code: def genetic_feat(df, num_gen=20, num_comp=10):
      function_set = [
          "add",
          "sub",
          "mul",
          "div",
          "sqrt",
          "log",
          "abs",
          "neg",
          "inv",
          "tan",
      ]
  
      gp = SymbolicTransformer(
          generations=num_gen,
          population_size=200,
          hall_of_fame=100,
          n_components=num_comp,
          function_set=function_set,
          parsimony_coefficient=0.0005,
          max_samples=0.9,
          verbose=1,
          random_state=0,
          n_jobs=6,
      )
  
      gen_feats = gp.fit_transform(
          df.drop("Close_1", axis=1), df["Close_1"]
      )
      df.iloc[:, :8]
      gen_feats = pd.DataFrame(
          gen_feats,
          columns=["gen_" + str(a) for a in range(gen_feats.shape[1])],
      )
      gen_feats.index = df.index
      return pd.concat((df, gen_feats), axis=1)
  
file_path: deltapy/deltapy/interact.py
imports: import numpy
  import pandas
  from statsmodels.tsa.ar_model import AR
  from timeit import default_timer
  from sklearn.tree import DecisionTreeRegressor
  from math import sin, cos, sqrt, atan2, radians, ceil
  import ta
  from gplearn.genetic import SymbolicTransformer
  from scipy import linalg
  import math
label_desc: Use image node_id 8 for calling a global function with example usage: genetic_feat(df, num_gen, num_comp) and returns: pd