def evaluate(self, ground_truth, result):
"""Return AUC metric."""
binary_labels = labels_to_binary(ground_truth)
binary_labels = np.hstack([
binary_labels[column].values.reshape(-1, 1)
for column in result.columns
])
predicted_labels = np.hstack([
result[column].values.reshape(-1, 1) for column in result.columns
])
return log_loss(binary_labels, predicted_labels)
def evaluate(self, ground_truth, result):
"""Return AUC metric."""
classwise_auc = {}
binary_labels = labels_to_binary(ground_truth)
binary_labels = np.hstack([
binary_labels[column].values.reshape(-1, 1)
for column in result.columns
])
predicted_labels = np.hstack([
result[column].values.reshape(-1, 1) for column in result.columns
])
return roc_auc_score(binary_labels, predicted_labels, average='macro')
def evaluate(self, ground_truth, result):
"""Return AUC metric."""
ground_truth = [yi[1] for yi in ground_truth]
classes = list(set(ground_truth) | set(result.columns))
classwise_auc = {}
binary_labels = labels_to_binary(ground_truth)
default = np.zeros(shape=[len(ground_truth), 1], dtype=np.int32)
binary_labels = np.hstack([
binary_labels[column].values.reshape(-1, 1)
if column in binary_labels else default for column in classes
])
predicted_labels = np.hstack([
result[column].values.reshape(-1, 1)
if column in result.columns else default for column in classes
])
return roc_auc_score(binary_labels, predicted_labels, average='macro')