mc_confusion_matrix

bayesflow.diagnostics.mc_confusion_matrix(pred_models: dict[str, ndarray] | ndarray, true_models: dict[str, ndarray] | ndarray, model_names: Sequence[str] = None, fig_size: 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, cmap: Colormap | str = None, title: bool = True) → Figure

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

Parameters:

pred_modelsnp.ndarray of shape (num_data_sets, num_models)

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

true_modelsnp.ndarray of shape (num_data_sets, 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.

fig_sizetuple 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’}, default=None

Passed to sklearn.metrics.confusion_matrix. Normalizes confusion matrix over the true (rows), predicted (columns) conditions or all the population. If None, confusion matrix will not be normalized.

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.

Returns:

figplt.Figure - the figure instance for optional saving