-
-
- The following code illustrates the usage of TPOT with the Boston house prices data set.
-from tpot import TPOTRegressor
-from sklearn.datasets import load_boston
-from sklearn.model_selection import train_test_split
-
-digits = load_boston()
-X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target,
- train_size=0.75, test_size=0.25)
-
-tpot = TPOTRegressor(generations=5, population_size=20, verbosity=2)
-tpot.fit(X_train, y_train)
-print(tpot.score(X_test, y_test))
-tpot.export('tpot_boston_pipeline.py')
-Running this code should discover a pipeline that achieves about 12.77 mean squared error (MSE).
-For details on how the fit(), score() and export() functions work, see the usage documentation.
After running the above code, the corresponding Python code should be exported to the tpot_boston_pipeline.py file and look similar to the following:
import numpy as np
-
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import ExtraTreesRegressor
-from sklearn.pipeline import make_pipeline
-
-# NOTE: Make sure that the target 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(
- ExtraTreesRegressor(max_features=0.76, n_estimators=500)
-)
-
-exported_pipeline.fit(training_features, training_classes)
-results = exported_pipeline.predict(testing_features)
-