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())