def main():
num_reps = 10
# Smaller datasets.
dataset = [["IMDB-BINARY", False], ["IMDB-MULTI", False], ["NCI1", True],
["NCI109", True], ["PROTEINS", True], ["PTC_FM", True],
["REDDIT-BINARY", False], ["ENZYMES", True]]
results = []
for d, use_labels in dataset:
dp.get_dataset(d)
acc, s_1, s_2 = gnn_evaluation(GIN0,
d, [1, 2, 3, 4, 5], [32, 64, 128],
max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps,
all_std=True)
print(d + " " + "GIN0 " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GIN0 " + str(acc) + " " + str(s_1) + " " +
str(s_2))
acc, s_1, s_2 = gnn_evaluation(GIN,
d, [1, 2, 3, 4, 5], [32, 64, 128],
max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps,
all_std=True)
print(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " +
str(s_2))
num_reps = 3
# Larger datasets with edge labels.
dataset = [["Yeast", True], ["YeastH", True], ["UACC257", True],
["UACC257H", True], ["OVCAR-8", True], ["OVCAR-8H", True]]
dataset = [["YeastH", True], ["UACC257", True], ["UACC257H", True],
["OVCAR-8", True], ["OVCAR-8H", True]]
for d, use_labels in dataset:
dp.get_dataset(d)
acc, s_1, s_2 = gnn_evaluation(GINE,
d, [2], [64],
max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps,
all_std=True)
print(d + " " + "GINE " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINE " + str(acc) + " " + str(s_1) + " " +
str(s_2))
acc, s_1, s_2 = gnn_evaluation(GINE0,
d, [2], [64],
max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps,
all_std=True)
print(d + " " + "GINE0 " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINE0 " + str(acc) + " " + str(s_1) + " " +
str(s_2))
import auxiliarymethods.datasets as dp
from auxiliarymethods.gnn_evaluation import gnn_evaluation
from gnn_baselines.gnn_architectures import GIN
from auxiliarymethods.reader import tud_to_networkx
dataset = "PROTEINS"
use_labels = True
# Download dataset.
dp.get_dataset(dataset)
# Optimize the number of layers ({1,2,3,4,5}) and
# the number of hidden features ({32,64,128}),
# set the maximum nummber of epochs to 200,
# batch size to 64,
# start learning rate to 0.01, and
# number of repetitions for 10-CV to 10.
print(
gnn_evaluation(GIN,
dataset, [1, 2, 3, 4, 5], [32, 64, 128],
max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=10,
all_std=True))
def main():
num_reps = 10
### Smaller datasets.
dataset = [["IMDB-BINARY", False], ["IMDB-MULTI", False], ["NCI1", True], ["PROTEINS", True],
["REDDIT-BINARY", False], ["ENZYMES", True]]
results = []
for d, use_labels in dataset:
# Download dataset.
dp.get_dataset(d)
# GIN, dataset d, layers in [1:6], hidden dimension in {32,64,128}.
acc, s_1, s_2 = gnn_evaluation(GIN, d, [1, 2, 3, 4, 5], [32, 64, 128], max_num_epochs=200, batch_size=64,
start_lr=0.01, num_repetitions=num_reps, all_std=True)
print(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " + str(s_2))
# GIN with jumping knowledge, dataset d, layers in [1:6], hidden dimension in {32,64,128}.
acc, s_1, s_2 = gnn_evaluation(GINWithJK, d, [1, 2, 3, 4, 5], [32, 64, 128], max_num_epochs=200,
batch_size=64,
start_lr=0.01, num_repetitions=num_reps, all_std=True)
print(d + " " + "GINWithJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINWithJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
num_reps = 3
print(num_reps)
### Midscale datasets.
dataset = [["MOLT-4", True, True], ["Yeast", True, True], ["MCF-7", True, True]]
for d, use_labels, _ in dataset:
print(d)
dp.get_dataset(d)
# GINE (GIN with edge labels), dataset d, 3 layers, hidden dimension in {64}.
acc, s_1, s_2 = gnn_evaluation(GINE, d, [3], [64], max_num_epochs=200,
batch_size=64, start_lr=0.01,
num_repetitions=num_reps, all_std=True)
print(d + " " + "GINE " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINE " + str(acc) + " " + str(s_1) + " " + str(s_2))
# GINE (GIN with edge labels) with jumping knowledge, dataset d, 3 layers, hidden dimension in {64}.
acc, s_1, s_2 = gnn_evaluation(GINEWithJK, d, [3], [64], max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps, all_std=True)
print(d + " " + "GINEJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINEJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
dataset = [["reddit_threads", False, False],
["github_stargazers", False, False],
]
for d, use_labels, _ in dataset:
print(d)
dp.get_dataset(d)
# GINE (GIN with edge labels), dataset d, 3 layers, hidden dimension in {64}.
acc, s_1, s_2 = gnn_evaluation(GIN, d, [3], [64], max_num_epochs=200,
batch_size=64, start_lr=0.01,
num_repetitions=num_reps, all_std=True)
print(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GIN " + str(acc) + " " + str(s_1) + " " + str(s_2))
# GINE (GIN with edge labels) with jumping knowledge, dataset d, 3 layers, hidden dimension in {64}.
acc, s_1, s_2 = gnn_evaluation(GINWithJK, d, [3], [64], max_num_epochs=200,
batch_size=64,
start_lr=0.01,
num_repetitions=num_reps, all_std=True)
print(d + " " + "GINJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
results.append(d + " " + "GINJK " + str(acc) + " " + str(s_1) + " " + str(s_2))
for r in results:
print(r)