def main(force_test=False):
mp.set_start_method("spawn", force=True)
parser = (argparse.ArgumentParser(description='Order embedding arguments')
if not HYPERPARAM_SEARCH else HyperOptArgumentParser(
strategy='grid_search'))
utils.parse_optimizer(parser)
parse_encoder(parser)
args = parser.parse_args()
print(args)
args.n_workers = 1
if force_test:
args.test = True
# Currently due to parallelism in multi-gpu training, this code performs
# sequential hyperparameter tuning.
# All gpus are used for every run of training in hyperparameter search.
if HYPERPARAM_SEARCH:
for i, hparam_trial in enumerate(
args.trials(HYPERPARAM_SEARCH_N_TRIALS)):
print("Running hyperparameter search trial", i)
print(hparam_trial)
train_loop(hparam_trial)
else:
train_loop(args)
def main():
if not os.path.exists("plots/"):
os.makedirs("plots/")
if not os.path.exists("results/"):
os.makedirs("results/")
parser = argparse.ArgumentParser(description='Alignment arguments')
utils.parse_optimizer(parser)
parse_encoder(parser)
parser.add_argument('--query_path',
type=str,
help='path of query graph',
default="")
parser.add_argument('--target_path',
type=str,
help='path of target graph',
default="")
args = parser.parse_args()
args.test = True
if args.query_path:
with open(args.query_path, "rb") as f:
query = pickle.load(f)
else:
query = nx.gnp_random_graph(8, 0.25)
if args.target_path:
with open(args.target_path, "rb") as f:
target = pickle.load(f)
else:
target = nx.gnp_random_graph(16, 0.25)
model = build_model(args)
mat = gen_alignment_matrix(model,
query,
target,
method_type=args.method_type)
np.save("results/alignment.npy", mat)
print("Saved alignment matrix in results/alignment.npy")
plt.imshow(mat, interpolation="nearest")
plt.savefig("plots/alignment.png")
print("Saved alignment matrix plot in plots/alignment.png")
import pickle
import torch.multiprocessing as mp
from sklearn.decomposition import PCA
from common import data
from common import models
from common import utils
from subgraph_matching.config import parse_encoder
# Now we load the model and a dataset to analyze embeddings on, here ENZYMES.
from subgraph_matching.train import make_data_source
parser = argparse.ArgumentParser()
utils.parse_optimizer(parser)
parse_encoder(parser)
args = parser.parse_args("")
args.model_path = os.path.join("..", args.model_path)
print("Using dataset {}".format(args.dataset))
model = models.OrderEmbedder(1, args.hidden_dim, args)
model.to(utils.get_device())
model.eval()
model.load_state_dict(
torch.load(args.model_path, map_location=utils.get_device()))
train, test, task = data.load_dataset("wn18")
from collections import Counter