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 `_) :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 _`) :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 `_ :param kwargs: keyworded arguments from `CountVectorizer `_ """ 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]