df_regr.py

# ===============================================================================
# Copyright 2020-2021 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===============================================================================

import argparse

import bench


def main():
    from sklearn.ensemble import RandomForestRegressor

    # Load and convert data
    X_train, X_test, y_train, y_test = bench.load_data(params)

    # Create our random forest regressor
    regr = RandomForestRegressor(criterion=params.criterion,
                                 n_estimators=params.num_trees,
                                 max_depth=params.max_depth,
                                 max_features=params.max_features,
                                 min_samples_split=params.min_samples_split,
                                 max_leaf_nodes=params.max_leaf_nodes,
                                 min_impurity_decrease=params.min_impurity_decrease,
                                 bootstrap=params.bootstrap,
                                 random_state=params.seed,
                                 n_jobs=params.n_jobs)

    fit_time, _ = bench.measure_function_time(regr.fit, X_train, y_train, params=params)

    y_pred = regr.predict(X_train)
    train_rmse = bench.rmse_score(y_train, y_pred)
    train_r2 = bench.r2_score(y_train, y_pred)

    predict_time, y_pred = bench.measure_function_time(
        regr.predict, X_test, params=params)
    test_rmse = bench.rmse_score(y_test, y_pred)
    test_r2 = bench.r2_score(y_test, y_pred)

    bench.print_output(
        library='sklearn',
        algorithm='df_regr',
        stages=['training', 'prediction'],
        params=params,
        functions=['df_regr.fit', 'df_regr.predict'],
        times=[fit_time, predict_time],
        metric_type=['rmse', 'r2_score'],
        metrics=[[train_rmse, test_rmse], [train_r2, test_r2]],
        data=[X_train, X_test],
        alg_instance=regr,
    )


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='scikit-learn random forest '
                                     'regression benchmark')

    parser.add_argument('--criterion', type=str, default='mse',
                        choices=('mse', 'mae'),
                        help='The function to measure the quality of a split')
    parser.add_argument('--num-trees', type=int, default=100,
                        help='Number of trees in the forest')
    parser.add_argument('--max-features', type=bench.float_or_int, default=None,
                        help='Upper bound on features used at each split')
    parser.add_argument('--max-depth', type=int, default=None,
                        help='Upper bound on depth of constructed trees')
    parser.add_argument('--min-samples-split', type=bench.float_or_int, default=2,
                        help='Minimum samples number for node splitting')
    parser.add_argument('--max-leaf-nodes', type=int, default=None,
                        help='Grow trees with max_leaf_nodes in best-first fashion'
                        'if it is not None')
    parser.add_argument('--min-impurity-decrease', type=float, default=0.,
                        help='Needed impurity decrease for node splitting')
    parser.add_argument('--no-bootstrap', dest='bootstrap', default=True,
                        action='store_false', help="Don't control bootstraping")

    params = bench.parse_args(parser)
    bench.run_with_context(params, main)