update samples from Release-61 as a part of SDK release

how-to-use-azureml/automated-machine-learning/forecasting-bike-share/forecasting_script.py

@@ -1,37 +1,21 @@
 import argparse
 import azureml.train.automl
-from azureml.automl.runtime.shared import forecasting_models
 from azureml.core import Run
 from sklearn.externals import joblib
-import forecasting_helper
 
 
 parser = argparse.ArgumentParser()
-parser.add_argument(
-    '--max_horizon', type=int, dest='max_horizon',
-    default=10, help='Max Horizon for forecasting')
 parser.add_argument(
     '--target_column_name', type=str, dest='target_column_name',
     help='Target Column Name')
-parser.add_argument(
-    '--time_column_name', type=str, dest='time_column_name',
-    help='Time Column Name')
-parser.add_argument(
-    '--frequency', type=str, dest='freq',
-    help='Frequency of prediction')
 
 args = parser.parse_args()
-max_horizon = args.max_horizon
 target_column_name = args.target_column_name
-time_column_name = args.time_column_name
-freq = args.freq
 
 run = Run.get_context()
 # get input dataset by name
 test_dataset = run.input_datasets['test_data']
 
-grain_column_names = []
-
 df = test_dataset.to_pandas_dataframe().reset_index(drop=True)
 
 X_test_df = test_dataset.drop_columns(columns=[target_column_name]).to_pandas_dataframe().reset_index(drop=True)
@@ -39,14 +23,12 @@ y_test_df = test_dataset.with_timestamp_columns(None).keep_columns(columns=[targ
 
 fitted_model = joblib.load('model.pkl')
 
-df_all = forecasting_helper.do_rolling_forecast(
-    fitted_model,
-    X_test_df,
-    y_test_df.values.T[0],
-    target_column_name,
-    time_column_name,
-    max_horizon,
-    freq)
+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}
+df_all = X_test_df.assign(**assign_dict)
 
 file_name = 'outputs/predictions.csv'
 export_csv = df_all.to_csv(file_name, header=True)