refactor(qav3): cleanup multi objective best trial heuristic

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index 7015783d7899e00bc260581d85c9e798a00608be..0141b16e78b9b7b2084cc4292b758ec391353664 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -389,31 +389,39 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             return None
         best_trials = study.best_trials
         if namespace == "label":
-            range_sizes = [trial.values[1] for trial in best_trials]
-            median_range_size = np.median(range_sizes)
+            peaks_sizes = [trial.values[1] for trial in best_trials]
+            median_peaks_size = np.median(peaks_sizes)
             equal_median_trials = [
                 trial
                 for trial in best_trials
-                if np.isclose(trial.values[1], median_range_size)
+                if np.isclose(trial.values[1], median_peaks_size)
             ]
             if equal_median_trials:
                 return max(equal_median_trials, key=lambda trial: trial.values[0])
-            nearest_above_median = (np.inf, -np.inf, None)
-            nearest_below_median = (-np.inf, -np.inf, None)
+            nearest_above_median = (
+                np.inf,
+                -np.inf,
+                None,
+            )  # (trial_peaks_size, trial_peaks_range, trial_index)
+            nearest_below_median = (
+                -np.inf,
+                -np.inf,
+                None,
+            )  # (trial_peaks_size, trial_peaks_range, trial_index)
             for idx, trial in enumerate(best_trials):
-                range_size = trial.values[1]
-                if range_size >= median_range_size:
-                    if range_size < nearest_above_median[0] or (
-                        range_size == nearest_above_median[0]
-                        and trial.values[0] > nearest_above_median[1]
+                peaks_size = trial.values[1]
+                if peaks_size >= median_peaks_size:
+                    if peaks_size < nearest_above_median[0] or (
+                        peaks_size == nearest_above_median[0]
+                        and trial.values[1] > nearest_above_median[1]
                     ):
-                        nearest_above_median = (range_size, trial.values[0], idx)
-                if range_size <= median_range_size:
-                    if range_size > nearest_below_median[0] or (
-                        range_size == nearest_below_median[0]
-                        and trial.values[0] > nearest_below_median[1]
+                        nearest_above_median = (peaks_size, trial.values[1], idx)
+                if peaks_size <= median_peaks_size:
+                    if peaks_size > nearest_below_median[0] or (
+                        peaks_size == nearest_below_median[0]
+                        and trial.values[1] > nearest_below_median[1]
                     ):
-                        nearest_below_median = (range_size, trial.values[0], idx)
+                        nearest_below_median = (peaks_size, trial.values[1], idx)
             if nearest_above_median[2] is None or nearest_below_median[2] is None:
                 return None
             above_median_trial = best_trials[nearest_above_median[2]]