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- import argparse
- import os
- import pandas as pd
- from sklearn.metrics import mean_absolute_error
- from sklearn.metrics import mean_squared_error
- from sklearn.ensemble import GradientBoostingRegressor
- from pipeline.utils.tools import JobConfig
- def main(config="../../config.yaml", param="./gbdt_config_multi.yaml"):
-
- 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"]
- print('config is {}'.format(config))
- if isinstance(config, str):
- config = JobConfig.load_from_file(config)
- data_base_dir = config["data_base_dir"]
- print('data base dir is', data_base_dir)
- else:
- data_base_dir = config.data_base_dir
-
- df_guest = pd.read_csv(os.path.join(data_base_dir, data_guest), index_col=idx)
- df_host = pd.read_csv(os.path.join(data_base_dir, data_host), index_col=idx)
- df = pd.concat([df_guest, df_host], axis=0)
- y = df[label_name]
- X = df.drop(label_name, axis=1)
- X_guest = df_guest.drop(label_name, axis=1)
- y_guest = df_guest[label_name]
- clf = GradientBoostingRegressor(n_estimators=40)
- clf.fit(X, y)
- y_predict = clf.predict(X_guest)
- result = {"mean_squared_error": mean_squared_error(y_guest, y_predict),
- "mean_absolute_error": mean_absolute_error(y_guest, y_predict)
- }
- 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.config is not None:
- main(args.param)
- main()
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