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62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
"""
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This is the script that is executed on the compute instance. It relies
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on the model.pkl file which is uploaded along with this script to the
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compute instance.
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"""
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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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from pandas.tseries.frequencies import to_offset
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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 = test_dataset.to_pandas_dataframe().reset_index(drop=True)
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y_test = X_test.pop(target_column_name).values
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# generate forecast
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fitted_model = joblib.load("model.pkl")
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# We have default quantiles values set as below(95th percentile)
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quantiles = [0.025, 0.5, 0.975]
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predicted_column_name = "predicted"
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PI = "prediction_interval"
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fitted_model.quantiles = quantiles
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pred_quantiles = fitted_model.forecast_quantiles(X_test)
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pred_quantiles[PI] = pred_quantiles[[min(quantiles), max(quantiles)]].apply(
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lambda x: "[{}, {}]".format(x[0], x[1]), axis=1
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)
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X_test[target_column_name] = y_test
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X_test[PI] = pred_quantiles[PI]
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X_test[predicted_column_name] = pred_quantiles[0.5]
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# drop rows where prediction or actuals are nan
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# happens because of missing actuals
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# or at edges of time due to lags/rolling windows
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clean = X_test[
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X_test[[target_column_name, predicted_column_name]].notnull().all(axis=1)
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]
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file_name = "outputs/predictions.csv"
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export_csv = clean.to_csv(file_name, header=True, index=False) # added Index
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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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