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"""
Testing for mean shift clustering methods
"""
import numpy as np
import warnings
import pytest
from scipy import sparse
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_allclose
from sklearn.cluster import MeanShift
from sklearn.cluster import mean_shift
from sklearn.cluster import estimate_bandwidth
from sklearn.cluster import get_bin_seeds
from sklearn.datasets import make_blobs
from sklearn.metrics import v_measure_score
n_clusters = 3
centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
X, _ = make_blobs(n_samples=300, n_features=2, centers=centers,
cluster_std=0.4, shuffle=True, random_state=11)
def test_estimate_bandwidth():
# Test estimate_bandwidth
bandwidth = estimate_bandwidth(X, n_samples=200)
assert 0.9 <= bandwidth <= 1.5
def test_estimate_bandwidth_1sample():
# Test estimate_bandwidth when n_samples=1 and quantile<1, so that
# n_neighbors is set to 1.
bandwidth = estimate_bandwidth(X, n_samples=1, quantile=0.3)
assert bandwidth == pytest.approx(0., abs=1e-5)
@pytest.mark.parametrize("bandwidth, cluster_all, expected, "
"first_cluster_label",
[(1.2, True, 3, 0), (1.2, False, 4, -1)])
def test_mean_shift(bandwidth, cluster_all, expected, first_cluster_label):
# Test MeanShift algorithm
ms = MeanShift(bandwidth=bandwidth, cluster_all=cluster_all)
labels = ms.fit(X).labels_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
assert n_clusters_ == expected
assert labels_unique[0] == first_cluster_label
cluster_centers, labels_mean_shift = mean_shift(X, cluster_all=cluster_all)
labels_mean_shift_unique = np.unique(labels_mean_shift)
n_clusters_mean_shift = len(labels_mean_shift_unique)
assert n_clusters_mean_shift == expected
assert labels_mean_shift_unique[0] == first_cluster_label
def test_mean_shift_negative_bandwidth():
bandwidth = -1
ms = MeanShift(bandwidth=bandwidth)
msg = (r"bandwidth needs to be greater than zero or None,"
r" got -1\.000000")
with pytest.raises(ValueError, match=msg):
ms.fit(X)
def test_estimate_bandwidth_with_sparse_matrix():
# Test estimate_bandwidth with sparse matrix
X = sparse.lil_matrix((1000, 1000))
msg = "A sparse matrix was passed, but dense data is required."
assert_raise_message(TypeError, msg, estimate_bandwidth, X)
def test_parallel():
centers = np.array([[1, 1], [-1, -1], [1, -1]]) + 10
X, _ = make_blobs(n_samples=50, n_features=2, centers=centers,
cluster_std=0.4, shuffle=True, random_state=11)
ms1 = MeanShift(n_jobs=2)
ms1.fit(X)
ms2 = MeanShift()
ms2.fit(X)
assert_array_almost_equal(ms1.cluster_centers_, ms2.cluster_centers_)
assert_array_equal(ms1.labels_, ms2.labels_)
def test_meanshift_predict():
# Test MeanShift.predict
ms = MeanShift(bandwidth=1.2)
labels = ms.fit_predict(X)
labels2 = ms.predict(X)
assert_array_equal(labels, labels2)
def test_meanshift_all_orphans():
# init away from the data, crash with a sensible warning
ms = MeanShift(bandwidth=0.1, seeds=[[-9, -9], [-10, -10]])
msg = "No point was within bandwidth=0.1"
assert_raise_message(ValueError, msg, ms.fit, X,)
def test_unfitted():
# Non-regression: before fit, there should be not fitted attributes.
ms = MeanShift()
assert not hasattr(ms, "cluster_centers_")
assert not hasattr(ms, "labels_")
def test_cluster_intensity_tie():
X = np.array([[1, 1], [2, 1], [1, 0],
[4, 7], [3, 5], [3, 6]])
c1 = MeanShift(bandwidth=2).fit(X)
X = np.array([[4, 7], [3, 5], [3, 6],
[1, 1], [2, 1], [1, 0]])
c2 = MeanShift(bandwidth=2).fit(X)
assert_array_equal(c1.labels_, [1, 1, 1, 0, 0, 0])
assert_array_equal(c2.labels_, [0, 0, 0, 1, 1, 1])
def test_bin_seeds():
# Test the bin seeding technique which can be used in the mean shift
# algorithm
# Data is just 6 points in the plane
X = np.array([[1., 1.], [1.4, 1.4], [1.8, 1.2],
[2., 1.], [2.1, 1.1], [0., 0.]])
# With a bin coarseness of 1.0 and min_bin_freq of 1, 3 bins should be
# found
ground_truth = {(1., 1.), (2., 1.), (0., 0.)}
test_bins = get_bin_seeds(X, 1, 1)
test_result = set(tuple(p) for p in test_bins)
assert len(ground_truth.symmetric_difference(test_result)) == 0
# With a bin coarseness of 1.0 and min_bin_freq of 2, 2 bins should be
# found
ground_truth = {(1., 1.), (2., 1.)}
test_bins = get_bin_seeds(X, 1, 2)
test_result = set(tuple(p) for p in test_bins)
assert len(ground_truth.symmetric_difference(test_result)) == 0
# With a bin size of 0.01 and min_bin_freq of 1, 6 bins should be found
# we bail and use the whole data here.
with warnings.catch_warnings(record=True):
test_bins = get_bin_seeds(X, 0.01, 1)
assert_array_almost_equal(test_bins, X)
# tight clusters around [0, 0] and [1, 1], only get two bins
X, _ = make_blobs(n_samples=100, n_features=2, centers=[[0, 0], [1, 1]],
cluster_std=0.1, random_state=0)
test_bins = get_bin_seeds(X, 1)
assert_array_equal(test_bins, [[0, 0], [1, 1]])
@pytest.mark.parametrize('max_iter', [1, 100])
def test_max_iter(max_iter):
clusters1, _ = mean_shift(X, max_iter=max_iter)
ms = MeanShift(max_iter=max_iter).fit(X)
clusters2 = ms.cluster_centers_
assert ms.n_iter_ <= ms.max_iter
assert len(clusters1) == len(clusters2)
for c1, c2 in zip(clusters1, clusters2):
assert np.allclose(c1, c2)
def test_mean_shift_zero_bandwidth():
# Check that mean shift works when the estimated bandwidth is 0.
X = np.array([1, 1, 1, 2, 2, 2, 3, 3]).reshape(-1, 1)
# estimate_bandwidth with default args returns 0 on this dataset
bandwidth = estimate_bandwidth(X)
assert bandwidth == 0
# get_bin_seeds with a 0 bin_size should return the dataset itself
assert get_bin_seeds(X, bin_size=bandwidth) is X
# MeanShift with binning and a 0 estimated bandwidth should be equivalent
# to no binning.
ms_binning = MeanShift(bin_seeding=True, bandwidth=None).fit(X)
ms_nobinning = MeanShift(bin_seeding=False).fit(X)
expected_labels = np.array([0, 0, 0, 1, 1, 1, 2, 2])
assert v_measure_score(ms_binning.labels_, expected_labels) == 1
assert v_measure_score(ms_nobinning.labels_, expected_labels) == 1
assert_allclose(ms_binning.cluster_centers_, ms_nobinning.cluster_centers_)
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