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| from src.predict import predict | |
| from sklearn.metrics import precision_recall_fscore_support, accuracy_score, classification_report | |
| def evaluate(model, loader, count_loss=True, report=False): | |
| # Model Preidction (Inference) | |
| all_preds, all_true, loss = predict(model, loader, count_loss) | |
| class_report = None | |
| # Get evaluation metric | |
| precision, recall, f1, _ = precision_recall_fscore_support(all_true, all_preds, average='macro', zero_division=0) | |
| acc = accuracy_score(all_true, all_preds) | |
| # Get classification report | |
| if report: | |
| class_report = classification_report(all_true, all_preds) | |
| return precision, recall, f1, acc, loss, class_report | |
| def evaluate_ignore_O(model, loader): | |
| pass |