syne_tune.experiments.visualization.aggregate_results module

syne_tune.experiments.visualization.aggregate_results.fill_trajectory(performance_list, time_list, replace_nan=nan)[source]

Return type:: (ndarray, ndarray)

syne_tune.experiments.visualization.aggregate_results.compute_mean_and_ci(metrics_runs, time)[source]

Aggregate is the mean, error bars are empirical estimate of 95% confidence interval for the true mean.

Note: Error bar scale depends on number of runs n via 1 / sqrt(n).

Return type:: Dict[str, ndarray]

syne_tune.experiments.visualization.aggregate_results.compute_median_percentiles(metrics_runs, time)[source]

Aggregate is the median, error bars are 25 and 75 percentiles.

Note: Error bar scale does not depend on number of runs.

Return type:: Dict[str, ndarray]

syne_tune.experiments.visualization.aggregate_results.compute_iqm_bootstrap(metrics_runs, time)[source]

The aggregate is the interquartile mean (IQM). Error bars are bootstrap estimate of 95% confidence interval for true IQM. This is the normal interval, based on the bootstrap variance estimate. While other bootstrap CI estimates are available, they are more expensive to compute.

Note: Error bar scale depends on number of runs n via 1 / sqrt(n).

Return type:: Dict[str, ndarray]

syne_tune.experiments.visualization.aggregate_results.aggregate_and_errors_over_time(errors, runtimes, mode='mean_and_ci')[source]

Return type:: Dict[str, ndarray]