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update samples from Release-116 as a part of SDK release

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amlrelsa-ms
2021-11-08 16:09:41 +00:00
parent c7e1241e20
commit aebe34b4e8
51 changed files with 2362 additions and 983 deletions
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34
how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py
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@@ -4,11 +4,14 @@ from sklearn.externals import joblib
parser = argparse.ArgumentParser()
parser.add_argument(
'--target_column_name', type=str, dest='target_column_name',
help='Target Column Name')
"--target_column_name",
type=str,
dest="target_column_name",
help="Target Column Name",
)
parser.add_argument(
'--test_dataset', type=str, dest='test_dataset',
help='Test Dataset')
"--test_dataset", type=str, dest="test_dataset", help="Test Dataset"
)
args = parser.parse_args()
target_column_name = args.target_column_name
@@ -20,19 +23,30 @@ ws = run.experiment.workspace
# get the input dataset by id
test_dataset = Dataset.get_by_id(ws, id=test_dataset_id)
X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[target_column_name]).to_pandas_dataframe()
X_test_df = (
test_dataset.drop_columns(columns=[target_column_name])
.to_pandas_dataframe()
.reset_index(drop=True)
)
y_test_df = (
test_dataset.with_timestamp_columns(None)
.keep_columns(columns=[target_column_name])
.to_pandas_dataframe()
)
fitted_model = joblib.load('model.pkl')
fitted_model = joblib.load("model.pkl")
y_pred, X_trans = fitted_model.rolling_evaluation(X_test_df, y_test_df.values)
# 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}
assign_dict = {
"horizon_origin": X_trans["horizon_origin"].values,
"predicted": y_pred,
target_column_name: y_test_df[target_column_name].values,
}
df_all = X_test_df.assign(**assign_dict)
file_name = 'outputs/predictions.csv'
file_name = "outputs/predictions.csv"
export_csv = df_all.to_csv(file_name, header=True)
# Upload the predictions into artifacts