# -*- coding: utf-8 -*-
# coding=utf-8
from __future__ import unicode_literals
import array
from collections import Mapping, defaultdict
import numbers
from operator import itemgetter
import re
import unicodedata
import numpy as np
import scipy.sparse as sp
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.externals import six
from sklearn.externals.six.moves import xrange
from sklearn.preprocessing import normalize
from sklearn.feature_extraction.hashing import FeatureHasher
from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS
from sklearn.utils import deprecated
from sklearn.utils.fixes import frombuffer_empty, bincount
from sklearn.utils.validation import check_is_fitted
from sklearn import preprocessing
__all__ = ['CountVectorizer',
'ENGLISH_STOP_WORDS',
'TfidfTransformer',
'TfidfVectorizer',
'strip_accents_ascii',
'strip_accents_unicode',
'strip_tags']
def strip_accents_unicode(s):
"""Transform accentuated unicode symbols into their simple counterpart
Warning: the python-level loop and join operations make this
implementation 20 times slower than the strip_accents_ascii basic
normalization.
See also
--------
strip_accents_ascii
Remove accentuated char for any unicode symbol that has a direct
ASCII equivalent.
"""
normalized = unicodedata.normalize('NFKD', s)
if normalized == s:
return s
else:
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.
See also
--------
strip_accents_unicode
Remove accentuated char for any unicode symbol.
"""
nkfd_form = unicodedata.normalize('NFKD', s)
return nkfd_form.encode('ASCII', 'ignore').decode('ASCII')
def strip_tags(s):
"""Basic regexp based HTML / XML tag stripper function
For serious HTML/XML preprocessing you should rather use an external
library such as lxml or BeautifulSoup.
"""
return re.compile(r"<([^>]+)>", flags=re.UNICODE).sub(" ", s)
def _check_stop_list(stop):
if stop == "english":
return ENGLISH_STOP_WORDS
elif isinstance(stop, six.string_types):
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(object):
"""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.
"""
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
tokens = []
n_original_tokens = len(original_tokens)
for n in xrange(min_n,
min(max_n + 1, n_original_tokens + 1)):
for i in xrange(n_original_tokens - n + 1):
tokens.append(" ".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)
ngrams = []
min_n, max_n = self.ngram_range
for n in xrange(min_n, min(max_n + 1, text_len + 1)):
for i in xrange(text_len - n + 1):
ngrams.append(text_document[i: i + n])
return ngrams
def _char_wb_ngrams(self, text_document):
"""Whitespace sensitive char-n-gram tokenization.
Tokenize text_document into a sequence of character n-grams
excluding any whitespace (operating only inside word boundaries)"""
# normalize white spaces
text_document = self._white_spaces.sub(" ", text_document)
min_n, max_n = self.ngram_range
ngrams = []
for w in text_document.split():
w = ' ' + w + ' '
w_len = len(w)
for n in xrange(min_n, max_n + 1):
offset = 0
ngrams.append(w[offset:offset + n])
while offset + n < w_len:
offset += 1
ngrams.append(w[offset:offset + n])
if offset == 0: # count a short word (w_len < n) only once
break
return ngrams
def build_preprocessor(self):
"""Return a function to preprocess the text before tokenization"""
if self.preprocessor is not None:
return self.preprocessor
# unfortunately python functools package does not have an efficient
# `compose` function that would have allowed us to chain a dynamic
# number of functions. However the cost of a lambda call is a few
# hundreds of nanoseconds which is negligible when compared to the
# cost of tokenizing a string of 1000 chars for instance.
noop = lambda x: x
# accent stripping
if not self.strip_accents:
strip_accents = noop
elif 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)
if self.lowercase:
return lambda x: strip_accents(x.lower())
else:
return strip_accents
def build_tokenizer(self):
"""Return a function that splits a string into a sequence of tokens"""
if self.tokenizer is not None:
return self.tokenizer
token_pattern = re.compile(self.token_pattern)
return lambda doc: token_pattern.findall(doc)
def get_stop_words(self):
"""Build or fetch the effective stop words list"""
return _check_stop_list(self.stop_words)
def build_analyzer(self):
"""Return a callable that handles preprocessing and tokenization"""
if callable(self.analyzer):
return self.analyzer
preprocess = self.build_preprocessor()
if self.analyzer == 'char':
return lambda doc: self._char_ngrams(preprocess(self.decode(doc)))
elif self.analyzer == 'char_wb':
return lambda doc: self._char_wb_ngrams(
preprocess(self.decode(doc)))
elif self.analyzer == 'word':
stop_words = self.get_stop_words()
tokenize = self.build_tokenizer()
return lambda doc: self._word_ngrams(
tokenize(preprocess(self.decode(doc))), stop_words)
else:
raise ValueError('%s is not a valid tokenization scheme/analyzer' %
self.analyzer)
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(six.itervalues(vocabulary))
if len(indices) != len(vocabulary):
raise ValueError("Vocabulary contains repeated indices.")
for i in xrange(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 fit-ed)"""
msg = "%(name)s - Vocabulary wasn't fitted."
check_is_fitted(self, 'vocabulary_', msg=msg),
if len(self.vocabulary_) == 0:
raise ValueError("Vocabulary is empty")
class HashingVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a collection of text documents to a matrix of token occurrences
It turns a collection of text documents into a scipy.sparse matrix holding
token occurrence counts (or binary occurrence information), possibly
normalized as token frequencies if norm='l1' or projected on the euclidean
unit sphere if norm='l2'.
This text vectorizer implementation uses the hashing trick to find the
token string name to feature integer index mapping.
This strategy has several 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.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, default='utf-8'
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
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', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char', 'char_wb'} or callable
Whether the feature should be made of word or character n-grams.
Option 'char_wb' creates character n-grams only from text inside
word boundaries.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
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.
stop_words : string {'english'}, list, or None (default)
If 'english', a built-in stop word list for English is used.
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'``.
lowercase : boolean, default=True
Convert all characters to lowercase before tokenizing.
token_pattern : string
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).
n_features : integer, 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.
norm : 'l1', 'l2' or None, optional
Norm used to normalize term vectors. None for no normalization.
binary: boolean, 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, optional
Type of the matrix returned by fit_transform() or transform().
non_negative : boolean, default=False
Whether output matrices should contain non-negative values only;
effectively calls abs on the matrix prior to returning it.
When True, output values can be interpreted as frequencies.
When False, output values will have expected value zero.
See also
--------
CountVectorizer, TfidfVectorizer
"""
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', non_negative=False,
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.non_negative = non_negative
self.dtype = dtype
def partial_fit(self, X, y=None):
"""Does nothing: this transformer is stateless.
This method is just there to mark the fact that this transformer
can work in a streaming setup.
"""
return self
def fit(self, X, y=None):
"""Does nothing: this transformer is stateless."""
# triggers a parameter validation
self._get_hasher().fit(X, y=y)
return self
def transform(self, X, y=None):
"""Transform a sequence of 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 : (ignored)
Returns
-------
X : scipy.sparse matrix, shape = (n_samples, self.n_features)
Document-term matrix.
"""
analyzer = self.build_analyzer()
X = self._get_hasher().transform(analyzer(doc) for doc in X)
if self.binary:
X.data.fill(1)
if self.norm is not None:
X = normalize(X, norm=self.norm, copy=False)
return X
# Alias transform to fit_transform for convenience
fit_transform = transform
def _get_hasher(self):
return FeatureHasher(n_features=self.n_features,
input_type='string', dtype=self.dtype,
non_negative=self.non_negative)
def _document_frequency(X):
"""Count the number of non-zero values for each feature in sparse X."""
if sp.isspmatrix_csr(X):
# return np.sum(X,axis=0)
return bincount(X.indices, minlength=X.shape[1])
else:
return np.diff(sp.csc_matrix(X, copy=False).indptr)
class CountVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts using
scipy.sparse.coo_matrix.
If you do not provide an a-priori dictionary and you do not use an analyzer
that does some kind of feature selection then the number of features will
be equal to the vocabulary size found by analyzing the data.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, 'utf-8' by default.
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
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', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char', 'char_wb'} or callable
Whether the feature should be made of word or character n-grams.
Option 'char_wb' creates character n-grams only from text inside
word boundaries.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
ngram_range : tuple (min_n, max_n)
The lower and upper boundary of the range of n-values for different
n-grams to be extracted. All values of n such that min_n <= n <= max_n
will be used.
stop_words : string {'english'}, list, or None (default)
If 'english', a built-in stop word list for English is used.
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.
lowercase : boolean, True by default
Convert all characters to lowercase before tokenizing.
token_pattern : string
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).
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 or None, 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, optional
Either a Mapping (e.g., a dict) where keys are terms and values are
indices in the feature matrix, or an iterable over terms. If not
given, a vocabulary is determined from the input documents. Indices
in the mapping should not be repeated and should not have any gap
between 0 and the largest index.
binary : boolean, 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, optional
Type of the matrix returned by fit_transform() or transform().
Attributes
----------
vocabulary_ : dict
A mapping of terms to feature indices.
stop_words_ : set
Terms that were ignored because they 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, 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.
"""
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(six.iteritems(vocabulary))
map_index = np.empty(len(sorted_features), dtype=np.int32)
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)
tfs = np.asarray(X.sum(axis=0)).ravel()
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:
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(six.iteritems(vocabulary)):
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 = _make_int_array()
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")
j_indices = np.asarray(j_indices, dtype=np.intc)
indptr = np.frombuffer(indptr, dtype=np.intc)
values = frombuffer_empty(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 _count_vocab_2(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 = _make_int_array()
# values = _make_int_array()
values = array.array(str("f"))
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([i * 1.0 / sum(feature_counter.values()) for i in 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")
j_indices = np.asarray(j_indices, dtype=np.intc)
indptr = np.frombuffer(indptr, dtype=np.intc)
values = frombuffer_empty(values, dtype=np.float32)
X = sp.csr_matrix((values, j_indices, indptr),
shape=(len(indptr) - 1, len(vocabulary)))
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.fit_transform(raw_documents)
return self
def fit_transform(self, raw_documents, y=None):
"""Learn the vocabulary dictionary and return term-document 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, [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.
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_:
X = self._sort_features(X, 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")
X, self.stop_words_ = self._limit_features(X, vocabulary,
max_doc_count,
min_doc_count,
max_features)
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, [n_samples, n_features]
Document-term matrix.
"""
if not hasattr(self, 'vocabulary_'):
self._validate_vocabulary()
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 get_term_topic(self, X):
n_features = X.shape[1]
id2word = self.vocabulary_
word2topic = {}
with open('word_topic.txt', 'r') as f:
for line in f:
strs = line.decode('utf-8').strip('\n').split('\t')
word2topic[strs[0]] = strs[2]
topic = np.zeros((len(id2word),))
for i, key in enumerate(id2word):
if key in word2topic:
topic[id2word[key]] = word2topic[key]
else:
print key
topic = preprocessing.MinMaxScaler().fit_transform(topic)
# topic = sp.spdiags(topic, diags=0, m=n_features,
# n=n_features, format='csr')
return topic
def transform2(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, [n_samples, n_features]
Document-term matrix.
"""
if not hasattr(self, 'vocabulary_'):
self._validate_vocabulary()
self._check_vocabulary()
# use the same matrix-building strategy as fit_transform
_, X = self._count_vocab_2(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, sparse matrix}, shape = [n_samples, n_features]
Returns
-------
X_inv : list of arrays, len = n_samples
List of arrays of terms.
"""
self._check_vocabulary()
if sp.issparse(X):
# We need CSR format for fast row manipulations.
X = X.tocsr()
else:
# We need to convert X to a matrix, so that the indexing
# returns 2D objects
X = np.asmatrix(X)
n_samples = X.shape[0]
terms = np.array(list(self.vocabulary_.keys()))
indices = np.array(list(self.vocabulary_.values()))
inverse_vocabulary = terms[np.argsort(indices)]
return [inverse_vocabulary[X[i, :].nonzero()[1]].ravel()
for i in range(n_samples)]
def get_feature_names(self):
"""Array mapping from feature integer indices to feature name"""
self._check_vocabulary()
return [t for t, i in sorted(six.iteritems(self.vocabulary_),
key=itemgetter(1))]
def _make_int_array():
"""Construct an array.array of a type suitable for scipy.sparse indices."""
return array.array(str("i"))
class TfidfTransformer(BaseEstimator, TransformerMixin):
"""Transform a count matrix to a normalized tf or tf-idf representation
Tf means term-frequency while tf-idf means term-frequency times inverse
document-frequency. This is a common term weighting scheme in information
retrieval, that has also found good use in document classification.
The goal of using tf-idf instead of the raw frequencies of occurrence of a
token in a given document is to scale down the impact of tokens that occur
very frequently in a given corpus and that are hence empirically less
informative than features that occur in a small fraction of the training
corpus.
The formula that is used to compute the tf-idf of term t is
tf-idf(d, t) = tf(t) * idf(d, t), and the idf is computed as
idf(d, t) = log [ n / df(d, t) ] + 1 (if ``smooth_idf=False``),
where n is the total number of documents and df(d, t) is the
document frequency; the document frequency is the number of documents d
that contain 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(d, t) = log [ n / (df(d, 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(d, t) = log [ (1 + n) / 1 + df(d, 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, optional
Norm used to normalize term vectors. None for no normalization.
use_idf : boolean, default=True
Enable inverse-document-frequency reweighting.
smooth_idf : boolean, 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 : boolean, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
References
----------
.. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
Information Retrieval. Addison Wesley, pp. 68-74.`
.. [MRS2008] `C.D. Manning, P. Raghavan and H. Schütze (2008).
Introduction to Information Retrieval. Cambridge University
Press, pp. 118-120.`
"""
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, termTopic=None):
"""Learn the idf vector (global term weights)
Parameters
----------
X : sparse matrix, [n_samples, n_features]
a matrix of term/token counts
"""
# todo http://nlpr-web.ia.ac.cn/cip/proceedings/klchen.pdf
# compute the normalized var
if y is not None:
aX = X
m = len(np.unique(y))
p = np.zeros((m, aX.shape[1]))
for j in range(np.min(y), m + np.min(y)):
w = aX[y == j, :]
tij = np.sum(w, axis=0)
lj = np.sum(tij)
p[j - np.min(y), :] = tij * 1.0 / lj
ave_p = np.sum(p, axis=0) * 1.0 / m
new_var = np.sqrt(np.sqrt(np.sum((p - ave_p) ** 2, axis=0)) * 1.0 / np.sum(p, axis=0))
if not sp.issparse(X):
X = sp.csc_matrix(X)
if self.use_idf:
n_samples, n_features = X.shape
df = _document_frequency(X)
# the number of all words
whole_df = np.sum(df)
# perform idf smoothing if required
df += int(self.smooth_idf)
n_samples += int(self.smooth_idf)
idf = np.log(whole_df * 1.0 / df * 1.0)
idf = idf * new_var
self._idf_diag = sp.spdiags(idf, diags=0, m=n_features,
n=n_features, format='csr')
return self
def transform(self, X, copy=True):
"""Transform a count matrix to a tf or tf-idf representation
Parameters
----------
X : sparse matrix, [n_samples, n_features]
a matrix of term/token counts
copy : boolean, default True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
vectors : sparse matrix, [n_samples, n_features]
"""
if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float):
# preserve float family dtype
X = sp.csr_matrix(X, copy=copy)
else:
# convert counts or binary occurrences to floats
X = sp.csr_matrix(X, dtype=np.float64, copy=copy)
n_samples, n_features = X.shape
if self.sublinear_tf:
np.log(X.data, X.data)
X.data += 1
if self.use_idf:
check_is_fitted(self, '_idf_diag', '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 = np.sqrt(X) * self._idf_diag
if self.norm:
X = normalize(X, norm=self.norm, copy=False)
return X
@property
def idf_(self):
if hasattr(self, "_idf_diag"):
return np.ravel(self._idf_diag.sum(axis=0))
else:
return None
class TfidfVectorizer(CountVectorizer):
"""Convert a collection of raw documents to a matrix of TF-IDF features.
Equivalent to CountVectorizer followed by TfidfTransformer.
Read more in the :ref:`User Guide <text_feature_extraction>`.
Parameters
----------
input : string {'filename', 'file', 'content'}
If 'filename', the sequence passed as an argument to fit is
expected to be a list of filenames that need reading to fetch
the raw content to analyze.
If 'file', the sequence items must have a 'read' method (file-like
object) that is called to fetch the bytes in memory.
Otherwise the input is expected to be the sequence strings or
bytes items are expected to be analyzed directly.
encoding : string, 'utf-8' by default.
If bytes or files are given to analyze, this encoding is used to
decode.
decode_error : {'strict', 'ignore', 'replace'}
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', None}
Remove accents during the preprocessing step.
'ascii' is a fast method that only works on characters that have
an direct ASCII mapping.
'unicode' is a slightly slower method that works on any characters.
None (default) does nothing.
analyzer : string, {'word', 'char'} or callable
Whether the feature should be made of word or character n-grams.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
preprocessor : callable or None (default)
Override the preprocessing (string transformation) stage while
preserving the tokenizing and n-grams generation steps.
tokenizer : callable or None (default)
Override the string tokenization step while preserving the
preprocessing and n-grams generation steps.
Only applies if ``analyzer == 'word'``.
ngram_range : tuple (min_n, max_n)
The lower and upper boundary of the range of n-values for different
n-grams to be extracted. All values of n such that min_n <= n <= max_n
will be used.
stop_words : string {'english'}, list, or None (default)
If a string, it is passed to _check_stop_list and the appropriate stop
list is returned. 'english' is currently the only supported string
value.
If a list, that list is assumed to contain stop words, all of which
will be removed from the resulting tokens.
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.
lowercase : boolean, default True
Convert all characters to lowercase before tokenizing.
token_pattern : string
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).
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 or None, 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, optional
Either a Mapping (e.g., a dict) where keys are terms and values are
indices in the feature matrix, or an iterable over terms. If not
given, a vocabulary is determined from the input documents.
binary : boolean, 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 : type, optional
Type of the matrix returned by fit_transform() or transform().
norm : 'l1', 'l2' or None, optional
Norm used to normalize term vectors. None for no normalization.
use_idf : boolean, default=True
Enable inverse-document-frequency reweighting.
smooth_idf : boolean, 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 : boolean, default=False
Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
Attributes
----------
vocabulary_ : dict
A mapping of terms to feature indices.
idf_ : array, shape = [n_features], or None
The learned idf vector (global term weights)
when ``use_idf`` is set to True, None otherwise.
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
Tokenize the documents and count the occurrences of token and return
them as a sparse matrix
TfidfTransformer
Apply Term Frequency Inverse Document Frequency normalization to a
sparse matrix of occurrence counts.
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.
"""
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.int64, norm='l2', use_idf=True, smooth_idf=True,
sublinear_tf=False):
super(TfidfVectorizer, self).__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_
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
Returns
-------
self : TfidfVectorizer
"""
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
# termTopic = super(TfidfVectorizer, self).get_term_topic(X)
self._tfidf.fit(X, y, None)
return self
def fit_transform(self, raw_documents, y=None):
"""Learn vocabulary and idf, return term-document 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 : sparse matrix, [n_samples, n_features]
Tf-idf-weighted document-term matrix.
"""
X = super(TfidfVectorizer, self).fit_transform(raw_documents)
self._tfidf.fit(X, y, None)
# X is already a transformed view of raw_documents so
# we set copy to False
return self._tfidf.transform(X, copy=False)
def transform(self, raw_documents, copy=True):
"""Transform 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
copy : boolean, default True
Whether to copy X and operate on the copy or perform in-place
operations.
Returns
-------
X : sparse matrix, [n_samples, n_features]
Tf-idf-weighted document-term matrix.
"""
check_is_fitted(self, '_tfidf', 'The tfidf vector is not fitted')
X = super(TfidfVectorizer, self).transform(raw_documents)
return self._tfidf.transform(X, copy=False)
