test_normalize

src/decagon_pytorch/normalize.py

@@ -2,17 +2,28 @@ import numpy as np
 import scipy.sparse as sp
 
 
-def normalize_adjacency_matrix(self, adj):
+def sparse_to_tuple(sparse_mx):
+    if not sp.isspmatrix_coo(sparse_mx):
+        sparse_mx = sparse_mx.tocoo()
+    coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
+    values = sparse_mx.data
+    shape = sparse_mx.shape
+    return coords, values, shape
+
+
+def normalize_adjacency_matrix(adj):
     adj = sp.coo_matrix(adj)
+
     if adj.shape[0] == adj.shape[1]:
         adj_ = adj + sp.eye(adj.shape[0])
         rowsum = np.array(adj_.sum(1))
-        degree_mat_inv_sqrt = sp.diags(np.power(rowsum, -0.5).flatten())
-        adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt).tocoo()
+        degree_mat_inv_sqrt = np.power(rowsum, -0.5).flatten()
+        degree_mat_inv_sqrt = sp.diags(degree_mat_inv_sqrt)
+        adj_normalized = adj_.dot(degree_mat_inv_sqrt).transpose().dot(degree_mat_inv_sqrt)
     else:
         rowsum = np.array(adj.sum(1))
         colsum = np.array(adj.sum(0))
         rowdegree_mat_inv = sp.diags(np.nan_to_num(np.power(rowsum, -0.5)).flatten())
         coldegree_mat_inv = sp.diags(np.nan_to_num(np.power(colsum, -0.5)).flatten())
         adj_normalized = rowdegree_mat_inv.dot(adj).dot(coldegree_mat_inv).tocoo()
-    return preprocessing.sparse_to_tuple(adj_normalized)
+    return sparse_to_tuple(adj_normalized)