recovery#

bayesflow.diagnostics.recovery(estimates: ~collections.abc.Mapping[str, ~numpy.ndarray] | ~numpy.ndarray, targets: ~collections.abc.Mapping[str, ~numpy.ndarray] | ~numpy.ndarray, variable_keys: ~collections.abc.Sequence[str] = None, variable_names: ~collections.abc.Sequence[str] = None, point_agg=<function median>, uncertainty_agg=<function median_abs_deviation>, add_corr: bool = True, figsize: ~collections.abc.Sequence[int] = None, label_fontsize: int = 16, title_fontsize: int = 18, metric_fontsize: int = 16, tick_fontsize: int = 12, color: str = '#132a70', num_col: int = None, num_row: int = None, xlabel: str = 'Ground truth', ylabel: str = 'Estimate', **kwargs) → Figure[source]#

Creates and plots publication-ready recovery plot with true estimate vs. point estimate + uncertainty. The point estimate can be controlled with the point_agg argument, and the uncertainty estimate can be controlled with the uncertainty_agg argument.

This plot yields similar information as the “posterior z-score”, but allows for generic point and uncertainty estimates:

https://betanalpha.github.io/assets/case_studies/principled_bayesian_workflow.html

Important: Posterior aggregates play no special role in Bayesian inference and should only be used heuristically. For instance, in the case of multi-modal posteriors, common point estimates, such as mean, (geometric) median, or maximum a posteriori (MAP) mean nothing.

Parameters:

estimatesnp.ndarray of shape (num_datasets, num_post_draws, num_params): The posterior draws obtained from num_datasets
targetsnp.ndarray of shape (num_datasets, num_params): The prior draws (true parameters) used for generating the num_datasets
variable_keyslist or None, optional, default: None: Select keys from the dictionaries provided in estimates and targets. By default, select all keys.
variable_nameslist or None, optional, default: None: The individual parameter names for nice plot titles. Inferred if None
point_aggfunction to compute point estimates. Default: median
uncertainty_aggfunction to compute uncertainty estimates. Default: MAD
add_corrboolean, default: True: Should correlations between estimates and ground truth values be shown?
figsizetuple or None, optional, defaultNone: The figure size passed to the matplotlib constructor. Inferred if None.
label_fontsizeint, optional, default: 16: The font size of the y-label text.
title_fontsizeint, optional, default: 18: The font size of the title text.
metric_fontsizeint, optional, default: 16: The font size of the metrics shown as text.
tick_fontsizeint, optional, default: 12: The font size of the axis ticklabels.
colorstr, optional, default: ‘#8f2727’: The color for the true vs. estimated scatter points and error bars.
num_rowint, optional, default: None: The number of rows for the subplots. Dynamically determined if None.
num_colint, optional, default: None: The number of columns for the subplots. Dynamically determined if None.
xlabel:
ylabel:

Returns:

fplt.Figure - the figure instance for optional saving

Raises:

ShapeError: If there is a deviation from the expected shapes of estimates and targets.