">qdrop_p</span> <span class="o">=</span> <span class="n">qdrop_p</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">doc_embedding_size</span> <span class="o">+</span> <span class="n">n_classes</span><span class="p">,</span>  <span class="c1"># +n_classes stands for the posterior probs. (concatenated)</span>
-                                  <span class="n">lstm_hidden_size</span><span class="p">,</span> <span class="n">lstm_nlayers</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="n">bidirectional</span><span class="p">,</span>
-                                  <span class="n">dropout</span><span class="o">=</span><span class="n">qdrop_p</span><span class="p">,</span> <span class="n">batch_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">qdrop_p</span><span class="p">)</span>
-
-        <span class="n">lstm_output_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span>
-        <span class="n">ff_input_size</span> <span class="o">=</span> <span class="n">lstm_output_size</span> <span class="o">+</span> <span class="n">stats_size</span>
-        <span class="n">prev_size</span> <span class="o">=</span> <span class="n">ff_input_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">lin_size</span> <span class="ow">in</span> <span class="n">ff_layers</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">lin_size</span><span class="p">))</span>
-            <span class="n">prev_size</span> <span class="o">=</span> <span class="n">lin_size</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">prev_size</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">device</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span> <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_init_hidden</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">directions</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">bidirectional</span> <span class="k">else</span> <span class="mi">1</span>
-        <span class="n">var_hidden</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
-        <span class="n">var_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span> <span class="o">*</span> <span class="n">directions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
-        <span class="k">if</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">:</span>
-            <span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span> <span class="o">=</span> <span class="n">var_hidden</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">var_cell</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">var_hidden</span><span class="p">,</span> <span class="n">var_cell</span>
-
-<div class="viewcode-block" id="QuaNetModule.forward">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._neural.QuaNetModule.forward">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
-        <span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">device</span>
-        <span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-        <span class="n">statistics</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">statistics</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_by</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">order</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">doc_posteriors</span><span class="p">[:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order_by</span><span class="p">])</span>
-            <span class="n">doc_embeddings</span> <span class="o">=</span> <span class="n">doc_embeddings</span><span class="p">[</span><span class="n">order</span><span class="p">]</span>
-            <span class="n">doc_posteriors</span> <span class="o">=</span> <span class="n">doc_posteriors</span><span class="p">[</span><span class="n">order</span><span class="p">]</span>
-
-        <span class="n">embeded_posteriors</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">doc_embeddings</span><span class="p">,</span> <span class="n">doc_posteriors</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="c1"># the entire set represents only one instance in quapy contexts, and so the batch_size=1</span>
-        <span class="c1"># the shape should be (1, number-of-instances, embedding-size + n_classes)</span>
-        <span class="n">embeded_posteriors</span> <span class="o">=</span> <span class="n">embeded_posteriors</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="o">.</span><span class="n">flatten_parameters</span><span class="p">()</span>
-        <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">rnn_hidden</span><span class="p">,</span><span class="n">_</span><span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span><span class="p">(</span><span class="n">embeded_posteriors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_hidden</span><span class="p">())</span>
-        <span class="n">rnn_hidden</span> <span class="o">=</span> <span class="n">rnn_hidden</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nlayers</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ndirections</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span><span class="p">)</span>
-        <span class="n">quant_embedding</span> <span class="o">=</span> <span class="n">rnn_hidden</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">quant_embedding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">quant_embedding</span><span class="p">,</span> <span class="n">statistics</span><span class="p">))</span>
-
-        <span class="n">abstracted</span> <span class="o">=</span> <span class="n">quant_embedding</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">linear</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ff_layers</span><span class="p">:</span>
-            <span class="n">abstracted</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">relu</span><span class="p">(</span><span class="n">linear</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)))</span>
-
-        <span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output</span><span class="p">(</span><span class="n">abstracted</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">prevalence</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">prevalence</span></div>
-</div>
-
-
-
-
-
-
-</pre></div>
-
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-  <h1>Source code for quapy.method._threshold_optim</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">abstractmethod</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BinaryAggregativeQuantifier</span>
-
-
-<div class="viewcode-block" id="ThresholdOptimization">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">ThresholdOptimization</span><span class="p">(</span><span class="n">BinaryAggregativeQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Abstract class of Threshold Optimization variants for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_.</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-<span class="sd">    The different variants are based on different heuristics for choosing a decision threshold</span>
-<span class="sd">    that would allow for more true positives and many more false positives, on the grounds this</span>
-<span class="sd">    would deliver larger denominators.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-
-<span class="sd">    :param n_jobs: number of parallel workers</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ThresholdOptimization.condition">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.condition">[docs]</a>
-    <span class="nd">@abstractmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Implements the criterion according to which the threshold should be selected.</span>
-<span class="sd">        This function should return the (float) score to be minimized.</span>
-
-<span class="sd">        :param tpr: float, true positive rate</span>
-<span class="sd">        :param fpr: float, false positive rate</span>
-<span class="sd">        :return: float, a score for the given `tpr` and `fpr`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="o">...</span></div>
-
-
-<div class="viewcode-block" id="ThresholdOptimization.discard">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.discard">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Indicates whether a combination of tpr and fpr should be discarded</span>
-
-<span class="sd">        :param tpr: float, true positive rate</span>
-<span class="sd">        :param fpr: float, false positive rate</span>
-<span class="sd">        :return: true if the combination is to be discarded, false otherwise</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span></div>
-
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_eval_candidate_thresholds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Seeks for the best `tpr` and `fpr` according to the score obtained at different</span>
-<span class="sd">        decision thresholds. The scoring function is implemented in function `_condition`.</span>
-
-<span class="sd">        :param decision_scores: array-like with the classification scores</span>
-<span class="sd">        :param y: predicted labels for the validation set (or for the training set via `k`-fold cross validation)</span>
-<span class="sd">        :return: best `tpr` and `fpr` and `threshold` according to `_condition`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">candidate_thresholds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">)</span>
-
-        <span class="n">candidates</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">candidate_threshold</span> <span class="ow">in</span> <span class="n">candidate_thresholds</span><span class="p">:</span>
-            <span class="n">y_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">[</span><span class="mi">1</span> <span class="o">*</span> <span class="p">(</span><span class="n">decision_scores</span> <span class="o">&gt;=</span> <span class="n">candidate_threshold</span><span class="p">)]</span>
-            <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_table</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">)</span>
-            <span class="n">tpr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_tpr</span><span class="p">(</span><span class="n">TP</span><span class="p">,</span> <span class="n">FN</span><span class="p">)</span>
-            <span class="n">fpr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_fpr</span><span class="p">(</span><span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">)</span>
-            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">discard</span><span class="p">(</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">):</span>
-                <span class="n">candidate_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">condition</span><span class="p">(</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span>
-                <span class="n">candidates</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">candidate_threshold</span><span class="p">])</span>
-                <span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">candidate_score</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="c1"># if no candidate gives rise to a valid combination of tpr and fpr, this method defaults to the standard</span>
-            <span class="c1"># classify &amp; count; this is akin to assign tpr=1, fpr=0, threshold=0</span>
-            <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">threshold</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
-            <span class="n">candidates</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">,</span> <span class="n">threshold</span><span class="p">])</span>
-            <span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">candidates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">candidates</span><span class="p">)</span>
-        <span class="n">candidates</span> <span class="o">=</span> <span class="n">candidates</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">scores</span><span class="p">)]</span>  <span class="c1"># sort candidates by candidate_score</span>
-
-        <span class="k">return</span> <span class="n">candidates</span>
-
-<div class="viewcode-block" id="ThresholdOptimization.aggregate_with_threshold">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate_with_threshold">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate_with_threshold</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">tprs</span><span class="p">,</span> <span class="n">fprs</span><span class="p">,</span> <span class="n">thresholds</span><span class="p">):</span>
-        <span class="c1"># This function performs the adjusted count for given tpr, fpr, and threshold.</span>
-        <span class="c1"># Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length &gt; 1</span>
-        <span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">thresholds</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">prevs_estims</span> <span class="o">=</span> <span class="p">(</span><span class="n">prevs_estims</span> <span class="o">-</span> <span class="n">fprs</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">tprs</span> <span class="o">-</span> <span class="n">fprs</span><span class="p">)</span>
-        <span class="n">prevs_estims</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">as_binary_prevalence</span><span class="p">(</span><span class="n">prevs_estims</span><span class="p">,</span> <span class="n">clip_if_necessary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">prevs_estims</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_table</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">y_</span><span class="p">):</span>
-        <span class="n">TP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">FP</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">FN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">!=</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">pos_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="n">TN</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">y</span> <span class="o">==</span> <span class="n">y_</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">neg_label</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-        <span class="k">return</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">FN</span><span class="p">,</span> <span class="n">TN</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_tpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TP</span><span class="p">,</span> <span class="n">FN</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">TP</span> <span class="o">+</span> <span class="n">FN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">1</span>
-        <span class="k">return</span> <span class="n">TP</span> <span class="o">/</span> <span class="p">(</span><span class="n">TP</span> <span class="o">+</span> <span class="n">FN</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_fpr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">FP</span><span class="p">,</span> <span class="n">TN</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">return</span> <span class="mi">0</span>
-        <span class="k">return</span> <span class="n">FP</span> <span class="o">/</span> <span class="p">(</span><span class="n">FP</span> <span class="o">+</span> <span class="n">TN</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ThresholdOptimization.aggregation_fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregation_fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
-        <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
-        <span class="c1"># the standard behavior is to keep the best threshold only</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="ThresholdOptimization.aggregate">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.ThresholdOptimization.aggregate">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="c1"># the standard behavior is to compute the adjusted count using the best threshold found</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fpr</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">threshold</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="T50">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">T50</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Threshold Optimization variant for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_ that looks</span>
-<span class="sd">    for the threshold that makes `tpr` closest to 0.5.</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-
-<div class="viewcode-block" id="T50.condition">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.T50.condition">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tpr</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="MAX">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">MAX</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Threshold Optimization variant for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_ that looks</span>
-<span class="sd">    for the threshold that maximizes `tpr-fpr`.</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-
-<div class="viewcode-block" id="MAX.condition">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MAX.condition">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="c1"># MAX strives to maximize (tpr - fpr), which is equivalent to minimize (fpr - tpr)</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">fpr</span> <span class="o">-</span> <span class="n">tpr</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="X">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">X</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Threshold Optimization variant for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_ that looks</span>
-<span class="sd">    for the threshold that yields `tpr=1-fpr`.</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-
-<div class="viewcode-block" id="X.condition">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.X.condition">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="k">return</span> <span class="nb">abs</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tpr</span> <span class="o">+</span> <span class="n">fpr</span><span class="p">))</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="MS">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">MS</span><span class="p">(</span><span class="n">ThresholdOptimization</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Median Sweep. Threshold Optimization variant for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_ that generates</span>
-<span class="sd">    class prevalence estimates for all decision thresholds and returns the median of them all.</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-
-<div class="viewcode-block" id="MS.condition">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.condition">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">condition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
-        <span class="k">return</span> <span class="mi">1</span></div>
-
-
-<div class="viewcode-block" id="MS.aggregation_fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregation_fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregation_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
-        <span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">classif_predictions</span><span class="p">,</span> <span class="n">labels</span>
-        <span class="c1"># keeps all candidates</span>
-        <span class="n">tprs_fprs_thresholds</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_eval_candidate_thresholds</span><span class="p">(</span><span class="n">decision_scores</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span> <span class="o">=</span> <span class="n">tprs_fprs_thresholds</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="MS.aggregate">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS.aggregate">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">aggregate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classif_predictions</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
-        <span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aggregate_with_threshold</span><span class="p">(</span><span class="n">classif_predictions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">fprs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">thresholds</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">prevalences</span><span class="o">.</span><span class="n">ndim</span><span class="o">==</span><span class="mi">2</span><span class="p">:</span>
-            <span class="n">prevalences</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevalences</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">prevalences</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="MS2">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">MS2</span><span class="p">(</span><span class="n">MS</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Median Sweep 2. Threshold Optimization variant for :class:`ACC` as proposed by</span>
-<span class="sd">    `Forman 2006 &lt;https://dl.acm.org/doi/abs/10.1145/1150402.1150423&gt;`_ and</span>
-<span class="sd">    `Forman 2008 &lt;https://link.springer.com/article/10.1007/s10618-008-0097-y&gt;`_ that generates</span>
-<span class="sd">    class prevalence estimates for all decision thresholds and returns the median of for cases in</span>
-<span class="sd">    which `tpr-fpr&gt;0.25`</span>
-<span class="sd">    The goal is to bring improved stability to the denominator of the adjustment.</span>
-
-<span class="sd">    :param classifier: a scikit-learn&#39;s BaseEstimator, or None, in which case the classifier is taken to be</span>
-<span class="sd">        the one indicated in `qp.environ[&#39;DEFAULT_CLS&#39;]`</span>
-<span class="sd">    :param fit_classifier: whether to train the learner (default is True). Set to False if the</span>
-<span class="sd">        learner has been trained outside the quantifier.</span>
-<span class="sd">    :param val_split: specifies the data used for generating classifier predictions. This specification</span>
-<span class="sd">        can be made as float in (0, 1) indicating the proportion of stratified held-out validation set to</span>
-<span class="sd">        be extracted from the training set; or as an integer (default 5), indicating that the predictions</span>
-<span class="sd">        are to be generated in a `k`-fold cross-validation manner (with this integer indicating the value</span>
-<span class="sd">        for `k`); or as a tuple (X,y) defining the specific set of data to use for validation.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">classifier</span><span class="p">:</span> <span class="n">BaseEstimator</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">classifier</span><span class="p">,</span> <span class="n">fit_classifier</span><span class="p">,</span> <span class="n">val_split</span><span class="p">)</span>
-
-<div class="viewcode-block" id="MS2.discard">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method._threshold_optim.MS2.discard">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">discard</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tpr</span><span class="p">,</span> <span class="n">fpr</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-        <span class="k">return</span> <span class="p">(</span><span class="n">tpr</span><span class="o">-</span><span class="n">fpr</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mf">0.25</span></div>
-</div>
-
-</pre></div>
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-  <h1>Source code for quapy.method.base</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">warnings</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">ABCMeta</span><span class="p">,</span> <span class="n">abstractmethod</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
-
-<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseEstimator</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-
-
-<span class="c1"># Base Quantifier abstract class</span>
-<span class="c1"># ------------------------------------</span>
-<div class="viewcode-block" id="BaseQuantifier">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">BaseQuantifier</span><span class="p">(</span><span class="n">BaseEstimator</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Abstract Quantifier. A quantifier is defined as an object of a class that implements the method :meth:`fit` on</span>
-<span class="sd">    a pair X, y, the method :meth:`predict`, and the :meth:`set_params` and</span>
-<span class="sd">    :meth:`get_params` for model selection (see :meth:`quapy.model_selection.GridSearchQ`)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-<div class="viewcode-block" id="BaseQuantifier.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.fit">[docs]</a>
-    <span class="nd">@abstractmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Generates a quantifier.</span>
-
-<span class="sd">        :param X: array-like, the training instances</span>
-<span class="sd">        :param y: array-like, the labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="o">...</span></div>
-
-
-<div class="viewcode-block" id="BaseQuantifier.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.predict">[docs]</a>
-    <span class="nd">@abstractmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Generate class prevalence estimates for the sample&#39;s instances</span>
-
-<span class="sd">        :param X: array-like, the test instances</span>
-<span class="sd">        :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="o">...</span></div>
-
-
-<div class="viewcode-block" id="BaseQuantifier.quantify">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BaseQuantifier.quantify">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Alias to :meth:`predict`, for old compatibility</span>
-
-<span class="sd">        :param X: array-like</span>
-<span class="sd">        :return: `np.ndarray` of shape `(n_classes,)` with class prevalence estimates.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="BinaryQuantifier">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.BinaryQuantifier">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">BinaryQuantifier</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Abstract class of binary quantifiers, i.e., quantifiers estimating class prevalence values for only two classes</span>
-<span class="sd">    (typically, to be interpreted as one class and its complement).</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_check_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">quantifier_name</span><span class="p">):</span>
-        <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-        <span class="k">assert</span> <span class="n">n_classes</span><span class="o">==</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> works only on problems of binary classification. &#39;</span> \
-                            <span class="sa">f</span><span class="s1">&#39;Use the class OneVsAll to enable </span><span class="si">{</span><span class="n">quantifier_name</span><span class="si">}</span><span class="s1"> work on single-label data.&#39;</span></div>
-
-
-
-<div class="viewcode-block" id="OneVsAll">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAll">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAll</span><span class="p">:</span>
-    <span class="k">pass</span></div>
-
-
-
-<div class="viewcode-block" id="newOneVsAll">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.newOneVsAll">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">newOneVsAll</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
-        <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">OneVsAllAggregative</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">OneVsAllGeneric</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="OneVsAllGeneric">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">OneVsAllGeneric</span><span class="p">(</span><span class="n">OneVsAll</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Allows any binary quantifier to perform quantification on single-label datasets. The method maintains one binary</span>
-<span class="sd">    quantifier for each class, and then l1-normalizes the outputs so that the class prevalence values sum up to 1.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">binary_quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">),</span> \
-            <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">binary_quantifier</span><span class="si">}</span><span class="s1"> does not seem to be a Quantifier&#39;</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">binary_quantifier</span><span class="p">,</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">AggregativeQuantifier</span><span class="p">):</span>
-            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s1">&#39;the quantifier seems to be an instance of qp.method.aggregative.AggregativeQuantifier; &#39;</span>
-                           <span class="sa">f</span><span class="s1">&#39;you might prefer instantiating </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">OneVsAllAggregative</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span> <span class="o">=</span> <span class="n">binary_quantifier</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
-
-<div class="viewcode-block" id="OneVsAllGeneric.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
-        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span><span class="o">!=</span><span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> expect non-binary data&#39;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span> <span class="o">=</span> <span class="p">{</span><span class="n">c</span><span class="p">:</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">binary_quantifier</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span><span class="p">}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_fit</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
-            <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;threading&#39;</span><span class="p">)(</span>
-                <span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">c</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes</span>
-            <span class="p">)</span>
-        <span class="p">)</span>
-
-<div class="viewcode-block" id="OneVsAllGeneric.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.base.OneVsAllGeneric.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">prevalences</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parallel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_delayed_binary_predict</span><span class="p">,</span> <span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">normalize_prevalence</span><span class="p">(</span><span class="n">prevalences</span><span class="p">)</span></div>
-
-
-    <span class="c1"># @property</span>
-    <span class="c1"># def classes_(self):</span>
-    <span class="c1">#     return sorted(self.dict_binary_quantifiers.keys())</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_delayed_binary_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="n">bindata</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">==</span> <span class="n">c</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="p">[</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">dict_binary_quantifiers</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">bindata</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span></div>
-
-</pre></div>
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-  <h1>Source code for quapy.method.non_aggregative</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span><span class="w"> </span><span class="nn">itertools</span><span class="w"> </span><span class="kn">import</span> <span class="n">product</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">tqdm</span><span class="w"> </span><span class="kn">import</span> <span class="n">tqdm</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Counter</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.feature_extraction.text</span><span class="w"> </span><span class="kn">import</span> <span class="n">CountVectorizer</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">normalize</span>
-
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.confidence</span><span class="w"> </span><span class="kn">import</span> <span class="n">WithConfidenceABC</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="kn">import</span> <span class="n">get_divergence</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">BinaryQuantifier</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._helper</span><span class="w"> </span><span class="kn">import</span> <span class="n">_labels_to_indices</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method._energy</span><span class="w"> </span><span class="kn">import</span> <span class="n">_EnergyDistanceCore</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy.functional</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">F</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.optimize</span><span class="w"> </span><span class="kn">import</span> <span class="n">lsq_linear</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">scipy</span><span class="w"> </span><span class="kn">import</span> <span class="n">sparse</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-
-
-<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">MaximumLikelihoodPrevalenceEstimation</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    The `Maximum Likelihood Prevalence Estimation` (MLPE) method is a lazy method that assumes there is no prior</span>
-<span class="sd">    probability shift between training and test instances (put it other way, that the i.i.d. assumpion holds).</span>
-<span class="sd">    The estimation of class prevalence values for any test sample is always (i.e., irrespective of the test sample</span>
-<span class="sd">    itself) the class prevalence seen during training. This method is considered to be a lower-bound quantifier that</span>
-<span class="sd">    any quantification method should beat.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Computes the training prevalence and stores it.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)`, the labels</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">classes_from_labels</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">prevalence_from_labels</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_classes_</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="MaximumLikelihoodPrevalenceEstimation.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.MaximumLikelihoodPrevalenceEstimation.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Ignores the input instances and returns, as the class prevalence estimantes, the training prevalence.</span>
-
-<span class="sd">        :param X: array-like (ignored)</span>
-<span class="sd">        :return: the class prevalence seen during training</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimated_prevalence</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="DMx">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">DMx</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Generic Distribution Matching quantifier for binary or multiclass quantification based on the space of covariates.</span>
-<span class="sd">    This implementation takes the number of bins, the divergence, and the possibility to work on CDF as hyperparameters.</span>
-
-<span class="sd">    :param nbins: number of bins used to discretize the distributions (default 8)</span>
-<span class="sd">    :param divergence: a string representing a divergence measure (currently, &quot;HD&quot; and &quot;topsoe&quot; are implemented)</span>
-<span class="sd">        or a callable function taking two ndarrays of the same dimension as input (default &quot;HD&quot;, meaning Hellinger</span>
-<span class="sd">        Distance)</span>
-<span class="sd">    :param cdf: whether to use CDF instead of PDF (default False)</span>
-<span class="sd">    :param search: string indicating the search strategy used to estimate the prevalence values.</span>
-<span class="sd">        Valid options are `optim_minimize` (default, works for binary and multiclass problems),</span>
-<span class="sd">        `linear_search` (binary only), and `ternary_search` (binary only)</span>
-<span class="sd">    :param n_jobs: number of parallel workers (default None)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">divergence</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;optim_minimize&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nbins</span> <span class="o">=</span> <span class="n">nbins</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">divergence</span> <span class="o">=</span> <span class="n">divergence</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span> <span class="o">=</span> <span class="n">cdf</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">search</span> <span class="o">=</span> <span class="n">search</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">n_jobs</span>
-
-<div class="viewcode-block" id="DMx.HDx">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.HDx">[docs]</a>
-    <span class="nd">@classmethod</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">HDx</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        `Hellinger Distance x &lt;https://www.sciencedirect.com/science/article/pii/S0020025512004069&gt;`_ (HDx).</span>
-<span class="sd">        HDx is a method for training binary quantifiers, that models quantification as the problem of</span>
-<span class="sd">        minimizing the average divergence (in terms of the Hellinger Distance) across the feature-specific normalized</span>
-<span class="sd">        histograms of two representations, one for the unlabelled examples, and another generated from the training</span>
-<span class="sd">        examples as a mixture model of the class-specific representations. The parameters of the mixture thus represent</span>
-<span class="sd">        the estimates of the class prevalence values.</span>
-
-<span class="sd">        The method computes all matchings for nbins in [10, 20, ..., 110] and reports the mean of the median.</span>
-<span class="sd">        The best prevalence is searched via linear search, from 0 to 1 stepping by 0.01.</span>
-
-<span class="sd">        :param n_jobs: number of parallel workers</span>
-<span class="sd">        :return: an instance of this class setup to mimick the performance of the HDx as originally proposed by</span>
-<span class="sd">            González-Castro, Alaiz-Rodríguez, Alegre (2013)</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.meta</span><span class="w"> </span><span class="kn">import</span> <span class="n">MedianEstimator</span>
-
-        <span class="n">dmx</span> <span class="o">=</span> <span class="n">DMx</span><span class="p">(</span><span class="n">divergence</span><span class="o">=</span><span class="s1">&#39;HD&#39;</span><span class="p">,</span> <span class="n">cdf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">search</span><span class="o">=</span><span class="s1">&#39;linear_search&#39;</span><span class="p">)</span>
-        <span class="n">nbins</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">110</span><span class="p">,</span> <span class="mi">11</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">int</span><span class="p">)}</span>
-        <span class="n">hdx</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">base_quantifier</span><span class="o">=</span><span class="n">dmx</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">nbins</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">hdx</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">__get_distributions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">histograms</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">feat_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">):</span>
-            <span class="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
-            <span class="n">feat_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span><span class="p">[</span><span class="n">feat_idx</span><span class="p">]</span>
-            <span class="n">hist</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">nbins</span><span class="p">,</span> <span class="nb">range</span><span class="o">=</span><span class="n">feat_range</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">norm_hist</span> <span class="o">=</span> <span class="n">hist</span> <span class="o">/</span> <span class="n">hist</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-            <span class="n">histograms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">norm_hist</span><span class="p">)</span>
-        <span class="n">distributions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">histograms</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cdf</span><span class="p">:</span>
-            <span class="n">distributions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">distributions</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">distributions</span>
-
-<div class="viewcode-block" id="DMx.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Generates the validation distributions out of the training data (covariates).</span>
-<span class="sd">        The validation distributions have shape `(n, nfeats, nbins)`, with `n` the number of classes, `nfeats`</span>
-<span class="sd">        the number of features, and `nbins` the number of bins.</span>
-<span class="sd">        In particular, let `V` be the validation distributions; then `di=V[i]` are the distributions obtained from</span>
-<span class="sd">        training data labelled with class `i`; while `dij = di[j]` is the discrete distribution for feature j in</span>
-<span class="sd">        training data labelled with class `i`, and `dij[k]` is the fraction of instances with a value in the `k`-th bin.</span>
-
-<span class="sd">        :param X: array-like of shape `(n_samples, n_features)`, the training instances</span>
-<span class="sd">        :param y: array-like of shape `(n_samples,)`, the labels</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">feat_ranges</span> <span class="o">=</span> <span class="n">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">_labels_to_indices</span><span class="p">(</span><span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="p">)</span>
-        <span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">classes</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span>
-            <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">y</span><span class="o">==</span><span class="n">cat</span><span class="p">])</span> <span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)]</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="DMx.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.DMx.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Searches for the mixture model parameter (the sought prevalence values) that yields a validation distribution</span>
-<span class="sd">        (the mixture) that best matches the test distribution, in terms of the divergence measure of choice.</span>
-<span class="sd">        The matching is computed as the average dissimilarity (in terms of the dissimilarity measure of choice)</span>
-<span class="sd">        between all feature-specific discrete distributions.</span>
-
-<span class="sd">        :param X: instances in the sample</span>
-<span class="sd">        :return: a vector of class prevalence estimates</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">nfeats</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span>
-
-        <span class="n">test_distribution</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">__get_distributions</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">divergence</span> <span class="o">=</span> <span class="n">get_divergence</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">divergence</span><span class="p">)</span>
-        <span class="n">n_classes</span><span class="p">,</span> <span class="n">n_feats</span><span class="p">,</span> <span class="n">nbins</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">shape</span>
-        <span class="k">def</span><span class="w"> </span><span class="nf">loss</span><span class="p">(</span><span class="n">prev</span><span class="p">):</span>
-            <span class="n">prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-            <span class="n">mixture_distribution</span> <span class="o">=</span> <span class="p">(</span><span class="n">prev</span> <span class="o">@</span> <span class="bp">self</span><span class="o">.</span><span class="n">validation_distribution</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_classes</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">n_feats</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="n">divs</span> <span class="o">=</span> <span class="p">[</span><span class="n">divergence</span><span class="p">(</span><span class="n">test_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">],</span> <span class="n">mixture_distribution</span><span class="p">[</span><span class="n">feat</span><span class="p">])</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_feats</span><span class="p">)]</span>
-            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">divs</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">argmin_prevalence</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">search</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="EDx">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">EDx</span><span class="p">(</span><span class="n">_EnergyDistanceCore</span><span class="p">,</span> <span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Energy Distance x (EDx), a covariate-space distribution-matching</span>
-<span class="sd">    quantifier based on energy distance.</span>
-
-<span class="sd">    EDx is the classifier-free counterpart of :class:`quapy.method.aggregative.EDy`.</span>
-<span class="sd">    Instead of representing each class through posterior-probability vectors, it</span>
-<span class="sd">    represents each class by the cloud of raw feature vectors observed in the</span>
-<span class="sd">    training set and estimates the test prevalence vector by solving the same</span>
-<span class="sd">    energy-distance quadratic program directly in feature space.</span>
-
-<span class="sd">    This implementation works for binary and multiclass single-label</span>
-<span class="sd">    quantification and relies on the optional ``quadprog`` dependency. The</span>
-<span class="sd">    current QuaPy adaptation shares its numerical core with EDy and keeps</span>
-<span class="sd">    credit to the original implementation available in</span>
-<span class="sd">    `quantificationlib &lt;https://github.com/AICGijon/quantificationlib&gt;`_.</span>
-
-<span class="sd">    The formulation follows the same references as EDy, namely:</span>
-
-<span class="sd">    * Alberto Castaño, Laura Morán-Fernández, Jaime Alonso,</span>
-<span class="sd">      Verónica Bolón-Canedo, Amparo Alonso-Betanzos, and Juan José del Coz.</span>
-<span class="sd">      *An analysis of quantification methods based on matching distributions*.</span>
-<span class="sd">    * Hideko Kawakubo, Marthinus Christoffel du Plessis, and Masashi Sugiyama</span>
-<span class="sd">      (2016). *Computationally efficient class-prior estimation under class</span>
-<span class="sd">      balance change using energy distance*. IEICE Transactions on Information</span>
-<span class="sd">      and Systems, 99(1):176-186.</span>
-
-<span class="sd">    :param distance: distance used to compare feature vectors. Valid string</span>
-<span class="sd">        aliases are ``&#39;manhattan&#39;`` (default) and ``&#39;euclidean&#39;``; a custom</span>
-<span class="sd">        callable compatible with pairwise-distance signatures can also be used</span>
-<span class="sd">    :param n_jobs: number of parallel workers (default ``None``, meaning the</span>
-<span class="sd">        value is taken from the environment)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">distance</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;manhattan&#39;</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">=</span> <span class="n">distance</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">train_distrib_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">train_n_cls_i_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">K_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">G_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">C_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">b_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">a_</span> <span class="o">=</span> <span class="kc">None</span>
-
-<div class="viewcode-block" id="EDx.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Fit class-conditional feature-space distributions from training data.&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_ed_init_parameters</span><span class="p">()</span>
-        <span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">train_distrib</span> <span class="o">=</span> <span class="p">[</span><span class="n">X</span><span class="p">[</span><span class="n">labels</span> <span class="o">==</span> <span class="n">class_</span><span class="p">]</span> <span class="k">for</span> <span class="n">class_</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">]</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fit_energy_model</span><span class="p">(</span><span class="n">train_distrib</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="EDx.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.EDx.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate class prevalences for a test sample of raw instances.&quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="p">,</span> <span class="p">(</span>
-            <span class="sa">f</span><span class="s1">&#39;wrong shape; expected </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_features_in_</span><span class="si">}</span><span class="s1">, found </span><span class="si">{</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s1">&#39;</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_predict_energy</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="ReadMe">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">ReadMe</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">WithConfidenceABC</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    ReadMe is a non-aggregative quantification system proposed by</span>
-<span class="sd">    `Daniel Hopkins and Gary King, 2007. A method of automated nonparametric content analysis for</span>
-<span class="sd">    social science. American Journal of Political Science, 54(1):229–247.</span>
-<span class="sd">    &lt;https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1540-5907.2009.00428.x&gt;`_.</span>
-<span class="sd">    The idea is to estimate `Q(Y=i)` directly from:</span>
-
-<span class="sd">    :math:`Q(X)=\\sum_{i=1} Q(X|Y=i) Q(Y=i)`</span>
-
-<span class="sd">    via least-squares regression, i.e., without incurring the cost of computing posterior probabilities.</span>
-<span class="sd">    However, this poses a very difficult representation in which the vector `Q(X)` and the matrix `Q(X|Y=i)`</span>
-<span class="sd">    can be of very high dimensions. In order to render the problem tracktable, ReadMe performs bagging in</span>
-<span class="sd">    the feature space. ReadMe also combines bagging with bootstrap in order to derive confidence intervals</span>
-<span class="sd">    around point estimations.</span>
-
-<span class="sd">    We use the same default parameters as in the official</span>
-<span class="sd">    `R implementation &lt;https://github.com/iqss-research/ReadMeV1/blob/master/R/prototype.R&gt;`_.</span>
-
-<span class="sd">    :param prob_model: str (&#39;naive&#39;, or &#39;full&#39;), selects the modality in which the probabilities `Q(X)` and</span>
-<span class="sd">        `Q(X|Y)` are to be modelled. Options include &quot;full&quot;, which corresponds to the original formulation of</span>
-<span class="sd">        ReadMe, in which X is constrained to be a binary matrix (e.g., of term presence/absence) and in which</span>
-<span class="sd">        `Q(X)` and `Q(X|Y)` are modelled, respectively, as matrices of `(2^K, 1)` and `(2^K, n)` values, where</span>
-<span class="sd">        `K` is the number of columns in the data matrix (i.e., `bagging_range`), and `n` is the number of classes.</span>
-<span class="sd">        Of course, this approach is computationally prohibited for large `K`, so the authors advised against computing it</span>
-<span class="sd">        for matrices with `K&gt;25` (although we recommend even smaller values of `K`). A much faster model is &quot;naive&quot;, which</span>
-<span class="sd">        considers the `Q(X)` and `Q(X|Y)` be multinomial distributions under the `bag-of-words` perspective. In this</span>
-<span class="sd">        case, `bagging_range` can be set to much larger values. Default is &quot;full&quot; (i.e., original ReadMe behavior).</span>
-<span class="sd">    :param bootstrap_trials: int, number of bootstrap trials (default 300)</span>
-<span class="sd">    :param bagging_trials: int, number of bagging trials (default 300)</span>
-<span class="sd">    :param bagging_range: int, number of features to keep for each bagging trial (default 15)</span>
-<span class="sd">    :param confidence_level: float, a value in (0,1) reflecting the desired confidence level (default 0.95)</span>
-<span class="sd">    :param region: str in &#39;intervals&#39;, &#39;ellipse&#39;, &#39;ellipse-clr&#39;; indicates the preferred method for</span>
-<span class="sd">        defining the confidence region (see :class:`WithConfidenceABC`)</span>
-<span class="sd">    :param bonferroni: bool (default False), whether to apply Bonferroni correction when</span>
-<span class="sd">        `region=&#39;intervals&#39;`. This parameter has no effect for ellipse-based regions.</span>
-<span class="sd">    :param random_state: int or None, allows replicability (default None)</span>
-<span class="sd">    :param verbose: bool, whether to display information during the process (default False)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span> <span class="o">=</span> <span class="mi">25</span>
-    <span class="n">PROBABILISTIC_MODELS</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;naive&quot;</span><span class="p">,</span> <span class="s2">&quot;full&quot;</span><span class="p">]</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">prob_model</span><span class="o">=</span><span class="s2">&quot;full&quot;</span><span class="p">,</span>
-                 <span class="n">bootstrap_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
-                 <span class="n">bagging_trials</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span>
-                 <span class="n">bagging_range</span><span class="o">=</span><span class="mi">15</span><span class="p">,</span>
-                 <span class="n">confidence_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span>
-                 <span class="n">region</span><span class="o">=</span><span class="s1">&#39;intervals&#39;</span><span class="p">,</span>
-                 <span class="n">bonferroni</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-        <span class="k">assert</span> <span class="n">prob_model</span> <span class="ow">in</span> <span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="p">,</span> \
-            <span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="n">prob_model</span><span class="si">=}</span><span class="s1">, valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">=</span> <span class="n">prob_model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span> <span class="o">=</span> <span class="n">bootstrap_trials</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span> <span class="o">=</span> <span class="n">bagging_trials</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span> <span class="o">=</span> <span class="n">bagging_range</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span> <span class="o">=</span> <span class="n">confidence_level</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">region</span> <span class="o">=</span> <span class="n">region</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span> <span class="o">=</span> <span class="n">bonferroni</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">random_state</span> <span class="o">=</span> <span class="n">random_state</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
-
-<div class="viewcode-block" id="ReadMe.fit">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">random_state</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">classes_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
-
-        <span class="n">Xsize</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="c1"># Bootstrap loop</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">):</span>
-            <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">Xsize</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="ReadMe.predict_conf">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict_conf">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict_conf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">confidence_level</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">ConfidenceRegionABC</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_check_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">confidence_level</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">confidence_level</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">confidence_level</span>
-
-        <span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">boots_prevalences</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">Xboots</span><span class="p">,</span> <span class="n">yboots</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span>
-                <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">Xboots</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">yboots</span><span class="p">),</span>
-                <span class="n">desc</span><span class="o">=</span><span class="s1">&#39;bootstrap predictions&#39;</span><span class="p">,</span> <span class="n">total</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bootstrap_trials</span><span class="p">,</span> <span class="n">disable</span><span class="o">=</span><span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span>
-        <span class="p">):</span>
-            <span class="n">bagging_estimates</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_trials</span><span class="p">):</span>
-                <span class="n">feat_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">n_features</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bagging_range</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-                <span class="n">Xboots_bagging</span> <span class="o">=</span> <span class="n">Xboots</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
-                <span class="n">X_boots_bagging</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span> <span class="n">feat_idx</span><span class="p">]</span>
-                <span class="n">bagging_prev</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_quantify_iteration</span><span class="p">(</span><span class="n">Xboots_bagging</span><span class="p">,</span> <span class="n">yboots</span><span class="p">,</span> <span class="n">X_boots_bagging</span><span class="p">)</span>
-                <span class="n">bagging_estimates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bagging_prev</span><span class="p">)</span>
-
-            <span class="n">boots_prevalences</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">bagging_estimates</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-
-        <span class="n">conf</span> <span class="o">=</span> <span class="n">WithConfidenceABC</span><span class="o">.</span><span class="n">construct_region</span><span class="p">(</span><span class="n">boots_prevalences</span><span class="p">,</span> <span class="n">confidence_level</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">region</span><span class="p">,</span> <span class="n">bonferroni</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">bonferroni</span><span class="p">)</span>
-        <span class="n">prev_estim</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">point_estimate</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="n">prev_estim</span><span class="p">,</span> <span class="n">conf</span></div>
-
-
-<div class="viewcode-block" id="ReadMe.predict">
-<a class="viewcode-back" href="../../../quapy.method.html#quapy.method.non_aggregative.ReadMe.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">prev_estim</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">predict_conf</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">prev_estim</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_quantify_iteration</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">Xtr</span><span class="p">,</span> <span class="n">ytr</span><span class="p">,</span> <span class="n">Xte</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Single ReadMe estimate.&quot;&quot;&quot;</span>
-        <span class="n">PX_given_Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xtr</span><span class="p">[</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">c</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">classes_</span><span class="p">)])</span>
-        <span class="n">PX</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_P</span><span class="p">(</span><span class="n">Xte</span><span class="p">)</span>
-
-        <span class="n">res</span> <span class="o">=</span> <span class="n">lsq_linear</span><span class="p">(</span><span class="n">A</span><span class="o">=</span><span class="n">PX_given_Y</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">b</span><span class="o">=</span><span class="n">PX</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
-        <span class="n">pY</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">maximum</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">pY</span> <span class="o">/</span> <span class="n">pY</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_check_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;the &quot;full&quot; model requires estimating empirical distributions; due to the high computational cost,</span>
-<span class="sd">                this function is only made available for binary matrices&quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_is_binary_matrix</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;the empirical distribution can only be computed efficiently on binary matrices&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_is_binary_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">data</span> <span class="k">if</span> <span class="n">sparse</span><span class="o">.</span><span class="n">issparse</span><span class="p">(</span><span class="n">X</span><span class="p">)</span> <span class="k">else</span> <span class="n">X</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">((</span><span class="n">data</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">data</span> <span class="o">==</span> <span class="mi">1</span><span class="p">))</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_P</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;naive&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_multinomial_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span> <span class="o">==</span> <span class="s1">&#39;full&#39;</span><span class="p">:</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_empirical_distribution</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unknown </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prob_model</span><span class="si">}</span><span class="s1">; valid ones are </span><span class="si">{</span><span class="n">ReadMe</span><span class="o">.</span><span class="n">PROBABILISTIC_MODELS</span><span class="si">=}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_empirical_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-
-        <span class="k">if</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the empirical distribution can only be computed efficiently for dimensions &#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;less or equal than </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">MAX_FEATURES_FOR_EMPIRICAL_ESTIMATION</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-        <span class="c1"># we first convert every binary row (e.g., 0 0 1 0 1) into the equivalent number (e.g., 5);</span>
-        <span class="c1"># this will speed up subsequent comparisons a lot</span>
-        <span class="n">K</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">binary_powers</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">K</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>     <span class="c1"># (2^K, ..., 32, 16, 8, 4, 2, 1)</span>
-        <span class="n">X_as_binary_numbers</span> <span class="o">=</span> <span class="n">X</span> <span class="o">@</span> <span class="n">binary_powers</span>         <span class="c1"># e.g., [0 0 1 0 1] @ [16, 8, 4, 2, 1] = 5</span>
-
-        <span class="c1"># count occurrences and compute probs</span>
-        <span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">X_as_binary_numbers</span><span class="p">,</span> <span class="n">minlength</span><span class="o">=</span><span class="mi">2</span> <span class="o">**</span> <span class="n">K</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">probs</span> <span class="o">=</span> <span class="n">counts</span> <span class="o">/</span> <span class="n">counts</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
-
-        <span class="k">return</span> <span class="n">probs</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_multinomial_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-        <span class="n">PX</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-        <span class="n">PX</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">PX</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;l1&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">PX</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span></div>
-
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">_get_features_range</span><span class="p">(</span><span class="n">X</span><span class="p">):</span>
-    <span class="n">feat_ranges</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">ncols</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="k">for</span> <span class="n">col_idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ncols</span><span class="p">):</span>
-        <span class="n">feature</span> <span class="o">=</span> <span class="n">X</span><span class="p">[:,</span><span class="n">col_idx</span><span class="p">]</span>
-        <span class="n">feat_ranges</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">feature</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">feature</span><span class="p">)))</span>
-    <span class="k">return</span> <span class="n">feat_ranges</span>
-
-
-<span class="c1">#---------------------------------------------------------------</span>
-<span class="c1"># aliases</span>
-<span class="c1">#---------------------------------------------------------------</span>
-
-
-<span class="n">HDx</span> <span class="o">=</span> <span class="n">DMx</span><span class="o">.</span><span class="n">HDx</span>
-<span class="n">DistributionMatchingX</span> <span class="o">=</span> <span class="n">DMx</span>
-<span class="n">EnergyDistanceX</span> <span class="o">=</span> <span class="n">EDx</span>
-<span class="n">HellingerDistanceX</span> <span class="o">=</span> <span class="n">HDx</span>
-</pre></div>
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-                  
-  <h1>Source code for quapy.model_selection</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">logging</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">copy</span><span class="w"> </span><span class="kn">import</span> <span class="n">deepcopy</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">enum</span><span class="w"> </span><span class="kn">import</span> <span class="n">Enum</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Callable</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">functools</span><span class="w"> </span><span class="kn">import</span> <span class="n">wraps</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn</span><span class="w"> </span><span class="kn">import</span> <span class="n">clone</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="kn">import</span> <span class="n">evaluation</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.protocol</span><span class="w"> </span><span class="kn">import</span> <span class="n">AbstractProtocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.data.base</span><span class="w"> </span><span class="kn">import</span> <span class="n">LabelledCollection</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.method.aggregative</span><span class="w"> </span><span class="kn">import</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">quapy.util</span><span class="w"> </span><span class="kn">import</span> <span class="n">timeout</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
-
-
-<div class="viewcode-block" id="Status">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.Status">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">Status</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-    <span class="n">SUCCESS</span> <span class="o">=</span> <span class="mi">1</span>
-    <span class="n">TIMEOUT</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="n">INVALID</span> <span class="o">=</span> <span class="mi">3</span>
-    <span class="n">ERROR</span> <span class="o">=</span> <span class="mi">4</span></div>
-
-
-
-<div class="viewcode-block" id="ConfigStatus">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">ConfigStatus</span><span class="p">:</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">params</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">=</span> <span class="n">status</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">msg</span> <span class="o">=</span> <span class="n">msg</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;:params:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="si">}</span><span class="s1"> :status:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">status</span><span class="si">}</span><span class="s1"> &#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">msg</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ConfigStatus.success">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.success">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">success</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
-
-
-<div class="viewcode-block" id="ConfigStatus.failed">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.ConfigStatus.failed">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">failed</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">status</span> <span class="o">!=</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span></div>
-</div>
-
-
-
-<div class="viewcode-block" id="GridSearchQ">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">GridSearchQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Grid Search optimization targeting a quantification-oriented metric.</span>
-
-<span class="sd">    Optimizes the hyperparameters of a quantification method, based on an evaluation method and on an evaluation</span>
-<span class="sd">    protocol for quantification.</span>
-
-<span class="sd">    :param model: the quantifier to optimize</span>
-<span class="sd">    :type model: BaseQuantifier</span>
-<span class="sd">    :param param_grid: a dictionary with keys the parameter names and values the list of values to explore</span>
-<span class="sd">    :param protocol: a sample generation protocol, an instance of :class:`quapy.protocol.AbstractProtocol`</span>
-<span class="sd">    :param error: an error function (callable) or a string indicating the name of an error function (valid ones</span>
-<span class="sd">        are those in :class:`quapy.error.QUANTIFICATION_ERROR`</span>
-<span class="sd">    :param refit: whether to refit the model on the whole labelled collection (training+validation) with</span>
-<span class="sd">        the best chosen hyperparameter combination. Ignored if protocol=&#39;gen&#39;</span>
-<span class="sd">    :param timeout: establishes a timer (in seconds) for each of the hyperparameters configurations being tested.</span>
-<span class="sd">        Whenever a run takes longer than this timer, that configuration will be ignored. If all configurations end up</span>
-<span class="sd">        being ignored, a TimeoutError exception is raised. If -1 (default) then no time bound is set.</span>
-<span class="sd">    :param raise_errors: boolean, if True then raises an exception when a param combination yields any error, if</span>
-<span class="sd">        otherwise is False (default), then the combination is marked with an error status, but the process goes on.</span>
-<span class="sd">        However, if no configuration yields a valid model, then a ValueError exception will be raised.</span>
-<span class="sd">    :param verbose: set to True to get information through the stdout</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">model</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span>
-                 <span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span>
-                 <span class="n">protocol</span><span class="p">:</span> <span class="n">AbstractProtocol</span><span class="p">,</span>
-                 <span class="n">error</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Callable</span><span class="p">,</span> <span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">,</span>
-                 <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                 <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
-                 <span class="n">n_jobs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-                 <span class="n">raise_errors</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                 <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">model</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">param_grid</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span> <span class="o">=</span> <span class="n">protocol</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">refit</span> <span class="o">=</span> <span class="n">refit</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">timeout</span> <span class="o">=</span> <span class="n">timeout</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">_get_njobs</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span> <span class="o">=</span> <span class="n">raise_errors</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span> <span class="o">=</span> <span class="n">verbose</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">__check_error_measure</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
-        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">protocol</span><span class="p">,</span> <span class="n">AbstractProtocol</span><span class="p">),</span> <span class="s1">&#39;unknown protocol&#39;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_sout</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">msg</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">verbose</span><span class="p">:</span>
-            <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">:</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1">]: </span><span class="si">{</span><span class="n">msg</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">__check_error_measure</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">error</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">error</span> <span class="ow">in</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
-        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">error</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">error</span><span class="p">,</span> <span class="s1">&#39;__call__&#39;</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">error</span> <span class="o">=</span> <span class="n">error</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;unexpected error type; must either be a callable function or a str representing</span><span class="se">\n</span><span class="s1">&#39;</span>
-                             <span class="sa">f</span><span class="s1">&#39;the name of an error function in </span><span class="si">{</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_classifier</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">):</span>
-        <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">cls_params</span><span class="p">):</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">cls_params</span><span class="p">)</span>
-            <span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier_fit_predict</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">predictions</span>
-
-        <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;[classifier fit] hyperparams=</span><span class="si">{</span><span class="n">cls_params</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_aggregation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">args</span><span class="p">):</span>
-        <span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">cls_took</span><span class="p">,</span> <span class="n">cls_params</span><span class="p">,</span> <span class="n">q_params</span> <span class="o">=</span> <span class="n">args</span>
-        <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
-        <span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="o">**</span><span class="n">cls_params</span><span class="p">,</span> <span class="o">**</span><span class="n">q_params</span><span class="p">}</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">q_params</span><span class="p">):</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">q_params</span><span class="p">)</span>
-            <span class="n">P</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">predictions</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">aggregation_fit</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">score</span>
-
-        <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">q_params</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">aggr_took</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="p">(</span><span class="n">cls_took</span><span class="o">+</span><span class="n">aggr_took</span><span class="p">)</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_prepare_nonaggr_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
-        <span class="n">model</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">job</span><span class="p">(</span><span class="n">params</span><span class="p">):</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="o">**</span><span class="n">params</span><span class="p">)</span>
-            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span><span class="p">)</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="p">)</span>
-            <span class="k">return</span> <span class="n">score</span>
-
-        <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_error_handler</span><span class="p">(</span><span class="n">job</span><span class="p">,</span> <span class="n">params</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_print_status</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_break_down_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Decides whether to break down the fit phase in two (classifier-fit followed by aggregation-fit).</span>
-<span class="sd">        In order to do so, some conditions should be met: a) the quantifier is of type aggregative,</span>
-<span class="sd">        b) the set of hyperparameters can be split into two disjoint non-empty groups.</span>
-
-<span class="sd">        :return: True if the conditions are met, False otherwise</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">):</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
-        <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cls_configs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">q_configs</span><span class="p">)</span><span class="o">==</span><span class="mi">1</span><span class="p">):</span>
-            <span class="k">return</span> <span class="kc">False</span>
-        <span class="k">return</span> <span class="kc">True</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="c1"># break down the set of hyperparameters into two: classifier-specific, quantifier-specific</span>
-        <span class="n">cls_configs</span><span class="p">,</span> <span class="n">q_configs</span> <span class="o">=</span> <span class="n">group_params</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
-
-        <span class="c1"># train all classifiers and get the predictions</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
-        <span class="n">cls_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_classifier</span><span class="p">,</span>
-            <span class="n">cls_configs</span><span class="p">,</span>
-            <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
-            <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="p">,</span>
-            <span class="n">asarray</span><span class="o">=</span><span class="kc">False</span>
-        <span class="p">)</span>
-
-        <span class="c1"># filter out classifier configurations that yielded any error</span>
-        <span class="n">success_outs</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">),</span> <span class="n">cls_config</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">cls_outs</span><span class="p">,</span> <span class="n">cls_configs</span><span class="p">):</span>
-            <span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
-                <span class="n">success_outs</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">model</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="n">took</span><span class="p">,</span> <span class="n">cls_config</span><span class="p">))</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">success_outs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;No valid configuration found for the classifier!&#39;</span><span class="p">)</span>
-
-        <span class="c1"># explore the quantifier-specific hyperparameters for each valid training configuration</span>
-        <span class="n">aggr_configs</span> <span class="o">=</span> <span class="p">[(</span><span class="o">*</span><span class="n">out</span><span class="p">,</span> <span class="n">q_config</span><span class="p">)</span> <span class="k">for</span> <span class="n">out</span><span class="p">,</span> <span class="n">q_config</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="n">success_outs</span><span class="p">,</span> <span class="n">q_configs</span><span class="p">)]</span>
-        <span class="n">aggr_outs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_aggregation</span><span class="p">,</span>
-            <span class="n">aggr_configs</span><span class="p">,</span>
-            <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
-            <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
-        <span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">aggr_outs</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_compute_scores_nonaggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-        <span class="n">configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_training_X</span> <span class="o">=</span> <span class="n">X</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_training_y</span> <span class="o">=</span> <span class="n">y</span>
-        <span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">parallel</span><span class="p">(</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_nonaggr_model</span><span class="p">,</span>
-            <span class="n">configs</span><span class="p">,</span>
-            <span class="n">seed</span><span class="o">=</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">),</span>
-            <span class="n">n_jobs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span>
-        <span class="p">)</span>
-        <span class="k">return</span> <span class="n">scores</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_print_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;hyperparams=[</span><span class="si">{</span><span class="n">params</span><span class="si">}</span><span class="s1">]</span><span class="se">\t</span><span class="s1"> got </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> = </span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1"> [took </span><span class="si">{</span><span class="n">took</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;error=</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="GridSearchQ.fit">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.fit">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot; Learning routine. Fits methods with all combinations of hyperparameters and selects the one minimizing</span>
-<span class="sd">            the error metric.</span>
-
-<span class="sd">        :param X: array-like, training covariates</span>
-<span class="sd">        :param y: array-like, labels of training data</span>
-<span class="sd">        :return: self</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">RuntimeWarning</span><span class="p">(</span>
-                <span class="sa">f</span><span class="s1">&#39;&quot;refit&quot; was requested, but the protocol does not implement &#39;</span>
-                <span class="sa">f</span><span class="s1">&#39;the </span><span class="si">{</span><span class="n">OnLabelledCollectionProtocol</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s1"> interface&#39;</span>
-            <span class="p">)</span>
-
-        <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;starting model selection with n_jobs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">n_jobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_break_down_fit</span><span class="p">():</span>
-            <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_aggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">results</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_scores_nonaggregative</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="k">for</span> <span class="n">model</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">score</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">status</span><span class="o">.</span><span class="n">success</span><span class="p">():</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">score</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="p">:</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="o">=</span> <span class="n">score</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span> <span class="o">=</span> <span class="n">params</span>
-                    <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span> <span class="o">=</span> <span class="n">model</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">score</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">param_scores_</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">params</span><span class="p">)]</span> <span class="o">=</span> <span class="n">status</span><span class="o">.</span><span class="n">status</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">status</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;no combination of hyperparameters seemed to work&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;optimization finished: best params </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_params_</span><span class="si">}</span><span class="s1"> (score=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score_</span><span class="si">:</span><span class="s1">.5f</span><span class="si">}</span><span class="s1">) &#39;</span>
-                   <span class="sa">f</span><span class="s1">&#39;[took </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">fit_time_</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s]&#39;</span><span class="p">)</span>
-
-        <span class="n">no_errors</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">)</span>
-        <span class="k">if</span> <span class="n">no_errors</span><span class="o">&gt;</span><span class="mi">0</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;warning: </span><span class="si">{</span><span class="n">no_errors</span><span class="si">}</span><span class="s1"> errors found&#39;</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">err</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">error_collector</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">err</span><span class="p">)</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">refit</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="p">,</span> <span class="n">OnLabelledCollectionProtocol</span><span class="p">):</span>
-                <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">_sout</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;refitting on the whole development set&#39;</span><span class="p">)</span>
-                <span class="n">validation_collection</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">get_labelled_collection</span><span class="p">()</span>
-                <span class="n">training_collection</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="n">validation_collection</span><span class="o">.</span><span class="n">classes</span><span class="p">)</span>
-                <span class="n">devel_collection</span> <span class="o">=</span> <span class="n">training_collection</span> <span class="o">+</span> <span class="n">validation_collection</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">devel_collection</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
-                <span class="n">tend</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">refit_time_</span> <span class="o">=</span> <span class="n">tend</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="c1"># already checked</span>
-                <span class="k">raise</span> <span class="ne">RuntimeWarning</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;the model cannot be refit on the whole dataset&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="bp">self</span></div>
-
-
-<div class="viewcode-block" id="GridSearchQ.predict">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.predict">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Estimate class prevalence values using the best model found after calling the :meth:`fit` method.</span>
-
-<span class="sd">        :param X: sample contanining the instances</span>
-<span class="sd">        :return: a ndarray of shape `(n_classes)` with class prevalence estimates as according to the best model found</span>
-<span class="sd">            by the model selection process.</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;best_model_&#39;</span><span class="p">),</span> <span class="s1">&#39;quantify called before fit&#39;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="GridSearchQ.set_params">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.set_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">set_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Sets the hyper-parameters to explore.</span>
-
-<span class="sd">        :param parameters: a dictionary with keys the parameter names and values the list of values to explore</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span> <span class="o">=</span> <span class="n">parameters</span></div>
-
-
-<div class="viewcode-block" id="GridSearchQ.get_params">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.get_params">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">get_params</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the dictionary of hyper-parameters to explore (`param_grid`)</span>
-
-<span class="sd">        :param deep: Unused</span>
-<span class="sd">        :return: the dictionary `param_grid`</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">param_grid</span></div>
-
-
-<div class="viewcode-block" id="GridSearchQ.best_model">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.GridSearchQ.best_model">[docs]</a>
-    <span class="k">def</span><span class="w"> </span><span class="nf">best_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Returns the best model found after calling the :meth:`fit` method, i.e., the one trained on the combination</span>
-<span class="sd">        of hyper-parameters that minimized the error function.</span>
-
-<span class="sd">        :return: a trained quantifier</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;best_model_&#39;</span><span class="p">):</span>
-            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_model_</span>
-        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;best_model called before fit&#39;</span><span class="p">)</span></div>
-
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">_error_handler</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Endorses one job with two returned values: the status, and the time of execution</span>
-
-<span class="sd">        :param func: the function to be called</span>
-<span class="sd">        :param params: parameters of the function</span>
-<span class="sd">        :return: `tuple(out, status, time)` where `out` is the function output,</span>
-<span class="sd">            `status` is an enum value from `Status`, and `time` is the time it</span>
-<span class="sd">            took to complete the call</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">output</span> <span class="o">=</span> <span class="kc">None</span>
-
-        <span class="k">def</span><span class="w"> </span><span class="nf">_handle</span><span class="p">(</span><span class="n">status</span><span class="p">,</span> <span class="n">exception</span><span class="p">):</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">raise_errors</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">exception</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">return</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">msg</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">exception</span><span class="p">))</span>
-
-        <span class="k">try</span><span class="p">:</span>
-            <span class="k">with</span> <span class="n">timeout</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">timeout</span><span class="p">):</span>
-                <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
-                <span class="n">output</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
-                <span class="n">status</span> <span class="o">=</span> <span class="n">ConfigStatus</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">Status</span><span class="o">.</span><span class="n">SUCCESS</span><span class="p">)</span>
-
-        <span class="k">except</span> <span class="ne">TimeoutError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-            <span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">TIMEOUT</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
-
-        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-            <span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">INVALID</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
-
-        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
-            <span class="n">status</span> <span class="o">=</span> <span class="n">_handle</span><span class="p">(</span><span class="n">Status</span><span class="o">.</span><span class="n">ERROR</span><span class="p">,</span> <span class="n">e</span><span class="p">)</span>
-
-        <span class="n">took</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
-        <span class="k">return</span> <span class="n">output</span><span class="p">,</span> <span class="n">status</span><span class="p">,</span> <span class="n">took</span></div>
-
-
-
-<div class="viewcode-block" id="cross_val_predict">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.cross_val_predict">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">cross_val_predict</span><span class="p">(</span><span class="n">quantifier</span><span class="p">:</span> <span class="n">BaseQuantifier</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">LabelledCollection</span><span class="p">,</span> <span class="n">nfolds</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Akin to `scikit-learn&#39;s cross_val_predict &lt;https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_predict.html&gt;`_</span>
-<span class="sd">    but for quantification.</span>
-
-<span class="sd">    :param quantifier: a quantifier issuing class prevalence values</span>
-<span class="sd">    :param data: a labelled collection</span>
-<span class="sd">    :param nfolds: number of folds for k-fold cross validation generation</span>
-<span class="sd">    :param random_state: random seed for reproducibility</span>
-<span class="sd">    :return: a vector of class prevalence values</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">total_prev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">kFCV</span><span class="p">(</span><span class="n">nfolds</span><span class="o">=</span><span class="n">nfolds</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">random_state</span><span class="p">):</span>
-        <span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="o">*</span><span class="n">train</span><span class="o">.</span><span class="n">Xy</span><span class="p">)</span>
-        <span class="n">fold_prev</span> <span class="o">=</span> <span class="n">quantifier</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-        <span class="n">rel_size</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">test</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-        <span class="n">total_prev</span> <span class="o">+=</span> <span class="n">fold_prev</span><span class="o">*</span><span class="n">rel_size</span>
-
-    <span class="k">return</span> <span class="n">total_prev</span></div>
-
-
-
-<div class="viewcode-block" id="expand_grid">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.expand_grid">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">expand_grid</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Expands a param_grid dictionary as a list of configurations.</span>
-<span class="sd">    Example:</span>
-
-<span class="sd">    &gt;&gt;&gt; combinations = expand_grid({&#39;A&#39;: [1, 10, 100], &#39;B&#39;: [True, False]})</span>
-<span class="sd">    &gt;&gt;&gt; print(combinations)</span>
-<span class="sd">    &gt;&gt;&gt; [{&#39;A&#39;: 1, &#39;B&#39;: True}, {&#39;A&#39;: 1, &#39;B&#39;: False}, {&#39;A&#39;: 10, &#39;B&#39;: True}, {&#39;A&#39;: 10, &#39;B&#39;: False}, {&#39;A&#39;: 100, &#39;B&#39;: True}, {&#39;A&#39;: 100, &#39;B&#39;: False}]</span>
-
-<span class="sd">    :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range</span>
-<span class="sd">        to explore for that hyper-parameter</span>
-<span class="sd">    :return: a list of configurations, i.e., combinations of hyper-parameter assignments in the grid.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">params_keys</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_grid</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-    <span class="n">params_values</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">param_grid</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
-    <span class="n">configs</span> <span class="o">=</span> <span class="p">[{</span><span class="n">k</span><span class="p">:</span> <span class="n">combs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">params_keys</span><span class="p">)}</span> <span class="k">for</span> <span class="n">combs</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">params_values</span><span class="p">)]</span>
-    <span class="k">return</span> <span class="n">configs</span></div>
-
-
-
-<div class="viewcode-block" id="group_params">
-<a class="viewcode-back" href="../../quapy.html#quapy.model_selection.group_params">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">group_params</span><span class="p">(</span><span class="n">param_grid</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Partitions a param_grid dictionary as two lists of configurations, one for the classifier-specific</span>
-<span class="sd">    hyper-parameters, and another for que quantifier-specific hyper-parameters</span>
-
-<span class="sd">    :param param_grid: dictionary with keys representing hyper-parameter names, and values representing the range</span>
-<span class="sd">        to explore for that hyper-parameter</span>
-<span class="sd">    :return: two expanded grids of configurations, one for the classifier, another for the quantifier</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">classifier_params</span><span class="p">,</span> <span class="n">quantifier_params</span> <span class="o">=</span> <span class="p">{},</span> <span class="p">{}</span>
-    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="n">param_grid</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-        <span class="k">if</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">&#39;classifier__&#39;</span><span class="p">)</span> <span class="ow">or</span> <span class="n">key</span> <span class="o">==</span> <span class="s1">&#39;val_split&#39;</span><span class="p">:</span>
-            <span class="n">classifier_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">quantifier_params</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">values</span>
-
-    <span class="n">classifier_configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="n">classifier_params</span><span class="p">)</span>
-    <span class="n">quantifier_configs</span> <span class="o">=</span> <span class="n">expand_grid</span><span class="p">(</span><span class="n">quantifier_params</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">classifier_configs</span><span class="p">,</span> <span class="n">quantifier_configs</span></div>
-
-
-</pre></div>
-
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docs/build/html/_modules/quapy/tests/test_base.html

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-  <h1>Source code for quapy.tests.test_base</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">pytest</span>
-
-<div class="viewcode-block" id="test_import">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_base.test_import">[docs]</a>
-<span class="k">def</span> <span class="nf">test_import</span><span class="p">():</span>
-    <span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-    <span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">__version__</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span></div>
-
-</pre></div>
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docs/build/html/_modules/quapy/tests/test_datasets.html

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-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.tests.test_datasets</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">pytest</span>
-
-<span class="kn">from</span> <span class="nn">quapy.data.datasets</span> <span class="kn">import</span> <span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="p">,</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span><span class="p">,</span> \
-    <span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span><span class="p">,</span> <span class="n">UCI_BINARY_DATASETS</span><span class="p">,</span> <span class="n">LEQUA2022_TASKS</span><span class="p">,</span> <span class="n">UCI_MULTICLASS_DATASETS</span><span class="p">,</span>\
-    <span class="n">fetch_reviews</span><span class="p">,</span> <span class="n">fetch_twitter</span><span class="p">,</span> <span class="n">fetch_UCIBinaryDataset</span><span class="p">,</span> <span class="n">fetch_lequa2022</span><span class="p">,</span> <span class="n">fetch_UCIMulticlassLabelledCollection</span>
-
-
-<div class="viewcode-block" id="test_fetch_reviews">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_reviews">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset_name&#39;</span><span class="p">,</span> <span class="n">REVIEWS_SENTIMENT_DATASETS</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training set stats&#39;</span><span class="p">)</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test set stats&#39;</span><span class="p">)</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span></div>
-
-
-
-<div class="viewcode-block" id="test_fetch_twitter">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_twitter">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset_name&#39;</span><span class="p">,</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TEST</span> <span class="o">+</span> <span class="n">TWITTER_SENTIMENT_DATASETS_TRAIN</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
-    <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ve</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">&#39;semeval&#39;</span> <span class="ow">and</span> <span class="n">ve</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span>
-                <span class="s1">&#39;dataset &quot;semeval&quot; can only be used for model selection.&#39;</span><span class="p">):</span>
-            <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_twitter</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">,</span> <span class="n">for_model_selection</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training set stats&#39;</span><span class="p">)</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test set stats&#39;</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="test_fetch_UCIDataset">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_UCIDataset">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset_name&#39;</span><span class="p">,</span> <span class="n">UCI_BINARY_DATASETS</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_fetch_UCIDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
-    <span class="k">except</span> <span class="ne">FileNotFoundError</span> <span class="k">as</span> <span class="n">fnfe</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">dataset_name</span> <span class="o">==</span> <span class="s1">&#39;pageblocks.5&#39;</span> <span class="ow">and</span> <span class="n">fnfe</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">find</span><span class="p">(</span>
-                <span class="s1">&#39;If this is the first time you attempt to load this dataset&#39;</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The pageblocks.5 dataset requires some hand processing to be usable, skipping this test.&#39;</span><span class="p">)</span>
-            <span class="k">return</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training set stats&#39;</span><span class="p">)</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test set stats&#39;</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="test_fetch_UCIMultiDataset">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_UCIMultiDataset">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset_name&#39;</span><span class="p">,</span> <span class="n">UCI_MULTICLASS_DATASETS</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_fetch_UCIMultiDataset</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">fetch_UCIMulticlassLabelledCollection</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Dataset </span><span class="si">{</span><span class="n">dataset_name</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training set stats&#39;</span><span class="p">)</span>
-    <span class="n">dataset</span><span class="o">.</span><span class="n">stats</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test set stats&#39;</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="test_fetch_lequa2022">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_datasets.test_fetch_lequa2022">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset_name&#39;</span><span class="p">,</span> <span class="n">LEQUA2022_TASKS</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_fetch_lequa2022</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">):</span>
-    <span class="n">train</span><span class="p">,</span> <span class="n">gen_val</span><span class="p">,</span> <span class="n">gen_test</span> <span class="o">=</span> <span class="n">fetch_lequa2022</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">train</span><span class="o">.</span><span class="n">stats</span><span class="p">())</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Val:&#39;</span><span class="p">,</span> <span class="n">gen_val</span><span class="o">.</span><span class="n">total</span><span class="p">())</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Test:&#39;</span><span class="p">,</span> <span class="n">gen_test</span><span class="o">.</span><span class="n">total</span><span class="p">())</span></div>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2024, Alejandro Moreo.</p>
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-  <h1>Source code for quapy.tests.test_evaluation</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
-
-<span class="kn">from</span> <span class="nn">quapy.error</span> <span class="kn">import</span> <span class="n">QUANTIFICATION_ERROR_SINGLE</span><span class="p">,</span> <span class="n">QUANTIFICATION_ERROR</span><span class="p">,</span> <span class="n">QUANTIFICATION_ERROR_NAMES</span><span class="p">,</span> \
-    <span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span>
-<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">EMQ</span><span class="p">,</span> <span class="n">PCC</span>
-<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BaseQuantifier</span>
-
-
-<div class="viewcode-block" id="EvalTestCase">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase">[docs]</a>
-<span class="k">class</span> <span class="nc">EvalTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-<div class="viewcode-block" id="EvalTestCase.test_eval_speedup">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase.test_eval_speedup">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_eval_speedup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;hp&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span>
-
-        <span class="n">protocol</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
-            <span class="k">def</span> <span class="nf">predict_proba</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">):</span>
-                <span class="kn">import</span> <span class="nn">time</span>
-                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-                <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
-
-        <span class="n">emq</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
-
-        <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
-        <span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">emq</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">aggr_speedup</span><span class="o">=</span><span class="s1">&#39;force&#39;</span><span class="p">)</span>
-        <span class="n">tend_optim</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;evaluation (with optimization) took </span><span class="si">{</span><span class="n">tend_optim</span><span class="si">}</span><span class="s1">s [MAE=</span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">]&#39;</span><span class="p">)</span>
-
-        <span class="k">class</span> <span class="nc">NonAggregativeEMQ</span><span class="p">(</span><span class="n">BaseQuantifier</span><span class="p">):</span>
-
-            <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">cls</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">emq</span> <span class="o">=</span> <span class="n">EMQ</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
-
-            <span class="k">def</span> <span class="nf">quantify</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">instances</span><span class="p">):</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">emq</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">instances</span><span class="p">)</span>
-
-            <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">emq</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-                <span class="k">return</span> <span class="bp">self</span>
-
-        <span class="n">emq</span> <span class="o">=</span> <span class="n">NonAggregativeEMQ</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
-
-        <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
-        <span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">emq</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">tend_no_optim</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;evaluation (w/o optimization) took </span><span class="si">{</span><span class="n">tend_no_optim</span><span class="si">}</span><span class="s1">s [MAE=</span><span class="si">{</span><span class="n">score</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">]&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">tend_no_optim</span><span class="o">&gt;</span><span class="p">(</span><span class="n">tend_optim</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="EvalTestCase.test_evaluation_output">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_evaluation.EvalTestCase.test_evaluation_output">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_evaluation_output</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;hp&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">test</span>
-
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span><span class="o">=</span><span class="mi">100</span>
-
-        <span class="n">protocol</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">protocol</span><span class="o">.</span><span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="n">q</span> <span class="o">=</span> <span class="n">PCC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
-
-        <span class="n">single_errors</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">QUANTIFICATION_ERROR_SINGLE_NAMES</span><span class="p">)</span>
-        <span class="n">averaged_errors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;m&#39;</span><span class="o">+</span><span class="n">e</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">single_errors</span><span class="p">]</span>
-        <span class="n">single_errors</span> <span class="o">=</span> <span class="n">single_errors</span> <span class="o">+</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">single_errors</span><span class="p">]</span>
-        <span class="n">averaged_errors</span> <span class="o">=</span> <span class="n">averaged_errors</span> <span class="o">+</span> <span class="p">[</span><span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">from_name</span><span class="p">(</span><span class="n">e</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">averaged_errors</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">error_metric</span><span class="p">,</span> <span class="n">averaged_error_metric</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">single_errors</span><span class="p">,</span> <span class="n">averaged_errors</span><span class="p">):</span>
-            <span class="n">score</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="n">averaged_error_metric</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">score</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>
-
-            <span class="n">scores</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">protocol</span><span class="p">,</span> <span class="n">error_metric</span><span class="o">=</span><span class="n">error_metric</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertTrue</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span>
-
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">score</span><span class="p">)</span></div>
-</div>
-
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
-
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docs/build/html/_modules/quapy/tests/test_hierarchy.html

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-      <li class="breadcrumb-item active">quapy.tests.test_hierarchy</li>
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-             
-  <h1>Source code for quapy.tests.test_hierarchy</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="o">*</span>
-
-
-<div class="viewcode-block" id="HierarchyTestCase">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase">[docs]</a>
-<span class="k">class</span> <span class="nc">HierarchyTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-
-<div class="viewcode-block" id="HierarchyTestCase.test_aggregative">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_aggregative">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_aggregative</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">CC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PCC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">ACC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="HierarchyTestCase.test_binary">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_binary">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_binary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">HDy</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="HierarchyTestCase.test_probabilistic">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_hierarchy.HierarchyTestCase.test_probabilistic">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_probabilistic</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">()</span>
-        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">CC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">ACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeCrispQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">),</span> <span class="kc">False</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="p">[</span><span class="n">PCC</span><span class="p">(</span><span class="n">lr</span><span class="p">),</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)]:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeCrispQuantifier</span><span class="p">),</span> <span class="kc">False</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">AggregativeSoftQuantifier</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
-</div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
-
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docs/build/html/_modules/quapy/tests/test_labelcollection.html

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-      <li class="breadcrumb-item active">quapy.tests.test_labelcollection</li>
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-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.tests.test_labelcollection</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.sparse</span> <span class="kn">import</span> <span class="n">csr_matrix</span>
-
-<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-
-
-<div class="viewcode-block" id="LabelCollectionTestCase">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase">[docs]</a>
-<span class="k">class</span> <span class="nc">LabelCollectionTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-<div class="viewcode-block" id="LabelCollectionTestCase.test_split">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase.test_split">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_split</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span>
-        <span class="n">tr</span><span class="p">,</span> <span class="n">te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_random</span><span class="p">(</span><span class="mf">0.7</span><span class="p">)</span>
-        <span class="n">check_prev</span> <span class="o">=</span> <span class="n">tr</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span><span class="o">*</span><span class="mf">0.7</span> <span class="o">+</span> <span class="n">te</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span><span class="o">*</span><span class="mf">0.3</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tr</span><span class="p">),</span> <span class="mi">70</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">te</span><span class="p">),</span> <span class="mi">30</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">allclose</span><span class="p">(</span><span class="n">check_prev</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()),</span> <span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">tr</span><span class="o">+</span><span class="n">te</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
-
-
-<div class="viewcode-block" id="LabelCollectionTestCase.test_join">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_labelcollection.LabelCollectionTestCase.test_join">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_join</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
-        <span class="n">data1</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">200</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-        <span class="n">data2</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-        <span class="n">data3</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-
-        <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">data3</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data1</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data2</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data3</span><span class="p">))</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">all</span><span class="p">(</span><span class="n">combined</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">6</span><span class="p">)),</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-        <span class="n">data4</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
-            <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data1</span><span class="p">,</span> <span class="n">data2</span><span class="p">,</span> <span class="n">data3</span><span class="p">,</span> <span class="n">data4</span><span class="p">)</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
-        <span class="n">data5</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data4</span><span class="p">)</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">data5</span><span class="p">))</span>
-
-        <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-        <span class="n">data6</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
-            <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">,</span> <span class="n">data6</span><span class="p">)</span>
-
-        <span class="n">data4</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">data4</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">Exception</span><span class="p">):</span>
-            <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
-        <span class="n">data5</span><span class="o">.</span><span class="n">instances</span> <span class="o">=</span> <span class="n">csr_matrix</span><span class="p">(</span><span class="n">data5</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-        <span class="n">combined</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">LabelledCollection</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">data4</span><span class="p">,</span> <span class="n">data5</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">combined</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data4</span><span class="p">)</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">data5</span><span class="p">))</span></div>
-</div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
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-  <h1>Source code for quapy.tests.test_methods</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">pytest</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">LinearSVC</span>
-
-<span class="kn">import</span> <span class="nn">method.aggregative</span>
-<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
-<span class="kn">from</span> <span class="nn">quapy.method.base</span> <span class="kn">import</span> <span class="n">BinaryQuantifier</span>
-<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">LabelledCollection</span>
-<span class="kn">from</span> <span class="nn">quapy.method</span> <span class="kn">import</span> <span class="n">AGGREGATIVE_METHODS</span><span class="p">,</span> <span class="n">NON_AGGREGATIVE_METHODS</span>
-<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">Ensemble</span>
-<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span>
-<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">DMy</span>
-<span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">MedianEstimator</span>
-
-<span class="c1"># datasets = [pytest.param(qp.datasets.fetch_twitter(&#39;hcr&#39;, pickle=True), id=&#39;hcr&#39;),</span>
-<span class="c1">#             pytest.param(qp.datasets.fetch_UCIDataset(&#39;ionosphere&#39;), id=&#39;ionosphere&#39;)]</span>
-
-<span class="n">tinydatasets</span> <span class="o">=</span> <span class="p">[</span><span class="n">pytest</span><span class="o">.</span><span class="n">param</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_twitter</span><span class="p">(</span><span class="s1">&#39;hcr&#39;</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(),</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;tiny_hcr&#39;</span><span class="p">),</span>
-                <span class="n">pytest</span><span class="o">.</span><span class="n">param</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="s1">&#39;ionosphere&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(),</span> <span class="nb">id</span><span class="o">=</span><span class="s1">&#39;tiny_ionosphere&#39;</span><span class="p">)]</span>
-
-<span class="n">learners</span> <span class="o">=</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">,</span> <span class="n">LinearSVC</span><span class="p">]</span>
-
-
-<div class="viewcode-block" id="test_aggregative_methods">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_aggregative_methods">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;aggregative_method&#39;</span><span class="p">,</span> <span class="n">AGGREGATIVE_METHODS</span><span class="p">)</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;learner&#39;</span><span class="p">,</span> <span class="n">learners</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_aggregative_methods</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">aggregative_method</span><span class="p">,</span> <span class="n">learner</span><span class="p">):</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">aggregative_method</span><span class="p">(</span><span class="n">learner</span><span class="p">())</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;skipping the test of binary model </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">return</span>
-
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
-
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
-
-
-
-<div class="viewcode-block" id="test_non_aggregative_methods">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_non_aggregative_methods">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;non_aggregative_method&#39;</span><span class="p">,</span> <span class="n">NON_AGGREGATIVE_METHODS</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_non_aggregative_methods</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">non_aggregative_method</span><span class="p">):</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">non_aggregative_method</span><span class="p">()</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;skipping the test of binary model </span><span class="si">{</span><span class="n">model</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">return</span>
-
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
-
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
-
-
-
-<div class="viewcode-block" id="test_ensemble_method">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_ensemble_method">[docs]</a>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;base_method&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">ACC</span><span class="p">,</span> <span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">PACC</span><span class="p">])</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;learner&#39;</span><span class="p">,</span> <span class="p">[</span><span class="n">LogisticRegression</span><span class="p">])</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="n">tinydatasets</span><span class="p">)</span>
-<span class="nd">@pytest</span><span class="o">.</span><span class="n">mark</span><span class="o">.</span><span class="n">parametrize</span><span class="p">(</span><span class="s1">&#39;policy&#39;</span><span class="p">,</span> <span class="n">Ensemble</span><span class="o">.</span><span class="n">VALID_POLICIES</span><span class="p">)</span>
-<span class="k">def</span> <span class="nf">test_ensemble_method</span><span class="p">(</span><span class="n">base_method</span><span class="p">,</span> <span class="n">learner</span><span class="p">,</span> <span class="n">dataset</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">policy</span><span class="p">):</span>
-
-    <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">20</span>
-
-    <span class="n">base_quantifier</span><span class="o">=</span><span class="n">base_method</span><span class="p">(</span><span class="n">learner</span><span class="p">())</span>
-
-    <span class="k">if</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span> <span class="ow">and</span> <span class="n">policy</span><span class="o">==</span><span class="s1">&#39;ds&#39;</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;skipping the test of binary policy ds on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">return</span>
-
-    <span class="n">model</span> <span class="o">=</span> <span class="n">Ensemble</span><span class="p">(</span><span class="n">quantifier</span><span class="o">=</span><span class="n">base_quantifier</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">policy</span><span class="o">=</span><span class="n">policy</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
-
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
-
-
-
-<div class="viewcode-block" id="test_quanet_method">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_quanet_method">[docs]</a>
-<span class="k">def</span> <span class="nf">test_quanet_method</span><span class="p">():</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="kn">import</span> <span class="nn">quapy.classification.neural</span>
-    <span class="k">except</span> <span class="ne">ModuleNotFoundError</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;skipping QuaNet test due to missing torch package&#39;</span><span class="p">)</span>
-        <span class="k">return</span>
-
-    <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
-
-    <span class="c1"># load the kindle dataset as text, and convert words to numerical indexes</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;kindle&#39;</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">reduce</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span> <span class="mi">200</span><span class="p">)</span>
-    <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">CNNnet</span>
-    <span class="n">cnn</span> <span class="o">=</span> <span class="n">CNNnet</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">vocabulary_size</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">n_classes</span><span class="p">)</span>
-
-    <span class="kn">from</span> <span class="nn">quapy.classification.neural</span> <span class="kn">import</span> <span class="n">NeuralClassifierTrainer</span>
-    <span class="n">learner</span> <span class="o">=</span> <span class="n">NeuralClassifierTrainer</span><span class="p">(</span><span class="n">cnn</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-
-    <span class="kn">from</span> <span class="nn">quapy.method.meta</span> <span class="kn">import</span> <span class="n">QuaNet</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">QuaNet</span><span class="p">(</span><span class="n">learner</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">BinaryQuantifier</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">dataset</span><span class="o">.</span><span class="n">binary</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;skipping the test of binary model </span><span class="si">{</span><span class="n">model</span><span class="si">}</span><span class="s1"> on non-binary dataset </span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="k">return</span>
-
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">estim_prevalences</span><span class="p">)</span>
-
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span></div>
-
-
-
-<div class="viewcode-block" id="test_str_label_names">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_str_label_names">[docs]</a>
-<span class="k">def</span> <span class="nf">test_str_label_names</span><span class="p">():</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="n">aggregative</span><span class="o">.</span><span class="n">CC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
-
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;imdb&#39;</span><span class="p">,</span> <span class="n">pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="o">*</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()),</span>
-                      <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mf">0.25</span><span class="p">,</span> <span class="mf">0.75</span><span class="p">))</span>
-    <span class="n">qp</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">preprocessing</span><span class="o">.</span><span class="n">text2tfidf</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-
-    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">int_estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">int_estim_prevalences</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span>
-
-    <span class="n">dataset_str</span> <span class="o">=</span> <span class="n">Dataset</span><span class="p">(</span><span class="n">LabelledCollection</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span>
-                                             <span class="p">[</span><span class="s1">&#39;one&#39;</span> <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;zero&#39;</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">labels</span><span class="p">]),</span>
-                          <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">,</span>
-                                             <span class="p">[</span><span class="s1">&#39;one&#39;</span> <span class="k">if</span> <span class="n">label</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;zero&#39;</span> <span class="k">for</span> <span class="n">label</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">labels</span><span class="p">]))</span>
-    <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="s1">&#39;wrong indexation&#39;</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">training</span><span class="p">)</span>
-
-    <span class="n">str_estim_prevalences</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">)</span>
-    <span class="n">true_prevalences</span> <span class="o">=</span> <span class="n">dataset_str</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">prevalence</span><span class="p">()</span>
-
-    <span class="n">error</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">,</span> <span class="n">str_estim_prevalences</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">error</span><span class="p">)</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">float64</span>
-
-    <span class="nb">print</span><span class="p">(</span><span class="n">true_prevalences</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">int_estim_prevalences</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">str_estim_prevalences</span><span class="p">)</span>
-
-    <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">int_estim_prevalences</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                      <span class="n">str_estim_prevalences</span><span class="p">[</span><span class="nb">list</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classes_</span><span class="p">)</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="s1">&#39;one&#39;</span><span class="p">)])</span></div>
-
-
-<span class="c1"># helper</span>
-<span class="k">def</span> <span class="nf">__fit_test</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">):</span>
-    <span class="n">quantifier</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
-    <span class="n">test_samples</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
-    <span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">evaluation</span><span class="o">.</span><span class="n">prediction</span><span class="p">(</span><span class="n">quantifier</span><span class="p">,</span> <span class="n">test_samples</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">qp</span><span class="o">.</span><span class="n">error</span><span class="o">.</span><span class="n">mae</span><span class="p">(</span><span class="n">true_prevs</span><span class="p">,</span> <span class="n">estim_prevs</span><span class="p">),</span> <span class="n">estim_prevs</span>
-
-
-<div class="viewcode-block" id="test_median_meta">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_median_meta">[docs]</a>
-<span class="k">def</span> <span class="nf">test_median_meta</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This test compares the performance of the MedianQuantifier with respect to computing the median of the predictions</span>
-<span class="sd">    of a differently parameterized quantifier. We use the DistributionMatching base quantifier and the median is</span>
-<span class="sd">    computed across different values of nbins</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
-
-    <span class="c1"># grid of values</span>
-    <span class="n">nbins_grid</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">))</span>
-
-    <span class="n">dataset</span> <span class="o">=</span> <span class="s1">&#39;kindle&#39;</span>
-    <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
-    <span class="n">prevs</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">nbins</span> <span class="ow">in</span> <span class="n">nbins_grid</span><span class="p">:</span>
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">nbins</span><span class="o">=</span><span class="n">nbins</span><span class="p">)</span>
-            <span class="n">mae</span><span class="p">,</span> <span class="n">estim_prevs</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
-            <span class="n">prevs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">estim_prevs</span><span class="p">)</span>
-            <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mae</span><span class="p">)</span>
-            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">dataset</span><span class="si">}</span><span class="s1"> DistributionMatching(nbins=</span><span class="si">{</span><span class="n">nbins</span><span class="si">}</span><span class="s1">) got MAE </span><span class="si">{</span><span class="n">mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-    <span class="n">prevs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">prevs</span><span class="p">)</span>
-    <span class="n">mae</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">median_mae</span><span class="p">,</span> <span class="n">prev</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">median_mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="n">np</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">assert_almost_equal</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">prevs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">prev</span><span class="p">)</span>
-    <span class="k">assert</span> <span class="n">median_mae</span> <span class="o">&lt;</span> <span class="n">mae</span><span class="p">,</span> <span class="s1">&#39;the median-based quantifier provided a higher error...&#39;</span></div>
-
-
-
-<div class="viewcode-block" id="test_median_meta_modsel">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_methods.test_median_meta_modsel">[docs]</a>
-<span class="k">def</span> <span class="nf">test_median_meta_modsel</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    This test checks the median-meta quantifier with model selection</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">100</span>
-
-    <span class="n">dataset</span> <span class="o">=</span> <span class="s1">&#39;kindle&#39;</span>
-    <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
-    <span class="n">train</span><span class="p">,</span> <span class="n">val</span> <span class="o">=</span> <span class="n">train</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-    <span class="n">nbins_grid</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">]</span>
-
-    <span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">median_mae</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">median_mae</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="n">q</span> <span class="o">=</span> <span class="n">DMy</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">())</span>
-    <span class="n">lr_params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)}</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">MedianEstimator</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;nbins&#39;</span><span class="p">:</span> <span class="n">nbins_grid</span><span class="p">},</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">lr_params</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">APP</span><span class="p">(</span><span class="n">val</span><span class="p">),</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">optimized_median_ave</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">__fit_test</span><span class="p">(</span><span class="n">q</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">MAE=</span><span class="si">{</span><span class="n">optimized_median_ave</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
-
-    <span class="k">assert</span> <span class="n">optimized_median_ave</span> <span class="o">&lt;</span> <span class="n">median_mae</span><span class="p">,</span> <span class="s2">&quot;the optimized method yielded worse performance...&quot;</span></div>
-
-</pre></div>
-
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-             
-  <h1>Source code for quapy.tests.test_modsel</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
-
-<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">PACC</span>
-<span class="kn">from</span> <span class="nn">quapy.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchQ</span>
-<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span>
-<span class="kn">import</span> <span class="nn">time</span>
-
-
-<div class="viewcode-block" id="ModselTestCase">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase">[docs]</a>
-<span class="k">class</span> <span class="nc">ModselTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-
-<div class="viewcode-block" id="ModselTestCase.test_modsel">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_modsel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;imdb&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
-        <span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
-            <span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;best params&#39;</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;best score&#39;</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ModselTestCase.test_modsel_parallel">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_parallel">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_modsel_parallel</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;imdb&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># test = data.test</span>
-
-        <span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
-        <span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
-            <span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;best params&#39;</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;best score&#39;</span><span class="p">,</span> <span class="n">q</span><span class="o">.</span><span class="n">best_score_</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_params_</span><span class="p">[</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">q</span><span class="o">.</span><span class="n">best_model</span><span class="p">()</span><span class="o">.</span><span class="n">get_params</span><span class="p">()[</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">],</span> <span class="mf">10.0</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ModselTestCase.test_modsel_parallel_speedup">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_parallel_speedup">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_modsel_parallel_speedup</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
-            <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-                <span class="k">return</span> <span class="nb">super</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">)</span>
-
-        <span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">5000</span><span class="p">))</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;imdb&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">7</span><span class="p">)}</span>
-        <span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-        <span class="n">GridSearchQ</span><span class="p">(</span>
-            <span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
-        <span class="n">tend_nooptim</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tinit</span>
-
-        <span class="n">tinit</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-        <span class="n">GridSearchQ</span><span class="p">(</span>
-            <span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span>
-        <span class="n">tend_optim</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">tinit</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;parallel training took </span><span class="si">{</span><span class="n">tend_optim</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;sequential training took </span><span class="si">{</span><span class="n">tend_nooptim</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">s&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">tend_optim</span> <span class="o">&lt;</span> <span class="p">(</span><span class="mf">0.5</span><span class="o">*</span><span class="n">tend_nooptim</span><span class="p">),</span> <span class="kc">True</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="ModselTestCase.test_modsel_timeout">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_modsel.ModselTestCase.test_modsel_timeout">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_modsel_timeout</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="k">class</span> <span class="nc">SlowLR</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">):</span>
-            <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-                <span class="kn">import</span> <span class="nn">time</span>
-                <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
-                <span class="nb">super</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="p">)</span>
-
-        <span class="n">q</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">SlowLR</span><span class="p">())</span>
-
-        <span class="n">data</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_reviews</span><span class="p">(</span><span class="s1">&#39;imdb&#39;</span><span class="p">,</span> <span class="n">tfidf</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">min_df</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">training</span><span class="p">,</span> <span class="n">validation</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">training</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="c1"># test = data.test</span>
-
-        <span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;classifier__C&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">logspace</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">7</span><span class="p">)}</span>
-        <span class="n">app</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">validation</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="n">q</span> <span class="o">=</span> <span class="n">GridSearchQ</span><span class="p">(</span>
-            <span class="n">q</span><span class="p">,</span> <span class="n">param_grid</span><span class="p">,</span> <span class="n">protocol</span><span class="o">=</span><span class="n">app</span><span class="p">,</span> <span class="n">error</span><span class="o">=</span><span class="s1">&#39;mae&#39;</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">timeout</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
-        <span class="p">)</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">TimeoutError</span><span class="p">):</span>
-            <span class="n">q</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">training</span><span class="p">)</span></div>
-</div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
-
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docs/build/html/_modules/quapy/tests/test_protocols.html

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-           <div itemprop="articleBody">
-             
-  <h1>Source code for quapy.tests.test_protocols</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">quapy.functional</span>
-<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
-<span class="kn">from</span> <span class="nn">quapy.protocol</span> <span class="kn">import</span> <span class="n">APP</span><span class="p">,</span> <span class="n">NPP</span><span class="p">,</span> <span class="n">UPP</span><span class="p">,</span> <span class="n">DomainMixer</span><span class="p">,</span> <span class="n">AbstractStochasticSeededProtocol</span>
-
-
-<div class="viewcode-block" id="mock_labelled_collection">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.mock_labelled_collection">[docs]</a>
-<span class="k">def</span> <span class="nf">mock_labelled_collection</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">):</span>
-    <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span> <span class="o">+</span> <span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">*</span> <span class="mi">250</span>
-    <span class="n">X</span> <span class="o">=</span> <span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;-&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">yi</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">yi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">y</span><span class="p">)]</span>
-    <span class="k">return</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">classes</span><span class="o">=</span><span class="nb">sorted</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y</span><span class="p">)))</span></div>
-
-
-
-<div class="viewcode-block" id="samples_to_str">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.samples_to_str">[docs]</a>
-<span class="k">def</span> <span class="nf">samples_to_str</span><span class="p">(</span><span class="n">protocol</span><span class="p">):</span>
-    <span class="n">samples_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-    <span class="k">for</span> <span class="n">instances</span><span class="p">,</span> <span class="n">prev</span> <span class="ow">in</span> <span class="n">protocol</span><span class="p">():</span>
-        <span class="n">samples_str</span> <span class="o">+=</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="n">instances</span><span class="si">}</span><span class="se">\t</span><span class="si">{</span><span class="n">prev</span><span class="si">}</span><span class="se">\n</span><span class="s1">&#39;</span>
-    <span class="k">return</span> <span class="n">samples_str</span></div>
-
-
-
-<div class="viewcode-block" id="TestProtocols">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols">[docs]</a>
-<span class="k">class</span> <span class="nc">TestProtocols</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-
-<div class="viewcode-block" id="TestProtocols.test_app_sanity_check">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_sanity_check">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_app_sanity_check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">n_prevpoints</span> <span class="o">=</span> <span class="mi">101</span>
-        <span class="n">repeats</span> <span class="o">=</span> <span class="mi">10</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">RuntimeError</span><span class="p">):</span>
-            <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="n">repeats</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-        <span class="n">n_combinations</span> <span class="o">=</span> \
-            <span class="n">quapy</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">num_prevalence_combinations</span><span class="p">(</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">n_classes</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">n_classes</span><span class="p">,</span> <span class="n">n_repeats</span><span class="o">=</span><span class="n">repeats</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="n">n_combinations</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="n">n_prevpoints</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">sanity_check</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_app_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_app_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>  <span class="c1"># &lt;- random_state is by default set to 0</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_app_not_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_not_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_app_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_app_number">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_app_number">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_app_number</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">APP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">n_prevalences</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="c1"># surprisingly enough, for some n_prevalences the test fails, notwithstanding</span>
-        <span class="c1"># everything is correct. The problem is that in function APP.prevalence_grid()</span>
-        <span class="c1"># there is sometimes one rounding error that gets cumulated and</span>
-        <span class="c1"># surpasses 1.0 (by a very small float value, 0.0000000000002 or sthe like)</span>
-        <span class="c1"># so these tuples are mistakenly removed... I have tried with np.close, and</span>
-        <span class="c1"># other workarounds, but eventually happens that there is some negative probability</span>
-        <span class="c1"># in the sampling function...</span>
-
-        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">p</span><span class="p">():</span>
-            <span class="n">count</span><span class="o">+=</span><span class="mi">1</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">p</span><span class="o">.</span><span class="n">total</span><span class="p">())</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_npp_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_npp_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_npp_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>  <span class="c1"># &lt;- random_state is by default set to 0</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_npp_not_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_npp_not_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_npp_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_kraemer_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_kraemer_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_kraemer_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>  <span class="c1"># &lt;- random_state is by default set to 0</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_kraemer_not_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_kraemer_not_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_kraemer_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">UPP</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">repeats</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_covariate_shift_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_covariate_shift_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_covariate_shift_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">dataA</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">&#39;domA&#39;</span><span class="p">)</span>
-        <span class="n">dataB</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">&#39;domB&#39;</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>  <span class="c1"># &lt;- random_state is by default set to 0</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_covariate_shift_not_replicate">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_covariate_shift_not_replicate">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_covariate_shift_not_replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">dataA</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">&#39;domA&#39;</span><span class="p">)</span>
-        <span class="n">dataB</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">(</span><span class="s1">&#39;domB&#39;</span><span class="p">)</span>
-        <span class="n">p</span> <span class="o">=</span> <span class="n">DomainMixer</span><span class="p">(</span><span class="n">dataA</span><span class="p">,</span> <span class="n">dataB</span><span class="p">,</span> <span class="n">sample_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">mixture_points</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-
-        <span class="n">samples1</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-        <span class="n">samples2</span> <span class="o">=</span> <span class="n">samples_to_str</span><span class="p">(</span><span class="n">p</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">samples1</span><span class="p">,</span> <span class="n">samples2</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="TestProtocols.test_no_seed_init">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_protocols.TestProtocols.test_no_seed_init">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_no_seed_init</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">class</span> <span class="nc">NoSeedInit</span><span class="p">(</span><span class="n">AbstractStochasticSeededProtocol</span><span class="p">):</span>
-            <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">mock_labelled_collection</span><span class="p">()</span>
-
-            <span class="k">def</span> <span class="nf">samples_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-                <span class="c1"># return a matrix containing sampling indexes in the rows</span>
-                <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">),</span> <span class="mi">10</span><span class="o">*</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
-
-            <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">params</span><span class="p">):</span>
-                <span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">params</span><span class="p">)</span>
-                <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">sampling_from_index</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
-
-        <span class="n">p</span> <span class="o">=</span> <span class="n">NoSeedInit</span><span class="p">()</span>
-
-        <span class="c1"># this should raise a ValueError, since the class is said to be AbstractStochasticSeededProtocol but the</span>
-        <span class="c1"># random_seed has never been passed to super(NoSeedInit, self).__init__(random_seed)</span>
-        <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">assertRaises</span><span class="p">(</span><span class="ne">ValueError</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">sample</span> <span class="ow">in</span> <span class="n">p</span><span class="p">():</span>
-                <span class="k">pass</span>
-            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;done&#39;</span><span class="p">)</span></div>
-</div>
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
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-    <p>&#169; Copyright 2024, Alejandro Moreo.</p>
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docs/build/html/_modules/quapy/tests/test_replicability.html

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-             
-  <h1>Source code for quapy.tests.test_replicability</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">unittest</span>
-<span class="kn">import</span> <span class="nn">quapy</span> <span class="k">as</span> <span class="nn">qp</span>
-<span class="kn">from</span> <span class="nn">quapy.data</span> <span class="kn">import</span> <span class="n">LabelledCollection</span>
-<span class="kn">from</span> <span class="nn">quapy.functional</span> <span class="kn">import</span> <span class="n">strprev</span>
-<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">LogisticRegression</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">quapy.method.aggregative</span> <span class="kn">import</span> <span class="n">PACC</span>
-<span class="kn">import</span> <span class="nn">quapy.functional</span> <span class="k">as</span> <span class="nn">F</span>
-
-
-<div class="viewcode-block" id="MyTestCase">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase">[docs]</a>
-<span class="k">class</span> <span class="nc">MyTestCase</span><span class="p">(</span><span class="n">unittest</span><span class="o">.</span><span class="n">TestCase</span><span class="p">):</span>
-
-<div class="viewcode-block" id="MyTestCase.test_prediction_replicability">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_prediction_replicability">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_prediction_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">dataset</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIBinaryDataset</span><span class="p">(</span><span class="s1">&#39;yeast&#39;</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-            <span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
-            <span class="n">prev</span> <span class="o">=</span> <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-            <span class="n">str_prev1</span> <span class="o">=</span> <span class="n">strprev</span><span class="p">(</span><span class="n">prev</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">lr</span> <span class="o">=</span> <span class="n">LogisticRegression</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>
-            <span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
-            <span class="n">prev2</span> <span class="o">=</span> <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">training</span><span class="p">)</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">dataset</span><span class="o">.</span><span class="n">test</span><span class="o">.</span><span class="n">X</span><span class="p">)</span>
-            <span class="n">str_prev2</span> <span class="o">=</span> <span class="n">strprev</span><span class="p">(</span><span class="n">prev2</span><span class="p">,</span> <span class="n">prec</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">str_prev1</span><span class="p">,</span> <span class="n">str_prev2</span><span class="p">)</span>  <span class="c1"># add assertion here</span></div>
-
-
-
-<div class="viewcode-block" id="MyTestCase.test_samping_replicability">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_samping_replicability">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_samping_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="k">def</span> <span class="nf">equal_collections</span><span class="p">(</span><span class="n">c1</span><span class="p">,</span> <span class="n">c2</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">Xtr</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">Xtr</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">ytr</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">ytr</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">value</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">c1</span><span class="o">.</span><span class="n">classes_</span> <span class="o">==</span> <span class="n">c2</span><span class="o">.</span><span class="n">classes_</span><span class="p">),</span> <span class="n">value</span><span class="p">)</span>
-
-        <span class="n">X</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="nb">str</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)))</span>
-        <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">LabelledCollection</span><span class="p">(</span><span class="n">instances</span><span class="o">=</span><span class="n">X</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">y</span><span class="p">)</span>
-
-        <span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
-        <span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
-
-        <span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">])</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">sample1</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">sample2</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">],</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1</span><span class="p">,</span> <span class="n">sample2</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample1_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">sample2_tr</span><span class="p">,</span> <span class="n">sample2_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample2_tr</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_te</span><span class="p">,</span> <span class="n">sample2_te</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample1_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">sample2_tr</span><span class="p">,</span> <span class="n">sample2_te</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">split_stratified</span><span class="p">(</span><span class="n">train_prop</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_tr</span><span class="p">,</span> <span class="n">sample2_tr</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-        <span class="n">equal_collections</span><span class="p">(</span><span class="n">sample1_te</span><span class="p">,</span> <span class="n">sample2_te</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="MyTestCase.test_parallel_replicability">
-<a class="viewcode-back" href="../../../quapy.tests.html#quapy.tests.test_replicability.MyTestCase.test_parallel_replicability">[docs]</a>
-    <span class="k">def</span> <span class="nf">test_parallel_replicability</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-
-        <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">fetch_UCIMulticlassDataset</span><span class="p">(</span><span class="s1">&#39;dry-bean&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">train_test</span>
-
-        <span class="n">test</span> <span class="o">=</span> <span class="n">test</span><span class="o">.</span><span class="n">sampling</span><span class="p">(</span><span class="mi">500</span><span class="p">,</span> <span class="o">*</span><span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">])</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
-            <span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-            <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-            <span class="n">prev1</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-            <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-            <span class="n">prev2</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
-
-        <span class="k">with</span> <span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
-            <span class="n">pacc</span> <span class="o">=</span> <span class="n">PACC</span><span class="p">(</span><span class="n">LogisticRegression</span><span class="p">(),</span> <span class="n">val_split</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">n_jobs</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-            <span class="n">pacc</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val_split</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
-            <span class="n">prev3</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">strprev</span><span class="p">(</span><span class="n">pacc</span><span class="o">.</span><span class="n">quantify</span><span class="p">(</span><span class="n">test</span><span class="o">.</span><span class="n">instances</span><span class="p">))</span>
-
-        <span class="nb">print</span><span class="p">(</span><span class="n">prev1</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">prev2</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">prev3</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertNotEqual</span><span class="p">(</span><span class="n">prev1</span><span class="p">,</span> <span class="n">prev2</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">assertEqual</span><span class="p">(</span><span class="n">prev2</span><span class="p">,</span> <span class="n">prev3</span><span class="p">)</span></div>
-</div>
-
-
-
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="n">unittest</span><span class="o">.</span><span class="n">main</span><span class="p">()</span>
-</pre></div>
-
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-                  
-  <h1>Source code for quapy.util</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span><span class="w"> </span><span class="nn">contextlib</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">itertools</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">multiprocessing</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">os</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">pickle</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">urllib</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">pathlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Path</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">contextlib</span><span class="w"> </span><span class="kn">import</span> <span class="n">ExitStack</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">quapy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">qp</span>
-
-<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">joblib</span><span class="w"> </span><span class="kn">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
-<span class="kn">from</span><span class="w"> </span><span class="nn">time</span><span class="w"> </span><span class="kn">import</span> <span class="n">time</span>
-<span class="kn">import</span><span class="w"> </span><span class="nn">signal</span>
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">_get_parallel_slices</span><span class="p">(</span><span class="n">n_tasks</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">n_jobs</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-        <span class="n">n_jobs</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
-    <span class="n">batch</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n_tasks</span> <span class="o">/</span> <span class="n">n_jobs</span><span class="p">)</span>
-    <span class="n">remainder</span> <span class="o">=</span> <span class="n">n_tasks</span> <span class="o">%</span> <span class="n">n_jobs</span>
-    <span class="k">return</span> <span class="p">[</span><span class="nb">slice</span><span class="p">(</span><span class="n">job</span> <span class="o">*</span> <span class="n">batch</span><span class="p">,</span> <span class="p">(</span><span class="n">job</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">batch</span> <span class="o">+</span> <span class="p">(</span><span class="n">remainder</span> <span class="k">if</span> <span class="n">job</span> <span class="o">==</span> <span class="n">n_jobs</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_jobs</span><span class="p">)]</span>
-
-
-<div class="viewcode-block" id="map_parallel">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.map_parallel">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">map_parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Applies func to n_jobs slices of args. E.g., if args is an array of 99 items and n_jobs=2, then</span>
-<span class="sd">    func is applied in two parallel processes to args[0:50] and to args[50:99]. func is a function</span>
-<span class="sd">    that already works with a list of arguments.</span>
-
-<span class="sd">    :param func: function to be parallelized</span>
-<span class="sd">    :param args: array-like of arguments to be passed to the function in different parallel calls</span>
-<span class="sd">    :param n_jobs: the number of workers</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">args</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
-    <span class="n">slices</span> <span class="o">=</span> <span class="n">_get_parallel_slices</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">),</span> <span class="n">n_jobs</span><span class="p">)</span>
-    <span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">func</span><span class="p">)(</span><span class="n">args</span><span class="p">[</span><span class="n">slice_i</span><span class="p">])</span> <span class="k">for</span> <span class="n">slice_i</span> <span class="ow">in</span> <span class="n">slices</span>
-    <span class="p">)</span>
-    <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">results</span><span class="p">))</span></div>
-
-
-
-<div class="viewcode-block" id="parallel">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">parallel</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;loky&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A wrapper of multiprocessing:</span>
-
-<span class="sd">    &gt;&gt;&gt; Parallel(n_jobs=n_jobs)(</span>
-<span class="sd">    &gt;&gt;&gt;      delayed(func)(args_i) for args_i in args</span>
-<span class="sd">    &gt;&gt;&gt; )</span>
-
-<span class="sd">    that takes the `quapy.environ` variable as input silently.</span>
-<span class="sd">    Seeds the child processes to ensure reproducibility when n_jobs&gt;1.</span>
-
-<span class="sd">    :param func: callable</span>
-<span class="sd">    :param args: args of func</span>
-<span class="sd">    :param seed: the numeric seed</span>
-<span class="sd">    :param asarray: set to True to return a np.ndarray instead of a list</span>
-<span class="sd">    :param backend: indicates the backend used for handling parallel works</span>
-<span class="sd">    :param open_args: if True, then the delayed function is called on *args_i, instead of on args_i</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;N_JOBS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="c1">#set a context with a temporal seed to ensure results are reproducibles in parallel</span>
-        <span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-    
-    <span class="n">out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">func_dec</span><span class="p">)(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">,</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">seed</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="n">asarray</span><span class="p">:</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">out</span></div>
-
-
-
-<div class="viewcode-block" id="parallel_unpack">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.parallel_unpack">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">parallel_unpack</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">n_jobs</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">asarray</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s1">&#39;loky&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A wrapper of multiprocessing:</span>
-
-<span class="sd">    &gt;&gt;&gt; Parallel(n_jobs=n_jobs)(</span>
-<span class="sd">    &gt;&gt;&gt;      delayed(func)(*args_i) for args_i in args</span>
-<span class="sd">    &gt;&gt;&gt; )</span>
-
-<span class="sd">    that takes the `quapy.environ` variable as input silently.</span>
-<span class="sd">    Seeds the child processes to ensure reproducibility when n_jobs&gt;1.</span>
-
-<span class="sd">    :param func: callable</span>
-<span class="sd">    :param args: args of func</span>
-<span class="sd">    :param seed: the numeric seed</span>
-<span class="sd">    :param asarray: set to True to return a np.ndarray instead of a list</span>
-<span class="sd">    :param backend: indicates the backend used for handling parallel works</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="nf">func_dec</span><span class="p">(</span><span class="n">environ</span><span class="p">,</span> <span class="n">seed</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span> <span class="o">=</span> <span class="n">environ</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;N_JOBS&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="c1"># set a context with a temporal seed to ensure results are reproducibles in parallel</span>
-        <span class="k">with</span> <span class="n">ExitStack</span><span class="p">()</span> <span class="k">as</span> <span class="n">stack</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                <span class="n">stack</span><span class="o">.</span><span class="n">enter_context</span><span class="p">(</span><span class="n">qp</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">temp_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">))</span>
-            <span class="k">return</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-
-    <span class="n">out</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">n_jobs</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">)(</span>
-        <span class="n">delayed</span><span class="p">(</span><span class="n">func_dec</span><span class="p">)(</span><span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">,</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">seed</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="o">*</span><span class="n">args_i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">args_i</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
-    <span class="p">)</span>
-    <span class="k">if</span> <span class="n">asarray</span><span class="p">:</span>
-        <span class="n">out</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">out</span></div>
-
-
-<div class="viewcode-block" id="temp_seed">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.temp_seed">[docs]</a>
-<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
-<span class="k">def</span><span class="w"> </span><span class="nf">temp_seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Can be used in a &quot;with&quot; context to set a temporal seed without modifying the outer numpy&#39;s current state. E.g.:</span>
-
-<span class="sd">    &gt;&gt;&gt; with temp_seed(random_seed):</span>
-<span class="sd">    &gt;&gt;&gt;  pass # do any computation depending on np.random functionality</span>
-
-<span class="sd">    :param random_state: the seed to set within the &quot;with&quot; context</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="n">state</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">get_state</span><span class="p">()</span>
-        <span class="c1">#save the seed just in case is needed (for instance for setting the seed to child processes)</span>
-        <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;_R_SEED&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">random_state</span>
-        <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">random_state</span><span class="p">)</span>
-    <span class="k">try</span><span class="p">:</span>
-        <span class="k">yield</span>
-    <span class="k">finally</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">random_state</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">set_state</span><span class="p">(</span><span class="n">state</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="download_file">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Downloads a file from a url</span>
-
-<span class="sd">    :param url: the url</span>
-<span class="sd">    :param archive_filename: destination filename</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">progress</span><span class="p">(</span><span class="n">blocknum</span><span class="p">,</span> <span class="n">bs</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
-        <span class="n">total_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">size</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
-        <span class="n">current_sz_mb</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%.2f</span><span class="s1"> MB&#39;</span> <span class="o">%</span> <span class="p">((</span><span class="n">blocknum</span> <span class="o">*</span> <span class="n">bs</span><span class="p">)</span> <span class="o">/</span> <span class="mf">1e6</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\r</span><span class="s1">downloaded </span><span class="si">%s</span><span class="s1"> / </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">current_sz_mb</span><span class="p">,</span> <span class="n">total_sz_mb</span><span class="p">),</span> <span class="n">end</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Downloading </span><span class="si">%s</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">url</span><span class="p">)</span>
-    <span class="n">urllib</span><span class="o">.</span><span class="n">request</span><span class="o">.</span><span class="n">urlretrieve</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">filename</span><span class="o">=</span><span class="n">archive_filename</span><span class="p">,</span> <span class="n">reporthook</span><span class="o">=</span><span class="n">progress</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="download_file_if_not_exists">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.download_file_if_not_exists">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">download_file_if_not_exists</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Downloads a file (using :meth:`download_file`) if the file does not exist.</span>
-
-<span class="sd">    :param url: the url</span>
-<span class="sd">    :param archive_filename: destination filename</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">archive_filename</span><span class="p">):</span>
-        <span class="k">return</span>
-    <span class="n">create_if_not_exist</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">archive_filename</span><span class="p">))</span>
-    <span class="n">download_file</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">archive_filename</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="create_if_not_exist">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.create_if_not_exist">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">create_if_not_exist</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    An alias to `os.makedirs(path, exist_ok=True)` that also returns the path. This is useful in cases like, e.g.:</span>
-
-<span class="sd">    &gt;&gt;&gt; path = create_if_not_exist(os.path.join(dir, subdir, anotherdir))</span>
-
-<span class="sd">    :param path: path to create</span>
-<span class="sd">    :return: the path itself</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">path</span></div>
-
-
-
-<div class="viewcode-block" id="get_quapy_home">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.get_quapy_home">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">get_quapy_home</span><span class="p">():</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Gets the home directory of QuaPy, i.e., the directory where QuaPy saves permanent data, such as dowloaded datasets.</span>
-<span class="sd">    This directory is `~/quapy_data`</span>
-
-<span class="sd">    :return: a string representing the path</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">home</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">home</span><span class="p">()),</span> <span class="s1">&#39;quapy_data&#39;</span><span class="p">)</span>
-    <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">home</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="k">return</span> <span class="n">home</span></div>
-
-
-
-<div class="viewcode-block" id="create_parent_dir">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.create_parent_dir">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Creates the parent dir (if any) of a given path, if not exists. E.g., for `./path/to/file.txt`, the path `./path/to`</span>
-<span class="sd">    is created.</span>
-
-<span class="sd">    :param path: the path</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">parentdir</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="n">path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span>
-    <span class="k">if</span> <span class="n">parentdir</span><span class="p">:</span>
-        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">parentdir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="save_text_file">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.save_text_file">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">save_text_file</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">text</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Saves a text file to disk, given its full path, and creates the parent directory if missing.</span>
-
-<span class="sd">    :param path: path where to save the path.</span>
-<span class="sd">    :param text: text to save.</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">create_parent_dir</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;wt&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fout</span><span class="p">:</span>
-        <span class="n">fout</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">text</span><span class="p">)</span></div>
-
-
-
-<div class="viewcode-block" id="pickled_resource">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.pickled_resource">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">pickled_resource</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">generation_func</span><span class="p">:</span><span class="nb">callable</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Allows for fast reuse of resources that are generated only once by calling generation_func(\\*args). The next times</span>
-<span class="sd">    this function is invoked, it loads the pickled resource. Example:</span>
-
-<span class="sd">    &gt;&gt;&gt; def some_array(n):  # a mock resource created with one parameter (`n`)</span>
-<span class="sd">    &gt;&gt;&gt;     return np.random.rand(n)</span>
-<span class="sd">    &gt;&gt;&gt; pickled_resource(&#39;./my_array.pkl&#39;, some_array, 10)  # the resource does not exist: it is created by calling some_array(10)</span>
-<span class="sd">    &gt;&gt;&gt; pickled_resource(&#39;./my_array.pkl&#39;, some_array, 10)  # the resource exists; it is loaded from &#39;./my_array.pkl&#39;</span>
-
-<span class="sd">    :param pickle_path: the path where to save (first time) and load (next times) the resource</span>
-<span class="sd">    :param generation_func: the function that generates the resource, in case it does not exist in pickle_path</span>
-<span class="sd">    :param args: any arg that generation_func uses for generating the resources</span>
-<span class="sd">    :return: the resource</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">pickle_path</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">):</span>
-            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fin</span><span class="p">:</span>
-                <span class="n">instance</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fin</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">instance</span> <span class="o">=</span> <span class="n">generation_func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">)</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">)</span><span class="o">.</span><span class="n">parent</span><span class="p">),</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">pickle_path</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">foo</span><span class="p">:</span>
-                <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">instance</span><span class="p">,</span> <span class="n">foo</span><span class="p">,</span> <span class="n">pickle</span><span class="o">.</span><span class="n">HIGHEST_PROTOCOL</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">instance</span></div>
-
-
-
-<span class="k">def</span><span class="w"> </span><span class="nf">_check_sample_size</span><span class="p">(</span><span class="n">sample_size</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">sample_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-        <span class="k">assert</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> \
-            <span class="s1">&#39;error: sample_size set to None, and cannot be resolved from the environment&#39;</span>
-        <span class="n">sample_size</span> <span class="o">=</span> <span class="n">qp</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;SAMPLE_SIZE&#39;</span><span class="p">]</span>
-    <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sample_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">)</span> <span class="ow">and</span> <span class="n">sample_size</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">,</span> \
-        <span class="s1">&#39;error: sample_size is not a positive integer&#39;</span>
-    <span class="k">return</span> <span class="n">sample_size</span>
-
-
-<div class="viewcode-block" id="load_report">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.load_report">[docs]</a>
-<span class="k">def</span><span class="w"> </span><span class="nf">load_report</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">as_dict</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
-    <span class="k">def</span><span class="w"> </span><span class="nf">str2prev_arr</span><span class="p">(</span><span class="n">strprev</span><span class="p">):</span>
-        <span class="n">within</span> <span class="o">=</span> <span class="n">strprev</span><span class="o">.</span><span class="n">strip</span><span class="p">(</span><span class="s1">&#39;[]&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
-        <span class="n">float_list</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">within</span><span class="p">]</span>
-        <span class="n">float_list</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span> <span class="o">-</span> <span class="nb">sum</span><span class="p">(</span><span class="n">float_list</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">float_list</span><span class="p">)</span>
-
-    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">index_col</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
-    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;estim-prev&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;estim-prev&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">str2prev_arr</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">as_dict</span><span class="p">:</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="p">{}</span>
-        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">values</span><span class="p">:</span>
-            <span class="n">vals</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-            <span class="k">if</span> <span class="n">col</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;true-prev&#39;</span><span class="p">,</span> <span class="s1">&#39;estim-prev&#39;</span><span class="p">]:</span>
-                <span class="n">vals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
-            <span class="n">d</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">vals</span>
-        <span class="k">return</span> <span class="n">d</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">df</span></div>
-
-
-
-<div class="viewcode-block" id="EarlyStop">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.EarlyStop">[docs]</a>
-<span class="k">class</span><span class="w"> </span><span class="nc">EarlyStop</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    A class implementing the early-stopping condition typically used for training neural networks.</span>
-
-<span class="sd">    &gt;&gt;&gt; earlystop = EarlyStop(patience=2, lower_is_better=True)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop(0.9, epoch=0)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop(0.7, epoch=1)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop.IMPROVED  # is True</span>
-<span class="sd">    &gt;&gt;&gt; earlystop(1.0, epoch=2)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop.STOP  # is False (patience=1)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop(1.0, epoch=3)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop.STOP  # is True (patience=0)</span>
-<span class="sd">    &gt;&gt;&gt; earlystop.best_epoch  # is 1</span>
-<span class="sd">    &gt;&gt;&gt; earlystop.best_score  # is 0.7</span>
-
-<span class="sd">    :param patience: the number of (consecutive) times that a monitored evaluation metric (typically obtaind in a</span>
-<span class="sd">        held-out validation split) can be found to be worse than the best one obtained so far, before flagging the</span>
-<span class="sd">        stopping condition. An instance of this class is `callable`, and is to be used as follows:</span>
-<span class="sd">    :param lower_is_better: if True (default) the metric is to be minimized.</span>
-<span class="sd">    :ivar best_score: keeps track of the best value seen so far</span>
-<span class="sd">    :ivar best_epoch: keeps track of the epoch in which the best score was set</span>
-<span class="sd">    :ivar STOP: flag (boolean) indicating the stopping condition</span>
-<span class="sd">    :ivar IMPROVED: flag (boolean) indicating whether there was an improvement in the last call</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">patience</span><span class="p">,</span> <span class="n">lower_is_better</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span> <span class="o">=</span> <span class="n">patience</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">better</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">a</span><span class="p">,</span><span class="n">b</span><span class="p">:</span> <span class="n">a</span><span class="o">&lt;</span><span class="n">b</span> <span class="k">if</span> <span class="n">lower_is_better</span> <span class="k">else</span> <span class="n">a</span><span class="o">&gt;</span><span class="n">b</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">best_epoch</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">False</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="kc">False</span>
-
-    <span class="k">def</span><span class="w"> </span><span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">watch_score</span><span class="p">,</span> <span class="n">epoch</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Commits the new score found in epoch `epoch`. If the score improves over the best score found so far, then</span>
-<span class="sd">        the patiente counter gets reset. If otherwise, the patience counter is decreased, and in case it reachs 0,</span>
-<span class="sd">        the flag STOP becomes True.</span>
-
-<span class="sd">        :param watch_score: the new score</span>
-<span class="sd">        :param epoch: the current epoch</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">better</span><span class="p">(</span><span class="n">watch_score</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_score</span><span class="p">))</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">IMPROVED</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">best_score</span> <span class="o">=</span> <span class="n">watch_score</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">best_epoch</span> <span class="o">=</span> <span class="n">epoch</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">PATIENCE_LIMIT</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">-=</span> <span class="mi">1</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="bp">self</span><span class="o">.</span><span class="n">STOP</span> <span class="o">=</span> <span class="kc">True</span></div>
-
-
-
-<div class="viewcode-block" id="timeout">
-<a class="viewcode-back" href="../../quapy.html#quapy.util.timeout">[docs]</a>
-<span class="nd">@contextlib</span><span class="o">.</span><span class="n">contextmanager</span>
-<span class="k">def</span><span class="w"> </span><span class="nf">timeout</span><span class="p">(</span><span class="n">seconds</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Opens a context that will launch an exception if not closed after a given number of seconds</span>
-
-<span class="sd">    &gt;&gt;&gt; def func(start_msg, end_msg):</span>
-<span class="sd">    &gt;&gt;&gt;     print(start_msg)</span>
-<span class="sd">    &gt;&gt;&gt;     sleep(2)</span>
-<span class="sd">    &gt;&gt;&gt;     print(end_msg)</span>
-<span class="sd">    &gt;&gt;&gt;</span>
-<span class="sd">    &gt;&gt;&gt; with timeout(1):</span>
-<span class="sd">    &gt;&gt;&gt;     func(&#39;begin function&#39;, &#39;end function&#39;)</span>
-<span class="sd">    &gt;&gt;&gt; Out[]</span>
-<span class="sd">    &gt;&gt;&gt; begin function</span>
-<span class="sd">    &gt;&gt;&gt; TimeoutError</span>
-
-
-<span class="sd">    :param seconds: number of seconds, set to &lt;=0 to ignore the timer</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="k">def</span><span class="w"> </span><span class="nf">handler</span><span class="p">(</span><span class="n">signum</span><span class="p">,</span> <span class="n">frame</span><span class="p">):</span>
-            <span class="k">raise</span> <span class="ne">TimeoutError</span><span class="p">()</span>
-
-        <span class="n">signal</span><span class="o">.</span><span class="n">signal</span><span class="p">(</span><span class="n">signal</span><span class="o">.</span><span class="n">SIGALRM</span><span class="p">,</span> <span class="n">handler</span><span class="p">)</span>
-        <span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="n">seconds</span><span class="p">)</span>
-
-    <span class="k">yield</span>
-
-    <span class="k">if</span> <span class="n">seconds</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">signal</span><span class="o">.</span><span class="n">alarm</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
-
-
-</pre></div>
-
-                </article>
-              
-              
-              
-              
-              
-                <footer class="prev-next-footer d-print-none">
-                  
-<div class="prev-next-area">
-</div>
-                </footer>
-              
-            </div>
-            
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-              
-            
-          </div>
-          <footer class="bd-footer-content">
-            
-          </footer>
-        
-      </main>
-    </div>
-  </div>
-  
-  <!-- Scripts loaded after <body> so the DOM is not blocked -->
-  <script defer src="../../_static/scripts/bootstrap.js?digest=90905a2f556bf617f1a9"></script>
-<script defer src="../../_static/scripts/pydata-sphinx-theme.js?digest=90905a2f556bf617f1a9"></script>
-
-  <footer class="bd-footer">
-<div class="bd-footer__inner bd-page-width">
-  
-    <div class="footer-items__start">
-      
-        <div class="footer-item">
-
-  <p class="copyright">
-    
-      © Copyright 2024, Alejandro Moreo.
-      <br/>
-    
-  </p>
-</div>
-      
-        <div class="footer-item">
-
-  <p class="sphinx-version">
-    Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 9.0.4.
-    <br/>
-  </p>
-</div>
-      
-    </div>
-  
-  
-  
-    <div class="footer-items__end">
-      
-        <div class="footer-item">
-<p class="theme-version">
-  <!-- # L10n: Setting the PST URL as an argument as this does not need to be localized -->
-  Built with the <a href="https://pydata-sphinx-theme.readthedocs.io/en/stable/index.html">PyData Sphinx Theme</a> 0.20.0.
-</p></div>
-      
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-  
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-  </footer>
-  </body>
-</html>

docs/build/html/_sources/quapy.method.rst.txt

@@ -52,22 +52,6 @@ quapy.method.non\_aggregative module
    :undoc-members:
    :show-inheritance:
 
-quapy.method.composable module
-------------------------------
-
-.. automodule:: quapy.method.composable
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-quapy.method.confidence module
-------------------------------
-
-.. automodule:: quapy.method.confidence
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 Module contents
 ---------------
 

docs/build/html/_static/_sphinx_javascript_frameworks_compat.js

@@ -1,123 +0,0 @@
-/* Compatability shim for jQuery and underscores.js.
- *
- * Copyright Sphinx contributors
- * Released under the two clause BSD licence
- */
-
-/**
- * small helper function to urldecode strings
- *
- * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
- */
-jQuery.urldecode = function(x) {
-    if (!x) {
-        return x
-    }
-    return decodeURIComponent(x.replace(/\+/g, ' '));
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
-    if (typeof s === 'undefined')
-        s = document.location.search;
-    var parts = s.substr(s.indexOf('?') + 1).split('&');
-    var result = {};
-    for (var i = 0; i < parts.length; i++) {
-        var tmp = parts[i].split('=', 2);
-        var key = jQuery.urldecode(tmp[0]);
-        var value = jQuery.urldecode(tmp[1]);
-        if (key in result)
-            result[key].push(value);
-        else
-            result[key] = [value];
-    }
-    return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
-    function highlight(node, addItems) {
-        if (node.nodeType === 3) {
-            var val = node.nodeValue;
-            var pos = val.toLowerCase().indexOf(text);
-            if (pos >= 0 &&
-                !jQuery(node.parentNode).hasClass(className) &&
-                !jQuery(node.parentNode).hasClass("nohighlight")) {
-                var span;
-                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
-                if (isInSVG) {
-                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-                } else {
-                    span = document.createElement("span");
-                    span.className = className;
-                }
-                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
-                    document.createTextNode(val.substr(pos + text.length)),
-                    node.nextSibling));
-                node.nodeValue = val.substr(0, pos);
-                if (isInSVG) {
-                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
-                    var bbox = node.parentElement.getBBox();
-                    rect.x.baseVal.value = bbox.x;
-                    rect.y.baseVal.value = bbox.y;
-                    rect.width.baseVal.value = bbox.width;
-                    rect.height.baseVal.value = bbox.height;
-                    rect.setAttribute('class', className);
-                    addItems.push({
-                        "parent": node.parentNode,
-                        "target": rect});
-                }
-            }
-        }
-        else if (!jQuery(node).is("button, select, textarea")) {
-            jQuery.each(node.childNodes, function() {
-                highlight(this, addItems);
-            });
-        }
-    }
-    var addItems = [];
-    var result = this.each(function() {
-        highlight(this, addItems);
-    });
-    for (var i = 0; i < addItems.length; ++i) {
-        jQuery(addItems[i].parent).before(addItems[i].target);
-    }
-    return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
-    jQuery.uaMatch = function(ua) {
-        ua = ua.toLowerCase();
-
-        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
-            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
-            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
-            /(msie) ([\w.]+)/.exec(ua) ||
-            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
-            [];
-
-        return {
-            browser: match[ 1 ] || "",
-            version: match[ 2 ] || "0"
-        };
-    };
-    jQuery.browser = {};
-    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}

docs/build/html/_static/basic.css

@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 360px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}

docs/build/html/_static/css/badge_only.css

@@ -1 +0,0 @@
-.clearfix{*zoom:1}.clearfix:after,.clearfix:before{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-style:normal;font-weight:400;src:url(fonts/fontawesome-webfont.eot?674f50d287a8c48dc19ba404d20fe713?#iefix) format("embedded-opentype"),url(fonts/fontawesome-webfont.woff2?af7ae505a9eed503f8b8e6982036873e) format("woff2"),url(fonts/fontawesome-webfont.woff?fee66e712a8a08eef5805a46892932ad) format("woff"),url(fonts/fontawesome-webfont.ttf?b06871f281fee6b241d60582ae9369b9) format("truetype"),url(fonts/fontawesome-webfont.svg?912ec66d7572ff821749319396470bde#FontAwesome) format("svg")}.fa:before{font-family:FontAwesome;font-style:normal;font-weight:400;line-height:1}.fa:before,a .fa{text-decoration:inherit}.fa:before,a .fa,li .fa{display:inline-block}li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-.8em}ul.fas li .fa{width:.8em}ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before,.icon-book:before{content:"\f02d"}.fa-caret-down:before,.icon-caret-down:before{content:"\f0d7"}.fa-caret-up:before,.icon-caret-up:before{content:"\f0d8"}.fa-caret-left:before,.icon-caret-left:before{content:"\f0d9"}.fa-caret-right:before,.icon-caret-right:before{content:"\f0da"}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;font-family:Lato,proxima-nova,Helvetica Neue,Arial,sans-serif;z-index:400}.rst-versions a{color:#2980b9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27ae60}.rst-versions .rst-current-version:after{clear:both;content:"";display:block}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book,.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#e74c3c;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#f1c40f;color:#000}.rst-versions.shift-up{height:auto;max-height:100%;overflow-y:scroll}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:grey;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:1px solid #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions .rst-other-versions .rtd-current-item{font-weight:700}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px;max-height:90%}.rst-versions.rst-badge .fa-book,.rst-versions.rst-badge .icon-book{float:none;line-height:30px}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book,.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge>.rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width:768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}#flyout-search-form{padding:6px}

docs/build/html/_static/doctools.js

@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);

docs/build/html/_static/documentation_options.js

@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '0.1.8',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};

docs/build/html/_static/js/badge_only.js

@@ -1 +0,0 @@
-!function(e){var t={};function r(n){if(t[n])return t[n].exports;var o=t[n]={i:n,l:!1,exports:{}};return e[n].call(o.exports,o,o.exports,r),o.l=!0,o.exports}r.m=e,r.c=t,r.d=function(e,t,n){r.o(e,t)||Object.defineProperty(e,t,{enumerable:!0,get:n})},r.r=function(e){"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},r.t=function(e,t){if(1&t&&(e=r(e)),8&t)return e;if(4&t&&"object"==typeof e&&e&&e.__esModule)return e;var n=Object.create(null);if(r.r(n),Object.defineProperty(n,"default",{enumerable:!0,value:e}),2&t&&"string"!=typeof e)for(var o in e)r.d(n,o,function(t){return e[t]}.bind(null,o));return n},r.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return r.d(t,"a",t),t},r.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)},r.p="",r(r.s=4)}({4:function(e,t,r){}});

docs/build/html/_static/js/html5shiv-printshiv.min.js

@@ -1,4 +0,0 @@
-/**
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-*/
-!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=y.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=y.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),y.elements=c+" "+a,j(b)}function f(a){var b=x[a[v]];return b||(b={},w++,a[v]=w,x[w]=b),b}function g(a,c,d){if(c||(c=b),q)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():u.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||t.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),q)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return y.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(y,b.frag)}function j(a){a||(a=b);var d=f(a);return!y.shivCSS||p||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),q||i(a,d),a}function k(a){for(var b,c=a.getElementsByTagName("*"),e=c.length,f=RegExp("^(?:"+d().join("|")+")$","i"),g=[];e--;)b=c[e],f.test(b.nodeName)&&g.push(b.applyElement(l(b)));return g}function l(a){for(var b,c=a.attributes,d=c.length,e=a.ownerDocument.createElement(A+":"+a.nodeName);d--;)b=c[d],b.specified&&e.setAttribute(b.nodeName,b.nodeValue);return e.style.cssText=a.style.cssText,e}function m(a){for(var b,c=a.split("{"),e=c.length,f=RegExp("(^|[\\s,>+~])("+d().join("|")+")(?=[[\\s,>+~#.:]|$)","gi"),g="$1"+A+"\\:$2";e--;)b=c[e]=c[e].split("}"),b[b.length-1]=b[b.length-1].replace(f,g),c[e]=b.join("}");return c.join("{")}function n(a){for(var b=a.length;b--;)a[b].removeNode()}function o(a){function b(){clearTimeout(g._removeSheetTimer),d&&d.removeNode(!0),d=null}var d,e,g=f(a),h=a.namespaces,i=a.parentWindow;return!B||a.printShived?a:("undefined"==typeof h[A]&&h.add(A),i.attachEvent("onbeforeprint",function(){b();for(var f,g,h,i=a.styleSheets,j=[],l=i.length,n=Array(l);l--;)n[l]=i[l];for(;h=n.pop();)if(!h.disabled&&z.test(h.media)){try{f=h.imports,g=f.length}catch(o){g=0}for(l=0;g>l;l++)n.push(f[l]);try{j.push(h.cssText)}catch(o){}}j=m(j.reverse().join("")),e=k(a),d=c(a,j)}),i.attachEvent("onafterprint",function(){n(e),clearTimeout(g._removeSheetTimer),g._removeSheetTimer=setTimeout(b,500)}),a.printShived=!0,a)}var p,q,r="3.7.3",s=a.html5||{},t=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,u=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,v="_html5shiv",w=0,x={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",p="hidden"in a,q=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){p=!0,q=!0}}();var y={elements:s.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:r,shivCSS:s.shivCSS!==!1,supportsUnknownElements:q,shivMethods:s.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=y,j(b);var z=/^$|\b(?:all|print)\b/,A="html5shiv",B=!q&&function(){var c=b.documentElement;return!("undefined"==typeof b.namespaces||"undefined"==typeof b.parentWindow||"undefined"==typeof c.applyElement||"undefined"==typeof c.removeNode||"undefined"==typeof a.attachEvent)}();y.type+=" print",y.shivPrint=o,o(b),"object"==typeof module&&module.exports&&(module.exports=y)}("undefined"!=typeof window?window:this,document);

docs/build/html/_static/js/html5shiv.min.js

@@ -1,4 +0,0 @@
-/**
-* @preserve HTML5 Shiv 3.7.3 | @afarkas @jdalton @jon_neal @rem | MIT/GPL2 Licensed
-*/
-!function(a,b){function c(a,b){var c=a.createElement("p"),d=a.getElementsByTagName("head")[0]||a.documentElement;return c.innerHTML="x<style>"+b+"</style>",d.insertBefore(c.lastChild,d.firstChild)}function d(){var a=t.elements;return"string"==typeof a?a.split(" "):a}function e(a,b){var c=t.elements;"string"!=typeof c&&(c=c.join(" ")),"string"!=typeof a&&(a=a.join(" ")),t.elements=c+" "+a,j(b)}function f(a){var b=s[a[q]];return b||(b={},r++,a[q]=r,s[r]=b),b}function g(a,c,d){if(c||(c=b),l)return c.createElement(a);d||(d=f(c));var e;return e=d.cache[a]?d.cache[a].cloneNode():p.test(a)?(d.cache[a]=d.createElem(a)).cloneNode():d.createElem(a),!e.canHaveChildren||o.test(a)||e.tagUrn?e:d.frag.appendChild(e)}function h(a,c){if(a||(a=b),l)return a.createDocumentFragment();c=c||f(a);for(var e=c.frag.cloneNode(),g=0,h=d(),i=h.length;i>g;g++)e.createElement(h[g]);return e}function i(a,b){b.cache||(b.cache={},b.createElem=a.createElement,b.createFrag=a.createDocumentFragment,b.frag=b.createFrag()),a.createElement=function(c){return t.shivMethods?g(c,a,b):b.createElem(c)},a.createDocumentFragment=Function("h,f","return function(){var n=f.cloneNode(),c=n.createElement;h.shivMethods&&("+d().join().replace(/[\w\-:]+/g,function(a){return b.createElem(a),b.frag.createElement(a),'c("'+a+'")'})+");return n}")(t,b.frag)}function j(a){a||(a=b);var d=f(a);return!t.shivCSS||k||d.hasCSS||(d.hasCSS=!!c(a,"article,aside,dialog,figcaption,figure,footer,header,hgroup,main,nav,section{display:block}mark{background:#FF0;color:#000}template{display:none}")),l||i(a,d),a}var k,l,m="3.7.3-pre",n=a.html5||{},o=/^<|^(?:button|map|select|textarea|object|iframe|option|optgroup)$/i,p=/^(?:a|b|code|div|fieldset|h1|h2|h3|h4|h5|h6|i|label|li|ol|p|q|span|strong|style|table|tbody|td|th|tr|ul)$/i,q="_html5shiv",r=0,s={};!function(){try{var a=b.createElement("a");a.innerHTML="<xyz></xyz>",k="hidden"in a,l=1==a.childNodes.length||function(){b.createElement("a");var a=b.createDocumentFragment();return"undefined"==typeof a.cloneNode||"undefined"==typeof a.createDocumentFragment||"undefined"==typeof a.createElement}()}catch(c){k=!0,l=!0}}();var t={elements:n.elements||"abbr article aside audio bdi canvas data datalist details dialog figcaption figure footer header hgroup main mark meter nav output picture progress section summary template time video",version:m,shivCSS:n.shivCSS!==!1,supportsUnknownElements:l,shivMethods:n.shivMethods!==!1,type:"default",shivDocument:j,createElement:g,createDocumentFragment:h,addElements:e};a.html5=t,j(b),"object"==typeof module&&module.exports&&(module.exports=t)}("undefined"!=typeof window?window:this,document);

docs/build/html/_static/language_data.js

@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+

docs/build/html/_static/pygments.css

@@ -0,0 +1,74 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */

docs/build/html/_static/searchtools.js

@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);

docs/build/html/_static/sphinx_highlight.js

@@ -1,159 +0,0 @@
-/* Highlighting utilities for Sphinx HTML documentation. */
-"use strict";
-
-const SPHINX_HIGHLIGHT_ENABLED = true;
-
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0
-      && !parent.classList.contains(className)
-      && !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      const rest = document.createTextNode(val.substr(pos + text.length));
-      parent.insertBefore(span, parent.insertBefore(rest, node.nextSibling));
-      node.nodeValue = val.substr(0, pos);
-      /* There may be more occurrences of search term in this node. So call this
-       * function recursively on the remaining fragment.
-       */
-      _highlight(rest, addItems, text, className);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect",
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target),
-  );
-};
-
-/**
- * Small JavaScript module for the documentation.
- */
-const SphinxHighlight = {
-  /**
-   * highlight the search words provided in localstorage in the text
-   */
-  highlightSearchWords: () => {
-    if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
-
-    // get and clear terms from localstorage
-    const url = new URL(window.location);
-    const highlight =
-      localStorage.getItem("sphinx_highlight_terms")
-      || url.searchParams.get("highlight")
-      || "";
-    localStorage.removeItem("sphinx_highlight_terms");
-    // Update history only if '?highlight' is present; otherwise it
-    // clears text fragments (not set in window.location by the browser)
-    if (url.searchParams.has("highlight")) {
-      url.searchParams.delete("highlight");
-      window.history.replaceState({}, "", url);
-    }
-
-    // get individual terms from highlight string
-    const terms = highlight
-      .toLowerCase()
-      .split(/\s+/)
-      .filter((x) => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">'
-            + '<a href="javascript:SphinxHighlight.hideSearchWords()">'
-            + _("Hide Search Matches")
-            + "</a></p>",
-        ),
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    localStorage.removeItem("sphinx_highlight_terms");
-  },
-
-  initEscapeListener: () => {
-    // only install a listener if it is really needed
-    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
-
-    document.addEventListener("keydown", (event) => {
-      // bail for input elements
-      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName))
-        return;
-      // bail with special keys
-      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey)
-        return;
-      if (
-        DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
-        && event.key === "Escape"
-      ) {
-        SphinxHighlight.hideSearchWords();
-        event.preventDefault();
-      }
-    });
-  },
-};
-
-_ready(() => {
-  /* Do not call highlightSearchWords() when we are on the search page.
-   * It will highlight words from the *previous* search query.
-   */
-  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
-  SphinxHighlight.initEscapeListener();
-});