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test.py
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import warnings
import logging
import argparse
import torch
from utils.initialization_utils import (initialize_experiment,
initialize_models)
from preprocess.datasets import DataLoader, MyDataset
from tqdm import tqdm
from sklearn.metrics import roc_auc_score, average_precision_score
warnings.filterwarnings('ignore')
def test(params):
dataset = MyDataset(dataset=params.dataset, num_classes=params.num_classes,
max_dim=params.max_dim, dim=params.dim, iterations=params.iter)
scn, san, gat, writer = initialize_models(params, mode='train')
def custom_collate(X):
return X[0]
dataloader = DataLoader(dataset, batch_size=1,
num_workers=16, collate_fn=custom_collate)
with torch.no_grad():
H0 = []
H1 = []
H2 = []
H3 = []
H4 = []
labels0 = []
labels1 = []
with tqdm(dataloader) as tepoch:
for pos_embeddings, neg_embeddings, laplacians, boundaries, order, idx, label, subgraph in tepoch:
label, subgraph = label.to(params.device), subgraph.to(params.device)
pos_embeddings = [ x.to(params.device) if x is not None else None for x in pos_embeddings]
neg_embeddings = [ x.to(params.device) if x is not None else None for x in neg_embeddings]
laplacians = [ x.to(params.device) if x is not None else None for x in laplacians]
boundaries = [ x.to(params.device) if x is not None else None for x in boundaries]
try:
if order > 0: # makes no sense to perform node classification since node embedding will be 0.
pred0 = (torch.sum(pos_embeddings[0][:order+1], dim=0)!=0).long().squeeze()
pred1 = (torch.prod(pos_embeddings[0][:order+1], dim=0)!=0).long().squeeze()
H0.append((pred0==label).long())
H1.append((pred1==label).long())
labels0.append(label)
pred2_pos = gat(subgraph, pos_embeddings[0], order, label)
pred2_neg = gat(subgraph, neg_embeddings[0], order, label)
pred3_pos = scn(pos_embeddings, laplacians, boundaries, order, idx, label)
pred3_neg = scn(neg_embeddings, laplacians, boundaries, order, idx, label)
pred4_pos = san(pos_embeddings, laplacians, boundaries, order, idx, label)
pred4_neg = san(neg_embeddings, laplacians, boundaries, order, idx, label)
# H1.append((torch.round(torch.sigmoid(pred1))==label).long())
# H2.append((torch.round(torch.sigmoid(pred2))==label).long())
H2.append(pred2_pos)
H2.append(pred2_neg)
H3.append(pred3_pos)
H3.append(pred3_neg)
H4.append(pred4_pos)
H4.append(pred4_neg)
labels1.append(torch.ones_like(pred2_pos))
labels1.append(torch.zeros_like(pred2_neg))
torch.cuda.empty_cache()
except:
pass
H0, H1, H2, H3, H4, labels0, labels1 = torch.stack(H0), torch.stack(H1), torch.cat(H2), torch.cat(H3), torch.cat(H4), torch.stack(labels0), torch.cat(labels1)
mask = (labels0.sum(dim=0)!=0)
labels0 = labels0[:,mask]
H0 = H0[:,mask]
H1 = H1[:,mask]
A1 = roc_auc_score(labels0.cpu().numpy(), H0.cpu().numpy(), average='weighted')
B1 = roc_auc_score(labels0.cpu().numpy(), H1.cpu().numpy(), average='weighted')
C1 = roc_auc_score(labels1.cpu().numpy(), H2.cpu().numpy(), average='weighted')
D1 = roc_auc_score(labels1.cpu().numpy(), H3.cpu().numpy(), average='weighted')
E1 = roc_auc_score(labels1.cpu().numpy(), H4.cpu().numpy(), average='weighted')
A2 = average_precision_score(labels0.cpu().numpy(), H0.cpu().numpy(), average='weighted')
B2 = average_precision_score(labels0.cpu().numpy(), H1.cpu().numpy(), average='weighted')
C2 = average_precision_score(labels1.cpu().numpy(), H2.cpu().numpy(), average='weighted')
D2 = average_precision_score(labels1.cpu().numpy(), H3.cpu().numpy(), average='weighted')
E2 = average_precision_score(labels1.cpu().numpy(), H4.cpu().numpy(), average='weighted')
result = {
'auc' : {
'Union' : A1, 'Intersection' : B1, 'Graph Attention Model' : C1, 'Simplicial Convolution Model' : D1, 'Simplicial Attention Model' : E1
},
'auc_pr': {
'Union' : A2, 'Intersection' : B2, 'Graph Attention Model' : C2, 'Simplicial Convolution Model' : D2, 'Simplicial Attention Model' : E2
}
}
logging.info(f'AUC : ')
[ logging.info(f'{key} : {value}') for key, value in result['auc'].items() ]
logging.info(f'AUC PR : ')
[ logging.info(f'{key} : {value}') for key, value in result['auc_pr'].items() ]
write = ','.join([str(params.dim), str(params.iter)]) + ',' + ','.join([str(value) for key, value in result['auc'].items()]) + ',' + ','.join([str(value) for key, value in result['auc_pr'].items()])
with open(params.test_csv, "a") as f:
f.write(f'{write}\n')
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(description='Inductive H-KGC training')
parser.add_argument("--experiment_name", "-e", type=str, help="experiment name", required=True)
parser.add_argument("--dataset", "-d", type=str, help="dataset folder name", required=True)
parser.add_argument("--num_classes", type=int, help="number of relation types", required=True)
parser.add_argument("--max_dim", type=int, default=4, help="maximum dimension of simplex to consider")
parser.add_argument("--dim", type=int, default=None, help="particular dimension to infere on")
parser.add_argument("--iter", type=int, default=10000, help="number of iterations")
parser.add_argument("--batch_size", type=int, default=32, help="batch size")
parser.add_argument("--gpu", type=int, default=0, help="Which GPU to use?")
parser.add_argument('--disable_cuda', action='store_true', help='Disable CUDA')
params = parser.parse_args()
params.reset_model = False
if not params.disable_cuda and torch.cuda.is_available():
params.device = torch.device('cuda:%d' % params.gpu)
else:
params.device = torch.device('cpu')
initialize_experiment(params, mode='test')
test(params)