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sklearn-lr-multi.py
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sklearn-lr-multi.py
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#
# 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 os
import pandas
from pipeline.utils.tools import JobConfig
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import precision_score, accuracy_score, recall_score
def main(config="../../config.yaml", param="./vechile_config.yaml"):
# obtain config
if isinstance(param, str):
param = JobConfig.load_from_file(param)
assert isinstance(param, dict)
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
config_param = {
"penalty": param["penalty"],
"max_iter": param["max_iter"],
"alpha": param["alpha"],
"learning_rate": "optimal",
"eta0": param["learning_rate"]
}
# 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 = LogisticRegression(max_iter=20)
lm = SGDClassifier(loss="log", **config_param, shuffle=False)
lm_fit = lm.fit(X, y)
y_pred = lm_fit.predict(X)
recall = recall_score(y, y_pred, average="macro")
pr = precision_score(y, y_pred, average="macro")
acc = accuracy_score(y, y_pred)
result = {"accuracy": acc}
print(result)
return {}, result
if __name__ == "__main__":
parser = argparse.ArgumentParser("BENCHMARK-QUALITY SKLEARN 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)