From e0fab81979acdb041c3611276677f3e67a356b6f Mon Sep 17 00:00:00 2001
From: Fize Jacques <jacques.fize@cirad.fr>
Date: Thu, 4 Feb 2021 14:11:44 +0100
Subject: [PATCH] Debug
---
evalNE_script.py | 4 ++--
generate_theoric_random_graph.py | 27 ++++++++++++++++-----------
lib/random.py | 6 +++---
run_eval_par.py | 2 +-
4 files changed, 22 insertions(+), 17 deletions(-)
diff --git a/evalNE_script.py b/evalNE_script.py
index 7f29050..4449d10 100644
--- a/evalNE_script.py
+++ b/evalNE_script.py
@@ -59,8 +59,8 @@ methods = ['random_prediction',
"preferential_attachment",
"resource_allocation_index",
"stochastic_block_model",
- "stochastic_block_model_edge_probs",
- "stochastic_block_model_degree_corrected"
+ "stochastic_block_model_degree_corrected",
+ "spatial_link_prediction"
]
# Evaluate baselines
diff --git a/generate_theoric_random_graph.py b/generate_theoric_random_graph.py
index 39ec3dc..8d6ef8e 100644
--- a/generate_theoric_random_graph.py
+++ b/generate_theoric_random_graph.py
@@ -17,8 +17,9 @@ parser.add_argument("output_dir")
args = parser.parse_args()
-GRAPH_SIZE = [100,150,200]
-EDGE_SIZE = [300,500]
+GRAPH_SIZE = [80,800]
+EDGE_SIZE = [2,3]
+
OUTPUT_DIR = args.output_dir
if not os.path.exists(OUTPUT_DIR):
raise FileExistsError("Output directory does not exists !")
@@ -28,7 +29,7 @@ parameters = {
"stochastic_block_model_graph": {
"nb_nodes":GRAPH_SIZE,
"nb_edges":EDGE_SIZE,
- "nb_com" :[2,5],
+ "nb_com" :[2,5,8,16],
"percentage_edge_betw":[0.1,0.01]
},
"ER_graph": {
@@ -38,10 +39,11 @@ parameters = {
"powerlaw_graph": { # configuration_model
"nb_nodes":GRAPH_SIZE,
"nb_edges":EDGE_SIZE,
- "exponent":[2,3]
+ "exponent":[2,3],
+ "tries":[100]
},
"spatial_graph":{
- "nb_nodes":[100,150],
+ "nb_nodes":GRAPH_SIZE,
"nb_edges":EDGE_SIZE,
"coords":["random","country"],
}
@@ -63,11 +65,14 @@ for method,args in pbar:
list_of_params = get_params(parameters[method])
func = getattr(ra,method)
for ix,params in enumerate(list_of_params):
- # try:
- G = func(**params)
- G.graph.update(params)
- nx.write_gml(G, OUTPUT_DIR+"/graph_{method}_{ix}.gml".format(method=method,ix=ix),stringizer=str)
- # except Exception as e:
- # print(e)
+ params["nb_edges"] = params["nb_edges"]*params["nb_nodes"]
+ print(params)
+ try:
+ G = func(**params)
+ G.graph.update(params)
+ nx.write_gml(G, OUTPUT_DIR+"/graph_{method}_{ix}.gml".format(method=method,ix=ix),stringizer=str)
+ except Exception as e:
+ print(e)
+ print("Can't generate graphs using these parameters")
diff --git a/lib/random.py b/lib/random.py
index c57df34..afe6a83 100644
--- a/lib/random.py
+++ b/lib/random.py
@@ -101,7 +101,7 @@ def _conf_model(degree_seq):
return G
-def powerlaw_graph(nb_nodes, nb_edges, exponent=2, tries=1000, min_deg=1):
+def powerlaw_graph(nb_nodes, nb_edges, exponent=2, tries=1000, min_deg=0):
"""
Generate a graph with a defined number of vertices, edges, and a degree distribution that fit the power law.
Using the Molloy-Reed algorithm to
@@ -186,7 +186,7 @@ def spatial_graph(nb_nodes, nb_edges, coords="country", dist_func=lambda a, b: n
for j in range(nb_nodes):
if i == j and not self_link:
continue
- data.append([i, j, dist_func(coords[i], coords[j])])
+ data.append([i, j, 1/(1+(dist_func(coords[i], coords[j])**2))])
df = pd.DataFrame(data, columns="src tar weight".split()).astype({"src": int, "tar": int})
df["hash"] = df.apply(lambda x: "_".join(sorted([str(int(x.src)), str(int(x.tar))])), axis=1)
df = df.drop_duplicates(subset="hash")
@@ -259,7 +259,7 @@ def stochastic_block_model_graph(nb_nodes, nb_edges, nb_com, percentage_edge_bet
percentage_edge_within = 1 - percentage_edge_betw
- G = nx.planted_partition_graph(nb_com, int(np.round(nb_nodes / nb_com)), 1, 1)
+ G = nx.planted_partition_graph(nb_com, nb_nodes//nb_com, 1, 1)
if verbose:
print(G.size())
diff --git a/run_eval_par.py b/run_eval_par.py
index 16e4a1f..ba738dc 100644
--- a/run_eval_par.py
+++ b/run_eval_par.py
@@ -21,7 +21,7 @@ args = parser.parse_args()
fns = sorted(glob.glob(args.dataset_dir + "/*." + args.format))
def run_eval(fn):
- command = "python evalNE_script.py {0} -f {1} -n".format(fn, args.format).split()
+ command = "python evalNE_script.py {0} -f {1}".format(fn, args.format).split()
output = subprocess.run(command)
if not output.returncode == 0:
print("Error! for the command :", " ".join(command))
--
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