check nn graphs building against pdist reference

Author: naspert

pygsp/tests/test_graphs.py

@@ -9,6 +9,7 @@
 
 import numpy as np
 import scipy.linalg
+import scipy.sparse.linalg
 from skimage import data, img_as_float
 
 from pygsp import graphs
@@ -199,20 +200,45 @@ def test_nngraph(self):
                     graphs.NNGraph(Xin, NNtype='knn', 
                                    backend=cur_backend, 
                                    dist_type=dist_type, order=order)
+        self.assertRaises(ValueError, graphs.NNGraph, Xin, 
+                                      NNtype='badtype', backend=cur_backend, 
+                                      dist_type=dist_type)
+        self.assertRaises(ValueError, graphs.NNGraph, Xin, 
+                                      NNtype='knn', backend='badtype', 
+                                      dist_type=dist_type)
 
     def test_nngraph_consistency(self):
-        #Xin = np.arange(180).reshape(60, 3)
         Xin = np.random.uniform(-5, 5, (60, 3))
         dist_types = ['euclidean', 'manhattan', 'max_dist', 'minkowski']
         backends = ['scipy-kdtree', 'flann']
-        num_neighbors=5
+        num_neighbors=4 
+        epsilon=0.1
 
+        # use pdist as ground truth
         G = graphs.NNGraph(Xin, NNtype='knn', 
                            backend='scipy-pdist', k=num_neighbors)
-         for cur_backend in backends:
+        for cur_backend in backends:
             for dist_type in dist_types:
+                if cur_backend == 'flann' and dist_type == 'max_dist':
+                    continue
+                #print("backend={} dist={}".format(cur_backend, dist_type))
                 Gt = graphs.NNGraph(Xin, NNtype='knn', 
                                     backend=cur_backend, k=num_neighbors)
+                d = scipy.sparse.linalg.norm(G.W - Gt.W)
+                self.assertTrue(d < 0.01, 'Graphs (knn) are not identical error='.format(d))
+                
+        G = graphs.NNGraph(Xin, NNtype='radius', 
+                           backend='scipy-pdist', epsilon=epsilon)
+        for cur_backend in backends:
+            for dist_type in dist_types:
+                if cur_backend == 'flann' and dist_type == 'max_dist':
+                    continue
+                #print("backend={} dist={}".format(cur_backend, dist_type))
+                Gt = graphs.NNGraph(Xin, NNtype='radius', 
+                                    backend=cur_backend, epsilon=epsilon)
+                d = scipy.sparse.linalg.norm(G.W - Gt.W, ord=1)
+                self.assertTrue(d < 0.01, 
+                                'Graphs (radius) are not identical error='.format(d))
 
     def test_bunny(self):
         graphs.Bunny()