class_docstr: Class for application of :obj:`PolynomialFeatures` operation on data,
  where only not encoded features (were not converted from categorical using
  ``OneHot encoding``) are used
  
  Args:
      params: OperationParameters with the arguments
class_docstr_tok: ["Class","for","application","of",":","obj",":","`","PolynomialFeatures","`","operation","on","data",",","where","only","not","encoded","features","(","were","not","converted","from","categorical","using","``","OneHot","encoding","``",")","are","used","Args",":","params",":","OperationParameters","with","the","arguments"]
docstr: Firstly perform filtration of columns
          
docstr_tok: ["Firstly","perform","filtration","of","columns"]
code: def transform(self, input_data: InputData) -> OutputData:
      """Firstly perform filtration of columns"""
  
      clipped_input_data = input_data
      if self.columns_to_take is not None:
          clipped_input_data = input_data.subset_features(
              self.columns_to_take
          )
      output_data = super().transform(clipped_input_data)
  
      if self.columns_to_take is not None:
          # Get generated features from poly function
          generated_features = output_data.predict[:, self.th_columns :]
          # Concat source features with generated one
          all_features = np.hstack(
              (input_data.features, generated_features)
          )
          output_data.predict = all_features
      return output_data
  
imports: import random
  from typing import Optional
  import numpy
  import pandas
  from sklearn.decomposition import FastICA, KernelPCA, PCA
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import MinMaxScaler, PolynomialFeatures, StandardScaler
  from fedot.core.data.data import InputData, OutputData, data_type_is_table
  from fedot.core.data.data_preprocessing import convert_into_column, data_has_categorical_features, divide_data_categorical_numerical, find_categorical_columns, replace_inf_with_nans
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation, EncodedInvariantImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
  from fedot.preprocessing.data_types import TYPE_TO_ID
label_desc: Use image node_id 3 for calling the PolyFeaturesImplementation obj's underlying member method code with example usage: obj.transform(input_data) and returns: output_data