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_auto_extract_hier():
# Generate sample data
np.random.seed(0)
n_points_per_cluster = 250
X = np.empty((0, 2))
X = np.r_[X, [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)]
# Compute OPTICS
clust = OPTICS(eps=30.3, min_samples=9)
# Run the fit
clust.fit(X)
# Extract the result
# eps not used for 'auto' extract
clust.extract(0.0, 'auto')
assert_equal(len(set(clust.labels_)), 6)
def test_dbscan_optics_parity(eps, min_samples):
# Test that OPTICS clustering labels are <= 5% difference of DBSCAN
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# calculate optics with dbscan extract at 0.3 epsilon
op = OPTICS(min_samples=min_samples).fit(X)
core_optics, labels_optics = op.extract_dbscan(eps)
# calculate dbscan labels
db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
contingency = contingency_matrix(db.labels_, labels_optics)
agree = min(np.sum(np.max(contingency, axis=0)),
np.sum(np.max(contingency, axis=1)))
disagree = X.shape[0] - agree
# verify core_labels match
assert_array_equal(core_optics, db.core_sample_indices_)
non_core_count = len(labels_optics) - len(core_optics)
percent_mismatch = np.round((disagree - 1) / non_core_count, 2)
# verify label mismatch is <= 5% labels
assert percent_mismatch <= 0.05
def test_bad_reachability():
msg = "All reachability values are inf. Set a larger max_eps."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
with pytest.warns(UserWarning, match=msg):
clust = OPTICS(max_eps=5.0 * 0.003, min_samples=10, eps=0.015)
clust.fit(X)
def test_bad_extract():
# Test an extraction of eps too close to original eps
msg = "Specify an epsilon smaller than 0.15. Got 0.3."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 0.03, min_samples=10)
clust2 = clust.fit(X)
assert_raise_message(ValueError, msg, clust2.extract_dbscan, 0.3)
def test_min_cluster_size(min_cluster_size):
redX = X[::2] # reduce for speed
clust = OPTICS(min_samples=9, min_cluster_size=min_cluster_size).fit(redX)
cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
if cluster_sizes.size:
assert min(cluster_sizes) >= min_cluster_size
# check behaviour is the same when min_cluster_size is a fraction
clust_frac = OPTICS(min_samples=9,
min_cluster_size=min_cluster_size / redX.shape[0])
clust_frac.fit(redX)
assert_array_equal(clust.labels_, clust_frac.labels_)
def test_bad_extract():
# Test an extraction of eps too close to original eps
msg = "Specify an epsilon smaller than 0.15. Got 0.3."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 0.03, min_samples=10)
clust2 = clust.fit(X)
assert_raise_message(ValueError, msg, clust2.extract_dbscan, 0.3)
def test_bad_reachability():
msg = "All reachability values are inf. Set a larger max_eps."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750,
centers=centers,
cluster_std=0.4,
random_state=0)
with pytest.warns(UserWarning, match=msg):
clust = OPTICS(max_eps=5.0 * 0.003, min_samples=10, eps=0.015)
clust.fit(X)
def test_min_cluster_size(min_cluster_size):
redX = X[::2] # reduce for speed
clust = OPTICS(min_samples=9, min_cluster_size=min_cluster_size).fit(redX)
cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
if cluster_sizes.size:
assert min(cluster_sizes) >= min_cluster_size
# check behaviour is the same when min_cluster_size is a fraction
clust_frac = OPTICS(min_samples=9,
min_cluster_size=min_cluster_size / redX.shape[0])
clust_frac.fit(redX)
assert_array_equal(clust.labels_, clust_frac.labels_)
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_bad_extract():
# Test an extraction of eps too close to original eps
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750,
centers=centers,
cluster_std=0.4,
random_state=0)
# Compute OPTICS
clust = OPTICS(max_bound=5.0 * 0.003, min_samples=10)
clust2 = clust.fit(X)
assert_raises(ValueError, clust2.extract_dbscan, 0.3)
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_optics2():
# Tests the optics clustering method and all functions inside it
# 'dbscan' mode
# Compute OPTICS
X = [[1, 1]]
clust = OPTICS(eps=0.3, min_samples=10)
# Run the fit
clust2 = clust.fit(X)
assert clust2 is None
def test_bad_extract():
# Test an extraction of eps too close to original eps
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
##########################################################################
# Compute OPTICS
clust = OPTICS(eps=0.003, min_samples=10)
clust2 = clust.fit(X)
assert clust2.extract(0.3) is None
def test_close_extract():
# Test extract where extraction eps is close to scaled epsPrime
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(eps=0.2, min_samples=10)
clust3 = clust.fit(X)
clust3.extract(0.3, clustering='dbscan')
assert max(clust3.labels_) == 3
def test_extract_xi():
# small and easy test (no clusters around other clusters)
# but with a clear noise data.
rng = np.random.RandomState(0)
n_points_per_cluster = 5
C1 = [-5, -2] + .8 * rng.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .1 * rng.randn(n_points_per_cluster, 2)
C3 = [1, -2] + .2 * rng.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .3 * rng.randn(n_points_per_cluster, 2)
C5 = [3, -2] + .6 * rng.randn(n_points_per_cluster, 2)
C6 = [5, 6] + .2 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, np.array([[100, 100]]), C6))
expected_labels = np.r_[[2] * 5, [0] * 5, [1] * 5, [3] * 5, [1] * 5, -1,
[4] * 5]
X, expected_labels = shuffle(X, expected_labels, random_state=rng)
clust = OPTICS(min_samples=3,
min_cluster_size=2,
max_eps=20,
cluster_method='xi',
xi=0.4).fit(X)
assert_array_equal(clust.labels_, expected_labels)
X = np.vstack((C1, C2, C3, C4, C5, np.array([[100, 100]] * 2), C6))
expected_labels = np.r_[[1] * 5, [3] * 5, [2] * 5, [0] * 5, [2] * 5, -1,
-1, [4] * 5]
X, expected_labels = shuffle(X, expected_labels, random_state=rng)
clust = OPTICS(min_samples=3,
min_cluster_size=3,
max_eps=20,
cluster_method='xi',
xi=0.3).fit(X)
# this may fail if the predecessor correction is not at work!
assert_array_equal(clust.labels_, expected_labels)
C1 = [[0, 0], [0, 0.1], [0, -.1], [0.1, 0]]
C2 = [[10, 10], [10, 9], [10, 11], [9, 10]]
C3 = [[100, 100], [100, 90], [100, 110], [90, 100]]
X = np.vstack((C1, C2, C3))
expected_labels = np.r_[[0] * 4, [1] * 4, [2] * 4]
X, expected_labels = shuffle(X, expected_labels, random_state=rng)
clust = OPTICS(min_samples=2,
min_cluster_size=2,
max_eps=np.inf,
cluster_method='xi',
xi=0.04).fit(X)
assert_array_equal(clust.labels_, expected_labels)
def test_close_extract():
# Test extract where extraction eps is close to scaled epsPrime
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=1.0, min_samples=10)
clust3 = clust.fit(X)
# check warning when centers are passed
assert_warns(RuntimeWarning, clust3.extract_dbscan, .3)
# Cluster ordering starts at 0; max cluster label = 2 is 3 clusters
assert_equal(max(clust3.extract_dbscan(.3)[1]), 2)
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(eps=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_close_extract():
# Test extract where extraction eps is close to scaled epsPrime
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=1.0, min_samples=10)
clust3 = clust.fit(X)
# check warning when centers are passed
assert_warns(RuntimeWarning, clust3.extract_dbscan, .3)
# Cluster ordering starts at 0; max cluster label = 2 is 3 clusters
assert_equal(max(clust3.extract_dbscan(.3)[1]), 2)
def test_min_cluster_size_invalid(min_cluster_size):
clust = OPTICS(min_cluster_size=min_cluster_size)
with pytest.raises(ValueError, match="must be a positive integer or a "):
clust.fit(X)
clust = OPTICS(min_cluster_size=min_cluster_size, metric='euclidean')
with pytest.raises(ValueError, match="must be a positive integer or a "):
clust.fit(sparse.lil_matrix(X))
def test_min_cluster_size_invalid2():
clust = OPTICS(min_cluster_size=len(X) + 1)
with pytest.raises(ValueError, match="must be no greater than the "):
clust.fit(X)
clust = OPTICS(min_cluster_size=len(X) + 1, metric='euclidean')
with pytest.raises(ValueError, match="must be no greater than the "):
clust.fit(sparse.lil_matrix(X))
def test_dbscan_optics_parity(eps, min_samples):
# Test that OPTICS clustering labels are <= 5% difference of DBSCAN
for metric in ['minkowski', 'euclidean']:
centers = [[1, 1], [-1, -1], [1, -1]]
_X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
X = _X if metric == 'minkowski' else sparse.lil_matrix(_X)
# calculate optics with dbscan extract at 0.3 epsilon
op = OPTICS(min_samples=min_samples, cluster_method='dbscan',
eps=eps,
metric=metric).fit(X)
# calculate dbscan labels
db = DBSCAN(eps=eps, min_samples=min_samples).fit(X)
contingency = contingency_matrix(db.labels_, op.labels_)
agree = min(np.sum(np.max(contingency, axis=0)),
np.sum(np.max(contingency, axis=1)))
disagree = X.shape[0] - agree
percent_mismatch = np.round((disagree - 1) / X.shape[0], 2)
# verify label mismatch is <= 5% labels
assert percent_mismatch <= 0.05
def test_processing_order():
"""Early dev version of OPTICS would not consider all unprocessed points,
but only direct neighbors. This tests against this mistake."""
Y = [[0], [10], [-10], [25]]
clust = OPTICS(min_samples=3, max_eps=15).fit(Y)
assert_array_equal(clust.reachability_, [np.inf, 10, 10, 15])
assert_array_equal(clust.core_distances_, [10, 15, 20, 25])
assert_array_equal(clust.ordering_, [0, 1, 2, 3])
def test_processing_order():
# Ensure that we consider all unprocessed points,
# not only direct neighbors. when picking the next point.
Y = [[0], [10], [-10], [25]]
clust = OPTICS(min_samples=3, max_eps=15).fit(Y)
assert_array_equal(clust.reachability_, [np.inf, 10, 10, 15])
assert_array_equal(clust.core_distances_, [10, 15, np.inf, np.inf])
assert_array_equal(clust.ordering_, [0, 1, 2, 3])
def test_min_samples_min_clusters_float_int_parity():
X, labels_true = make_blobs(n_samples=750,
centers=centers,
cluster_std=0.4,
random_state=0)
# calculate optics with dbscan extract at 0.3 epsilon
op_both_int = OPTICS(min_samples=7, min_cluster_size=3)
op_int_none = OPTICS(min_samples=7)
op_both_float = OPTICS(min_samples=0.01, min_cluster_size=0.05)
op_float_none = OPTICS(min_samples=0.01)
assert op_both_int.min_samples == op_both_float.min_samples
assert op_both_int.min_cluster_size == op_both_float.min_cluster_size
assert op_int_none.min_samples == op_float_none.min_samples
assert op_int_none.min_cluster_size == op_float_none.min_cluster_size
def test_bad_reachability():
msg = "All reachability values are inf. Set a larger max_eps."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
clust = OPTICS(max_eps=5.0 * 0.003, min_samples=10)
assert_raise_message(ValueError, msg, clust.fit, X)
def test_min_cluster_size(min_cluster_size):
_redX = X[::2] # reduce for speed
for metric in ['minkowski', 'euclidean']:
redX = _redX if metric == 'minkowski' else sparse.lil_matrix(_redX)
clust = OPTICS(min_samples=9, min_cluster_size=min_cluster_size,
metric=metric).fit(redX)
cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
if cluster_sizes.size:
assert min(cluster_sizes) >= min_cluster_size
# check behaviour is the same when min_cluster_size is a fraction
clust_frac = OPTICS(min_samples=9,
min_cluster_size=min_cluster_size / redX.shape[0],
metric=metric)
clust_frac.fit(redX)
assert_array_equal(clust.labels_, clust_frac.labels_)
def test_minimum_number_of_sample_check():
# test that we check a minimum number of samples
msg = "min_samples must be no greater than"
# Compute OPTICS
X = [[1, 1]]
clust = OPTICS(max_eps=5.0 * 0.3, min_samples=10, min_cluster_size=1)
# Run the fit
assert_raise_message(ValueError, msg, clust.fit, X)
# Compute OPTICS
X = sparse.lil_matrix([[1, 1]])
clust = OPTICS(max_eps=5.0 * 0.3, min_samples=10, min_cluster_size=1,
metric='euclidean')
# Run the fit
assert_raise_message(ValueError, msg, clust.fit, X)
def test_cluster_hierarchy_():
rng = np.random.RandomState(0)
n_points_per_cluster = 100
C1 = [0, 0] + 2 * rng.randn(n_points_per_cluster, 2)
C2 = [0, 0] + 50 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2))
X = shuffle(X, random_state=0)
clusters = OPTICS(min_samples=20, xi=.1).fit(X).cluster_hierarchy_
assert clusters.shape == (2, 2)
diff = np.sum(clusters - np.array([[0, 99], [0, 199]]))
assert diff / len(X) < 0.05
clust = OPTICS(min_samples=20, xi=.1,
metric='euclidean').fit(sparse.lil_matrix(X))
clusters = clust.cluster_hierarchy_
assert clusters.shape == (2, 2)
diff = np.sum(clusters - np.array([[0, 99], [0, 199]]))
assert diff / len(X) < 0.05
def test_optics2():
# Tests the optics clustering method and all functions inside it
# 'dbscan' mode
# Compute OPTICS
X = [[1, 1]]
clust = OPTICS(max_bound=5.0 * 0.3, min_samples=10)
# Run the fit
assert_raises(ValueError, clust.fit, X)
def run_optics(w2v_2d_array, industry_lst):
clust = OPTICS(min_samples=2)
opt = clust.fit_predict(w2v_2d_array)
labels = clust.labels_
optics_dict = defaultdict(list)
for i, v in enumerate(labels):
optics_dict[v].append(industry_lst[i])
max_k = max(opt)
for k in range(0, max_k):
Xk = w2v_2d_array[clust.labels_ == k]
plt.scatter(Xk[:, 0], Xk[:, 1], label=k)
plt.legend()
plt.title('OPTICS Clustering\n'
'min_samples= 4, rejection_ratio=0.67')
plt.scatter(w2v_2d_array[clust.labels_ == -1, 0], w2v_2d_array[clust.labels_ == -1, 1], c='black', marker='+')
plt.show()
return max_k
def test_close_extract():
# Test extract where extraction eps is close to scaled max_eps
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=1.0, cluster_method='dbscan',
eps=0.3, min_samples=10).fit(X)
# Cluster ordering starts at 0; max cluster label = 2 is 3 clusters
assert max(clust.labels_) == 2
# Compute OPTICS
clust = OPTICS(max_eps=1.0, cluster_method='dbscan',
eps=0.3, min_samples=10,
metric='euclidean').fit(sparse.lil_matrix(X))
# Cluster ordering starts at 0; max cluster label = 2 is 3 clusters
assert max(clust.labels_) == 2
def test_bad_extract():
# Test an extraction of eps too close to original eps
msg = "Specify an epsilon smaller than 0.15. Got 0.3."
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=750, centers=centers,
cluster_std=0.4, random_state=0)
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 0.03,
cluster_method='dbscan',
eps=0.3, min_samples=10)
assert_raise_message(ValueError, msg, clust.fit, X)
# Compute OPTICS
clust = OPTICS(max_eps=5.0 * 0.03,
cluster_method='dbscan',
eps=0.3, min_samples=10,
metric='euclidean')
assert_raise_message(ValueError, msg, clust.fit, sparse.lil_matrix(X))
def test_cluster_hierarchy_():
n_points_per_cluster = 100
C1 = [0, 0] + 2 * rng.randn(n_points_per_cluster, 2)
C2 = [0, 0] + 10 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2))
X = shuffle(X, random_state=0)
clusters = OPTICS(min_samples=20, xi=.1).fit(X).cluster_hierarchy_
assert clusters.shape == (2, 2)
diff = np.sum(clusters - np.array([[0, 99], [0, 199]]))
assert diff / len(X) < 0.05
def test_minimum_number_of_sample_check():
# test that we check a minimum number of samples
msg = ("Number of training samples (n_samples=1) must be greater than "
"min_samples (min_samples=10) used for clustering.")
# Compute OPTICS
X = [[1, 1]]
clust = OPTICS(max_bound=5.0 * 0.3, min_samples=10)
# Run the fit
assert_raise_message(ValueError, msg, clust.fit, X)
def test_processing_order():
for metric in ['minkowski', 'euclidean']:
# Ensure that we consider all unprocessed points,
# not only direct neighbors. when picking the next point.
_Y = [[0], [10], [-10], [25]]
Y = _Y if metric == 'minkowski' else sparse.lil_matrix(_Y)
clust = OPTICS(min_samples=3, max_eps=15, metric=metric).fit(Y)
assert_array_equal(clust.reachability_, [np.inf, 10, 10, 15])
assert_array_equal(clust.core_distances_, [10, 15, np.inf, np.inf])
assert_array_equal(clust.ordering_, [0, 1, 2, 3])
def test_min_samples_edge_case():
C1 = [[0, 0], [0, 0.1], [0, -.1]]
C2 = [[10, 10], [10, 9], [10, 11]]
C3 = [[100, 100], [100, 96], [100, 106]]
X = np.vstack((C1, C2, C3))
expected_labels = np.r_[[0] * 3, [1] * 3, [2] * 3]
clust = OPTICS(min_samples=3, max_eps=7, cluster_method='xi',
xi=0.04).fit(X)
assert_array_equal(clust.labels_, expected_labels)
expected_labels = np.r_[[0] * 3, [1] * 3, [-1] * 3]
clust = OPTICS(min_samples=3, max_eps=3, cluster_method='xi',
xi=0.04).fit(X)
assert_array_equal(clust.labels_, expected_labels)
expected_labels = np.r_[[-1] * 9]
with pytest.warns(UserWarning, match="All reachability values"):
clust = OPTICS(min_samples=4, max_eps=3, cluster_method='xi',
xi=0.04).fit(X)
assert_array_equal(clust.labels_, expected_labels)
def test_auto_extract_hier():
# Generate sample data
np.random.seed(0)
n_points_per_cluster = 250
C1 = [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2)
C3 = [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2)
C5 = [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)
C6 = [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, C6))
# Compute OPTICS
clust = OPTICS(min_samples=9)
# Run the fit
clust.fit(X)
assert_equal(len(set(clust.labels_)), 6)
def test_extract_dbscan():
# testing an easy dbscan case. Not including clusters with different
# densities.
rng = np.random.RandomState(0)
n_points_per_cluster = 20
C1 = [-5, -2] + .2 * rng.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .2 * rng.randn(n_points_per_cluster, 2)
C3 = [1, 2] + .2 * rng.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .2 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4))
clust = OPTICS(cluster_method='dbscan', eps=.5).fit(X)
assert_array_equal(np.sort(np.unique(clust.labels_)), [0, 1, 2, 3])
def test_auto_extract_hier():
# Tests auto extraction gets correct # of clusters with varying density
# Generate sample data
rng = np.random.RandomState(0)
n_points_per_cluster = 250
C1 = [-5, -2] + .8 * rng.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .1 * rng.randn(n_points_per_cluster, 2)
C3 = [1, -2] + .2 * rng.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .3 * rng.randn(n_points_per_cluster, 2)
C5 = [3, -2] + 1.6 * rng.randn(n_points_per_cluster, 2)
C6 = [5, 6] + 2 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, C6))
# Compute OPTICS
clust = OPTICS(min_samples=9)
# Run the fit
clust.fit(X)
assert_equal(len(set(clust.labels_)), 6)
def test_auto_extract_hier():
# Tests auto extraction gets correct # of clusters with varying density
# Generate sample data
rng = np.random.RandomState(0)
n_points_per_cluster = 250
C1 = [-5, -2] + .8 * rng.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .1 * rng.randn(n_points_per_cluster, 2)
C3 = [1, -2] + .2 * rng.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .3 * rng.randn(n_points_per_cluster, 2)
C5 = [3, -2] + 1.6 * rng.randn(n_points_per_cluster, 2)
C6 = [5, 6] + 2 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, C6))
# Compute OPTICS
clust = OPTICS(min_samples=9)
# Run the fit
clust.fit(X)
assert_equal(len(set(clust.labels_)), 6)
def test_filter():
# Tests the filter function.
n_clusters = 3
X = generate_clustered_data(n_clusters=n_clusters)
# Parameters chosen specifically for this task.
clust = OPTICS(eps=6.0, min_samples=4, metric='euclidean')
# Run filter (before computing OPTICS)
bool_memb = clust.filter(X, 0.5)
idx_memb = clust.filter(X, 0.5, index_type='idx')
# Test for equivalence between 'idx' and 'bool' extraction
assert_equal(sum(bool_memb), len(idx_memb))
# Compute OPTICS
clust.fit(X)
clust.extract(0.5, clustering='dbscan')
# core points from filter and extract should be the same within 1 point,
# with extract occasionally underestimating due to start point of the
# OPTICS algorithm. Here we test for at least 95% similarity in
# classification of core/not core
agree = sum(clust._is_core == bool_memb)
assert_greater_equal(float(agree)/len(X), 0.95)
def test_reach_dists():
# Tests against known extraction array
np.random.seed(0)
n_points_per_cluster = 250
X = np.empty((0, 2))
X = np.r_[X, [-5, -2] + .8 * np.random.randn(n_points_per_cluster, 2)]
# eps not used for 'auto' extract
X = np.r_[X, [4, -1] + .1 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)]
# Compute OPTICS
clust = OPTICS(eps=30.3, min_samples=10, metric='minkowski')
# Run the fit
clust.fit(X)
# Expected values
# Skip to line 370
v = [np.inf, 0.606005, 0.472013, 0.162951, 0.161000, 0.385547, 0.179715,
0.213507, 0.348468, 0.308146, 0.560519, 0.266072, 0.764384, 0.253164,
0.435716, 0.153696, 0.363924, 0.194267, 0.392313, 0.230589, 0.260023,
0.535348, 0.168173, 0.296736, 0.310583, 0.277204, 0.250654, 0.153696,
0.215533, 0.175710, 0.168173, 0.283134, 0.256372, 0.313931, 0.234164,
0.179715, 0.352957, 0.277052, 0.180986, 0.203819, 0.296022, 0.356691,
0.515438, 0.219208, 0.265821, 0.346630, 0.275305, 0.229332, 0.433715,
0.153696, 0.584960, 0.265821, 0.471049, 0.259154, 0.461707, 0.400021,
0.422748, 0.300699, 0.162951, 0.290504, 0.315199, 0.327130, 0.168864,
0.462826, 0.188862, 0.259784, 0.216788, 0.259784, 0.195673, 0.315199,
0.313931, 0.189128, 0.461707, 0.265821, 0.233594, 0.433715, 0.222260,
0.251734, 0.352957, 0.218134, 0.453792, 0.179715, 0.296736, 0.260023,
0.311162, 0.214549, 0.266072, 0.318744, 0.180986, 0.194267, 0.262882,
0.420186, 0.352957, 0.288388, 0.360962, 0.328054, 0.293849, 0.198271,
0.248772, 0.461707, 0.216788, 0.396450, 0.352957, 0.289448, 0.241311,
0.213742, 0.220516, 0.218134, 0.153696, 0.516090, 0.218134, 0.221507,
0.328647, 0.255933, 0.195766, 0.233594, 0.205270, 0.296736, 0.726008,
0.251991, 0.168173, 0.214027, 0.262882, 0.342089, 0.260023, 0.266072,
0.253164, 0.230345, 0.262882, 0.296022, 0.227047, 0.205974, 0.328647,
0.184315, 0.196304, 0.831185, 0.514116, 0.168173, 0.189784, 0.664306,
0.327130, 0.379139, 0.208932, 0.266140, 0.362751, 0.168173, 0.764384,
0.327130, 0.187107, 0.194267, 0.414196, 0.251734, 0.220516, 0.363924,
0.166886, 0.327130, 0.233594, 0.203819, 0.230589, 0.203819, 0.222972,
0.311526, 0.218134, 0.422748, 0.314870, 0.315199, 0.315199, 0.594179,
0.328647, 0.415638, 0.244046, 0.250654, 0.214027, 0.203819, 0.213507,
0.260023, 0.311442, 0.168173, 0.389432, 0.229343, 0.162951, 0.311162,
0.153696, 0.214027, 0.250654, 0.315199, 0.172484, 0.153696, 0.352957,
0.314870, 0.328647, 0.546505, 0.378118, 0.260023, 0.387830, 0.199714,
0.262882, 0.250654, 0.345254, 0.396450, 0.250654, 0.179715, 0.328647,
0.179715, 0.263104, 0.265821, 0.231714, 0.514116, 0.213507, 0.474255,
0.212568, 0.376760, 0.196304, 0.844945, 0.194267, 0.264914, 0.210320,
0.316374, 0.184315, 0.179715, 0.250654, 0.153696, 0.162951, 0.315199,
0.179965, 0.297876, 0.213507, 0.475420, 0.439372, 0.241311, 0.260927,
0.194267, 0.422748, 0.222260, 0.411940, 0.414733, 0.260923, 0.396450,
0.380672, 0.333277, 0.290504, 0.196014, 0.844945, 0.506989, 0.153696,
0.218134, 0.392313, 0.698970, 0.168173, 0.227047, 0.028856, 0.033243,
0.028506, 0.057003, 0.038335, 0.051183, 0.063923, 0.022363, 0.030677,
0.036155, 0.017748, 0.062887, 0.036041, 0.051183, 0.078198, 0.068936,
0.032418, 0.040634, 0.022188, 0.022112, 0.036858, 0.040199, 0.025549,
0.083975, 0.032209, 0.025525, 0.032952, 0.034727, 0.068887, 0.040634,
0.048985, 0.047450, 0.022422, 0.023767, 0.028092, 0.047450, 0.029202,
0.026105, 0.030542, 0.032250, 0.062887, 0.038335, 0.026753, 0.028092,
0.099391, 0.021430, 0.020496, 0.021430, 0.025043, 0.023868, 0.050069,
0.023868, 0.044140, 0.038032, 0.022112, 0.044140, 0.031528, 0.028092,
0.020065, 0.055926, 0.031508, 0.025549, 0.028062, 0.036155, 0.023694,
0.029423, 0.026105, 0.028497, 0.023868, 0.044808, 0.035783, 0.033090,
0.038779, 0.032146, 0.038421, 0.057328, 0.020065, 0.020065, 0.028858,
0.021337, 0.041226, 0.022507, 0.028506, 0.030257, 0.057912, 0.050876,
0.120109, 0.020065, 0.034727, 0.038596, 0.037008, 0.031609, 0.095640,
0.083728, 0.064906, 0.030677, 0.057003, 0.037008, 0.018705, 0.030677,
0.044140, 0.034727, 0.045226, 0.032146, 0.032418, 0.029332, 0.030104,
0.033243, 0.030104, 0.032209, 0.026405, 0.024092, 0.048441, 0.036379,
0.030745, 0.023454, 0.018705, 0.124248, 0.041114, 0.020700, 0.042633,
0.042455, 0.028497, 0.029202, 0.057859, 0.053157, 0.036155, 0.029534,
0.032209, 0.038032, 0.024617, 0.023071, 0.033090, 0.023694, 0.047277,
0.024617, 0.023868, 0.043916, 0.025549, 0.046198, 0.041086, 0.042003,
0.022507, 0.021430, 0.038779, 0.025043, 0.036379, 0.036326, 0.029421,
0.023454, 0.058683, 0.025549, 0.039904, 0.022507, 0.046198, 0.029332,
0.032209, 0.036155, 0.038421, 0.025043, 0.023694, 0.030104, 0.022363,
0.048544, 0.035180, 0.030677, 0.022112, 0.030677, 0.036678, 0.022507,
0.024092, 0.064231, 0.022507, 0.032209, 0.025043, 0.221152, 0.029840,
0.038779, 0.040634, 0.024617, 0.032418, 0.025525, 0.033298, 0.028092,
0.045754, 0.032209, 0.017748, 0.033090, 0.017748, 0.048931, 0.038689,
0.022112, 0.027129, 0.032952, 0.036858, 0.027704, 0.032146, 0.052191,
0.042633, 0.071638, 0.044140, 0.022507, 0.046647, 0.028270, 0.050525,
0.036772, 0.058995, 0.038335, 0.025185, 0.022507, 0.040293, 0.032418,
0.064308, 0.026023, 0.036155, 0.032418, 0.038032, 0.018705, 0.040293,
0.030104, 0.030845, 0.064906, 0.025525, 0.036155, 0.022507, 0.022363,
0.032418, 0.021430, 0.032209, 0.102770, 0.036960, 0.031062, 0.025043,
0.036155, 0.031609, 0.036379, 0.030845, 0.048985, 0.021848, 0.025549,
0.022507, 0.035783, 0.023698, 0.034422, 0.032418, 0.022507, 0.023868,
0.020065, 0.023694, 0.040634, 0.055633, 0.054549, 0.044662, 0.087660,
0.048066, 0.143571, 0.068669, 0.065049, 0.076927, 0.044359, 0.041577,
0.052364, 0.100317, 0.062146, 0.067578, 0.054549, 0.047239, 0.062809,
0.033917, 0.087660, 0.077113, 0.055633, 0.061854, 0.059756, 0.059537,
0.052364, 0.060347, 0.170251, 0.108492, 0.046370, 0.070684, 0.049589,
0.044662, 0.049013, 0.043303, 0.069573, 0.075044, 0.054354, 0.065072,
0.073135, 0.046126, 0.055569, 0.047239, 0.062146, 0.056093, 0.059986,
0.096182, 0.100317, 0.051649, 0.054354, 0.077420, 0.100317, 0.046370,
0.043303, 0.045845, 0.061422, 0.091580, 0.206234, 0.051405, 0.071684,
0.061574, 0.063666, 0.052692, 0.051649, 0.100124, 0.077909, 0.033917,
0.058680, 0.044359, 0.065498, 0.080214, 0.123231, 0.052957, 0.056582,
0.061540, 0.076794, 0.043303, 0.054884, 0.044359, 0.145249, 0.081741,
0.041577, 0.056093, 0.076799, 0.044359, 0.068483, 0.051649, 0.092275,
0.044359, 0.108492, 0.092275, 0.046126, 0.106422, 0.054354, 0.052957,
0.073329, 0.046126, 0.086402, 0.048194, 0.128569, 0.104042, 0.061854,
0.069573, 0.070035, 0.050346, 0.043303, 0.053576, 0.054549, 0.033917,
0.063666, 0.058680, 0.099130, 0.080198, 0.050118, 0.054549, 0.041577,
0.143571, 0.095965, 0.047643, 0.052364, 0.105168, 0.048685, 0.043303,
0.052814, 0.076927, 0.054549, 0.041577, 0.066657, 0.189930, 0.046370,
0.075044, 0.121331, 0.043303, 0.223897, 0.198621, 0.150328, 0.100317,
0.053576, 0.070708, 0.100898, 0.047239, 0.043613, 0.065049, 0.049146,
0.068669, 0.055569, 0.062124, 0.096408, 0.044662, 0.087660, 0.083012,
0.050118, 0.069573, 0.046126, 0.049146, 0.049146, 0.050808, 0.080198,
0.059986, 0.071974, 0.047239, 0.050808, 0.059986, 0.065850, 0.044863,
0.052814, 0.044359, 0.052364, 0.108492, 0.143571, 0.050926, 0.049146,
0.049146, 0.055569, 0.033917, 0.527659, 0.143547, 0.077113, 0.046126,
0.106422, 0.068669, 0.108492, 0.063666, 0.054549, 0.054884, 0.056907,
0.068669, 0.080198, 0.120887, 0.054549, 0.052692, 0.085801, 0.054884,
0.050808, 0.094595, 0.059545, 0.054354, 0.062124, 0.087660, 0.052814,
0.086715, 0.146253, 0.046370, 0.041577, 0.116083, 0.076927, 0.047239,
0.084375, 0.134652, 0.217969, 0.063559, 0.061540, 0.044662, 0.054354,
0.063666, 0.145466, 0.101700, 0.090491, 0.078536, 0.054884, 0.062124,
0.041577, 0.043303, 0.194473, 0.079780, 0.059704, 0.054780, 0.048194,
0.062146, 0.069573, 0.086898, 0.046675, 0.056258, 0.074141, 0.048066,
0.052957, 0.057982, 0.058966, 0.061048, 0.050885, 0.049146, 0.080993,
0.056093, 0.061854, 0.124025, 0.062146, 0.060906, 0.150328, 0.058680,
0.077420, 0.051800, 0.102359, 0.113301, 0.073096, 0.116715, 0.131476,
0.140601, 0.097667, 0.051800, 0.051800, 0.127964, 0.108870, 0.111926,
0.093532, 0.102390, 0.144266, 0.098271, 0.102541, 0.136497, 0.127964,
0.085569, 0.157863, 0.096739, 0.054008, 0.106219, 0.076838, 0.099076,
0.093532, 0.059861, 0.079975, 0.116715, 0.133625, 0.053641, 0.066110,
0.122302, 0.081313, 0.140601, 0.259889, 0.094437, 0.098271, 0.105776,
0.225742, 0.100097, 0.147592, 0.099076, 0.093128, 0.093532, 0.134946,
0.133625, 0.120869, 0.065932, 0.103395, 0.125172, 0.147842, 0.105278,
0.173584, 0.168241, 0.111524, 0.093532, 0.099076, 0.100426, 0.137132,
0.065356, 0.091108, 0.141202, 0.054008, 0.075298, 0.073717, 0.122817,
0.105278, 0.094437, 0.067080, 0.108530, 0.115467, 0.093532, 0.085569,
0.145180, 0.100426, 0.116715, 0.151726, 0.073096, 0.193781, 0.090614,
0.081162, 0.051800, 0.133625, 0.136497, 0.100670, 0.081313, 0.506893,
0.084567, 0.108530, 0.087353, 0.063184, 0.123639, 0.168333, 0.314422,
0.091108, 0.079975, 0.091108, 0.136497, 0.122302, 0.167297, 0.067080,
0.144266, 0.065932, 0.087667, 0.100426, 0.099460, 0.091108, 0.100637,
0.116715, 0.079975, 0.077977, 0.090340, 0.136723, 1.943026, 0.108870,
0.090340, 0.065932, 0.102245, 0.157863, 0.157863, 0.215574, 0.156830,
0.093532, 0.122302, 0.097667, 0.063000, 0.116715, 0.076838, 0.148372,
0.093532, 0.099076, 0.141202, 0.096505, 0.096739, 0.091108, 0.099076,
0.079975, 0.108870, 0.102390, 0.079975, 0.244121, 0.167071, 0.096739,
0.102390, 0.103395, 0.073096, 0.094887, 0.065932, 0.190667, 0.099460,
0.102390, 0.096739, 0.102390, 0.116715, 0.100637, 0.256554, 0.103395,
0.081313, 0.068962, 0.109645, 0.059364, 0.147842, 0.099460, 0.079262,
0.099460, 0.065932, 0.123687, 0.090614, 0.131352, 0.098271, 0.102541,
0.098983, 0.057224, 0.074797, 0.057224, 0.250559, 0.079975, 0.103395,
0.100426, 0.065932, 0.120661, 0.079262, 0.065932, 0.118665, 0.081162,
0.066283, 0.099076, 0.102359, 0.108530, 0.079975, 0.168333, 0.096739,
0.168333, 0.097008, 0.055288, 0.172411, 0.092801, 0.051800, 0.102541,
0.084567, 0.054008, 0.090991, 0.172411, 0.057224, 0.148396, 0.200965,
0.076838, 0.157863, 0.053535, 0.121919, 0.126609, 0.123890, 0.118081,
0.097008, 0.125311, 0.099460, 0.122302, 0.134946, 0.080975, 0.084567,
0.110093, 0.102245, 0.103395, 0.171601, 0.094887, 0.126240, 0.137742,
0.099954, 0.108530, 0.157863, 0.096739, 0.051800, 0.127964, 0.066110,
0.061021, 0.105147, 0.100426, 0.079975, 0.088187, 0.116421, 0.076838,
0.098271, 0.116715, 0.137656, 0.075298, 0.148396, 0.112166, 1.083905,
0.326598, 0.428987, 0.395963, 0.224541, 0.326598, 0.030677, 0.410454,
0.122771, 1.140305, 0.641074, 0.432159, 0.429335, 0.422908, 0.461926,
0.293083, 0.477078, 0.714856, 0.515861, 0.405418, 0.054354, 0.341177,
0.410008, 0.514245, 0.641074, 0.816459, 0.455115, 0.400707, 0.382240,
0.431832, 1.618970, 0.683953, 0.182992, 0.763699, 0.515861, 0.717145,
0.409629, 0.074134, 0.398273, 0.864974, 0.400707, 0.591403, 0.435354,
0.514245, 1.337152, 0.841077, 0.410008, 0.683953, 0.338649, 0.557595,
0.442092, 0.326598, 0.984189, 0.429608, 0.395963, 1.152055, 0.587222,
1.748492, 0.477078, 0.395459, 0.717145, 0.575811, 0.210115, 0.487785,
0.431832, 0.383852, 0.806708, 0.428987, 0.278405, 0.395963, 0.395459,
0.383852, 1.083905, 0.428510, 0.326598, 0.108492, 0.541644, 0.612110,
0.382240, 0.833511, 0.382240, 0.456628, 0.326598, 0.458880, 0.398273,
0.957748, 0.326598, 0.295049, 0.629646, 0.429765, 0.439942, 0.633617,
0.566297, 0.429335, 0.086507, 0.477078, 0.526753, 0.375240, 0.584436,
0.355776, 0.395963, 0.644924, 0.129793, 0.484880, 0.470001, 0.572306,
0.383852, 1.110081, 0.841077, 0.395963, 0.683953, 0.428745, 0.387752,
0.545299, 0.686537, 0.635219, 0.840499, 0.527659, 0.400707, 0.480982,
0.541644, 0.714856, 0.942673, 0.398273, 0.428987, 0.356781, 0.428510,
1.140961, 0.395963, 0.356781, 0.410454, 0.541644, 0.641074, 0.484778,
0.410008, 0.433108, 0.278405, 0.278405, 0.503141, 0.428745, 0.125103,
0.633617, 0.410454, 0.124025, 0.461926, 0.398273, 0.410008, 1.181303,
0.635219, 0.593537, 0.395963, 0.717145, 0.409629, 0.492595, 0.806708,
0.503820, 0.423834, 0.557595, 0.429335, 0.470749, 0.461926, 1.890036,
0.236343, 0.806708, 0.123561, 0.433744, 0.427348, 0.427348, 0.962234,
0.395963, 0.409629, 0.527659, 0.425727, 0.602549, 0.901331, 0.326598,
0.635949, 0.541644, 0.375240, 0.598969, 1.140961, 0.391998, 0.719443,
0.410008, 0.515861, 0.714856, 0.842273, 0.410454, 0.389377, 0.431078,
0.515861, 0.515861, 0.429335, 0.332495, 0.398273, 0.428987, 0.635219,
0.387752, 0.384289, 0.383852, 0.430504, 0.428510, 0.431832, 0.375240,
0.278405, 0.374102, 0.428745, 0.692878, 1.152055, 0.503820, 0.428745,
0.352868, 0.429335, 0.375240, 0.400707, 0.427348, 0.256183, 0.962234,
0.505376, 0.058995, 0.410454, 0.172880, 0.395963, 0.470749, 0.356781,
1.332700, 0.683953, 0.395963, 0.806708, 0.400707, 0.330982, 0.427731,
0.934845, 0.375240, 0.191534, 0.047239, 1.083905, 0.348794, 0.409708,
0.503820, 0.557595, 0.429335, 0.498780, 0.293083, 0.363069, 0.442092,
1.152055, 0.375240, 0.335677, 0.452443, 0.655156, 0.929928, 0.614869,
1.411031, 1.101132, 0.469030, 0.404976, 0.538209, 0.655828, 0.674748,
0.365182, 0.641612, 0.555434, 0.521651, 0.386679, 0.386679, 0.980304,
0.659111, 0.651366, 0.538209, 0.521651, 0.884780, 1.287829, 0.558322,
0.446161, 0.817970, 0.568499, 0.533507, 0.639746, 0.484404, 0.591751,
0.913016, 0.446161, 0.533907, 0.606885, 0.672320, 1.150642, 0.655828,
0.365182, 0.665088, 1.094242, 0.629401, 0.540676, 0.733026, 1.248265,
1.273499, 0.867854, 0.538656, 0.386679, 0.922273, 0.515686, 1.321022,
0.624444, 0.655828, 0.922273, 0.386679, 0.762191, 0.779432, 0.601851,
0.655156, 0.926213, 0.762191, 0.641612, 0.558322, 1.025370, 0.641067,
0.651366, 0.633434, 0.459580, 0.859221, 0.552291, 0.591751, 0.819965,
0.669977, 1.185083, 0.499338, 0.533907, 0.752871, 0.571388, 0.539772,
0.449182, 1.025370, 0.365182, 1.321022, 0.926213, 0.886360, 0.562272,
0.669977, 0.796046, 0.557598, 0.596776, 0.672336, 0.659111, 0.453719,
0.477716, 0.477716, 1.592069, 0.591751, 0.539772, 0.641612, 0.946254,
0.744165, 0.386679, 0.593825, 0.539772, 0.449182, 0.604273, 0.794951,
0.752871, 0.539772, 0.648732, 0.469030, 0.665088, 1.332700, 1.341388,
0.533507, 0.544212, 1.025992, 0.645967, 0.612945, 0.868492, 0.648732,
0.752300, 0.624444, 1.219748, 0.446161, 0.520818, 0.469044, 0.669977,
0.926213, 0.638752, 0.762191, 0.922273, 0.794951, 0.606885, 0.669977,
0.550113, 0.641067, 0.733026, 0.604273, 0.648732, 0.533507, 0.746506,
0.733026, 0.980683, 0.538209, 0.669977, 0.469030, 0.648732, 0.609190,
1.219748, 0.373113, 0.539772, 1.744047, 1.004716, 0.926213, 0.562272,
0.752871, 0.538656, 0.449182, 0.365182, 0.469030, 0.446161, 0.484404,
0.768592, 0.648732, 0.655156, 0.521651, 0.779432, 0.446161, 0.596776,
0.538209, 0.726740, 0.539772, 0.469030, 0.521651, 0.561950, 0.601851,
0.533907, 0.922273, 1.248265, 0.476800, 0.737990, 0.817970, 0.792127,
0.533907, 0.486038, 0.624444, 0.798241, 0.476800, 1.059373, 0.645967,
0.619940, 0.528726, 0.669977, 0.865406, 0.980683, 0.980683, 0.834671,
1.001353, 0.752871, 0.449182, 1.096520, 0.449182, 0.593825, 0.636558,
0.762191, 0.638591, 0.538209, 0.865406, 0.779432, 0.469044, 0.645967,
0.557598, 0.499338, 0.484404, 0.515686, 0.794951, 0.619456, 0.733026,
0.821769, 0.752300, 0.643302, 0.636558, 0.655156, 0.655156, 0.484404,
0.648732, 0.726023, 0.365182, 0.606885, 0.499338, 0.520818, 0.612945,
0.446161, 0.557598, 0.469044, 1.134650, 0.629401, 0.538656, 0.561950,
1.364861, 0.459580, 1.025370, 0.980304, 0.607592, 0.533907, 1.134650,
0.446161, 0.629962]
assert_array_almost_equal(clust.reachability_, np.array(v))
def test_empty_extract():
# Test extract where fit() has not yet been run.
clust = OPTICS(eps=0.3, min_samples=10)
assert clust.extract(0.01, clustering='auto') is None
def test_wrong_cluster_method():
clust = OPTICS(cluster_method='superfancy')
with pytest.raises(ValueError, match="cluster_method should be one of "):
clust.fit(X)
def test_min_cluster_size_invalid2():
clust = OPTICS(min_cluster_size=len(X) + 1)
with pytest.raises(ValueError, match="must be no greater than the "):
clust.fit(X)
def test_min_cluster_size_invalid(min_cluster_size):
clust = OPTICS(min_cluster_size=min_cluster_size)
with pytest.raises(ValueError, match="must be a positive integer or a "):
clust.fit(X)
def test_empty_extract():
# Test extract where fit() has not yet been run.
msg = ("This OPTICS instance is not fitted yet. Call 'fit' with "
"appropriate arguments before using this method.")
clust = OPTICS(max_bound=5.0 * 0.3, min_samples=10)
assert_raise_message(ValueError, msg, clust.extract_dbscan, 0.01)
X = np.r_[X, [1, -2] + .2 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [-2, 3] + .3 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [3, -2] + 1.6 * np.random.randn(n_points_per_cluster, 2)]
X = np.r_[X, [5, 6] + 2 * np.random.randn(n_points_per_cluster, 2)]
# Plot scatterplot of points
plt.figure(figsize=(10, 10))
plt.subplot(221)
plt.plot(X[:, 0], X[:, 1], 'b.', ms=2)
plt.title("Raw Data")
# Compute OPTICS
clust = OPTICS(eps=30.3, min_samples=9, metric='minkowski')
# Run the fit
clust.fit(X)
# Plot result
core_samples_mask = np.zeros_like(clust.labels_, dtype=bool)
core_samples_mask[clust.core_sample_indices_] = True
# Black removed and is used for noise instead.
unique_labels = set(clust.labels_)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
plt.subplot(222)
def test_reach_dists():
# Tests against known extraction array
rng = np.random.RandomState(0)
n_points_per_cluster = 250
C1 = [-5, -2] + .8 * rng.randn(n_points_per_cluster, 2)
C2 = [4, -1] + .1 * rng.randn(n_points_per_cluster, 2)
C3 = [1, -2] + .2 * rng.randn(n_points_per_cluster, 2)
C4 = [-2, 3] + .3 * rng.randn(n_points_per_cluster, 2)
C5 = [3, -2] + 1.6 * rng.randn(n_points_per_cluster, 2)
C6 = [5, 6] + 2 * rng.randn(n_points_per_cluster, 2)
X = np.vstack((C1, C2, C3, C4, C5, C6))
# Compute OPTICS
clust = OPTICS(min_samples=10, metric='minkowski')
# Run the fit
clust.fit(X)
# Expected values, matches 'RD' results from:
# http://chemometria.us.edu.pl/download/optics.py
v = [np.inf, 0.606005, 0.472013, 0.162951, 0.161000, 0.385547, 0.179715,
0.213507, 0.348468, 0.308146, 0.560519, 0.266072, 0.764384, 0.253164,
0.435716, 0.153696, 0.363924, 0.194267, 0.392313, 0.230589, 0.260023,
0.535348, 0.168173, 0.296736, 0.310583, 0.277204, 0.250654, 0.153696,
0.215533, 0.175710, 0.168173, 0.283134, 0.256372, 0.313931, 0.234164,
0.179715, 0.352957, 0.277052, 0.180986, 0.203819, 0.296022, 0.356691,
0.515438, 0.219208, 0.265821, 0.346630, 0.275305, 0.229332, 0.433715,
0.153696, 0.584960, 0.265821, 0.471049, 0.259154, 0.461707, 0.400021,
0.422748, 0.300699, 0.162951, 0.290504, 0.315199, 0.327130, 0.168864,
0.462826, 0.188862, 0.259784, 0.216788, 0.259784, 0.195673, 0.315199,
0.313931, 0.189128, 0.461707, 0.265821, 0.233594, 0.433715, 0.222260,
0.251734, 0.352957, 0.218134, 0.453792, 0.179715, 0.296736, 0.260023,
0.311162, 0.214549, 0.266072, 0.318744, 0.180986, 0.194267, 0.262882,
0.420186, 0.352957, 0.288388, 0.360962, 0.328054, 0.293849, 0.198271,
0.248772, 0.461707, 0.216788, 0.396450, 0.352957, 0.289448, 0.241311,
0.213742, 0.220516, 0.218134, 0.153696, 0.516090, 0.218134, 0.221507,
0.328647, 0.255933, 0.195766, 0.233594, 0.205270, 0.296736, 0.726008,
0.251991, 0.168173, 0.214027, 0.262882, 0.342089, 0.260023, 0.266072,
0.253164, 0.230345, 0.262882, 0.296022, 0.227047, 0.205974, 0.328647,
0.184315, 0.196304, 0.831185, 0.514116, 0.168173, 0.189784, 0.664306,
0.327130, 0.379139, 0.208932, 0.266140, 0.362751, 0.168173, 0.764384,
0.327130, 0.187107, 0.194267, 0.414196, 0.251734, 0.220516, 0.363924,
0.166886, 0.327130, 0.233594, 0.203819, 0.230589, 0.203819, 0.222972,
0.311526, 0.218134, 0.422748, 0.314870, 0.315199, 0.315199, 0.594179,
0.328647, 0.415638, 0.244046, 0.250654, 0.214027, 0.203819, 0.213507,
0.260023, 0.311442, 0.168173, 0.389432, 0.229343, 0.162951, 0.311162,
0.153696, 0.214027, 0.250654, 0.315199, 0.172484, 0.153696, 0.352957,
0.314870, 0.328647, 0.546505, 0.378118, 0.260023, 0.387830, 0.199714,
0.262882, 0.250654, 0.345254, 0.396450, 0.250654, 0.179715, 0.328647,
0.179715, 0.263104, 0.265821, 0.231714, 0.514116, 0.213507, 0.474255,
0.212568, 0.376760, 0.196304, 0.844945, 0.194267, 0.264914, 0.210320,
0.316374, 0.184315, 0.179715, 0.250654, 0.153696, 0.162951, 0.315199,
0.179965, 0.297876, 0.213507, 0.475420, 0.439372, 0.241311, 0.260927,
0.194267, 0.422748, 0.222260, 0.411940, 0.414733, 0.260923, 0.396450,
0.380672, 0.333277, 0.290504, 0.196014, 0.844945, 0.506989, 0.153696,
0.218134, 0.392313, 0.698970, 0.168173, 0.227047, 0.028856, 0.033243,
0.028506, 0.057003, 0.038335, 0.051183, 0.063923, 0.022363, 0.030677,
0.036155, 0.017748, 0.062887, 0.036041, 0.051183, 0.078198, 0.068936,
0.032418, 0.040634, 0.022188, 0.022112, 0.036858, 0.040199, 0.025549,
0.083975, 0.032209, 0.025525, 0.032952, 0.034727, 0.068887, 0.040634,
0.048985, 0.047450, 0.022422, 0.023767, 0.028092, 0.047450, 0.029202,
0.026105, 0.030542, 0.032250, 0.062887, 0.038335, 0.026753, 0.028092,
0.099391, 0.021430, 0.020496, 0.021430, 0.025043, 0.023868, 0.050069,
0.023868, 0.044140, 0.038032, 0.022112, 0.044140, 0.031528, 0.028092,
0.020065, 0.055926, 0.031508, 0.025549, 0.028062, 0.036155, 0.023694,
0.029423, 0.026105, 0.028497, 0.023868, 0.044808, 0.035783, 0.033090,
0.038779, 0.032146, 0.038421, 0.057328, 0.020065, 0.020065, 0.028858,
0.021337, 0.041226, 0.022507, 0.028506, 0.030257, 0.057912, 0.050876,
0.120109, 0.020065, 0.034727, 0.038596, 0.037008, 0.031609, 0.095640,
0.083728, 0.064906, 0.030677, 0.057003, 0.037008, 0.018705, 0.030677,
0.044140, 0.034727, 0.045226, 0.032146, 0.032418, 0.029332, 0.030104,
0.033243, 0.030104, 0.032209, 0.026405, 0.024092, 0.048441, 0.036379,
0.030745, 0.023454, 0.018705, 0.124248, 0.041114, 0.020700, 0.042633,
0.042455, 0.028497, 0.029202, 0.057859, 0.053157, 0.036155, 0.029534,
0.032209, 0.038032, 0.024617, 0.023071, 0.033090, 0.023694, 0.047277,
0.024617, 0.023868, 0.043916, 0.025549, 0.046198, 0.041086, 0.042003,
0.022507, 0.021430, 0.038779, 0.025043, 0.036379, 0.036326, 0.029421,
0.023454, 0.058683, 0.025549, 0.039904, 0.022507, 0.046198, 0.029332,
0.032209, 0.036155, 0.038421, 0.025043, 0.023694, 0.030104, 0.022363,
0.048544, 0.035180, 0.030677, 0.022112, 0.030677, 0.036678, 0.022507,
0.024092, 0.064231, 0.022507, 0.032209, 0.025043, 0.221152, 0.029840,
0.038779, 0.040634, 0.024617, 0.032418, 0.025525, 0.033298, 0.028092,
0.045754, 0.032209, 0.017748, 0.033090, 0.017748, 0.048931, 0.038689,
0.022112, 0.027129, 0.032952, 0.036858, 0.027704, 0.032146, 0.052191,
0.042633, 0.071638, 0.044140, 0.022507, 0.046647, 0.028270, 0.050525,
0.036772, 0.058995, 0.038335, 0.025185, 0.022507, 0.040293, 0.032418,
0.064308, 0.026023, 0.036155, 0.032418, 0.038032, 0.018705, 0.040293,
0.030104, 0.030845, 0.064906, 0.025525, 0.036155, 0.022507, 0.022363,
0.032418, 0.021430, 0.032209, 0.102770, 0.036960, 0.031062, 0.025043,
0.036155, 0.031609, 0.036379, 0.030845, 0.048985, 0.021848, 0.025549,
0.022507, 0.035783, 0.023698, 0.034422, 0.032418, 0.022507, 0.023868,
0.020065, 0.023694, 0.040634, 0.055633, 0.054549, 0.044662, 0.087660,
0.048066, 0.143571, 0.068669, 0.065049, 0.076927, 0.044359, 0.041577,
0.052364, 0.100317, 0.062146, 0.067578, 0.054549, 0.047239, 0.062809,
0.033917, 0.087660, 0.077113, 0.055633, 0.061854, 0.059756, 0.059537,
0.052364, 0.060347, 0.170251, 0.108492, 0.046370, 0.070684, 0.049589,
0.044662, 0.049013, 0.043303, 0.069573, 0.075044, 0.054354, 0.065072,
0.073135, 0.046126, 0.055569, 0.047239, 0.062146, 0.056093, 0.059986,
0.096182, 0.100317, 0.051649, 0.054354, 0.077420, 0.100317, 0.046370,
0.043303, 0.045845, 0.061422, 0.091580, 0.206234, 0.051405, 0.071684,
0.061574, 0.063666, 0.052692, 0.051649, 0.100124, 0.077909, 0.033917,
0.058680, 0.044359, 0.065498, 0.080214, 0.123231, 0.052957, 0.056582,
0.061540, 0.076794, 0.043303, 0.054884, 0.044359, 0.145249, 0.081741,
0.041577, 0.056093, 0.076799, 0.044359, 0.068483, 0.051649, 0.092275,
0.044359, 0.108492, 0.092275, 0.046126, 0.106422, 0.054354, 0.052957,
0.073329, 0.046126, 0.086402, 0.048194, 0.128569, 0.104042, 0.061854,
0.069573, 0.070035, 0.050346, 0.043303, 0.053576, 0.054549, 0.033917,
0.063666, 0.058680, 0.099130, 0.080198, 0.050118, 0.054549, 0.041577,
0.143571, 0.095965, 0.047643, 0.052364, 0.105168, 0.048685, 0.043303,
0.052814, 0.076927, 0.054549, 0.041577, 0.066657, 0.189930, 0.046370,
0.075044, 0.121331, 0.043303, 0.223897, 0.198621, 0.150328, 0.100317,
0.053576, 0.070708, 0.100898, 0.047239, 0.043613, 0.065049, 0.049146,
0.068669, 0.055569, 0.062124, 0.096408, 0.044662, 0.087660, 0.083012,
0.050118, 0.069573, 0.046126, 0.049146, 0.049146, 0.050808, 0.080198,
0.059986, 0.071974, 0.047239, 0.050808, 0.059986, 0.065850, 0.044863,
0.052814, 0.044359, 0.052364, 0.108492, 0.143571, 0.050926, 0.049146,
0.049146, 0.055569, 0.033917, 0.527659, 0.143547, 0.077113, 0.046126,
0.106422, 0.068669, 0.108492, 0.063666, 0.054549, 0.054884, 0.056907,
0.068669, 0.080198, 0.120887, 0.054549, 0.052692, 0.085801, 0.054884,
0.050808, 0.094595, 0.059545, 0.054354, 0.062124, 0.087660, 0.052814,
0.086715, 0.146253, 0.046370, 0.041577, 0.116083, 0.076927, 0.047239,
0.084375, 0.134652, 0.217969, 0.063559, 0.061540, 0.044662, 0.054354,
0.063666, 0.145466, 0.101700, 0.090491, 0.078536, 0.054884, 0.062124,
0.041577, 0.043303, 0.194473, 0.079780, 0.059704, 0.054780, 0.048194,
0.062146, 0.069573, 0.086898, 0.046675, 0.056258, 0.074141, 0.048066,
0.052957, 0.057982, 0.058966, 0.061048, 0.050885, 0.049146, 0.080993,
0.056093, 0.061854, 0.124025, 0.062146, 0.060906, 0.150328, 0.058680,
0.077420, 0.051800, 0.102359, 0.113301, 0.073096, 0.116715, 0.131476,
0.140601, 0.097667, 0.051800, 0.051800, 0.127964, 0.108870, 0.111926,
0.093532, 0.102390, 0.144266, 0.098271, 0.102541, 0.136497, 0.127964,
0.085569, 0.157863, 0.096739, 0.054008, 0.106219, 0.076838, 0.099076,
0.093532, 0.059861, 0.079975, 0.116715, 0.133625, 0.053641, 0.066110,
0.122302, 0.081313, 0.140601, 0.259889, 0.094437, 0.098271, 0.105776,
0.225742, 0.100097, 0.147592, 0.099076, 0.093128, 0.093532, 0.134946,
0.133625, 0.120869, 0.065932, 0.103395, 0.125172, 0.147842, 0.105278,
0.173584, 0.168241, 0.111524, 0.093532, 0.099076, 0.100426, 0.137132,
0.065356, 0.091108, 0.141202, 0.054008, 0.075298, 0.073717, 0.122817,
0.105278, 0.094437, 0.067080, 0.108530, 0.115467, 0.093532, 0.085569,
0.145180, 0.100426, 0.116715, 0.151726, 0.073096, 0.193781, 0.090614,
0.081162, 0.051800, 0.133625, 0.136497, 0.100670, 0.081313, 0.506893,
0.084567, 0.108530, 0.087353, 0.063184, 0.123639, 0.168333, 0.314422,
0.091108, 0.079975, 0.091108, 0.136497, 0.122302, 0.167297, 0.067080,
0.144266, 0.065932, 0.087667, 0.100426, 0.099460, 0.091108, 0.100637,
0.116715, 0.079975, 0.077977, 0.090340, 0.136723, 1.943026, 0.108870,
0.090340, 0.065932, 0.102245, 0.157863, 0.157863, 0.215574, 0.156830,
0.093532, 0.122302, 0.097667, 0.063000, 0.116715, 0.076838, 0.148372,
0.093532, 0.099076, 0.141202, 0.096505, 0.096739, 0.091108, 0.099076,
0.079975, 0.108870, 0.102390, 0.079975, 0.244121, 0.167071, 0.096739,
0.102390, 0.103395, 0.073096, 0.094887, 0.065932, 0.190667, 0.099460,
0.102390, 0.096739, 0.102390, 0.116715, 0.100637, 0.256554, 0.103395,
0.081313, 0.068962, 0.109645, 0.059364, 0.147842, 0.099460, 0.079262,
0.099460, 0.065932, 0.123687, 0.090614, 0.131352, 0.098271, 0.102541,
0.098983, 0.057224, 0.074797, 0.057224, 0.250559, 0.079975, 0.103395,
0.100426, 0.065932, 0.120661, 0.079262, 0.065932, 0.118665, 0.081162,
0.066283, 0.099076, 0.102359, 0.108530, 0.079975, 0.168333, 0.096739,
0.168333, 0.097008, 0.055288, 0.172411, 0.092801, 0.051800, 0.102541,
0.084567, 0.054008, 0.090991, 0.172411, 0.057224, 0.148396, 0.200965,
0.076838, 0.157863, 0.053535, 0.121919, 0.126609, 0.123890, 0.118081,
0.097008, 0.125311, 0.099460, 0.122302, 0.134946, 0.080975, 0.084567,
0.110093, 0.102245, 0.103395, 0.171601, 0.094887, 0.126240, 0.137742,
0.099954, 0.108530, 0.157863, 0.096739, 0.051800, 0.127964, 0.066110,
0.061021, 0.105147, 0.100426, 0.079975, 0.088187, 0.116421, 0.076838,
0.098271, 0.116715, 0.137656, 0.075298, 0.148396, 0.112166, 1.083905,
0.326598, 0.428987, 0.395963, 0.224541, 0.326598, 0.030677, 0.410454,
0.122771, 1.140305, 0.641074, 0.432159, 0.429335, 0.422908, 0.461926,
0.293083, 0.477078, 0.714856, 0.515861, 0.405418, 0.054354, 0.341177,
0.410008, 0.514245, 0.641074, 0.816459, 0.455115, 0.400707, 0.382240,
0.431832, 1.618970, 0.683953, 0.182992, 0.763699, 0.515861, 0.717145,
0.409629, 0.074134, 0.398273, 0.864974, 0.400707, 0.591403, 0.435354,
0.514245, 1.337152, 0.841077, 0.410008, 0.683953, 0.338649, 0.557595,
0.442092, 0.326598, 0.984189, 0.429608, 0.395963, 1.152055, 0.587222,
1.748492, 0.477078, 0.395459, 0.717145, 0.575811, 0.210115, 0.487785,
0.431832, 0.383852, 0.806708, 0.428987, 0.278405, 0.395963, 0.395459,
0.383852, 1.083905, 0.428510, 0.326598, 0.108492, 0.541644, 0.612110,
0.382240, 0.833511, 0.382240, 0.456628, 0.326598, 0.458880, 0.398273,
0.957748, 0.326598, 0.295049, 0.629646, 0.429765, 0.439942, 0.633617,
0.566297, 0.429335, 0.086507, 0.477078, 0.526753, 0.375240, 0.584436,
0.355776, 0.395963, 0.644924, 0.129793, 0.484880, 0.470001, 0.572306,
0.383852, 1.110081, 0.841077, 0.395963, 0.683953, 0.428745, 0.387752,
0.545299, 0.686537, 0.635219, 0.840499, 0.527659, 0.400707, 0.480982,
0.541644, 0.714856, 0.942673, 0.398273, 0.428987, 0.356781, 0.428510,
1.140961, 0.395963, 0.356781, 0.410454, 0.541644, 0.641074, 0.484778,
0.410008, 0.433108, 0.278405, 0.278405, 0.503141, 0.428745, 0.125103,
0.633617, 0.410454, 0.124025, 0.461926, 0.398273, 0.410008, 1.181303,
0.635219, 0.593537, 0.395963, 0.717145, 0.409629, 0.492595, 0.806708,
0.503820, 0.423834, 0.557595, 0.429335, 0.470749, 0.461926, 1.890036,
0.236343, 0.806708, 0.123561, 0.433744, 0.427348, 0.427348, 0.962234,
0.395963, 0.409629, 0.527659, 0.425727, 0.602549, 0.901331, 0.326598,
0.635949, 0.541644, 0.375240, 0.598969, 1.140961, 0.391998, 0.719443,
0.410008, 0.515861, 0.714856, 0.842273, 0.410454, 0.389377, 0.431078,
0.515861, 0.515861, 0.429335, 0.332495, 0.398273, 0.428987, 0.635219,
0.387752, 0.384289, 0.383852, 0.430504, 0.428510, 0.431832, 0.375240,
0.278405, 0.374102, 0.428745, 0.692878, 1.152055, 0.503820, 0.428745,
0.352868, 0.429335, 0.375240, 0.400707, 0.427348, 0.256183, 0.962234,
0.505376, 0.058995, 0.410454, 0.172880, 0.395963, 0.470749, 0.356781,
1.332700, 0.683953, 0.395963, 0.806708, 0.400707, 0.330982, 0.427731,
0.934845, 0.375240, 0.191534, 0.047239, 1.083905, 0.348794, 0.409708,
0.503820, 0.557595, 0.429335, 0.498780, 0.293083, 0.363069, 0.442092,
1.152055, 0.375240, 0.335677, 0.452443, 0.655156, 0.929928, 0.614869,
1.411031, 1.101132, 0.469030, 0.404976, 0.538209, 0.655828, 0.674748,
0.365182, 0.641612, 0.555434, 0.521651, 0.386679, 0.386679, 0.980304,
0.659111, 0.651366, 0.538209, 0.521651, 0.884780, 1.287829, 0.558322,
0.446161, 0.817970, 0.568499, 0.533507, 0.639746, 0.484404, 0.591751,
0.913016, 0.446161, 0.533907, 0.606885, 0.672320, 1.150642, 0.655828,
0.365182, 0.665088, 1.094242, 0.629401, 0.540676, 0.733026, 1.248265,
1.273499, 0.867854, 0.538656, 0.386679, 0.922273, 0.515686, 1.321022,
0.624444, 0.655828, 0.922273, 0.386679, 0.762191, 0.779432, 0.601851,
0.655156, 0.926213, 0.762191, 0.641612, 0.558322, 1.025370, 0.641067,
0.651366, 0.633434, 0.459580, 0.859221, 0.552291, 0.591751, 0.819965,
0.669977, 1.185083, 0.499338, 0.533907, 0.752871, 0.571388, 0.539772,
0.449182, 1.025370, 0.365182, 1.321022, 0.926213, 0.886360, 0.562272,
0.669977, 0.796046, 0.557598, 0.596776, 0.672336, 0.659111, 0.453719,
0.477716, 0.477716, 1.592069, 0.591751, 0.539772, 0.641612, 0.946254,
0.744165, 0.386679, 0.593825, 0.539772, 0.449182, 0.604273, 0.794951,
0.752871, 0.539772, 0.648732, 0.469030, 0.665088, 1.332700, 1.341388,
0.533507, 0.544212, 1.025992, 0.645967, 0.612945, 0.868492, 0.648732,
0.752300, 0.624444, 1.219748, 0.446161, 0.520818, 0.469044, 0.669977,
0.926213, 0.638752, 0.762191, 0.922273, 0.794951, 0.606885, 0.669977,
0.550113, 0.641067, 0.733026, 0.604273, 0.648732, 0.533507, 0.746506,
0.733026, 0.980683, 0.538209, 0.669977, 0.469030, 0.648732, 0.609190,
1.219748, 0.373113, 0.539772, 1.744047, 1.004716, 0.926213, 0.562272,
0.752871, 0.538656, 0.449182, 0.365182, 0.469030, 0.446161, 0.484404,
0.768592, 0.648732, 0.655156, 0.521651, 0.779432, 0.446161, 0.596776,
0.538209, 0.726740, 0.539772, 0.469030, 0.521651, 0.561950, 0.601851,
0.533907, 0.922273, 1.248265, 0.476800, 0.737990, 0.817970, 0.792127,
0.533907, 0.486038, 0.624444, 0.798241, 0.476800, 1.059373, 0.645967,
0.619940, 0.528726, 0.669977, 0.865406, 0.980683, 0.980683, 0.834671,
1.001353, 0.752871, 0.449182, 1.096520, 0.449182, 0.593825, 0.636558,
0.762191, 0.638591, 0.538209, 0.865406, 0.779432, 0.469044, 0.645967,
0.557598, 0.499338, 0.484404, 0.515686, 0.794951, 0.619456, 0.733026,
0.821769, 0.752300, 0.643302, 0.636558, 0.655156, 0.655156, 0.484404,
0.648732, 0.726023, 0.365182, 0.606885, 0.499338, 0.520818, 0.612945,
0.446161, 0.557598, 0.469044, 1.134650, 0.629401, 0.538656, 0.561950,
1.364861, 0.459580, 1.025370, 0.980304, 0.607592, 0.533907, 1.134650,
0.446161, 0.629962]
# FIXME: known failure in 32bit Linux; numerical imprecision results in
# different ordering in quick_scan
if _IS_32BIT: # pragma: no cover
assert_allclose(clust.reachability_, np.array(v), rtol=1e-2)
else:
# we compare to truncated decimals, so use atol
assert_allclose(clust.reachability_, np.array(v), atol=1e-5)
