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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.
62 lines
2.0 KiB
Python
62 lines
2.0 KiB
Python
import argparse
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import lightgbm as lgb
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import os
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import pandas as pd
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from azureml.core import Run
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class LightGBMCallbackHandler():
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def __init__(self):
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pass
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def callback(self, env: lgb.callback.CallbackEnv) -> None:
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"""Callback method to collect metrics produced by LightGBM.
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See https://lightgbm.readthedocs.io/en/latest/_modules/lightgbm/callback.html
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"""
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# loop on all the evaluation results tuples
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print("env.evaluation_result_list:", env.evaluation_result_list)
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for data_name, eval_name, result, _ in env.evaluation_result_list:
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run = Run.get_context()
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run.log(f"{data_name}_{eval_name}", result)
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def main(args):
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"""Main function of the script."""
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train_path = os.path.join(args.train_data, 'data.csv')
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print("traning_path:", train_path)
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test_path = os.path.join(args.test_data, 'data.csv')
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train_set = lgb.Dataset(train_path)
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test_set = lgb.Dataset(test_path)
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callbacks_handler = LightGBMCallbackHandler()
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config = {"header": True, "objective": "binary", "label_column": 30, "metric": "binary_error",
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"n_estimators": args.n_estimators, "learning_rate": args.learning_rate}
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gbm = lgb.train(
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config,
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train_set,
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valid_sets=[test_set],
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valid_names=["eval"],
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callbacks=[
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callbacks_handler.callback,
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],
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)
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print('Saving model...')
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# save model to file
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gbm.save_model(os.path.join(args.model, 'model.txt'))
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if __name__ == "__main__":
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# input and output arguments
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parser = argparse.ArgumentParser()
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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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parser.add_argument("--n_estimators", required=False, default=100, type=int)
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parser.add_argument("--learning_rate", required=False, default=0.1, type=float)
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parser.add_argument("--model", type=str, help="path to output directory")
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args = parser.parse_args()
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main(args)
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