class_docstr: TextSpecialFeatureGenerator generates text specific features from incoming raw text features.
  These include word counts, character counts, symbol counts, capital letter ratios, and much more.
  Features generated by this generator will have 'text_special' as a special type.
  
  Parameters
  ----------
  symbols : List[str], optional
      List of string symbols to compute counts and ratios for as features.
      If not specified, defaults to ['!', '?', '@', '%', '$', '*', '&', '#', '^', '.', ':', ' ', '/', ';', '-', '=']
  min_occur_ratio : float, default 0.01
      Minimum ratio of symbol occurrence to consider as a feature.
      If a symbol appears in fewer than 1 in 1/min_occur_ratio samples, it will not be used as a feature.
  min_occur_offset : int, default 10
      Minimum symbol occurrences to consider as a feature. This is added to the threshold calculated from min_occur_ratio.
  bin_features : bool, default True
      If True, adds a BinnedFeatureGenerator to the front of post_generators such that all features generated from this generator are then binned.
      This is useful for 'text_special' features because it lowers the chance models will overfit on the features and reduces their memory usage.
  post_drop_duplicates : bool, default True
      Identical to AbstractFeatureGenerator's post_drop_duplicates, except it is defaulted to True instead of False.
      This helps to clean the output of this generator when symbols aren't present in the data.
  **kwargs :
      Refer to AbstractFeatureGenerator documentation for details on valid keyword arguments.
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code: def __init__(
      self,
      symbols: List[str] = None,
      min_occur_ratio=0.01,
      min_occur_offset=10,
      bin_features: bool = True,
      post_drop_duplicates: bool = True,
      **kwargs,
  ):
      super().__init__(
          post_drop_duplicates=post_drop_duplicates, **kwargs
      )
      if symbols is None:
          symbols = [
              "!",
              "?",
              "@",
              "%",
              "$",
              "*",
              "&",
              "#",
              "^",
              ".",
              ":",
              " ",
              "/",
              ";",
              "-",
              "=",
          ]
      self._symbols = (
          symbols  # Symbols to generate count and ratio features for.
      )
      self._symbols_per_feature = {}
      self._min_occur_ratio = min_occur_ratio
      self._min_occur_offset = min_occur_offset
      if bin_features:
          self._post_generators = [
              BinnedFeatureGenerator()
          ] + self._post_generators
  
file_path: autogluon/features/src/autogluon/features/generators/text_special.py
imports: import logging
  from typing import List, Tuple
  import numpy
  import pandas
  from pandas import DataFrame, Series
  from autogluon.common.features.types import S_IMAGE_BYTEARRAY, S_IMAGE_PATH, S_TEXT, S_TEXT_SPECIAL
  from .abstract import AbstractFeatureGenerator
  from .binned import BinnedFeatureGenerator
label_desc: Use image node_id 1 to create a new TextSpecialFeatureGenerator object from inherited base classes: AbstractFeatureGenerator with example: obj = TextSpecialFeatureGenerator(symbols, min_occur_ratio, min_occur_offset, bin_features, post_drop_duplicates)