def test_correct_number_of_clusters():
# in 'auto' mode
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# Parameters chosen specifically for this task.
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 6.0, min_samples=4, xi=.1)
clust.fit(X)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
assert n_clusters_1 == n_clusters
# check attribute types and sizes
assert clust.labels_.shape == (len(X),)
assert clust.labels_.dtype.kind == 'i'
assert clust.reachability_.shape == (len(X),)
assert clust.reachability_.dtype.kind == 'f'
assert clust.core_distances_.shape == (len(X),)
assert clust.core_distances_.dtype.kind == 'f'
assert clust.ordering_.shape == (len(X),)
assert clust.ordering_.dtype.kind == 'i'
assert set(clust.ordering_) == set(range(len(X)))
def test_correct_number_of_clusters():
# in 'auto' mode
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# Parameters chosen specifically for this task.
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 6.0, min_samples=4)
clust.fit(X)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
assert_equal(n_clusters_1, n_clusters)
# check attribute types and sizes
assert clust.core_sample_indices_.ndim == 1
assert clust.core_sample_indices_.size > 0
assert clust.core_sample_indices_.dtype.kind == 'i'
assert clust.labels_.shape == (len(X),)
assert clust.labels_.dtype.kind == 'i'
assert clust.reachability_.shape == (len(X),)
assert clust.reachability_.dtype.kind == 'f'
assert clust.core_distances_.shape == (len(X),)
assert clust.core_distances_.dtype.kind == 'f'
assert clust.ordering_.shape == (len(X),)
assert clust.ordering_.dtype.kind == 'i'
assert set(clust.ordering_) == set(range(len(X)))
def test_correct_number_of_clusters():
# in 'auto' mode
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# Parameters chosen specifically for this task.
# Compute OPTICS
clust = OPTICS(max_bound=5.0 * 6.0, min_samples=4, metric='euclidean')
clust.fit(X)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
assert_equal(n_clusters_1, n_clusters)
def test_correct_number_of_clusters():
# in 'auto' mode
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# Parameters chosen specifically for this task.
# Compute OPTICS
clust = OPTICS(max_bound=5.0 * 6.0, min_samples=4, metric='euclidean')
clust.fit(X)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
assert_equal(n_clusters_1, n_clusters)
def test_optics():
# Tests the optics clustering method and all functions inside it
# 'auto' mode
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
print(np.shape(X))
# Parameters chosen specifically for this task.
# Compute OPTICS
clust = OPTICS(max_bound=5.0 * 6.0, min_samples=4, metric='euclidean')
clust.fit(X)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(clust.labels_)) - int(-1 in clust.labels_)
assert_equal(n_clusters_1, n_clusters)
def test_dbscan():
# Test chaining RadiusNeighborsTransformer and DBSCAN
radius = 0.3
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# compare the chained version and the compact version
est_chain = make_pipeline(
RadiusNeighborsTransformer(radius=radius, mode='distance'),
DBSCAN(metric='precomputed', eps=radius))
est_compact = DBSCAN(eps=radius)
labels_chain = est_chain.fit_predict(X)
labels_compact = est_compact.fit_predict(X)
assert_array_almost_equal(labels_chain, labels_compact)
def test_birch_predict():
# Test the predict method predicts the nearest centroid.
rng = np.random.RandomState(0)
X = generate_clustered_data(n_clusters=3, n_features=3,
n_samples_per_cluster=10)
# n_samples * n_samples_per_cluster
shuffle_indices = np.arange(30)
rng.shuffle(shuffle_indices)
X_shuffle = X[shuffle_indices, :]
brc = Birch(n_clusters=4, threshold=1.)
brc.fit(X_shuffle)
centroids = brc.subcluster_centers_
assert_array_equal(brc.labels_, brc.predict(X_shuffle))
nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
def test_birch_predict():
# Test the predict method predicts the nearest centroid.
rng = np.random.RandomState(0)
X = generate_clustered_data(n_clusters=3, n_features=3,
n_samples_per_cluster=10)
# n_samples * n_samples_per_cluster
shuffle_indices = np.arange(30)
rng.shuffle(shuffle_indices)
X_shuffle = X[shuffle_indices, :]
brc = Birch(n_clusters=4, threshold=1.)
brc.fit(X_shuffle)
centroids = brc.subcluster_centers_
assert_array_equal(brc.labels_, brc.predict(X_shuffle))
nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0)
from scipy.spatial import distance
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.cluster.dbscan_ import DBSCAN
from sklearn.cluster.dbscan_ import dbscan
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.metrics.pairwise import pairwise_distances
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
def test_dbscan_similarity():
# Tests the DBSCAN algorithm with a similarity array.
# Parameters chosen specifically for this task.
eps = 0.15
min_samples = 10
# Compute similarities
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
# Compute DBSCAN
core_samples, labels = dbscan(D, metric="precomputed", eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - (1 if -1 in labels else 0)
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import if_matplotlib
from hdbscan import RobustSingleLinkage
from hdbscan import robust_single_linkage
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn import datasets
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters, n_samples_per_cluster=50)
def test_rsl_distance_matrix():
D = distance.squareform(distance.pdist(X))
D /= np.max(D)
labels, tree = robust_single_linkage(D, 0.25, metric='precomputed')
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels) # ignore noise
#assert_equal(n_clusters_1, n_clusters)
labels = RobustSingleLinkage(metric="precomputed").fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
#assert_equal(n_clusters_2, n_clusters)
def test_rsl_feature_vector():
def clustered_data(n_clusters):
return generate_clustered_data(n_clusters=n_clusters)
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import if_matplotlib
from hdbscan import HDBSCAN
from hdbscan import hdbscan
from sklearn.cluster.tests.common import generate_clustered_data
from scipy.stats import mode
from tempfile import mkdtemp
from sklearn import datasets
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters, n_samples_per_cluster=50)
def relabel(labels):
result = np.zeros(labels.shape[0])
labels_to_go = set(labels)
i = 0
new_l = 0
while len(labels_to_go) > 0:
l = labels[i]
if l in labels_to_go:
result[labels == l] = new_l
new_l += 1
labels_to_go.remove(l)
i += 1
return result
