class_docstr: Configuration of a :class:`.CooccurrenceVectorizer` object
class_docstr_tok: ["Configuration","of","a",":","class",":","`",".CooccurrenceVectorizer","`","object"]
docstr: Args:
      window_size (int, optional): if provided, word cooccurrences will
          be taken into account only in a context window of size
          :attr:`window_size`. If the window size is 3 then given a word
          w[i], the vectorizer will only extract the following pairs:
          (w[i], w[i + 1]), (w[i], w[i + 2]) and (w[i], w[i + 3]).
          Defaults to None, which means that we consider all words
      unknown_words_replacement_string (str, optional)
      filter_stop_words (bool, optional): if True, stop words are ignored
          when computing cooccurrences
      keep_order (bool, optional): if True then cooccurrence are computed
          taking the words order into account, which means the pairs
          (w1, w2) and (w2, w1) will count as two separate features.
          Defaults to `True`.
code: def __init__(
      self,
      window_size=None,
      unknown_words_replacement_string=None,
      filter_stop_words=True,
      keep_order=True,
  ):
      """
      Args:
          window_size (int, optional): if provided, word cooccurrences will
              be taken into account only in a context window of size
              :attr:`window_size`. If the window size is 3 then given a word
              w[i], the vectorizer will only extract the following pairs:
              (w[i], w[i + 1]), (w[i], w[i + 2]) and (w[i], w[i + 3]).
              Defaults to None, which means that we consider all words
          unknown_words_replacement_string (str, optional)
          filter_stop_words (bool, optional): if True, stop words are ignored
              when computing cooccurrences
          keep_order (bool, optional): if True then cooccurrence are computed
              taking the words order into account, which means the pairs
              (w1, w2) and (w2, w1) will count as two separate features.
              Defaults to `True`.
      """
      self.window_size = window_size
      self.unknown_words_replacement_string = (
          unknown_words_replacement_string
      )
      self.filter_stop_words = filter_stop_words
      self.keep_order = keep_order
  
imports: from __future__ import unicode_literals
  from snips_nlu.common.from_dict import FromDict
  from snips_nlu.constants import CUSTOM_ENTITY_PARSER_USAGE, NOISE, STEMS, STOP_WORDS, WORD_CLUSTERS
  from snips_nlu.entity_parser.custom_entity_parser import CustomEntityParserUsage
  from snips_nlu.pipeline.configs import Config, ProcessingUnitConfig
  from snips_nlu.resources import merge_required_resources
label_desc: Use image node_id 1 to create a new CooccurrenceVectorizerConfig object from inherited base classes: FromDict, ProcessingUnitConfig with example: obj = CooccurrenceVectorizerConfig(window_size, unknown_words_replacement_string, filter_stop_words, keep_order)