diff --git a/evalNE_script.py b/evalNE_script.py
index f11e56f1cb79f55d2eda0f253f9558b677e6a1e2..29fdf12ee5638f4522d1f835550b896689b093f6 100644
--- a/evalNE_script.py
+++ b/evalNE_script.py
@@ -3,27 +3,48 @@ from evalne.evaluation.split import EvalSplit as LPEvalSplit
from evalne.evaluation.score import Scoresheet
from evalne.utils import preprocess as pp
import networkx as nx
+from tqdm import tqdm
from lib.utils import load_edgelist
import argparse
parser = argparse.ArgumentParser()
-parser.add_argument("edgelist_graph_filename")
-parser.add_argument("--ne","--network-embedding",action="store_true",help="If you want to use neural network embedding for link prediction")
+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"])
-args = parser.parse_args()#("data/fb_country_country_sample_6_size1000.txt".split())
+args = parser.parse_args()
+
+def log(x):
+ if args.verbose:
+ print(x)
# Load and preprocess the network
-G = nx.read_gexf(args.edgelist_graph_filename)#load_edgelist(args.edgelist_graph_filename,is_directed=True,weighted=True)
-G, _ = pp.prep_graph(G,maincc=True)
+log("Load Input Graph...")
+G = None
+if args.format == "txt":
+ G = load_edgelist(path=args.graph_filename,weighted=True)
+elif args.format == "gml":
+ G = nx.read_gml(args.graph_filename)
+else:
+ G = nx.read_gexf(args.graph_filename)
+G, _ = pp.prep_graph(G,maincc=True)
+log("Graph Loaded !")
+log("Size "+str(len(G)))
+log("Nb of Edges "+str(len(list(G.edges()))))
+log("Density "+ str(len(G)/len(list(G.edges()))))
+log("Building link prediction dataset...")
# Create an evaluator and generate train/test edge split
traintest_split = LPEvalSplit()
traintest_split.compute_splits(G,split_alg="spanning_tree",train_frac=0.8,fe_ratio=1)
nee = LPEvaluator(traintest_split)
+
+log("Dataset Built !")
+
# Create a Scoresheet to store the results
scoresheet = Scoresheet()
@@ -40,7 +61,9 @@ methods = ['random_prediction',
]
# Evaluate baselines
-for method in methods:
+pbar = tqdm(methods,disable= (not args.verbose))
+for method in pbar:
+ pbar.set_description("Evaluate "+method)
result = nee.evaluate_baseline(method=method, )
scoresheet.log_results(result)
@@ -63,7 +86,9 @@ if args.network_embedding:
edge_emb = ['average', 'hadamard']
# Evaluate embedding methods
- for i in range(len(methods)):
+ pbar = tqdm(enumerate(methods), disable=(not args.verbose))
+ for i,method in pbar:
+ pbar.set_description("Evaluate "+method)
command = commands[i] + " --input {} --output {} --representation-size {}"
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)
@@ -76,4 +101,6 @@ if args.network_embedding:
# Get output
if args.verbose:
scoresheet.print_tabular()
-scoresheet.write_all(args.edgelist_graph_filename+"_results_lp")
\ No newline at end of file
+log("Saving Output in " + args.graph_filename + "_results_lp")
+scoresheet.write_all(args.graph_filename+"_results_lp")
+log("Output Saved !")
\ No newline at end of file
diff --git a/generate_theoric_random_graph.py b/generate_theoric_random_graph.py
index cf1e588d14cd988a58e1cc5549fc41bc889a8511..b6f83f2d01b716f364683a884e9bb0282a5309b9 100644
--- a/generate_theoric_random_graph.py
+++ b/generate_theoric_random_graph.py
@@ -19,13 +19,13 @@ def generate_sbm_prob_matrix(nb_of_blocks,prob_btw_block=0.1):
M[j,i] = prob_btw_block
return M
-GRAPH_SIZE = [50,100,200,500]
-OUTPUT_DIR = "test_dataset/"
+GRAPH_SIZE = [50,75,100]
+OUTPUT_DIR = "data/theoric_graph_1/"
parameters = {
"planted_partition_graph": {
"l": [3,5,8],
- "k": [10,20,30],
+ "k": [10,20],
"p_in": [0.2,0.5,0.7],
"p_out": [0.1,0.2,0.3]
},
@@ -52,7 +52,7 @@ parameters = {
},
"geographical_threshold_graph": {
"n": GRAPH_SIZE,
- "theta": [0.1,0.2,0.4,0.6]
+ "theta": [0.1,0.2,0.3]
},
}
# Generating transition matrices for stochastic block model
diff --git a/run_eval.py b/run_eval.py
index 29bbd10ade84af934527440c76bebf3d194bb76e..163f01737b01a01a336e7637848b85a467cfaf9d 100644
--- a/run_eval.py
+++ b/run_eval.py
@@ -14,22 +14,32 @@ import argparse
parser = argparse.ArgumentParser()
parser.add_argument("dataset_dir")
parser.add_argument("output_filename")
+parser.add_argument("-f", "--format", default="gexf", choices=["gexf", "gml", "txt"])
args = parser.parse_args()
-fns = glob.glob(args.dataset_dir + "/*.gexf")
+fns = glob.glob(args.dataset_dir + "/*." + args.format)
all_res = []
-for fn in tqdm(fns):
- print("run eval on ", fn)
- command = "python evalNE_script.py {0} -v".format(fn).split()
+pbar = tqdm(fns)
+for fn in pbar:
+ pbar.set_description("run eval on "+ fn)
+ command = "python evalNE_script.py {0} -f {1} -n".format(fn, args.format).split()
output = subprocess.run(command)
if not output.returncode == 0:
print("Error! for the command :", " ".join(command))
continue
df_results = parse_evalne_output(open(fn + "_results_lp").read())
name = os.path.basename(fn)
- G = nx.read_gexf(fn)
- top10node = pd.DataFrame(list(G.degree()), columns="node degree".split()).sort_values("degree",ascending=False).head(10).node.values
+ G = None
+ if args.format == "edgelist":
+ G = load_edgelist(path=fn)
+ elif args.format == "gml":
+ G = nx.read_gml(fn)
+ else:
+ G = nx.read_gexf(fn)
+
+ top10node = pd.DataFrame(list(G.degree()), columns="node degree".split()).sort_values("degree",
+ ascending=False).head(10).node.values
df_results["nb_edge"] = len(list(G.edges()))
df_results["transitivity"] = nx.transitivity(G)
df_results["density"] = nx.density(G)
@@ -38,4 +48,4 @@ for fn in tqdm(fns):
df_results["filename"] = name
all_res.append(df_results)
-pd.concat(all_res).to_csv(args.output_filename,sep="\t",index=None)
\ No newline at end of file
+pd.concat(all_res).to_csv(args.output_filename, sep="\t", index=False)