def compute_uboost_multipliers(self, sample_weight, score, y):
        signed_score = score * self.signed_uniform_label
        signed_score_cut = compute_cut_for_efficiency(self.target_efficiency, y == self.uniform_label, signed_score)
        global_score_cut = signed_score_cut * self.signed_uniform_label

        local_efficiencies = compute_group_efficiencies(signed_score, self.knn_indices, cut=signed_score_cut,
                                                        smoothing=self.smoothing)

        # pay attention - sample_weight should be used only here
        e_prime = np.average(np.abs(local_efficiencies - self.target_efficiency),
                             weights=sample_weight)

        is_uniform_class = (y == self.uniform_label)

        # beta = np.log((1.0 - e_prime) / e_prime)
        # changed to log(1. / e_prime), otherwise this can lead to the situation
        # where beta is negative (which is a disaster).
        # Mike (uboost author) said he didn't take that into account.
        beta = np.log(1. / e_prime)
        boost_weights = np.exp((self.target_efficiency - local_efficiencies) * is_uniform_class *
                               (beta * self.uniforming_rate))

        if self.keep_debug_info:
            self.debug_dict['local_effs'].append(local_efficiencies.copy())

        return boost_weights, global_score_cut
Restore internal state from object returned by getstate().
dataset/ETHPy150Open anaderi/lhcb_trigger_ml/hep_ml/uboost.py/uBoostBDT.compute_uboost_multipliers--crossover with--dataset/ETHPy150Open babble/babble/include/jython/Lib/random.py/Random.setstate