"""Utilities to build feature vectors from text documents.""" # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import array import re import unicodedata import warnings from collections import defaultdict from collections.abc import Mapping from functools import partial from numbers import Integral from operator import itemgetter import numpy as np import scipy.sparse as sp from sklearn.utils import metadata_routing from ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context from ..exceptions import NotFittedError from ..preprocessing import normalize from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions from ..utils.fixes import _IS_32BIT from ..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted, validate_data from ._hash import FeatureHasher from ._stop_words import ENGLISH_STOP_WORDS __all__ = [ "HashingVectorizer", "CountVectorizer", "ENGLISH_STOP_WORDS", "TfidfTransformer", "TfidfVectorizer", "strip_accents_ascii", "strip_accents_unicode", "strip_tags", ] def _preprocess(doc, accent_function=None, lower=False): """Chain together an optional series of text preprocessing steps to apply to a document. Parameters ---------- doc: str The string to preprocess accent_function: callable, default=None Function for handling accented characters. Common strategies include normalizing and removing. lower: bool, default=False Whether to use str.lower to lowercase all of the text Returns ------- doc: str preprocessed string """ if lower: doc = doc.lower() if accent_function is not None: doc = accent_function(doc) return doc def _analyze( doc, analyzer=None, tokenizer=None, ngrams=None, preprocessor=None, decoder=None, stop_words=None, ): """Chain together an optional series of text processing steps to go from a single document to ngrams, with or without tokenizing or preprocessing. If analyzer is used, only the decoder argument is used, as the analyzer is intended to replace the preprocessor, tokenizer, and ngrams steps. Parameters ---------- analyzer: callable, default=None tokenizer: callable, default=None ngrams: callable, default=None preprocessor: callable, default=None decoder: callable, default=None stop_words: list, default=None Returns ------- ngrams: list A sequence of tokens, possibly with pairs, triples, etc. """ if decoder is not None: doc = decoder(doc) if analyzer is not None: doc = analyzer(doc) else: if preprocessor is not None: doc = preprocessor(doc) if tokenizer is not None: doc = tokenizer(doc) if ngrams is not None: if stop_words is not None: doc = ngrams(doc, stop_words) else: doc = ngrams(doc) return doc 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. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. See Also -------- strip_accents_ascii : Remove accentuated char for any unicode symbol that has a direct ASCII equivalent. """ try: # If `s` is ASCII-compatible, then it does not contain any accented # characters and we can avoid an expensive list comprehension s.encode("ASCII", errors="strict") return s except UnicodeEncodeError: normalized = unicodedata.normalize("NFKD", s) return "".join([c for c in normalized 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. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. 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. Parameters ---------- s : str The string to strip. Returns ------- s : str The stripped string. """ return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s) def _check_stop_list(stop): if stop == "english": return ENGLISH_STOP_WORDS elif isinstance(stop, str): raise ValueError("not a built-in stop list: %s" % stop) elif stop is None: return None else: # assume it's a collection return frozenset(stop) class _VectorizerMixin: """Provides common code for text vectorizers (tokenization logic).""" _white_spaces = re.compile(r"\s\s+") def decode(self, doc): """Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters ---------- doc : bytes or str The string to decode. Returns ------- doc: str A string of unicode symbols. """ 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.encoding, self.decode_error) if doc is np.nan: raise ValueError( "np.nan is an invalid document, expected byte or unicode string." ) 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 if min_n == 1: # no need to do any slicing for unigrams # just iterate through the original tokens tokens = list(original_tokens) min_n += 1 else: tokens = [] n_original_tokens = len(original_tokens) # bind method outside of loop to reduce overhead tokens_append = tokens.append space_join = " ".join for n in range(min_n, min(max_n + 1, n_original_tokens + 1)): for i in range(n_original_tokens - n + 1): tokens_append(space_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(" ", text_document) text_len = len(text_document) min_n, max_n = self.ngram_range if min_n == 1: # no need to do any slicing for unigrams # iterate through the string ngrams = list(text_document) min_n += 1 else: ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for n in range(min_n, min(max_n + 1, text_len + 1)): for i in range(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 operating only inside word boundaries. n-grams at the edges of words are padded with space.""" # normalize white spaces text_document = self._white_spaces.sub(" ", text_document) min_n, max_n = self.ngram_range ngrams = [] # bind method outside of loop to reduce overhead ngrams_append = ngrams.append for w in text_document.split(): w = " " + w + " " w_len = len(w) for n in range(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. Returns ------- preprocessor: callable A function to preprocess the text before tokenization. """ if self.preprocessor is not None: return self.preprocessor # accent stripping if not self.strip_accents: strip_accents = None elif callable(self.strip_accents): 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 ) return partial(_preprocess, accent_function=strip_accents, lower=self.lowercase) def build_tokenizer(self): """Return a function that splits a string into a sequence of tokens. Returns ------- tokenizer: callable A function to split a string into a sequence of tokens. """ if self.tokenizer is not None: return self.tokenizer token_pattern = re.compile(self.token_pattern) if token_pattern.groups > 1: raise ValueError( "More than 1 capturing group in token pattern. Only a single " "group should be captured." ) return token_pattern.findall def get_stop_words(self): """Build or fetch the effective stop words list. Returns ------- stop_words: list or None A list of stop words. """ return _check_stop_list(self.stop_words) def _check_stop_words_consistency(self, stop_words, preprocess, tokenize): """Check if stop words are consistent Returns ------- is_consistent : True if stop words are consistent with the preprocessor and tokenizer, False if they are not, None if the check was previously performed, "error" if it could not be performed (e.g. because of the use of a custom preprocessor / tokenizer) """ if id(self.stop_words) == getattr(self, "_stop_words_id", None): # Stop words are were previously validated return None # NB: stop_words is validated, unlike self.stop_words try: inconsistent = set() for w in stop_words or (): tokens = list(tokenize(preprocess(w))) for token in tokens: if token not in stop_words: inconsistent.add(token) self._stop_words_id = id(self.stop_words) if inconsistent: warnings.warn( "Your stop_words may be inconsistent with " "your preprocessing. Tokenizing the stop " "words generated tokens %r not in " "stop_words." % sorted(inconsistent) ) return not inconsistent except Exception: # Failed to check stop words consistency (e.g. because a custom # preprocessor or tokenizer was used) self._stop_words_id = id(self.stop_words) return "error" def build_analyzer(self): """Return a callable to process input data. The callable handles preprocessing, tokenization, and n-grams generation. Returns ------- analyzer: callable A function to handle preprocessing, tokenization and n-grams generation. """ if callable(self.analyzer): return partial(_analyze, analyzer=self.analyzer, decoder=self.decode) preprocess = self.build_preprocessor() if self.analyzer == "char": return partial( _analyze, ngrams=self._char_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "char_wb": return partial( _analyze, ngrams=self._char_wb_ngrams, preprocessor=preprocess, decoder=self.decode, ) elif self.analyzer == "word": stop_words = self.get_stop_words() tokenize = self.build_tokenizer() self._check_stop_words_consistency(stop_words, preprocess, tokenize) return partial( _analyze, ngrams=self._word_ngrams, tokenizer=tokenize, preprocessor=preprocess, decoder=self.decode, stop_words=stop_words, ) else: raise ValueError( "%s is not a valid tokenization scheme/analyzer" % self.analyzer ) def _validate_vocabulary(self): vocabulary = self.vocabulary if vocabulary is not None: if isinstance(vocabulary, set): vocabulary = sorted(vocabulary) if not isinstance(vocabulary, Mapping): vocab = {} for i, t in enumerate(vocabulary): if vocab.setdefault(t, i) != i: msg = "Duplicate term in vocabulary: %r" % t raise ValueError(msg) vocabulary = vocab else: indices = set(vocabulary.values()) if len(indices) != len(vocabulary): raise ValueError("Vocabulary contains repeated indices.") for i in range(len(vocabulary)): if i not in indices: msg = "Vocabulary of size %d doesn't contain index %d." % ( len(vocabulary), i, ) raise ValueError(msg) if not vocabulary: raise ValueError("empty vocabulary passed to fit") self.fixed_vocabulary_ = True self.vocabulary_ = dict(vocabulary) else: self.fixed_vocabulary_ = False def _check_vocabulary(self): """Check if vocabulary is empty or missing (not fitted)""" if not hasattr(self, "vocabulary_"): self._validate_vocabulary() if not self.fixed_vocabulary_: raise NotFittedError("Vocabulary not fitted or provided") if len(self.vocabulary_) == 0: raise ValueError("Vocabulary is empty") def _validate_ngram_range(self): """Check validity of ngram_range parameter""" min_n, max_m = self.ngram_range if min_n > max_m: raise ValueError( "Invalid value for ngram_range=%s " "lower boundary larger than the upper boundary." % str(self.ngram_range) ) def _warn_for_unused_params(self): if self.tokenizer is not None and self.token_pattern is not None: warnings.warn( "The parameter 'token_pattern' will not be used" " since 'tokenizer' is not None'" ) if self.preprocessor is not None and callable(self.analyzer): warnings.warn( "The parameter 'preprocessor' will not be used" " since 'analyzer' is callable'" ) if ( self.ngram_range != (1, 1) and self.ngram_range is not None and callable(self.analyzer) ): warnings.warn( "The parameter 'ngram_range' will not be used" " since 'analyzer' is callable'" ) if self.analyzer != "word" or callable(self.analyzer): if self.stop_words is not None: warnings.warn( "The parameter 'stop_words' will not be used" " since 'analyzer' != 'word'" ) if ( self.token_pattern is not None and self.token_pattern != r"(?u)\b\w\w+\b" ): warnings.warn( "The parameter 'token_pattern' will not be used" " since 'analyzer' != 'word'" ) if self.tokenizer is not None: warnings.warn( "The parameter 'tokenizer' will not be used" " since 'analyzer' != 'word'" ) class HashingVectorizer( TransformerMixin, _VectorizerMixin, BaseEstimator, auto_wrap_output_keys=None ): r"""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 advantages: - 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 as 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. For an efficiency comparison of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. For an example of document clustering and comparison with :class:`~sklearn.feature_extraction.text.TfidfVectorizer`, see :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`. Read more in the :ref:`User Guide `. Parameters ---------- input : {'filename', 'file', 'content'}, default='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 a 'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore', 'replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and 'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any character. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) 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. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' 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; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. n_features : int, default=(2 ** 20) 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. binary : bool, default=False 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. norm : {'l1', 'l2'}, default='l2' Norm used to normalize term vectors. None for no normalization. alternate_sign : bool, default=True When True, an alternating sign is added to the features as to approximately conserve the inner product in the hashed space even for small n_features. This approach is similar to sparse random projection. .. versionadded:: 0.19 dtype : type, default=np.float64 Type of the matrix returned by fit_transform() or transform(). See Also -------- CountVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Notes ----- This estimator is :term:`stateless` and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.feature_extraction.text import HashingVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = HashingVectorizer(n_features=2**4) >>> X = vectorizer.fit_transform(corpus) >>> print(X.shape) (4, 16) """ _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "n_features": [Interval(Integral, 1, np.iinfo(np.int32).max, closed="left")], "binary": ["boolean"], "norm": [StrOptions({"l1", "l2"}), None], "alternate_sign": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", n_features=(2**20), binary=False, norm="l2", alternate_sign=True, dtype=np.float64, ): self.input = input self.encoding = encoding self.decode_error = decode_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.alternate_sign = alternate_sign self.dtype = dtype @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ return self @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : ndarray of shape [n_samples, n_features] Training data. y : Ignored Not used, present for API consistency by convention. Returns ------- self : object HashingVectorizer instance. """ # triggers a parameter validation if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._warn_for_unused_params() self._validate_ngram_range() self._get_hasher().fit(X, y=y) return self def transform(self, X): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(X, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() 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 def fit_transform(self, X, y=None): """Transform a sequence of documents to a document-term matrix. Parameters ---------- X : iterable over raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ return self.fit(X, y).transform(X) def _get_hasher(self): return FeatureHasher( n_features=self.n_features, input_type="string", dtype=self.dtype, alternate_sign=self.alternate_sign, ) def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.string = True tags.input_tags.two_d_array = False return tags def _document_frequency(X): """Count the number of non-zero values for each feature in sparse X.""" if sp.issparse(X) and X.format == "csr": return np.bincount(X.indices, minlength=X.shape[1]) else: return np.diff(X.indptr) class CountVectorizer(_VectorizerMixin, BaseEstimator): r"""Convert a collection of text documents to a matrix of token counts. This implementation produces a sparse representation of the counts using scipy.sparse.csr_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 analyzing the data. For an efficiency comparison of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. Read more in the :ref:`User Guide `. Parameters ---------- input : {'filename', 'file', 'content'}, default='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 a 'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore', 'replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and 'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (strip_accents and lowercase) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. stop_words : {'english'}, list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word n-gram or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``filename`` or ``file``, the data is first read from the file and then passed to the given callable analyzer. max_df : float in range [0.0, 1.0] or int, default=1.0 When building the vocabulary ignore terms that have a document 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, default=1 When building the vocabulary ignore terms that have a document 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 : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None 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. Indices in the mapping should not be repeated and should not have any gap between 0 and the largest index. binary : bool, default=False 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 : dtype, default=np.int64 Type of the matrix returned by fit_transform() or transform(). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. See Also -------- HashingVectorizer : Convert a collection of text documents to a matrix of token counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. Examples -------- >>> from sklearn.feature_extraction.text import CountVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = CountVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'], ...) >>> print(X.toarray()) [[0 1 1 1 0 0 1 0 1] [0 2 0 1 0 1 1 0 1] [1 0 0 1 1 0 1 1 1] [0 1 1 1 0 0 1 0 1]] >>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2)) >>> X2 = vectorizer2.fit_transform(corpus) >>> vectorizer2.get_feature_names_out() array(['and this', 'document is', 'first document', 'is the', 'is this', 'second document', 'the first', 'the second', 'the third', 'third one', 'this document', 'this is', 'this the'], ...) >>> print(X2.toarray()) [[0 0 1 1 0 0 1 0 0 0 0 1 0] [0 1 0 1 0 1 0 1 0 0 1 0 0] [1 0 0 1 0 0 0 0 1 1 0 1 0] [0 0 1 0 1 0 1 0 0 0 0 0 1]] """ # raw_documents should not be in the routing mechanism. It should have been # called X in the first place. __metadata_request__fit = {"raw_documents": metadata_routing.UNUSED} __metadata_request__transform = {"raw_documents": metadata_routing.UNUSED} _parameter_constraints: dict = { "input": [StrOptions({"filename", "file", "content"})], "encoding": [str], "decode_error": [StrOptions({"strict", "ignore", "replace"})], "strip_accents": [StrOptions({"ascii", "unicode"}), None, callable], "lowercase": ["boolean"], "preprocessor": [callable, None], "tokenizer": [callable, None], "stop_words": [StrOptions({"english"}), list, None], "token_pattern": [str, None], "ngram_range": [tuple], "analyzer": [StrOptions({"word", "char", "char_wb"}), callable], "max_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "min_df": [ Interval(RealNotInt, 0, 1, closed="both"), Interval(Integral, 1, None, closed="left"), ], "max_features": [Interval(Integral, 1, None, closed="left"), None], "vocabulary": [Mapping, HasMethods("__iter__"), None], "binary": ["boolean"], "dtype": "no_validation", # delegate to numpy } def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), analyzer="word", max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.int64, ): self.input = input self.encoding = encoding self.decode_error = decode_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 self.ngram_range = ngram_range self.vocabulary = vocabulary self.binary = binary self.dtype = dtype def _sort_features(self, X, vocabulary): """Sort features by name Returns a reordered matrix and modifies the vocabulary in place """ sorted_features = sorted(vocabulary.items()) map_index = np.empty(len(sorted_features), dtype=X.indices.dtype) for new_val, (term, old_val) in enumerate(sorted_features): vocabulary[term] = new_val map_index[old_val] = new_val X.indices = map_index.take(X.indices, mode="clip") return X def _limit_features(self, X, vocabulary, high=None, low=None, limit=None): """Remove too rare or too common features. Prune features that are non zero in more samples than high or less documents than low, modifying the vocabulary, and restricting it to at most the limit most frequent. This does not prune samples with zero features. """ if high is None and low is None and limit is None: return X, set() # Calculate a mask based on document frequencies dfs = _document_frequency(X) mask = np.ones(len(dfs), dtype=bool) if high is not None: mask &= dfs <= high if low is not None: mask &= dfs >= low if limit is not None and mask.sum() > limit: tfs = np.asarray(X.sum(axis=0)).ravel() mask_inds = (-tfs[mask]).argsort()[:limit] new_mask = np.zeros(len(dfs), dtype=bool) new_mask[np.where(mask)[0][mask_inds]] = True mask = new_mask new_indices = np.cumsum(mask) - 1 # maps old indices to new for term, old_index in list(vocabulary.items()): if mask[old_index]: vocabulary[term] = new_indices[old_index] else: del vocabulary[term] kept_indices = np.where(mask)[0] if len(kept_indices) == 0: raise ValueError( "After pruning, no terms remain. Try a lower min_df or a higher max_df." ) return X[:, kept_indices] def _count_vocab(self, raw_documents, fixed_vocab): """Create sparse feature matrix, and vocabulary where fixed_vocab=False""" if fixed_vocab: vocabulary = self.vocabulary_ else: # Add a new value when a new vocabulary item is seen vocabulary = defaultdict() vocabulary.default_factory = vocabulary.__len__ analyze = self.build_analyzer() j_indices = [] indptr = [] values = _make_int_array() indptr.append(0) for doc in raw_documents: feature_counter = {} for feature in analyze(doc): try: feature_idx = vocabulary[feature] if feature_idx not in feature_counter: feature_counter[feature_idx] = 1 else: feature_counter[feature_idx] += 1 except KeyError: # Ignore out-of-vocabulary items for fixed_vocab=True continue j_indices.extend(feature_counter.keys()) values.extend(feature_counter.values()) indptr.append(len(j_indices)) if not fixed_vocab: # disable defaultdict behaviour vocabulary = dict(vocabulary) if not vocabulary: raise ValueError( "empty vocabulary; perhaps the documents only contain stop words" ) if indptr[-1] > np.iinfo(np.int32).max: # = 2**31 - 1 if _IS_32BIT: raise ValueError( ( "sparse CSR array has {} non-zero " "elements and requires 64 bit indexing, " "which is unsupported with 32 bit Python." ).format(indptr[-1]) ) indices_dtype = np.int64 else: indices_dtype = np.int32 j_indices = np.asarray(j_indices, dtype=indices_dtype) indptr = np.asarray(indptr, dtype=indices_dtype) values = np.frombuffer(values, dtype=np.intc) X = sp.csr_matrix( (values, j_indices, indptr), shape=(len(indptr) - 1, len(vocabulary)), dtype=self.dtype, ) X.sort_indices() return vocabulary, X 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 generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- self : object Fitted vectorizer. """ self.fit_transform(raw_documents) return self @_fit_context(prefer_skip_nested_validation=True) def fit_transform(self, raw_documents, y=None): """Learn the vocabulary dictionary and return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : array of shape (n_samples, n_features) Document-term matrix. """ # We intentionally don't call the transform method to make # fit_transform overridable without unwanted side effects in # TfidfVectorizer. if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._validate_ngram_range() self._warn_for_unused_params() self._validate_vocabulary() max_df = self.max_df min_df = self.min_df max_features = self.max_features if self.fixed_vocabulary_ and self.lowercase: for term in self.vocabulary: if any(map(str.isupper, term)): warnings.warn( "Upper case characters found in" " vocabulary while 'lowercase'" " is True. These entries will not" " be matched with any documents" ) break vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_) if self.binary: X.data.fill(1) if not self.fixed_vocabulary_: n_doc = X.shape[0] max_doc_count = max_df if isinstance(max_df, Integral) else max_df * n_doc min_doc_count = min_df if isinstance(min_df, Integral) else min_df * n_doc if max_doc_count < min_doc_count: raise ValueError("max_df corresponds to < documents than min_df") if max_features is not None: X = self._sort_features(X, vocabulary) X = self._limit_features( X, vocabulary, max_doc_count, min_doc_count, max_features ) if max_features is None: X = self._sort_features(X, vocabulary) self.vocabulary_ = vocabulary return X def transform(self, raw_documents): """Transform documents to document-term matrix. Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of shape (n_samples, n_features) Document-term matrix. """ if isinstance(raw_documents, str): raise ValueError( "Iterable over raw text documents expected, string object received." ) self._check_vocabulary() # use the same matrix-building strategy as fit_transform _, X = self._count_vocab(raw_documents, fixed_vocab=True) if self.binary: X.data.fill(1) return X def inverse_transform(self, X): """Return terms per document with nonzero entries in X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Document-term matrix. Returns ------- X_inv : list of arrays of shape (n_samples,) List of arrays of terms. """ self._check_vocabulary() # We need CSR format for fast row manipulations. X = check_array(X, accept_sparse="csr") n_samples = X.shape[0] terms = np.array(list(self.vocabulary_.keys())) indices = np.array(list(self.vocabulary_.values())) inverse_vocabulary = terms[np.argsort(indices)] if sp.issparse(X): return [ inverse_vocabulary[X[i, :].nonzero()[1]].ravel() for i in range(n_samples) ] else: return [ inverse_vocabulary[np.flatnonzero(X[i, :])].ravel() for i in range(n_samples) ] def get_feature_names_out(self, input_features=None): """Get output feature names for transformation. Parameters ---------- input_features : array-like of str or None, default=None Not used, present here for API consistency by convention. Returns ------- feature_names_out : ndarray of str objects Transformed feature names. """ self._check_vocabulary() return np.asarray( [t for t, i in sorted(self.vocabulary_.items(), key=itemgetter(1))], dtype=object, ) def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.string = True tags.input_tags.two_d_array = False return tags def _make_int_array(): """Construct an array.array of a type suitable for scipy.sparse indices.""" return array.array(str("i")) class TfidfTransformer( OneToOneFeatureMixin, TransformerMixin, BaseEstimator, auto_wrap_output_keys=None ): """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. The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if ``smooth_idf=False``), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t. The effect of adding "1" to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored. (Note that the idf formula above differs from the standard textbook notation that defines the idf as idf(t) = log [ n / (df(t) + 1) ]). If ``smooth_idf=True`` (the default), the constant "1" is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1. Furthermore, the formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR as follows: Tf is "n" (natural) by default, "l" (logarithmic) when ``sublinear_tf=True``. Idf is "t" when use_idf is given, "n" (none) otherwise. Normalization is "c" (cosine) when ``norm='l2'``, "n" (none) when ``norm=None``. Read more in the :ref:`User Guide `. Parameters ---------- norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True 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 : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- idf_ : array of shape (n_features) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. .. versionadded:: 0.20 n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 1.0 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfVectorizer : Convert a collection of raw documents to a matrix of TF-IDF features. HashingVectorizer : Convert a collection of text documents to a matrix of token occurrences. References ---------- .. [Yates2011] R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68-74. .. [MRS2008] C.D. Manning, P. Raghavan and H. Schütze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120. Examples -------- >>> from sklearn.feature_extraction.text import TfidfTransformer >>> from sklearn.feature_extraction.text import CountVectorizer >>> from sklearn.pipeline import Pipeline >>> corpus = ['this is the first document', ... 'this document is the second document', ... 'and this is the third one', ... 'is this the first document'] >>> vocabulary = ['this', 'document', 'first', 'is', 'second', 'the', ... 'and', 'one'] >>> pipe = Pipeline([('count', CountVectorizer(vocabulary=vocabulary)), ... ('tfid', TfidfTransformer())]).fit(corpus) >>> pipe['count'].transform(corpus).toarray() array([[1, 1, 1, 1, 0, 1, 0, 0], [1, 2, 0, 1, 1, 1, 0, 0], [1, 0, 0, 1, 0, 1, 1, 1], [1, 1, 1, 1, 0, 1, 0, 0]]) >>> pipe['tfid'].idf_ array([1. , 1.22314355, 1.51082562, 1. , 1.91629073, 1. , 1.91629073, 1.91629073]) >>> pipe.transform(corpus).shape (4, 8) """ _parameter_constraints: dict = { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } 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 @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Learn the idf vector (global term weights). Parameters ---------- X : sparse matrix of shape (n_samples, n_features) A matrix of term/token counts. y : None This parameter is not needed to compute tf-idf. Returns ------- self : object Fitted transformer. """ # large sparse data is not supported for 32bit platforms because # _document_frequency uses np.bincount which works on arrays of # dtype NPY_INTP which is int32 for 32bit platforms. See #20923 X = validate_data( self, X, accept_sparse=("csr", "csc"), accept_large_sparse=not _IS_32BIT ) if not sp.issparse(X): X = sp.csr_matrix(X) dtype = X.dtype if X.dtype in (np.float64, np.float32) else np.float64 if self.use_idf: n_samples, _ = X.shape df = _document_frequency(X) df = df.astype(dtype, copy=False) # perform idf smoothing if required df += float(self.smooth_idf) n_samples += int(self.smooth_idf) # log+1 instead of log makes sure terms with zero idf don't get # suppressed entirely. # Force the dtype of `idf_` to be the same as `df`. In NumPy < 2, the dtype # was depending on the value of `n_samples`. self.idf_ = np.full_like(df, fill_value=n_samples, dtype=dtype) self.idf_ /= df # `np.log` preserves the dtype of `df` and thus `dtype`. np.log(self.idf_, out=self.idf_) self.idf_ += 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 of (n_samples, n_features) A matrix of term/token counts. copy : bool, default=True Whether to copy X and operate on the copy or perform in-place operations. `copy=False` will only be effective with CSR sparse matrix. Returns ------- vectors : sparse matrix of shape (n_samples, n_features) Tf-idf-weighted document-term matrix. """ check_is_fitted(self) X = validate_data( self, X, accept_sparse="csr", dtype=[np.float64, np.float32], copy=copy, reset=False, ) if not sp.issparse(X): X = sp.csr_matrix(X, dtype=X.dtype) if self.sublinear_tf: np.log(X.data, X.data) X.data += 1.0 if hasattr(self, "idf_"): # the columns of X (CSR matrix) can be accessed with `X.indices `and # multiplied with the corresponding `idf` value X.data *= self.idf_[X.indices] if self.norm is not None: X = normalize(X, norm=self.norm, copy=False) return X def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.sparse = True # FIXME: np.float16 could be preserved if _inplace_csr_row_normalize_l2 # accepted it. tags.transformer_tags.preserves_dtype = ["float64", "float32"] return tags class TfidfVectorizer(CountVectorizer): r"""Convert a collection of raw documents to a matrix of TF-IDF features. Equivalent to :class:`CountVectorizer` followed by :class:`TfidfTransformer`. For an example of usage, see :ref:`sphx_glr_auto_examples_text_plot_document_classification_20newsgroups.py`. For an efficiency comparison of the different feature extractors, see :ref:`sphx_glr_auto_examples_text_plot_hashing_vs_dict_vectorizer.py`. For an example of document clustering and comparison with :class:`~sklearn.feature_extraction.text.HashingVectorizer`, see :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`. Read more in the :ref:`User Guide `. Parameters ---------- input : {'filename', 'file', 'content'}, default='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 a 'read' method (file-like object) that is called to fetch the bytes in memory. - If `'content'`, the input is expected to be a sequence of items that can be of type string or byte. encoding : str, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode. decode_error : {'strict', 'ignore', 'replace'}, default='strict' Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given `encoding`. By default, it is 'strict', meaning that a UnicodeDecodeError will be raised. Other values are 'ignore' and 'replace'. strip_accents : {'ascii', 'unicode'} or callable, default=None Remove accents and perform other character normalization during the preprocessing step. 'ascii' is a fast method that only works on characters that have a direct ASCII mapping. 'unicode' is a slightly slower method that works on any characters. None (default) means no character normalization is performed. Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`. lowercase : bool, default=True Convert all characters to lowercase before tokenizing. preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer`` is not callable. tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``. analyzer : {'word', 'char', 'char_wb'} or callable, default='word' 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; n-grams at the edges of words are padded with space. If a callable is passed it is used to extract the sequence of features out of the raw, unprocessed input. .. versionchanged:: 0.21 Since v0.21, if ``input`` is ``'filename'`` or ``'file'``, the data is first read from the file and then passed to the given callable analyzer. stop_words : {'english'}, list, default=None 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. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if ``analyzer == 'word'``. If None, no stop words will be used. In this case, setting `max_df` to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. token_pattern : str, default=r"(?u)\\b\\w\\w+\\b" Regular expression denoting what constitutes a "token", only used if ``analyzer == 'word'``. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. ngram_range : tuple (min_n, max_n), default=(1, 1) 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. For example an ``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means unigrams and bigrams, and ``(2, 2)`` means only bigrams. Only applies if ``analyzer`` is not callable. max_df : float or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. min_df : float or int, default=1 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. max_features : int, default=None If not None, build a vocabulary that only consider the top `max_features` ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not None. vocabulary : Mapping or iterable, default=None 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 : bool, default=False If True, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set `binary` to True, `use_idf` to False and `norm` to None to get 0/1 outputs). dtype : dtype, default=float64 Type of the matrix returned by fit_transform() or transform(). norm : {'l1', 'l2'} or None, default='l2' Each output row will have unit norm, either: - 'l2': Sum of squares of vector elements is 1. The cosine similarity between two vectors is their dot product when l2 norm has been applied. - 'l1': Sum of absolute values of vector elements is 1. See :func:`~sklearn.preprocessing.normalize`. - None: No normalization. use_idf : bool, default=True Enable inverse-document-frequency reweighting. If False, idf(t) = 1. smooth_idf : bool, default=True 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 : bool, default=False Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf). Attributes ---------- vocabulary_ : dict A mapping of terms to feature indices. fixed_vocabulary_ : bool True if a fixed vocabulary of term to indices mapping is provided by the user. idf_ : array of shape (n_features,) The inverse document frequency (IDF) vector; only defined if ``use_idf`` is True. See Also -------- CountVectorizer : Transforms text into a sparse matrix of n-gram counts. TfidfTransformer : Performs the TF-IDF transformation from a provided matrix of counts. Examples -------- >>> from sklearn.feature_extraction.text import TfidfVectorizer >>> corpus = [ ... 'This is the first document.', ... 'This document is the second document.', ... 'And this is the third one.', ... 'Is this the first document?', ... ] >>> vectorizer = TfidfVectorizer() >>> X = vectorizer.fit_transform(corpus) >>> vectorizer.get_feature_names_out() array(['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'], ...) >>> print(X.shape) (4, 9) """ _parameter_constraints: dict = {**CountVectorizer._parameter_constraints} _parameter_constraints.update( { "norm": [StrOptions({"l1", "l2"}), None], "use_idf": ["boolean"], "smooth_idf": ["boolean"], "sublinear_tf": ["boolean"], } ) def __init__( self, *, input="content", encoding="utf-8", decode_error="strict", strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer="word", stop_words=None, token_pattern=r"(?u)\b\w\w+\b", ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=np.float64, norm="l2", use_idf=True, smooth_idf=True, sublinear_tf=False, ): super().__init__( input=input, encoding=encoding, decode_error=decode_error, strip_accents=strip_accents, lowercase=lowercase, preprocessor=preprocessor, tokenizer=tokenizer, analyzer=analyzer, stop_words=stop_words, token_pattern=token_pattern, ngram_range=ngram_range, max_df=max_df, min_df=min_df, max_features=max_features, vocabulary=vocabulary, binary=binary, dtype=dtype, ) self.norm = norm self.use_idf = use_idf self.smooth_idf = smooth_idf self.sublinear_tf = sublinear_tf # Broadcast the TF-IDF parameters to the underlying transformer instance # for easy grid search and repr @property def idf_(self): """Inverse document frequency vector, only defined if `use_idf=True`. Returns ------- ndarray of shape (n_features,) """ if not hasattr(self, "_tfidf"): raise NotFittedError( f"{self.__class__.__name__} is not fitted yet. Call 'fit' with " "appropriate arguments before using this attribute." ) return self._tfidf.idf_ @idf_.setter def idf_(self, value): if not self.use_idf: raise ValueError("`idf_` cannot be set when `user_idf=False`.") if not hasattr(self, "_tfidf"): # We should support transferring `idf_` from another `TfidfTransformer` # and therefore, we need to create the transformer instance it does not # exist yet. self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) self._validate_vocabulary() if hasattr(self, "vocabulary_"): if len(self.vocabulary_) != len(value): raise ValueError( "idf length = %d must be equal to vocabulary size = %d" % (len(value), len(self.vocabulary)) ) self._tfidf.idf_ = value def _check_params(self): if self.dtype not in FLOAT_DTYPES: warnings.warn( "Only {} 'dtype' should be used. {} 'dtype' will " "be converted to np.float64.".format(FLOAT_DTYPES, self.dtype), UserWarning, ) @_fit_context(prefer_skip_nested_validation=True) def fit(self, raw_documents, y=None): """Learn vocabulary and idf from training set. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is not needed to compute tfidf. Returns ------- self : object Fitted vectorizer. """ self._check_params() self._warn_for_unused_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().fit_transform(raw_documents) self._tfidf.fit(X) return self def fit_transform(self, raw_documents, y=None): """Learn vocabulary and idf, return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. y : None This parameter is ignored. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ self._check_params() self._tfidf = TfidfTransformer( norm=self.norm, use_idf=self.use_idf, smooth_idf=self.smooth_idf, sublinear_tf=self.sublinear_tf, ) X = super().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): """Transform documents to document-term matrix. Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform). Parameters ---------- raw_documents : iterable An iterable which generates either str, unicode or file objects. Returns ------- X : sparse matrix of (n_samples, n_features) Tf-idf-weighted document-term matrix. """ check_is_fitted(self, msg="The TF-IDF vectorizer is not fitted") X = super().transform(raw_documents) return self._tfidf.transform(X, copy=False) def __sklearn_tags__(self): tags = super().__sklearn_tags__() tags.input_tags.string = True tags.input_tags.two_d_array = False tags._skip_test = True return tags