class_docstr: Transformer for binarizing categorical and ordinal features.
  
  For use with BooleanRuleCG, LogisticRuleRegression and LinearRuleRegression
class_docstr_tok: ["Transformer","for","binarizing","categorical","and","ordinal","features",".","For","use","with","BooleanRuleCG",",","LogisticRuleRegression","and","LinearRuleRegression"]
docstr: Args:
      colCateg (list): Categorical features ('object' dtype automatically treated as categorical)
      numThresh (int): Number of quantile thresholds used to binarize ordinal variables
      negations (bool): Append negations
      threshStr (bool): Convert thresholds on ordinal features to strings
      returnOrd (bool): Also return standardized ordinal features
docstr_tok: ["Args",":","colCateg","(","list",")",":","Categorical","features","(","'object","'","dtype","automatically","treated","as","categorical",")","numThresh","(","int",")",":","Number","of","quantile","thresholds","used","to","binarize","ordinal","variables","negations","(","bool",")",":","Append","negations","threshStr","(","bool",")",":","Convert","thresholds","on","ordinal","features","to","strings","returnOrd","(","bool",")",":","Also","return","standardized","ordinal","features"]
code: def __init__(
      self,
      colCateg=[],
      numThresh=9,
      negations=False,
      threshStr=False,
      returnOrd=False,
      **kwargs,
  ):
      """
      Args:
          colCateg (list): Categorical features ('object' dtype automatically treated as categorical)
          numThresh (int): Number of quantile thresholds used to binarize ordinal variables
          negations (bool): Append negations
          threshStr (bool): Convert thresholds on ordinal features to strings
          returnOrd (bool): Also return standardized ordinal features
      """
      # List of categorical columns
      if type(colCateg) is pd.Series:
          self.colCateg = colCateg.tolist()
      elif type(colCateg) is not list:
          self.colCateg = [colCateg]
      else:
          self.colCateg = colCateg
      # Number of quantile thresholds used to binarize ordinal features
      self.numThresh = numThresh
      self.thresh = {}
      # whether to append negations
      self.negations = negations
      # whether to convert thresholds on ordinal features to strings
      self.threshStr = threshStr
      # Also return standardized ordinal features
      self.returnOrd = returnOrd
  
file_path: AIX360/aix360/algorithms/rbm/features.py
imports: from typing import Optional, Tuple, Union
  import numpy
  import pandas
  from numpy import ndarray
  from pandas import DataFrame, Series
  from sklearn.base import TransformerMixin
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from sklearn.tree import DecisionTreeClassifier
label_desc: Use image node_id 1 to create a new FeatureBinarizer object from inherited base classes: TransformerMixin with example: obj = FeatureBinarizer(colCateg, numThresh, negations, threshStr, returnOrd)