forked from moreo/QuaPy
168 lines
7.9 KiB
Python
168 lines
7.9 KiB
Python
import numpy as np
|
|
from scipy.sparse import spmatrix
|
|
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
|
|
from tqdm import tqdm
|
|
|
|
import quapy as qp
|
|
from quapy.data.base import Dataset
|
|
from quapy.util import parallelize
|
|
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
|
|
"""
|
|
__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.n_classes)
|
|
dataset.test = LabelledCollection(test_documents, dataset.test.labels, dataset.n_classes)
|
|
dataset.vocabulary = vectorizer.vocabulary_
|
|
return dataset
|
|
else:
|
|
training = LabelledCollection(training_documents, dataset.training.labels.copy(), dataset.n_classes)
|
|
test = LabelledCollection(test_documents, dataset.test.labels.copy(), dataset.n_classes)
|
|
return Dataset(training, test, vectorizer.vocabulary_)
|
|
|
|
|
|
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_occurrences_ 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
|
|
"""
|
|
__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.n_classes)
|
|
test = LabelledCollection(Xte, dataset.test.labels.copy(), dataset.n_classes)
|
|
return Dataset(training, test)
|
|
|
|
|
|
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.
|
|
"""
|
|
__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)
|
|
|
|
if inplace:
|
|
dataset.training = LabelledCollection(training_index, dataset.training.labels, dataset.n_classes)
|
|
dataset.test = LabelledCollection(test_index, dataset.test.labels, dataset.n_classes)
|
|
dataset.vocabulary = indexer.vocabulary_
|
|
return dataset
|
|
else:
|
|
training = LabelledCollection(training_index, dataset.training.labels.copy(), dataset.n_classes)
|
|
test = LabelledCollection(test_index, dataset.test.labels.copy(), dataset.n_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:
|
|
|
|
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):
|
|
"""
|
|
: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=-1):
|
|
# given the number of tasks and the number of jobs, generates the slices for the parallel threads
|
|
assert self.unk != -1, 'transform called before fit'
|
|
indexed = parallelize(func=self.index, args=X, n_jobs=n_jobs)
|
|
return np.asarray(indexed)
|
|
|
|
def index(self, documents):
|
|
vocab = self.vocabulary_.copy()
|
|
return [[vocab.getscore(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
|
|
|
|
def fit_transform(self, X, n_jobs=-1):
|
|
return self.fit(X).transform(X, n_jobs=n_jobs)
|
|
|
|
def vocabulary_size(self):
|
|
return len(self.vocabulary_)
|
|
|
|
def add_word(self, word, id=None, nogaps=True):
|
|
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]
|
|
|