libcity.model.loss

libcity.model.loss.explained_variance_score_np(preds, labels)

libcity.model.loss.explained_variance_score_torch(preds, labels)

libcity.model.loss.huber_loss(preds, labels, delta=1.0)

libcity.model.loss.log_cosh_loss(preds, labels)

libcity.model.loss.masked_mae_loss(y_pred, y_true)

libcity.model.loss.masked_mae_np(preds, labels, null_val=nan)

libcity.model.loss.masked_mae_torch(preds, labels, null_val=nan)

libcity.model.loss.masked_mape_np(preds, labels, null_val=nan)

libcity.model.loss.masked_mape_torch(preds, labels, null_val=nan, eps=0)

libcity.model.loss.masked_mse_np(preds, labels, null_val=nan)

libcity.model.loss.masked_mse_torch(preds, labels, null_val=nan)

libcity.model.loss.masked_rmse_np(preds, labels, null_val=nan)

libcity.model.loss.masked_rmse_torch(preds, labels, null_val=nan)

libcity.model.loss.quantile_loss(preds, labels, delta=0.25)

libcity.model.loss.r2_score_np(preds, labels)

libcity.model.loss.r2_score_torch(preds, labels)