Cleaned test and docstring

pysr/sr.py

@@ -1427,12 +1427,6 @@ class PySRRegressor(BaseEstimator, RegressorMixin, MultiOutputMixin):
             If :param`X` is a pandas dataframe, the column names will be used.
             If variable_names are specified
 
-        from_equation_file : bool, default=False
-            Allows model to be initialized/fit from a previous run that has
-            been saved to a file. If true, a value of y still needs to be
-            passed such that `nout_` can be determined, however, the values of
-            y are irrelevant and can be all zeros.
-
         Returns
         -------
         self : object

test/test_torch.py

@@ -23,6 +23,16 @@ class TestTorch(unittest.TestCase):
         )
 
     def test_pipeline_pandas(self):
+        X = pd.DataFrame(np.random.randn(100, 10))
+        y = np.ones(X.shape[0])
+        model = PySRRegressor(
+            max_evals=10000,
+            model_selection="accuracy",
+            extra_sympy_mappings={},
+            output_torch_format=True,
+        )
+        model.fit(X, y)
+
         equations = pd.DataFrame(
             {
                 "Equation": ["1.0", "cos(x1)", "square(cos(x1))"],
@@ -35,15 +45,6 @@ class TestTorch(unittest.TestCase):
             "equation_file.csv.bkup", sep="|"
         )
 
-        X = pd.DataFrame(np.random.randn(100, 10))
-        y = np.ones(X.shape[0])
-        model = PySRRegressor(
-            max_evals=10000,
-            model_selection="accuracy",
-            extra_sympy_mappings={},
-            output_torch_format=True,
-        )
-        model.fit(X, y)
         model.refresh(checkpoint_file="equation_file.csv")
         tformat = model.pytorch()
         self.assertEqual(str(tformat), "_SingleSymPyModule(expression=cos(x1)**2)")