| import sklearn.metrics | |
| import numpy as np | |
| def f1_macro(targets, predictions): | |
| targets, predictions = np.asarray(targets), np.asarray(predictions) | |
| unique_labels = np.unique(np.concatenate((targets, predictions))) | |
| return {"f1_macro": 100 * sklearn.metrics.f1_score(targets, predictions, labels=unique_labels, average='macro')} | |