def test_pca_inverse(svd_solver, whiten): # Test that the projection of data can be inverted rng = np.random.RandomState(0) n, p = 50, 3 X = rng.randn(n, p) # spherical data X[:, 1] *= .00001 # make middle component relatively small X += [5, 4, 3] # make a large mean # same check that we can find the original data from the transformed # signal (since the data is almost of rank n_components) pca = PCA(n_components=2, svd_solver=svd_solver, whiten=whiten).fit(X) Y = pca.transform(X) Y_inverse = pca.inverse_transform(Y) assert_allclose(X, Y_inverse, rtol=5e-6)
def test_pipeline_transform(): # Test whether pipeline works with a transformer at the end. # Also test pipeline.transform and pipeline.inverse_transform X = iris.data pca = PCA(n_components=2, svd_solver='full') pipeline = Pipeline([('pca', pca)]) # test transform and fit_transform: X_trans = pipeline.fit(X).transform(X) X_trans2 = pipeline.fit_transform(X) X_trans3 = pca.fit_transform(X) assert_array_almost_equal(X_trans, X_trans2) assert_array_almost_equal(X_trans, X_trans3) X_back = pipeline.inverse_transform(X_trans) X_back2 = pca.inverse_transform(X_trans) assert_array_almost_equal(X_back, X_back2)
def test_whitening(): # Test that PCA and IncrementalPCA transforms match to sign flip. X = datasets.make_low_rank_matrix(1000, 10, tail_strength=0., effective_rank=2, random_state=1999) prec = 3 n_samples, n_features = X.shape for nc in [None, 9]: pca = PCA(whiten=True, n_components=nc).fit(X) ipca = IncrementalPCA(whiten=True, n_components=nc, batch_size=250).fit(X) Xt_pca = pca.transform(X) Xt_ipca = ipca.transform(X) assert_almost_equal(np.abs(Xt_pca), np.abs(Xt_ipca), decimal=prec) Xinv_ipca = ipca.inverse_transform(Xt_ipca) Xinv_pca = pca.inverse_transform(Xt_pca) assert_almost_equal(X, Xinv_ipca, decimal=prec) assert_almost_equal(X, Xinv_pca, decimal=prec) assert_almost_equal(Xinv_pca, Xinv_ipca, decimal=prec)