text: # -*- coding: utf-8 -*-
  # Authors: Olivier Grisel <olivier.grisel@ensta.org>
  #          Mathieu Blondel <mathieu@mblondel.org>
  #          Lars Buitinck
  #          Robert Layton <robertlayton@gmail.com>
  #          Jochen Wersdoerfer <jochen@wersdoerfer.de>
  #          Roman Sinayev <roman.sinayev@gmail.com>
  #          Arnold Gaius <jiangcmd@qq.com>
  # 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,division
  
  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.feature_extraction.text import BaseEstimator, TransformerMixin
  from sklearn.feature_extraction.text import six
  from sklearn.feature_extraction.text import xrange
  from sklearn.feature_extraction.text import normalize
  from sklearn.feature_extraction.text import FeatureHasher
  from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
  from sklearn.feature_extraction.text import deprecated
  from sklearn.feature_extraction.text import frombuffer_empty, bincount
  from sklearn.feature_extraction.text import check_is_fitted
  
  __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
          if isinstance(X, six.string_types):
              raise ValueError(
                  "Iterable over raw text documents expected, "
                  "string object received.")
  
          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.
  
          """
          if isinstance(X, six.string_types):
              raise ValueError(
                  "Iterable over raw text documents expected, "
                  "string object received.")
  
          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 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 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.
          if isinstance(raw_documents, six.string_types):
              raise ValueError(
                  "Iterable over raw text documents expected, "
                  "string object received.")
  
          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 isinstance(raw_documents, six.string_types):
              raise ValueError(
                  "Iterable over raw text documents expected, "
                  "string object received.")
  
          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 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 TfidfPro_Transformer(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. Sch眉tze  (2008).
                     Introduction to Information Retrieval. Cambridge University
                     Press, pp. 118-120.`
      """
  
      def __init__(self, norm='l2',use_Wt=False, use_idf=True, smooth_idf=True,
                   sublinear_tf=False):
          self.norm = norm
          self.use_idf = use_idf
          self.use_Wt = use_Wt        
          self.smooth_idf = smooth_idf
          self.sublinear_tf = sublinear_tf
  
  
      def Ntcount(self,x,y):
          
          indices = x.indices
          indptr = x.indptr
          Nt = [0]*x.shape[1]
          new_indptr = []
          y=np.array(y)
          #
          for i in range(len(indptr)-1):
              start= indptr[i]
              stop = indptr[i+1]
              for j in range(start,stop):
                  new_indptr.append(i)
                  
          new_indptr = np.array(new_indptr)        
          for word_index in indices:
              if Nt[word_index] == 0:
                  index_list=np.where(indices==word_index)[0]
                  cate_list=y[[new_indptr[index_list]]]
                  Nt[word_index] = len(np.unique(cate_list))
              
          return np.array(Nt)
      
      def fit(self, X, y):
          """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
              df = _document_frequency(X)
  
              # 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(float(n_samples) / df) + 1.0
              self._idf_diag = sp.spdiags(idf, diags=0, m=n_features, 
                                          n=n_features, format='csr')
              
          if self.use_Wt:
              n_samples, n_features = X.shape
              wt = np.log(len(np.unique(y))/self.Ntcount(X,list(y))+1.0)
  #             print len(np.unique(y)),'\t',self.Ntcount(X,list(y))
  #             wt = len(np.unique(y))/self.Ntcount(X,list(y))
  #             wt = wt*wt
              self._wt = sp.spdiags(wt, 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 = X * self._idf_diag
              
          if self.use_Wt:
              check_is_fitted(self, '_wt', 'idf vector is not fitted')
  
              expected_n_features = self._wt.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._wt
  
          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 TfidfPro_Vectorizer(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,use_Wt=False):
  
          super(TfidfPro_Vectorizer, 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 = TfidfPro_Transformer(norm=norm, use_idf=use_idf,use_Wt=use_Wt,
                                         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 use_Wt(self):
          return self._tfidf.use_Wt
      
      @use_Wt.setter
      def use_Wt(self, value):
          self._tfidf.use_Wt = 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(TfidfPro_Vectorizer, self).fit_transform(raw_documents)
          self._tfidf.fit(X)
          return self
  
      def fit_transform(self, raw_documents, y):
          """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(TfidfPro_Vectorizer, 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
  
          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(TfidfPro_Vectorizer, self).transform(raw_documents)
          return self._tfidf.transform(X, copy=False)
  
  
  