dodiscover.metrics.confusion_matrix_networks

dodiscover.metrics.confusion_matrix_networks(true_graph, pred_graph, labels=None, normalize=None)

Compute the confusion matrix comparing a predicted graph from the true graph.

Converts the graphs into an undirected graph, and then compares their adjacency matrix, which are symmetric.

Parameters:

true_graphinstance of causal graph

The true graph.

pred_graphinstance of causal graph

The predicted graph. The predicted graph and true graph must be the same type.

labelsarray_like of shape (n_classes), default=None

List of labels to index the matrix. This may be used to reorder or select a subset of labels. If None is given, those that appear at least once in y_true or y_pred are used in sorted order.

normalize{‘true’, ‘pred’, ‘all’}, default=None

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

Returns:

cmnp.ndarray of shape (2, 2)

The confusion matrix.

See also

sklearn.metrics.confusion_matrix

Notes

This function only compares the graph’s adjacency structure, which does not take into consideration the directionality of edges.