text: import numpy as np
  import pandas as pd
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import train_test_split
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.pipeline import make_pipeline, make_union
  from tpot.builtins import StackingEstimator
  
  # NOTE: Make sure that the class is labeled 'target' in the data file
  tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64)
  features = tpot_data.drop('target', axis=1).values
  training_features, testing_features, training_target, testing_target = \
              train_test_split(features, tpot_data['target'].values, random_state=2019)
  
  # Average CV score on the training set was:0.907447774750227
  exported_pipeline = make_pipeline(
      StackingEstimator(estimator=MultinomialNB(alpha=0.1, fit_prior=True)),
      LogisticRegression(C=25.0, dual=False, penalty="l2")
  )
  
  exported_pipeline.fit(training_features, training_target)
  results = exported_pipeline.predict(testing_features)
  