accuracy#

bayesflow.diagnostics.metrics.accuracy(predictions: ndarray, true_models: ndarray, model_names: Sequence[str] | None = None, per_model: bool = True) → dict[str, Any][source]#

Compute classification accuracy for model comparison networks.

Parameters:

predictionsnp.ndarray of shape (N, M): Predicted (log) probabilities. The argmax is used as the predicted label.
true_modelsnp.ndarray of shape (N, M): One-hot encoded true model indices.
model_namesSequence[str], optional (default = None): Optional model names to show in the output. Only used when per_model is True. By default, models are called “M_” + model index.
per_modelbool, optional (default = True): If True, returns the per-model accuracy, i.e., the diagonal entries of the confusion matrix normalized over the true (row) labels. This is equivalent to the recall / true-positive-rate of each model. If False, returns the overall (aggregate) accuracy as a single float.

Returns:

dict[str, any] or float

If per_model is True, a dictionary containing:

“values”np.ndarray
The per-model accuracy (diagonal of the row-normalized confusion matrix).
“metric_name”str
The name of the metric (“Accuracy”).
“model_names”list[str]
The (inferred) model names.

If per_model is False, the fraction of datasets for which the predicted model matches the true model (a single float).

Examples

>>> import numpy as np
>>> from bayesflow.diagnostics.metrics import accuracy
>>> probs = np.array([[0.8, 0.1, 0.1], [0.1, 0.9, 0.0]])
>>> one_hot = np.array([[1, 0, 0], [0, 1, 0]])
>>> accuracy(probs, one_hot, per_model=False)
1.0