python-autosklearn-randomsearch

import sklearn.model_selection
import sklearn.datasets
import sklearn.metrics

from smac.facade.roar_facade import ROAR
from smac.scenario.scenario import Scenario

import autosklearn.classification


def get_roar_object_callback(
    scenario_dict,
    seed,
    ta,
    backend,
    metalearning_configurations,
    runhistory,
):
    """Random online adaptive racing."""
    scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob()
    scenario = Scenario(scenario_dict)
    return ROAR(
        scenario=scenario,
        rng=seed,
        tae_runner=ta,
        runhistory=runhistory,
        run_id=seed,
    )


def get_random_search_object_callback(
        scenario_dict,
        seed,
        ta,
        backend,
        metalearning_configurations,
        runhistory,
):
    """Random search."""
    scenario_dict['input_psmac_dirs'] = backend.get_smac_output_glob()
    scenario_dict['minR'] = len(scenario_dict['instances'])
    scenario_dict['initial_incumbent'] = 'RANDOM'
    scenario = Scenario(scenario_dict)
    return ROAR(
        scenario=scenario,
        rng=seed,
        tae_runner=ta,
        runhistory=runhistory,
        run_id=seed,
    )


def main():
    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
    X_train, X_test, y_train, y_test = \
        sklearn.model_selection.train_test_split(X, y, random_state=1)

    automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=120, per_run_time_limit=30,
        tmp_folder='/tmp/autosklearn_random_search_example_tmp',
        output_folder='/tmp/autosklearn_random_search_example_out',
        get_smac_object_callback=get_roar_object_callback,
        initial_configurations_via_metalearning=0,
    )
    automl.fit(X_train, y_train, dataset_name='breast_cancer')

    print('#' * 80)
    print('Results for ROAR.')
    # Print the final ensemble constructed by auto-sklearn via ROAR.
    print(automl.show_models())
    predictions = automl.predict(X_test)
    # Print statistics about the auto-sklearn run such as number of
    # iterations, number of models failed with a time out.
    print(automl.sprint_statistics())
    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

    automl = autosklearn.classification.AutoSklearnClassifier(
        time_left_for_this_task=120,
        per_run_time_limit=30,
        tmp_folder='/tmp/autosklearn_random_search_example_tmp',
        output_folder='/tmp/autosklearn_random_search_example_out',
        get_smac_object_callback=get_random_search_object_callback,
        initial_configurations_via_metalearning=0,
    )
    automl.fit(X_train, y_train, dataset_name='breast_cancer')

    print('#' * 80)
    print('Results for random search.')
    # Print the final ensemble constructed by auto-sklearn via random search.
    print(automl.show_models())
    predictions = automl.predict(X_test)
    # Print statistics about the auto-sklearn run such as number of
    # iterations, number of models failed with a time out.
    print(automl.sprint_statistics())
    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))


if __name__ == '__main__':
    main()