# -*- 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. """ from collections import Mapping from operator import itemgetter import re import unicodedata import warnings import numbers import numpy as np import scipy.sparse as sp from ..base import BaseEstimator, TransformerMixin from ..preprocessing import normalize from ..utils.fixes import Counter from .hashing import FeatureHasher from .stop_words import ENGLISH_STOP_WORDS __all__ = ['CountVectorizer', 'ENGLISH_STOP_WORDS', 'TfidfTransformer', 'TfidfVectorizer', 'strip_accents_ascii', 'strip_accents_unicode', 'strip_tags'] 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 VectorizerMixin(object): """Provides common code for text vectorizers (tokenization logic).""" _white_spaces = re.compile(ur"\s\s+") 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': with open(doc, 'rb') as fh: doc = fh.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 min_n, max_n = self.ngram_range if max_n != 1: original_tokens = tokens tokens = [] n_original_tokens = len(original_tokens) for n in xrange(min_n, min(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 = [] min_n, max_n = self.ngram_range for n in xrange(min_n, min(max_n + 1, text_len + 1)): for i in xrange(text_len - n + 1): ngrams.append(text_document[i: i + n]) return ngrams def _char_wb_ngrams(self, text_document): """Whitespace sensitive char-n-gram tokenization. Tokenize text_document into a sequence of character n-grams excluding any whitespace (operating only inside word boundaries)""" # normalize white spaces text_document = self._white_spaces.sub(u" ", text_document) min_n, max_n = self.ngram_range ngrams = [] for w in text_document.split(): w = u' ' + w + u' ' w_len = len(w) for n in xrange(min_n, max_n + 1): offset = 0 ngrams.append(w[offset:offset + n]) while offset + n < w_len: offset += 1 ngrams.append(w[offset:offset + n]) if offset == 0: # count a short word (w_len < n) only once break 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 cost 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 == 'char_wb': return lambda doc: self._char_wb_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/analyzer' % self.analyzer) class HashingVectorizer(BaseEstimator, VectorizerMixin): """Convert a collection of text documents to a matrix of token occurrences It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm='l1' or projected on the euclidean unit sphere if norm='l2'. This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping. This strategy has several advantage: - it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory - it is fast to pickle and un-pickle has it holds no state besides the constructor parameters - it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit. There are also a couple of cons (vs using a CountVectorizer with an in-memory vocabulary): - there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model. - there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems). - no IDF weighting as this would render the transformer stateful. The hash function employed is the signed 32-bit version of Murmurhash3. 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', 'char_wb'} or callable Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries. 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. ngram_range: tuple (min_n, max_n) The lower and 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. 'english' 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. lowercase: boolean, default True Convert all characters to lowercase before tokenizing. 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). n_features : interger, optional, (2 ** 20) by default The number of features (columns) in the output matrices. Small numbers of features are likely to cause hash collisions, but large numbers will cause larger coefficient dimensions in linear learners. norm : 'l1', 'l2' or None, optional Norm used to normalize term vectors. None for no normalization. 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(). non_negative : boolean, optional Whether output matrices should contain non-negative values only; effectively calls abs on the matrix prior to returning it. When True, output values will be multinomially distributed. When False, output values will be normally distributed (Gaussian) with mean 0, assuming a good hash function. See also -------- CountVectorizer, TfidfVectorizer """ 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"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer='word', n_features=(2 ** 20), binary=False, norm='l2', non_negative=False, dtype=np.float64): 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.token_pattern = token_pattern self.stop_words = stop_words self.n_features = n_features self.ngram_range = ngram_range self.binary = binary self.norm = norm self.non_negative = non_negative self.dtype = dtype def partial_fit(self, X, y=None): """Does nothing: this transformer is stateless. This method is just there to mark the fact that this transformer can work in a streaming setup. """ return self def fit(self, X, y=None): """Does nothing: this transformer is stateless.""" # triggers a parameter validation self._get_hasher().fit(X, y=y) return self def transform(self, X, y=None): """Transform a sequence of instances to a scipy.sparse matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, filen ame or file object depending on the constructor argument) which will be tokenized and hashed. y : (ignored) Returns ------- X : scipy.sparse matrix, shape = (n_samples, self.n_features) Feature matrix, for use with estimators or further transformers. """ analyzer = self.build_analyzer() X = self._get_hasher().transform(analyzer(doc) for doc in X) if self.binary: X.data.fill(1) if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X # Alias transform to fit_transform for convenience fit_transform = transform def _get_hasher(self): return FeatureHasher(n_features=self.n_features, input_type='string', dtype=self.dtype, non_negative=self.non_negative) class CountVectorizer(BaseEstimator, VectorizerMixin): """Convert a collection of text 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', 'char_wb'} or callable Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries. 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. ngram_range : tuple (min_n, max_n) The lower and 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. 'english' 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. lowercase : boolean, default True Convert all characters to lowercase befor tokenizing. 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] or int, 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). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float in range [0.0, 1.0] or int, optional, 2 by default When building the vocabulary ignore terms that have a term frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. 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. vocabulary : Mapping or iterable, optional Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. 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(). Attributes ---------- `vocabulary_` : dict A mapping of terms to feature indices. `stop_words_` : set Terms that were ignored because they occurred in either too many (`max_df`) or in too few (`min_df`) documents. This is only available if no vocabulary was given. See also -------- HashingVectorizer, TfidfVectorizer """ 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"(?u)\b\w\w+\b", ngram_range=(1, 1), min_n=None, max_n=None, analyzer='word', max_df=1.0, min_df=2, 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.token_pattern = token_pattern self.stop_words = stop_words self.max_df = max_df self.min_df = min_df self.max_features = max_features if not (max_n is None) or not (min_n is None): warnings.warn('Parameters max_n and min_n are deprecated. Use ' 'ngram_range instead. This will be removed in 0.14.', DeprecationWarning, stacklevel=2) if min_n is None: min_n = 1 if max_n is None: max_n = min_n ngram_range = (min_n, max_n) self.ngram_range = ngram_range if vocabulary is not None: if not isinstance(vocabulary, Mapping): vocabulary = dict((t, i) for i, t in enumerate(vocabulary)) if not vocabulary: raise ValueError("empty vocabulary passed to fit") self.fixed_vocabulary = True self.vocabulary_ = vocabulary else: self.fixed_vocabulary = False self.binary = binary self.dtype = dtype 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 = (i + 1, max(vocabulary.itervalues()) + 1) spmatrix = sp.coo_matrix((values, (i_indices, j_indices)), shape=shape, dtype=self.dtype) if self.binary: spmatrix.data.fill(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() 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) document_counts.update(term_count_current.iterkeys()) term_counts_per_doc.append(term_count_current) n_doc = len(term_counts_per_doc) max_features = self.max_features max_df = self.max_df min_df = self.min_df max_doc_count = (max_df if isinstance(max_df, numbers.Integral) else max_df * n_doc) min_doc_count = (min_df if isinstance(min_df, numbers.Integral) else min_df * n_doc) # filter out stop words: terms that occur in almost all documents if max_doc_count < n_doc or min_doc_count > 1: stop_words = set(t for t, dc in document_counts.iteritems() if dc > max_doc_count or dc < min_doc_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.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. vocab = dict(((t, i) for i, t in enumerate(sorted(terms)))) if not vocab: raise ValueError("empty vocabulary; training set may have" " contained only stop words or min_df (resp. " "max_df) may be too high (resp. too low).") self.vocabulary_ = vocab # 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 indices 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))] @property def max_df_stop_words_(self): warnings.warn( "The 'stop_words_ attribute was renamed to 'max_df_stop_words'. " "The old attribute will be removed in 0.15.", DeprecationWarning) return self.stop_words_ 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 "n" (natural) by default, "l" (logarithmic) when sublinear_tf=True. Idf is "t" idf is "t" when use_idf is given, "n" (none) otherwise. Normalization is "c" (cosine) when norm='l2', "n" (none) when 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 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 idf = np.log(float(n_samples) / df) + 1.0 idf_diag = sp.lil_matrix((n_features, n_features)) idf_diag.setdiag(idf) self._idf_diag = sp.csc_matrix(idf_diag) 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: if not hasattr(self, "_idf_diag"): raise ValueError("idf vector not fitted") expected_n_features = self._idf_diag.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)) # *= doesn't work X = X * self._idf_diag if self.norm: X = normalize(X, norm=self.norm, copy=False) return X @property def idf_(self): if hasattr(self, "_idf_diag"): return np.ravel(self._idf_diag.sum(axis=0)) else: return None class TfidfVectorizer(CountVectorizer): """Convert a collection of raw documents to a matrix of TF-IDF features. Equivalent to CountVectorizer followed by TfidfTransformer. 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. ngram_range : tuple (min_n, max_n) The lower and 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. 'english' 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. lowercase : boolean, default True Convert all characters to lowercase befor tokenizing. 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] or int, 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). If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float in range [0.0, 1.0] or int, optional, 2 by default When building the vocabulary ignore terms that have a term frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. 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. vocabulary : Mapping or iterable, optional Either a Mapping (e.g., a dict) where keys are terms and values are indices in the feature matrix, or an iterable over terms. If not given, a vocabulary is determined from the input documents. 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(). 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). 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"(?u)\b\w\w+\b", min_n=None, max_n=None, ngram_range=(1, 1), max_df=1.0, min_df=2, 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, ngram_range=ngram_range, max_df=max_df, min_df=min_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, y=None): """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)