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- import numpy as np
- from autogl.datasets import build_dataset_from_name
- from autogl.solver import AutoHeteroNodeClassifier
- from helper import get_encoder_decoder_hp
- from tqdm import tqdm
-
- def fixed(**kwargs):
- return [{
- 'parameterName': k,
- "type": "FIXED",
- "value": v
- } for k, v in kwargs.items()]
-
- if __name__ == '__main__':
- import argparse
- parser = argparse.ArgumentParser()
- parser.add_argument("--model", type=str, choices=["han", "hgt", "HeteroRGCN"], default="hgt")
- parser.add_argument("--epoch", type=int, default=200)
- parser.add_argument("--lr", type=float, default=1e-3)
- parser.add_argument("--weight_decay", type=float, default=1e-2)
- parser.add_argument("--device", type=str, default="cuda")
- parser.add_argument("--repeat", type=int, default=10)
-
- args = parser.parse_args()
-
- dataset = {
- "han": "hetero-acm-han",
- "hgt": "hetero-acm-hgt",
- "HeteroRGCN": "hetero-acm-hgt"
- }
-
- dataset = build_dataset_from_name(dataset[args.model])
-
- model_hp, _ = get_encoder_decoder_hp(args.model)
-
- accs = []
- process = tqdm(total=args.repeat)
- for rep in range(args.repeat):
- solver = AutoHeteroNodeClassifier(
- graph_models=[args.model],
- hpo_module="random",
- ensemble_module=None,
- max_evals=1,
- device=args.device,
- trainer_hp_space=fixed(
- max_epoch=args.epoch,
- early_stopping_round=args.epoch + 1,
- lr=args.lr,
- weight_decay=args.weight_decay
- ),
- model_hp_spaces=[fixed(**model_hp)]
- )
- solver.fit(dataset)
- acc = solver.evaluate()
- accs.append(acc)
- process.update(1)
- process.set_postfix(mean=np.mean(accs), std=np.std(accs))
- process.close()
- print("mean: {:.4f} ~ std: {:.4f}".format(np.mean(accs), np.std(accs)))
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