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20 lines
1.0 KiB
Python
20 lines
1.0 KiB
Python
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score
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from torch.utils.tensorboard import SummaryWriter
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from logging import getLogger
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logger = getLogger(__name__)
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def evaluate(perdicted_class, predicted_proba, true_class, writer: SummaryWriter, epoch: int):
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accuracy = accuracy_score(true_class, perdicted_class)
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precision = precision_score(true_class, perdicted_class)
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recall = recall_score(true_class, perdicted_class)
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f1 = f1_score(true_class, perdicted_class)
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roc_auc = roc_auc_score(true_class, predicted_proba)
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writer.add_scalar('Eval/Accuracy', accuracy, epoch)
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writer.add_scalar('Eval/Precision', precision, epoch)
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writer.add_scalar('Eval/Recall', recall, epoch)
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writer.add_scalar('Eval/F1_Score', f1, epoch)
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writer.add_scalar('Eval/ROC_AUC', roc_auc, epoch)
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logger.info(f"Eval Metrics - Epoch {epoch}: Accuracy: {accuracy:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, F1 Score: {f1:.4f}, ROC AUC: {roc_auc:.4f}")
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