profile lg

tests/profile_lg.py

+import argparse
+import time
+
+import dgl
+import dgl.backend as F
+import igraph
+import networkx as nx
+import numpy as np
+import scipy.sparse as sp
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--bt', action='store_true', help='BackTracking')
+parser.add_argument('--n-nodes', type=int, help='Number of NODES')
+args = parser.parse_args()
+
+n = args.n_nodes
+a = sp.random(n, n, 1 / n, data_rvs=lambda n: np.ones(n))
+b = sp.triu(a, 1) + sp.triu(a, 1).transpose()
+g = dgl.DGLGraph()
+g.from_scipy_sparse_matrix(b)
+
+N = 10
+
+t0 = time.time()
+for i in range(N):
+    lg_sparse = g.line_graph(args.bt)
+    g._graph._cache.clear()
+t = (time.time() - t0) / N
+print('dgl.DGLGraph.line_graph: %f' % t)
+
+t0 = time.time()
+for i in range(N):
+    lg = g._line_graph(args.bt)
+    g._graph._cache.clear()
+t = (time.time() - t0) / N
+print('dgl.DGLGraph._line_graph: %f' % t)
+
+g = igraph.Graph()
+g.add_vertices(n)
+g.add_edges(list(zip(a.row.tolist(), a.col.tolist())))
+
+'''
+t0 = time.time()
+for i in range(N):
+    lg = g.linegraph()
+t = (time.time() - t0) / N
+print('igraph.Graph._line_graph: %f' % t)
+'''
+
+t = 0
+for i in range(N):
+    g = igraph.Graph()
+    g.add_vertices(n)
+    g.add_edges(list(zip(a.row.tolist(), a.col.tolist())))
+    t0 = time.time()
+    lg = g.linegraph()
+    t += time.time() - t0
+t /= N
+print('igraph.Graph.linegraph: %f' % t)