Adding more test cases to HDBSCAN

Signed-off-by: Julio Faracco <[email protected]>

tests/ml/cluster/test_hdbscan.py

@@ -3,35 +3,80 @@
 import unittest
 import numpy as np
 
+from parameterized import parameterized_class
+
 try:
     import cupy as cp
 except ImportError:
     pass
 
-from sklearn.datasets import make_blobs
-
-try:
-    from dasf.ml.cluster import HDBSCAN
-except ImportError:
-    raise unittest.SkipTest("HDBSCAN is probably affected by CVE-2022-21797")
+from sklearn.datasets import make_blobs, make_moons
 
+from dasf.ml.cluster import HDBSCAN
 from dasf.utils.types import is_cpu_array
 from dasf.utils.types import is_gpu_array
 from dasf.utils.funcs import is_gpu_supported
 
 
+def generate_blobs():
+    blobs = []
+
+    X, y, c = make_blobs(n_samples=1000,
+                         centers=3,
+                         n_features=2,
+                         return_centers=True,
+                         random_state=42)
+
+    blobs.append({'X': X, 'y': y, 'centroids': c})
+
+    X, y, c = make_blobs(n_samples=1000,
+                         return_centers=True,
+                         random_state=30)
+
+    blobs.append({'X': X, 'y': y, 'centroids': c})
+
+#    X, y, c = make_blobs(n_samples=4000,
+#                         centers=[(-0.75,2.25),
+#                                  (1.0, 2.0),
+#                                  (1.0, 1.0),
+#                                  (2.0, -0.5),
+#                                  (-1.0, -1.0),
+#                                  (0.0, 0.0)],
+#                         cluster_std=0.5,
+#                         return_centers=True,
+#                         random_state=12)
+#
+#    blobs.append({'id': 3, 'X': X, 'y': y, 'centroids': c})
+
+    X, y, c = make_blobs(n_samples=2000,
+                         n_features=10,
+                         return_centers=True,
+                         random_state=10)
+
+    blobs.append({'X': X, 'y': y, 'centroids': c})
+
+    X, y = make_moons(n_samples=3000,
+                         noise=0.1,
+                         random_state=42)
+
+    blobs.append({'X': X, 'y': y, 'centroids': []})
+
+    return blobs
+
+
 class TestHDBSCAN(unittest.TestCase):
     def setUp(self):
-        self.size = 1000
-        self.centers = 3
+        size = 1000
+        centers = 3
         random_state = 42
 
-        self.X, self.y, self.centroids = make_blobs(n_samples=self.size,
-                                                    centers=self.centers,
+        self.X, self.y, self.centroids = make_blobs(n_samples=size,
+                                                    centers=centers,
                                                     n_features=2,
                                                     return_centers=True,
                                                     random_state=random_state)
 
+
     def __match_randomly_labels_created(self, y1, y2):
         y2 = (y2 * -1) - 1
 
@@ -83,3 +128,33 @@ def test_hdbscan_gpu(self):
         y1, y2 = self.__match_randomly_labels_created(y.get(), self.y)
 
         self.assertTrue(float(len(np.where(y1 != y2)[0])/len(y1))*100 < 5.0)
+
+
+@parameterized_class(generate_blobs())
+class TestHDBSCANMatches(unittest.TestCase):
+    def __match_randomly_labels_created(self, y1, y2):
+        y2 = (y2 * -1) - 1
+
+        for i in range(len(y1)):
+            if y2[i] < 0:
+                y2[y2 == y2[i]] = y1[i]
+
+            if not np.any(y2[y2 < 0]):
+                break
+
+        return y1, y2
+
+    @unittest.skipIf(not is_gpu_supported(),
+                     "not supported CUDA in this platform")
+    def test_hdbscan(self):
+        sc_cpu = HDBSCAN()
+        sc_gpu = HDBSCAN()
+
+        cp_X = cp.asarray(self.X)
+
+        y_cpu = sc_cpu._fit_predict_cpu(self.X)
+        y_gpu = sc_gpu._fit_predict_gpu(cp_X)
+
+        y1, y2 = self.__match_randomly_labels_created(y_gpu.get(), y_cpu)
+
+        self.assertTrue(float(len(np.where(y1 != y2)[0])/len(y1))*100 < 5.0)