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update samples from Release-167 as a part of SDK release

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amlrelsa-ms
2022-11-08 01:17:48 +00:00
parent f45b815221
commit 0814eee151
9 changed files with 170 additions and 335 deletions
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12
how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py
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@@ -36,18 +36,18 @@ y_test_df = (
fitted_model = joblib.load("model.pkl")
y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
X_rf = fitted_model.rolling_forecast(X_test_df, y_test_df.values, step=1)
# Add predictions, actuals, and horizon relative to rolling origin to the test feature data
assign_dict = {
"horizon_origin": X_trans["horizon_origin"].values,
"predicted": y_pred,
target_column_name: y_test_df[target_column_name].values,
fitted_model.forecast_origin_column_name: "forecast_origin",
fitted_model.forecast_column_name: "predicted",
fitted_model.actual_column_name: target_column_name,
}
df_all = X_test_df.assign(**assign_dict)
X_rf.rename(columns=assign_dict, inplace=True)
file_name = "outputs/predictions.csv"
export_csv = df_all.to_csv(file_name, header=True)
export_csv = X_rf.to_csv(file_name, header=True)
# Upload the predictions into artifacts
run.upload_file(name=file_name, path_or_stream=file_name)