wehub-resource-sync
34 lines
992 B
Python
34 lines
992 B
Python
import numpy as np
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import torch
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def evaluate_model(model, loss, data_iter):
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model.eval()
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l_sum, n = 0.0, 0
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with torch.no_grad():
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for x, y in data_iter:
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y_pred = model(x).view(len(x), -1)
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l = loss(y_pred, y)
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l_sum += l.item() * y.shape[0]
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n += y.shape[0]
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return l_sum / n
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def evaluate_metric(model, data_iter, scaler):
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model.eval()
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with torch.no_grad():
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mae, mape, mse = [], [], []
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for x, y in data_iter:
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y = scaler.inverse_transform(y.cpu().numpy()).reshape(-1)
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y_pred = scaler.inverse_transform(
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model(x).view(len(x), -1).cpu().numpy()
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).reshape(-1)
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d = np.abs(y - y_pred)
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mae += d.tolist()
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mape += (d / y).tolist()
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mse += (d**2).tolist()
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MAE = np.array(mae).mean()
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MAPE = np.array(mape).mean()
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RMSE = np.sqrt(np.array(mse).mean())
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return MAE, MAPE, RMSE
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