Format code with black

test/test_explained_variance.py

@@ -13,7 +13,7 @@ def rng():
 @pytest.fixture
 def toy_model(rng):
     model = BaseModel()
-    model.weights = [rng.random((10, 3)), rng.random((8, 3)), rng.random((5,3))]
+    model.weights = [rng.random((10, 3)), rng.random((8, 3)), rng.random((5, 3))]
     return model
 
 
@@ -29,53 +29,72 @@ def synthetic_views(rng):
     view3 -= view3.mean(axis=0)
     return [view1, view2, view3]
 
+
 def test_explained_variance_ratio(toy_model, synthetic_views):
     explained_variance_ratios = toy_model.explained_variance_ratio(synthetic_views)
 
     # Verify if the ratios are between 0 and 1 for each latent dimension in each view
     for ratios in explained_variance_ratios:
         for ratio in ratios:
-            assert 0 <= ratio <= 1, f"Explained variance ratio should be between 0 and 1, but got {ratio}"
+            assert (
+                0 <= ratio <= 1
+            ), f"Explained variance ratio should be between 0 and 1, but got {ratio}"
 
 
 def test_transformed_covariance_ratio(toy_model, synthetic_views):
     maximum_dimension = min([view.shape[1] for view in synthetic_views])
     pls = MPLS(latent_dimensions=maximum_dimension).fit(synthetic_views)
     pls_cov_ratios = pls.explained_covariance_ratio(synthetic_views)
     # sum of these should be 1 within a small tolerance
-    assert np.isclose(np.sum(pls_cov_ratios), 1, atol=1e-2), "Expected sum of ratios to be 1"
+    assert np.isclose(
+        np.sum(pls_cov_ratios), 1, atol=1e-2
+    ), "Expected sum of ratios to be 1"
 
     cov_ratios = toy_model.explained_covariance_ratio(synthetic_views)
 
     # Verify if the ratios are between 0 and 1 for each latent dimension in each view
     for ratio in cov_ratios:
-        assert 0 <= ratio <= 1, f"Explained covariance ratio should be between 0 and 1, but got {ratio}"
+        assert (
+            0 <= ratio <= 1
+        ), f"Explained covariance ratio should be between 0 and 1, but got {ratio}"
 
 
 def test_explained_variance(toy_model, synthetic_views):
     explained_vars = toy_model.explained_variance(synthetic_views)
-    assert all(isinstance(var, np.ndarray) for var in explained_vars), "Expected numpy arrays"
+    assert all(
+        isinstance(var, np.ndarray) for var in explained_vars
+    ), "Expected numpy arrays"
     assert all(var.ndim == 1 for var in explained_vars), "Expected 1-dimensional arrays"
 
+
 def test_explained_variance_cumulative(toy_model, synthetic_views):
     cumulative_ratios = toy_model.explained_variance_cumulative(synthetic_views)
     # Verifying if the ratios are increasing for each latent dimension in each view
     for ratios in cumulative_ratios:
-        assert np.all(np.diff(ratios) >= 0), "Expected cumulative ratios to be non-decreasing"
+        assert np.all(
+            np.diff(ratios) >= 0
+        ), "Expected cumulative ratios to be non-decreasing"
+
 
 def test_explained_covariance(toy_model, synthetic_views):
     explained_covariances = toy_model.explained_covariance(synthetic_views)
     assert isinstance(explained_covariances, np.ndarray), "Expected a numpy array"
     assert explained_covariances.ndim == 1, "Expected 1-dimensional array"
 
+
 def test_explained_covariance_ratio(toy_model, synthetic_views):
     explained_covariance_ratios = toy_model.explained_covariance_ratio(synthetic_views)
     # Verifying if the ratios are between 0 and 1 for each latent dimension in each view
     for ratio in explained_covariance_ratios:
-        assert 0 <= ratio <= 1, f"Explained covariance ratio should be between 0 and 1, but got {ratio}"
+        assert (
+            0 <= ratio <= 1
+        ), f"Explained covariance ratio should be between 0 and 1, but got {ratio}"
+
 
 def test_explained_covariance_cumulative(toy_model, synthetic_views):
     cumulative_ratios = toy_model.explained_covariance_cumulative(synthetic_views)
     # Verifying if the ratios are increasing for each latent dimension in each view
     for ratios in cumulative_ratios:
-        assert np.all(np.diff(ratios) >= 0), "Expected cumulative ratios to be non-decreasing"
+        assert np.all(
+            np.diff(ratios) >= 0
+        ), "Expected cumulative ratios to be non-decreasing"