mc_confusion_matrix

bayesflow.diagnostics.plots.mc_confusion_matrix(pred_models: Mapping[str, ndarray] | ndarray, true_models: Mapping[str, ndarray] | ndarray, model_names: Sequence[str] = None, figsize: tuple = (5, 5), label_fontsize: int = 16, title_fontsize: int = 18, value_fontsize: int = 10, tick_fontsize: int = 12, xtick_rotation: int = None, ytick_rotation: int = None, normalize: str | None = 'true', cmap: Colormap | str = None, title: bool = True, ax: Axes = None) → Figure

Plots a confusion matrix for validating a neural network trained for Bayesian model comparison.

Parameters:

pred_modelsnp.ndarray of shape (num_datasets, num_models)

The predicted posterior model probabilities (PMPs) per data set.

true_modelsnp.ndarray of shape (num_datasets, num_models)

The one-hot-encoded true model indices per data set.

model_nameslist or None, optional, default: None

The model names for nice plot titles. Inferred if None.

figsizetuple or None, optional, default: (5, 5)

The figure size passed to the matplotlib constructor. Inferred if None

label_fontsizeint, optional, default: 16

The font size of the y-label and y-label texts

title_fontsizeint, optional, default: 18

The font size of the title text.

value_fontsizeint, optional, default: 10

The font size of the text annotations and the colorbar tick labels.

tick_fontsizeint, optional, default: 12

The font size of the axis label and model name texts.

xtick_rotation: int, optional, default: None

Rotation of x-axis tick labels (helps with long model names).

ytick_rotation: int, optional, default: None

Rotation of y-axis tick labels (helps with long model names).

normalize{‘true’, ‘pred’, ‘all’} or None, optional, default: ‘true’

Passed to the confusion matrix. Normalizes the counts over the true (rows), predicted (columns) conditions, or the whole population, yielding values in [0, 1] that are annotated as fractions (.2f). With the default 'true' each row sums to 1, so the diagonal reads as the per-model classification accuracy. Pass None to display raw integer counts instead.

cmapmatplotlib.colors.Colormap or str, optional, default: None

Colormap to be used for the cells. If a str, it should be the name of a registered colormap, e.g., ‘viridis’. Default colormap matches the BayesFlow defaults by ranging from white to red.

titlebool, optional, default True

A flag for adding ‘Confusion Matrix’ above the matrix.

axmatplotlib.axes.Axes, optional, default: None

An existing axis to draw into. If None, a new figure and axis are created. When provided, figsize is ignored and the parent figure is returned, enabling composition (e.g. side-by-side panels).

Returns:

figplt.Figure - the figure instance for optional saving