Add test for custom torch operator

test/test_torch.py

@@ -36,7 +36,7 @@ class TestTorch(unittest.TestCase):
 
         equations = get_hof(
             "equation_file.csv",
-            n_features=2,
+            n_features=2,  # TODO: Why is this 2 and not 3?
             variables_names="x1 x2 x3".split(" "),
             extra_sympy_mappings={},
             output_torch_format=True,
@@ -68,3 +68,37 @@ class TestTorch(unittest.TestCase):
         np.testing.assert_array_almost_equal(
             true_out.detach(), torch_out.detach(), decimal=4
         )
+
+    def test_custom_operator(self):
+        X = np.random.randn(100, 3)
+
+        equations = pd.DataFrame(
+            {
+                "Equation": ["1.0", "mycustomoperator(x0)"],
+                "MSE": [1.0, 0.1],
+                "Complexity": [1, 2],
+            }
+        )
+
+        equations["Complexity MSE Equation".split(" ")].to_csv(
+            "equation_file_custom_operator.csv.bkup", sep="|"
+        )
+
+        equations = get_hof(
+            "equation_file_custom_operator.csv",
+            n_features=3,
+            variables_names="x1 x2 x3".split(" "),
+            extra_sympy_mappings={"mycustomoperator": sympy.sin},
+            extra_torch_mappings={"mycustomoperator": torch.sin},
+            output_torch_format=True,
+            multioutput=False,
+            nout=1,
+            selection=[0, 1, 2],
+        )
+
+        tformat = equations.iloc[-1].torch_format
+        np.testing.assert_almost_equal(
+            tformat(torch.tensor(X)).detach().numpy(),
+            np.sin(X[:, 0]),  # Selection 1st feature
+            decimal=4,
+        )