| import numpy as np |
| from sklearn.metrics import matthews_corrcoef |
| from sklearn.metrics import accuracy_score,f1_score |
| from scipy.stats import pearsonr, spearmanr |
| from statistics import * |
| from scipy.special import softmax |
|
|
| def metric_multi_accuracy(logits, labels, options_num): |
| logits = np.reshape(softmax(logits, -1)[:,1], (len(logits)//options_num, options_num)) |
| labels = np.argmax(np.reshape(labels, (len(labels)//options_num, options_num)),-1) |
| return metric_accuracy(logits, labels) |
|
|
| def metric_accuracy(logits, labels): |
| predicts = np.argmax(logits, axis=1) |
| return accuracy_score(labels, predicts) |
|
|
| def metric_f1(logits, labels): |
| predicts = np.argmax(logits, axis=1) |
| return f1_score(labels, predicts) |
|
|
| def metric_macro_f1(logits, ground_truth, labels=[0,1]): |
| predicts = np.argmax(logits, axis=1) |
| f1=[] |
| for l in labels: |
| binary_g = (ground_truth==l).astype(int) |
| binary_p = (predicts==l).astype(int) |
| f1.append(f1_score(binary_g, binary_p)) |
| return float(np.mean(f1)) |
|
|
| def metric_mcc(logits, labels): |
| predicts = np.argmax(logits, axis=1) |
| return matthews_corrcoef(labels, predicts) |
|
|
