import numpy as np
from scipy.sparse import spmatrix
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm
import quapy as qp
from quapy.data.base import Dataset
from quapy.util import map_parallel
from .base import LabelledCollection
def text2tfidf(dataset:Dataset, min_df=3, sublinear_tf=True, inplace=False, **kwargs):
"""
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 <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html>`_)
: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)
vectorizer = TfidfVectorizer(min_df=min_df, sublinear_tf=sublinear_tf, **kwargs)
training_documents = vectorizer.fit_transform(dataset.training.instances)
test_documents = vectorizer.transform(dataset.test.instances)
if inplace:
dataset.training = LabelledCollection(training_documents, dataset.training.labels, dataset.classes_)
dataset.test = LabelledCollection(test_documents, dataset.test.labels, dataset.classes_)
dataset.vocabulary = vectorizer.vocabulary_
return dataset
else:
training = LabelledCollection(training_documents, dataset.training.labels.copy(), dataset.classes_)
test = LabelledCollection(test_documents, dataset.test.labels.copy(), dataset.classes_)
return Dataset(training, test, vectorizer.vocabulary_)
def reduce_columns(dataset: Dataset, min_df=5, inplace=False):
"""
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)
assert dataset.training.instances.shape[1] == dataset.test.instances.shape[1], 'unaligned vector spaces'
def filter_by_occurrences(X, W):
column_prevalence = np.asarray((X > 0).sum(axis=0)).flatten()
take_columns = column_prevalence >= min_df
X = X[:, take_columns]
W = W[:, take_columns]
return X, W
Xtr, Xte = filter_by_occurrences(dataset.training.instances, dataset.test.instances)
if inplace:
dataset.training.instances = Xtr
dataset.test.instances = Xte
return dataset
else:
training = LabelledCollection(Xtr, dataset.training.labels.copy(), dataset.classes_)
test = LabelledCollection(Xte, dataset.test.labels.copy(), dataset.classes_)
return Dataset(training, test)
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: an instance of :class:`quapy.data.base.Dataset`
"""
s = StandardScaler(copy=not inplace)
training = s.fit_transform(dataset.training.instances)
test = s.transform(dataset.test.instances)
if inplace:
return dataset
else:
return Dataset(training, test, dataset.vocabulary, dataset.name)
def index(dataset: Dataset, min_df=5, inplace=False, **kwargs):
"""
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 <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>_`)
: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)
indexer = IndexTransformer(min_df=min_df, **kwargs)
training_index = indexer.fit_transform(dataset.training.instances)
test_index = indexer.transform(dataset.test.instances)
training_index = np.asarray(training_index, dtype=object)
test_index = np.asarray(test_index, dtype=object)
if inplace:
dataset.training = LabelledCollection(training_index, dataset.training.labels, dataset.classes_)
dataset.test = LabelledCollection(test_index, dataset.test.labels, dataset.classes_)
dataset.vocabulary = indexer.vocabulary_
return dataset
else:
training = LabelledCollection(training_index, dataset.training.labels.copy(), dataset.classes_)
test = LabelledCollection(test_index, dataset.test.labels.copy(), dataset.classes_)
return Dataset(training, test, indexer.vocabulary_)
def __check_type(container, container_type=None, element_type=None):
if container_type:
assert isinstance(container, container_type), \
f'unexpected type of container (expected {container_type}, found {type(container)})'
if element_type:
assert isinstance(container[0], element_type), \
f'unexpected type of element (expected {container_type}, found {type(container)})'
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 <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
:param kwargs: keyworded arguments from
`CountVectorizer <https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html>`_
"""
def __init__(self, **kwargs):
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
"""
self.vect.fit(X)
self.analyzer = self.vect.build_analyzer()
self.vocabulary_ = self.vect.vocabulary_
self.unk = self.add_word(qp.environ['UNK_TOKEN'], qp.environ['UNK_INDEX'])
self.pad = self.add_word(qp.environ['PAD_TOKEN'], qp.environ['PAD_INDEX'])
return self
def transform(self, X, n_jobs=None):
"""
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'
n_jobs = qp._get_njobs(n_jobs)
return map_parallel(func=self._index, args=X, n_jobs=n_jobs)
def _index(self, documents):
vocab = self.vocabulary_.copy()
return [[vocab.get(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
def fit_transform(self, X, n_jobs=None):
"""
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:
# add the word with the next id
self.vocabulary_[word] = len(self.vocabulary_)
else:
id2word = {id_:word_ for word_, id_ in self.vocabulary_.items()}
if id in id2word:
old_word = id2word[id]
self.vocabulary_[word] = id
del self.vocabulary_[old_word]
self.add_word(old_word)
elif nogaps:
if id > self.vocabulary_size()+1:
raise ValueError(f'word {word} added with id {id}, while the current vocabulary size '
f'is of {self.vocabulary_size()}, and id gaps are not allowed')
return self.vocabulary_[word]