default="../configs/nodeclf_ladies_reproduction.yml",
help="configuration file to adopt",
)
argument_parser.add_argument("--seed", type=int, default=0, help="random seed")
argument_parser.add_argument("--device", default=0, type=int, help="GPU device")
arguments = argument_parser.parse_args()
if torch.cuda.is_available():
torch.cuda.set_device(arguments.device)
seed = arguments.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
dataset = build_dataset_from_name(arguments.dataset)
configs = yaml.load(
open(arguments.configs, "r").read(),
Loader=yaml.FullLoader
)
autoClassifier = AutoNodeClassifier.from_config(configs)
# The running time is likely to exceed 1 hour when CiteSeer or Reddit dataset is adopted
autoClassifier.fit(dataset, time_limit=24 * 3600, evaluation_method=[MicroF1])
autoClassifier.get_leaderboard().show()
predict_result = autoClassifier.predict_proba()
res = autoClassifier.evaluate(metric=[MicroF1, 'acc'])
print("Final Micro F1 {:.4f} Acc {:.4f}".format(res[0], res[1]))
import os
os.environ["AUTOGL_BACKEND"] = "pyg"
import sys
sys.path.append('../')
from autogl.datasets import build_dataset_from_name
from autogl.solver import AutoNodeClassifier
from autogl.module.train import Acc
from autogl.solver.utils import set_seed
import argparse
if __name__ == '__main__':
set_seed(202106)
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='../configs/nodeclf_nas_gasso.yml')
parser.add_argument('--dataset', choices=['cora', 'citeseer', 'pubmed'], default='citeseer', type=str)
args = parser.parse_args()
dataset = build_dataset_from_name(args.dataset)
solver = AutoNodeClassifier.from_config(args.config)
solver.fit(dataset)
solver.get_leaderboard().show()
print('acc on dataset', solver.evaluate())