from azureml.train.estimator import Estimator


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()

    est = Estimator(source_directory=inference_folder,
                    entry_script='forecasting_script.py',
                    script_params={
                        '--target_column_name': target_column_name
                    },
                    inputs=[test_dataset.as_named_input('test_data')],
                    compute_target=compute_target,
                    environment_definition=inference_env)

    run = test_experiment.submit(est,
                                 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