335 lines
12 KiB
Python
335 lines
12 KiB
Python
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"""Testing for Spectral Clustering methods"""
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import pickle
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import re
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import numpy as np
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import pytest
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from scipy.linalg import LinAlgError
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from sklearn.cluster import SpectralClustering, spectral_clustering
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from sklearn.cluster._spectral import cluster_qr, discretize
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from sklearn.datasets import make_blobs
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from sklearn.feature_extraction import img_to_graph
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from sklearn.metrics import adjusted_rand_score
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from sklearn.metrics.pairwise import kernel_metrics, rbf_kernel
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from sklearn.neighbors import NearestNeighbors
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from sklearn.utils import check_random_state
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils.fixes import COO_CONTAINERS, CSR_CONTAINERS
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try:
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from pyamg import smoothed_aggregation_solver # noqa
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amg_loaded = True
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except ImportError:
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amg_loaded = False
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centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
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X, _ = make_blobs(
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n_samples=60,
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n_features=2,
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centers=centers,
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cluster_std=0.4,
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shuffle=True,
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random_state=0,
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)
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@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
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@pytest.mark.parametrize("eigen_solver", ("arpack", "lobpcg"))
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@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
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def test_spectral_clustering(eigen_solver, assign_labels, csr_container):
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S = np.array(
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[
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[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
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[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
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[1.0, 1.0, 1.0, 0.2, 0.0, 0.0, 0.0],
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[0.2, 0.2, 0.2, 1.0, 1.0, 1.0, 1.0],
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[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
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[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
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[0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0],
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]
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)
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for mat in (S, csr_container(S)):
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model = SpectralClustering(
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random_state=0,
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n_clusters=2,
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affinity="precomputed",
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eigen_solver=eigen_solver,
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assign_labels=assign_labels,
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).fit(mat)
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labels = model.labels_
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if labels[0] == 0:
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labels = 1 - labels
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assert adjusted_rand_score(labels, [1, 1, 1, 0, 0, 0, 0]) == 1
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model_copy = pickle.loads(pickle.dumps(model))
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assert model_copy.n_clusters == model.n_clusters
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assert model_copy.eigen_solver == model.eigen_solver
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assert_array_equal(model_copy.labels_, model.labels_)
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@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
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@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
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def test_spectral_clustering_sparse(assign_labels, coo_container):
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X, y = make_blobs(
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n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
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)
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S = rbf_kernel(X, gamma=1)
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S = np.maximum(S - 1e-4, 0)
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S = coo_container(S)
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labels = (
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SpectralClustering(
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random_state=0,
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n_clusters=2,
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affinity="precomputed",
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assign_labels=assign_labels,
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)
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.fit(S)
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.labels_
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)
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assert adjusted_rand_score(y, labels) == 1
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def test_precomputed_nearest_neighbors_filtering():
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# Test precomputed graph filtering when containing too many neighbors
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X, y = make_blobs(
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n_samples=200, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
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)
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n_neighbors = 2
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results = []
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for additional_neighbors in [0, 10]:
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nn = NearestNeighbors(n_neighbors=n_neighbors + additional_neighbors).fit(X)
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graph = nn.kneighbors_graph(X, mode="connectivity")
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labels = (
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SpectralClustering(
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random_state=0,
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n_clusters=2,
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affinity="precomputed_nearest_neighbors",
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n_neighbors=n_neighbors,
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)
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.fit(graph)
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.labels_
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)
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results.append(labels)
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assert_array_equal(results[0], results[1])
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def test_affinities():
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# Note: in the following, random_state has been selected to have
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# a dataset that yields a stable eigen decomposition both when built
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# on OSX and Linux
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X, y = make_blobs(
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n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
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)
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# nearest neighbors affinity
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sp = SpectralClustering(n_clusters=2, affinity="nearest_neighbors", random_state=0)
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with pytest.warns(UserWarning, match="not fully connected"):
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sp.fit(X)
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assert adjusted_rand_score(y, sp.labels_) == 1
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sp = SpectralClustering(n_clusters=2, gamma=2, random_state=0)
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labels = sp.fit(X).labels_
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assert adjusted_rand_score(y, labels) == 1
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X = check_random_state(10).rand(10, 5) * 10
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kernels_available = kernel_metrics()
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for kern in kernels_available:
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# Additive chi^2 gives a negative similarity matrix which
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# doesn't make sense for spectral clustering
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if kern != "additive_chi2":
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sp = SpectralClustering(n_clusters=2, affinity=kern, random_state=0)
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labels = sp.fit(X).labels_
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assert (X.shape[0],) == labels.shape
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sp = SpectralClustering(n_clusters=2, affinity=lambda x, y: 1, random_state=0)
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labels = sp.fit(X).labels_
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assert (X.shape[0],) == labels.shape
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def histogram(x, y, **kwargs):
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# Histogram kernel implemented as a callable.
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assert kwargs == {} # no kernel_params that we didn't ask for
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return np.minimum(x, y).sum()
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sp = SpectralClustering(n_clusters=2, affinity=histogram, random_state=0)
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labels = sp.fit(X).labels_
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assert (X.shape[0],) == labels.shape
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def test_cluster_qr():
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# cluster_qr by itself should not be used for clustering generic data
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# other than the rows of the eigenvectors within spectral clustering,
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# but cluster_qr must still preserve the labels for different dtypes
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# of the generic fixed input even if the labels may be meaningless.
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random_state = np.random.RandomState(seed=8)
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n_samples, n_components = 10, 5
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data = random_state.randn(n_samples, n_components)
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labels_float64 = cluster_qr(data.astype(np.float64))
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# Each sample is assigned a cluster identifier
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assert labels_float64.shape == (n_samples,)
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# All components should be covered by the assignment
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assert np.array_equal(np.unique(labels_float64), np.arange(n_components))
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# Single precision data should yield the same cluster assignments
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labels_float32 = cluster_qr(data.astype(np.float32))
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assert np.array_equal(labels_float64, labels_float32)
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def test_cluster_qr_permutation_invariance():
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# cluster_qr must be invariant to sample permutation.
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random_state = np.random.RandomState(seed=8)
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n_samples, n_components = 100, 5
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data = random_state.randn(n_samples, n_components)
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perm = random_state.permutation(n_samples)
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assert np.array_equal(
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cluster_qr(data)[perm],
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cluster_qr(data[perm]),
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)
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@pytest.mark.parametrize("coo_container", COO_CONTAINERS)
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@pytest.mark.parametrize("n_samples", [50, 100, 150, 500])
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def test_discretize(n_samples, coo_container):
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# Test the discretize using a noise assignment matrix
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random_state = np.random.RandomState(seed=8)
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for n_class in range(2, 10):
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# random class labels
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y_true = random_state.randint(0, n_class + 1, n_samples)
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y_true = np.array(y_true, float)
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# noise class assignment matrix
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y_indicator = coo_container(
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(np.ones(n_samples), (np.arange(n_samples), y_true)),
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shape=(n_samples, n_class + 1),
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)
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y_true_noisy = y_indicator.toarray() + 0.1 * random_state.randn(
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n_samples, n_class + 1
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)
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y_pred = discretize(y_true_noisy, random_state=random_state)
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assert adjusted_rand_score(y_true, y_pred) > 0.8
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# TODO: Remove when pyamg does replaces sp.rand call with np.random.rand
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# https://github.com/scikit-learn/scikit-learn/issues/15913
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@pytest.mark.filterwarnings(
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"ignore:scipy.rand is deprecated:DeprecationWarning:pyamg.*"
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)
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# TODO: Remove when pyamg removes the use of np.float
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@pytest.mark.filterwarnings(
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"ignore:`np.float` is a deprecated alias:DeprecationWarning:pyamg.*"
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)
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# TODO: Remove when pyamg removes the use of pinv2
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@pytest.mark.filterwarnings(
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"ignore:scipy.linalg.pinv2 is deprecated:DeprecationWarning:pyamg.*"
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)
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# TODO: Remove when pyamg removes the use of np.find_common_type
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@pytest.mark.filterwarnings(
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"ignore:np.find_common_type is deprecated:DeprecationWarning:pyamg.*"
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)
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def test_spectral_clustering_with_arpack_amg_solvers():
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# Test that spectral_clustering is the same for arpack and amg solver
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# Based on toy example from plot_segmentation_toy.py
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# a small two coin image
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x, y = np.indices((40, 40))
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center1, center2 = (14, 12), (20, 25)
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radius1, radius2 = 8, 7
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circle1 = (x - center1[0]) ** 2 + (y - center1[1]) ** 2 < radius1**2
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circle2 = (x - center2[0]) ** 2 + (y - center2[1]) ** 2 < radius2**2
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circles = circle1 | circle2
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mask = circles.copy()
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img = circles.astype(float)
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graph = img_to_graph(img, mask=mask)
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graph.data = np.exp(-graph.data / graph.data.std())
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labels_arpack = spectral_clustering(
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graph, n_clusters=2, eigen_solver="arpack", random_state=0
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)
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assert len(np.unique(labels_arpack)) == 2
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if amg_loaded:
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labels_amg = spectral_clustering(
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graph, n_clusters=2, eigen_solver="amg", random_state=0
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)
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assert adjusted_rand_score(labels_arpack, labels_amg) == 1
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else:
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with pytest.raises(ValueError):
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spectral_clustering(graph, n_clusters=2, eigen_solver="amg", random_state=0)
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def test_n_components():
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# Test that after adding n_components, result is different and
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# n_components = n_clusters by default
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X, y = make_blobs(
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n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
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)
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sp = SpectralClustering(n_clusters=2, random_state=0)
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labels = sp.fit(X).labels_
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# set n_components = n_cluster and test if result is the same
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labels_same_ncomp = (
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SpectralClustering(n_clusters=2, n_components=2, random_state=0).fit(X).labels_
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)
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# test that n_components=n_clusters by default
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assert_array_equal(labels, labels_same_ncomp)
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# test that n_components affect result
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# n_clusters=8 by default, and set n_components=2
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labels_diff_ncomp = (
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SpectralClustering(n_components=2, random_state=0).fit(X).labels_
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)
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assert not np.array_equal(labels, labels_diff_ncomp)
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@pytest.mark.parametrize("assign_labels", ("kmeans", "discretize", "cluster_qr"))
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def test_verbose(assign_labels, capsys):
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# Check verbose mode of KMeans for better coverage.
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X, y = make_blobs(
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n_samples=20, random_state=0, centers=[[1, 1], [-1, -1]], cluster_std=0.01
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)
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SpectralClustering(n_clusters=2, random_state=42, verbose=1).fit(X)
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captured = capsys.readouterr()
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assert re.search(r"Computing label assignment using", captured.out)
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if assign_labels == "kmeans":
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assert re.search(r"Initialization complete", captured.out)
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assert re.search(r"Iteration [0-9]+, inertia", captured.out)
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def test_spectral_clustering_np_matrix_raises():
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"""Check that spectral_clustering raises an informative error when passed
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a np.matrix. See #10993"""
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X = np.matrix([[0.0, 2.0], [2.0, 0.0]])
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msg = r"np\.matrix is not supported. Please convert to a numpy array"
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with pytest.raises(TypeError, match=msg):
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spectral_clustering(X)
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def test_spectral_clustering_not_infinite_loop(capsys, monkeypatch):
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"""Check that discretize raises LinAlgError when svd never converges.
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Non-regression test for #21380
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"""
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def new_svd(*args, **kwargs):
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raise LinAlgError()
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monkeypatch.setattr(np.linalg, "svd", new_svd)
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vectors = np.ones((10, 4))
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with pytest.raises(LinAlgError, match="SVD did not converge"):
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discretize(vectors)
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