def calculate_multiple_f1_scores(preds, labels): if len(labels.shape) == 1: return calculate_multiclass_f1_score(preds, labels) else: return [ compute_f1_score(preds[:, i], labels[:, i], average="weighted") for i in range(labels.shape[-1]) ]
def flat_f1(settings, preds, labels): pred_flat = np.argmax(preds, axis=1).flatten() labels_flat = labels.flatten() labels = list(range(0, settings.get_num_classifier_labels())) f1 = compute_f1_score(labels_flat, pred_flat, labels, average='weighted', zero_division=0) return f1
def calculate_multiclass_f1_score(preds, labels): preds = torch.argmax(preds, dim=1).detach().numpy() labels = labels.numpy() f1score = compute_f1_score(labels, preds, average="weighted") return f1score
def calculate_multiclass_f1_score(preds, labels): f1score = compute_f1_score(labels, preds, average="weighted") return f1score
def calculate_f1_score(logits, labels): preds = np.argmax(logits, axis=1) f1score = compute_f1_score(labels, preds, average="weighted") return f1score