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utils.py
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utils.py
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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