ridge.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
from cuml import Ridge


parser = argparse.ArgumentParser(description='cuML ridge regression '
                                             'benchmark')
parser.add_argument('--no-fit-intercept', dest='fit_intercept', default=True,
                    action='store_false',
                    help="Don't fit intercept (assume data already centered)")
parser.add_argument('--solver', default='eig', choices=('eig', 'cd', 'svd'),
                    help='Solver used for training')
parser.add_argument('--alpha', type=float, default=1.0,
                    help='Regularization strength')
params = bench.parse_args(parser)

# Load data
X_train, X_test, y_train, y_test = bench.load_data(
    params, generated_data=['X_train', 'y_train'])

# Create our regression object
regr = Ridge(fit_intercept=params.fit_intercept, alpha=params.alpha,
             solver=params.solver)

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

# Time predict
predict_time, yp = bench.measure_function_time(regr.predict, X_test, params=params)

test_rmse = bench.rmse_score(yp, y_test)
yp = regr.predict(X_train)
train_rmse = bench.rmse_score(yp, y_train)

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