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test_linux.py
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test_linux.py
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from utils.utils import *
from argparse import ArgumentParser
from model.GSC import GSC
import torch.nn as nn
import torch
from tqdm import tqdm, trange
import torch.nn.functional as F
from scipy.stats import spearmanr, kendalltau
from torch_geometric.data import Batch
@torch.no_grad()
def evaluate(testing_graphs, training_graphs, model, loss_func, dataset: DatasetLocal, config):
model.eval()
scores = np.empty((len(testing_graphs), len(training_graphs)))
ground_truth = np.empty((len(testing_graphs), len(training_graphs)))
ground_truth_ged = np.empty((len(testing_graphs), len(training_graphs)))
prediction_mat = np.empty((len(testing_graphs), len(training_graphs)))
rho_list = []
tau_list = []
prec_at_10_list = []
prec_at_20_list = []
num_test_pairs = len(testing_graphs) * len(training_graphs)
t = tqdm(total=num_test_pairs)
for i,g in enumerate(testing_graphs):
source_batch = Batch.from_data_list([g] * len(training_graphs))
target_batch = Batch.from_data_list(training_graphs)
data = dataset.transform_batch((source_batch, target_batch), config)
target = data["target"]
# target = data["norm_ged"]
ground_truth[i] = target
target_ged = data["target_ged"]
ground_truth_ged[i] = target_ged
prediction, loss_cl = model(data)
prediction_mat[i] = prediction.cpu().detach().numpy()
scores[i] = ( F.mse_loss(prediction.cpu().detach(), target, reduction="none").numpy())
rho_list.append(
calculate_ranking_correlation(
spearmanr, prediction_mat[i], ground_truth[i]
)
)
tau_list.append(
calculate_ranking_correlation(
kendalltau, prediction_mat[i], ground_truth[i]
)
)
prec_at_10_list.append(
calculate_prec_at_k(
10, prediction_mat[i], ground_truth[i], ground_truth_ged[i]
)
)
prec_at_20_list.append(
calculate_prec_at_k(
20, prediction_mat[i], ground_truth[i], ground_truth_ged[i]
)
)
t.update(len(training_graphs))
rho = np.mean(rho_list).item()
tau = np.mean(tau_list).item()
prec_at_10 = np.mean(prec_at_10_list).item()
prec_at_20 = np.mean(prec_at_20_list).item()
model_mse_error = np.mean(scores).item()
return model_mse_error, rho, tau, prec_at_10, prec_at_20
if __name__ == '__main__':
model_name = 'GSC_GNN'
# dataset = 'AIDS700nef'
parser = ArgumentParser()
parser.add_argument('--dataset', type=str, default= 'LINUX')
parser.add_argument('--num_workers', type=int, default=8, choices=[0,8])
parser.add_argument('--seed', type=int, default=1234, choices=[0, 1, 1234])
parser.add_argument('--data_dir', type=str, default="/data1/zhuowei/datasets/GED_Datasets/")
parser.add_argument('--custom_data_dir', type=str, default='datasets/GED/')
parser.add_argument('--hyper_file', type=str, default= 'config/')
parser.add_argument('--recache', action="store_true", help ="clean up the old adj data", default=True)
parser.add_argument('--no_dev', action="store_true" , default = False)
parser.add_argument('--patience', type = int , default = -1)
parser.add_argument('--gpu_id', type = int , default = 3)
parser.add_argument('--model', type = str, default ='GSC_GNN') # GCN, GAT or other
parser.add_argument('--train_first', type = bool, default = True)
parser.add_argument('--save_model', type = bool, default = False)
args = parser.parse_args()
# CONFIG_PATH = "model_saved/LINUX/2022-03-20_09-55-10"
CONFIG_PATH = "model_saved/LINUX/2022-03-28_19-49-36"
# CONFIG_PATH = "model_saved/AIDS700nef/2022-03-19_10-00-20"
config_path = osp.join(CONFIG_PATH, 'config' + '.yml')
# config_path = osp.join('config/',args.dataset +'.yml')
config = get_config(config_path)
print(config)
config = config[args.model]
config['dataset_name'] = args.dataset
print(config)
dataset = load_data(args, False)
dataset.load(config)
model = GSC(config, dataset.input_dim).cuda()
best_val_model_path = osp.join(CONFIG_PATH, 'GSC_GNN_{}_checkpoint.pth'.format(args.dataset))
model.load_state_dict(torch.load(best_val_model_path))
model_mse, test_rho, test_tau, test_prec_at_10, test_prec_at_20 = evaluate(dataset.testing_graphs, dataset.trainval_graphs, model, nn.MSELoss(), dataset, config)
def print_evaluation(model_mse,test_rho,test_tau,test_prec_at_10,test_prec_at_20):
"""
Printing the error rates.
"""
print("\nmse(10^-3): " + str(round(model_mse * 1000, 5)) + ".")
print("Spearman's rho: " + str(round(test_rho, 5)) + ".")
print("Kendall's tau: " + str(round(test_tau, 5)) + ".")
print("p@10: " + str(round(test_prec_at_10, 5)) + ".")
print("p@20: " + str(round(test_prec_at_20, 5)) + ".")
print_evaluation(model_mse, test_rho, test_tau, test_prec_at_10, test_prec_at_20)