123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
"""
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Testing for mean shift clustering methods
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"""
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import numpy as np
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import warnings
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from scipy import sparse
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from sklearn.utils.testing import assert_equal
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from sklearn.utils.testing import assert_false
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_array_equal
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from sklearn.utils.testing import assert_raise_message
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from sklearn.cluster import MeanShift
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from sklearn.cluster import mean_shift
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from sklearn.cluster import estimate_bandwidth
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from sklearn.cluster import get_bin_seeds
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from sklearn.datasets.samples_generator import make_blobs
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n_clusters = 3
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centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
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X, _ = make_blobs(n_samples=300, n_features=2, centers=centers,
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cluster_std=0.4, shuffle=True, random_state=11)
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def test_estimate_bandwidth():
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# Test estimate_bandwidth
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bandwidth = estimate_bandwidth(X, n_samples=200)
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assert_true(0.9 <= bandwidth <= 1.5)
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def test_mean_shift():
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# Test MeanShift algorithm
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bandwidth = 1.2
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ms = MeanShift(bandwidth=bandwidth)
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labels = ms.fit(X).labels_
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labels_unique = np.unique(labels)
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n_clusters_ = len(labels_unique)
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assert_equal(n_clusters_, n_clusters)
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cluster_centers, labels = mean_shift(X, bandwidth=bandwidth)
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labels_unique = np.unique(labels)
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n_clusters_ = len(labels_unique)
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assert_equal(n_clusters_, n_clusters)
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def test_estimate_bandwidth_with_sparse_matrix():
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# Test estimate_bandwidth with sparse matrix
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X = sparse.lil_matrix((1000, 1000))
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msg = "A sparse matrix was passed, but dense data is required."
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assert_raise_message(TypeError, msg, estimate_bandwidth, X, 200)
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def test_parallel():
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ms1 = MeanShift(n_jobs=2)
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ms1.fit(X)
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ms2 = MeanShift()
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ms2.fit(X)
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assert_array_equal(ms1.cluster_centers_, ms2.cluster_centers_)
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assert_array_equal(ms1.labels_, ms2.labels_)
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def test_meanshift_predict():
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# Test MeanShift.predict
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ms = MeanShift(bandwidth=1.2)
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labels = ms.fit_predict(X)
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labels2 = ms.predict(X)
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assert_array_equal(labels, labels2)
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def test_meanshift_all_orphans():
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# init away from the data, crash with a sensible warning
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ms = MeanShift(bandwidth=0.1, seeds=[[-9, -9], [-10, -10]])
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msg = "No point was within bandwidth=0.1"
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assert_raise_message(ValueError, msg, ms.fit, X,)
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def test_unfitted():
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# Non-regression: before fit, there should be not fitted attributes.
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ms = MeanShift()
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assert_false(hasattr(ms, "cluster_centers_"))
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assert_false(hasattr(ms, "labels_"))
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def test_bin_seeds():
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# Test the bin seeding technique which can be used in the mean shift
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# algorithm
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# Data is just 6 points in the plane
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X = np.array([[1., 1.], [1.4, 1.4], [1.8, 1.2],
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[2., 1.], [2.1, 1.1], [0., 0.]])
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# With a bin coarseness of 1.0 and min_bin_freq of 1, 3 bins should be
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# found
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ground_truth = set([(1., 1.), (2., 1.), (0., 0.)])
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test_bins = get_bin_seeds(X, 1, 1)
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test_result = set([tuple(p) for p in test_bins])
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assert_true(len(ground_truth.symmetric_difference(test_result)) == 0)
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# With a bin coarseness of 1.0 and min_bin_freq of 2, 2 bins should be
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# found
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ground_truth = set([(1., 1.), (2., 1.)])
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test_bins = get_bin_seeds(X, 1, 2)
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test_result = set([tuple(p) for p in test_bins])
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assert_true(len(ground_truth.symmetric_difference(test_result)) == 0)
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# With a bin size of 0.01 and min_bin_freq of 1, 6 bins should be found
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# we bail and use the whole data here.
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with warnings.catch_warnings(record=True):
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test_bins = get_bin_seeds(X, 0.01, 1)
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assert_array_equal(test_bins, X)
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# tight clusters around [0, 0] and [1, 1], only get two bins
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X, _ = make_blobs(n_samples=100, n_features=2, centers=[[0, 0], [1, 1]],
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cluster_std=0.1, random_state=0)
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test_bins = get_bin_seeds(X, 1)
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assert_array_equal(test_bins, [[0, 0], [1, 1]])
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