Add TestCsrVcorrcoef

Test class for unit testing `csr_vcorrcoef`.

tests/preprocessing/test_utils.py

@@ -0,0 +1,76 @@
+import pytest
+
+import numpy as np
+from scipy.sparse import csr_matrix, spmatrix
+
+from scvelo.preprocessing.utils import csr_vcorrcoef
+
+
+class TestCsrVcorrcoef:
+    @pytest.mark.parametrize(
+        "X",
+        (
+            np.zeros(3),
+            np.array([1, 0, -4]),
+            np.array([-0.3, 0.5, 0.93]),
+            np.zeros(shape=(3, 3)),
+            np.eye(3),
+            np.array([[1, 2, 3], [1, -1, 1]]),
+            np.array([[0.1, -0.3, 7.5], [8.3, 0.4, -0.9]]),
+        ),
+    )
+    @pytest.mark.parametrize(
+        "y",
+        (
+            np.zeros(3),
+            np.ones(3),
+            np.array([1, 0, 0]),
+            np.array([1, 2, 3]),
+            np.array([-0.24, 0.7, 0.4]),
+        ),
+    )
+    def test_dense_arrays(self, X: np.ndarray, y: np.ndarray):
+        pearsonr = csr_vcorrcoef(X=X, y=y)
+
+        if X.ndim == 1:
+            np.testing.assert_almost_equal(np.corrcoef(X, y)[0, 1], pearsonr)
+        else:
+            assert all(
+                np.allclose(np.corrcoef(sample, y)[0, 1], corr, equal_nan=True)
+                for corr, sample in zip(pearsonr, X)
+            )
+
+    @pytest.mark.parametrize(
+        "X",
+        (
+            csr_matrix(np.zeros(3)),
+            csr_matrix(np.array([1, 0, -4])),
+            csr_matrix(np.array([-0.3, 0.5, 0.93])),
+            csr_matrix(np.zeros(shape=(3, 3))),
+            csr_matrix(np.eye(3)),
+            csr_matrix(np.array([[1, 2, 3], [1, -1, 1]])),
+            csr_matrix(np.array([[0.1, -0.3, 7.5], [8.3, 0.4, -0.9]])),
+        ),
+    )
+    @pytest.mark.parametrize(
+        "y",
+        (
+            np.zeros(3),
+            np.ones(3),
+            np.array([1, 0, 0]),
+            np.array([1, 2, 3]),
+            np.array([-0.24, 0.7, 0.4]),
+        ),
+    )
+    def test_sparse_arrays(self, X: spmatrix, y: np.ndarray):
+        pearsonr = csr_vcorrcoef(X=X, y=y)
+
+        X_dense = X.A.squeeze()
+
+        if X_dense.ndim == 1:
+            np.testing.assert_almost_equal(np.corrcoef(X_dense, y)[0, 1], pearsonr)
+        else:
+            assert all(
+                np.allclose(np.corrcoef(sample, y)[0, 1], corr, equal_nan=True)
+                for corr, sample in zip(pearsonr, X_dense)
+            )