Dev (#29)

sb_arch_opt/__init__.py

@@ -1 +1 @@
-__version__ = '1.5.4'
+__version__ = '1.5.5'

sb_arch_opt/algo/segomoe_interface/algo.py

@@ -184,6 +184,7 @@ def set_pop(self, pop: Population = None):
             self._x = self._x_failed = self._y = None
         else:
             self._x, self._x_failed, self._y = self._get_xy(pop)
+        self._save_results()
 
     def run_optimization(self):
         capture_log()

sb_arch_opt/problem.py

@@ -464,8 +464,9 @@ def _correct_x(self, x: np.ndarray, is_active: np.ndarray):
         """
 
     def __repr__(self):
-        """repr() of the class, should be unique for unique Pareto fronts"""
-        raise NotImplementedError
+        """repr() of the class, should be unique for unique Pareto fronts. As fallback it returns a random repr."""
+        import random
+        return f'{self.__class__.__name__}{random.randint(1000000, 9999999)}()'
 
 
 class ArchOptRepair(Repair):

sb_arch_opt/tests/test_problem.py

@@ -498,3 +498,16 @@ def test_explicit_design_space():
     problem_try_mod = DummyExplicitDesignSpaceProblem(write_x_out=True)
     with pytest.raises(ValueError):
         problem_try_mod.evaluate(x_all)
+
+
+def test_ds_stub_problem():
+    problem = HierarchicalDummyProblem()
+    stub_problem = ArchOptProblemBase(problem.design_space)
+    x = HierarchicalSampling().do(stub_problem, 100).get('X')
+    assert x.shape[0] > 0
+
+    n = 12
+    problem = HierarchicalDummyProblem(n=n)
+    stub_problem = ArchOptProblemBase(problem.design_space)
+    x = HierarchicalSampling().do(stub_problem, 100).get('X')
+    assert x.shape[0] > 0