h2o-py/h2o/model/metrics_base.py
def metric(self, metric, thresholds=None):
        """
        :param str metric: The desired metric.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then
            the thresholds in this set of metrics will be used.
        :returns: The set of metrics for the list of thresholds.
        """
        assert_is_type(thresholds, None, [numeric])
        if not thresholds: thresholds = [self.find_threshold_by_max_metric(metric)]
        thresh2d = self._metric_json['thresholds_and_metric_scores']
        metrics = []
        for t in thresholds:
            idx = self.find_idx_by_threshold(t)
            metrics.append([t, thresh2d[metric][idx]])
        return metrics
def metric(self, metric, thresholds=None):
        """
        :param str metric: The desired metric.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then
            the thresholds in this set of metrics will be used.
        :returns: The set of metrics for the list of thresholds.
        """
        assert_is_type(thresholds, None, [numeric])
        if not thresholds: thresholds = [self.find_threshold_by_max_metric(metric)]
        thresh2d = self._metric_json['thresholds_and_metric_scores']
        metrics = []
        for t in thresholds:
            idx = self.find_idx_by_threshold(t)
            metrics.append([t, thresh2d[metric][idx]])
        return metrics
:param str metric: The desired metric.
        :param thresholds: thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then
            the thresholds in this set of metrics will be used.
        :returns: The set of metrics for the list of thresholds.