def test_hdbscan_approximate_predict_score():
clusterer = HDBSCAN(min_cluster_size=200).fit(X)
# no prediction data error
assert_raises(ValueError, approximate_predict_scores, clusterer, X)
clusterer.generate_prediction_data()
# wrong dimensions error
assert_raises(ValueError, approximate_predict_scores, clusterer, np.array([[1, 2, 3]]))
with warnings.catch_warnings(record=True) as w:
approximate_predict_scores(clusterer, np.array([[1.5, -1.0]]))
# no clusters warning
assert 'Clusterer does not have any defined clusters' in str(w[-1].message)
clusterer = HDBSCAN(prediction_data=True).fit(X)
scores = approximate_predict_scores(clusterer, X)
assert_array_almost_equal(scores, clusterer.outlier_scores_)
assert scores.min() >= 0
assert scores.max() <= 1
def predict(self, dim_reduced_vecs, outlier_labels, scores, contamination,
min_cluster_size, allow_noise):
print("Clustering ...")
clusterer = HDBSCAN(min_cluster_size=min_cluster_size,
prediction_data=True,
metric="euclidean").fit(dim_reduced_vecs)
print("Get prediction data ...")
clusterer.generate_prediction_data()
try:
cluster_pred = clusterer.labels_ if allow_noise else np.argmax(
all_points_membership_vectors(clusterer)[:, 1:], axis=1)
except IndexError:
print(
"Got IndexError and will not enforce cluster membership (allow noise) ..."
)
print(all_points_membership_vectors(clusterer))
cluster_pred = clusterer.labels_
# scoring
print("Get scores ...")
# GLOSH
threshold = pd.Series(clusterer.outlier_scores_).quantile(0.9)
outlier_pred = np.where(clusterer.outlier_scores_ > threshold, -1, 1)
scores["cluster_n"] = len(np.unique(clusterer.labels_))
scores["homogeneity"] = homogeneity_score(outlier_labels, cluster_pred)
scores["completeness"] = completeness_score(outlier_labels,
cluster_pred)
scores["v_measure"] = v_measure_score(outlier_labels, cluster_pred)
scores = get_scores(scores, outlier_labels, outlier_pred)
print(
f"Homogeneity - {homogeneity_score(outlier_labels, cluster_pred)*100:.1f} \
cluster_n - {len(np.unique(clusterer.labels_))}")
return scores, clusterer.outlier_scores_
