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_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)
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=14, alpha=0.5)
xy = X[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=2, alpha=0.5)
plt.title("Automatic Clustering \n Estimated number of clusters: %d"
% clust.n_clusters)
# (Re)-extract clustering structure, using a single eps to show comparison
# with DBSCAN. This can be run for any clustering distance, and can be run
# multiple times without rerunning OPTICS. OPTICS does need to be re-run to c
# hange the min-pts parameter.
clust.extract(.15, 'dbscan')
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(223)
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = 'k'
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
