test: backport sklearn estimator skip handling

Co-authored-by: Miles Cranmer <miles.cranmer@gmail.com>

Author: MilesCranmerBot

pysr/sr.py

@@ -2662,16 +2662,23 @@ def get_hof(self, search_output=None) -> pd.DataFrame | list[pd.DataFrame]:
 
         equation_file_contents = cast(List[pd.DataFrame], self.equation_file_contents_)
 
-        ret_outputs = [
-            pd.concat(
-                [
-                    output,
-                    *([calculate_scores(output)] if self.loss_scale == "log" else []),
-                    self.expression_spec_.create_exports(
-                        self, output, search_output, i if self.nout_ > 1 else None
-                    ),
-                ],
-                axis=1,
+        ret_outputs: list[pd.DataFrame] = [
+            cast(
+                pd.DataFrame,
+                pd.concat(
+                    [
+                        output,
+                        *(
+                            [calculate_scores(output)]
+                            if self.loss_scale == "log"
+                            else []
+                        ),
+                        self.expression_spec_.create_exports(
+                            self, output, search_output, i if self.nout_ > 1 else None
+                        ),
+                    ],
+                    axis=1,
+                ),
             )
             for i, output in enumerate(equation_file_contents)
         ]

pysr/test/test_main.py

@@ -1022,6 +1022,8 @@ def test_scikit_learn_compatibility(self):
                     warnings.simplefilter("ignore")
                     check(model)
                 print("Passed", check.func.__name__)
+            except unittest.SkipTest as exc:
+                print("Skipped", check.func.__name__, "with:", str(exc))
             except Exception:
                 error_message = str(traceback.format_exc())
                 exception_messages.append(