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174 | def classification_metrics(
y_true: np.ndarray,
y_pred: np.ndarray,
conf: np.ndarray,
class_names: list[str],
y_train_labels: Iterable,
cost_matrix: Optional[np.ndarray] = None,
) -> dict:
y_true = _encode_labels(y_true, class_names)
y_pred = _encode_labels(y_pred, class_names)
conf = np.asarray(conf, dtype=float)
y_train_arr = _encode_labels(y_train_labels, class_names)
n = len(y_true)
num_classes = len(class_names)
classes = list(range(num_classes))
cm = confusion_matrix(y_true, y_pred, labels=classes)
# Probability-vector MAE/RMSE (Weka's definition for classification).
# Weka's Evaluation.meanAbsoluteError() divides by N * num_classes (the
# per-class differences are summed inside, then the total is divided by
# m_WithClass which is N — but the accumulation runs over classes too, so
# the effective denominator is N*C). Confirmed against the OpenML server
# for run 1 / dataset 9 (autos): port gave 0.8895, server had 0.1271,
# ratio is exactly 7 = num_classes.
onehot = np.eye(num_classes)[y_true]
mae = float(np.abs(conf - onehot).mean())
rmse = float(np.sqrt(np.square(conf - onehot).mean()))
prior = class_ratios(y_train_arr, num_classes)
mae_prior = float(np.abs(onehot - prior).mean())
rmse_prior = float(np.sqrt(np.square(onehot - prior).mean()))
h_prior = float(entropy(prior, base=2)) if prior.sum() > 0 else 0.0
p, r, f, _ = precision_recall_fscore_support(
y_true, y_pred, labels=classes, average=None, zero_division=0
)
instances_per_class = cm.sum(axis=1)
total_inst = int(instances_per_class.sum())
weights = (
instances_per_class / total_inst if total_inst > 0 else np.zeros(num_classes)
)
try:
auroc_per_class = roc_auc_score(
np.eye(num_classes)[y_true],
conf,
average=None,
multi_class="ovr",
labels=classes,
)
auroc_per_class = [float(x) for x in auroc_per_class]
auroc_global = (
float(np.average(auroc_per_class, weights=weights))
if weights.sum() > 0
else None
)
except (ValueError, IndexError):
auroc_per_class = [None] * num_classes
auroc_global = None
result: dict = {
"mean_absolute_error": mae,
"mean_prior_absolute_error": mae_prior,
"root_mean_squared_error": rmse,
"root_mean_prior_squared_error": rmse_prior,
"relative_absolute_error": (mae / mae_prior) if mae_prior > 0 else 0.0,
"root_relative_squared_error": (rmse / rmse_prior) if rmse_prior > 0 else 0.0,
"prior_entropy": h_prior,
"kb_relative_information_score": kb_relative_information(y_true, conf, prior),
"predictive_accuracy": float(accuracy_score(y_true, y_pred)),
"kappa": float(cohen_kappa_score(y_true, y_pred)),
"precision": (
float(np.average(p, weights=weights)) if weights.sum() > 0 else 0.0
),
"weighted_recall": (
float(np.average(r, weights=weights)) if weights.sum() > 0 else 0.0
),
"unweighted_recall": float(r.mean()),
"f_measure": (
float(np.average(f, weights=weights)) if weights.sum() > 0 else 0.0
),
"number_of_instances": float(n),
"confusion_matrix": cm.tolist(),
"_per_class": {
"precision": p.tolist(),
"recall": r.tolist(),
"f_measure": f.tolist(),
"auroc": auroc_per_class,
"instances_per_class": instances_per_class.tolist(),
},
}
if auroc_global is not None:
result["area_under_roc_curve"] = auroc_global
if cost_matrix is not None:
cm_cost = np.asarray(cost_matrix, dtype=float)
total_cost = float((cm * cm_cost).sum())
result["total_cost"] = total_cost
result["average_cost"] = total_cost / n if n > 0 else 0.0
return result
|