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,
--
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