Debug Node2vec

evalNE_script.py

@@ -42,11 +42,12 @@ log("Building link prediction dataset...")
 # Create an evaluator and generate train/test edge split
 traintest_split = LPEvalSplit()
 try:
-    traintest_split.compute_splits(G, split_alg="spanning_tree", train_frac=args.train_frac, fe_ratio=1)
+    traintest_split.compute_splits(G, split_alg="random", train_frac=args.train_frac, fe_ratio=1)
 except ValueError:
     traintest_split.compute_splits(G, split_alg="fast", train_frac=args.train_frac, fe_ratio=1)
+print("BEFORE", len(traintest_split.test_edges))
 nee = LPEvaluator(traintest_split)
-
+print("AFTER",len(traintest_split.test_edges))
 log("Dataset Built !")
 
 # Create a Scoresheet to store the results
@@ -93,9 +94,12 @@ if args.network_embedding:
         pbar = tqdm(enumerate(methods), disable=(not args.verbose))
         for i,method in pbar:
             pbar.set_description("Evaluate "+method)
+            is_weighted = nx.is_weighted(G)
             command = commands[i] + " --input {} --output {} --representation-size {}"
+            if is_weighted:
+                command = command + " --weighted"
             results = nee.evaluate_cmd(method_name=methods[i], method_type='ne', command=command,
-                                       edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ',  verbose=args.verbose,write_weights=nx.is_weighted(G))
+                                       edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ',  verbose=args.verbose,write_weights=is_weighted)
             scoresheet.log_results(results)
 
     except ImportError:

generate_theoric_random_graph.py

@@ -18,7 +18,7 @@ args = parser.parse_args()
 
 
 GRAPH_SIZE = [80,800]
-EDGE_SIZE = [2,3]
+EDGE_SIZE = [2,4,5]
 sample_per_params  = 4
 
 OUTPUT_DIR = args.output_dir

run_eval.py

@@ -16,6 +16,7 @@ parser.add_argument("dataset_dir")
 parser.add_argument("output_filename")
 parser.add_argument("-f", "--format", default="gexf", choices=["gexf", "gml", "txt"])
 parser.add_argument("-t","--train-frac",default=0.9,type=float)
+parser.add_argument("-v","--verbose",action="store_true")
 
 args = parser.parse_args()
 fns = sorted(glob.glob(args.dataset_dir + "/*." + args.format))
@@ -24,9 +25,9 @@ all_res = []
 pbar = tqdm(fns)
 for fn in pbar:
     pbar.set_description("run eval on "+ fn)
-
+    verbose_cmd = "-v" if args.v else ""
     if not os.path.exists(fn + "_results_lp"):
-        command = "python evalNE_script.py {0} -f {1} -n --train-frac {2}".format(fn, args.format,args.train_frac).split()
+        command = "python evalNE_script.py {0} -f {1} -n --train-frac {2} {3}".format(fn, args.format,args.train_frac,verbose_cmd).split()
         output = subprocess.run(command)
         if not output.returncode == 0:
             print("Error! for the command :", " ".join(command))