class_docstr: Сlass for storing, cleaning and processing your dataset
class_docstr_tok: ["\u0421lass","for","storing",",","cleaning","and","processing","your","dataset"]
docstr: Description of _encode_features:
      Encode car features
  
  Args:
      data (pd.DataFrame):
      cat_encoder_name (str): cat Encoder name
  
  Returns:
      pd.DataFrame
docstr_tok: ["Description","of","_encode_features",":","Encode","car","features","Args",":","data","(","pd.DataFrame",")",":","cat_encoder_name","(","str",")",":","cat","Encoder","name","Returns",":","pd.DataFrame"]
code: def gen_cat_encodet_features(self, data, cat_encoder_name):
      """
      Description of _encode_features:
          Encode car features
  
      Args:
          data (pd.DataFrame):
          cat_encoder_name (str): cat Encoder name
  
      Returns:
          pd.DataFrame
  
      """
  
      if isinstance(cat_encoder_name, str):
          if (
              cat_encoder_name in self.cat_encoder_names_list
              and cat_encoder_name
              not in self.target_encoders_names_list
          ):
              if cat_encoder_name == "HashingEncoder":
                  encoder = self.cat_encoders_names[cat_encoder_name][
                      0
                  ](
                      cols=self.cat_features,
                      n_components=int(
                          np.log(len(data.columns)) * 1000
                      ),
                      drop_invariant=True,
                  )
              else:
                  encoder = self.cat_encoders_names[cat_encoder_name][
                      0
                  ](cols=self.cat_features, drop_invariant=True)
              data_encodet = encoder.fit_transform(data)
              data_encodet = data_encodet.add_prefix(
                  cat_encoder_name + "_"
              )
          else:
              print(f"{cat_encoder_name} is not supported!")
              return ("", "")
      else:
          encoder = copy.deepcopy(cat_encoder_name)
          data_encodet = encoder.transform(data)
          data_encodet = data_encodet.add_prefix(
              str(cat_encoder_name).split("(")[0] + "_"
          )
  
      return (data_encodet, encoder)
  
imports: import pandas
  import numpy
  from itertools import combinations
  from sklearn.preprocessing import StandardScaler
  from category_encoders import HashingEncoder, SumEncoder, PolynomialEncoder, BackwardDifferenceEncoder
  from category_encoders import OneHotEncoder, HelmertEncoder, OrdinalEncoder, CountEncoder, BaseNEncoder
  from category_encoders import TargetEncoder, CatBoostEncoder, WOEEncoder, JamesSteinEncoder
  from category_encoders.glmm import GLMMEncoder
  from sklearn.preprocessing import LabelEncoder
  from category_encoders.wrapper import PolynomialWrapper
  from .encoders import FrequencyEncoder
  from .None import settings
  import pdb
  import copy
  import dask
  import dask.dataframe
label_desc: Use image node_id 5 for calling the DataBunch obj's underlying member method code with example usage: obj.gen_cat_encodet_features(data, cat_encoder_name) and returns: data_encodet,  encoder, str,  str