class_docstr: Configuration of a :class:`.Featurizer` object
class_docstr_tok: ["Configuration","of","a",":","class",":","`",".Featurizer","`","object"]
docstr: Args:
      tfidf_vectorizer_config (:class:`.TfidfVectorizerConfig`, optional):
          empty configuration of the featurizer's
          :attr:`tfidf_vectorizer`
      cooccurrence_vectorizer_config: (:class:`.CooccurrenceVectorizerConfig`, optional):
          configuration of the featurizer's
          :attr:`cooccurrence_vectorizer`
      pvalue_threshold (float): after fitting the training set to
          extract tfidf features, a univariate feature selection is
          applied. Features are tested for independence using a Chi-2
          test, under the null hypothesis that each feature should be
          equally present in each class. Only features having a p-value
          lower than the threshold are kept
      added_cooccurrence_feature_ratio (float, optional): proportion of
          cooccurrence features to add with respect to the number of
          tfidf features. For instance with a ratio of 0.5, if 100 tfidf
          features are remaining after feature selection, a maximum of 50
          cooccurrence features will be added
code: def __init__(
      self,
      tfidf_vectorizer_config=None,
      cooccurrence_vectorizer_config=None,
      pvalue_threshold=0.4,
      added_cooccurrence_feature_ratio=0,
  ):
      """
      Args:
          tfidf_vectorizer_config (:class:`.TfidfVectorizerConfig`, optional):
              empty configuration of the featurizer's
              :attr:`tfidf_vectorizer`
          cooccurrence_vectorizer_config: (:class:`.CooccurrenceVectorizerConfig`, optional):
              configuration of the featurizer's
              :attr:`cooccurrence_vectorizer`
          pvalue_threshold (float): after fitting the training set to
              extract tfidf features, a univariate feature selection is
              applied. Features are tested for independence using a Chi-2
              test, under the null hypothesis that each feature should be
              equally present in each class. Only features having a p-value
              lower than the threshold are kept
          added_cooccurrence_feature_ratio (float, optional): proportion of
              cooccurrence features to add with respect to the number of
              tfidf features. For instance with a ratio of 0.5, if 100 tfidf
              features are remaining after feature selection, a maximum of 50
              cooccurrence features will be added
      """
      self.pvalue_threshold = pvalue_threshold
      self.added_cooccurrence_feature_ratio = (
          added_cooccurrence_feature_ratio
      )
  
      if tfidf_vectorizer_config is None:
          tfidf_vectorizer_config = TfidfVectorizerConfig()
      elif isinstance(tfidf_vectorizer_config, dict):
          tfidf_vectorizer_config = TfidfVectorizerConfig.from_dict(
              tfidf_vectorizer_config
          )
      self.tfidf_vectorizer_config = tfidf_vectorizer_config
  
      if cooccurrence_vectorizer_config is None:
          cooccurrence_vectorizer_config = (
              CooccurrenceVectorizerConfig()
          )
      elif isinstance(cooccurrence_vectorizer_config, dict):
          cooccurrence_vectorizer_config = (
              CooccurrenceVectorizerConfig.from_dict(
                  cooccurrence_vectorizer_config
              )
          )
      self.cooccurrence_vectorizer_config = (
          cooccurrence_vectorizer_config
      )
  
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 FeaturizerConfig object from inherited base classes: FromDict, ProcessingUnitConfig with example: obj = FeaturizerConfig(tfidf_vectorizer_config, cooccurrence_vectorizer_config, pvalue_threshold, added_cooccurrence_feature_ratio)