From f5f22683b10cc5f0181a3b7a67b638a95c5bd5c5 Mon Sep 17 00:00:00 2001 From: Fize Jacques <jacques.fize@cirad.fr> Date: Wed, 10 Feb 2021 11:10:57 +0100 Subject: [PATCH] Debug --- evalNE_script.py | 5 +++-- generate_theoric_random_graph.py | 34 ++++++++++++++++---------------- 2 files changed, 20 insertions(+), 19 deletions(-) diff --git a/evalNE_script.py b/evalNE_script.py index e36d763..99d70ee 100644 --- a/evalNE_script.py +++ b/evalNE_script.py @@ -14,6 +14,7 @@ parser.add_argument("graph_filename") parser.add_argument("-n","--network-embedding",action="store_true",help="If you want to use neural network embedding for link prediction") parser.add_argument("-v","--verbose",action="store_true") parser.add_argument("-f","--format",default="gexf",choices=["gexf","gml","txt"]) +parser.add_argument("-t","--train-frac",default=0.9,type=float) args = parser.parse_args() @@ -41,9 +42,9 @@ 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=0.9, fe_ratio=1) + traintest_split.compute_splits(G, split_alg="spanning_tree", train_frac=args.train_frac, fe_ratio=1) except ValueError: - traintest_split.compute_splits(G, split_alg="fast", train_frac=0.9, fe_ratio=1) + traintest_split.compute_splits(G, split_alg="fast", train_frac=args.train_frac, fe_ratio=1) nee = LPEvaluator(traintest_split) log("Dataset Built !") diff --git a/generate_theoric_random_graph.py b/generate_theoric_random_graph.py index 1777fa7..2a3a2ff 100644 --- a/generate_theoric_random_graph.py +++ b/generate_theoric_random_graph.py @@ -19,7 +19,7 @@ args = parser.parse_args() GRAPH_SIZE = [80,800] EDGE_SIZE = [2,3] -sample_per_params = 1 +sample_per_params = 4 OUTPUT_DIR = args.output_dir if not os.path.exists(OUTPUT_DIR): @@ -27,22 +27,22 @@ if not os.path.exists(OUTPUT_DIR): parameters = { - "stochastic_block_model_graph": { - "nb_nodes":GRAPH_SIZE, - "nb_edges":EDGE_SIZE, - "nb_com" :[2,5,8,16], - "percentage_edge_betw":[0.1,0.01] - }, - "ER_graph": { - "nb_nodes":GRAPH_SIZE, - "nb_edges":EDGE_SIZE - }, - "powerlaw_graph": { # configuration_model - "nb_nodes":GRAPH_SIZE, - "nb_edges":EDGE_SIZE, - "exponent":[2,3], - "tries":[100] - }, + # "stochastic_block_model_graph": { + # "nb_nodes":GRAPH_SIZE, + # "nb_edges":EDGE_SIZE, + # "nb_com" :[2,5,8,16], + # "percentage_edge_betw":[0.1,0.01] + # }, + # "ER_graph": { + # "nb_nodes":GRAPH_SIZE, + # "nb_edges":EDGE_SIZE + # }, + # "powerlaw_graph": { # configuration_model + # "nb_nodes":GRAPH_SIZE, + # "nb_edges":EDGE_SIZE, + # "exponent":[2,3], + # "tries":[100] + # }, "spatial_graph":{ "nb_nodes":GRAPH_SIZE, "nb_edges":EDGE_SIZE, -- GitLab