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54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
import argparse
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from azureml.core import Dataset, 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",
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type=str,
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dest="target_column_name",
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help="Target Column Name",
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)
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parser.add_argument(
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"--test_dataset", type=str, dest="test_dataset", help="Test Dataset"
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)
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args = parser.parse_args()
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target_column_name = args.target_column_name
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test_dataset_id = args.test_dataset
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run = Run.get_context()
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ws = run.experiment.workspace
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# get the input dataset by id
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test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
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X_test_df = (
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test_dataset.drop_columns(columns=[target_column_name])
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.to_pandas_dataframe()
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.reset_index(drop=True)
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)
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y_test_df = (
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test_dataset.with_timestamp_columns(None)
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.keep_columns(columns=[target_column_name])
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.to_pandas_dataframe()
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)
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fitted_model = joblib.load("model.pkl")
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X_rf = fitted_model.rolling_forecast(X_test_df, y_test_df.values, step=1)
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# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
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assign_dict = {
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fitted_model.forecast_origin_column_name: "forecast_origin",
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fitted_model.forecast_column_name: "predicted",
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fitted_model.actual_column_name: target_column_name,
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}
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X_rf.rename(columns=assign_dict, inplace=True)
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file_name = "outputs/predictions.csv"
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export_csv = X_rf.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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