File Content: test_isomap.py from itertools import product import numpy as np from numpy.testing import (assert_almost_equal, assert_array_almost_equal, assert_equal) from sklearn import datasets from sklearn import manifold from sklearn import neighbors from sklearn import pipeline from sklearn import preprocessing from sklearn.utils.testing import assert_less eigen_solvers = ['auto', 'dense', 'arpack'] path_methods = ['auto', 'FW', 'D'] def test_isomap_simple_grid(): # Isomap should preserve distances when all neighbors are used N_per_side = 5 Npts = N_per_side ** 2 n_neighbors = Npts - 1 # grid of equidistant points in 2D, n_components = n_dim X = np.array(list(product(range(N_per_side), repeat=2))) # distances from each point to all others G = neighbors.kneighbors_graph(X, n_neighbors, mode='distance').toarray() for eigen_solver in eigen_solvers: for path_method in path_methods: clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2, eigen_solver=eigen_solver, path_method=path_method) clf.fit(X) G_iso = neighbors.kneighbors_graph(clf.embedding_, n_neighbors, mode='distance').toarray() assert_array_almost_equal(G, G_iso) def test_isomap_reconstruction_error(): # Same setup as in test_isomap_simple_grid, with an added dimension N_per_side = 5 Npts = N_per_side ** 2 n_neighbors = Npts - 1 # grid of equidistant points in 2D, n_components = n_dim X = np.array(list(product(range(N_per_side), repeat=2))) # add noise in a third dimension rng = np.random.RandomState(0) noise = 0.1 * rng.randn(Npts, 1) X = np.concatenate((X, noise), 1) # compute input kernel G = neighbors.kneighbors_graph(X, n_neighbors, mode='distance').toarray() centerer = preprocessing.KernelCenterer() K = centerer.fit_transform(-0.5 * G ** 2) for eigen_solver in eigen_solvers: for path_method in path_methods: clf = manifold.Isomap(n_neighbors=n_neighbors, n_components=2, eigen_solver=eigen_solver, path_method=path_method) clf.fit(X) # compute output kernel G_iso = neighbors.kneighbors_graph(clf.embedding_, n_neighbors, mode='distance').toarray() K_iso = centerer.fit_transform(-0.5 * G_iso ** 2) # make sure error agrees reconstruction_error = np.linalg.norm(K - K_iso) / Npts assert_almost_equal(reconstruction_error, clf.reconstruction_error()) def test_transform(): n_samples = 200 n_components = 10 noise_scale = 0.01 # Create S-curve dataset X, y = datasets.samples_generator.make_s_curve(n_samples, random_state=0) # Compute isomap embedding iso = manifold.Isomap(n_components, 2) X_iso = iso.fit_transform(X) # Re-embed a noisy version of the points rng = np.random.RandomState(0) noise = noise_scale * rng.randn(*X.shape) X_iso2 = iso.transform(X + noise) # Make sure the rms error on re-embedding is comparable to noise_scale assert_less(np.sqrt(np.mean((X_iso - X_iso2) ** 2)), 2 * noise_scale) def test_pipeline(): # check that Isomap works fine as a transformer in a Pipeline # only checks that no error is raised. # TODO check that it actually does something useful X, y = datasets.make_blobs(random_state=0) clf = pipeline.Pipeline( [('isomap', manifold.Isomap()), ('clf', neighbors.KNeighborsClassifier())]) clf.fit(X, y) assert_less(.9, clf.score(X, y)) def test_isomap_clone_bug(): # regression test for bug reported in #6062 model = manifold.Isomap() for n_neighbors in [10, 15, 20]: model.set_params(n_neighbors=n_neighbors) model.fit(np.random.rand(50, 2)) assert_equal(model.nbrs_.n_neighbors, n_neighbors) Submit