def test_mem_vec_same_clusters():
"""
Verify membership vector produces same n_clusters as clusterer
"""
# Given a flat clustering trained for n_clusters picked by HDBSCAN,
n_clusters_fit = None
clusterer = HDBSCAN_flat(X, n_clusters=n_clusters_fit)
# When membership_vector_flat is called with new data,
memberships = membership_vector_flat(clusterer, X_test)
# Then the number of clusters in memberships matches those of clusterer,
assert_equal(memberships.shape[1],
n_clusters_from_labels(clusterer.labels_))
# and the number of points should equal those in the test set
assert_equal(len(memberships), len(X_test))
# and all probabilities are <= 1.
assert_array_less(memberships, np.ones(memberships.shape) + 1.e-14)
# ========================================
# Given a flat clustering for a specified n_clusters,
n_clusters_fit = n_clusters_from_labels(clusterer.labels_) - 2
clusterer = HDBSCAN_flat(X, n_clusters=n_clusters_fit)
# When membership_vector_flat is called with new data,
memberships = membership_vector_flat(clusterer, X_test)
# Then the number of clusters in memberships matches those of clusterer,
assert_equal(memberships.shape[1], n_clusters_fit)
# and the number of points should equal those in the test set
assert_equal(len(memberships), len(X_test))
# and all probabilities are <= 1.
assert_array_less(memberships, np.ones(memberships.shape) + 1.e-14)
return
def test_mem_vec_diff_clusters():
"""
Verify membership vector produces as many clusters as requested
"""
# Ignore user warnings in this function
warnings.filterwarnings("ignore", category=UserWarning)
# Given a flat clustering trained for n_clusters picked by HDBSCAN,
n_clusters_fit = None
clusterer = HDBSCAN_flat(X, n_clusters=n_clusters_fit)
n_clusters_fitted = n_clusters_from_labels(clusterer.labels_)
# When membership_vector_flat is called with new data for some n_clusters,
n_clusters_predict = n_clusters_fitted + 3
memberships = membership_vector_flat(clusterer,
X_test,
n_clusters=n_clusters_predict)
# Then the number of clusters in memberships should be as requested,
assert_equal(memberships.shape[1], n_clusters_predict)
# and the number of points should equal those in the test set
assert_equal(len(memberships), len(X_test))
# and all probabilities are <= 1.
assert_array_less(memberships, np.ones(memberships.shape) + 1.e-14)
# ========================================
# Given a flat clustering for a specified n_clusters,
n_clusters_fit = n_clusters_from_labels(clusterer.labels_) + 2
clusterer = HDBSCAN_flat(X, n_clusters=n_clusters_fit)
# When membership_vector_flat is called with new data for some n_clusters,
n_clusters_predict = n_clusters_fit + 3
memberships = membership_vector_flat(clusterer,
X_test,
n_clusters=n_clusters_predict)
# Then the number of clusters in memberships should be as requested,
assert_equal(memberships.shape[1], n_clusters_predict)
# and the number of points should equal those in the test set
assert_equal(len(memberships), len(X_test))
# and all probabilities are <= 1.
assert_array_less(memberships, np.ones(memberships.shape) + 1.e-14)
return