forked from moreo/QuaPy
151 lines
7.0 KiB
Python
151 lines
7.0 KiB
Python
import numpy as np
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from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
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from data.base import Dataset
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from scipy.sparse import spmatrix
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from utils.util import parallelize
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from .base import LabelledCollection
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from tqdm import tqdm
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def text2tfidf(dataset:Dataset, min_df=3, sublinear_tf=True, inplace=False, **kwargs):
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"""
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Transforms a Dataset of textual instances into a Dataset of tfidf weighted sparse vectors
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:param dataset: a Dataset where the instances are lists of str
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:param min_df: minimum number of occurrences for a word to be considered as part of the vocabulary
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:param sublinear_tf: whether or not to apply the log scalling to the tf counters
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:param inplace: whether or not to apply the transformation inplace, or to a new copy
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:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.TfidfVectorizer)
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:return: a new Dataset in csr_matrix format (if inplace=False) or a reference to the current Dataset (inplace=True)
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where the instances are stored in a csr_matrix of real-valued tfidf scores
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"""
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__check_type(dataset.training.instances, np.ndarray, str)
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__check_type(dataset.test.instances, np.ndarray, str)
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vectorizer = TfidfVectorizer(min_df=min_df, sublinear_tf=sublinear_tf, **kwargs)
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training_documents = vectorizer.fit_transform(dataset.training.instances)
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test_documents = vectorizer.transform(dataset.test.instances)
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if inplace:
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dataset.training = LabelledCollection(training_documents, dataset.training.labels, dataset.n_classes)
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dataset.test = LabelledCollection(test_documents, dataset.test.labels, dataset.n_classes)
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dataset.vocabulary = vectorizer.vocabulary_
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return dataset
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else:
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training = LabelledCollection(training_documents, dataset.training.labels.copy(), dataset.n_classes)
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test = LabelledCollection(test_documents, dataset.test.labels.copy(), dataset.n_classes)
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return Dataset(training, test, vectorizer.vocabulary_)
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def reduce_columns(dataset:Dataset, min_df=5, inplace=False):
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"""
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Reduces the dimensionality of the csr_matrix by removing the columns of words which are not present in at least
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_min_occurrences_ instances
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:param dataset: a Dataset in sparse format (any subtype of scipy.sparse.spmatrix)
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:param min_df: minimum number of instances below which the columns are removed
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:param inplace: whether or not to apply the transformation inplace, or to a new copy
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:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
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where the dimensions corresponding to infrequent instances have been removed
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"""
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__check_type(dataset.training.instances, spmatrix)
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__check_type(dataset.test.instances, spmatrix)
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assert dataset.training.instances.shape[1] == dataset.test.instances.shape[1], 'unaligned vector spaces'
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def filter_by_occurrences(X, W):
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column_prevalence = np.asarray((X > 0).sum(axis=0)).flatten()
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take_columns = column_prevalence >= min_df
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X = X[:, take_columns]
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W = W[:, take_columns]
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return X, W
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Xtr, Xte = filter_by_occurrences(dataset.training.instances, dataset.test.instances)
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if inplace:
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dataset.training.instances = Xtr
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dataset.test.instances = Xte
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return dataset
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else:
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training = LabelledCollection(Xtr, dataset.training.labels.copy(), dataset.n_classes)
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test = LabelledCollection(Xte, dataset.test.labels.copy(), dataset.n_classes)
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return Dataset(training, test)
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def index(dataset: Dataset, min_df=5, inplace=False, **kwargs):
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"""
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Indexes a dataset of strings. To index a document means to replace each different token by a unique numerical index.
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Rare words (i.e., words occurring less than _min_df_ times) are replaced by a special token UNK
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:param dataset: a Dataset where the instances are lists of str
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:param min_df: minimum number of instances below which the term is replaced by a UNK index
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:param inplace: whether or not to apply the transformation inplace, or to a new copy
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:param kwargs: the rest of parameters of the transformation (as for sklearn.feature_extraction.text.CountVectorizer)
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:return: a new Dataset (if inplace=False) or a reference to the current Dataset (inplace=True)
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consisting of lists of integer values representing indices.
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"""
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__check_type(dataset.training.instances, np.ndarray, str)
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__check_type(dataset.test.instances, np.ndarray, str)
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indexer = IndexTransformer(min_df=min_df, **kwargs)
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training_index = indexer.fit_transform(dataset.training.instances)
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test_index = indexer.transform(dataset.test.instances)
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if inplace:
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dataset.training = LabelledCollection(training_index, dataset.training.labels, dataset.n_classes)
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dataset.test = LabelledCollection(test_index, dataset.test.labels, dataset.n_classes)
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dataset.vocabulary = indexer.vocabulary_
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return dataset
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else:
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training = LabelledCollection(training_index, dataset.training.labels.copy(), dataset.n_classes)
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test = LabelledCollection(test_index, dataset.test.labels.copy(), dataset.n_classes)
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return Dataset(training, test, indexer.vocabulary_)
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def __check_type(container, container_type=None, element_type=None):
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if container_type:
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assert isinstance(container, container_type), \
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f'unexpected type of container (expected {container_type}, found {type(container)})'
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if element_type:
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assert isinstance(container[0], element_type), \
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f'unexpected type of element (expected {container_type}, found {type(container)})'
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class IndexTransformer:
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def __init__(self, **kwargs):
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"""
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:param kwargs: keyworded arguments from _sklearn.feature_extraction.text.CountVectorizer_
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"""
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self.vect = CountVectorizer(**kwargs)
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self.unk = -1 # a valid index is assigned after fit
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def fit(self, X):
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"""
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:param X: a list of strings
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:return: self
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"""
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self.vect.fit(X)
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self.analyzer = self.vect.build_analyzer()
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self.vocabulary_ = self.vect.vocabulary_
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self.unk = self.add_word('UNK')
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return self
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def transform(self, X, n_jobs=-1):
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# given the number of tasks and the number of jobs, generates the slices for the parallel threads
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assert self.unk > 0, 'transform called before fit'
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indexed = parallelize(func=self.index, args=X, n_jobs=n_jobs)
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return np.asarray(indexed)
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def index(self, documents):
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vocab = self.vocabulary_.copy()
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return [[vocab.get(word, self.unk) for word in self.analyzer(doc)] for doc in tqdm(documents, 'indexing')]
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def fit_transform(self, X, n_jobs=-1):
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return self.fit(X).transform(X, n_jobs=n_jobs)
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def vocabulary_size(self):
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return len(self.vocabulary_)
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def add_word(self, word):
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if word in self.vocabulary_:
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raise ValueError(f'word {word} already in dictionary')
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self.vocabulary_[word] = len(self.vocabulary_)
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return self.vocabulary_[word]
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