ENH smaller confidence intervals in plot_reliability_diagram (#190)

lorentzenchr · Nov 27, 2024 · 3e9a8bd · 3e9a8bd
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diff --git a/docs/examples/classification.ipynb b/docs/examples/classification.ipynb
diff --git a/docs/examples/quantile_regression.ipynb b/docs/examples/quantile_regression.ipynb
diff --git a/docs/examples/regression_on_workers_compensation.ipynb b/docs/examples/regression_on_workers_compensation.ipynb
diff --git a/src/model_diagnostics/calibration/plots.py b/src/model_diagnostics/calibration/plots.py
@@ -229,8 +229,11 @@ def iso_statistic(y_obs, y_pred, weights=None, x_values=None):
 
             # We make the interval conservatively monotone increasing by applying
             # np.maximum.accumulate etc.
-            lower = -np.minimum.accumulate(-boot.confidence_interval.low)
-            upper = np.maximum.accumulate(boot.confidence_interval.high)
+            # Conservative here means smaller intervals such that it is more likely
+            # for the prediction to be out of the intervals leading to the conclusion
+            # of "not auto-calibrated".
+            lower = np.maximum.accumulate(boot.confidence_interval.low)
+            upper = np.minimum.accumulate(boot.confidence_interval.high[::-1])[::-1]
             if diagram_type == "bias":
                 lower = iso.X_thresholds_ - lower
                 upper = iso.X_thresholds_ - upper