get_confidence_intervals.py

def get_confidence_intervals(preds_test, ci_multiplier):
    global scaler
    
    preds_test = torch.tensor(preds_test)
    
    pred_mean = preds_test.mean(1)
    pred_std = preds_test.std(1).detach().cpu().numpy()

    pred_std = torch.tensor((pred_std))
    
    upper_bound = pred_mean + (pred_std * ci_multiplier)
    lower_bound = pred_mean - (pred_std * ci_multiplier)
    #gather unscaled confidence intervals

    pred_mean_final = pred_mean.unsqueeze(1).detach().cpu().numpy()
    pred_mean_unscaled = scaler.inverse_transform(pred_mean_final)

    upper_bound_unscaled = upper_bound.unsqueeze(1).detach().cpu().numpy()
    upper_bound_unscaled = scaler.inverse_transform(upper_bound_unscaled)
    
    lower_bound_unscaled = lower_bound.unsqueeze(1).detach().cpu().numpy()
    lower_bound_unscaled = scaler.inverse_transform(lower_bound_unscaled)
    
    return pred_mean_unscaled, upper_bound_unscaled, lower_bound_unscaled