visualize_bohb.py

import matplotlib.pyplot as plt
import hpbandster.core.result as hpres
import hpbandster.visualization as hpvis
import argparse


def main(args):
    # load the example run from the log files
    result = hpres.logged_results_to_HBS_result(args.run_id)

    # get all executed runs
    all_runs = result.get_all_runs()

    # get the 'dict' that translates config ids to the actual configurations
    id2conf = result.get_id2config_mapping()


    # Here is how you get he incumbent (best configuration)
    inc_id = result.get_incumbent_id()

    # let's grab the run on the highest budget
    inc_runs = result.get_runs_by_id(inc_id)
    inc_run = inc_runs[-1]


    # We have access to all information: the config, the loss observed during
    #optimization, and all the additional information
    inc_loss = inc_run.loss
    inc_config = id2conf[inc_id]['config']

    print('Best found configuration:')
    print(inc_config)


    # Let's plot the observed losses grouped by budget,
    hpvis.losses_over_time(all_runs)

    # the number of concurent runs,
    hpvis.concurrent_runs_over_time(all_runs)

    # and the number of finished runs.
    hpvis.finished_runs_over_time(all_runs)

    # This one visualizes the spearman rank correlation coefficients of the losses
    # between different budgets.
    hpvis.correlation_across_budgets(result)

    # For model based optimizers, one might wonder how much the model actually helped.
    # The next plot compares the performance of configs picked by the model vs. random ones
    hpvis.performance_histogram_model_vs_random(all_runs, id2conf)

    plt.show()

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--run_id', type=str,   help='run id')
    args = parser.parse_args()
    main(args)