146 lines
5.4 KiB
Python
146 lines
5.4 KiB
Python
from itertools import product
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import numpy as np
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from numpy.testing import assert_almost_equal, assert_array_almost_equal
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from scipy import linalg
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import pytest
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from sklearn import neighbors, manifold
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from sklearn.manifold._locally_linear import barycenter_kneighbors_graph
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from sklearn.utils._testing import ignore_warnings
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from sklearn.utils._testing import assert_raise_message
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eigen_solvers = ['dense', 'arpack']
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# ----------------------------------------------------------------------
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# Test utility routines
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def test_barycenter_kneighbors_graph():
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X = np.array([[0, 1], [1.01, 1.], [2, 0]])
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A = barycenter_kneighbors_graph(X, 1)
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assert_array_almost_equal(
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A.toarray(),
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[[0., 1., 0.],
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[1., 0., 0.],
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[0., 1., 0.]])
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A = barycenter_kneighbors_graph(X, 2)
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# check that columns sum to one
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assert_array_almost_equal(np.sum(A.toarray(), 1), np.ones(3))
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pred = np.dot(A.toarray(), X)
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assert linalg.norm(pred - X) / X.shape[0] < 1
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# ----------------------------------------------------------------------
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# Test LLE by computing the reconstruction error on some manifolds.
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def test_lle_simple_grid():
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# note: ARPACK is numerically unstable, so this test will fail for
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# some random seeds. We choose 2 because the tests pass.
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rng = np.random.RandomState(2)
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# grid of equidistant points in 2D, n_components = n_dim
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X = np.array(list(product(range(5), repeat=2)))
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X = X + 1e-10 * rng.uniform(size=X.shape)
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n_components = 2
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clf = manifold.LocallyLinearEmbedding(n_neighbors=5,
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n_components=n_components,
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random_state=rng)
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tol = 0.1
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N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
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reconstruction_error = linalg.norm(np.dot(N, X) - X, 'fro')
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assert reconstruction_error < tol
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for solver in eigen_solvers:
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clf.set_params(eigen_solver=solver)
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clf.fit(X)
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assert clf.embedding_.shape[1] == n_components
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reconstruction_error = linalg.norm(
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np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
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assert reconstruction_error < tol
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assert_almost_equal(clf.reconstruction_error_,
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reconstruction_error, decimal=1)
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# re-embed a noisy version of X using the transform method
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noise = rng.randn(*X.shape) / 100
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X_reembedded = clf.transform(X + noise)
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assert linalg.norm(X_reembedded - clf.embedding_) < tol
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def test_lle_manifold():
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rng = np.random.RandomState(0)
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# similar test on a slightly more complex manifold
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X = np.array(list(product(np.arange(18), repeat=2)))
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X = np.c_[X, X[:, 0] ** 2 / 18]
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X = X + 1e-10 * rng.uniform(size=X.shape)
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n_components = 2
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for method in ["standard", "hessian", "modified", "ltsa"]:
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clf = manifold.LocallyLinearEmbedding(n_neighbors=6,
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n_components=n_components,
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method=method, random_state=0)
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tol = 1.5 if method == "standard" else 3
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N = barycenter_kneighbors_graph(X, clf.n_neighbors).toarray()
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reconstruction_error = linalg.norm(np.dot(N, X) - X)
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assert reconstruction_error < tol
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for solver in eigen_solvers:
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clf.set_params(eigen_solver=solver)
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clf.fit(X)
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assert clf.embedding_.shape[1] == n_components
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reconstruction_error = linalg.norm(
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np.dot(N, clf.embedding_) - clf.embedding_, 'fro') ** 2
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details = ("solver: %s, method: %s" % (solver, method))
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assert reconstruction_error < tol, details
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assert (np.abs(clf.reconstruction_error_ -
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reconstruction_error) <
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tol * reconstruction_error), details
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# Test the error raised when parameter passed to lle is invalid
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def test_lle_init_parameters():
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X = np.random.rand(5, 3)
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clf = manifold.LocallyLinearEmbedding(eigen_solver="error")
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msg = "unrecognized eigen_solver 'error'"
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assert_raise_message(ValueError, msg, clf.fit, X)
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clf = manifold.LocallyLinearEmbedding(method="error")
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msg = "unrecognized method 'error'"
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assert_raise_message(ValueError, msg, clf.fit, X)
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def test_pipeline():
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# check that LocallyLinearEmbedding works fine as a Pipeline
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# only checks that no error is raised.
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# TODO check that it actually does something useful
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from sklearn import pipeline, datasets
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X, y = datasets.make_blobs(random_state=0)
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clf = pipeline.Pipeline(
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[('filter', manifold.LocallyLinearEmbedding(random_state=0)),
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('clf', neighbors.KNeighborsClassifier())])
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clf.fit(X, y)
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assert .9 < clf.score(X, y)
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# Test the error raised when the weight matrix is singular
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def test_singular_matrix():
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M = np.ones((10, 3))
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f = ignore_warnings
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with pytest.raises(ValueError):
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f(manifold.locally_linear_embedding(M, n_neighbors=2, n_components=1,
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method='standard',
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eigen_solver='arpack'))
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# regression test for #6033
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def test_integer_input():
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rand = np.random.RandomState(0)
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X = rand.randint(0, 100, size=(20, 3))
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for method in ["standard", "hessian", "modified", "ltsa"]:
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clf = manifold.LocallyLinearEmbedding(method=method, n_neighbors=10)
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clf.fit(X) # this previously raised a TypeError
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