70c416bbd7ebc468fda10c37b1f2965d43059249,skopt/learning/gbrt.py,GradientBoostingQuantileRegressor,predict,#GradientBoostingQuantileRegressor#,54

Before Change

    def predict(self, X):
        Predict for each quantile.
        return np.asarray([rgr.predict(X) for rgr in self.regressors_]).T

After Change

        
        predicted_quantiles = np.asarray(
            [rgr.predict(X) for rgr in self.regressors_])
        if not return_std:
            return predicted_quantiles.T
        else:
            std_quantiles = [0.16, 0.5, 0.84]
            is_present_mask = np.in1d(std_quantiles, self.quantiles)
            if not np.all(is_present_mask):
                raise ValueError(
                    "return_std works only if the quan")
            low = self.regressors_[self.quantiles.index(0.16)].predict(X)
            high = self.regressors_[self.quantiles.index(0.84)].predict(X)
            mean = self.regressors_[self.quantiles.index(0.5)].predict(X)
            return mean, ((high - low) / 2.0)

Instances