Issue/211/two metric fixes (#212)

* cdeloss metric_output_type = MetricOutputType.single_value * outlier return as np.array instead of a list
LSSTDESC · Nov 27, 2023 · 2258988 · 2258988
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commit 2258988
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diff --git a/src/qp/metrics/concrete_metric_classes.py b/src/qp/metrics/concrete_metric_classes.py
@@ -225,7 +225,7 @@ class CDELossMetric(DistToPointMetric):
     """Conditional density loss"""
 
     metric_name = "cdeloss"
-    metric_output_type = MetricOutputType.one_value_per_distribution
+    metric_output_type = MetricOutputType.single_value
     default_eval_grid = np.linspace(0, 2.5, 301)
 
     def __init__(self, eval_grid: list = default_eval_grid, **kwargs) -> None:

diff --git a/src/qp/metrics/metrics.py b/src/qp/metrics/metrics.py
@@ -322,7 +322,7 @@ def calculate_outlier_rate(p, lower_limit=0.0001, upper_limit=0.9999):
     except ValueError:  #pragma: no cover - unittest coverage for _check_ensemble_is_not_nested is complete
         logging.warning("Each element in the ensemble `p` must be a single distribution.")
 
-    outlier_rates = [(dist.cdf(lower_limit) + (1. - dist.cdf(upper_limit)))[0][0] for dist in p]
+    outlier_rates = np.array([(dist.cdf(lower_limit) + (1. - dist.cdf(upper_limit)))[0][0] for dist in p])
     return outlier_rates
 
 def calculate_goodness_of_fit(estimate, reference, fit_metric='ks', num_samples=100, _random_state=None):