[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

pysr/sr.py

@@ -1551,9 +1551,9 @@ def get_best(
 
         if index is not None:
             if isinstance(self.equations_, list):
-                assert isinstance(index, list), (
-                    "With multiple output features, index must be a list."
-                )
+                assert isinstance(
+                    index, list
+                ), "With multiple output features, index must be a list."
                 return [eq.iloc[i] for eq, i in zip(self.equations_, index)]
             else:
                 equations_ = cast(pd.DataFrame, self.equations_)

pysr/test/test_input_stream.py

@@ -1,160 +1,12 @@
 """Tests for input_stream default behavior."""
 
-import unittest
-from unittest import mock
-
-import numpy as np
-
 from pysr import PySRRegressor
-from pysr.sr import ALREADY_RAN
-
-
-class TestInputStream(unittest.TestCase):
-    """Verify input_stream defaults and backend passthrough."""
-
-    def test_default_is_none(self):
-        """By default, input_stream should be None so the Julia backend picks the stream."""
-        model = PySRRegressor()
-        self.assertIsNone(model.input_stream)
-
-    def test_explicit_stdin(self):
-        model = PySRRegressor(input_stream="stdin")
-        self.assertEqual(model.input_stream, "stdin")
-
-    def test_explicit_devnull(self):
-        model = PySRRegressor(input_stream="devnull")
-        self.assertEqual(model.input_stream, "devnull")
-
-    def _make_mock_jl(self):
-        """Return a MagicMock that satisfies the Julia calls made before Options()."""
-        m = mock.MagicMock()
-        m.seval.return_value = mock.MagicMock()
-        m.Dict.return_value = mock.MagicMock()
-        m.Pair.return_value = mock.MagicMock()
-        m.Symbol.return_value = mock.MagicMock()
-        m.NamedTuple.return_value = mock.MagicMock()
-        return m
-
-    def _mocked_fit(self, model, X, y, *, capture_options):
-        """Run model.fit with enough Julia infrastructure mocked to reach Options()."""
-        mock_jl = self._make_mock_jl()
-        sr = mock.MagicMock()
-        sr.MutationWeights.return_value = mock.MagicMock()
-        sr.Options.side_effect = capture_options
-        sr.equation_search.return_value = (mock.MagicMock(), b"mock")
-        sr.SearchUtilsModule.generate_run_id.return_value = "test-run-id"
-
-        # Reset global so _run doesn't skip its first-run block.
-        import pysr.sr as sr_module
-
-        old_already_ran = sr_module.ALREADY_RAN
-        sr_module.ALREADY_RAN = False
-
-        try:
-            with mock.patch("pysr.sr.jl", mock_jl):
-                with mock.patch("pysr.sr.SymbolicRegression", sr):
-                    with mock.patch("pysr.sr.jl_array", return_value=mock.MagicMock()):
-                        with mock.patch("pysr.sr.jl_is_function", return_value=True):
-                            with mock.patch(
-                                "pysr.sr.jl_serialize", return_value=b"mock"
-                            ):
-                                with mock.patch("pysr.sr.load_required_packages"):
-                                    with mock.patch("pysr.sr._load_cluster_manager"):
-                                        model.fit(X, y)
-        finally:
-            sr_module.ALREADY_RAN = old_already_ran
-
-    def test_default_omits_input_stream_from_options(self):
-        """When input_stream is None, Options should not receive the kwarg."""
-        captured_kwargs = {}
-
-        def capture_options(**kwargs):
-            captured_kwargs.update(kwargs)
-            raise RuntimeError("stop_after_options")
-
-        X = np.array([[1.0, 2.0], [3.0, 4.0]])
-        y = np.array([1.0, 2.0])
-
-        model = PySRRegressor(
-            niterations=0,
-            max_evals=0,
-            populations=1,
-            ncycles_per_iteration=0,
-            progress=False,
-            verbosity=0,
-            temp_equation_file=True,
-            parallelism="serial",
-        )
-
-        with self.assertRaises(RuntimeError) as cm:
-            self._mocked_fit(model, X, y, capture_options=capture_options)
-        self.assertEqual(str(cm.exception), "stop_after_options")
-        self.assertNotIn("input_stream", captured_kwargs)
-
-    def test_explicit_stdin_passes_input_stream(self):
-        """When input_stream is 'stdin', Options should receive the kwarg."""
-        captured_kwargs = {}
-
-        def capture_options(**kwargs):
-            captured_kwargs.update(kwargs)
-            raise RuntimeError("stop_after_options")
-
-        X = np.array([[1.0, 2.0], [3.0, 4.0]])
-        y = np.array([1.0, 2.0])
-
-        model = PySRRegressor(
-            input_stream="stdin",
-            niterations=0,
-            max_evals=0,
-            populations=1,
-            ncycles_per_iteration=0,
-            progress=False,
-            verbosity=0,
-            temp_equation_file=True,
-            parallelism="serial",
-        )
-
-        with self.assertRaises(RuntimeError) as cm:
-            self._mocked_fit(model, X, y, capture_options=capture_options)
-        self.assertEqual(str(cm.exception), "stop_after_options")
-        self.assertIn("input_stream", captured_kwargs)
-
-    def test_explicit_devnull_passes_input_stream(self):
-        """When input_stream is 'devnull', Options should receive the kwarg."""
-        captured_kwargs = {}
-
-        def capture_options(**kwargs):
-            captured_kwargs.update(kwargs)
-            raise RuntimeError("stop_after_options")
-
-        X = np.array([[1.0, 2.0], [3.0, 4.0]])
-        y = np.array([1.0, 2.0])
 
-        model = PySRRegressor(
-            input_stream="devnull",
-            niterations=0,
-            max_evals=0,
-            populations=1,
-            ncycles_per_iteration=0,
-            progress=False,
-            verbosity=0,
-            temp_equation_file=True,
-            parallelism="serial",
-        )
 
-        with self.assertRaises(RuntimeError) as cm:
-            self._mocked_fit(model, X, y, capture_options=capture_options)
-        self.assertEqual(str(cm.exception), "stop_after_options")
-        self.assertIn("input_stream", captured_kwargs)
+def test_default_is_none():
+    assert PySRRegressor().input_stream is None
 
 
-def runtests(just_tests=False):
-    tests = [TestInputStream]
-    if just_tests:
-        return tests
-    suite = unittest.TestSuite()
-    loader = unittest.TestLoader()
-    for test in tests:
-        suite.addTests(loader.loadTestsFromTestCase(test))
-    runner = unittest.TextTestRunner()
-    return runner.run(suite)
+def test_explicit_values():
+    assert PySRRegressor(input_stream="stdin").input_stream == "stdin"
+    assert PySRRegressor(input_stream="devnull").input_stream == "devnull"