added test_pca_lobpcg_solver to test_pca

lobpcg · lobpcg · commit 7f9ab5a1c6cb · 2018-10-10T17:30:21.000-04:00
diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py
@@ -137,6 +137,43 @@ def test_pca_randomized_solver():
                      svd_solver='randomized', random_state=0).svd_solver)
 
 
+def test_pca_lobpcg_solver():
+    # PCA on dense arrays
+    X = iris.data
+
+    # Loop excluding the 0, invalid for lobpcg
+    for n_comp in np.arange(1, X.shape[1]):
+        pca = PCA(n_components=n_comp, svd_solver='lobpcg', random_state=0)
+
+        X_r = pca.fit(X).transform(X)
+        np.testing.assert_equal(X_r.shape[1], n_comp)
+
+        X_r2 = pca.fit_transform(X)
+        assert_array_almost_equal(X_r, X_r2)
+
+        X_r = pca.transform(X)
+        assert_array_almost_equal(X_r, X_r2)
+
+        # Test get_covariance and get_precision
+        cov = pca.get_covariance()
+        precision = pca.get_precision()
+        assert_array_almost_equal(np.dot(cov, precision),
+                                  np.eye(X.shape[1]), 12)
+
+    pca = PCA(n_components=0, svd_solver='lobpcg', random_state=0)
+    assert_raises(ValueError, pca.fit, X)
+
+    pca = PCA(n_components=0, svd_solver='lobpcg', random_state=0)
+    assert_raises(ValueError, pca.fit, X)
+    # Check internal state
+    assert_equal(pca.n_components,
+                 PCA(n_components=0,
+                     svd_solver='lobpcg', random_state=0).n_components)
+    assert_equal(pca.svd_solver,
+                 PCA(n_components=0,
+                     svd_solver='lobpcg', random_state=0).svd_solver)
+
+
 def test_no_empty_slice_warning():
     # test if we avoid numpy warnings for computing over empty arrays
     n_components = 10
