Suppress warnings from numpy in hypervolume computation

optuna/_hypervolume/hssp.py

@@ -44,10 +44,12 @@ def _solve_hssp_on_unique_loss_vals(
     subset_size: int,
     reference_point: np.ndarray,
 ) -> np.ndarray:
-    assert not np.any(reference_point - rank_i_loss_vals <= 0)
+    if not np.isfinite(reference_point).all():
+        return rank_i_indices[:subset_size]
+    diff_of_loss_vals_and_ref_point = reference_point - rank_i_loss_vals
     assert subset_size <= rank_i_indices.size
     n_objectives = reference_point.size
-    contribs = np.prod(reference_point - rank_i_loss_vals, axis=-1)
+    contribs = np.prod(diff_of_loss_vals_and_ref_point, axis=-1)
     selected_indices = np.zeros(subset_size, dtype=int)
     selected_vecs = np.empty((subset_size, n_objectives))
     indices = np.arange(rank_i_loss_vals.shape[0], dtype=int)

optuna/_hypervolume/utils.py

@@ -14,6 +14,8 @@ def _compute_2d(solution_set: np.ndarray, reference_point: np.ndarray) -> float:
             The reference point to compute the hypervolume.
     """
     assert solution_set.shape[1] == 2 and reference_point.shape[0] == 2
+    if not np.isfinite(reference_point).all():
+        return float("inf")
 
     # Ascending order in the 1st objective, and descending order in the 2nd objective.
     sorted_solution_set = solution_set[np.lexsort((-solution_set[:, 1], solution_set[:, 0]))]

optuna/_hypervolume/wfg.py

@@ -20,6 +20,8 @@ def __init__(self) -> None:
         self._reference_point: np.ndarray | None = None
 
     def _compute(self, solution_set: np.ndarray, reference_point: np.ndarray) -> float:
+        if not np.isfinite(reference_point).all():
+            return float("inf")
         self._reference_point = reference_point.astype(np.float64)
         if self._reference_point.shape[0] == 2:
             return _compute_2d(solution_set, self._reference_point)

tests/hypervolume_tests/test_hssp.py

@@ -1,4 +1,5 @@
 import itertools
+import math
 from typing import Tuple
 
 import numpy as np
@@ -11,11 +12,14 @@ def _compute_hssp_truth_and_approx(test_case: np.ndarray, subset_size: int) -> T
     r = 1.1 * np.max(test_case, axis=0)
     truth = 0.0
     for subset in itertools.permutations(test_case, subset_size):
-        truth = max(truth, optuna._hypervolume.WFG().compute(np.asarray(subset), r))
+        hv = optuna._hypervolume.WFG().compute(np.asarray(subset), r)
+        assert not math.isnan(hv)
+        truth = max(truth, hv)
     indices = optuna._hypervolume.hssp._solve_hssp(
         test_case, np.arange(len(test_case)), subset_size, r
     )
     approx = optuna._hypervolume.WFG().compute(test_case[indices], r)
+    assert not math.isnan(approx)
     return truth, approx
 
 
@@ -35,19 +39,13 @@ def test_solve_hssp_infinite_loss() -> None:
     rng = np.random.RandomState(128)
 
     subset_size = 4
-    test_case = rng.rand(9, 2)
-    test_case[-1].fill(float("inf"))
-    truth, approx = _compute_hssp_truth_and_approx(test_case, subset_size)
-    assert np.isinf(truth)
-    assert np.isinf(approx)
-
-    test_case = rng.rand(9, 3)
-    test_case[-1].fill(float("inf"))
-    truth, approx = _compute_hssp_truth_and_approx(test_case, subset_size)
-    assert truth == 0
-    assert np.isnan(approx)
-
     for dim in range(2, 4):
+        test_case = rng.rand(9, dim)
+        test_case[-1].fill(float("inf"))
+        truth, approx = _compute_hssp_truth_and_approx(test_case, subset_size)
+        assert np.isinf(truth)
+        assert np.isinf(approx)
+
         test_case = rng.rand(9, dim)
         test_case[-1].fill(-float("inf"))
         truth, approx = _compute_hssp_truth_and_approx(test_case, subset_size)