Blocked Algorithms still run for AutoML Experiment

Question

I have been trying to run an AutoML Forecasting Experiment with only allowing one algorithm (FBProphet) to run and no other supported algorithms. The issue I run into is that even though I specify the blocked algorithms, they still run in the experiment taking up unnecessary runtime. For eg, my experiment should run only for 1-2 hours but it ends up running for 24-30 hours because it still runs the undesired algorithms. Is there any way I can stop making the blocked algorithms from running in my experiment so I can save up on significant runtime? I have attached a screenshot and my AutoML config code to help understand this issue better.

Code:

n_test_periods = 60
blocked_algos = ['ExtremeRandomTrees','DecisionTree','ElasticNet','LassoLars']
time_series_settings = {
    'time_column_name': time_column_name,
    'grain_column_names': grain_column_names ,
    'forecast_horizon': n_test_periods
}

automl_config = AutoMLConfig(task='forecasting',
                             debug_log='Logs/prophet_forecasting_errors.log',
                             primary_metric='normalized_mean_absolute_error',
                             training_data=train_data,
                             label_column_name=target_column_name,
                             compute_target=compute_target,
                             featurization= 'off',
                             blocked_model = blocked_algos,
                             allowed_models = ['Prophet'],
                             n_cross_validations= 3,
                             verbosity=logging.INFO,
                             max_cores_per_iteration=6,
                             **time_series_settings)

remote_run = experiment.submit(automl_config, show_output=True)

Screenshot of the Experiment: (This took 32h when it should ideally take 56 mins)

Answer 1

@Megha Lokanadham it's always good to have the latest version if possible, latest version is 1.18.0 as of today.

Can you please try the following 1. Correct the name of the parameter( blocked_model) to "blocked_models" and keep both "blocked_models" and "allowed_models".