Further hdbscan coverage improvements

lmcinnes · Aug 20, 2016 · 916b1cd · 916b1cd
1 parent 81cc4ce
commit 916b1cd
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Showing 2 changed files with 12 additions and 3 deletions.
diff --git a/hdbscan/hdbscan_.py b/hdbscan/hdbscan_.py
@@ -75,7 +75,7 @@ def _hdbscan_generic(X, min_samples=5, alpha=1.0,
     if gen_min_span_tree:
         result_min_span_tree = min_spanning_tree.copy()
         for index, row in enumerate(result_min_span_tree[1:], 1):
-            candidates = np.where(np.isclose(mutual_reachability_[row[1]], row[2]))[0]
+            candidates = np.where(np.isclose(mutual_reachability_[int(row[1])], row[2]))[0]
             candidates = np.intersect1d(candidates, min_spanning_tree[:index, :2].astype(int))
             candidates = candidates[candidates != row[1]]
             assert (len(candidates) > 0)

diff --git a/hdbscan/tests/test_hdbscan.py b/hdbscan/tests/test_hdbscan.py
@@ -157,7 +157,7 @@ def test_hdbscan_prims_balltree():
                   metric='cosine')
 
 def test_hdbscan_boruvka_kdtree():
-    labels, p, persist, ctree, ltree, mtree = hdbscan(X, algorithm='boruvka_kdtree')
+    labels, p, persist, ctree, ltree, mtree = hdbscan(X, algorithm='boruvka_kdtree', leaf_size=5)
     n_clusters_1 = len(set(labels)) - int(-1 in labels)
     assert_equal(n_clusters_1, n_clusters)
 
@@ -172,7 +172,7 @@ def test_hdbscan_boruvka_kdtree():
                   metric='russelrao')
 
 def test_hdbscan_boruvka_balltree():
-    labels, p, persist, ctree, ltree, mtree = hdbscan(X, algorithm='boruvka_balltree')
+    labels, p, persist, ctree, ltree, mtree = hdbscan(X, algorithm='boruvka_balltree', leaf_size=5)
     n_clusters_1 = len(set(labels)) - int(-1 in labels)
     assert_equal(n_clusters_1, n_clusters)
 
@@ -186,6 +186,15 @@ def test_hdbscan_boruvka_balltree():
                   algorithm='boruvka_balltree',
                   metric='cosine')
 
+def test_hdbscan_generic():
+    labels, p, persist, ctree, ltree, mtree = hdbscan(X, algorithm='generic')
+    n_clusters_1 = len(set(labels)) - int(-1 in labels)
+    assert_equal(n_clusters_1, n_clusters)
+
+    labels = HDBSCAN(algorithm='generic', gen_min_span_tree=True).fit(X).labels_
+    n_clusters_2 = len(set(labels)) - int(-1 in labels)
+    assert_equal(n_clusters_2, n_clusters)
+
 
 def test_hdbscan_high_dimensional():
     H, y = make_blobs(n_samples=50, random_state=0, n_features=64)