| import numpy as np |
| from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score |
| |
| def compute_metrics_bce(eval_pred): |
| logits, labels = eval_pred |
|
|
| logits[logits>=0.5] = 1 |
| logits[logits<0.5] = 0 |
| predictions = logits.reshape(-1) |
| labels = labels.reshape(-1) |
|
|
| accuracy = accuracy_score(labels, predictions) |
| f1 = f1_score(labels, predictions, pos_label=1, average='binary') |
| precision = precision_score(labels, predictions, pos_label=1, average='binary') |
| recall = recall_score(labels, predictions, pos_label=1, average='binary') |
|
|
| return {"accuracy": accuracy, "f1": f1, "precision": precision, "recall": recall} |
