# -*- coding: utf-8 -*- # Authors: Olivier Grisel # Mathieu Blondel # Lars Buitinck # Robert Layton # # License: BSD Style. """ The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to build feature vectors from text documents. """ import re import unicodedata from operator import itemgetter import warnings import numpy as np import scipy.sparse as sp from ..base import BaseEstimator, TransformerMixin from ..preprocessing import normalize from ..utils.fixes import Counter from .stop_words import ENGLISH_STOP_WORDS def strip_accents_unicode(s): """Transform accentuated unicode symbols into their simple counterpart Warning: the python-level loop and join operations make this implementation 20 times slower than the strip_accents_ascii basic normalization. See also -------- strip_accents_ascii Remove accentuated char for any unicode symbol that has a direct ASCII equivalent. """ return u''.join([c for c in unicodedata.normalize('NFKD', s) if not unicodedata.combining(c)]) def strip_accents_ascii(s): """Transform accentuated unicode symbols into ascii or nothing Warning: this solution is only suited for languages that have a direct transliteration to ASCII symbols. See also -------- strip_accents_unicode Remove accentuated char for any unicode symbol. """ nkfd_form = unicodedata.normalize('NFKD', s) return nkfd_form.encode('ASCII', 'ignore').decode('ASCII') def strip_tags(s): """Basic regexp based HTML / XML tag stripper function For serious HTML/XML preprocessing you should rather use an external library such as lxml or BeautifulSoup. """ return re.compile(ur"<([^>]+)>", flags=re.UNICODE).sub(u" ", s) def _check_stop_list(stop): if stop == "english": return ENGLISH_STOP_WORDS elif isinstance(stop, str) or isinstance(stop, unicode): raise ValueError("not a built-in stop list: %s" % stop) else: # assume it's a collection return stop class CountVectorizer(BaseEstimator): """Convert a collection of raw documents to a matrix of token counts This implementation produces a sparse representation of the counts using scipy.sparse.coo_matrix. If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analysing the data. The default analyzer does simple stop word filtering for English. Parameters ---------- input: string {'filename', 'file', 'content'} If filename, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. If 'file', the sequence items must have 'read' method (file-like object) it is called to fetch the bytes in memory. Otherwise the input is expected to be the sequence strings or bytes items are expected to be analyzed directly. charset: string, 'utf-8' by default. If bytes or files are given to analyze, this charset is used to decode. charset_error: {'strict', 'ignore', 'replace'} Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `charset`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and 'replace'. strip_accents: {'ascii', 'unicode', None} Remove accents during the preprocessing step. 'ascii' is a fast method that only works on characters that have an direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) does nothing. analyzer: string, {'word', 'char'} or callable Whether the feature should be made of word or character n-grams. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. preprocessor: callable or None (default) Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. tokenizer: callable or None (default) Override the string tokenization step while preserving the preprocessing and n-grams generation steps. min_n: integer The lower boundary of the range of n-values for different n-grams to be extracted. max_n: integer The upper boundary of the range of n-values for different n-grams to be extracted. All values of n such that min_n <= n <= max_n will be used. stop_words: string {'english'}, list, or None (default) If a string, it is passed to _check_stop_list and the appropriate stop list is returned is currently the only supported string value. If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern: string Regular expression denoting what constitutes a "token", only used if `tokenize == 'word'`. The default regexp select tokens of 2 or more letters characters (punctuation is completely ignored and always treated as a token separator). max_df : float in range [0.0, 1.0], optional, 1.0 by default When building the vocabulary ignore terms that have a term frequency strictly higher than the given threshold (corpus specific stop words). This parameter is ignored if vocabulary is not None. max_features : optional, None by default If not None, build a vocabulary that only consider the top max_features ordered by term frequency across the corpus. This parameter is ignored if vocabulary is not None. binary: boolean, False by default. If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts. dtype: type, optional Type of the matrix returned by fit_transform() or transform(). """ _white_spaces = re.compile(ur"\s\s+") def __init__(self, input='content', charset='utf-8', charset_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=ur"\b\w\w+\b", min_n=1, max_n=1, analyzer='word', max_df=1.0, max_features=None, vocabulary=None, binary=False, dtype=long): self.input = input self.charset = charset self.charset_error = charset_error self.strip_accents = strip_accents self.preprocessor = preprocessor self.tokenizer = tokenizer self.analyzer = analyzer self.lowercase = lowercase self.min_n = min_n self.max_n = max_n self.token_pattern = token_pattern self.stop_words = stop_words self.max_df = max_df self.max_features = max_features if vocabulary is not None: self.fixed_vocabulary = True if not hasattr(vocabulary, 'get'): vocabulary = dict((t, i) for i, t in enumerate(vocabulary)) self.vocabulary_ = vocabulary else: self.fixed_vocabulary = False self.binary = binary self.dtype = dtype def decode(self, doc): """Decode the input into a string of unicode symbols The decoding strategy depends on the vectorizer parameters. """ if self.input == 'filename': doc = open(doc, 'rb').read() elif self.input == 'file': doc = doc.read() if isinstance(doc, bytes): doc = doc.decode(self.charset, self.charset_error) return doc def _word_ngrams(self, tokens, stop_words=None): """Turn tokens into a sequence of n-grams after stop words filtering""" # handle stop words if stop_words is not None: tokens = [w for w in tokens if w not in stop_words] # handle token n-grams if self.min_n != 1 or self.max_n != 1: original_tokens = tokens tokens = [] n_original_tokens = len(original_tokens) for n in xrange(self.min_n, min(self.max_n + 1, n_original_tokens + 1)): for i in xrange(n_original_tokens - n + 1): tokens.append(u" ".join(original_tokens[i: i + n])) return tokens def _char_ngrams(self, text_document): """Tokenize text_document into a sequence of character n-grams""" # normalize white spaces text_document = self._white_spaces.sub(u" ", text_document) text_len = len(text_document) ngrams = [] for n in xrange(self.min_n, min(self.max_n + 1, text_len + 1)): for i in xrange(text_len - n + 1): ngrams.append(text_document[i: i + n]) return ngrams def build_preprocessor(self): """Return a function to preprocess the text before tokenization""" if self.preprocessor is not None: return self.preprocessor # unfortunately python functools package does not have an efficient # `compose` function that would have allowed us to chain a dynamic # number of functions. However the however of a lambda call is a few # hundreds of nanoseconds which is negligible when compared to the # cost of tokenizing a string of 1000 chars for instance. noop = lambda x: x # accent stripping if not self.strip_accents: strip_accents = noop elif hasattr(self.strip_accents, '__call__'): strip_accents = self.strip_accents elif self.strip_accents == 'ascii': strip_accents = strip_accents_ascii elif self.strip_accents == 'unicode': strip_accents = strip_accents_unicode else: raise ValueError('Invalid value for "strip_accents": %s' % self.strip_accents) if self.lowercase: return lambda x: strip_accents(x.lower()) else: return strip_accents def build_tokenizer(self): """Return a function that split a string in sequence of tokens""" if self.tokenizer is not None: return self.tokenizer token_pattern = re.compile(self.token_pattern) return lambda doc: token_pattern.findall(doc) def get_stop_words(self): """Build or fetch the effective stop words list""" return _check_stop_list(self.stop_words) def build_analyzer(self): """Return a callable that handles preprocessing and tokenization""" if hasattr(self.analyzer, '__call__'): return self.analyzer preprocess = self.build_preprocessor() if self.analyzer == 'char': return lambda doc: self._char_ngrams(preprocess(self.decode(doc))) elif self.analyzer == 'word': stop_words = self.get_stop_words() tokenize = self.build_tokenizer() return lambda doc: self._word_ngrams( tokenize(preprocess(self.decode(doc))), stop_words) else: raise ValueError('%s is not a valid tokenization scheme' % self.tokenize) def _term_count_dicts_to_matrix(self, term_count_dicts): i_indices = [] j_indices = [] values = [] vocabulary = self.vocabulary_ for i, term_count_dict in enumerate(term_count_dicts): for term, count in term_count_dict.iteritems(): j = vocabulary.get(term) if j is not None: i_indices.append(i) j_indices.append(j) values.append(count) # free memory as we go term_count_dict.clear() shape = (len(term_count_dicts), max(vocabulary.itervalues()) + 1) spmatrix = sp.coo_matrix((values, (i_indices, j_indices)), shape=shape, dtype=self.dtype) if self.binary: spmatrix.data[:] = 1 return spmatrix def fit(self, raw_documents, y=None): """Learn a vocabulary dictionary of all tokens in the raw documents Parameters ---------- raw_documents: iterable an iterable which yields either str, unicode or file objects Returns ------- self """ self.fit_transform(raw_documents) return self def fit_transform(self, raw_documents, y=None): """Learn the vocabulary dictionary and return the count vectors This is more efficient than calling fit followed by transform. Parameters ---------- raw_documents: iterable an iterable which yields either str, unicode or file objects Returns ------- vectors: array, [n_samples, n_features] """ if self.fixed_vocabulary: # No need to fit anything, directly perform the transformation. # We intentionally don't call the transform method to make it # fit_transform overridable without unwanted side effects in # TfidfVectorizer analyze = self.build_analyzer() term_counts_per_doc = [Counter(analyze(doc)) for doc in raw_documents] return self._term_count_dicts_to_matrix(term_counts_per_doc) self.vocabulary_ = {} # result of document conversion to term count dicts term_counts_per_doc = [] term_counts = Counter() # term counts across entire corpus (count each term maximum once per # document) document_counts = Counter() max_df = self.max_df max_features = self.max_features analyze = self.build_analyzer() # TODO: parallelize the following loop with joblib? # (see XXX up ahead) for doc in raw_documents: term_count_current = Counter(analyze(doc)) term_counts.update(term_count_current) if max_df < 1.0: document_counts.update(term_count_current.iterkeys()) term_counts_per_doc.append(term_count_current) n_doc = len(term_counts_per_doc) # filter out stop words: terms that occur in almost all documents if max_df < 1.0: max_document_count = max_df * n_doc stop_words = set(t for t, dc in document_counts.iteritems() if dc > max_document_count) else: stop_words = set() # list the terms that should be part of the vocabulary if max_features is None: terms = set(term_counts) - stop_words else: # extract the most frequent terms for the vocabulary terms = set() for t, tc in term_counts.most_common(): if t not in stop_words: terms.add(t) if len(terms) >= max_features: break # store the learned stop words to make it easier to debug the value of # max_df self.max_df_stop_words_ = stop_words # store map from term name to feature integer index: we sort the term # to have reproducible outcome for the vocabulary structure: otherwise # the mapping from feature name to indices might depend on the memory # layout of the machine. Furthermore sorted terms might make it # possible to perform binary search in the feature names array. self.vocabulary_ = dict(((t, i) for i, t in enumerate(sorted(terms)))) # the term_counts and document_counts might be useful statistics, are # we really sure want we want to drop them? They take some memory but # can be useful for corpus introspection return self._term_count_dicts_to_matrix(term_counts_per_doc) def transform(self, raw_documents): """Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided in the constructor. Parameters ---------- raw_documents: iterable an iterable which yields either str, unicode or file objects Returns ------- vectors: sparse matrix, [n_samples, n_features] """ if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0: raise ValueError("Vocabulary wasn't fitted or is empty!") # raw_documents can be an iterable so we don't know its size in # advance # XXX @larsmans tried to parallelize the following loop with joblib. # The result was some 20% slower than the serial version. analyze = self.build_analyzer() term_counts_per_doc = [Counter(analyze(doc)) for doc in raw_documents] return self._term_count_dicts_to_matrix(term_counts_per_doc) def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array, sparse matrix}, shape = [n_samples, n_features] Returns ------- X_inv : list of arrays, len = n_samples List of arrays of terms. """ if sp.isspmatrix_coo(X): # COO matrix is not indexable X = X.tocsr() elif not sp.issparse(X): # We need to convert X to a matrix, so that the indexing # returns 2D objects X = np.asmatrix(X) n_samples = X.shape[0] terms = np.array(self.vocabulary_.keys()) indices = np.array(self.vocabulary_.values()) inverse_vocabulary = terms[np.argsort(indices)] return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in xrange(n_samples)] def get_feature_names(self): """Array mapping from feature integer indicex to feature name""" if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0: raise ValueError("Vocabulary wasn't fitted or is empty!") return [t for t, i in sorted(self.vocabulary_.iteritems(), key=itemgetter(1))] class TfidfTransformer(BaseEstimator, TransformerMixin): """Transform a count matrix to a normalized tf or tf–idf representation Tf means term-frequency while tf–idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification. The goal of using tf–idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus. In the SMART notation used in IR, this class implements several tf–idf variants. Tf is always "n" (natural), idf is "t" iff use_idf is given, "n" otherwise, and normalization is "c" iff norm='l2', "n" iff norm=None. Parameters ---------- norm : 'l1', 'l2' or None, optional Norm used to normalize term vectors. None for no normalization. use_idf : boolean, optional Enable inverse-document-frequency reweighting. smooth_idf : boolean, optional Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions. sublinear_tf : boolean, optional Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). References ---------- .. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68–74.` .. [MSR2008] `C.D. Manning, H. Schütze and P. Raghavan (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 121–125.` """ def __init__(self, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False): self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf self.idf_ = None def fit(self, X, y=None): """Learn the idf vector (global term weights) Parameters ---------- X: sparse matrix, [n_samples, n_features] a matrix of term/token counts """ if self.use_idf: if not hasattr(X, 'nonzero'): X = sp.csr_matrix(X) n_samples, n_features = X.shape df = np.bincount(X.nonzero()[1]) if df.shape[0] < n_features: # bincount might return fewer bins than there are features df = np.concatenate([df, np.zeros(n_features - df.shape[0])]) # perform idf smoothing if required df += int(self.smooth_idf) n_samples += int(self.smooth_idf) # avoid division by zeros for features that occur in all documents self.idf_ = np.log(float(n_samples) / df) + 1.0 return self def transform(self, X, copy=True): """Transform a count matrix to a tf or tf–idf representation Parameters ---------- X: sparse matrix, [n_samples, n_features] a matrix of term/token counts Returns ------- vectors: sparse matrix, [n_samples, n_features] """ if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float): # preserve float family dtype X = sp.csr_matrix(X, copy=copy) else: # convert counts or binary occurrences to floats X = sp.csr_matrix(X, dtype=np.float64, copy=copy) n_samples, n_features = X.shape if self.sublinear_tf: np.log(X.data, X.data) X.data += 1 if self.use_idf: expected_n_features = self.idf_.shape[0] if n_features != expected_n_features: raise ValueError("Input has n_features=%d while the model" " has been trained with n_features=%d" % ( n_features, expected_n_features)) d = sp.lil_matrix((n_features, n_features)) d.setdiag(self.idf_) # *= doesn't work X = X * d if self.norm: X = normalize(X, norm=self.norm, copy=False) return X class TfidfVectorizer(CountVectorizer): """Convert a collection of raw documents to a matrix of TF-IDF features. Equivalent to CountVectorizer followed by TfidfTransformer. See also -------- CountVectorizer Tokenize the documents and count the occurrences of token and return them as a sparse matrix TfidfTransformer Apply Term Frequency Inverse Document Frequency normalization to a sparse matrix of occurrence counts. """ def __init__(self, input='content', charset='utf-8', charset_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern=ur"\b\w\w+\b", min_n=1, max_n=1, max_df=1.0, max_features=None, vocabulary=None, binary=False, dtype=long, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False): super(TfidfVectorizer, self).__init__( input=input, charset=charset, charset_error=charset_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, stop_words=stop_words, token_pattern=token_pattern, min_n=min_n, max_n=max_n, max_df=max_df, max_features=max_features, vocabulary=vocabulary, binary=False, dtype=dtype) self._tfidf = TfidfTransformer(norm=norm, use_idf=use_idf, smooth_idf=smooth_idf, sublinear_tf=sublinear_tf) # Broadcast the TF-IDF parameters to the underlying transformer instance # for easy grid search and repr @property def norm(self): return self._tfidf.norm @norm.setter def norm(self, value): self._tfidf.norm = value @property def use_idf(self): return self._tfidf.use_idf @use_idf.setter def use_idf(self, value): self._tfidf.use_idf = value @property def smooth_idf(self): return self._tfidf.smooth_idf @smooth_idf.setter def smooth_idf(self, value): self._tfidf.smooth_idf = value @property def sublinear_tf(self): return self._tfidf.sublinear_tf @sublinear_tf.setter def sublinear_tf(self, value): self._tfidf.sublinear_tf = value def fit(self, raw_documents): """Learn a conversion law from documents to array data""" X = super(TfidfVectorizer, self).fit_transform(raw_documents) self._tfidf.fit(X) return self def fit_transform(self, raw_documents, y=None): """Learn the representation and return the vectors. Parameters ---------- raw_documents: iterable an iterable which yields either str, unicode or file objects Returns ------- vectors: array, [n_samples, n_features] """ X = super(TfidfVectorizer, self).fit_transform(raw_documents) self._tfidf.fit(X) # X is already a transformed view of raw_documents so # we set copy to False return self._tfidf.transform(X, copy=False) def transform(self, raw_documents, copy=True): """Transform raw text documents to tf–idf vectors Parameters ---------- raw_documents: iterable an iterable which yields either str, unicode or file objects Returns ------- vectors: sparse matrix, [n_samples, n_features] """ X = super(TfidfVectorizer, self).transform(raw_documents) return self._tfidf.transform(X, copy) class Vectorizer(TfidfVectorizer): """Vectorizer is eprecated in 0.11, use TfidfVectorizer instead""" def __init__(self, input='content', charset='utf-8', charset_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern=ur"\b\w\w+\b", min_n=1, max_n=1, max_df=1.0, max_features=None, vocabulary=None, binary=False, dtype=long, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False): warnings.warn("Vectorizer is deprecated in 0.11 and will be removed" " in 0.13. Please use TfidfVectorizer instead.", category=DeprecationWarning) super(Vectorizer, self).__init__( input=input, charset=charset, charset_error=charset_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, stop_words=stop_words, token_pattern=token_pattern, min_n=min_n, max_n=max_n, max_df=max_df, max_features=max_features, vocabulary=vocabulary, binary=False, dtype=dtype, norm=norm, use_idf=use_idf, smooth_idf=smooth_idf, sublinear_tf=sublinear_tf)