| import sklearn | |
| from sklearn.metrics import roc_auc_score,precision_recall_curve,accuracy_score,matthews_corrcoef | |
| # gt_np = [0,1,1,0] | |
| # pred_np = [0.2,0.7,0.8,0.3] | |
| # fps,tps,thresholds = sklearn.metrics._ranking._binary_clf_curve(gt_np, pred_np) | |
| # precision, recall, thresholds = precision_recall_curve(gt_np, pred_np) | |
| # print(fps,tps,thresholds) # tps An increasing count of true positives | |
| # print(precision[:-1], recall[:-1], thresholds) | |
| # tns = tps/recall[:-1] - tps | |
| # print(tns + fps + tps) | |
| # fns = | |
| # [0. 1. 2.] [2. 2. 2.] [0.8 0.3 0.2] | |
| # precision, recall, thresholds = precision_recall_curve(gt_np, pred_np) | |
| # numerator = 2 * recall * precision | |
| # denom = recall + precision | |
| # f1_scores = np.divide(numerator, denom, out=np.zeros_like(denom), where=(denom!=0)) | |
| # max_f1 = np.max(f1_scores) | |
| # max_f1_thresh = thresholds[np.argmax(f1_scores)] | |
| # print('The max_f1_thresh is', max_f1_thresh) | |
| # print('The average f1_score is', max_f1) | |
| # print('The average acc_score is', accuracy_score(gt_np, pred_np>max_f1_thresh)) | |