Combine builtin variable names test with noisy data test

pysr/test/test.py

@@ -272,7 +272,7 @@ def test_warm_start_set_at_init(self):
         regressor = PySRRegressor(warm_start=True, max_evals=10)
         regressor.fit(self.X, y)
 
-    def test_noisy(self):
+    def test_noisy_builtin_variable_names(self):
         y = self.X[:, [0, 1]] ** 2 + self.rstate.randn(self.X.shape[0], 1) * 0.05
         model = PySRRegressor(
             # Test that passing a single operator works:
@@ -289,9 +289,12 @@ def test_noisy(self):
         model.set_params(model_selection="best")
         # Also try without a temp equation file:
         model.set_params(temp_equation_file=False)
-        model.fit(self.X, y)
+        # We also test builtin variable names
+        model.fit(self.X, y, variable_names=["exec", "hash", "x3", "x4", "x5"])
         self.assertLessEqual(model.get_best()[1]["loss"], 1e-2)
         self.assertLessEqual(model.get_best()[1]["loss"], 1e-2)
+        self.assertIn("exec", model.latex()[1])
+        self.assertIn("hash", model.latex()[1])
 
     def test_pandas_resample_with_nested_constraints(self):
         X = pd.DataFrame(
@@ -654,19 +657,6 @@ def test_bad_variable_names_fail(self):
             model.fit(X, y, variable_names=["f{c}"])
         self.assertIn("Invalid variable name", str(cm.exception))
 
-    def test_python_built_in_python_funcs_as_variable_names(self):
-        model = PySRRegressor()
-        X = np.random.randn(100, 2)
-        y = np.random.randn(100)
-
-        # Should not throw an error
-        try:
-            model.fit(X, y, variable_names=["exec", "hash"])
-        except Exception:
-            self.fail(
-                "Should not have thrown when a variable name is a builtin_function_or_method"
-            )
-
     def test_bad_kwargs(self):
         bad_kwargs = [
             dict(