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"""
Testing for mean shift clustering methods
"""
import warnings
import numpy as np
import pytest
from sklearn.cluster import MeanShift, estimate_bandwidth, get_bin_seeds, mean_shift
from sklearn.datasets import make_blobs
from sklearn.metrics import v_measure_score
from sklearn.utils._testing import assert_allclose, assert_array_equal
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_convergence_of_1d_constant_data():
# Test convergence using 1D constant data
# Non-regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/28926
model = MeanShift()
n_iter = model.fit(np.ones(10).reshape(-1, 1)).n_iter_
assert n_iter < model.max_iter
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(global_dtype):
# Test estimate_bandwidth when n_samples=1 and quantile<1, so that
# n_neighbors is set to 1.
bandwidth = estimate_bandwidth(
X.astype(global_dtype, copy=False), n_samples=1, quantile=0.3
)
assert bandwidth.dtype == X.dtype
assert bandwidth == pytest.approx(0.0, 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(
global_dtype, bandwidth, cluster_all, expected, first_cluster_label
):
# Test MeanShift algorithm
X_with_global_dtype = X.astype(global_dtype, copy=False)
ms = MeanShift(bandwidth=bandwidth, cluster_all=cluster_all)
labels = ms.fit(X_with_global_dtype).labels_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
assert n_clusters_ == expected
assert labels_unique[0] == first_cluster_label
assert ms.cluster_centers_.dtype == global_dtype
cluster_centers, labels_mean_shift = mean_shift(
X_with_global_dtype, 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
assert cluster_centers.dtype == global_dtype
def test_parallel(global_dtype, global_random_seed):
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=global_random_seed,
)
X = X.astype(global_dtype, copy=False)
ms1 = MeanShift(n_jobs=2)
ms1.fit(X)
ms2 = MeanShift()
ms2.fit(X)
assert_allclose(ms1.cluster_centers_, ms2.cluster_centers_)
assert ms1.cluster_centers_.dtype == ms2.cluster_centers_.dtype
assert_array_equal(ms1.labels_, ms2.labels_)
def test_meanshift_predict(global_dtype):
# Test MeanShift.predict
ms = MeanShift(bandwidth=1.2)
X_with_global_dtype = X.astype(global_dtype, copy=False)
labels = ms.fit_predict(X_with_global_dtype)
labels2 = ms.predict(X_with_global_dtype)
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"
with pytest.raises(ValueError, match=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(global_dtype):
X = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]], dtype=global_dtype)
c1 = MeanShift(bandwidth=2).fit(X)
X = np.array([[4, 7], [3, 5], [3, 6], [1, 1], [2, 1], [1, 0]], dtype=global_dtype)
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(global_dtype):
# 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(
[[1.0, 1.0], [1.4, 1.4], [1.8, 1.2], [2.0, 1.0], [2.1, 1.1], [0.0, 0.0]],
dtype=global_dtype,
)
# With a bin coarseness of 1.0 and min_bin_freq of 1, 3 bins should be
# found
ground_truth = {(1.0, 1.0), (2.0, 1.0), (0.0, 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 = {(1.0, 1.0), (2.0, 1.0)}
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_allclose(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,
)
X = X.astype(global_dtype, copy=False)
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(global_dtype):
# Check that mean shift works when the estimated bandwidth is 0.
X = np.array([1, 1, 1, 2, 2, 2, 3, 3], dtype=global_dtype).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) == pytest.approx(1)
assert v_measure_score(ms_nobinning.labels_, expected_labels) == pytest.approx(1)
assert_allclose(ms_binning.cluster_centers_, ms_nobinning.cluster_centers_)
|
