Fix bug in IndividualMultiGenome and add test

Add test for local search with MultiGenome
Fix bug in IndividualMultiGenome.update_parameters function

Author: mschmidt87

cgp/individual.py

@@ -211,9 +211,10 @@ def to_sympy(self, simplify: bool = True) -> List["sympy_expr.Expr"]:
 
     def update_parameters_from_torch_class(self, torch_cls: List["torch.nn.Module"]) -> None:
         any_parameter_updated = any(
-            self._update_parameters_from_torch_class(g, tcls)
-            for g, tcls in zip(self.genome, torch_cls)
+            [
+                self._update_parameters_from_torch_class(g, tcls)
+                for g, tcls in zip(self.genome, torch_cls)
+            ]
         )
-
         if any_parameter_updated:
             self.fitness = None

test/test_local_search.py

@@ -32,3 +32,43 @@ def objective(f):
     ind.genome.parameter_names_to_values["<p1>"] = 1.0
     cgp.local_search.gradient_based(ind, objective, 0.05, 1)
     assert ind.genome.parameter_names_to_values["<p1>"] == pytest.approx(1.0)
+
+
+def test_gradient_based_step_towards_maximum_multi_genome():
+    torch = pytest.importorskip("torch")
+
+    primitives = (cgp.Parameter,)
+    genome = cgp.Genome(1, 1, 1, 1, 1, primitives)
+    # f(x) = c
+    genome.dna = [ID_INPUT_NODE, ID_NON_CODING_GENE, 0, 0, ID_OUTPUT_NODE, 1]
+    genome2 = genome.clone()
+    ind = cgp.individual.IndividualMultiGenome(None, [genome, genome2])
+
+    def objective(f):
+        x_dummy = torch.zeros((1, 1), dtype=torch.double)  # not used
+        target_value = torch.ones((1, 1), dtype=torch.double)
+        loss = torch.nn.MSELoss()(f[0](x_dummy), target_value) + 2 * torch.nn.MSELoss()(
+            f[1](x_dummy), target_value
+        )
+        return loss
+
+    # test increase parameter value if too small
+    ind.genome[0].parameter_names_to_values["<p1>"] = 0.9
+    ind.genome[1].parameter_names_to_values["<p1>"] = 0.9
+    cgp.local_search.gradient_based(ind, objective, 0.05, 1)
+    assert ind.genome[0].parameter_names_to_values["<p1>"] == pytest.approx(0.91)
+    assert ind.genome[1].parameter_names_to_values["<p1>"] == pytest.approx(0.92)
+
+    # test decrease parameter value if too large
+    ind.genome[0].parameter_names_to_values["<p1>"] = 1.1
+    ind.genome[1].parameter_names_to_values["<p1>"] = 1.1
+    cgp.local_search.gradient_based(ind, objective, 0.05, 1)
+    assert ind.genome[0].parameter_names_to_values["<p1>"] == pytest.approx(1.09)
+    assert ind.genome[1].parameter_names_to_values["<p1>"] == pytest.approx(1.08)
+
+    # test no change of parameter value if at optimum
+    ind.genome[0].parameter_names_to_values["<p1>"] = 1.0
+    ind.genome[1].parameter_names_to_values["<p1>"] = 1.0
+    cgp.local_search.gradient_based(ind, objective, 0.05, 1)
+    assert ind.genome[0].parameter_names_to_values["<p1>"] == pytest.approx(1.0)
+    assert ind.genome[1].parameter_names_to_values["<p1>"] == pytest.approx(1.0)