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ai-content-maker/.venv/Lib/site-packages/sklearn/feature_extraction/text.py

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# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gmail.com>
#
# License: BSD 3 clause
"""
The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to
build feature vectors from text documents.
"""
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 ..base import BaseEstimator, OneToOneFeatureMixin, TransformerMixin, _fit_context
from ..exceptions import NotFittedError
from ..preprocessing import normalize
from ..utils import _IS_32BIT
from ..utils._param_validation import HasMethods, Interval, RealNotInt, StrOptions
from ..utils.validation import FLOAT_DTYPES, check_array, check_is_fitted
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`.
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'} 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 _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.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 <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'} 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.
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
--------
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.
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 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]]
"""
_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
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 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.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 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 _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(
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 <text_feature_extraction>`.
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 = self._validate_data(
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 FLOAT_DTYPES else np.float64
if self.use_idf:
n_samples, n_features = X.shape
df = _document_frequency(X)
df = df.astype(dtype, copy=False)
# 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)
Tf-idf-weighted document-term matrix.
"""
X = self._validate_data(
X, accept_sparse="csr", dtype=FLOAT_DTYPES, copy=copy, reset=False
)
if not sp.issparse(X):
X = sp.csr_matrix(X, dtype=np.float64)
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")
X = X @ self._idf_diag
if self.norm is not None:
X = normalize(X, norm=self.norm, copy=False)
return X
@property
def idf_(self):
"""Inverse document frequency vector, only defined if `use_idf=True`.
Returns
-------
ndarray of shape (n_features,)
"""
# 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": ["2darray", "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`.
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`.
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'} 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.
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)
>>> 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 _more_tags(self):
return {"X_types": ["string"], "_skip_test": True}