1875 lines
69 KiB
Python
1875 lines
69 KiB
Python
# -*- coding: utf-8 -*-
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# Authors: Olivier Grisel <olivier.grisel@ensta.org>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Lars Buitinck
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# Robert Layton <robertlayton@gmail.com>
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# Jochen Wersdörfer <jochen@wersdoerfer.de>
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# Roman Sinayev <roman.sinayev@gmail.com>
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#
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# License: BSD 3 clause
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"""
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The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to
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build feature vectors from text documents.
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"""
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import array
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from collections import defaultdict
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from collections.abc import Mapping
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from functools import partial
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import numbers
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from operator import itemgetter
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import re
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import unicodedata
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import warnings
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import numpy as np
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import scipy.sparse as sp
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from ..base import BaseEstimator, TransformerMixin
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from ..preprocessing import normalize
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from ._hash import FeatureHasher
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from ._stop_words import ENGLISH_STOP_WORDS
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from ..utils.validation import check_is_fitted, check_array, FLOAT_DTYPES
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from ..utils import _IS_32BIT
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from ..utils.fixes import _astype_copy_false
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from ..exceptions import NotFittedError
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from ..utils.validation import _deprecate_positional_args
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__all__ = ['HashingVectorizer',
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'CountVectorizer',
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'ENGLISH_STOP_WORDS',
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'TfidfTransformer',
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'TfidfVectorizer',
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'strip_accents_ascii',
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'strip_accents_unicode',
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'strip_tags']
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def _preprocess(doc, accent_function=None, lower=False):
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"""Chain together an optional series of text preprocessing steps to
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apply to a document.
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Parameters
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----------
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doc: str
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The string to preprocess
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accent_function: callable, default=None
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Function for handling accented characters. Common strategies include
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normalizing and removing.
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lower: bool, default=False
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Whether to use str.lower to lowercase all fo the text
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Returns
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-------
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doc: str
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preprocessed string
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"""
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if lower:
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doc = doc.lower()
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if accent_function is not None:
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doc = accent_function(doc)
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return doc
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def _analyze(doc, analyzer=None, tokenizer=None, ngrams=None,
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preprocessor=None, decoder=None, stop_words=None):
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"""Chain together an optional series of text processing steps to go from
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a single document to ngrams, with or without tokenizing or preprocessing.
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If analyzer is used, only the decoder argument is used, as the analyzer is
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intended to replace the preprocessor, tokenizer, and ngrams steps.
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Parameters
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----------
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analyzer: callable, default=None
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tokenizer: callable, default=None
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ngrams: callable, default=None
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preprocessor: callable, default=None
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decoder: callable, default=None
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stop_words: list, default=None
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Returns
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-------
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ngrams: list
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A sequence of tokens, possibly with pairs, triples, etc.
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"""
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if decoder is not None:
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doc = decoder(doc)
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if analyzer is not None:
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doc = analyzer(doc)
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else:
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if preprocessor is not None:
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doc = preprocessor(doc)
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if tokenizer is not None:
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doc = tokenizer(doc)
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if ngrams is not None:
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if stop_words is not None:
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doc = ngrams(doc, stop_words)
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else:
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doc = ngrams(doc)
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return doc
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def strip_accents_unicode(s):
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"""Transform accentuated unicode symbols into their simple counterpart
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Warning: the python-level loop and join operations make this
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implementation 20 times slower than the strip_accents_ascii basic
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normalization.
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Parameters
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----------
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s : string
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The string to strip
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See Also
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--------
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strip_accents_ascii : Remove accentuated char for any unicode symbol that
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has a direct ASCII equivalent.
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"""
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try:
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# If `s` is ASCII-compatible, then it does not contain any accented
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# characters and we can avoid an expensive list comprehension
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s.encode("ASCII", errors="strict")
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return s
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except UnicodeEncodeError:
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normalized = unicodedata.normalize('NFKD', s)
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return ''.join([c for c in normalized if not unicodedata.combining(c)])
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def strip_accents_ascii(s):
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"""Transform accentuated unicode symbols into ascii or nothing
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Warning: this solution is only suited for languages that have a direct
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transliteration to ASCII symbols.
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Parameters
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----------
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s : string
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The string to strip
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See Also
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--------
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strip_accents_unicode : Remove accentuated char for any unicode symbol.
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"""
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nkfd_form = unicodedata.normalize('NFKD', s)
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return nkfd_form.encode('ASCII', 'ignore').decode('ASCII')
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def strip_tags(s):
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"""Basic regexp based HTML / XML tag stripper function
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For serious HTML/XML preprocessing you should rather use an external
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library such as lxml or BeautifulSoup.
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Parameters
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----------
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s : string
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The string to strip
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"""
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return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s)
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def _check_stop_list(stop):
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if stop == "english":
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return ENGLISH_STOP_WORDS
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elif isinstance(stop, str):
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raise ValueError("not a built-in stop list: %s" % stop)
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elif stop is None:
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return None
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else: # assume it's a collection
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return frozenset(stop)
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class _VectorizerMixin:
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"""Provides common code for text vectorizers (tokenization logic)."""
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_white_spaces = re.compile(r"\s\s+")
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def decode(self, doc):
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"""Decode the input into a string of unicode symbols.
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The decoding strategy depends on the vectorizer parameters.
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Parameters
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----------
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doc : str
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The string to decode.
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Returns
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-------
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doc: str
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A string of unicode symbols.
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"""
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if self.input == 'filename':
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with open(doc, 'rb') as fh:
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doc = fh.read()
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elif self.input == 'file':
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doc = doc.read()
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if isinstance(doc, bytes):
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doc = doc.decode(self.encoding, self.decode_error)
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if doc is np.nan:
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raise ValueError("np.nan is an invalid document, expected byte or "
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"unicode string.")
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return doc
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def _word_ngrams(self, tokens, stop_words=None):
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"""Turn tokens into a sequence of n-grams after stop words filtering"""
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# handle stop words
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if stop_words is not None:
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tokens = [w for w in tokens if w not in stop_words]
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# handle token n-grams
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min_n, max_n = self.ngram_range
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if max_n != 1:
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original_tokens = tokens
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if min_n == 1:
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# no need to do any slicing for unigrams
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# just iterate through the original tokens
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tokens = list(original_tokens)
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min_n += 1
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else:
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tokens = []
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n_original_tokens = len(original_tokens)
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# bind method outside of loop to reduce overhead
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tokens_append = tokens.append
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space_join = " ".join
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for n in range(min_n,
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min(max_n + 1, n_original_tokens + 1)):
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for i in range(n_original_tokens - n + 1):
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tokens_append(space_join(original_tokens[i: i + n]))
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return tokens
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def _char_ngrams(self, text_document):
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"""Tokenize text_document into a sequence of character n-grams"""
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# normalize white spaces
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text_document = self._white_spaces.sub(" ", text_document)
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text_len = len(text_document)
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min_n, max_n = self.ngram_range
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if min_n == 1:
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# no need to do any slicing for unigrams
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# iterate through the string
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ngrams = list(text_document)
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min_n += 1
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else:
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ngrams = []
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# bind method outside of loop to reduce overhead
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ngrams_append = ngrams.append
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for n in range(min_n, min(max_n + 1, text_len + 1)):
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for i in range(text_len - n + 1):
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ngrams_append(text_document[i: i + n])
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return ngrams
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def _char_wb_ngrams(self, text_document):
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"""Whitespace sensitive char-n-gram tokenization.
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Tokenize text_document into a sequence of character n-grams
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operating only inside word boundaries. n-grams at the edges
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of words are padded with space."""
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# normalize white spaces
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text_document = self._white_spaces.sub(" ", text_document)
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min_n, max_n = self.ngram_range
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ngrams = []
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# bind method outside of loop to reduce overhead
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ngrams_append = ngrams.append
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for w in text_document.split():
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w = ' ' + w + ' '
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w_len = len(w)
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for n in range(min_n, max_n + 1):
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offset = 0
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ngrams_append(w[offset:offset + n])
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while offset + n < w_len:
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offset += 1
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ngrams_append(w[offset:offset + n])
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if offset == 0: # count a short word (w_len < n) only once
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break
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return ngrams
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def build_preprocessor(self):
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"""Return a function to preprocess the text before tokenization.
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Returns
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-------
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preprocessor: callable
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A function to preprocess the text before tokenization.
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"""
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if self.preprocessor is not None:
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return self.preprocessor
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# accent stripping
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if not self.strip_accents:
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strip_accents = None
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elif callable(self.strip_accents):
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strip_accents = self.strip_accents
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elif self.strip_accents == 'ascii':
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strip_accents = strip_accents_ascii
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elif self.strip_accents == 'unicode':
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strip_accents = strip_accents_unicode
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else:
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raise ValueError('Invalid value for "strip_accents": %s' %
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self.strip_accents)
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return partial(
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_preprocess, accent_function=strip_accents, lower=self.lowercase
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)
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def build_tokenizer(self):
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"""Return a function that splits a string into a sequence of tokens.
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Returns
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-------
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tokenizer: callable
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A function to split a string into a sequence of tokens.
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"""
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if self.tokenizer is not None:
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return self.tokenizer
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token_pattern = re.compile(self.token_pattern)
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if token_pattern.groups > 1:
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raise ValueError(
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"More than 1 capturing group in token pattern. Only a single "
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"group should be captured."
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)
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return token_pattern.findall
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def get_stop_words(self):
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"""Build or fetch the effective stop words list.
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Returns
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-------
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stop_words: list or None
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A list of stop words.
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"""
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return _check_stop_list(self.stop_words)
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def _check_stop_words_consistency(self, stop_words, preprocess, tokenize):
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"""Check if stop words are consistent
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Returns
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-------
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is_consistent : True if stop words are consistent with the preprocessor
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and tokenizer, False if they are not, None if the check
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was previously performed, "error" if it could not be
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performed (e.g. because of the use of a custom
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preprocessor / tokenizer)
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"""
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if id(self.stop_words) == getattr(self, '_stop_words_id', None):
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# Stop words are were previously validated
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return None
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# NB: stop_words is validated, unlike self.stop_words
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try:
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inconsistent = set()
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for w in stop_words or ():
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tokens = list(tokenize(preprocess(w)))
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for token in tokens:
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if token not in stop_words:
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inconsistent.add(token)
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self._stop_words_id = id(self.stop_words)
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if inconsistent:
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warnings.warn('Your stop_words may be inconsistent with '
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'your preprocessing. Tokenizing the stop '
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'words generated tokens %r not in '
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'stop_words.' % sorted(inconsistent))
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return not inconsistent
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except Exception:
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# Failed to check stop words consistency (e.g. because a custom
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# preprocessor or tokenizer was used)
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self._stop_words_id = id(self.stop_words)
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return 'error'
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def build_analyzer(self):
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"""Return a callable that handles preprocessing, tokenization
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and n-grams generation.
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Returns
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-------
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analyzer: callable
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A function to handle preprocessing, tokenization
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and n-grams generation.
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"""
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if callable(self.analyzer):
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return partial(
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_analyze, analyzer=self.analyzer, decoder=self.decode
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)
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preprocess = self.build_preprocessor()
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if self.analyzer == 'char':
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return partial(_analyze, ngrams=self._char_ngrams,
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preprocessor=preprocess, decoder=self.decode)
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elif self.analyzer == 'char_wb':
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return partial(_analyze, ngrams=self._char_wb_ngrams,
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preprocessor=preprocess, decoder=self.decode)
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elif self.analyzer == 'word':
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stop_words = self.get_stop_words()
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tokenize = self.build_tokenizer()
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self._check_stop_words_consistency(stop_words, preprocess,
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tokenize)
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return partial(_analyze, ngrams=self._word_ngrams,
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tokenizer=tokenize, preprocessor=preprocess,
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decoder=self.decode, stop_words=stop_words)
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else:
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raise ValueError('%s is not a valid tokenization scheme/analyzer' %
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self.analyzer)
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def _validate_vocabulary(self):
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vocabulary = self.vocabulary
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if vocabulary is not None:
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if isinstance(vocabulary, set):
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vocabulary = sorted(vocabulary)
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if not isinstance(vocabulary, Mapping):
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vocab = {}
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for i, t in enumerate(vocabulary):
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if vocab.setdefault(t, i) != i:
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msg = "Duplicate term in vocabulary: %r" % t
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raise ValueError(msg)
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vocabulary = vocab
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else:
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indices = set(vocabulary.values())
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if len(indices) != len(vocabulary):
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raise ValueError("Vocabulary contains repeated indices.")
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for i in range(len(vocabulary)):
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if i not in indices:
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msg = ("Vocabulary of size %d doesn't contain index "
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"%d." % (len(vocabulary), i))
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raise ValueError(msg)
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if not vocabulary:
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raise ValueError("empty vocabulary passed to fit")
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self.fixed_vocabulary_ = True
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self.vocabulary_ = dict(vocabulary)
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else:
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self.fixed_vocabulary_ = False
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def _check_vocabulary(self):
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"""Check if vocabulary is empty or missing (not fitted)"""
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if not hasattr(self, 'vocabulary_'):
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self._validate_vocabulary()
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if not self.fixed_vocabulary_:
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raise NotFittedError("Vocabulary not fitted or provided")
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if len(self.vocabulary_) == 0:
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raise ValueError("Vocabulary is empty")
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def _validate_params(self):
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"""Check validity of ngram_range parameter"""
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min_n, max_m = self.ngram_range
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if min_n > max_m:
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raise ValueError(
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"Invalid value for ngram_range=%s "
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"lower boundary larger than the upper boundary."
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% str(self.ngram_range))
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def _warn_for_unused_params(self):
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if self.tokenizer is not None and self.token_pattern is not None:
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warnings.warn("The parameter 'token_pattern' will not be used"
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" since 'tokenizer' is not None'")
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if self.preprocessor is not None and callable(self.analyzer):
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warnings.warn("The parameter 'preprocessor' will not be used"
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" since 'analyzer' is callable'")
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if (self.ngram_range != (1, 1) and self.ngram_range is not None
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and callable(self.analyzer)):
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warnings.warn("The parameter 'ngram_range' will not be used"
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" since 'analyzer' is callable'")
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if self.analyzer != 'word' or callable(self.analyzer):
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if self.stop_words is not None:
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warnings.warn("The parameter 'stop_words' will not be used"
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" since 'analyzer' != 'word'")
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if self.token_pattern is not None and \
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self.token_pattern != r"(?u)\b\w\w+\b":
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warnings.warn("The parameter 'token_pattern' will not be used"
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" since 'analyzer' != 'word'")
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if self.tokenizer is not None:
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warnings.warn("The parameter 'tokenizer' will not be used"
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" since 'analyzer' != 'word'")
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|
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class HashingVectorizer(TransformerMixin, _VectorizerMixin, BaseEstimator):
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r"""Convert a collection of text documents to a matrix of token occurrences
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It turns a collection of text documents into a scipy.sparse matrix holding
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token occurrence counts (or binary occurrence information), possibly
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normalized as token frequencies if norm='l1' or projected on the euclidean
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unit sphere if norm='l2'.
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This text vectorizer implementation uses the hashing trick to find the
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token string name to feature integer index mapping.
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This strategy has several advantages:
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- it is very low memory scalable to large datasets as there is no need to
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store a vocabulary dictionary in memory
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- it is fast to pickle and un-pickle as it holds no state besides the
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constructor parameters
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- it can be used in a streaming (partial fit) or parallel pipeline as there
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is no state computed during fit.
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There are also a couple of cons (vs using a CountVectorizer with an
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in-memory vocabulary):
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- there is no way to compute the inverse transform (from feature indices to
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string feature names) which can be a problem when trying to introspect
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which features are most important to a model.
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- there can be collisions: distinct tokens can be mapped to the same
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feature index. However in practice this is rarely an issue if n_features
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is large enough (e.g. 2 ** 18 for text classification problems).
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- no IDF weighting as this would render the transformer stateful.
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|
The hash function employed is the signed 32-bit version of Murmurhash3.
|
|
|
|
Read more in the :ref:`User Guide <text_feature_extraction>`.
|
|
|
|
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 : string, 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'}, 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
|
|
an direct ASCII mapping.
|
|
'unicode' is a slightly slower method that works on any characters.
|
|
None (default) does nothing.
|
|
|
|
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, 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().
|
|
|
|
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)
|
|
|
|
See Also
|
|
--------
|
|
CountVectorizer, TfidfVectorizer
|
|
|
|
"""
|
|
@_deprecate_positional_args
|
|
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
|
|
|
|
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.
|
|
|
|
Parameters
|
|
----------
|
|
X : ndarray of shape [n_samples, n_features]
|
|
Training data.
|
|
"""
|
|
return self
|
|
|
|
def fit(self, X, y=None):
|
|
"""Does nothing: this transformer is stateless.
|
|
|
|
Parameters
|
|
----------
|
|
X : ndarray of shape [n_samples, n_features]
|
|
Training data.
|
|
"""
|
|
# 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_params()
|
|
|
|
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_params()
|
|
|
|
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 _more_tags(self):
|
|
return {'X_types': ['string']}
|
|
|
|
|
|
def _document_frequency(X):
|
|
"""Count the number of non-zero values for each feature in sparse X."""
|
|
if sp.isspmatrix_csr(X):
|
|
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.
|
|
|
|
Read more in the :ref:`User Guide <text_feature_extraction>`.
|
|
|
|
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 : string, 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'}, 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
|
|
an direct ASCII mapping.
|
|
'unicode' is a slightly slower method that works on any characters.
|
|
None (default) does nothing.
|
|
|
|
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. max_df can be set to a value
|
|
in the range [0.7, 1.0) to automatically detect and filter stop
|
|
words based on intra corpus document frequency of terms.
|
|
|
|
token_pattern : str, 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.
|
|
|
|
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 : type, 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_: boolean
|
|
True if a fixed vocabulary of term to indices mapping
|
|
is provided by the user
|
|
|
|
stop_words_ : set
|
|
Terms that were ignored because they either:
|
|
|
|
- occurred in too many documents (`max_df`)
|
|
- occurred in too few documents (`min_df`)
|
|
- were cut off by feature selection (`max_features`).
|
|
|
|
This is only available if no vocabulary was given.
|
|
|
|
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)
|
|
>>> print(vectorizer.get_feature_names())
|
|
['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)
|
|
>>> print(vectorizer2.get_feature_names())
|
|
['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]]
|
|
|
|
See Also
|
|
--------
|
|
HashingVectorizer, TfidfVectorizer
|
|
|
|
Notes
|
|
-----
|
|
The ``stop_words_`` attribute can get large and increase the model size
|
|
when pickling. This attribute is provided only for introspection and can
|
|
be safely removed using delattr or set to None before pickling.
|
|
"""
|
|
@_deprecate_positional_args
|
|
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
|
|
if max_df < 0 or min_df < 0:
|
|
raise ValueError("negative value for max_df or min_df")
|
|
self.max_features = max_features
|
|
if max_features is not None:
|
|
if (not isinstance(max_features, numbers.Integral) or
|
|
max_features <= 0):
|
|
raise ValueError(
|
|
"max_features=%r, neither a positive integer nor None"
|
|
% 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
|
|
removed_terms = set()
|
|
for term, old_index in list(vocabulary.items()):
|
|
if mask[old_index]:
|
|
vocabulary[term] = new_indices[old_index]
|
|
else:
|
|
del vocabulary[term]
|
|
removed_terms.add(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], removed_terms
|
|
|
|
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 yields either str, unicode or file objects.
|
|
|
|
Returns
|
|
-------
|
|
self
|
|
"""
|
|
self._warn_for_unused_params()
|
|
self.fit_transform(raw_documents)
|
|
return self
|
|
|
|
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 yields either str, unicode or file objects.
|
|
|
|
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_params()
|
|
self._validate_vocabulary()
|
|
max_df = self.max_df
|
|
min_df = self.min_df
|
|
max_features = self.max_features
|
|
|
|
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, numbers.Integral)
|
|
else max_df * n_doc)
|
|
min_doc_count = (min_df
|
|
if isinstance(min_df, numbers.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.stop_words_ = 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 yields 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(self):
|
|
"""Array mapping from feature integer indices to feature name.
|
|
|
|
Returns
|
|
-------
|
|
feature_names : list
|
|
A list of feature names.
|
|
"""
|
|
|
|
self._check_vocabulary()
|
|
|
|
return [t for t, i in sorted(self.vocabulary_.items(),
|
|
key=itemgetter(1))]
|
|
|
|
def _more_tags(self):
|
|
return {'X_types': ['string']}
|
|
|
|
|
|
def _make_int_array():
|
|
"""Construct an array.array of a type suitable for scipy.sparse indices."""
|
|
return array.array(str("i"))
|
|
|
|
|
|
class TfidfTransformer(TransformerMixin, BaseEstimator):
|
|
"""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 <text_feature_extraction>`.
|
|
|
|
Parameters
|
|
----------
|
|
norm : {'l1', 'l2'}, 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:`preprocessing.normalize`
|
|
|
|
use_idf : bool, default=True
|
|
Enable inverse-document-frequency reweighting.
|
|
|
|
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
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.feature_extraction.text import TfidfTransformer
|
|
>>> from sklearn.feature_extraction.text import CountVectorizer
|
|
>>> from sklearn.pipeline import Pipeline
|
|
>>> import numpy as np
|
|
>>> 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)
|
|
|
|
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.
|
|
"""
|
|
@_deprecate_positional_args
|
|
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 of shape n_samples, n_features)
|
|
A matrix of term/token counts.
|
|
"""
|
|
X = check_array(X, accept_sparse=('csr', 'csc'))
|
|
if not sp.issparse(X):
|
|
X = sp.csr_matrix(X)
|
|
dtype = X.dtype if X.dtype in FLOAT_DTYPES else np.float64
|
|
|
|
if self.use_idf:
|
|
n_samples, n_features = X.shape
|
|
df = _document_frequency(X)
|
|
df = df.astype(dtype, **_astype_copy_false(df))
|
|
|
|
# perform idf smoothing if required
|
|
df += int(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.
|
|
idf = np.log(n_samples / df) + 1
|
|
self._idf_diag = sp.diags(idf, offsets=0,
|
|
shape=(n_features, n_features),
|
|
format='csr',
|
|
dtype=dtype)
|
|
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
vectors : sparse matrix of shape (n_samples, n_features)
|
|
"""
|
|
X = check_array(X, accept_sparse='csr', dtype=FLOAT_DTYPES, copy=copy)
|
|
if not sp.issparse(X):
|
|
X = sp.csr_matrix(X, dtype=np.float64)
|
|
|
|
n_samples, n_features = X.shape
|
|
|
|
if self.sublinear_tf:
|
|
np.log(X.data, X.data)
|
|
X.data += 1
|
|
|
|
if self.use_idf:
|
|
# idf_ being a property, the automatic attributes detection
|
|
# does not work as usual and we need to specify the attribute
|
|
# name:
|
|
check_is_fitted(self, attributes=["idf_"],
|
|
msg='idf vector is 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 _idf_diag is not set, this will raise an attribute error,
|
|
# which means hasattr(self, "idf_") is False
|
|
return np.ravel(self._idf_diag.sum(axis=0))
|
|
|
|
@idf_.setter
|
|
def idf_(self, value):
|
|
value = np.asarray(value, dtype=np.float64)
|
|
n_features = value.shape[0]
|
|
self._idf_diag = sp.spdiags(value, diags=0, m=n_features,
|
|
n=n_features, format='csr')
|
|
|
|
def _more_tags(self):
|
|
return {'X_types': 'sparse'}
|
|
|
|
|
|
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`.
|
|
|
|
Read more in the :ref:`User Guide <text_feature_extraction>`.
|
|
|
|
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'}, 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
|
|
an direct ASCII mapping.
|
|
'unicode' is a slightly slower method that works on any characters.
|
|
None (default) does nothing.
|
|
|
|
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. max_df can be set to a value
|
|
in the range [0.7, 1.0) to automatically detect and filter stop
|
|
words based on intra corpus document frequency of terms.
|
|
|
|
token_pattern : 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.
|
|
|
|
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 idf and normalization to False to get 0/1 outputs).
|
|
|
|
dtype : dtype, default=float64
|
|
Type of the matrix returned by fit_transform() or transform().
|
|
|
|
norm : {'l1', 'l2'}, 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:`preprocessing.normalize`.
|
|
|
|
use_idf : bool, default=True
|
|
Enable inverse-document-frequency reweighting.
|
|
|
|
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.
|
|
|
|
stop_words_ : set
|
|
Terms that were ignored because they either:
|
|
|
|
- occurred in too many documents (`max_df`)
|
|
- occurred in too few documents (`min_df`)
|
|
- were cut off by feature selection (`max_features`).
|
|
|
|
This is only available if no vocabulary was given.
|
|
|
|
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.
|
|
|
|
Notes
|
|
-----
|
|
The ``stop_words_`` attribute can get large and increase the model size
|
|
when pickling. This attribute is provided only for introspection and can
|
|
be safely removed using delattr or set to None before pickling.
|
|
|
|
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)
|
|
>>> print(vectorizer.get_feature_names())
|
|
['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
|
|
>>> print(X.shape)
|
|
(4, 9)
|
|
"""
|
|
@_deprecate_positional_args
|
|
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._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
|
|
|
|
@property
|
|
def idf_(self):
|
|
return self._tfidf.idf_
|
|
|
|
@idf_.setter
|
|
def idf_(self, value):
|
|
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)
|
|
|
|
def fit(self, raw_documents, y=None):
|
|
"""Learn vocabulary and idf from training set.
|
|
|
|
Parameters
|
|
----------
|
|
raw_documents : iterable
|
|
An iterable which yields 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()
|
|
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 yields 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()
|
|
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 yields 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 _more_tags(self):
|
|
return {'X_types': ['string'], '_skip_test': True}
|