| 828 | return 2 / (1 / precision + 1 / recall), precision, recall |
| 829 | |
| 830 | def _fast_hist(self, label_true, label_pred, n_class): |
| 831 | mask = (label_true >= 0) & (label_true < n_class) |
| 832 | mask &= (label_pred >= 0) & (label_pred < n_class) |
| 833 | |
| 834 | if self.ignore_index is not None: |
| 835 | mask = mask & (label_true != self.ignore_index) |
| 836 | |
| 837 | hist = np.bincount( |
| 838 | n_class * label_true[mask].astype(int) + |
| 839 | label_pred[mask], minlength=n_class**2) |
| 840 | |
| 841 | # print(np.unique(label_true)) |
| 842 | # print(np.unique(label_pred)) |
| 843 | hist = hist.reshape(n_class, n_class) |
| 844 | |
| 845 | return hist |
| 846 | |
| 847 | def update(self, label_preds, label_trues): |
| 848 | self.reduced_confusion_matrix = None |