in cp_examples/mip_finetune/train_mip.py [0:0]
def fetch_pos_weights(csv, label_list, uncertain_label, nan_label):
pos = (csv[label_list] == 1).sum()
neg = (csv[label_list] == 0).sum()
if uncertain_label == 1:
pos = pos + (csv[label_list] == -1).sum()
elif uncertain_label == -1:
neg = neg + (csv[label_list] == -1).sum()
if nan_label == 1:
pos = pos + (csv[label_list].isna()).sum()
elif nan_label == -1:
neg = neg + (csv[label_list].isna()).sum()
pos_weights = torch.tensor((neg / np.maximum(pos, 1)).values.astype(np.float))
return pos_weights