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Commit b84f9818 authored by Fize Jacques's avatar Fize Jacques
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parent 613e6a5c
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......@@ -34,7 +34,7 @@ def load_data(fn, graph_dir):
df = pd.read_csv(fn, sep="\t")
df["type_graph"] = df.filename.apply(lambda x: x[6:]).apply(lambda x: re.sub("_[\d]+.gml", "", x).replace("_", " "))
df["parameters"] = df.filename.apply(lambda x: get_graph_attr(x, graph_dir))
df["sample"] = df.filename.apply(get_sample_id_old)
df["sample"] = df.filename.apply(get_sample_id)
non_ne = {'random_prediction', 'common_neighbours', 'jaccard_coefficient', 'adamic_adar_index',
'preferential_attachment', 'resource_allocation_index', 'stochastic_block_model',
'stochastic_block_model_degree_corrected', 'spatial_link_prediction'}
......
......@@ -42,7 +42,7 @@ 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="random", train_frac=args.train_frac, 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=args.train_frac, fe_ratio=1)
print("BEFORE", len(traintest_split.test_edges))
......@@ -88,7 +88,7 @@ if args.network_embedding:
"python -m openne --method grarep --epochs 100"
# "python -m openne --method lap --epochs 100",
]
edge_emb = ['average', 'hadamard']
edge_emb = [ 'hadamard'] #'average',
# Evaluate embedding methods
pbar = tqdm(enumerate(methods), disable=(not args.verbose))
......
......@@ -16,12 +16,15 @@ parser = argparse.ArgumentParser()
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("-n","--n-jobs",default=2,type=int)
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}".format(fn, args.format).split()
verbose_cmd = "-v" if args.verbose else ""
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))
......@@ -29,7 +32,7 @@ def run_eval(fn):
all_res = []
# Run link prediction
Parallel(n_jobs=4,backend="multiprocessing")(delayed(run_eval)(fn) for fn in tqdm(fns))
Parallel(n_jobs=args.n_jobs,backend="multiprocessing")(delayed(run_eval)(fn) for fn in tqdm(fns))
pbar = tqdm(fns)
for fn in pbar:
......
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