207 lines
6.6 KiB
Python
207 lines
6.6 KiB
Python
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"""
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Testing for mean shift clustering methods
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"""
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import warnings
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import numpy as np
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import pytest
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from sklearn.cluster import MeanShift, estimate_bandwidth, get_bin_seeds, mean_shift
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from sklearn.datasets import make_blobs
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from sklearn.metrics import v_measure_score
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from sklearn.utils._testing import assert_allclose, assert_array_equal
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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(
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n_samples=300,
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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=11,
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)
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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 0.9 <= bandwidth <= 1.5
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def test_estimate_bandwidth_1sample(global_dtype):
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# Test estimate_bandwidth when n_samples=1 and quantile<1, so that
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# n_neighbors is set to 1.
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bandwidth = estimate_bandwidth(
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X.astype(global_dtype, copy=False), n_samples=1, quantile=0.3
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)
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assert bandwidth.dtype == X.dtype
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assert bandwidth == pytest.approx(0.0, abs=1e-5)
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@pytest.mark.parametrize(
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"bandwidth, cluster_all, expected, first_cluster_label",
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[(1.2, True, 3, 0), (1.2, False, 4, -1)],
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)
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def test_mean_shift(
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global_dtype, bandwidth, cluster_all, expected, first_cluster_label
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):
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# Test MeanShift algorithm
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X_with_global_dtype = X.astype(global_dtype, copy=False)
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ms = MeanShift(bandwidth=bandwidth, cluster_all=cluster_all)
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labels = ms.fit(X_with_global_dtype).labels_
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labels_unique = np.unique(labels)
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n_clusters_ = len(labels_unique)
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assert n_clusters_ == expected
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assert labels_unique[0] == first_cluster_label
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assert ms.cluster_centers_.dtype == global_dtype
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cluster_centers, labels_mean_shift = mean_shift(
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X_with_global_dtype, cluster_all=cluster_all
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)
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labels_mean_shift_unique = np.unique(labels_mean_shift)
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n_clusters_mean_shift = len(labels_mean_shift_unique)
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assert n_clusters_mean_shift == expected
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assert labels_mean_shift_unique[0] == first_cluster_label
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assert cluster_centers.dtype == global_dtype
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def test_parallel(global_dtype):
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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=50,
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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=11,
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)
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X = X.astype(global_dtype, copy=False)
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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_allclose(ms1.cluster_centers_, ms2.cluster_centers_)
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assert ms1.cluster_centers_.dtype == ms2.cluster_centers_.dtype
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assert_array_equal(ms1.labels_, ms2.labels_)
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def test_meanshift_predict(global_dtype):
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# Test MeanShift.predict
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ms = MeanShift(bandwidth=1.2)
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X_with_global_dtype = X.astype(global_dtype, copy=False)
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labels = ms.fit_predict(X_with_global_dtype)
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labels2 = ms.predict(X_with_global_dtype)
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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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with pytest.raises(ValueError, match=msg):
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ms.fit(
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X,
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)
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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 not hasattr(ms, "cluster_centers_")
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assert not hasattr(ms, "labels_")
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def test_cluster_intensity_tie(global_dtype):
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X = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]], dtype=global_dtype)
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c1 = MeanShift(bandwidth=2).fit(X)
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X = np.array([[4, 7], [3, 5], [3, 6], [1, 1], [2, 1], [1, 0]], dtype=global_dtype)
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c2 = MeanShift(bandwidth=2).fit(X)
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assert_array_equal(c1.labels_, [1, 1, 1, 0, 0, 0])
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assert_array_equal(c2.labels_, [0, 0, 0, 1, 1, 1])
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def test_bin_seeds(global_dtype):
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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(
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[[1.0, 1.0], [1.4, 1.4], [1.8, 1.2], [2.0, 1.0], [2.1, 1.1], [0.0, 0.0]],
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dtype=global_dtype,
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)
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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 = {(1.0, 1.0), (2.0, 1.0), (0.0, 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 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 = {(1.0, 1.0), (2.0, 1.0)}
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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 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_allclose(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(
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n_samples=100,
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n_features=2,
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centers=[[0, 0], [1, 1]],
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cluster_std=0.1,
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random_state=0,
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)
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X = X.astype(global_dtype, copy=False)
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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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@pytest.mark.parametrize("max_iter", [1, 100])
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def test_max_iter(max_iter):
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clusters1, _ = mean_shift(X, max_iter=max_iter)
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ms = MeanShift(max_iter=max_iter).fit(X)
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clusters2 = ms.cluster_centers_
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assert ms.n_iter_ <= ms.max_iter
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assert len(clusters1) == len(clusters2)
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for c1, c2 in zip(clusters1, clusters2):
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assert np.allclose(c1, c2)
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def test_mean_shift_zero_bandwidth(global_dtype):
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# Check that mean shift works when the estimated bandwidth is 0.
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X = np.array([1, 1, 1, 2, 2, 2, 3, 3], dtype=global_dtype).reshape(-1, 1)
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# estimate_bandwidth with default args returns 0 on this dataset
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bandwidth = estimate_bandwidth(X)
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assert bandwidth == 0
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# get_bin_seeds with a 0 bin_size should return the dataset itself
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assert get_bin_seeds(X, bin_size=bandwidth) is X
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# MeanShift with binning and a 0 estimated bandwidth should be equivalent
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# to no binning.
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ms_binning = MeanShift(bin_seeding=True, bandwidth=None).fit(X)
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ms_nobinning = MeanShift(bin_seeding=False).fit(X)
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expected_labels = np.array([0, 0, 0, 1, 1, 1, 2, 2])
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assert v_measure_score(ms_binning.labels_, expected_labels) == pytest.approx(1)
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assert v_measure_score(ms_nobinning.labels_, expected_labels) == pytest.approx(1)
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assert_allclose(ms_binning.cluster_centers_, ms_nobinning.cluster_centers_)
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