class_docstr: Сlass for storing, cleaning and processing your dataset
class_docstr_tok: ["\u0421lass","for","storing",",","cleaning","and","processing","your","dataset"]
docstr: Description of numeric_interaction_terms:
      Numerical interaction generator features: A/B, A*B, A-B,
  
  Args:
      df (pd.DataFrame):
      columns (list): num columns names
      operations (list): operations type
  
  Returns:
      pd.DataFrame
docstr_tok: ["Description","of","numeric_interaction_terms",":","Numerical","interaction","generator","features",":","A\/B",",","A","*","B",",","A-B",",","Args",":","df","(","pd.DataFrame",")",":","columns","(","list",")",":","num","columns","names","operations","(","list",")",":","operations","type","Returns",":","pd.DataFrame"]
code: def gen_numeric_interaction_features(
      self,
      df,
      columns,
      operations=["/", "*", "-", "+"],
  ):
      """
      Description of numeric_interaction_terms:
          Numerical interaction generator features: A/B, A*B, A-B,
  
      Args:
          df (pd.DataFrame):
          columns (list): num columns names
          operations (list): operations type
  
      Returns:
          pd.DataFrame
  
      """
      copy_columns = copy.deepcopy(columns)
      fe_df = pd.DataFrame()
      for combo_col in combinations(columns, 2):
          if "/" in operations:
              fe_df[
                  "{}_div_by_{}".format(combo_col[0], combo_col[1])
              ] = (df[combo_col[0]] * 1.0) / df[combo_col[1]]
          if "*" in operations:
              fe_df[
                  "{}_mult_by_{}".format(combo_col[0], combo_col[1])
              ] = (df[combo_col[0]] * df[combo_col[1]])
          if "-" in operations:
              fe_df[
                  "{}_minus_{}".format(combo_col[0], combo_col[1])
              ] = (df[combo_col[0]] - df[combo_col[1]])
          if "+" in operations:
              fe_df["{}_plus_{}".format(combo_col[0], combo_col[1])] = (
                  df[combo_col[0]] + df[combo_col[1]]
              )
  
      for each_col in copy_columns:
          fe_df["{}_squared".format(each_col)] = df[each_col].pow(2)
      return fe_df
  
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 7 for calling the DataBunch obj's underlying member method code with example usage: obj.gen_numeric_interaction_features(df, columns, operations) and returns: fe_df