from azureml.core import ScriptRunConfig


def run_rolling_forecast(test_experiment, compute_target, train_run,
                         test_dataset, target_column_name,
                         inference_folder='./forecast'):
    train_run.download_file('outputs/model.pkl',
                            inference_folder + '/model.pkl')

    inference_env = train_run.get_environment()

    config = ScriptRunConfig(source_directory=inference_folder,
                             script='forecasting_script.py',
                             arguments=['--target_column_name',
                                        target_column_name,
                                        '--test_dataset',
                                        test_dataset.as_named_input(test_dataset.name)],
                             compute_target=compute_target,
                             environment=inference_env)

    run = test_experiment.submit(config,
                                 tags={'training_run_id':
                                       train_run.id,
                                       'run_algorithm':
                                       train_run.properties['run_algorithm'],
                                       'valid_score':
                                       train_run.properties['score'],
                                       'primary_metric':
                                       train_run.properties['primary_metric']})

    run.log("run_algorithm", run.tags['run_algorithm'])
    return run