Do not calculate HV for single-dimension problems

sb_arch_opt/algo/pymoo_interface/metrics.py

@@ -123,7 +123,7 @@ def do(self, f, *args, **kwargs):
         if f.ndim == 1:
             f = f[None, :]
 
-        if len(f) <= 1:
+        if f.shape[1] == 0:
             return 0.
 
         if self.ref_point is None:
@@ -138,6 +138,12 @@ def do(self, f, *args, **kwargs):
             self.normalization = ZeroToOneNormalization(ideal, nadir)
             self.ref_point = self.normalization.forward(nadir)
 
+        if f.shape[1] == 1:
+            # Calculate HV here due to bug in moocore: https://github.com/multi-objective/moocore/issues/58
+            f = self.normalization.forward(f)
+            hv = self.ref_point[0] - np.min(f)
+            return hv if hv >= 0. else 0.
+
         return super().do(f, *args, **kwargs)
 
 

sb_arch_opt/tests/algo/test_pymoo.py

@@ -7,6 +7,7 @@
 from typing import Optional
 from sb_arch_opt.problem import *
 from sb_arch_opt.sampling import *
+from sb_arch_opt.problems.discrete import MDBranin
 from sb_arch_opt.algo.pymoo_interface import *
 from sb_arch_opt.algo.pymoo_interface.random_search import RandomSearchAlgorithm
 
@@ -33,6 +34,16 @@ def test_nsga2(problem: ArchOptProblemBase):
     assert np.all(pop.get('X') == x_imp)
 
 
+def test_nsga2_so():
+    problem = MDBranin()
+    nsga2 = get_nsga2(pop_size=100)
+    result = minimize(problem, nsga2, termination=('n_gen', 10), verbose=True, progress=True)
+    pop = result.pop
+
+    x_imp, _ = problem.correct_x(pop.get('X'))
+    assert np.all(pop.get('X') == x_imp)
+
+
 def test_termination(problem: ArchOptProblemBase):
     nsga2 = get_nsga2(pop_size=100)
     assert minimize(problem, nsga2, get_default_termination(problem, tol=1e-4), verbose=True, progress=True)