[KG] partition a KG. (dmlc#1351)

* partition a KG.

* get tid.

* support builtin

* fix a minor bug.

Co-authored-by: Chao Ma <[email protected]>

apps/kg/dataloader/KGDataset.py

@@ -387,8 +387,7 @@ def __init__(self, path, name, files, format, read_triple=True, only_train=False
             super(KGDatasetUDD, self).__init__(os.path.join(path, files[0]),
                                                os.path.join(path, files[1]),
                                                os.path.join(path, files[2]),
-                                               os.path.join(path, None),
-                                               os.path.join(path, None),
+                                               None, None,
                                                format=format,
                                                read_triple=read_triple,
                                                only_train=only_train)

apps/kg/partition.py

@@ -0,0 +1,54 @@
+from dataloader import get_dataset
+import scipy as sp
+import numpy as np
+import argparse
+import signal
+import dgl
+from dgl import backend as F
+from dgl.data.utils import load_graphs, save_graphs
+
+def main():
+    parser = argparse.ArgumentParser(description='Partition a knowledge graph')
+    parser.add_argument('--data_path', type=str, default='data',
+                        help='root path of all dataset')
+    parser.add_argument('--dataset', type=str, default='FB15k',
+                        help='dataset name, under data_path')
+    parser.add_argument('--data_files', type=str, default=None, nargs='+',
+                        help='a list of data files, e.g. entity relation train valid test')
+    parser.add_argument('--format', type=str, default='built_in',
+                        help='the format of the dataset, it can be built_in,'\
+                                'raw_udd_{htr} and udd_{htr}')
+    parser.add_argument('-k', '--num-parts', required=True, type=int,
+                        help='The number of partitions')
+    args = parser.parse_args()
+    num_parts = args.num_parts
+
+    # load dataset and samplers
+    dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)
+
+    src, etype_id, dst = dataset.train
+    coo = sp.sparse.coo_matrix((np.ones(len(src)), (src, dst)),
+            shape=[dataset.n_entities, dataset.n_entities])
+    g = dgl.DGLGraph(coo, readonly=True, sort_csr=True)
+    g.edata['tid'] = F.tensor(etype_id, F.int64)
+
+    part_dict = dgl.transform.metis_partition(g, num_parts, 1)
+
+    tot_num_inner_edges = 0
+    for part_id in part_dict:
+        part = part_dict[part_id]
+
+        num_inner_nodes = len(np.nonzero(F.asnumpy(part.ndata['inner_node']))[0])
+        num_inner_edges = len(np.nonzero(F.asnumpy(part.edata['inner_edge']))[0])
+        print('part {} has {} nodes and {} edges. {} nodes and {} edges are inside the partition'.format(
+              part_id, part.number_of_nodes(), part.number_of_edges(),
+              num_inner_nodes, num_inner_edges))
+        tot_num_inner_edges += num_inner_edges
+
+        part.copy_from_parent()
+        save_graphs(args.data_path + '/part_' + str(part_id) + '.dgl', [part])
+    print('there are {} edges in the graph and {} edge cuts for {} partitions.'.format(
+        g.number_of_edges(), g.number_of_edges() - tot_num_inner_edges, len(part_dict)))
+
+if __name__ == '__main__':
+    main()