63 lines
2.0 KiB
Python
Executable File
63 lines
2.0 KiB
Python
Executable File
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def masked_mae_loss(scaler, mask_value):
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def loss(preds, labels):
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if scaler:
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preds = scaler.inverse_transform(preds)
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labels = scaler.inverse_transform(labels)
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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 masked_mae_loss(scaler, mask_value=0.0).to(args['device'])
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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': mask1 = None
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if mask2 == 'None': 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)) |