demo.py

from tpot import TPOTClassifier
from sklearn.cross_validation import train_test_split
import pandas as pd 
import numpy as np

#load the data
telescope=pd.read_csv('MAGIC Gamma Telescope Data.csv')

#clean the data
telescope_shuffle=telescope.iloc[np.random.permutation(len(telescope))]
tele=telescope_shuffle.reset_index(drop=True)

#Store 2 classes
tele['Class']=tele['Class'].map({'g':0, 'h':1})
tele_class = tele['Class'].values

#Split training, testing, and validation data
training_indices, validation_indices = training_indices, testing_indices = train_test_split(tele.index,
	stratify= tele_class, train_size=0.75, test_size=0.25)

#Let Genetic Programming find best ML model and hyperparameters
tpot = TPOTClassifier(generations=5, verbosity=2)
tpot.fit(tele.drop('Class', axis=1).loc[training_indices].values,
	tele.loc[training_indicss, 'Class'].values)

#Score the accuracy
tpot.score(tele.drop('Class', axis=1).loc[validation_indices].values,
	tele.loc[validation_indices, 'Class'].values)

#Export the generated code
tpot.export('pipeline.py')