def test_dbscan_core_samples_toy(algorithm):
X = [[0], [2], [3], [4], [6], [8], [10]]
n_samples = len(X)
# Degenerate case: every sample is a core sample, either with its own
# cluster or including other close core samples.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=1)
assert_array_equal(core_samples, np.arange(n_samples))
assert_array_equal(labels, [0, 1, 1, 1, 2, 3, 4])
# With eps=1 and min_samples=2 only the 3 samples from the denser area
# are core samples. All other points are isolated and considered noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=2)
assert_array_equal(core_samples, [1, 2, 3])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# Only the sample in the middle of the dense area is core. Its two
# neighbors are edge samples. Remaining samples are noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=3)
assert_array_equal(core_samples, [2])
assert_array_equal(labels, [-1, 0, 0, 0, -1, -1, -1])
# It's no longer possible to extract core samples with eps=1:
# everything is noise.
core_samples, labels = dbscan(X, algorithm=algorithm, eps=1, min_samples=4)
assert_array_equal(core_samples, [])
assert_array_equal(labels, np.full(n_samples, -1.))
def test_dbscan_sparse():
core_sparse, labels_sparse = dbscan(sparse.lil_matrix(X),
eps=.8,
min_samples=10)
core_dense, labels_dense = dbscan(X, eps=.8, min_samples=10)
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_boundaries():
# ensure min_samples is inclusive of core point
core, _ = dbscan([[0], [1]], eps=2, min_samples=2)
assert 0 in core
# ensure eps is inclusive of circumference
core, _ = dbscan([[0], [1], [1]], eps=1, min_samples=2)
assert 0 in core
core, _ = dbscan([[0], [1], [1]], eps=.99, min_samples=2)
assert 0 not in core
def test_dbscan_input_not_modified(use_sparse, metric):
# test that the input is not modified by dbscan
X = np.random.RandomState(0).rand(10, 10)
X = sparse.csr_matrix(X) if use_sparse else X
X_copy = X.copy()
dbscan(X, metric=metric)
if use_sparse:
assert_array_equal(X.toarray(), X_copy.toarray())
else:
assert_array_equal(X, X_copy)
def test_dbscan_sparse_precomputed_different_eps():
# test that precomputed neighbors graph is filtered if computed with
# a radius larger than DBSCAN's eps.
lower_eps = 0.2
nn = NearestNeighbors(radius=lower_eps).fit(X)
D_sparse = nn.radius_neighbors_graph(X, mode='distance')
dbscan_lower = dbscan(D_sparse, eps=lower_eps, metric='precomputed')
higher_eps = lower_eps + 0.7
nn = NearestNeighbors(radius=higher_eps).fit(X)
D_sparse = nn.radius_neighbors_graph(X, mode='distance')
dbscan_higher = dbscan(D_sparse, eps=lower_eps, metric='precomputed')
assert_array_equal(dbscan_lower[0], dbscan_higher[0])
assert_array_equal(dbscan_lower[1], dbscan_higher[1])
def test_dbscan_callable():
# Tests the DBSCAN algorithm with a callable metric.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
# metric is the function reference, not the string key.
metric = distance.euclidean
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X,
metric=metric,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert n_clusters_1 == n_clusters
db = DBSCAN(metric=metric,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert n_clusters_2 == n_clusters
def test_dbscan_sparse_precomputed(include_self):
D = pairwise_distances(X)
nn = NearestNeighbors(radius=.9).fit(X)
X_ = X if include_self else None
D_sparse = nn.radius_neighbors_graph(X=X_, mode='distance')
# Ensure it is sparse not merely on diagonals:
assert D_sparse.nnz < D.shape[0] * (D.shape[0] - 1)
core_sparse, labels_sparse = dbscan(D_sparse,
eps=.8,
min_samples=10,
metric='precomputed')
core_dense, labels_dense = dbscan(D,
eps=.8,
min_samples=10,
metric='precomputed')
assert_array_equal(core_dense, core_sparse)
assert_array_equal(labels_dense, labels_sparse)
def test_dbscan_balltree():
# Tests the DBSCAN algorithm with balltree for neighbor calculation.
eps = 0.8
min_samples = 10
D = pairwise_distances(X)
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)) - int(-1 in labels)
assert n_clusters_1 == n_clusters
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert n_clusters_2 == n_clusters
db = DBSCAN(p=2.0, eps=eps, min_samples=min_samples, algorithm='kd_tree')
labels = db.fit(X).labels_
n_clusters_3 = len(set(labels)) - int(-1 in labels)
assert n_clusters_3 == n_clusters
db = DBSCAN(p=1.0, eps=eps, min_samples=min_samples, algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_4 = len(set(labels)) - int(-1 in labels)
assert n_clusters_4 == n_clusters
db = DBSCAN(leaf_size=20,
eps=eps,
min_samples=min_samples,
algorithm='ball_tree')
labels = db.fit(X).labels_
n_clusters_5 = len(set(labels)) - int(-1 in labels)
assert n_clusters_5 == n_clusters
def test_dbscan_feature():
# Tests the DBSCAN algorithm with a feature vector array.
# Parameters chosen specifically for this task.
# Different eps to other test, because distance is not normalised.
eps = 0.8
min_samples = 10
metric = 'euclidean'
# Compute DBSCAN
# parameters chosen for task
core_samples, labels = dbscan(X,
metric=metric,
eps=eps,
min_samples=min_samples)
# number of clusters, ignoring noise if present
n_clusters_1 = len(set(labels)) - int(-1 in labels)
assert n_clusters_1 == n_clusters
db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
labels = db.fit(X).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert n_clusters_2 == 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)
assert n_clusters_1 == n_clusters
db = DBSCAN(metric="precomputed", eps=eps, min_samples=min_samples)
labels = db.fit(D).labels_
n_clusters_2 = len(set(labels)) - int(-1 in labels)
assert n_clusters_2 == n_clusters
def test_weighted_dbscan():
# ensure sample_weight is validated
with pytest.raises(ValueError):
dbscan([[0], [1]], sample_weight=[2])
with pytest.raises(ValueError):
dbscan([[0], [1]], sample_weight=[2, 3, 4])
# ensure sample_weight has an effect
assert_array_equal([],
dbscan([[0], [1]], sample_weight=None,
min_samples=6)[0])
assert_array_equal([],
dbscan([[0], [1]], sample_weight=[5, 5],
min_samples=6)[0])
assert_array_equal([0],
dbscan([[0], [1]], sample_weight=[6, 5],
min_samples=6)[0])
assert_array_equal([0, 1],
dbscan([[0], [1]], sample_weight=[6, 6],
min_samples=6)[0])
# points within eps of each other:
assert_array_equal([0, 1],
dbscan([[0], [1]],
eps=1.5,
sample_weight=[5, 1],
min_samples=6)[0])
# and effect of non-positive and non-integer sample_weight:
assert_array_equal([],
dbscan([[0], [1]],
sample_weight=[5, 0],
eps=1.5,
min_samples=6)[0])
assert_array_equal([0, 1],
dbscan([[0], [1]],
sample_weight=[5.9, 0.1],
eps=1.5,
min_samples=6)[0])
assert_array_equal([0, 1],
dbscan([[0], [1]],
sample_weight=[6, 0],
eps=1.5,
min_samples=6)[0])
assert_array_equal([],
dbscan([[0], [1]],
sample_weight=[6, -1],
eps=1.5,
min_samples=6)[0])
# for non-negative sample_weight, cores should be identical to repetition
rng = np.random.RandomState(42)
sample_weight = rng.randint(0, 5, X.shape[0])
core1, label1 = dbscan(X, sample_weight=sample_weight)
assert len(label1) == len(X)
X_repeated = np.repeat(X, sample_weight, axis=0)
core_repeated, label_repeated = dbscan(X_repeated)
core_repeated_mask = np.zeros(X_repeated.shape[0], dtype=bool)
core_repeated_mask[core_repeated] = True
core_mask = np.zeros(X.shape[0], dtype=bool)
core_mask[core1] = True
assert_array_equal(np.repeat(core_mask, sample_weight), core_repeated_mask)
# sample_weight should work with precomputed distance matrix
D = pairwise_distances(X)
core3, label3 = dbscan(D,
sample_weight=sample_weight,
metric='precomputed')
assert_array_equal(core1, core3)
assert_array_equal(label1, label3)
# sample_weight should work with estimator
est = DBSCAN().fit(X, sample_weight=sample_weight)
core4 = est.core_sample_indices_
label4 = est.labels_
assert_array_equal(core1, core4)
assert_array_equal(label1, label4)
est = DBSCAN()
label5 = est.fit_predict(X, sample_weight=sample_weight)
core5 = est.core_sample_indices_
assert_array_equal(core1, core5)
assert_array_equal(label1, label5)
assert_array_equal(label1, est.labels_)
def test_dbscan_badargs(args):
# Test bad argument values: these should all raise ValueErrors
with pytest.raises(ValueError):
dbscan(X, **args)