class_docstr: Configuration used by a :class:`.LogRegIntentClassifier` which defines
  how to augment data to improve the training of the classifier
class_docstr_tok: ["Configuration","used","by","a",":","class",":","`",".LogRegIntentClassifier","`","which","defines","how","to","augment","data","to","improve","the","training","of","the","classifier"]
docstr: Args:
      min_utterances (int, optional): The minimum number of utterances to
          automatically generate for each intent, based on the existing
          utterances. Default is 20.
      noise_factor (int, optional): Defines the size of the noise to
          generate to train the implicit *None* intent, as a multiplier
          of the average size of the other intents. Default is 5.
      add_builtin_entities_examples (bool, optional): If True, some
          builtin entity examples will be automatically added to the
          training data. Default is True.
code: def __init__(
      self,
      min_utterances=20,
      noise_factor=5,
      add_builtin_entities_examples=True,
      unknown_word_prob=0,
      unknown_words_replacement_string=None,
      max_unknown_words=None,
  ):
      """
      Args:
          min_utterances (int, optional): The minimum number of utterances to
              automatically generate for each intent, based on the existing
              utterances. Default is 20.
          noise_factor (int, optional): Defines the size of the noise to
              generate to train the implicit *None* intent, as a multiplier
              of the average size of the other intents. Default is 5.
          add_builtin_entities_examples (bool, optional): If True, some
              builtin entity examples will be automatically added to the
              training data. Default is True.
      """
      self.min_utterances = min_utterances
      self.noise_factor = noise_factor
      self.add_builtin_entities_examples = add_builtin_entities_examples
      self.unknown_word_prob = unknown_word_prob
      self.unknown_words_replacement_string = (
          unknown_words_replacement_string
      )
      if max_unknown_words is not None and max_unknown_words < 0:
          raise ValueError("max_unknown_words must be None or >= 0")
      self.max_unknown_words = max_unknown_words
      if (
          unknown_word_prob > 0
          and unknown_words_replacement_string is None
      ):
          raise ValueError(
              "unknown_word_prob is positive (%s) but the "
              "replacement string is None" % unknown_word_prob
          )
  
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 IntentClassifierDataAugmentationConfig object from inherited base classes: FromDict, Config with example: obj = IntentClassifierDataAugmentationConfig(min_utterances, noise_factor, add_builtin_entities_examples, unknown_word_prob, unknown_words_replacement_string, max_unknown_words)