Overview¶
An experiment aggregation method consolidates information from the empirical metric distributions of individual experiments, and creates an aggregate distribution that summarizes the average performance for all experiments in the same experiment group.
The configuration oft the experiment aggregator must be specified along with the metric, preferably using the Study.add_metric method. The aggregation key must correspond to one of the aliases listed in the table below.
To add several experiments to the same ExperimentGroup, use the Study.add_experiment method, and pass the experiment name as '${GROUP_NAME}/${EXPERIMENT_NAME}', where ${GROUP_NAME} is the name of the ExperimentGroup, and ${EXPERIMENT_NAME} is the name of the Experiment.
The following aliases are available:
| Alias | Method |
|---|---|
| 'beta' | BetaAggregator |
| 'beta_conflation' | BetaAggregator |
| 'fe' | FEGaussianAggregator |
| 'fe_gaussian' | FEGaussianAggregator |
| 'fe_normal' | FEGaussianAggregator |
| 'fixed_effect' | FEGaussianAggregator |
| 'gamma' | GammaAggregator |
| 'gamma_conflation' | GammaAggregator |
| 'gaussian' | FEGaussianAggregator |
| 'hist' | HistogramAggregator |
| 'histogram' | HistogramAggregator |
| 'identity' | SingletonAggregator |
| 'normal' | FEGaussianAggregator |
| 'random_effect' | REGaussianAggregator |
| 're' | REGaussianAggregator |
| 're_gaussian' | REGaussianAggregator |
| 're_normal' | REGaussianAggregator |
| 'singleton' | SingletonAggregator |