Format code with black

pysr/sr.py

@@ -798,7 +798,7 @@ class PySRRegressor(BaseEstimator, RegressorMixin):
         if self.multioutput:
             return [eq["jax_format"] for eq in best]
         return best["jax_format"]
-        
+
     def pytorch(self):
         self.set_params(output_torch_format=True)
         self.refresh()

test/test.py

@@ -154,7 +154,7 @@ class TestPipeline(unittest.TestCase):
         self.assertIn("T", model.latex())
         self.assertIn("x", model.latex())
         self.assertLessEqual(model.get_best()["loss"], 1e-2)
-        fn = model.get_best()['lambda_format']
+        fn = model.get_best()["lambda_format"]
         self.assertListEqual(list(sorted(fn._selection)), [0, 1])
         X2 = pd.DataFrame(
             {
@@ -202,7 +202,7 @@ class TestBest(unittest.TestCase):
     def test_best_lambda(self):
         X = np.random.randn(10, 2)
         y = np.cos(X[:, 0]) ** 2
-        for f in [self.model.predict, self.equations.iloc[-1]['lambda_format']]:
+        for f in [self.model.predict, self.equations.iloc[-1]["lambda_format"]]:
             np.testing.assert_almost_equal(f(X), y, decimal=4)
 
 
@@ -231,4 +231,4 @@ class TestFeatureSelection(unittest.TestCase):
         self.assertEqual(set(selected_var_names), set("x2 x3".split(" ")))
         np.testing.assert_array_equal(
             np.sort(selected_X, axis=1), np.sort(X[:, [2, 3]], axis=1)
-        )
+        )