import numpy as np

from sklearn.cross_validation import train_test_split

from sklearn.decomposition import RandomizedPCA

from sklearn.ensemble import AdaBoostClassifier, VotingClassifier

from sklearn.naive_bayes import GaussianNB

from sklearn.pipeline import make_pipeline, make_union

from sklearn.preprocessing import FunctionTransformer, RobustScaler

# NOTE: Make sure that the class is labeled 'class' in the data file

tpot_data = np.recfromcsv('PATH/TO/DATA/FILE', delimiter='COLUMN_SEPARATOR', dtype=np.float64)

features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)

training_features, testing_features, training_classes, testing_classes = \

train_test_split(features, tpot_data['class'], random_state=42)

exported_pipeline = make_pipeline(

)

exported_pipeline.fit(training_features, training_classes)

results = exported_pipeline.predict(testing_features)