convert to pytest

tests/unit/calibration/models/test_linear_model.py

@@ -1,63 +1,70 @@
-import unittest
-from functools import cached_property
-
 import numpy as np
+import pytest
 
 from depiction.calibration.models.linear_model import LinearModel
 
 
-class TestLinearModel(unittest.TestCase):
-    def setUp(self) -> None:
-        self.mock_coef = [1, 2]
+@pytest.fixture
+def mock_coef() -> list[float]:
+    return [1, 2]
+
+
+@pytest.fixture
+def mock_model(mock_coef: list[float]) -> LinearModel:
+    return LinearModel(coef=mock_coef)
+
+
+def test_coef(mock_model: LinearModel) -> None:
+    np.testing.assert_array_equal(np.array([1, 2]), mock_model.coef, strict=True)
+
+
+def test_raise_error_when_invalid_coef() -> None:
+    with pytest.raises(ValueError):
+        LinearModel(coef=[1, 2, 3])
+
+
+def test_is_zero_when_false(mock_model: LinearModel) -> None:
+    assert not mock_model.is_zero
+
+
+def test_is_zero_when_true(mock_model: LinearModel) -> None:
+    mock_coef = [0.0, 0]
+    assert LinearModel(coef=mock_coef).is_zero
 
-    @cached_property
-    def mock_model(self):
-        return LinearModel(coef=self.mock_coef)
 
-    def test_coef(self) -> None:
-        np.testing.assert_array_equal(np.array([1, 2]), self.mock_model.coef, strict=True)
+def test_predict(mock_model: LinearModel) -> None:
+    np.testing.assert_array_equal(
+        np.array([3.0, 5]),
+        mock_model.predict([1.0, 2]),
+    )
 
-    def test_raise_error_when_invalid_coef(self) -> None:
-        with self.assertRaises(ValueError):
-            LinearModel(coef=[1, 2, 3])
 
-    def test_is_zero_when_false(self) -> None:
-        self.assertFalse(self.mock_model.is_zero)
+def test_identity() -> None:
+    np.testing.assert_array_equal(
+        np.array([0, 1]),
+        LinearModel.identity().coef,
+    )
 
-    def test_is_zero_when_true(self) -> None:
-        self.assertTrue(LinearModel.zero().is_zero)
-        self.mock_coef = [0.0, 0]
-        self.assertTrue(self.mock_model.is_zero)
 
-    def test_predict(self) -> None:
-        np.testing.assert_array_equal(
-            np.array([3.0, 5]),
-            self.mock_model.predict([1.0, 2]),
-        )
+def test_zero() -> None:
+    np.testing.assert_array_equal(
+        np.array([0, 0]),
+        LinearModel.zero().coef,
+    )
 
-    def test_identity(self) -> None:
-        np.testing.assert_array_equal(
-            np.array([0, 1]),
-            LinearModel.identity().coef,
-        )
 
-    def test_zero(self) -> None:
-        np.testing.assert_array_equal(
-            np.array([0, 0]),
-            LinearModel.zero().coef,
-        )
+def test_fit_lsq() -> None:
+    model = LinearModel.fit_lsq(np.array([1, 2, 3]), np.array([4, 5, 6]))
+    assert model.intercept == pytest.approx(3, abs=1e-7)
+    assert model.slope == pytest.approx(1, abs=1e-7)
 
-    def test_fit_lsq(self) -> None:
-        model = LinearModel.fit_lsq(np.array([1, 2, 3]), np.array([4, 5, 6]))
-        self.assertAlmostEqual(3, model.intercept, places=7)
-        self.assertAlmostEqual(1, model.slope, places=7)
 
-    def test_fit_linear_siegelslopes(self) -> None:
-        mock_x = np.array([100, 200, 300])
-        mock_y = np.array([1, 2, 3])
-        model = LinearModel.fit_siegelslopes(mock_x, mock_y)
-        np.testing.assert_array_almost_equal(np.array([0, 0.01]), model.coef, decimal=7)
+def test_fit_linear_siegelslopes() -> None:
+    mock_x = np.array([100, 200, 300])
+    mock_y = np.array([1, 2, 3])
+    model = LinearModel.fit_siegelslopes(mock_x, mock_y)
+    np.testing.assert_array_almost_equal(np.array([0, 0.01]), model.coef, decimal=7)
 
 
 if __name__ == "__main__":
-    unittest.main()
+    pytest.main()