root_mean_squared_error

bayesflow.diagnostics.root_mean_squared_error(estimates: ~typing.Mapping[str, ~numpy.ndarray] | ~numpy.ndarray, targets: ~typing.Mapping[str, ~numpy.ndarray] | ~numpy.ndarray, variable_keys: ~typing.Sequence[str] = None, variable_names: ~typing.Sequence[str] = None, normalize: bool = True, aggregation: ~typing.Callable = <function median>) → Mapping[str, Any]

Computes the (Normalized) Root Mean Squared Error (RMSE/NRMSE) for the given posterior and prior samples.

Parameters:

estimatesnp.ndarray of shape (num_datasets, num_draws_post, num_variables)

Posterior samples, comprising num_draws_post random draws from the posterior distribution for each data set from num_datasets.

targetsnp.ndarray of shape (num_datasets, num_variables)

Prior samples, comprising num_datasets ground truths.

variable_keysSequence[str], optional (default = None)

Select keys from the dictionaries provided in estimates and targets. By default, select all keys.

variable_namesSequence[str], optional (default = None)

Optional variable names to show in the output.

normalizebool, optional (default = True)

Whether to normalize the RMSE using the range of the prior samples.

aggregationcallable, optional (default = np.median)

Function to aggregate the RMSE across draws. Typically np.mean or np.median.

Returns:

resultdict

Dictionary containing:

• “values”np.ndarray

  The aggregated (N)RMSE for each variable.

• “metric_name”str

  The name of the metric (“RMSE” or “NRMSE”).

• “variable_names”str

  The (inferred) variable names.

Notes

Aggregation is performed after computing the RMSE for each posterior draw, instead of first aggregating the posterior draws and then computing the RMSE between aggregates and ground truths.