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- import torch
- import numpy as np
- from sklearn.metrics import f1_score, roc_auc_score
-
-
- def accuracy(output, labels):
- preds = output.max(1)[1].type_as(labels)
- correct = preds.eq(labels).double()
- correct = correct.sum()
- return correct / len(labels)
-
-
- def class_f1(output, labels, type='micro', pos_label=1):
- preds = output.max(1)[1].type_as(labels)
- return f1_score(labels.detach().cpu().numpy(), preds.cpu(), average=type, pos_label=pos_label)
-
-
- def roc_auc(output, labels):
- return roc_auc_score(labels.cpu().numpy(), output.detach().cpu().numpy())
-
-
- def loss(output,labels, weights=None):
- if weights is None:
- weights = torch.ones(labels.shape[0])
- return torch.sum(- weights * (labels.float() * output).sum(1), -1)
-
-
- def half_normalize(mx):
- rowsum = mx.sum(1).float()
- r_inv = rowsum.pow(-1).flatten()
- r_inv[torch.isinf(r_inv)] = 0.
- r_mat_inv = torch.diag(r_inv)
- mx = r_mat_inv.mm(mx)
- return mx
-
-
- def encode_onehot_torch(labels,num_classes=None):
- if num_classes is None:
- num_classes = int(labels.max() + 1)
- y = torch.eye(num_classes)
- return y[labels]
-
-
- def calculate_imbalance_weight(idx,labels):
- weights = torch.ones(len(labels))
- for i in range(labels.max()+1):
- sub_node = torch.where(labels == i)[0]
- sub_idx = [x.item() for x in sub_node if x in idx]
- weights[sub_idx] = 1 - len(sub_idx)/ len(idx)
- return weights
-
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