67 lines
2.1 KiB
Python
Executable File
67 lines
2.1 KiB
Python
Executable File
def masked_mae_loss(scaler, mask_value):
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def loss(preds, labels):
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# 仅对预测反归一化；标签在数据管道中保持原始量纲
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if scaler:
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preds = scaler.inverse_transform(preds)
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return mae_torch(pred=preds, true=labels, mask_value=mask_value)
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return loss
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def get_loss_function(args, scaler):
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if args["loss_func"] == "mask_mae":
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# Return callable loss (no .to for function closures); disable masking by default
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return masked_mae_loss(scaler, mask_value=None)
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elif args["loss_func"] == "mae":
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return torch.nn.L1Loss().to(args["device"])
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elif args["loss_func"] == "mse":
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return torch.nn.MSELoss().to(args["device"])
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elif args["loss_func"] == "Huber":
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return torch.nn.HuberLoss().to(args["device"])
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else:
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raise ValueError("Unsupported loss function: {}".format(args.loss_func))
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import torch
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def mae_torch(pred, true, mask_value=None):
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if mask_value is not None:
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mask = torch.gt(true, mask_value)
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pred = torch.masked_select(pred, mask)
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true = torch.masked_select(true, mask)
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return torch.mean(torch.abs(true - pred))
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def rmse_torch(pred, true, mask_value=None):
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if mask_value is not None:
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mask = torch.gt(true, mask_value)
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pred = torch.masked_select(pred, mask)
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true = torch.masked_select(true, mask)
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return torch.sqrt(torch.mean((pred - true) ** 2))
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def mape_torch(pred, true, mask_value=None):
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if mask_value is not None:
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mask = torch.gt(true, mask_value)
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pred = torch.masked_select(pred, mask)
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true = torch.masked_select(true, mask)
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return torch.mean(torch.abs(torch.div((true - pred), (true + 0.001))))
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def all_metrics(pred, true, mask1, mask2):
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if mask1 == "None":
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mask1 = None
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if mask2 == "None":
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mask2 = None
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mae = mae_torch(pred, true, mask1)
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rmse = rmse_torch(pred, true, mask1)
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mape = mape_torch(pred, true, mask2)
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return mae, rmse, mape
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if __name__ == "__main__":
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pred = torch.Tensor([1, 2, 3, 4])
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true = torch.Tensor([2, 1, 4, 5])
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print(all_metrics(pred, true, None, None))
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