88 lines
3.2 KiB
Python
88 lines
3.2 KiB
Python
import torch
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from sklearn.metrics import f1_score
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def accuracy(predict, labels, evaluator=None):
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"""计算准确率
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:param predict: tensor(N) 预测标签
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:param labels: tensor(N) 正确标签
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:param evaluator: ogb.nodeproppred.Evaluator
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:return: float 准确率
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"""
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if evaluator is not None:
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y_true, y_pred = labels.unsqueeze(dim=1), predict.unsqueeze(dim=1)
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return evaluator.eval({'y_true': y_true, 'y_pred': y_pred})['acc']
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else:
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return torch.sum(predict == labels).item() / labels.shape[0]
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def macro_f1_score(predict, labels):
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"""计算Macro-F1得分
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:param predict: tensor(N) 预测标签
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:param labels: tensor(N) 正确标签
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:return: float Macro-F1得分
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"""
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return f1_score(labels.numpy(), predict.long().numpy(), average='macro')
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@torch.no_grad()
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def evaluate(
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model, loader, g, labels, num_classes, predict_ntype,
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train_idx, val_idx, test_idx, evaluator=None):
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"""评估模型性能
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:param model: nn.Module GNN模型
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:param loader: NodeDataLoader 图数据加载器
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:param g: DGLGraph 图
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:param labels: tensor(N) 顶点标签
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:param num_classes: int 类别数
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:param predict_ntype: str 目标顶点类型
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:param train_idx: tensor(N_train) 训练集顶点id
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:param val_idx: tensor(N_val) 验证集顶点id
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:param test_idx: tensor(N_test) 测试集顶点id
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:param evaluator: ogb.nodeproppred.Evaluator
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:return: train_acc, val_acc, test_acc, train_f1, val_f1, test_f1
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"""
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model.eval()
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logits = torch.zeros(g.num_nodes(predict_ntype), num_classes, device=train_idx.device)
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for input_nodes, output_nodes, blocks in loader:
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logits[output_nodes[predict_ntype]] = model(blocks, blocks[0].srcdata['feat'])
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return calc_metrics(logits, labels, train_idx, val_idx, test_idx, evaluator)
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@torch.no_grad()
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def evaluate_full(model, g, labels, train_idx, val_idx, test_idx):
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"""评估模型性能(full-batch)
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:param model: nn.Module GNN模型
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:param g: DGLGraph 图
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:param labels: tensor(N) 顶点标签
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:param train_idx: tensor(N_train) 训练集顶点id
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:param val_idx: tensor(N_val) 验证集顶点id
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:param test_idx: tensor(N_test) 测试集顶点id
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:return: train_acc, val_acc, test_acc, train_f1, val_f1, test_f1
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"""
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model.eval()
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logits = model(g, g.ndata['feat'])
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return calc_metrics(logits, labels, train_idx, val_idx, test_idx)
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def calc_metrics(logits, labels, train_idx, val_idx, test_idx, evaluator=None):
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predict = logits.detach().cpu().argmax(dim=1)
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labels = labels.cpu()
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train_acc = accuracy(predict[train_idx], labels[train_idx], evaluator)
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val_acc = accuracy(predict[val_idx], labels[val_idx], evaluator)
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test_acc = accuracy(predict[test_idx], labels[test_idx], evaluator)
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train_f1 = macro_f1_score(predict[train_idx], labels[train_idx])
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val_f1 = macro_f1_score(predict[val_idx], labels[val_idx])
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test_f1 = macro_f1_score(predict[test_idx], labels[test_idx])
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return train_acc, val_acc, test_acc, train_f1, val_f1, test_f1
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METRICS_STR = ' | '.join(
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f'{split} {metric} {{:.4f}}'
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for metric in ('Acc', 'Macro-F1') for split in ('Train', 'Val', 'Test')
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)
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