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Created August 23, 2014 13:40
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  1. psorianom created this gist Aug 23, 2014.
    1,370 changes: 1,370 additions & 0 deletions text.py
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    # -*- coding: utf-8 -*-
    # Authors: Olivier Grisel <[email protected]>
    # Mathieu Blondel <[email protected]>
    # Lars Buitinck <[email protected]>
    # Robert Layton <[email protected]>
    # Jochen Wersdörfer <[email protected]>
    # Roman Sinayev <[email protected]>
    #
    # License: BSD 3 clause
    """
    The :mod:`sklearn.feature_extraction.text` submodule gathers utilities to
    build feature vectors from text documents.
    """
    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 ..base import BaseEstimator, TransformerMixin
    from ..externals.six.moves import xrange
    from ..preprocessing import normalize
    from .hashing import FeatureHasher
    from .stop_words import ENGLISH_STOP_WORDS
    from ..utils import deprecated
    from ..externals import six


    __all__ = ['CountVectorizer',
    'ENGLISH_STOP_WORDS',
    'TfidfTransformer',
    'TfidfVectorizer',
    'strip_accents_ascii',
    'strip_accents_unicode',
    'strip_tags']


    def strip_accents_unicode(s):
    """Transform accentuated unicode symbols into their simple counterpart
    Warning: the python-level loop and join operations make this
    implementation 20 times slower than the strip_accents_ascii basic
    normalization.
    See also
    --------
    strip_accents_ascii
    Remove accentuated char for any unicode symbol that has a direct
    ASCII equivalent.
    """
    return ''.join([c for c in unicodedata.normalize('NFKD', s)
    if not unicodedata.combining(c)])


    def strip_accents_ascii(s):
    """Transform accentuated unicode symbols into ascii or nothing
    Warning: this solution is only suited for languages that have a direct
    transliteration to ASCII symbols.
    See also
    --------
    strip_accents_unicode
    Remove accentuated char for any unicode symbol.
    """
    nkfd_form = unicodedata.normalize('NFKD', s)
    return nkfd_form.encode('ASCII', 'ignore').decode('ASCII')


    def strip_tags(s):
    """Basic regexp based HTML / XML tag stripper function
    For serious HTML/XML preprocessing you should rather use an external
    library such as lxml or BeautifulSoup.
    """
    return re.compile(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)
    else: # assume it's a collection
    return 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 _check_vocabulary(self):
    vocabulary = self.vocabulary
    if vocabulary is not None:
    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

    @property
    @deprecated("The `fixed_vocabulary` attribute is deprecated and will be "
    "removed in 0.18. Please use `fixed_vocabulary_` instead.")
    def fixed_vocabulary(self):
    return self.fixed_vocabulary_


    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.
    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.
    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.
    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, optional, (2 ** 20) by default
    The number of features (columns) in the output matrices. Small numbers
    of features are likely to cause hash collisions, but large numbers
    will cause larger coefficient dimensions in linear learners.
    norm : 'l1', 'l2' or None, optional
    Norm used to normalize term vectors. None for no normalization.
    binary: boolean, False by default.
    If True, all non zero counts are set to 1. This is useful for discrete
    probabilistic models that model binary events rather than integer
    counts.
    dtype: type, optional
    Type of the matrix returned by fit_transform() or transform().
    non_negative : boolean, optional
    Whether output matrices should contain non-negative values only;
    effectively calls abs on the matrix prior to returning it.
    When True, output values 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 _add_sparse_column(sparse,column):
    import itertools
    addition = sp.lil_matrix(sparse.shape)
    sparse_coo = sparse.tocoo()
    for i,j,v in itertools.izip(sparse_coo.row, sparse_coo.col, sparse_coo.data):
    addition[i,j] = v + column[i,0]
    return addition.tocsr()

    def _class_frequencies(X, y):
    """Count the number of non-zero values for each class y in sparse X."""

    labels = np.unique(y)
    if len(labels) > 2:
    raise ValueError("Delta works only with binary classification problems")

    # Indices for each type of labels in y
    N1 = np.where(y == labels[0])[0]
    N2 = np.where(y == labels[1])[0]

    # Number of positive documents that each term appears on
    df1 = np.bincount(X[N1].nonzero()[1], minlength=X.shape[1])
    # Number of negative documents that each term appears on
    df2 = np.bincount(X[N2].nonzero()[1], minlength=X.shape[1])

    return N1.shape[0], df1, N2.shape[0], df2


    def _document_frequency(X):
    """Count the number of non-zero values for each feature in sparse X."""
    if sp.isspmatrix_csr(X):
    return np.bincount(X.indices, minlength=X.shape[1])
    else:
    return np.diff(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.
    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.
    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.
    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 `tokenize == '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, optional, 1.0 by default
    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, optional, 1 by default
    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 : optional, None by default
    If not None, build a vocabulary that only consider the top
    max_features ordered by term frequency across the corpus.
    This parameter is ignored if vocabulary is not None.
    vocabulary : Mapping or iterable, optional
    Either a Mapping (e.g., a dict) where keys are terms and values are
    indices in the feature matrix, or an iterable over terms. If not
    given, a vocabulary is determined from the input documents. Indices
    in the mapping should not be repeated and should not have any gap
    between 0 and the largest index.
    binary : boolean, False by default.
    If True, all non zero counts are set to 1. This is useful for discrete
    probabilistic models that model binary events rather than integer
    counts.
    dtype : type, optional
    Type of the matrix returned by fit_transform() or transform().
    Attributes
    ----------
    vocabulary_ : dict
    A mapping of terms to feature indices.
    stop_words_ : set
    Terms that were ignored because
    they occurred in either too many
    (`max_df`) or in too few (`min_df`) documents.
    This is only available if no vocabulary was given.
    See also
    --------
    HashingVectorizer, TfidfVectorizer
    """

    def __init__(self, input='content', 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 of 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):
    map_index[new_val] = old_val
    vocabulary[term] = new_val
    return X[:, map_index]

    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 = _make_int_array()
    indptr = _make_int_array()
    indptr.append(0)
    for doc in raw_documents:
    for feature in analyze(doc):
    try:
    j_indices.append(vocabulary[feature])
    except KeyError:
    # Ignore out-of-vocabulary items for fixed_vocab=True
    continue
    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")

    # some Python/Scipy versions won't accept an array.array:
    if j_indices:
    j_indices = np.frombuffer(j_indices, dtype=np.intc)
    else:
    j_indices = np.array([], dtype=np.int32)
    indptr = np.frombuffer(indptr, dtype=np.intc)
    values = np.ones(len(j_indices))

    X = sp.csr_matrix((values, j_indices, indptr),
    shape=(len(indptr) - 1, len(vocabulary)),
    dtype=self.dtype)
    X.sum_duplicates()
    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._check_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._check_vocabulary()

    if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
    raise ValueError("Vocabulary wasn't fitted or is empty!")

    # 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, sparse matrix}, shape = [n_samples, n_features]
    Returns
    -------
    X_inv : list of arrays, len = n_samples
    List of arrays of terms.
    """
    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"""
    if not hasattr(self, 'vocabulary_') or len(self.vocabulary_) == 0:
    raise ValueError("Vocabulary wasn't fitted or is empty!")

    return [t for t, i in sorted(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 actual formula used for tf-idf is tf * (idf + 1) = tf + tf * idf,
    instead of tf * idf. The effect of this is that terms with zero idf, i.e.
    that occur in all documents of a training set, will not be entirely
    ignored. 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.
    Parameters
    ----------
    norm : 'l1', 'l2' or None, optional
    Norm used to normalize term vectors. None for no normalization.
    use_idf : boolean, optional
    Enable inverse-document-frequency reweighting.
    smooth_idf : boolean, optional
    Smooth idf weights by adding one to document frequencies, as if an
    extra document was seen containing every term in the collection
    exactly once. Prevents zero divisions.
    sublinear_tf : boolean, optional
    Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
    References
    ----------
    .. [Yates2011] `R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern
    Information Retrieval. Addison Wesley, pp. 68-74.`
    .. [MRS2008] `C.D. Manning, P. Raghavan and H. Schuetze (2008).
    Introduction to Information Retrieval. Cambridge University
    Press, pp. 118-120.`
    """

    def __init__(self, norm='l2', use_idf=True, use_bm25idf=False, smooth_idf=True,
    delta_idf=False, sublinear_tf=False, bm25_tf=False):
    self.norm = norm
    self.use_idf = use_idf
    self.use_bm25idf = use_bm25idf
    self.smooth_idf = smooth_idf
    # Required for delta idf's
    self.delta_idf = delta_idf

    self.sublinear_tf = sublinear_tf
    self.bm25_tf = bm25_tf
    self.k = 1.2
    self.b = 0.95

    def fit(self, X, y=None):
    """Learn the idf vector (global term weights)
    Parameters
    ----------
    X : sparse matrix, [n_samples, n_features]
    a matrix of term/token counts
    """
    if not sp.issparse(X):
    X = sp.csc_matrix(X)

    if self.use_idf:
    n_samples, n_features = X.shape

    # BM25 idf
    if self.use_bm25idf:
    if self.delta_idf:
    if y is None:
    raise ValueError("Labels are needed to determine Delta idf")

    N1, df1, N2, df2 = _class_frequencies(X, y)
    delta_bm25idf = np.log(((N1 - df1 + 0.5) * df2 + 0.5) / ((N2 - df2 + 0.5) * df1 + 0.5))
    self._idf_diag = sp.spdiags(delta_bm25idf,
    diags=0, m=n_features, n=n_features)
    else:
    # vanilla bm25 idf
    df = _document_frequency(X)

    # perform idf smoothing if required
    df += int(self.smooth_idf)
    n_samples += int(self.smooth_idf)

    # log1p instead of log makes sure terms with zero idf don't get
    # suppressed entirely

    bm25idf = np.log((n_samples - df + 0.5) / (df + 0.5))
    self._idf_diag = sp.spdiags(bm25idf,
    diags=0, m=n_features, n=n_features)

    # Vanilla idf
    elif self.delta_idf:
    if y is None:
    raise ValueError("Labels are needed to determine Delta idf")

    N1, df1, N2, df2 = _class_frequencies(X, y)
    delta_idf = np.log((df1 * float(N2) + int(self.smooth_idf)) /
    (df2 * N1 + int(self.smooth_idf)))

    # Maybe scale delta_idf to only positive values (for Naive Bayes, etc) ?
    self._idf_diag = sp.spdiags(delta_idf,
    diags=0, m=n_features, n=n_features)

    else:
    df = _document_frequency(X)

    # perform idf smoothing if required
    df += int(self.smooth_idf)
    n_samples += int(self.smooth_idf)

    # log1p instead of log makes sure terms with zero idf don't get
    # suppressed entirely
    idf = np.log(float(n_samples) / df) + 1.0
    self._idf_diag = sp.spdiags(idf,
    diags=0, m=n_features, n=n_features)


    return self

    def transform(self, X, copy=True):
    """Transform a count matrix to a tf or tf-idf representation
    Parameters
    ----------
    X : sparse matrix, [n_samples, n_features]
    a matrix of term/token counts
    Returns
    -------
    vectors : sparse matrix, [n_samples, n_features]
    """
    if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float):
    # preserve float family dtype
    X = sp.csr_matrix(X, copy=copy)
    else:
    # convert counts or binary occurrences to floats
    X = sp.csr_matrix(X, dtype=np.float64, copy=copy)

    n_samples, n_features = X.shape

    if self.bm25_tf:

    # First calculate the denominator of BM25 equation
    # Sum the frequencies (sum of each row) to get the documents lengths
    D = (X.sum(1) / np.average(X.sum(1))).reshape((n_samples, 1))
    D = ((1 - self.b) + self.b * D) * self.k
    # D = sp.csr_matrix(np.multiply(np.ones((n_samples,n_features)),D))
    D_X = _add_sparse_column(X,D)

    # Then perform the main division
    # ...Find a better way to add a numpy ndarray to a sparse matrix
    np.divide(X.data * (self.k + 1), D_X.data, X.data)
    # np.divide(X.data * (self.k + 1), sp.csr_matrix(np.add(X.todense(), D)).data, X.data)

    elif self.sublinear_tf:
    np.log(X.data, X.data)
    X.data += 1

    if self.use_idf:
    if not hasattr(self, "_idf_diag"):
    raise ValueError("idf vector not fitted")
    expected_n_features = self._idf_diag.shape[0]
    if n_features != expected_n_features:
    raise ValueError("Input has n_features=%d while the model"
    " has been trained with n_features=%d" % (
    n_features, expected_n_features))
    # *= doesn't work
    X = X * self._idf_diag

    if self.norm:
    X = normalize(X, norm=self.norm, copy=False)

    return X

    @property
    def idf_(self):
    if hasattr(self, "_idf_diag"):
    return np.ravel(self._idf_diag.sum(axis=0))
    else:
    return None


    class TfidfVectorizer(CountVectorizer):
    """Convert a collection of raw documents to a matrix of TF-IDF features.
    Equivalent to CountVectorizer followed by TfidfTransformer.
    Parameters
    ----------
    input : string {'filename', 'file', 'content'}
    If 'filename', the sequence passed as an argument to fit is
    expected to be a list of filenames that need reading to fetch
    the raw content to analyze.
    If 'file', the sequence items must have 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.
    ngram_range : tuple (min_n, max_n)
    The lower and upper boundary of the range of n-values for different
    n-grams to be extracted. All values of n such that min_n <= n <= max_n
    will be used.
    stop_words : string {'english'}, list, or None (default)
    If a string, it is passed to _check_stop_list and the appropriate stop
    list is returned. 'english' is currently the only supported string
    value.
    If a list, that list is assumed to contain stop words, all of which
    will be removed from the resulting tokens.
    If None, no stop words will be used. max_df can be set to a value
    in the range [0.7, 1.0) to automatically detect and filter stop
    words based on intra corpus document frequency of terms.
    lowercase : boolean, default True
    Convert all characters to lowercase 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, optional, 1.0 by default
    When building the vocabulary ignore terms that have a term frequency
    strictly higher than the given threshold (corpus specific stop words).
    If float, the parameter represents a proportion of documents, integer
    absolute counts.
    This parameter is ignored if vocabulary is not None.
    min_df : float in range [0.0, 1.0] or int, optional, 1 by default
    When building the vocabulary ignore terms that have a term frequency
    strictly lower than the given threshold.
    This value is also called cut-off in the literature.
    If float, the parameter represents a proportion of documents, integer
    absolute counts.
    This parameter is ignored if vocabulary is not None.
    max_features : optional, None by default
    If not None, build a vocabulary that only consider the top
    max_features ordered by term frequency across the corpus.
    This parameter is ignored if vocabulary is not None.
    vocabulary : Mapping or iterable, optional
    Either a Mapping (e.g., a dict) where keys are terms and values are
    indices in the feature matrix, or an iterable over terms. If not
    given, a vocabulary is determined from the input documents.
    binary : boolean, False by default.
    If True, all non-zero 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, optional
    Enable inverse-document-frequency reweighting.
    smooth_idf : boolean, optional
    Smooth idf weights by adding one to document frequencies, as if an
    extra document was seen containing every term in the collection
    exactly once. Prevents zero divisions.
    sublinear_tf : boolean, optional
    Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
    Attributes
    ----------
    idf_ : array, shape = [n_features], or None
    The learned idf vector (global term weights)
    when ``use_idf`` is set to True, None otherwise.
    See also
    --------
    CountVectorizer
    Tokenize the documents and count the occurrences of token and return
    them as a sparse matrix
    TfidfTransformer
    Apply Term Frequency Inverse Document Frequency normalization to a
    sparse matrix of occurrence counts.
    """

    def __init__(self, input='content', 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, use_bm25idf = False,
    smooth_idf=True, delta_idf=False, sublinear_tf=False, bm25_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,
    use_bm25idf=use_bm25idf,
    smooth_idf=smooth_idf,
    delta_idf=delta_idf,
    sublinear_tf=sublinear_tf,
    bm25_tf=bm25_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)
    self._tfidf.fit(X)
    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)
    # 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
    Returns
    -------
    X : sparse matrix, [n_samples, n_features]
    Tf-idf-weighted document-term matrix.
    """
    X = super(TfidfVectorizer, self).transform(raw_documents)
    return self._tfidf.transform(X, copy=False)