def update(self, j, est):
        do_oob = est.subsample < 1
        # we need to take into account if we fit additional estimators.
        i = j - self.begin_at_stage  # iteration relative to the start iter
        if (i + 1) % self.verbose_mod == 0:
            oob_impr = est.oob_improvement_[j] if do_oob else 0
            remaining_time = ((est.n_estimators - (j + 1)) *
                              (time() - self.start_time) / float(i + 1))
            if remaining_time > 60:
                remaining_time = '{0:.2f}m'.format(remaining_time / 60.0)
            else:
                remaining_time = '{0:.2f}s'.format(remaining_time)
            print(self.verbose_fmt.format(iter=j + 1,
                                          train_score=est.train_score_[j],
                                          oob_impr=oob_impr,
                                          remaining_time=remaining_time))
            if self.verbose == 1 and ((i + 1) // (self.verbose_mod * 10) > 0):
                # adjust verbose frequency (powers of 10)
                self.verbose_mod *= 10
Update reporter with new iteration.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/ensemble/gradient_boosting.py/VerboseReporter.update