Generator of stratified folds to be used in k-fold cross validation. This function is only a wrapper around
+LabelledCollection.kFCV() that returns Dataset instances made of training and test folds.
+
+
Parameters
+
+
nfolds – integer (default 5), the number of folds to generate
+
nrepeats – integer (default 1), the number of rounds of k-fold cross validation to run
+
random_state – integer (default 0), guarantees that the folds generated are reproducible
+
+
+
Returns
+
yields nfolds * nrepeats folds for k-fold cross validation as instances of Dataset
Loads a training and a test labelled set of data and convert it into a Dataset 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 defined in quapy.data.reader module.
+
+
Parameters
+
+
train_path – string, the path to the file containing the training instances
+
test_path – string, the path to the file containing the test instances
+
loader_func – a custom function that implements the data loader and returns a tuple with instances and
+labels
+
classes – array-like, the classes according to which the instances are labelled
+
loader_kwargs – any argument that the loader_func function needs in order to read the instances.
+See LabelledCollection.load() for further details.
show – if set to True (default), prints the stats in standard output
+
+
Returns
+
a dictionary containing some stats of this collection for the training and test collections. The keys
+are train and test, and point to dedicated dictionaries of stats, for each collection, with keys
+#instances (the number of instances), type (the type representing the instances),
+#features (the number of features, if the instances are in array-like format), #classes (the classes of
+the collection), prevs (the prevalence values for each class)
A LabelledCollection is a set of objects each with a label associated to it.
+
A LabelledCollection is a set of objects each with a label associated to it. This class implements many sampling
+routines.
+
+
Parameters
+
+
instances – array-like (np.ndarray, list, or csr_matrix are supported)
+
labels – array-like with the same length of instances
+
classes – optional, list of classes from which labels are taken. If not specified, the classes are inferred
+from the labels. The classes must be indicated in cases in which some of the labels might have no examples
+(i.e., a prevalence of 0)
A generator of samples that implements the artificial prevalence protocol (APP). The APP consists of exploring
+a grid of prevalence values (e.g., [0, 0.05, 0.1, 0.15, …, 1]), and generating all valid combinations of
+prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], …,
+[1, 0, 0] prevalence values of size sample_size will be yielded). The number of samples for each valid
+combination of prevalence values is indicated by repeats
+
+
Parameters
+
+
sample_size – the number of instances in each sample
+
n_prevalences – the number of prevalence points to be taken from the [0,1] interval (including the
+limits {0,1}). E.g., if n_prevalences=11, then the prevalence points to take are [0, 0.1, 0.2, …, 1]
+
repeats – the number of samples to generate for each valid combination of prevalence values (default 1)
+
+
+
Returns
+
yield samples generated at artificially controlled prevalence values
A generator of sample indexes implementing the artificial prevalence protocol (APP).
+The APP consists of exploring
+a grid of prevalence values (e.g., [0, 0.05, 0.1, 0.15, …, 1]), and generating all valid combinations of
+prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], …,
+[1, 0, 0] prevalence values of size sample_size will be yielded). The number of sample indexes for each valid
+combination of prevalence values is indicated by repeats
+
+
Parameters
+
+
sample_size – the number of instances in each sample (i.e., length of each index)
+
n_prevalences – the number of prevalence points to be taken from the [0,1] interval (including the
+limits {0,1}). E.g., if n_prevalences=11, then the prevalence points to take are [0, 0.1, 0.2, …, 1]
+
repeats – the number of samples to generate for each valid combination of prevalence values (default 1)
+
+
+
Returns
+
yield the indexes that generate the samples according to APP
Loads a labelled set of data and convert it into a 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
+defined in quapy.data.reader module.
+
+
Parameters
+
+
path – string, the path to the file containing the labelled instances
+
loader_func – a custom function that implements the data loader and returns a tuple with instances and
+labels
+
classes – array-like, the classes according to which the instances are labelled
+
loader_kwargs – any argument that the loader_func function needs in order to read the instances, i.e.,
+these arguments are used to call loader_func(path, **loader_kwargs)
A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing
+samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
+
+
Parameters
+
+
sample_size – integer, the number of instances in each sample
A generator of sample indexes according to the natural prevalence protocol (NPP). The NPP consists of drawing
+samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
+
+
Parameters
+
+
sample_size – integer, the number of instances in each sample (i.e., the length of each index)
+
repeats – the number of indexes to generate
+
+
+
Returns
+
yield repeats instances of np.ndarray with shape (sample_size,)
Return a random sample (an instance of LabelledCollection) of desired size and desired prevalence
+values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
+the actual prevalence of the class, or with replacement otherwise.
+
+
Parameters
+
+
size – integer, the requested size
+
prevs – the prevalence for each class; the prevalence value for the last class can be lead empty since
+it is constrained. E.g., for binary collections, only the prevalence p for the first class (as listed in
+self.classes_ can be specified, while the other class takes prevalence value 1-p
+
shuffle – if set to True (default), shuffles the index before returning it
+
+
+
Returns
+
an instance of LabelledCollection with length == size and prevalence close to prevs (or
+prevalence == prevs if the exact prevalence values can be met as proportions of instances)
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.
+For each class, the sampling is drawn without replacement if the requested prevalence is larger than
+the actual prevalence of the class, or with replacement otherwise.
+
+
Parameters
+
+
size – integer, the requested size
+
prevs – the prevalence for each class; the prevalence value for the last class can be lead empty since
+it is constrained. E.g., for binary collections, only the prevalence p for the first class (as listed in
+self.classes_ can be specified, while the other class takes prevalence value 1-p
+
shuffle – if set to True (default), shuffles the index before returning it
Returns two instances of LabelledCollection split with stratification from this collection, at desired
+proportion.
+
+
Parameters
+
+
train_prop – the proportion of elements to include in the left-most returned collection (typically used
+as the training collection). The rest of elements are included in the right-most returned collection
+(typically used as a test collection).
+
random_state – if specified, guarantees reproducibility of the split.
+
+
+
Returns
+
two instances of LabelledCollection, the first one with train_prop elements, and the
+second one with 1-train_prop elements
show – if set to True (default), prints the stats in standard output
+
+
Returns
+
a dictionary containing some stats of this collection. Keys include #instances (the number of
+instances), type (the type representing the instances), #features (the number of features, if the
+instances are in array-like format), #classes (the classes of the collection), prevs (the prevalence
+values for each class)
Returns a uniform sample (an instance of LabelledCollection) of desired size. The sampling is drawn
+without replacement if the requested size is greater than the number of instances, or with replacement
+otherwise.
Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
+without replacement if the requested size is greater than the number of instances, or with replacement
+otherwise.
Load a Reviews dataset as a Dataset instance, as used in:
-Esuli, A., Moreo, A., and Sebastiani, F. “A recurrent neural network for sentiment quantification.”
-Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
-:param dataset_name: the name of the dataset: valid ones are ‘hp’, ‘kindle’, ‘imdb’
-:param tfidf: set to True to transform the raw documents into tfidf weighted matrices
-:param min_df: minimun number of documents that should contain a term in order for the term to be
-kept (ignored if tfidf==False)
-:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
-~/quay_data/ directory)
-:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
-faster subsequent invokations
-:return: a Dataset instance
Load a Twitter dataset as a Dataset instance, as used in:
-Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
-Social Network Analysis and Mining6(19), 1–22 (2016)
-The datasets ‘semeval13’, ‘semeval14’, ‘semeval15’ share the same training set.
dataset_name – the name of the dataset: valid ones are ‘gasp’, ‘hcr’, ‘omd’, ‘sanders’, ‘semeval13’,
+
+
dataset_name – the name of the dataset: valid ones are ‘gasp’, ‘hcr’, ‘omd’, ‘sanders’, ‘semeval13’,
+‘semeval14’, ‘semeval15’, ‘semeval16’, ‘sst’, ‘wa’, ‘wb’
+
for_model_selection – if True, then returns the train split as the training set and the devel split
+as the test set; if False, then returns the train+devel split as the training set and the test set as the
+test set
+
min_df – minimun number of documents that should contain a term in order for the term to be kept
+
data_home – specify the quapy home directory where collections will be dumped (leave empty to use the default
+~/quay_data/ directory)
+
pickle – set to True to pickle the Dataset object the first time it is generated, in order to allow for
+faster subsequent invokations
‘semeval14’, ‘semeval15’, ‘semeval16’, ‘sst’, ‘wa’, ‘wb’
-:param for_model_selection: if True, then returns the train split as the training set and the devel split
-as the test set; if False, then returns the train+devel split as the training set and the test set as the
-test set
-:param min_df: minimun number of documents that should contain a term in order for the term to be kept
-:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
-~/quay_data/ directory)
-:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
-faster subsequent invokations
-:return: a Dataset instance
This class implements a sklearn’s-style transformer that indexes text as numerical ids for the tokens it
+contains, and that would be generated by sklearn’s
+CountVectorizer
Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.
+Useful to define special tokens for codifying unknown words, or padding tokens.
+
+
Parameters
+
+
word – string, surface form of the token
+
id – integer, numerical value to assign to the token (leave as None for indicating the next valid id,
+default)
+
nogaps – if set to True (default) asserts that the id indicated leads to no numerical gaps with
+precedent ids stored so far
Indexes a dataset of strings. To index a document means to replace each different token by a unique numerical index.
-Rare words (i.e., words occurring less than _min_df_ times) are replaced by a special token UNK
-:param dataset: a Dataset where the instances are lists of str
-:param min_df: minimum number of instances below which the term is replaced by a UNK index
-:param inplace: whether or not to apply the transformation inplace, or to a new copy
-:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.CountVectorizer)
-:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
-consisting of lists of integer values representing indices.
+
Indexes the tokens of a textual quapy.data.base.Dataset of string documents.
+To index a document means to replace each different token by a unique numerical index.
+Rare words (i.e., words occurring less than min_df times) are replaced by a special token UNK
+
+
Parameters
+
+
dataset – a quapy.data.base.Dataset object where the instances of training and test documents
+are lists of str
+
min_df – minimum number of occurrences below which the term is replaced by a UNK index
+
inplace – whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+
kwargs – the rest of parameters of the transformation (as for sklearn’s
+
+
+
+
CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>_)
+:return: a new quapy.data.base.Dataset (if inplace=False) or a reference to the current
+
+
quapy.data.base.Dataset (inplace=True) consisting of lists of integer values representing indices.
Reduces the dimensionality of the csr_matrix by removing the columns of words which are not present in at least
-_min_df_ instances
-:param dataset: a Dataset in sparse format (any subtype of scipy.sparse.spmatrix)
-:param min_df: minimum number of instances below which the columns are removed
-:param inplace: whether or not to apply the transformation inplace, or to a new copy
-:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
-where the dimensions corresponding to infrequent instances have been removed
+
Reduces the dimensionality of the instances, represented as a csr_matrix (or any subtype of
+scipy.sparse.spmatrix), of training and test documents by removing the columns of words which are not present
+in at least min_df instances in the training set
+
+
Parameters
+
+
dataset – a quapy.data.base.Dataset in which instances are represented in sparse format (any
+subtype of scipy.sparse.spmatrix)
+
min_df – integer, minimum number of instances below which the columns are removed
+
inplace – whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+
+
+
Returns
+
a new quapy.data.base.Dataset (if inplace=False) or a reference to the current
+quapy.data.base.Dataset (inplace=True) where the dimensions corresponding to infrequent terms
+in the training set have been removed
Standardizes the real-valued columns of a quapy.data.base.Dataset.
+Standardization, aka z-scoring, of a variable X comes down to subtracting the average and normalizing by the
+standard deviation.
Transforms a Dataset of textual instances into a Dataset of tfidf weighted sparse vectors
-:param dataset: a Dataset where the instances are lists of str
-:param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary
-:param sublinear_tf: whether or not to apply the log scalling to the tf counters
-:param inplace: whether or not to apply the transformation inplace, or to a new copy
-:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.TfidfVectorizer)
-:return: a new Dataset in csr_matrix format (if inplace=False) or a reference to the current Dataset (inplace=True)
-where the instances are stored in a csr_matrix of real-valued tfidf scores
dataset – a quapy.data.base.Dataset where the instances of training and test collections are
+lists of str
+
min_df – minimum number of occurrences for a word to be considered as part of the vocabulary (default 3)
+
sublinear_tf – whether or not to apply the log scalling to the tf counters (default True)
+
inplace – whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+
kwargs – the rest of parameters of the transformation (as for sklearn’s
+TfidfVectorizer)
+
+
+
Returns
+
a new quapy.data.base.Dataset in csr_matrix format (if inplace=False) or a reference to the
+current Dataset (if inplace=True) where the instances are stored in a csr_matrix of real-valued tfidf scores
+
+
@@ -367,7 +884,24 @@ where the instances are stored in a csr_matrix of real-valued tfidf scores
Re-indexes a list of labels as a list of indexes, and returns the classnames corresponding to the indexes.
-E.g., y=[‘B’, ‘B’, ‘A’, ‘C’] -> [1,1,0,2], [‘A’,’B’,’C’]
-:param y: the list or array of original labels
-:return: a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.
a ndarray (int) of class indexes, and a ndarray of classnames corresponding to the indexes.
+
+
diff --git a/docs/build/html/quapy.html b/docs/build/html/quapy.html
index fc4066f..32f7f57 100644
--- a/docs/build/html/quapy.html
+++ b/docs/build/html/quapy.html
@@ -515,14 +515,20 @@ will be taken from the environment variable SAMPLE_SIZE (which has
Performs the predictions for all samples generated according to the artificial sampling protocol.
-:param model: the model in charge of generating the class prevalence estimations
-:param test: the test set on which to perform arificial sampling
-:param sample_size: the size of the samples
-:param n_prevpoints: the number of different prevalences to sample (or set to None if eval_budget is specified)
-:param n_repetitions: the number of repetitions for each prevalence
-:param eval_budget: if specified, sets a ceil on the number of evaluations to perform. For example, if there are 3
-classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be set to 5, since this will generate 15
+
Performs the predictions for all samples generated according to the artificial sampling protocol.
+
+
Parameters
+
+
model – the model in charge of generating the class prevalence estimations
+
test – the test set on which to perform arificial sampling
+
sample_size – the size of the samples
+
n_prevpoints – the number of different prevalences to sample (or set to None if eval_budget is specified)
+
n_repetitions – the number of repetitions for each prevalence
+
eval_budget – if specified, sets a ceil on the number of evaluations to perform. For example, if there are 3
+
+
+
+
classes, n_repetitions=1 and eval_budget=20, then n_prevpoints will be set to 5, since this will generate 15
different prevalences ([0, 0, 1], [0, 0.25, 0.75], [0, 0.5, 0.5] … [1, 0, 0]) and since setting it n_prevpoints
to 6 would produce more than 20 evaluations.
:param n_jobs: number of jobs to be run in parallel
@@ -601,7 +607,31 @@ contains the the prevalence estimations
Generates vectors of prevalence values artificially drawn from an exhaustive grid of prevalence values. The
+number of prevalence values explored for each dimension depends on n_prevalences, so that, if, for example,
+n_prevalences=11 then the prevalence values of the grid are taken from [0, 0.1, 0.2, …, 0.9, 1]. Only
+valid prevalence distributions are returned, i.e., vectors of prevalence values that sum up to 1. For each
+valid vector of prevalence values, repeat copies are returned. The vector of prevalence values can be
+implicit (by setting return_constrained_dim=False), meaning that the last dimension (which is constrained
+to 1 - sum of the rest) is not returned (note that, quite obviously, in this case the vector does not sum up to 1).
+
+
Parameters
+
+
dimensions – the number of classes
+
n_prevalences – the number of equidistant prevalence points to extract from the [0,1] interval for the grid
+(default is 21)
+
repeat – number of copies for each valid prevalence vector (default is 1)
+
return_constrained_dim – set to True to return all dimensions, or to False (default) for ommitting the
+constrained dimension
+
+
+
Returns
+
an ndarray of shape (n, dimensions) if return_constrained_dim=True or of shape (n, dimensions-1)
+if return_constrained_dim=False, where n is the number of valid combinations found in the grid multiplied
+by repeat
+
+
+
@@ -634,8 +664,21 @@ number of possible combinations are 5, i.e.: [0,1], [0.25,0.75], [0.50,0.50], [0
Produces a uniformly separated values of prevalence. By default, produces an array 21 prevalences, with step 0.05
-and with the limits smoothed, i.e.:
-[0.01, 0.05, 0.10, 0.15, …, 0.90, 0.95, 0.99]
-:param n_prevalences: the number of prevalence values to sample from the [0,1] interval (default 21)
-:param repeat: number of times each prevalence is to be repeated (defaults to 1)
-:param smooth_limits_epsilon: the quantity to add and subtract to the limits 0 and 1
-:return: an array of uniformly separated prevalence values
+
Produces a uniformly separated values of prevalence. By default, produces an array of 21 prevalence values, with
+step 0.05 and with the limits smoothed, i.e.:
+[0.01, 0.05, 0.10, 0.15, …, 0.90, 0.95, 0.99]
+
+
Parameters
+
+
n_prevalences – the number of prevalence values to sample from the [0,1] interval (default 21)
+
repeat – number of times each prevalence is to be repeated (defaults to 1)
+
smooth_limits_epsilon – the quantity to add and subtract to the limits 0 and 1
+
+
+
Returns
+
an array of uniformly separated prevalence values
+
+
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index f92c96d..02ef050 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/quapy/data/base.py b/quapy/data/base.py
index 3cb2392..8324bc4 100644
--- a/quapy/data/base.py
+++ b/quapy/data/base.py
@@ -1,6 +1,3 @@
-from abc import abstractmethod
-from typing import List, Union
-
import numpy as np
from scipy.sparse import issparse
from scipy.sparse import vstack
@@ -9,18 +6,19 @@ from sklearn.model_selection import train_test_split, RepeatedStratifiedKFold
from quapy.functional import artificial_prevalence_sampling, strprev
-
class LabelledCollection:
- '''
- A LabelledCollection is a set of objects each with a label associated to it.
- '''
+ """
+ A LabelledCollection is a set of objects each with a label associated to it. This class implements many sampling
+ routines.
+
+ :param instances: array-like (np.ndarray, list, or csr_matrix are supported)
+ :param labels: array-like with the same length of instances
+ :param classes_: optional, list of classes from which labels are taken. If not specified, the classes are inferred
+ from the labels. The classes must be indicated in cases in which some of the labels might have no examples
+ (i.e., a prevalence of 0)
+ """
def __init__(self, instances, labels, classes_=None):
- """
- :param instances: list of objects
- :param labels: list of labels, same length of instances
- :param classes_: optional, list of classes from which labels are taken. When used, must contain the set of values used in labels.
- """
if issparse(instances):
self.instances = instances
elif isinstance(instances, list) and len(instances) > 0 and isinstance(instances[0], str):
@@ -42,28 +40,81 @@ class LabelledCollection:
@classmethod
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
+ defined in :mod:`quapy.data.reader` module.
+
+ :param path: string, the path to the file containing the labelled instances
+ :param loader_func: a custom function that implements the data loader and returns a tuple with instances and
+ labels
+ :param classes: array-like, the classes according to which the instances are labelled
+ :param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances, i.e.,
+ these arguments are used to call `loader_func(path, **loader_kwargs)`
+ :return: a :class:`LabelledCollection` object
+ """
return LabelledCollection(*loader_func(path, **loader_kwargs), classes)
def __len__(self):
+ """
+ Returns the length of this collection (number of labelled instances)
+
+ :return: integer
+ """
return self.instances.shape[0]
def prevalence(self):
+ """
+ Returns the prevalence, or relative frequency, of the classes of interest.
+
+ :return: a np.ndarray of shape `(n_classes)` with the relative frequencies of each class, in the same order
+ as listed by `self.classes_`
+ """
return self.counts() / len(self)
def counts(self):
+ """
+ Returns the number of instances for each of the classes of interest.
+
+ :return: a np.ndarray of shape `(n_classes)` with the number of instances of each class, in the same order
+ as listed by `self.classes_`
+ """
return np.asarray([len(self.index[class_]) for class_ in self.classes_])
@property
def n_classes(self):
+ """
+ The number of classes
+
+ :return: integer
+ """
return len(self.classes_)
@property
def binary(self):
+ """
+ Returns True if the number of classes is 2
+
+ :return: boolean
+ """
return self.n_classes == 2
def sampling_index(self, size, *prevs, shuffle=True):
+ """
+ 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.
+ For each class, the sampling is drawn without replacement if the requested prevalence is larger than
+ the actual prevalence of the class, or with replacement otherwise.
+
+ :param size: integer, the requested size
+ :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since
+ it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in
+ `self.classes_` can be specified, while the other class takes prevalence value `1-p`
+ :param shuffle: if set to True (default), shuffles the index before returning it
+ :return: a np.ndarray of shape `(size)` with the indexes
+ """
if len(prevs) == 0: # no prevalence was indicated; returns an index for uniform sampling
- return np.random.choice(len(self), size, replace=False)
+ return self.uniform_sampling_index(size)
if len(prevs) == self.n_classes - 1:
prevs = prevs + (1 - sum(prevs),)
assert len(prevs) == self.n_classes, 'unexpected number of prevalences'
@@ -93,47 +144,142 @@ class LabelledCollection:
return indexes_sample
def uniform_sampling_index(self, size):
+ """
+ Returns an index to be used to extract a uniform sample of desired size. The sampling is drawn
+ without replacement if the requested size is greater than the number of instances, or with replacement
+ otherwise.
+
+ :param size: integer, the size of the uniform sample
+ :return: a np.ndarray of shape `(size)` with the indexes
+ """
return np.random.choice(len(self), size, replace=False)
- def uniform_sampling(self, size):
- unif_index = self.uniform_sampling_index(size)
- return self.sampling_from_index(unif_index)
-
def sampling(self, size, *prevs, shuffle=True):
+ """
+ Return a random sample (an instance of :class:`LabelledCollection`) of desired size and desired prevalence
+ values. For each class, the sampling is drawn without replacement if the requested prevalence is larger than
+ the actual prevalence of the class, or with replacement otherwise.
+
+ :param size: integer, the requested size
+ :param prevs: the prevalence for each class; the prevalence value for the last class can be lead empty since
+ it is constrained. E.g., for binary collections, only the prevalence `p` for the first class (as listed in
+ `self.classes_` can be specified, while the other class takes prevalence value `1-p`
+ :param shuffle: if set to True (default), shuffles the index before returning it
+ :return: an instance of :class:`LabelledCollection` with length == `size` and prevalence close to `prevs` (or
+ prevalence == `prevs` if the exact prevalence values can be met as proportions of instances)
+ """
prev_index = self.sampling_index(size, *prevs, shuffle=shuffle)
return self.sampling_from_index(prev_index)
+ def uniform_sampling(self, size):
+ """
+ Returns a uniform sample (an instance of :class:`LabelledCollection`) of desired size. The sampling is drawn
+ without replacement if the requested size is greater than the number of instances, or with replacement
+ otherwise.
+
+ :param size: integer, the requested size
+ :return: an instance of :class:`LabelledCollection` with length == `size`
+ """
+ unif_index = self.uniform_sampling_index(size)
+ return self.sampling_from_index(unif_index)
+
def sampling_from_index(self, index):
+ """
+ Returns an instance of :class:`LabelledCollection` whose elements are sampled from this collection using the
+ index.
+
+ :param index: np.ndarray
+ :return: an instance of :class:`LabelledCollection`
+ """
documents = self.instances[index]
labels = self.labels[index]
return LabelledCollection(documents, labels, classes_=self.classes_)
def split_stratified(self, train_prop=0.6, random_state=None):
- # with temp_seed(42):
+ """
+ Returns two instances of :class:`LabelledCollection` split with stratification from this collection, at desired
+ proportion.
+
+ :param train_prop: the proportion of elements to include in the left-most returned collection (typically used
+ as the training collection). The rest of elements are included in the right-most returned collection
+ (typically used as a test collection).
+ :param random_state: if specified, guarantees reproducibility of the split.
+ :return: two instances of :class:`LabelledCollection`, the first one with `train_prop` elements, and the
+ second one with `1-train_prop` elements
+ """
tr_docs, te_docs, tr_labels, te_labels = \
train_test_split(self.instances, self.labels, train_size=train_prop, stratify=self.labels,
random_state=random_state)
return LabelledCollection(tr_docs, tr_labels), LabelledCollection(te_docs, te_labels)
def artificial_sampling_generator(self, sample_size, n_prevalences=101, repeats=1):
+ """
+ A generator of samples that implements the artificial prevalence protocol (APP). The APP consists of exploring
+ a grid of prevalence values (e.g., [0, 0.05, 0.1, 0.15, ..., 1]), and generating all valid combinations of
+ prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
+ [1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of samples for each valid
+ combination of prevalence values is indicated by `repeats`
+
+ :param sample_size: the number of instances in each sample
+ :param n_prevalences: the number of prevalence points to be taken from the [0,1] interval (including the
+ limits {0,1}). E.g., if `n_prevalences=11`, then the prevalence points to take are [0, 0.1, 0.2, ..., 1]
+ :param repeats: the number of samples to generate for each valid combination of prevalence values (default 1)
+ :return: yield samples generated at artificially controlled prevalence values
+ """
dimensions = self.n_classes
for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
yield self.sampling(sample_size, *prevs)
def artificial_sampling_index_generator(self, sample_size, n_prevalences=101, repeats=1):
+ """
+ A generator of sample indexes implementing the artificial prevalence protocol (APP).
+ The APP consists of exploring
+ a grid of prevalence values (e.g., [0, 0.05, 0.1, 0.15, ..., 1]), and generating all valid combinations of
+ prevalence values for all classes (e.g., for 3 classes, samples with [0, 0, 1], [0, 0.05, 0.95], ...,
+ [1, 0, 0] prevalence values of size `sample_size` will be yielded). The number of sample indexes for each valid
+ combination of prevalence values is indicated by `repeats`
+
+ :param sample_size: the number of instances in each sample (i.e., length of each index)
+ :param n_prevalences: the number of prevalence points to be taken from the [0,1] interval (including the
+ limits {0,1}). E.g., if `n_prevalences=11`, then the prevalence points to take are [0, 0.1, 0.2, ..., 1]
+ :param repeats: the number of samples to generate for each valid combination of prevalence values (default 1)
+ :return: yield the indexes that generate the samples according to APP
+ """
dimensions = self.n_classes
for prevs in artificial_prevalence_sampling(dimensions, n_prevalences, repeats):
yield self.sampling_index(sample_size, *prevs)
def natural_sampling_generator(self, sample_size, repeats=100):
+ """
+ A generator of samples that implements the natural prevalence protocol (NPP). The NPP consists of drawing
+ samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
+
+ :param sample_size: integer, the number of instances in each sample
+ :param repeats: the number of samples to generate
+ :return: yield instances of :class:`LabelledCollection`
+ """
for _ in range(repeats):
yield self.uniform_sampling(sample_size)
def natural_sampling_index_generator(self, sample_size, repeats=100):
+ """
+ A generator of sample indexes according to the natural prevalence protocol (NPP). The NPP consists of drawing
+ samples uniformly at random, therefore approximately preserving the natural prevalence of the collection.
+
+ :param sample_size: integer, the number of instances in each sample (i.e., the length of each index)
+ :param repeats: the number of indexes to generate
+ :return: yield `repeats` instances of np.ndarray with shape `(sample_size,)`
+ """
for _ in range(repeats):
yield self.uniform_sampling_index(sample_size)
def __add__(self, other):
+ """
+ Returns a new :class:`LabelledCollection` as the union of this collection with another collection
+
+ :param other: another :class:`LabelledCollection`
+ :return: a :class:`LabelledCollection` representing the union of both collections
+ """
if other is None:
return self
elif issparse(self.instances) and issparse(other.instances):
@@ -149,9 +295,29 @@ class LabelledCollection:
@property
def Xy(self):
+ """
+ Gets the instances and labels. This is useful when working with `sklearn` estimators, e.g.:
+
+ >>> svm = LinearSVC().fit(*my_collection.Xy)
+
+ :return: a tuple `(instances, labels)` from this collection
+ """
return self.instances, self.labels
def stats(self, show=True):
+ """
+ Returns (and eventually prints) a dictionary with some stats of this collection. E.g.,:
+
+ >>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
+ >>> data.training.stats()
+ >>> #instances=3821, type=, #features=4403, #classes=[0 1], prevs=[0.081, 0.919]
+
+ :param show: if set to True (default), prints the stats in standard output
+ :return: a dictionary containing some stats of this collection. Keys include `#instances` (the number of
+ instances), `type` (the type representing the instances), `#features` (the number of features, if the
+ instances are in array-like format), `#classes` (the classes of the collection), `prevs` (the prevalence
+ values for each class)
+ """
ninstances = len(self)
instance_type = type(self.instances[0])
if instance_type == list:
@@ -171,6 +337,14 @@ class LabelledCollection:
return stats_
def kFCV(self, nfolds=5, nrepeats=1, random_state=0):
+ """
+ Generator of stratified folds to be used in k-fold cross validation.
+
+ :param nfolds: integer (default 5), the number of folds to generate
+ :param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run
+ :param random_state: integer (default 0), guarantees that the folds generated are reproducible
+ :return: yields `nfolds * nrepeats` folds for k-fold cross validation
+ """
kf = RepeatedStratifiedKFold(n_splits=nfolds, n_repeats=nrepeats, random_state=random_state)
for train_index, test_index in kf.split(*self.Xy):
train = self.sampling_from_index(train_index)
@@ -178,8 +352,15 @@ class LabelledCollection:
yield train, test
-
class Dataset:
+ """
+ Abstraction of training and test :class:`LabelledCollection` objects.
+
+ :param training: a :class:`LabelledCollection` instance
+ :param test: a :class:`LabelledCollection` instance
+ :param vocabulary: if indicated, is a dictionary of the terms used in this textual dataset
+ :param name: a string representing the name of the dataset
+ """
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'
@@ -190,45 +371,118 @@ class Dataset:
@classmethod
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`
+
+ :param collection: :class:`LabelledCollection`
+ :param train_size: the proportion of training documents (the rest conforms the test split)
+ :return: an instance of :class:`Dataset`
+ """
return Dataset(*collection.split_stratified(train_prop=train_size))
@property
def classes_(self):
+ """
+ The classes according to which the training collection is labelled
+
+ :return: The classes according to which the training collection is labelled
+ """
return self.training.classes_
@property
def n_classes(self):
+ """
+ The number of classes according to which the training collection is labelled
+
+ :return: integer
+ """
return self.training.n_classes
@property
def binary(self):
+ """
+ Returns True if the training collection is labelled according to two classes
+
+ :return: boolean
+ """
return self.training.binary
@classmethod
- def load(cls, train_path, test_path, loader_func: callable):
- training = LabelledCollection.load(train_path, loader_func)
- test = LabelledCollection.load(test_path, loader_func)
+ 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
+ the reading functions defined in :mod:`quapy.data.reader` module.
+
+ :param train_path: string, the path to the file containing the training instances
+ :param test_path: string, the path to the file containing the test instances
+ :param loader_func: a custom function that implements the data loader and returns a tuple with instances and
+ labels
+ :param classes: array-like, the classes according to which the instances are labelled
+ :param loader_kwargs: any argument that the `loader_func` function needs in order to read the instances.
+ See :meth:`LabelledCollection.load` for further details.
+ :return: a :class:`Dataset` object
+ """
+
+ training = LabelledCollection.load(train_path, loader_func, classes, **loader_kwargs)
+ test = LabelledCollection.load(test_path, loader_func, classes, **loader_kwargs)
return Dataset(training, test)
@property
def vocabulary_size(self):
+ """
+ If the dataset is textual, and the vocabulary was indicated, returns the size of the vocabulary
+
+ :return: integer
+ """
return len(self.vocabulary)
- def stats(self):
+ def stats(self, show):
+ """
+ Returns (and eventually prints) a dictionary with some stats of this dataset. E.g.,:
+
+ >>> data = qp.datasets.fetch_reviews('kindle', tfidf=True, min_df=5)
+ >>> data.stats()
+ >>> Dataset=kindle #tr-instances=3821, #te-instances=21591, type=, #features=4403, #classes=[0 1], tr-prevs=[0.081, 0.919], te-prevs=[0.063, 0.937]
+
+ :param show: if set to True (default), prints the stats in standard output
+ :return: a dictionary containing some stats of this collection for the training and test collections. The keys
+ are `train` and `test`, and point to dedicated dictionaries of stats, for each collection, with keys
+ `#instances` (the number of instances), `type` (the type representing the instances),
+ `#features` (the number of features, if the instances are in array-like format), `#classes` (the classes of
+ the collection), `prevs` (the prevalence values for each class)
+ """
tr_stats = self.training.stats(show=False)
te_stats = self.test.stats(show=False)
- print(f'Dataset={self.name} #tr-instances={tr_stats["instances"]}, #te-instances={te_stats["instances"]}, '
- f'type={tr_stats["type"]}, #features={tr_stats["features"]}, #classes={tr_stats["classes"]}, '
- f'tr-prevs={tr_stats["prevs"]}, te-prevs={te_stats["prevs"]}')
+ if show:
+ print(f'Dataset={self.name} #tr-instances={tr_stats["instances"]}, #te-instances={te_stats["instances"]}, '
+ f'type={tr_stats["type"]}, #features={tr_stats["features"]}, #classes={tr_stats["classes"]}, '
+ f'tr-prevs={tr_stats["prevs"]}, te-prevs={te_stats["prevs"]}')
return {'train': tr_stats, 'test': te_stats}
@classmethod
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.
+
+ :param nfolds: integer (default 5), the number of folds to generate
+ :param nrepeats: integer (default 1), the number of rounds of k-fold cross validation to run
+ :param random_state: integer (default 0), guarantees that the folds generated are reproducible
+ :return: yields `nfolds * nrepeats` folds for k-fold cross validation as instances of :class:`Dataset`
+ """
for i, (train, test) in enumerate(data.kFCV(nfolds=nfolds, nrepeats=nrepeats, random_state=random_state)):
yield Dataset(train, test, name=f'fold {(i % nfolds) + 1}/{nfolds} (round={(i // nfolds) + 1})')
def isbinary(data):
+ """
+ Returns True if `data` is either a binary :class:`Dataset` or a binary :class:`LabelledCollection`
+
+ :param data: a :class:`Dataset` or a :class:`LabelledCollection` object
+ :return: True if labelled according to two classes
+ """
if isinstance(data, Dataset) or isinstance(data, LabelledCollection):
return data.binary
return False
diff --git a/quapy/data/datasets.py b/quapy/data/datasets.py
index 575ffca..74e2a3e 100644
--- a/quapy/data/datasets.py
+++ b/quapy/data/datasets.py
@@ -5,9 +5,6 @@ warnings.warn = warn
import os
import zipfile
from os.path import join
-from urllib.error import HTTPError
-from sklearn.model_selection import StratifiedKFold
-
import pandas as pd
from quapy.data.base import Dataset, LabelledCollection
@@ -49,18 +46,20 @@ UCI_DATASETS = ['acute.a', 'acute.b',
def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle=False) -> Dataset:
"""
- Load a Reviews dataset as a Dataset instance, as used in:
- Esuli, A., Moreo, A., and Sebastiani, F. "A recurrent neural network for sentiment quantification."
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018.
+ 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."
+ Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018. `_.
+ The list of valid dataset names can be accessed in `quapy.data.datasets.REVIEWS_SENTIMENT_DATASETS`
+
:param dataset_name: the name of the dataset: valid ones are 'hp', 'kindle', 'imdb'
:param tfidf: set to True to transform the raw documents into tfidf weighted matrices
:param min_df: minimun number of documents that should contain a term in order for the term to be
- kept (ignored if tfidf==False)
+ kept (ignored if tfidf==False)
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
- ~/quay_data/ directory)
+ ~/quay_data/ directory)
:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
- faster subsequent invokations
- :return: a Dataset instance
+ faster subsequent invokations
+ :return: a :class:`quapy.data.base.Dataset` instance
"""
assert dataset_name in REVIEWS_SENTIMENT_DATASETS, \
f'Name {dataset_name} does not match any known dataset for sentiment reviews. ' \
@@ -93,22 +92,25 @@ def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None, pickle
def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_home=None, pickle=False) -> Dataset:
"""
- Load a Twitter dataset as a Dataset instance, as used in:
- Gao, W., Sebastiani, F.: From classification to quantification in tweet sentiment analysis.
- Social Network Analysis and Mining6(19), 1–22 (2016)
- The datasets 'semeval13', 'semeval14', 'semeval15' share the same training set.
+ 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.
+ Social Network Analysis and Mining6(19), 1–22 (2016) `_
+ Note that the datasets 'semeval13', 'semeval14', 'semeval15' share the same training set.
+ The list of valid dataset names corresponding to training sets can be accessed in
+ `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TRAIN`, while the test sets can be accessed in
+ `quapy.data.datasets.TWITTER_SENTIMENT_DATASETS_TEST`
:param dataset_name: the name of the dataset: valid ones are 'gasp', 'hcr', 'omd', 'sanders', 'semeval13',
- 'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'
+ 'semeval14', 'semeval15', 'semeval16', 'sst', 'wa', 'wb'
:param for_model_selection: if True, then returns the train split as the training set and the devel split
- as the test set; if False, then returns the train+devel split as the training set and the test set as the
- test set
+ as the test set; if False, then returns the train+devel split as the training set and the test set as the
+ test set
:param min_df: minimun number of documents that should contain a term in order for the term to be kept
:param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
- ~/quay_data/ directory)
+ ~/quay_data/ directory)
:param pickle: set to True to pickle the Dataset object the first time it is generated, in order to allow for
- faster subsequent invokations
- :return: a Dataset instance
+ faster subsequent invokations
+ :return: a :class:`quapy.data.base.Dataset` instance
"""
assert dataset_name in TWITTER_SENTIMENT_DATASETS_TRAIN + TWITTER_SENTIMENT_DATASETS_TEST, \
f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \
@@ -163,11 +165,58 @@ def fetch_twitter(dataset_name, for_model_selection=False, min_df=None, data_hom
def fetch_UCIDataset(dataset_name, data_home=None, test_split=0.3, 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).
+ Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
+ Information Fusion, 34, 87-100. `_
+ and
+ `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
+ Dynamic ensemble selection for quantification tasks.
+ Information Fusion, 45, 1-15. `_.
+ The datasets do not come with a predefined train-test split (see :meth:`fetch_UCILabelledCollection` for further
+ information on how to use these collections), and so a train-test split is generated at desired proportion.
+ The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
+
+ :param dataset_name: a dataset name
+ :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
+ ~/quay_data/ directory)
+ :param test_split: proportion of documents to be included in the test set. The rest conforms the training set
+ :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
+ :return: a :class:`quapy.data.base.Dataset` instance
+ """
data = fetch_UCILabelledCollection(dataset_name, data_home, verbose)
return Dataset(*data.split_stratified(1 - test_split, random_state=0))
def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) -> Dataset:
+ """
+ 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).
+ Using ensembles for problems with characterizable changes in data distribution: A case study on quantification.
+ Information Fusion, 34, 87-100. `_
+ and
+ `Pérez-Gállego, P., Castano, A., Quevedo, J. R., & del Coz, J. J. (2019).
+ Dynamic ensemble selection for quantification tasks.
+ Information Fusion, 45, 1-15. `_.
+ The datasets do not come with a predefined train-test split, and so Pérez-Gállego et al. adopted a 5FCVx2 evaluation
+ protocol, meaning that each collection was used to generate two rounds (hence the x2) of 5 fold cross validation.
+ This can be reproduced by using :meth:`quapy.data.base.Dataset.kFCV`, e.g.:
+
+ >>> import quapy as qp
+ >>> collection = qp.datasets.fetch_UCILabelledCollection("yeast")
+ >>> for data in qp.data.Dataset.kFCV(collection, nfolds=5, nrepeats=2):
+ >>> ...
+
+ The list of valid dataset names can be accessed in `quapy.data.datasets.UCI_DATASETS`
+
+ :param dataset_name: a dataset name
+ :param data_home: specify the quapy home directory where collections will be dumped (leave empty to use the default
+ ~/quay_data/ directory)
+ :param test_split: proportion of documents to be included in the test set. The rest conforms the training set
+ :param verbose: set to True (default is False) to get information (from the UCI ML repository) about the datasets
+ :return: a :class:`quapy.data.base.Dataset` instance
+ """
assert dataset_name in UCI_DATASETS, \
f'Name {dataset_name} does not match any known dataset from the UCI Machine Learning datasets repository. ' \
@@ -302,7 +351,7 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) ->
df = pd.read_csv(data_path, header=None, encoding='utf-16', sep='\t')
df[0] = df[0].apply(lambda x: float(x.replace(',', '.'))).astype(float, copy=False)
- [df_replace(df, col) for col in range(1, 6)]
+ [_df_replace(df, col) for col in range(1, 6)]
X = df.loc[:, 0:5].values
if dataset_name == 'acute.a':
y = binarize(df[6], pos_class='yes')
@@ -482,5 +531,5 @@ def fetch_UCILabelledCollection(dataset_name, data_home=None, verbose=False) ->
return data
-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)
\ No newline at end of file
diff --git a/quapy/data/preprocessing.py b/quapy/data/preprocessing.py
index 6e58718..f04f010 100644
--- a/quapy/data/preprocessing.py
+++ b/quapy/data/preprocessing.py
@@ -12,14 +12,18 @@ from .base import LabelledCollection
def text2tfidf(dataset:Dataset, min_df=3, sublinear_tf=True, inplace=False, **kwargs):
"""
- Transforms a Dataset of textual instances into a Dataset of tfidf weighted sparse vectors
- :param dataset: a Dataset where the instances are lists of str
- :param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary
- :param sublinear_tf: whether or not to apply the log scalling to the tf counters
- :param inplace: whether or not to apply the transformation inplace, or to a new copy
- :param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.TfidfVectorizer)
- :return: a new Dataset in csr_matrix format (if inplace=False) or a reference to the current Dataset (inplace=True)
- where the instances are stored in a csr_matrix of real-valued tfidf scores
+ Transforms a :class:`quapy.data.base.Dataset` of textual instances into a :class:`quapy.data.base.Dataset` of
+ tfidf weighted sparse vectors
+
+ :param dataset: a :class:`quapy.data.base.Dataset` where the instances of training and test collections are
+ lists of str
+ :param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary (default 3)
+ :param sublinear_tf: whether or not to apply the log scalling to the tf counters (default True)
+ :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+ :param kwargs: the rest of parameters of the transformation (as for sklearn's
+ `TfidfVectorizer `_)
+ :return: a new :class:`quapy.data.base.Dataset` in `csr_matrix` format (if inplace=False) or a reference to the
+ current Dataset (if inplace=True) where the instances are stored in a `csr_matrix` of real-valued tfidf scores
"""
__check_type(dataset.training.instances, np.ndarray, str)
__check_type(dataset.test.instances, np.ndarray, str)
@@ -41,13 +45,17 @@ def text2tfidf(dataset:Dataset, min_df=3, sublinear_tf=True, inplace=False, **kw
def reduce_columns(dataset: Dataset, min_df=5, inplace=False):
"""
- Reduces the dimensionality of the csr_matrix by removing the columns of words which are not present in at least
- _min_df_ instances
- :param dataset: a Dataset in sparse format (any subtype of scipy.sparse.spmatrix)
- :param min_df: minimum number of instances below which the columns are removed
- :param inplace: whether or not to apply the transformation inplace, or to a new copy
- :return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
- where the dimensions corresponding to infrequent instances have been removed
+ Reduces the dimensionality of the instances, represented as a `csr_matrix` (or any subtype of
+ `scipy.sparse.spmatrix`), of training and test documents by removing the columns of words which are not present
+ in at least `min_df` instances in the training set
+
+ :param dataset: a :class:`quapy.data.base.Dataset` in which instances are represented in sparse format (any
+ subtype of scipy.sparse.spmatrix)
+ :param min_df: integer, minimum number of instances below which the columns are removed
+ :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+ :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current
+ :class:`quapy.data.base.Dataset` (inplace=True) where the dimensions corresponding to infrequent terms
+ in the training set have been removed
"""
__check_type(dataset.training.instances, spmatrix)
__check_type(dataset.test.instances, spmatrix)
@@ -71,7 +79,17 @@ def reduce_columns(dataset: Dataset, min_df=5, inplace=False):
return Dataset(training, test)
-def standardize(dataset: Dataset, inplace=True):
+def standardize(dataset: Dataset, inplace=False):
+ """
+ Standardizes the real-valued columns of a :class:`quapy.data.base.Dataset`.
+ Standardization, aka z-scoring, of a variable `X` comes down to subtracting the average and normalizing by the
+ standard deviation.
+
+ :param dataset: a :class:`quapy.data.base.Dataset` object
+ :param inplace: set to True if the transformation is to be applied inplace, or to False (default) if a new
+ :class:`quapy.data.base.Dataset` is to be returned
+ :return:
+ """
s = StandardScaler(copy=not inplace)
training = s.fit_transform(dataset.training.instances)
test = s.transform(dataset.test.instances)
@@ -83,14 +101,18 @@ def standardize(dataset: Dataset, inplace=True):
def index(dataset: Dataset, min_df=5, inplace=False, **kwargs):
"""
- Indexes a dataset of strings. To index a document means to replace each different token by a unique numerical index.
- Rare words (i.e., words occurring less than _min_df_ times) are replaced by a special token UNK
- :param dataset: a Dataset where the instances are lists of str
- :param min_df: minimum number of instances below which the term is replaced by a UNK index
- :param inplace: whether or not to apply the transformation inplace, or to a new copy
- :param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.CountVectorizer)
- :return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
- consisting of lists of integer values representing indices.
+ Indexes the tokens of a textual :class:`quapy.data.base.Dataset` of string documents.
+ To index a document means to replace each different token by a unique numerical index.
+ Rare words (i.e., words occurring less than `min_df` times) are replaced by a special token `UNK`
+
+ :param dataset: a :class:`quapy.data.base.Dataset` object where the instances of training and test documents
+ are lists of str
+ :param min_df: minimum number of occurrences below which the term is replaced by a `UNK` index
+ :param inplace: whether or not to apply the transformation inplace (True), or to a new copy (False, default)
+ :param kwargs: the rest of parameters of the transformation (as for sklearn's
+ `CountVectorizer _`)
+ :return: a new :class:`quapy.data.base.Dataset` (if inplace=False) or a reference to the current
+ :class:`quapy.data.base.Dataset` (inplace=True) consisting of lists of integer values representing indices.
"""
__check_type(dataset.training.instances, np.ndarray, str)
__check_type(dataset.test.instances, np.ndarray, str)
@@ -120,17 +142,23 @@ def __check_type(container, container_type=None, element_type=None):
class IndexTransformer:
+ """
+ This class implements a sklearn's-style transformer that indexes text as numerical ids for the tokens it
+ contains, and that would be generated by sklearn's
+ `CountVectorizer `_
+
+ :param kwargs: keyworded arguments from `CountVectorizer `_
+ """
def __init__(self, **kwargs):
- """
- :param kwargs: keyworded arguments from _sklearn.feature_extraction.text.CountVectorizer_
- """
self.vect = CountVectorizer(**kwargs)
self.unk = -1 # a valid index is assigned after fit
self.pad = -2 # a valid index is assigned after fit
def fit(self, X):
"""
+ Fits the transformer, i.e., decides on the vocabulary, given a list of strings.
+
:param X: a list of strings
:return: self
"""
@@ -142,22 +170,52 @@ class IndexTransformer:
return self
def transform(self, X, n_jobs=-1):
+ """
+ Transforms the strings in `X` as lists of numerical ids
+
+ :param X: a list of strings
+ :param n_jobs: the number of parallel workers to carry out this task
+ :return: a `np.ndarray` of numerical ids
+ """
# given the number of tasks and the number of jobs, generates the slices for the parallel processes
assert self.unk != -1, 'transform called before fit'
- indexed = map_parallel(func=self.index, args=X, n_jobs=n_jobs)
+ indexed = map_parallel(func=self._index, args=X, n_jobs=n_jobs)
return np.asarray(indexed)
- def index(self, documents):
+ def _index(self, documents):
vocab = self.vocabulary_.copy()
return [[vocab.prevalence(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
def fit_transform(self, X, n_jobs=-1):
+ """
+ Fits the transform on `X` and transforms it.
+
+ :param X: a list of strings
+ :param n_jobs: the number of parallel workers to carry out this task
+ :return: a `np.ndarray` of numerical ids
+ """
return self.fit(X).transform(X, n_jobs=n_jobs)
def vocabulary_size(self):
+ """
+ Gets the length of the vocabulary according to which the document tokens have been indexed
+
+ :return: integer
+ """
return len(self.vocabulary_)
def add_word(self, word, id=None, nogaps=True):
+ """
+ Adds a new token (regardless of whether it has been found in the text or not), with dedicated id.
+ Useful to define special tokens for codifying unknown words, or padding tokens.
+
+ :param word: string, surface form of the token
+ :param id: integer, numerical value to assign to the token (leave as None for indicating the next valid id,
+ default)
+ :param nogaps: if set to True (default) asserts that the id indicated leads to no numerical gaps with
+ precedent ids stored so far
+ :return: integer, the numerical id for the new token
+ """
if word in self.vocabulary_:
raise ValueError(f'word {word} already in dictionary')
if id is None:
diff --git a/quapy/data/reader.py b/quapy/data/reader.py
index 675b9a1..8f8bc79 100644
--- a/quapy/data/reader.py
+++ b/quapy/data/reader.py
@@ -7,7 +7,10 @@ def from_text(path, encoding='utf-8', verbose=1, class2int=True):
"""
Reads a labelled colletion of documents.
File fomart <0 or 1>\t\n
+
:param path: path to the labelled collection
+ :param encoding: the text encoding used to open the file
+ :param verbose: if >0 (default) shows some progress information in standard output
:return: a list of sentences, and a list of labels
"""
all_sentences, all_labels = [], []
@@ -35,8 +38,9 @@ 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
+
:param path: path to the labelled collection
- :return: a csr_matrix containing the instances (rows), and a ndarray containing the labels
+ :return: a `csr_matrix` containing the instances (rows), and a ndarray containing the labels
"""
def split_col_val(col_val):
@@ -68,8 +72,10 @@ def from_csv(path, encoding='utf-8'):
"""
Reads a csv file in which columns are separated by ','.
File format