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* add pipeline tuner component and dependencies. * clean code. * do not need force rerun. * replace the resources. * update metrics retrieving. * Update test/pipeline_tuning_example/requirements.txt * Update test/pipeline_tuning_example/train/env.yaml * Update test/pipeline_tuning_example/tuner/env.yaml * Update test/pipeline_tuning_example/tuner/tuner_func.py * Update test/pipeline_tuning_example/data_prep/env.yaml * fix issues found by lint with flake8. * add documentation * add data. * do not need AML resource for local run. * AML -> AzureML * clean code. * Update website/docs/Examples/Tune-AzureML pipeline.md * rename and add pip install. * update figure name. * align docs with code. * remove extra line.
39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import os
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import argparse
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import logging
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logger = logging.getLogger(__name__)
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def main():
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"""Main function of the script."""
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# input and output arguments
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parser = argparse.ArgumentParser()
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parser.add_argument("--data", type=str, help="path to input data")
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parser.add_argument("--test_train_ratio", type=float, required=False, default=0.25)
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parser.add_argument("--train_data", type=str, help="path to train data")
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parser.add_argument("--test_data", type=str, help="path to test data")
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args = parser.parse_args()
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logger.info(" ".join(f"{k}={v}" for k, v in vars(args).items()))
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data_path = os.path.join(args.data, 'data.csv')
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df = pd.read_csv(data_path)
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train_df, test_df = train_test_split(
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df,
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test_size=args.test_train_ratio,
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)
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# output paths are mounted as folder, therefore, we are adding a filename to the path
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train_df.to_csv(os.path.join(args.train_data, "data.csv"), index=False)
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test_df.to_csv(os.path.join(args.test_data, "data.csv"), index=False)
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if __name__ == "__main__":
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main()
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