def main(params):
simplefilter(action='ignore', category=UserWarning)
simplefilter(action='ignore', category=SparseEfficiencyWarning)
graph_classifier = initialize_model(params, None, load_model=True)
logging.info(f"Device: {params.device}")
all_auc = []
auc_mean = 0
all_auc_pr = []
auc_pr_mean = 0
for r in range(1, params.runs + 1):
params.db_path = os.path.join(
params.main_dir,
f'data/{params.dataset}/test_subgraphs_{params.experiment_name}_{params.constrained_neg_prob}_en_{params.enclosing_sub_graph}'
)
generate_subgraph_datasets(
params,
splits=['test'],
saved_relation2id=graph_classifier.relation2id,
max_label_value=graph_classifier.gnn.max_label_value)
test = SubgraphDataset(
params.db_path,
'test_pos',
'test_neg',
params.file_paths,
graph_classifier.relation2id,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
use_kge_embeddings=params.use_kge_embeddings,
dataset=params.dataset,
kge_model=params.kge_model,
file_name=params.test_file)
test_evaluator = Evaluator(params, graph_classifier, test)
result = test_evaluator.eval(save=True)
logging.info('\nTest Set Performance:' + str(result))
all_auc.append(result['auc'])
auc_mean = auc_mean + (result['auc'] - auc_mean) / r
all_auc_pr.append(result['auc_pr'])
auc_pr_mean = auc_pr_mean + (result['auc_pr'] - auc_pr_mean) / r
auc_std = np.std(all_auc)
auc_pr_std = np.std(all_auc_pr)
logging.info('\nAvg test Set Performance -- mean auc :' +
str(np.mean(all_auc)) + ' std auc: ' + str(np.std(all_auc)))
logging.info('\nAvg test Set Performance -- mean auc_pr :' +
str(np.mean(all_auc_pr)) + ' std auc_pr: ' +
str(np.std(all_auc_pr)))
def main(params):
simplefilter(action='ignore', category=UserWarning)
simplefilter(action='ignore', category=SparseEfficiencyWarning)
params.db_path = os.path.join(
params.main_dir,
f'../../data/{params.dataset}/subgraphs_en_{params.enclosing_sub_graph}_neg_{params.num_neg_samples_per_link}_hop_{params.hop}'
)
if not os.path.isdir(params.db_path):
generate_subgraph_datasets(params)
train = SubgraphDataset(
params.db_path,
'train_pos',
'train_neg',
params.file_paths,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
dataset=params.dataset,
file_name=params.train_file)
valid = SubgraphDataset(
params.db_path,
'valid_pos',
'valid_neg',
params.file_paths,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
dataset=params.dataset,
file_name=params.valid_file)
params.num_rels = train.num_rels
params.aug_num_rels = train.aug_num_rels
params.inp_dim = train.n_feat_dim
# Log the max label value to save it in the model. This will be used to cap the labels generated on test set.
params.max_label_value = train.max_n_label
graph_classifier = initialize_model(params, dgl_model, params.load_model)
logging.info(f"Device: {params.device}")
logging.info(
f"Input dim : {params.inp_dim}, # Relations : {params.num_rels}, # Augmented relations : {params.aug_num_rels}"
)
valid_evaluator = Evaluator(params, graph_classifier, valid)
trainer = Trainer(params, graph_classifier, train, valid_evaluator)
logging.info('Starting training with full batch...')
trainer.train()
def main(params):
simplefilter(action='ignore', category=UserWarning)
simplefilter(action='ignore', category=SparseEfficiencyWarning)
params.db_path = os.path.join(
params.main_dir,
f'data/{params.dataset}/subgraphs_en_{params.enclosing_sub_graph}_neg_{params.num_neg_samples_per_link}_hop_{params.hop}'
)
if not os.path.isdir(params.db_path):
generate_subgraph_datasets(params)
train = SubgraphDataset(
params.db_path,
'train_pos',
'train_neg',
params.file_paths,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
use_kge_embeddings=params.use_kge_embeddings,
dataset=params.dataset,
kge_model=params.kge_model,
file_name=params.train_file)
#assert 0
valid = SubgraphDataset(
params.db_path,
'valid_pos',
'valid_neg',
params.file_paths,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
use_kge_embeddings=params.use_kge_embeddings,
dataset=params.dataset,
kge_model=params.kge_model,
file_name=params.valid_file,
ssp_graph=train.ssp_graph,
id2entity=train.id2entity,
id2relation=train.id2relation,
rel=train.num_rels,
graph=train.graph)
test = SubgraphDataset(
params.db_path,
'test_pos',
'test_neg',
params.file_paths,
add_traspose_rels=params.add_traspose_rels,
num_neg_samples_per_link=params.num_neg_samples_per_link,
use_kge_embeddings=params.use_kge_embeddings,
dataset=params.dataset,
kge_model=params.kge_model,
file_name=params.valid_file,
ssp_graph=train.ssp_graph,
id2entity=train.id2entity,
id2relation=train.id2relation,
rel=train.num_rels,
graph=train.graph)
params.num_rels = train.num_rels
params.aug_num_rels = train.aug_num_rels
params.inp_dim = train.n_feat_dim
params.train_rels = 200 if params.dataset == 'BioSNAP' else params.num_rels
params.num_nodes = 35000
# Log the max label value to save it in the model. This will be used to cap the labels generated on test set.
params.max_label_value = train.max_n_label
logging.info(f"Device: {params.device}")
logging.info(
f"Input dim : {params.inp_dim}, # Relations : {params.num_rels}, # Augmented relations : {params.aug_num_rels}"
)
graph_classifier = initialize_model(params, dgl_model, params.load_model)
if params.dataset == 'drugbank':
if params.feat == 'morgan':
import pickle
with open('data/{}/DB_molecular_feats.pkl'.format(params.dataset),
'rb') as f:
x = pickle.load(f, encoding='utf-8')
mfeat = []
for y in x['Morgan_Features']:
mfeat.append(y)
params.feat_dim = 1024
elif params.feat == 'pca':
mfeat = np.loadtxt('data/{}/PCA.txt'.format(params.dataset))
params.feat_dim = 200
elif params.feat == 'pretrained':
mfeat = np.loadtxt('data/{}/pretrained.txt'.format(params.dataset))
params.feat_dim = 200
elif params.dataset == 'BioSNAP':
mfeat = []
rfeat = []
import pickle
with open('data/{}/id2drug_feat.pkl'.format(params.dataset),
'rb') as f:
x = pickle.load(f, encoding='utf-8')
for z in x:
y = x[z]['Morgan']
mfeat.append(y)
y = x[z]['rdkit2d']
rfeat.append(y)
params.feat_dim = 1024
graph_classifier.drug_feat(
torch.FloatTensor(np.array(mfeat)).to(params.device))
valid_evaluator = Evaluator(
params, graph_classifier,
valid) if params.dataset == 'drugbank' else Evaluator_ddi2(
params, graph_classifier, valid)
test_evaluator = Evaluator(
params, graph_classifier,
test) if params.dataset == 'drugbank' else Evaluator_ddi2(
params, graph_classifier, test)
train_evaluator = Evaluator(
params, graph_classifier,
train) if params.dataset == 'drugbank' else Evaluator_ddi2(
params, graph_classifier, valid)
trainer = Trainer(params, graph_classifier, train, train_evaluator,
valid_evaluator, test_evaluator)
logging.info('Starting training with full batch...')
trainer.train()