ESSOptimizer: Fix priority for local search startpoints (ICB-DCM#1503)

Fixes a bug in ESSOptimizer that lead to incorrect priorities for the selection of candidate solutions for local searches. A numpy array was incorrectly initialized from a generator, which led to the `balance` hyperparameter being practically ignored, and candidate solutions being ranked only by objective function value.
dweindl · Nov 5, 2024 · c8640ff · c8640ff
1 parent a82e637
commit c8640ff
Showing 1 changed file with 12 additions and 7 deletions.
diff --git a/pypesto/optimize/ess/ess.py b/pypesto/optimize/ess/ess.py
@@ -487,14 +487,19 @@ def _do_local_search(
             quality_order = np.argsort(fx_best_children)
             # compute minimal distance between the best children and all local
             #  optima found so far
-            min_distances = np.array(
-                np.min(
-                    np.linalg.norm(
-                        y_i - optimizer_result.x[optimizer_result.free_indices]
+            min_distances = np.fromiter(
+                (
+                    min(
+                        np.linalg.norm(
+                            y_i
+                            - optimizer_result.x[optimizer_result.free_indices]
+                        )
+                        for optimizer_result in self.local_solutions
                     )
-                    for optimizer_result in self.local_solutions
-                )
-                for y_i in x_best_children
+                    for y_i in x_best_children
+                ),
+                dtype=np.float64,
+                count=len(x_best_children),
             )
             # sort by furthest distance to existing local optima
             diversity_order = np.argsort(min_distances)[::-1]