convert data format to SDCN

convert_data_sdcn.py

@@ -0,0 +1,141 @@
+import numpy as np
+import scipy.sparse as sp
+import sys
+import pickle as pkl
+import networkx as nx
+
+def parse_index_file(filename):
+    """Parse index file."""
+    index = []
+    for line in open(filename):
+        index.append(int(line.strip()))
+    return index
+
+def load_data(dataset_str): # {'pubmed', 'citeseer', 'cora'}
+    """Load data."""
+    names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
+    objects = []
+    for i in range(len(names)):
+        with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f:
+            if sys.version_info > (3, 0):
+                objects.append(pkl.load(f, encoding='latin1'))
+            else:
+                objects.append(pkl.load(f))
+
+    x, y, tx, ty, allx, ally, graph = tuple(objects)
+    test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str))
+    test_idx_range = np.sort(test_idx_reorder)
+
+    if dataset_str == 'citeseer':
+        # Fix citeseer dataset (there are some isolated nodes in the graph)
+        # Find isolated nodes, add them as zero-vecs into the right position
+        test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)
+        tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
+        tx_extended[test_idx_range-min(test_idx_range), :] = tx
+        tx = tx_extended
+        ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
+        ty_extended[test_idx_range-min(test_idx_range), :] = ty
+        ty = ty_extended
+
+    features = sp.vstack((allx, tx)).tolil()
+    features[test_idx_reorder, :] = features[test_idx_range, :]
+    adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
+
+    labels = np.vstack((ally, ty))
+    labels[test_idx_reorder, :] = labels[test_idx_range, :]
+
+    idx_test = test_idx_range.tolist()
+    idx_train = range(len(y))
+    idx_val = range(len(y), len(y)+500)
+    return adj, features, labels, idx_train, idx_val, idx_test
+
+def load_AN(dataset):
+    edge_file = open("data/{}.edge".format(dataset), 'r')
+    attri_file = open("data/{}.node".format(dataset), 'r')
+    label_file = open("data/{}.label".format(dataset),'r')
+    edges = edge_file.readlines()
+    attributes = attri_file.readlines()
+    labels_raw = label_file.readlines()
+    edge_file.close()
+    attri_file.close()
+    label_file.close()
+
+    node_num = int(edges[0].split()[1].strip())
+    edge_num = int(edges[1].split()[1].strip())
+    attribute_number = int(attributes[1].split()[1].strip())
+    print("dataset:{}, node_num:{},edge_num:{},attribute_num:{}".format(dataset, node_num, edge_num, attribute_number))
+    edges.pop(0)
+    edges.pop(0)
+    attributes.pop(0)
+    attributes.pop(0)
+    adj_row = []
+    adj_col = []
+
+    edge_num_no_selfloop= 0
+    for line in edges:
+        node1 = int(line.split()[0].strip())
+        node2 = int(line.split()[1].strip())
+        if node1==node2:
+            continue
+        adj_row.append(node1)
+        adj_col.append(node2)
+        edge_num_no_selfloop+=1
+    adj = sp.csc_matrix((np.ones(edge_num_no_selfloop), (adj_row, adj_col)), shape=(node_num, node_num))
+
+    att_row = []
+    att_col = []
+    for line in attributes:
+        node1 = int(line.split()[0].strip())
+        attribute1 = int(line.split()[1].strip())
+        att_row.append(node1)
+        att_col.append(attribute1)
+    attribute = sp.csc_matrix((np.ones(len(att_row)), (att_row, att_col)), shape=(node_num, attribute_number))
+
+    labels=[]
+    for label in labels_raw:
+        label = int(label.strip())
+        labels.append(label)
+    return adj, attribute, np.array(labels)
+
+dataset_name = sys.argv[1]
+if dataset_name in ['cora','citeseer']:
+    adj, attribute, y, idx_train, idx_val, idx_test = load_data(dataset_name)
+
+    y = np.argmax(y,1) # labels is in one-hot format
+    attribute = attribute.toarray()
+
+    wp = open('data/{}.txt'.format(dataset_name),'w')
+    for i in range(attribute.shape[0]):
+        for j in range(attribute.shape[1]):
+            wp.write("{} ".format(int(attribute[i][j])))
+        wp.write('\n')
+    wp.close()
+
+    wp = open('data/{}_label.txt'.format(dataset_name),'w')
+
+    for i in range(y.shape[0]):
+        wp.write('{}\n'.format(y[i]))
+    wp.close()
+
+    wp = open('data/{}_graph.txt'.format(dataset_name),'w')
+    adj = adj.toarray()
+    for i in range(adj.shape[0]):
+        for j in range(adj.shape[1]):
+            if adj[i][j]==1:
+                wp.write("{} {}\n".format(i,j))
+    wp.close()
+
+else:
+    adj,attribute, y = load_AN(dataset_name)
+
+    attribute = attribute.toarray()
+
+    wp = open('data/{}.txt'.format(dataset_name),'w')
+    for i in range(attribute.shape[0]):
+        for j in range(attribute.shape[1]):
+            wp.write("{} ".format(int(attribute[i][j])))
+        wp.write('\n')
+
+
+
+

vaece.py

@@ -249,7 +249,7 @@ def training(args):
         pre,gamma_c,z = model.predict_soft_assignment(mu_u,logvar_u,z)
         print("label mapping using Hungarian algorithm ")
 
-        # pre = label_mapping(tru,pre)
+        pre = label_mapping(tru,pre)
 
         with open("save_prediction.log",'w') as wp:
             for label in pre: