when prediction value is very small, metric logloss calculate error about deepctr-torch HOT 1 CLOSED

Same problem.
It is caused by different default float types between pytorch (float32) and numpy/sklearn (float64).
I solved by turning the model output to float64 (.astype("float64")). I modified two places in BaseModel.fit and BaseModel.predict methods and the problem is solved in my case.

fit():
    train_result[name].append(metric_fun(
                                    y.cpu().data.numpy(), y_pred.cpu().data.numpy().astype("float64")))
predict():
    return np.concatenate(pred_ans).astype("float64")

from deepctr-torch.