diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index 8a6f07e..10f0751 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -29,7 +29,7 @@ - + @@ -383,7 +383,6 @@
  • quapy.method._threshold_optim
  • quapy.method.aggregative
  • quapy.method.base
    • -
  • quapy.method.composable
  • quapy.method.confidence
  • quapy.method.meta
  • quapy.method.non_aggregative
    • @@ -391,9 +390,7 @@
  • quapy.plot
  • quapy.protocol
  • quapy.util
    • -
  • qunfold.methods.linear.losses
    • -
  • qunfold.methods.linear.representations
    • -
  • qunfold.sklearn
    • +
  • sklearn.utils._metadata_requests
    • diff --git a/docs/build/html/_modules/quapy/classification/neural.html b/docs/build/html/_modules/quapy/classification/neural.html index 871d391..42d1b4b 100644 --- a/docs/build/html/_modules/quapy/classification/neural.html +++ b/docs/build/html/_modules/quapy/classification/neural.html @@ -29,7 +29,7 @@ - + @@ -370,27 +370,27 @@

    Source code for quapy.classification.neural

    -import logging
-import os
-from abc import ABCMeta, abstractmethod
-from pathlib import Path
+import logging
+import os
+from abc import ABCMeta, abstractmethod
+from pathlib import Path
 
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from sklearn.metrics import accuracy_score, f1_score
-from torch.nn.utils.rnn import pad_sequence
-from tqdm import tqdm
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from sklearn.metrics import accuracy_score, f1_score
+from torch.nn.utils.rnn import pad_sequence
+from tqdm import tqdm
 
-import quapy as qp
-from quapy.data import LabelledCollection
-from quapy.util import EarlyStop
+import quapy as qp
+from quapy.data import LabelledCollection
+from quapy.util import EarlyStop
 
 
 
    
 [docs]
-class NeuralClassifierTrainer:
+class NeuralClassifierTrainer:
     """
     Trains a neural network for text classification.
 
@@ -408,7 +408,7 @@
         according to the evaluation in the held-out validation split (default '../checkpoint/classifier_net.dat')
     """
 
-    def __init__(self,
+    def __init__(self,
                  net: 'TextClassifierNet',
                  lr=1e-3,
                  weight_decay=0,
@@ -443,7 +443,7 @@
 
 
    
 [docs]
-    def reset_net_params(self, vocab_size, n_classes):
+    def reset_net_params(self, vocab_size, n_classes):
         """Reinitialize the network parameters
 
         :param vocab_size: the size of the vocabulary
@@ -456,7 +456,7 @@
 
 
    
 [docs]
-    def get_params(self):
+    def get_params(self):
         """Get hyper-parameters for this estimator
 
         :return: a dictionary with parameter names mapped to their values
@@ -466,7 +466,7 @@
 
 
    
 [docs]
-    def set_params(self, **params):
+    def set_params(self, **params):
         """Set the parameters of this trainer and the learner it is training.
         In this current version, parameter names for the trainer and learner should
         be disjoint.
@@ -492,14 +492,14 @@
 
 
     @property
-    def device(self):
+    def device(self):
         """ Gets the device in which the network is allocated
 
         :return: device
         """
         return next(self.net.parameters()).device
 
-    def _train_epoch(self, data, status, pbar, epoch):
+    def _train_epoch(self, data, status, pbar, epoch):
         self.net.train()
         criterion = torch.nn.CrossEntropyLoss()
         losses, predictions, true_labels = [], [], []
@@ -519,7 +519,7 @@
             status["f1"] = f1_score(true_labels, predictions, average='macro')
             self.__update_progress_bar(pbar, epoch)
 
-    def _test_epoch(self, data, status, pbar, epoch):
+    def _test_epoch(self, data, status, pbar, epoch):
         self.net.eval()
         criterion = torch.nn.CrossEntropyLoss()
         losses, predictions, true_labels = [], [], []
@@ -537,7 +537,7 @@
             status["f1"] = f1_score(true_labels, predictions, average='macro')
             self.__update_progress_bar(pbar, epoch)
 
-    def __update_progress_bar(self, pbar, epoch):
+    def __update_progress_bar(self, pbar, epoch):
         pbar.set_description(f'[{self.net.__class__.__name__}] training epoch={epoch} '
                              f'tr-loss={self.status["tr"]["loss"]:.5f} '
                              f'tr-acc={100 * self.status["tr"]["acc"]:.2f}% '
@@ -549,7 +549,7 @@
 
 
    
 [docs]
-    def fit(self, instances, labels, val_split=0.3):
+    def fit(self, instances, labels, val_split=0.3):
         """
         Fits the model according to the given training data.
 
@@ -599,7 +599,7 @@
 
 
    
 [docs]
-    def predict(self, instances):
+    def predict(self, instances):
         """
         Predicts labels for the instances
 
@@ -612,7 +612,7 @@
 
 
    
 [docs]
-    def predict_proba(self, instances):
+    def predict_proba(self, instances):
         """
         Predicts posterior probabilities for the instances
 
@@ -631,7 +631,7 @@
 
 
    
 [docs]
-    def transform(self, instances):
+    def transform(self, instances):
         """
         Returns the embeddings of the instances
 
@@ -653,7 +653,7 @@
 
 
    
 [docs]
-class TorchDataset(torch.utils.data.Dataset):
+class TorchDataset(torch.utils.data.Dataset):
     """
     Transforms labelled instances into a Torch's :class:`torch.utils.data.DataLoader` object
 
@@ -661,19 +661,19 @@
     :param labels: array-like of shape `(n_samples, n_classes)` with the class labels
     """
 
-    def __init__(self, instances, labels=None):
+    def __init__(self, instances, labels=None):
         self.instances = instances
         self.labels = labels
 
-    def __len__(self):
+    def __len__(self):
         return len(self.instances)
 
-    def __getitem__(self, index):
+    def __getitem__(self, index):
         return {'doc': self.instances[index], 'label': self.labels[index] if self.labels is not None else None}
 
 
    
 [docs]
-    def asDataloader(self, batch_size, shuffle, pad_length, device):
+    def asDataloader(self, batch_size, shuffle, pad_length, device):
         """
         Converts the labelled collection into a Torch DataLoader with dynamic padding for
         the batch
@@ -686,7 +686,7 @@
         :param device: whether to allocate tensors in cpu or in cuda
         :return: a :class:`torch.utils.data.DataLoader` object
         """
-        def collate(batch):
+        def collate(batch):
             data = [torch.LongTensor(item['doc'][:pad_length]) for item in batch]
             data = pad_sequence(data, batch_first=True, padding_value=qp.environ['PAD_INDEX']).to(device)
             targets = [item['label'] for item in batch]
@@ -704,7 +704,7 @@
 
 
    
 [docs]
-class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta):
+class TextClassifierNet(torch.nn.Module, metaclass=ABCMeta):
     """
     Abstract Text classifier (`torch.nn.Module`)
     """
@@ -712,7 +712,7 @@
 
    
 [docs]
     @abstractmethod
-    def document_embedding(self, x):
+    def document_embedding(self, x):
         """Embeds documents (i.e., performs the forward pass up to the
         next-to-last layer).
 
@@ -727,7 +727,7 @@
 
 
    
 [docs]
-    def forward(self, x):
+    def forward(self, x):
         """Performs the forward pass.
 
         :param x: a batch of instances, typically generated by a torch's `DataLoader`
@@ -741,7 +741,7 @@
 
 
    
 [docs]
-    def dimensions(self):
+    def dimensions(self):
         """Gets the number of dimensions of the embedding space
 
         :return: integer
@@ -751,7 +751,7 @@
 
 
    
 [docs]
-    def predict_proba(self, x):
+    def predict_proba(self, x):
         """
         Predicts posterior probabilities for the instances in `x`
 
@@ -766,7 +766,7 @@
 
 
    
 [docs]
-    def xavier_uniform(self):
+    def xavier_uniform(self):
         """
         Performs Xavier initialization of the network parameters
         """
@@ -778,7 +778,7 @@
 
    
 [docs]
     @abstractmethod
-    def get_params(self):
+    def get_params(self):
         """
         Get hyper-parameters for this estimator
 
@@ -788,7 +788,7 @@
 
 
     @property
-    def vocabulary_size(self):
+    def vocabulary_size(self):
         """
         Return the size of the vocabulary
 
@@ -800,7 +800,7 @@
 
 
    
 [docs]
-class LSTMnet(TextClassifierNet):
+class LSTMnet(TextClassifierNet):
     """
     An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on
     Long Short Term Memory networks.
@@ -814,7 +814,7 @@
     :param drop_p: drop probability for dropout (default 0.5)
     """
 
-    def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_class_nlayers=1,
+    def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100, lstm_class_nlayers=1,
                  drop_p=0.5):
 
         super().__init__()
@@ -836,7 +836,7 @@
         self.doc_embedder = torch.nn.Linear(hidden_size, self.dim)
         self.output = torch.nn.Linear(self.dim, n_classes)
 
-    def __init_hidden(self, set_size):
+    def __init_hidden(self, set_size):
         opt = self.hyperparams
         var_hidden = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size'])
         var_cell = torch.zeros(opt['lstm_class_nlayers'], set_size, opt['hidden_size'])
@@ -846,7 +846,7 @@
 
 
    
 [docs]
-    def document_embedding(self, x):
+    def document_embedding(self, x):
         """Embeds documents (i.e., performs the forward pass up to the
         next-to-last layer).
 
@@ -865,7 +865,7 @@
 
 
    
 [docs]
-    def get_params(self):
+    def get_params(self):
         """
         Get hyper-parameters for this estimator
 
@@ -875,7 +875,7 @@
 
 
     @property
-    def vocabulary_size(self):
+    def vocabulary_size(self):
         """
         Return the size of the vocabulary
 
@@ -887,7 +887,7 @@
 
 
    
 [docs]
-class CNNnet(TextClassifierNet):
+class CNNnet(TextClassifierNet):
     """
     An implementation of :class:`quapy.classification.neural.TextClassifierNet` based on
     Convolutional Neural Networks.
@@ -904,7 +904,7 @@
     :param drop_p: drop probability for dropout (default 0.5)
     """
 
-    def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100,
+    def __init__(self, vocabulary_size, n_classes, embedding_size=100, hidden_size=256, repr_size=100,
                  kernel_heights=[3, 5, 7], stride=1, padding=0, drop_p=0.5):
         super(CNNnet, self).__init__()
 
@@ -929,7 +929,7 @@
         self.doc_embedder = torch.nn.Linear(len(kernel_heights) * hidden_size, self.dim)
         self.output = nn.Linear(self.dim, n_classes)
 
-    def __conv_block(self, input, conv_layer):
+    def __conv_block(self, input, conv_layer):
         conv_out = conv_layer(input)  # conv_out.size() = (batch_size, out_channels, dim, 1)
         activation = F.relu(conv_out.squeeze(3))  # activation.size() = (batch_size, out_channels, dim1)
         max_out = F.max_pool1d(activation, activation.size()[2]).squeeze(2)  # maxpool_out.size() = (batch_size, out_channels)
@@ -937,7 +937,7 @@
 
 
    
 [docs]
-    def document_embedding(self, input):
+    def document_embedding(self, input):
         """Embeds documents (i.e., performs the forward pass up to the
         next-to-last layer).
 
@@ -962,7 +962,7 @@
 
 
    
 [docs]
-    def get_params(self):
+    def get_params(self):
         """
         Get hyper-parameters for this estimator
 
@@ -972,7 +972,7 @@
 
 
     @property
-    def vocabulary_size(self):
+    def vocabulary_size(self):
         """
         Return the size of the vocabulary
 
diff --git a/docs/build/html/_modules/quapy/classification/svmperf.html b/docs/build/html/_modules/quapy/classification/svmperf.html
index e855546..073c664 100644
--- a/docs/build/html/_modules/quapy/classification/svmperf.html
+++ b/docs/build/html/_modules/quapy/classification/svmperf.html
@@ -29,7 +29,7 @@
   
 
 
-    
+    
     
     
   
@@ -370,22 +370,22 @@
                 
    
                   
   
    Source code for quapy.classification.svmperf
    -import logging
-import random
-import shutil
-import subprocess
-import tempfile
-from os import remove, makedirs
-from os.path import join, exists
-from subprocess import PIPE, STDOUT
-import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.datasets import dump_svmlight_file
+import logging
+import random
+import shutil
+import subprocess
+import tempfile
+from os import remove, makedirs
+from os.path import join, exists
+from subprocess import PIPE, STDOUT
+import numpy as np
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.datasets import dump_svmlight_file
 
 
 
    
 [docs]
-class SVMperf(BaseEstimator, ClassifierMixin):
+class SVMperf(BaseEstimator, ClassifierMixin):
     """A wrapper for the `SVM-perf package <https://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html>`__ by Thorsten Joachims.
     When using losses for quantification, the source code has to be patched. See
     the `installation documentation <https://hlt-isti.github.io/QuaPy/build/html/Installation.html#svm-perf-with-quantification-oriented-losses>`__
@@ -407,7 +407,7 @@
     # losses with their respective codes in svm_perf implementation
     valid_losses = {'01':0, 'f1':1, 'kld':12, 'nkld':13, 'q':22, 'qacc':23, 'qf1':24, 'qgm':25, 'mae':26, 'mrae':27}
 
-    def __init__(self, svmperf_base, C=0.01, verbose=False, loss='01', host_folder=None):
+    def __init__(self, svmperf_base, C=0.01, verbose=False, loss='01', host_folder=None):
         assert exists(svmperf_base), \
             (f'path {svmperf_base} does not seem to point to a valid path;'
              f'did you install svm-perf? '
@@ -420,7 +420,7 @@
 
 
    
 [docs]
-    def fit(self, X, y):
+    def fit(self, X, y):
         """
         Trains the SVM for the multivariate performance loss
 
@@ -469,7 +469,7 @@
 
 
    
 [docs]
-    def predict(self, X):
+    def predict(self, X):
         """
         Predicts labels for the instances `X`
 
@@ -484,7 +484,7 @@
 
 
    
 [docs]
-    def decision_function(self, X, y=None):
+    def decision_function(self, X, y=None):
         """
         Evaluate the decision function for the samples in `X`.
 
@@ -520,7 +520,7 @@
         return scores
    
 
 
-    def __del__(self):
+    def __del__(self):
         if hasattr(self, 'tmpdir'):
             shutil.rmtree(self.tmpdir, ignore_errors=True)
    
 
diff --git a/docs/build/html/_modules/quapy/data/base.html b/docs/build/html/_modules/quapy/data/base.html
index 87203ec..7a4018c 100644
--- a/docs/build/html/_modules/quapy/data/base.html
+++ b/docs/build/html/_modules/quapy/data/base.html
@@ -29,7 +29,7 @@
   
 
 
-    
+    
     
     
   
@@ -370,23 +370,23 @@
                 
    
                   
   
    Source code for quapy.data.base
    -import itertools
-from functools import cached_property
-from typing import Iterable
+import itertools
+from functools import cached_property
+from typing import Iterable
 
-import numpy as np
-from scipy.sparse import issparse
-from scipy.sparse import vstack
-from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
-from numpy.random import RandomState
-from quapy.functional import strprev
-from quapy.util import temp_seed
-import quapy.functional as F
+import numpy as np
+from scipy.sparse import issparse
+from scipy.sparse import vstack
+from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
+from numpy.random import RandomState
+from quapy.functional import strprev
+from quapy.util import temp_seed
+import quapy.functional as F
 
 
 
    
 [docs]
-class LabelledCollection:
+class LabelledCollection:
     """
     A LabelledCollection is a set of objects each with a label attached to each of them. 
     This class implements several sampling routines and other utilities.
@@ -398,7 +398,7 @@
         (i.e., a prevalence of 0)
     """
 
-    def __init__(self, instances, labels, classes=None):
+    def __init__(self, instances, labels, classes=None):
         if issparse(instances):
             self.instances = instances
         elif isinstance(instances, list) and len(instances) > 0 and isinstance(instances[0], str):
@@ -417,7 +417,7 @@
         self._index = None
 
     @property
-    def index(self):
+    def index(self):
         if not hasattr(self, '_index') or self._index is None:
             self._index = {class_: np.arange(len(self))[self.labels == class_] for class_ in self.classes_}
         return self._index
@@ -425,7 +425,7 @@
 
    
 [docs]
     @classmethod
-    def load(cls, path: str, loader_func: callable, classes=None, **loader_kwargs):
+    def load(cls, path: str, loader_func: callable, classes=None, **loader_kwargs):
         """
         Loads a labelled set of data and convert it into a :class:`LabelledCollection` instance. The function in charge
         of reading the instances must be specified. This function can be a custom one, or any of the reading functions
@@ -442,7 +442,7 @@
         return LabelledCollection(*loader_func(path, **loader_kwargs), classes)
    
 
 
-    def __len__(self):
+    def __len__(self):
         """
         Returns the length of this collection (number of labelled instances)
 
@@ -452,7 +452,7 @@
 
 
    
 [docs]
-    def prevalence(self):
+    def prevalence(self):
         """
         Returns the prevalence, or relative frequency, of the classes in the codeframe.
 
@@ -464,7 +464,7 @@
 
 
    
 [docs]
-    def counts(self):
+    def counts(self):
         """
         Returns the number of instances for each of the classes in the codeframe.
 
@@ -475,7 +475,7 @@
 
 
     @property
-    def n_classes(self):
+    def n_classes(self):
         """
         The number of classes
 
@@ -484,7 +484,7 @@
         return len(self.classes_)
 
     @property
-    def n_instances(self):
+    def n_instances(self):
         """
         The number of instances
 
@@ -493,7 +493,7 @@
         return len(self.labels)
 
     @property
-    def binary(self):
+    def binary(self):
         """
         Returns True if the number of classes is 2
 
@@ -503,7 +503,7 @@
 
 
    
 [docs]
-    def sampling_index(self, size, *prevs, shuffle=True, random_state=None):
+    def sampling_index(self, size, *prevs, shuffle=True, random_state=None):
         """
         Returns an index to be used to extract a random sample of desired size and desired prevalence values. If the
         prevalence values are not specified, then returns the index of a uniform sampling.
@@ -566,7 +566,7 @@
 
 
    
 [docs]
-    def uniform_sampling_index(self, size, random_state=None):
+    def uniform_sampling_index(self, size, random_state=None):
         """
         Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
         with replacement.
@@ -584,7 +584,7 @@
 
 
    
 [docs]
-    def sampling(self, size, *prevs, shuffle=True, random_state=None):
+    def sampling(self, size, *prevs, shuffle=True, random_state=None):
         """
         Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence
         values. For each class, the sampling is drawn with replacement.
@@ -604,7 +604,7 @@
 
 
    
 [docs]
-    def uniform_sampling(self, size, random_state=None):
+    def uniform_sampling(self, size, random_state=None):
         """
         Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn
         with replacement.
@@ -619,7 +619,7 @@
 
 
    
 [docs]
-    def sampling_from_index(self, index):
+    def sampling_from_index(self, index):
         """
         Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the
         index.
@@ -634,7 +634,7 @@
 
 
    
 [docs]
-    def split_stratified(self, train_prop=0.6, random_state=None):
+    def split_stratified(self, train_prop=0.6, random_state=None):
         """
         Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired
         proportion.
@@ -656,7 +656,7 @@
 
 
    
 [docs]
-    def split_random(self, train_prop=0.6, random_state=None):
+    def split_random(self, train_prop=0.6, random_state=None):
         """
         Returns two instances of :class:`LabelledCollection` split randomly from this collection, at desired
         proportion.
@@ -683,7 +683,7 @@
         return training, test
    
 
 
-    def __add__(self, other):
+    def __add__(self, other):
         """
         Returns a new :class:`LabelledCollection` as the union of this collection with another collection.
         Both labelled collections must have the same classes.
@@ -699,7 +699,7 @@
 
    
 [docs]
     @classmethod
-    def join(cls, *args: Iterable['LabelledCollection']):
+    def join(cls, *args: Iterable['LabelledCollection']):
         """
         Returns a new :class:`LabelledCollection` as the union of the collections given in input.
 
@@ -742,7 +742,7 @@
 
 
     @property
-    def classes(self):
+    def classes(self):
         """
         Gets an array-like with the classes used in this collection
 
@@ -751,7 +751,7 @@
         return self.classes_
 
     @property
-    def Xy(self):
+    def Xy(self):
         """
         Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:
 
@@ -762,7 +762,7 @@
         return self.instances, self.labels
 
     @property
-    def Xp(self):
+    def Xp(self):
         """
         Gets the instances and the true prevalence. This is useful when implementing evaluation protocols from
         a :class:`LabelledCollection` object.
@@ -772,7 +772,7 @@
         return self.instances, self.prevalence()
 
     @property
-    def X(self):
+    def X(self):
         """
         An alias to self.instances
 
@@ -781,7 +781,7 @@
         return self.instances
 
     @property
-    def y(self):
+    def y(self):
         """
         An alias to self.labels
 
@@ -790,7 +790,7 @@
         return self.labels
 
     @property
-    def p(self):
+    def p(self):
         """
         An alias to self.prevalence()
 
@@ -801,7 +801,7 @@
 
 
    
 [docs]
-    def stats(self, show=True):
+    def stats(self, show=True):
         """
         Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:
 
@@ -836,7 +836,7 @@
 
 
    
 [docs]
-    def kFCV(self, nfolds=5, nrepeats=1, random_state=None):
+    def kFCV(self, nfolds=5, nrepeats=1, random_state=None):
         """
         Generator of stratified folds to be used in k-fold cross validation.
 
@@ -852,7 +852,7 @@
             yield train, test
    
 
 
-    def __repr__(self):
+    def __repr__(self):
         repr=f'<{self.n_instances} instances (dtype={type(self.instances[0])}), '
         repr+=f'n_classes={self.n_classes} {self.classes_}, prevalence={F.strprev(self.prevalence())}>'
         return repr
    
@@ -861,7 +861,7 @@
 
 
    
 [docs]
-class Dataset:
+class Dataset:
     """
     Abstraction of training and test :class:`LabelledCollection` objects.
 
@@ -871,7 +871,7 @@
     :param name: a string representing the name of the dataset
     """
 
-    def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''):
+    def __init__(self, training: LabelledCollection, test: LabelledCollection, vocabulary: dict = None, name=''):
         assert set(training.classes_) == set(test.classes_), 'incompatible labels in training and test collections'
         self.training = training
         self.test = test
@@ -881,7 +881,7 @@
 
    
 [docs]
     @classmethod
-    def SplitStratified(cls, collection: LabelledCollection, train_size=0.6):
+    def SplitStratified(cls, collection: LabelledCollection, train_size=0.6):
         """
         Generates a :class:`Dataset` from a stratified split of a :class:`LabelledCollection` instance.
         See :meth:`LabelledCollection.split_stratified`
@@ -894,7 +894,7 @@
 
 
     @property
-    def classes_(self):
+    def classes_(self):
         """
         The classes according to which the training collection is labelled
 
@@ -903,7 +903,7 @@
         return self.training.classes_
 
     @property
-    def n_classes(self):
+    def n_classes(self):
         """
         The number of classes according to which the training collection is labelled
 
@@ -912,7 +912,7 @@
         return self.training.n_classes
 
     @property
-    def binary(self):
+    def binary(self):
         """
         Returns True if the training collection is labelled according to two classes
 
@@ -923,7 +923,7 @@
 
    
 [docs]
     @classmethod
-    def load(cls, train_path, test_path, loader_func: callable, classes=None, **loader_kwargs):
+    def load(cls, train_path, test_path, loader_func: callable, classes=None, **loader_kwargs):
         """
         Loads a training and a test labelled set of data and convert it into a :class:`Dataset` instance.
         The function in charge of reading the instances must be specified. This function can be a custom one, or any of
@@ -945,7 +945,7 @@
 
 
     @property
-    def vocabulary_size(self):
+    def vocabulary_size(self):
         """
         If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary
 
@@ -954,7 +954,7 @@
         return len(self.vocabulary)
 
     @property
-    def train_test(self):
+    def train_test(self):
         """
         Alias to `self.training` and `self.test`
 
@@ -965,7 +965,7 @@
 
 
    
 [docs]
-    def stats(self, show=True):
+    def stats(self, show=True):
         """
         Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:
 
@@ -992,7 +992,7 @@
 
    
 [docs]
     @classmethod
-    def kFCV(cls, data: LabelledCollection, nfolds=5, nrepeats=1, random_state=0):
+    def kFCV(cls, data: LabelledCollection, nfolds=5, nrepeats=1, random_state=0):
         """
         Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around
         :meth:`LabelledCollection.kFCV` that returns :class:`Dataset` instances made of training and test folds.
@@ -1009,7 +1009,7 @@
 
 
    
 [docs]
-    def reduce(self, n_train=100, n_test=100, random_state=None):
+    def reduce(self, n_train=100, n_test=100, random_state=None):
         """
         Reduce the number of instances in place for quick experiments. Preserves the prevalence of each set.
 
@@ -1030,7 +1030,7 @@
         return self
    
 
 
-    def __repr__(self):
+    def __repr__(self):
         return f'training={self.training}; test={self.test}'
    
 
 
    
diff --git a/docs/build/html/_modules/quapy/data/datasets.html b/docs/build/html/_modules/quapy/data/datasets.html
index cc6a18b..a35e6ab 100644
--- a/docs/build/html/_modules/quapy/data/datasets.html
+++ b/docs/build/html/_modules/quapy/data/datasets.html
@@ -29,7 +29,7 @@
   
 
 
-    
+    
     
     
   
@@ -370,28 +370,28 @@
                 
    
                   
   
    Source code for quapy.data.datasets
    -import logging
-import os
-from contextlib import contextmanager
-import zipfile
-from os.path import join
-import pandas as pd
-from quapy.data.base import Dataset, LabelledCollection
-from quapy.data.preprocessing import text2tfidf, reduce_columns
-from quapy.data.preprocessing import standardize as standardizer
-from quapy.data.reader import *
-from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
-from sklearn.preprocessing import StandardScaler
+import logging
+import os
+from contextlib import contextmanager
+import zipfile
+from os.path import join
+import pandas as pd
+from quapy.data.base import Dataset, LabelledCollection
+from quapy.data.preprocessing import text2tfidf, reduce_columns
+from quapy.data.preprocessing import standardize as standardizer
+from quapy.data.reader import *
+from quapy.util import download_file_if_not_exists, download_file, get_quapy_home, pickled_resource
+from sklearn.preprocessing import StandardScaler
 
 
-def _fetch_ucirepo(*args, **kwargs):
+def _fetch_ucirepo(*args, **kwargs):
     try:
-        from ucimlrepo import fetch_ucirepo
+        from ucimlrepo import fetch_ucirepo
     except ImportError as exc:
         raise ImportError(
             "UCI dataset fetching requires the optional 'ucimlrepo' package. "
             "Install it to use fetch_UCIBinaryDataset or fetch_UCIMulticlassDataset."
-        ) from exc
+        ) from exc
     return fetch_ucirepo(*args, **kwargs)
 
 
@@ -498,7 +498,7 @@
 
 
    
 [docs]
-def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset:
+def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset:
     """
     Loads a Reviews dataset as a Dataset instance, as used in
     `Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."
@@ -548,7 +548,7 @@
 
 
    
 [docs]
-def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False) -> Dataset:
+def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False) -> Dataset:
     """
     Loads a Twitter dataset as a :class:`quapy.data.base.Dataset` instance, as used in:
     `Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
@@ -626,7 +626,7 @@
 
 
    
 [docs]
-def fetch_UCIBinaryDataset(dataset_name, data_home=None, test_split=0.3, standardize=True, verbose=False) -> Dataset:
+def fetch_UCIBinaryDataset(dataset_name, data_home=None, test_split=0.3, standardize=True, verbose=False) -> Dataset:
     """
     Loads a UCI dataset as an instance of :class:`quapy.data.base.Dataset`, as used in
     `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
@@ -660,7 +660,7 @@
 
 
    
 [docs]
-def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, standardize=True, verbose=False) -> LabelledCollection:
+def fetch_UCIBinaryLabelledCollection(dataset_name, data_home=None, standardize=True, verbose=False) -> LabelledCollection:
     """
     Loads a UCI collection as an instance of :class:`quapy.data.base.LabelledCollection`, as used in
     `Pérez-Gállego, P., Quevedo, J. R., & del Coz, J. J. (2017).
@@ -853,7 +853,7 @@
     file = join(data_home, "uci_datasets", dataset_group + ".pkl")
 
     @contextmanager
-    def download_tmp_file(url_group: str, filename: str):
+    def download_tmp_file(url_group: str, filename: str):
         """
         Download a data file for a group of datasets temporarely.
         When used as a context, the file is removed once the context exits.
@@ -871,7 +871,7 @@
         finally:
             os.remove(data_path)
 
-    def download(id: int | None, group: str) -> dict:
+    def download(id: int | None, group: str) -> dict:
         """
         Download the data to be pickled for a dataset group. Use the `fetch_ucirepo` api when possible.
 
@@ -937,7 +937,7 @@
 
         return data
 
-    def binarize_data(name, data: dict) -> LabelledCollection:
+    def binarize_data(name, data: dict) -> LabelledCollection:
         """
         Filter and transform data to extract a binary dataset.
 
@@ -982,7 +982,7 @@
 
 
    
 [docs]
-def fetch_UCIMulticlassDataset(
+def fetch_UCIMulticlassDataset(
         dataset_name,
         data_home=None,
         min_test_split=0.3,
@@ -1045,7 +1045,7 @@
 
 
    
 [docs]
-def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_class_support=100, standardize=True, verbose=False) -> LabelledCollection:
+def fetch_UCIMulticlassLabelledCollection(dataset_name, data_home=None, min_class_support=100, standardize=True, verbose=False) -> LabelledCollection:
     """
     Loads a UCI multiclass collection as an instance of :class:`quapy.data.base.LabelledCollection`.
 
@@ -1148,7 +1148,7 @@
 
     file = join(data_home, 'uci_multiclass', dataset_name+'.pkl')
     
-    def dummify_categorical_features(df_features, dataset_id):
+    def dummify_categorical_features(df_features, dataset_id):
         categorical_features = {
             158: ["S1", "C1", "S2", "C2", "S3", "C3", "S4", "C4", "S5", "C5"],  # poker_hand
         }
@@ -1162,7 +1162,7 @@
 
         return X
 
-    def download(id, name):
+    def download(id, name):
         df = _fetch_ucirepo(id=id)
 
         X_df = dummify_categorical_features(df.data.features, id)
@@ -1175,7 +1175,7 @@
         y = np.searchsorted(classes, y)
         return LabelledCollection(X, y)
 
-    def filter_classes(data: LabelledCollection, min_class_support):
+    def filter_classes(data: LabelledCollection, min_class_support):
         if min_class_support is None or min_class_support == 0.:
             return data
         if isinstance(min_class_support, float):
@@ -1209,18 +1209,18 @@
 
 
 
-def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
+def _df_replace(df, col, repl={'yes': 1, 'no':0}, astype=float):
     df[col] = df[col].apply(lambda x:repl[x]).astype(astype, copy=False)
 
 
-def _array_replace(arr, repl={"yes": 1, "no": 0}):
+def _array_replace(arr, repl={"yes": 1, "no": 0}):
     for k, v in repl.items():
         arr[arr == k] = v
 
 
 
    
 [docs]
-def fetch_lequa2022(task, data_home=None):
+def fetch_lequa2022(task, data_home=None):
     """
     Loads the official datasets provided for the `LeQua 2022 <https://lequa2022.github.io/index>`_ competition.
     In brief, there are 4 tasks (T1A, T1B, T2A, T2B) having to do with text quantification
@@ -1246,7 +1246,7 @@
         that return a series of samples stored in a directory which are labelled by prevalence.
     """
 
-    from quapy.data._lequa import load_raw_documents, load_vector_documents_2022, SamplesFromDir
+    from quapy.data._lequa import load_raw_documents, load_vector_documents_2022, SamplesFromDir
 
     assert task in LEQUA2022_TASKS, \
         f'Unknown task {task}. Valid ones are {LEQUA2022_TASKS}'
@@ -1260,7 +1260,7 @@
     lequa_dir = join(data_home, 'lequa2022')
     os.makedirs(lequa_dir, exist_ok=True)
 
-    def download_unzip_and_remove(unzipped_path, url):
+    def download_unzip_and_remove(unzipped_path, url):
         tmp_path = join(lequa_dir, task + '_tmp.zip')
         download_file_if_not_exists(url, tmp_path)
         with zipfile.ZipFile(tmp_path) as file:
@@ -1296,7 +1296,7 @@
 
 
    
 [docs]
-def fetch_lequa2024(task, data_home=None, merge_T3=False):
+def fetch_lequa2024(task, data_home=None, merge_T3=False):
     """
     Loads the official datasets provided for the `LeQua 2024 <https://lequa2024.github.io/index>`_ competition.
     LeQua 2024 defines four tasks (T1, T2, T3, T4) related to the problem of quantification;
@@ -1327,7 +1327,7 @@
         that return a series of samples stored in a directory which are labelled by prevalence.
     """
 
-    from quapy.data._lequa import load_vector_documents_2024, SamplesFromDir, LabelledCollectionsFromDir
+    from quapy.data._lequa import load_vector_documents_2024, SamplesFromDir, LabelledCollectionsFromDir
 
     assert task in LEQUA2024_TASKS, \
         f'Unknown task {task}. Valid ones are {LEQUA2024_TASKS}'
@@ -1346,7 +1346,7 @@
     lequa_dir = join(data_home, 'lequa2024')
     os.makedirs(lequa_dir, exist_ok=True)
 
-    def download_unzip_and_remove(unzipped_path, url):
+    def download_unzip_and_remove(unzipped_path, url):
         tmp_path = join(lequa_dir, task + '_tmp.zip')
         download_file_if_not_exists(url, tmp_path)
         with zipfile.ZipFile(tmp_path) as file:
@@ -1386,7 +1386,7 @@
 
 
    
 [docs]
-def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=None):
+def fetch_IFCB(single_sample_train=True, for_model_selection=False, data_home=None):
     """
     Loads the IFCB dataset for quantification from `Zenodo <https://zenodo.org/records/10036244>`_ (for more
     information on this dataset, please follow the zenodo link).
@@ -1411,7 +1411,7 @@
         i.e., a sampling protocol that returns a series of samples labelled by prevalence.
     """
 
-    from quapy.data._ifcb import IFCBTrainSamplesFromDir, IFCBTestSamples, get_sample_list, generate_modelselection_split
+    from quapy.data._ifcb import IFCBTrainSamplesFromDir, IFCBTestSamples, get_sample_list, generate_modelselection_split
 
     if data_home is None:
         data_home = get_quapy_home()
@@ -1423,7 +1423,7 @@
     ifcb_dir = join(data_home, 'ifcb')
     os.makedirs(ifcb_dir, exist_ok=True)
 
-    def download_unzip_and_remove(unzipped_path, url):
+    def download_unzip_and_remove(unzipped_path, url):
         tmp_path = join(ifcb_dir, 'ifcb_tmp.zip')
         download_file_if_not_exists(url, tmp_path)
         with zipfile.ZipFile(tmp_path) as file:
@@ -1468,7 +1468,7 @@
 
 
 
-def _fetch_image_embedding_splits(dataset_name, embedding, data_home=None) -> tuple[LabelledCollection,LabelledCollection,LabelledCollection]:
+def _fetch_image_embedding_splits(dataset_name, embedding, data_home=None) -> tuple[LabelledCollection,LabelledCollection,LabelledCollection]:
     """
     Loads a pre-generated embedding set (train, val, or test) of an image dataset from `Zenodo <https://zenodo.org/records/21131944>`_.
     
@@ -1498,7 +1498,7 @@
 
     trained_network = dataset_network[dataset_name]
 
-    def download_embedding_npz(dataset_name, trained_network, embedding):
+    def download_embedding_npz(dataset_name, trained_network, embedding):
         target_file = f'{dataset_name}_{trained_network}_{embedding}.npz'
         URL = f'https://zenodo.org/records/21131944/files/{target_file}'
         os.makedirs(join(data_home, 'image'), exist_ok=True)
@@ -1519,7 +1519,7 @@
 
 
    
 [docs]
-def fetch_image_embeddings(dataset_name, embedding, heldout_only=True, data_home=None) -> Dataset:
+def fetch_image_embeddings(dataset_name, embedding, heldout_only=True, data_home=None) -> Dataset:
     """
     Loads an image dataset with pre-generated embeddings. Available datasets include:
 
diff --git a/docs/build/html/_modules/quapy/data/reader.html b/docs/build/html/_modules/quapy/data/reader.html
index 617442d..88c6f46 100644
--- a/docs/build/html/_modules/quapy/data/reader.html
+++ b/docs/build/html/_modules/quapy/data/reader.html
@@ -29,7 +29,7 @@
   
 
 
-    
+    
     
     
   
@@ -370,16 +370,16 @@
                 
    
                   
   
    Source code for quapy.data.reader
    -import logging
+import logging
 
-import numpy as np
-from scipy.sparse import dok_matrix
-from tqdm import tqdm
+import numpy as np
+from scipy.sparse import dok_matrix
+from tqdm import tqdm
 
 
 
    
 [docs]
-def from_text(path, encoding='utf-8', verbose=1, class2int=True):
+def from_text(path, encoding='utf-8', verbose=1, class2int=True):
     """
     Reads a labelled colletion of documents.
     File fomart <0 or 1>\t<document>\n
@@ -413,7 +413,7 @@
 
 
    
 [docs]
-def from_sparse(path):
+def from_sparse(path):
     """
     Reads a labelled collection of real-valued instances expressed in sparse format
     File format <-1 or 0 or 1>[\s col(int):val(float)]\n
@@ -422,7 +422,7 @@
     :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels
     """
 
-    def split_col_val(col_val):
+    def split_col_val(col_val):
         col, val = col_val.split(':')
         col, val = int(col) - 1, float(val)
         return col, val
@@ -450,7 +450,7 @@
 
 
    
 [docs]
-def from_csv(path, encoding='utf-8'):
+def from_csv(path, encoding='utf-8'):
     """
     Reads a csv file in which columns are separated by ','.
     File format <label>,<feat1>,<feat2>,...,<featn>\n
@@ -473,7 +473,7 @@
 
 
    
 [docs]
-def reindex_labels(y):
+def reindex_labels(y):
     """
     Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
     E.g.:
@@ -496,7 +496,7 @@
 
 
    
 [docs]
-def binarize(y, pos_class):
+def binarize(y, pos_class):
     """
     Binarizes a categorical array-like collection of labels towards the positive class `pos_class`. E.g.,:
 
diff --git a/docs/build/html/_modules/quapy/functional.html b/docs/build/html/_modules/quapy/functional.html
index c275ca4..c2fc0bb 100644
--- a/docs/build/html/_modules/quapy/functional.html
+++ b/docs/build/html/_modules/quapy/functional.html
@@ -29,7 +29,7 @@
   
 
 
-    
+    
     
     
   
@@ -370,17 +370,17 @@
                 
    
                   
   
    Source code for quapy.functional
    -import warnings
-from abc import ABC, abstractmethod
-from collections import defaultdict
-from functools import lru_cache
-from typing import Literal, Union, Callable
-from numpy.typing import ArrayLike
+import warnings
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from functools import lru_cache
+from typing import Literal, Union, Callable
+from numpy.typing import ArrayLike
 
-import scipy
-import numpy as np
+import scipy
+import numpy as np
 
-import quapy as qp
+import quapy as qp
 
 
 # ------------------------------------------------------------------------------------------
@@ -389,7 +389,7 @@
 
 
    
 [docs]
-def classes_from_labels(labels):
+def classes_from_labels(labels):
     """
     Obtains a np.ndarray with the (sorted) classes
     :param labels: array-like with the instances' labels
@@ -403,7 +403,7 @@
 
 
    
 [docs]
-def num_classes_from_labels(labels):
+def num_classes_from_labels(labels):
     """
     Obtains the number of classes from an array-like of instance's labels
     :param labels: array-like with the instances' labels
@@ -418,7 +418,7 @@
 
 
    
 [docs]
-def counts_from_labels(labels: ArrayLike, classes: ArrayLike) -> np.ndarray:
+def counts_from_labels(labels: ArrayLike, classes: ArrayLike) -> np.ndarray:
     """
     Computes the raw count values from a vector of labels.
 
@@ -439,7 +439,7 @@
 
 
    
 [docs]
-def prevalence_from_labels(labels: ArrayLike, classes: ArrayLike):
+def prevalence_from_labels(labels: ArrayLike, classes: ArrayLike):
     """
     Computes the prevalence values from a vector of labels.
 
@@ -457,7 +457,7 @@
 
 
    
 [docs]
-def prevalence_from_probabilities(posteriors: ArrayLike, binarize: bool = False):
+def prevalence_from_probabilities(posteriors: ArrayLike, binarize: bool = False):
     """
     Returns a vector of prevalence values from a matrix of posterior probabilities.
 
@@ -481,7 +481,7 @@
 
 
    
 [docs]
-def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1) -> int:
+def num_prevalence_combinations(n_prevpoints:int, n_classes:int, n_repeats:int=1) -> int:
     """
     Computes the number of valid prevalence combinations in the n_classes-dimensional simplex if `n_prevpoints` equally
     distant prevalence values are generated and `n_repeats` repetitions are requested.
@@ -510,7 +510,7 @@
 
 
    
 [docs]
-def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1) -> int:
+def get_nprevpoints_approximation(combinations_budget:int, n_classes:int, n_repeats:int=1) -> int:
     """
     Searches for the largest number of (equidistant) prevalence points to define for each of the `n_classes` classes so
     that the number of valid prevalence values generated as combinations of prevalence points (points in a
@@ -538,7 +538,7 @@
 
 
    
 [docs]
-def as_binary_prevalence(positive_prevalence: Union[float, ArrayLike], clip_if_necessary: bool=False) -> np.ndarray:
+def as_binary_prevalence(positive_prevalence: Union[float, ArrayLike], clip_if_necessary: bool=False) -> np.ndarray:
     """
     Helper that, given a float representing the prevalence for the positive class, returns a np.ndarray of two
     values representing a binary distribution.
@@ -560,7 +560,7 @@
 
 
    
 [docs]
-def strprev(prevalences: ArrayLike, prec: int=3) -> str:
+def strprev(prevalences: ArrayLike, prec: int=3) -> str:
     """
     Returns a string representation for a prevalence vector. E.g.,
 
@@ -577,7 +577,7 @@
 
 
    
 [docs]
-def check_prevalence_vector(prevalences: ArrayLike, raise_exception: bool=False, tolerance: float=1e-08, aggr=True):
+def check_prevalence_vector(prevalences: ArrayLike, raise_exception: bool=False, tolerance: float=1e-08, aggr=True):
     """
     Checks that `prevalences` is a valid prevalence vector, i.e., it contains values in [0,1] and
     the values sum up to 1. In other words, verifies that the `prevalences` vectors lies in the
@@ -619,7 +619,7 @@
 
 
    
 [docs]
-def uniform_prevalence(n_classes):
+def uniform_prevalence(n_classes):
     """
     Returns a vector representing the uniform distribution for `n_classes`
 
@@ -635,7 +635,7 @@
 
 
    
 [docs]
-def normalize_prevalence(prevalences: ArrayLike, method='l1'):
+def normalize_prevalence(prevalences: ArrayLike, method='l1'):
     """
     Normalizes a vector or matrix of prevalence values. The normalization consists of applying a L1 normalization in
     cases in which the prevalence values are not all-zeros, and to convert the prevalence values into `1/n_classes` in
@@ -678,7 +678,7 @@
 
 
    
 [docs]
-def l1_norm(prevalences: ArrayLike) -> np.ndarray:
+def l1_norm(prevalences: ArrayLike) -> np.ndarray:
     """
     Applies L1 normalization to the `unnormalized_arr` so that it becomes a valid prevalence
     vector. Zero vectors are mapped onto the uniform distribution. Raises an exception if
@@ -704,7 +704,7 @@
 
 
    
 [docs]
-def clip(prevalences: ArrayLike) -> np.ndarray:
+def clip(prevalences: ArrayLike) -> np.ndarray:
     """
     Clips the values in [0,1] and then applies the L1 normalization.
 
@@ -719,7 +719,7 @@
 
 
    
 [docs]
-def projection_simplex_sort(unnormalized_arr: ArrayLike) -> np.ndarray:
+def projection_simplex_sort(unnormalized_arr: ArrayLike) -> np.ndarray:
     """Projects a point onto the probability simplex.
 
     The code is adapted from Mathieu Blondel's BSD-licensed
@@ -747,7 +747,7 @@
 
 
    
 [docs]
-def softmax(prevalences: ArrayLike) -> np.ndarray:
+def softmax(prevalences: ArrayLike) -> np.ndarray:
     """
     Applies the softmax function to all vectors even if the original vectors were valid distributions.
     If you want to leave valid vectors untouched, use condsoftmax instead.
@@ -762,7 +762,7 @@
 
 
    
 [docs]
-def condsoftmax(prevalences: ArrayLike) -> np.ndarray:
+def condsoftmax(prevalences: ArrayLike) -> np.ndarray:
     """
     Applies the softmax function only to vectors that do not represent valid distributions.
 
@@ -787,7 +787,7 @@
 
 
    
 [docs]
-def HellingerDistance(P: np.ndarray, Q: np.ndarray) -> float:
+def HellingerDistance(P: np.ndarray, Q: np.ndarray) -> float:
     """
     Computes the Hellingher Distance (HD) between (discretized) distributions `P` and `Q`.
     The HD for two discrete distributions of `k` bins is defined as:
@@ -805,7 +805,7 @@
 
 
    
 [docs]
-def TopsoeDistance(P: np.ndarray, Q: np.ndarray, epsilon: float=1e-20):
+def TopsoeDistance(P: np.ndarray, Q: np.ndarray, epsilon: float=1e-20):
     """
     Topsoe distance between two (discretized) distributions `P` and `Q`.
     The Topsoe distance for two discrete distributions of `k` bins is defined as:
@@ -824,7 +824,7 @@
 
 
    
 [docs]
-def get_divergence(divergence: Union[str, Callable]):
+def get_divergence(divergence: Union[str, Callable]):
     """
     Guarantees that the divergence received as argument is a function. That is, if this argument is already
     a callable, then it is returned, if it is instead a string, then tries to instantiate the corresponding
@@ -853,7 +853,7 @@
 
 
    
 [docs]
-def argmin_prevalence(loss: Callable,
+def argmin_prevalence(loss: Callable,
                       n_classes: int,
                       method: Literal["optim_minimize", "linear_search", "ternary_search"]='optim_minimize'):
     """
@@ -880,7 +880,7 @@
 
 
    
 [docs]
-def optim_minimize(loss: Callable, n_classes: int, return_loss=False):
+def optim_minimize(loss: Callable, n_classes: int, return_loss=False):
     """
     Searches for the optimal prevalence values, i.e., an `n_classes`-dimensional vector of the (`n_classes`-1)-simplex
     that yields the smallest lost. This optimization is carried out by means of a constrained search using scipy's
@@ -892,7 +892,7 @@
     :return: (ndarray) the best prevalence vector found or a tuple which also contains the value of the loss
         if return_loss=True
     """
-    from scipy import optimize
+    from scipy import optimize
 
     # the initial point is set as the uniform distribution
     uniform_distribution = uniform_prevalence(n_classes=n_classes)
@@ -911,7 +911,7 @@