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38 lines
1.3 KiB
Python
38 lines
1.3 KiB
Python
import argparse
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import azureml.train.automl
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from azureml.core import Run
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from sklearn.externals import joblib
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--target_column_name', type=str, dest='target_column_name',
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help='Target Column Name')
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args = parser.parse_args()
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target_column_name = args.target_column_name
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run = Run.get_context()
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# get input dataset by name
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test_dataset = run.input_datasets['test_data']
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df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
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X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
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y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
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fitted_model = joblib.load('model.pkl')
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y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
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# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
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assign_dict = {'horizon_origin': X_trans['horizon_origin'].values, 'predicted': y_pred,
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target_column_name: y_test_df[target_column_name].values}
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df_all = X_test_df.assign(**assign_dict)
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file_name = 'outputs/predictions.csv'
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export_csv = df_all.to_csv(file_name, header=True)
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# Upload the predictions into artifacts
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run.upload_file(name=file_name, path_or_stream=file_name)
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