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Faster get_stability_scores #422

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Faster get_stability_scores #422

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17 changes: 13 additions & 4 deletions hdbscan/_hdbscan_tree.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -593,13 +593,22 @@ cpdef np.ndarray[np.double_t, ndim=1] outlier_scores(np.ndarray tree):
cpdef np.ndarray get_stability_scores(np.ndarray labels, set clusters,
dict stability, np.double_t max_lambda):

cdef np.ndarray result, cluster_sizes, cluster_arr
cdef np.intp_t cluster_size
cdef np.intp_t n
cdef np.intp_t n, c

if np.isinf(max_lambda) or max_lambda == 0.0:
return np.ones(len(clusters), dtype=np.double)

cluster_sizes = np.bincount(labels[labels != -1], minlength=len(clusters))
cluster_arr = np.fromiter(clusters, dtype=np.intp, count=len(clusters))
cluster_arr.sort()

result = np.empty(len(clusters), dtype=np.double)
for n, c in enumerate(sorted(list(clusters))):
cluster_size = np.sum(labels == n)
if np.isinf(max_lambda) or max_lambda == 0.0 or cluster_size == 0:
for n in range(cluster_arr.shape[0]):
c = cluster_arr[n]
cluster_size = cluster_sizes[n]
if cluster_size == 0:
result[n] = 1.0
else:
result[n] = stability[c] / (cluster_size * max_lambda)
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