text: import numpy as np 
  import json, pickle
  import pandas as pd
  from sklearn.ensemble import ExtraTreesRegressor
  from sklearn.feature_selection import SelectPercentile, f_regression
  from sklearn.linear_model import ElasticNetCV
  from sklearn.model_selection import train_test_split
  from sklearn.pipeline import make_pipeline, make_union
  from tpot.builtins import StackingEstimator
  
  # NOTE: Make sure that the outcome column is labeled 'target' in the data file
  g=json.load(open('gender_tpot_regression.json'))
  tpot_data=np.array(g['labels'])
  features=np.array(g['data'])
  
  training_features, testing_features, training_target, testing_target = \
              train_test_split(features, tpot_data, random_state=None)
  
  # Average CV score on the training set was: -0.13558964188025885
  exported_pipeline = make_pipeline(
      SelectPercentile(score_func=f_regression, percentile=19),
      StackingEstimator(estimator=ElasticNetCV(l1_ratio=0.2, tol=0.0001)),
      StackingEstimator(estimator=ExtraTreesRegressor(bootstrap=True, max_features=0.7000000000000001, min_samples_leaf=14, min_samples_split=15, n_estimators=100)),
      ElasticNetCV(l1_ratio=0.9, tol=0.001)
  )
  
  exported_pipeline.fit(training_features, training_target)
  print('saving classifier to disk')
  f=open('gender_tpot_regression.pickle','wb')
  pickle.dump(exported_pipeline,f)
  f.close()
  