346 lines
12 KiB
Python
346 lines
12 KiB
Python
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from itertools import product
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import numpy as np
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import math
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import pytest
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from sklearn import datasets, clone
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from sklearn import manifold
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from sklearn import neighbors
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from sklearn import pipeline
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from sklearn import preprocessing
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from sklearn.datasets import make_blobs
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from sklearn.metrics.pairwise import pairwise_distances
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from sklearn.utils._testing import (
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assert_allclose,
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assert_allclose_dense_sparse,
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assert_array_equal,
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)
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from scipy.sparse import rand as sparse_rand
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eigen_solvers = ["auto", "dense", "arpack"]
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path_methods = ["auto", "FW", "D"]
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def create_sample_data(dtype, n_pts=25, add_noise=False):
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# grid of equidistant points in 2D, n_components = n_dim
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n_per_side = int(math.sqrt(n_pts))
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X = np.array(list(product(range(n_per_side), repeat=2))).astype(dtype, copy=False)
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if add_noise:
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# add noise in a third dimension
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rng = np.random.RandomState(0)
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noise = 0.1 * rng.randn(n_pts, 1).astype(dtype, copy=False)
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X = np.concatenate((X, noise), 1)
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return X
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@pytest.mark.parametrize("n_neighbors, radius", [(24, None), (None, np.inf)])
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@pytest.mark.parametrize("eigen_solver", eigen_solvers)
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@pytest.mark.parametrize("path_method", path_methods)
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def test_isomap_simple_grid(
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global_dtype, n_neighbors, radius, eigen_solver, path_method
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):
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# Isomap should preserve distances when all neighbors are used
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n_pts = 25
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X = create_sample_data(global_dtype, n_pts=n_pts, add_noise=False)
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# distances from each point to all others
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if n_neighbors is not None:
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G = neighbors.kneighbors_graph(X, n_neighbors, mode="distance")
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else:
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G = neighbors.radius_neighbors_graph(X, radius, mode="distance")
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clf = manifold.Isomap(
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n_neighbors=n_neighbors,
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radius=radius,
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n_components=2,
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eigen_solver=eigen_solver,
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path_method=path_method,
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)
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clf.fit(X)
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if n_neighbors is not None:
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G_iso = neighbors.kneighbors_graph(clf.embedding_, n_neighbors, mode="distance")
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else:
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G_iso = neighbors.radius_neighbors_graph(
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clf.embedding_, radius, mode="distance"
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)
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atol = 1e-5 if global_dtype == np.float32 else 0
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assert_allclose_dense_sparse(G, G_iso, atol=atol)
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@pytest.mark.parametrize("n_neighbors, radius", [(24, None), (None, np.inf)])
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@pytest.mark.parametrize("eigen_solver", eigen_solvers)
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@pytest.mark.parametrize("path_method", path_methods)
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def test_isomap_reconstruction_error(
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global_dtype, n_neighbors, radius, eigen_solver, path_method
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):
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if global_dtype is np.float32:
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pytest.skip(
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"Skipping test due to numerical instabilities on float32 data"
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"from KernelCenterer used in the reconstruction_error method"
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)
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# Same setup as in test_isomap_simple_grid, with an added dimension
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n_pts = 25
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X = create_sample_data(global_dtype, n_pts=n_pts, add_noise=True)
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# compute input kernel
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if n_neighbors is not None:
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G = neighbors.kneighbors_graph(X, n_neighbors, mode="distance").toarray()
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else:
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G = neighbors.radius_neighbors_graph(X, radius, mode="distance").toarray()
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centerer = preprocessing.KernelCenterer()
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K = centerer.fit_transform(-0.5 * G**2)
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clf = manifold.Isomap(
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n_neighbors=n_neighbors,
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radius=radius,
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n_components=2,
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eigen_solver=eigen_solver,
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path_method=path_method,
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)
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clf.fit(X)
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# compute output kernel
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if n_neighbors is not None:
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G_iso = neighbors.kneighbors_graph(clf.embedding_, n_neighbors, mode="distance")
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else:
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G_iso = neighbors.radius_neighbors_graph(
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clf.embedding_, radius, mode="distance"
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)
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G_iso = G_iso.toarray()
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K_iso = centerer.fit_transform(-0.5 * G_iso**2)
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# make sure error agrees
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reconstruction_error = np.linalg.norm(K - K_iso) / n_pts
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atol = 1e-5 if global_dtype == np.float32 else 0
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assert_allclose(reconstruction_error, clf.reconstruction_error(), atol=atol)
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@pytest.mark.parametrize("n_neighbors, radius", [(2, None), (None, 0.5)])
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def test_transform(global_dtype, n_neighbors, radius):
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n_samples = 200
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n_components = 10
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noise_scale = 0.01
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# Create S-curve dataset
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X, y = datasets.make_s_curve(n_samples, random_state=0)
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X = X.astype(global_dtype, copy=False)
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# Compute isomap embedding
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iso = manifold.Isomap(
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n_components=n_components, n_neighbors=n_neighbors, radius=radius
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)
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X_iso = iso.fit_transform(X)
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# Re-embed a noisy version of the points
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rng = np.random.RandomState(0)
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noise = noise_scale * rng.randn(*X.shape)
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X_iso2 = iso.transform(X + noise)
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# Make sure the rms error on re-embedding is comparable to noise_scale
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assert np.sqrt(np.mean((X_iso - X_iso2) ** 2)) < 2 * noise_scale
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@pytest.mark.parametrize("n_neighbors, radius", [(2, None), (None, 10.0)])
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def test_pipeline(n_neighbors, radius, global_dtype):
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# check that Isomap works fine as a transformer in 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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X, y = datasets.make_blobs(random_state=0)
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X = X.astype(global_dtype, copy=False)
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clf = pipeline.Pipeline(
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[
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("isomap", manifold.Isomap(n_neighbors=n_neighbors, radius=radius)),
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("clf", neighbors.KNeighborsClassifier()),
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]
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)
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clf.fit(X, y)
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assert 0.9 < clf.score(X, y)
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def test_pipeline_with_nearest_neighbors_transformer(global_dtype):
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# Test chaining NearestNeighborsTransformer and Isomap with
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# neighbors_algorithm='precomputed'
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algorithm = "auto"
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n_neighbors = 10
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X, _ = datasets.make_blobs(random_state=0)
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X2, _ = datasets.make_blobs(random_state=1)
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X = X.astype(global_dtype, copy=False)
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X2 = X2.astype(global_dtype, copy=False)
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# compare the chained version and the compact version
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est_chain = pipeline.make_pipeline(
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neighbors.KNeighborsTransformer(
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n_neighbors=n_neighbors, algorithm=algorithm, mode="distance"
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),
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manifold.Isomap(n_neighbors=n_neighbors, metric="precomputed"),
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)
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est_compact = manifold.Isomap(
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n_neighbors=n_neighbors, neighbors_algorithm=algorithm
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)
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Xt_chain = est_chain.fit_transform(X)
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Xt_compact = est_compact.fit_transform(X)
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assert_allclose(Xt_chain, Xt_compact)
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Xt_chain = est_chain.transform(X2)
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Xt_compact = est_compact.transform(X2)
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assert_allclose(Xt_chain, Xt_compact)
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@pytest.mark.parametrize(
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"metric, p, is_euclidean",
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[
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("euclidean", 2, True),
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("manhattan", 1, False),
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("minkowski", 1, False),
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("minkowski", 2, True),
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(lambda x1, x2: np.sqrt(np.sum(x1**2 + x2**2)), 2, False),
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],
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)
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def test_different_metric(global_dtype, metric, p, is_euclidean):
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# Isomap must work on various metric parameters work correctly
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# and must default to euclidean.
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X, _ = datasets.make_blobs(random_state=0)
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X = X.astype(global_dtype, copy=False)
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reference = manifold.Isomap().fit_transform(X)
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embedding = manifold.Isomap(metric=metric, p=p).fit_transform(X)
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if is_euclidean:
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assert_allclose(embedding, reference)
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else:
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with pytest.raises(AssertionError, match="Not equal to tolerance"):
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assert_allclose(embedding, reference)
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def test_isomap_clone_bug():
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# regression test for bug reported in #6062
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model = manifold.Isomap()
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for n_neighbors in [10, 15, 20]:
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model.set_params(n_neighbors=n_neighbors)
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model.fit(np.random.rand(50, 2))
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assert model.nbrs_.n_neighbors == n_neighbors
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@pytest.mark.parametrize("eigen_solver", eigen_solvers)
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@pytest.mark.parametrize("path_method", path_methods)
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def test_sparse_input(global_dtype, eigen_solver, path_method, global_random_seed):
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# TODO: compare results on dense and sparse data as proposed in:
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# https://github.com/scikit-learn/scikit-learn/pull/23585#discussion_r968388186
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X = sparse_rand(
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100,
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3,
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density=0.1,
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format="csr",
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dtype=global_dtype,
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random_state=global_random_seed,
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)
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iso_dense = manifold.Isomap(
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n_components=2,
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eigen_solver=eigen_solver,
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path_method=path_method,
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n_neighbors=8,
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)
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iso_sparse = clone(iso_dense)
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X_trans_dense = iso_dense.fit_transform(X.toarray())
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X_trans_sparse = iso_sparse.fit_transform(X)
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assert_allclose(X_trans_sparse, X_trans_dense, rtol=1e-4, atol=1e-4)
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def test_isomap_fit_precomputed_radius_graph(global_dtype):
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# Isomap.fit_transform must yield similar result when using
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# a precomputed distance matrix.
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X, y = datasets.make_s_curve(200, random_state=0)
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X = X.astype(global_dtype, copy=False)
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radius = 10
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g = neighbors.radius_neighbors_graph(X, radius=radius, mode="distance")
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isomap = manifold.Isomap(n_neighbors=None, radius=radius, metric="precomputed")
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isomap.fit(g)
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precomputed_result = isomap.embedding_
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isomap = manifold.Isomap(n_neighbors=None, radius=radius, metric="minkowski")
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result = isomap.fit_transform(X)
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atol = 1e-5 if global_dtype == np.float32 else 0
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assert_allclose(precomputed_result, result, atol=atol)
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def test_isomap_fitted_attributes_dtype(global_dtype):
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"""Check that the fitted attributes are stored accordingly to the
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data type of X."""
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iso = manifold.Isomap(n_neighbors=2)
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X = np.array([[1, 2], [3, 4], [5, 6]], dtype=global_dtype)
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iso.fit(X)
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assert iso.dist_matrix_.dtype == global_dtype
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assert iso.embedding_.dtype == global_dtype
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def test_isomap_dtype_equivalence():
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"""Check the equivalence of the results with 32 and 64 bits input."""
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iso_32 = manifold.Isomap(n_neighbors=2)
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X_32 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float32)
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iso_32.fit(X_32)
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iso_64 = manifold.Isomap(n_neighbors=2)
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X_64 = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float64)
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iso_64.fit(X_64)
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assert_allclose(iso_32.dist_matrix_, iso_64.dist_matrix_)
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def test_isomap_raise_error_when_neighbor_and_radius_both_set():
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# Isomap.fit_transform must raise a ValueError if
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# radius and n_neighbors are provided.
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X, _ = datasets.load_digits(return_X_y=True)
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isomap = manifold.Isomap(n_neighbors=3, radius=5.5)
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msg = "Both n_neighbors and radius are provided"
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with pytest.raises(ValueError, match=msg):
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isomap.fit_transform(X)
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def test_multiple_connected_components():
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# Test that a warning is raised when the graph has multiple components
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X = np.array([0, 1, 2, 5, 6, 7])[:, None]
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with pytest.warns(UserWarning, match="number of connected components"):
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manifold.Isomap(n_neighbors=2).fit(X)
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def test_multiple_connected_components_metric_precomputed(global_dtype):
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# Test that an error is raised when the graph has multiple components
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# and when X is a precomputed neighbors graph.
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X = np.array([0, 1, 2, 5, 6, 7])[:, None].astype(global_dtype, copy=False)
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# works with a precomputed distance matrix (dense)
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X_distances = pairwise_distances(X)
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with pytest.warns(UserWarning, match="number of connected components"):
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manifold.Isomap(n_neighbors=1, metric="precomputed").fit(X_distances)
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# does not work with a precomputed neighbors graph (sparse)
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X_graph = neighbors.kneighbors_graph(X, n_neighbors=2, mode="distance")
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with pytest.raises(RuntimeError, match="number of connected components"):
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manifold.Isomap(n_neighbors=1, metric="precomputed").fit(X_graph)
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def test_get_feature_names_out():
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"""Check get_feature_names_out for Isomap."""
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X, y = make_blobs(random_state=0, n_features=4)
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n_components = 2
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iso = manifold.Isomap(n_components=n_components)
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iso.fit_transform(X)
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names = iso.get_feature_names_out()
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assert_array_equal([f"isomap{i}" for i in range(n_components)], names)
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