tpot_exported_pipeline.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, make_union
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from tpot.builtins import StackingEstimator
from sklearn.preprocessing import FunctionTransformer
from copy import copy

# 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=42)

# Average CV score on the training set was:0.951388888888889
exported_pipeline = make_pipeline(
    make_union(
        FunctionTransformer(copy),
        make_union(
            FunctionTransformer(copy),
            make_union(
                FunctionTransformer(copy),
                make_union(
                    FunctionTransformer(copy),
                    FunctionTransformer(copy)
                )
            )
        )
    ),
    StandardScaler(),
    LinearSVC(C=25.0, dual=False, loss="squared_hinge", penalty="l2", tol=1e-05)
)

exported_pipeline.fit(training_features, training_target)
results = exported_pipeline.predict(testing_features)