FateWithShellmiaoComment/Fate/examples/benchmark_quality/hetero_linear_regression/local-linr.py

#
#  Copyright 2019 The FATE Authors. All Rights Reserved.
#
#  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 pandas
import numpy as np
import os
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score

from pipeline.utils.tools import JobConfig


def main(config="../../config.yaml", param="./linr_config.yaml"):
    # obtain config
    if isinstance(param, str):
        param = JobConfig.load_from_file(param)
    data_guest = param["data_guest"]
    data_host = param["data_host"]
    idx = param["idx"]
    label_name = param["label_name"]

    if isinstance(config, str):
        config = JobConfig.load_from_file(config)
        data_base_dir = config["data_base_dir"]
    else:
        data_base_dir = config.data_base_dir

    # prepare data
    df_guest = pandas.read_csv(os.path.join(data_base_dir, data_guest), index_col=idx)
    df_host = pandas.read_csv(os.path.join(data_base_dir, data_host), index_col=idx)
    df = df_guest.join(df_host, rsuffix="host")
    y = df[label_name]
    X = df.drop(label_name, axis=1)
    lm = SGDRegressor(loss="squared_loss", penalty=param["penalty"], random_state=42,
                      fit_intercept=True, max_iter=param["max_iter"], average=param["batch_size"])
    lm_fit = lm.fit(X, y)
    y_pred = lm_fit.predict(X)

    mse = mean_squared_error(y, y_pred)
    rmse = np.sqrt(mse)
    r2 = r2_score(y, y_pred)
    explained_var = explained_variance_score(y, y_pred)
    metric_summary = {"r2_score": r2,
                      "mean_squared_error": mse,
                      "root_mean_squared_error": rmse,
                      "explained_variance": explained_var}
    data_summary = {}
    return data_summary, metric_summary


if __name__ == "__main__":
    parser = argparse.ArgumentParser("BENCHMARK-QUALITY LOCAL JOB")
    parser.add_argument("-param", type=str,
                        help="config file for params")
    args = parser.parse_args()
    if args.param is not None:
        main(args.param)
    else:
        main()