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train.py
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train.py
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import argparse
import os
import pickle
import time
import numpy as np
import torch
import torch.nn as nn
import utils
from dataset import BaseDataset, collate_fn, UnderSampler
from gnn import gnn
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader
from tqdm import tqdm
parser = argparse.ArgumentParser()
parser.add_argument("--lr", help="learning rate", type=float, default=0.0001)
parser.add_argument("--epoch", help="epoch", type=int, default=30)
parser.add_argument("--ngpu", help="number of gpu", type=int, default=1)
parser.add_argument("--dataset", help="dataset", type=str, default="tiny")
parser.add_argument("--batch_size", help="batch_size", type=int, default=32)
parser.add_argument(
"--num_workers", help="number of workers", type=int, default=os.cpu_count()
)
parser.add_argument(
"--embedding_dim",
help="node embedding dim aka number of distinct node label",
type=int,
default=20,
)
parser.add_argument(
"--tatic",
help="tactic of defining number of hops",
type=str,
default="static",
choices=["static", "cont", "jump"],
)
parser.add_argument("--nhop", help="number of hops", type=int, default=1)
parser.add_argument("--branch", help="choosing branch",
type=str, default="both", choices=["both", "left", "right"])
parser.add_argument("--n_graph_layer",
help="number of GNN layer", type=int, default=4)
parser.add_argument(
"--d_graph_layer", help="dimension of GNN layer", type=int, default=140
)
parser.add_argument(
"--n_FC_layer", help="number of FC layer", type=int, default=4)
parser.add_argument(
"--d_FC_layer", help="dimension of FC layer", type=int, default=128)
parser.add_argument(
"--data_path", help="path to the data", type=str, default="data_processed"
)
parser.add_argument(
"--save_dir", help="save directory of model parameter", type=str, default="save/"
)
parser.add_argument("--log_dir", help="logging directory",
type=str, default="log/")
parser.add_argument("--dropout_rate", help="dropout_rate",
type=float, default=0.0)
parser.add_argument("--al_scale", help="attn_loss scale",
type=float, default=1.0)
parser.add_argument("--ckpt", help="Load ckpt file", type=str, default="")
parser.add_argument(
"--train_keys", help="train keys", type=str, default="train_keys.pkl"
)
parser.add_argument("--test_keys", help="test keys",
type=str, default="test_keys.pkl")
parser.add_argument("--tag", help="Additional tag for saving and logging folder",
type=str, default="")
def main(args):
# hyper parameters
num_epochs = args.epoch
lr = args.lr
data_path = os.path.join(args.data_path, args.dataset)
args.train_keys = os.path.join(data_path, args.train_keys)
args.test_keys = os.path.join(data_path, args.test_keys)
save_dir = os.path.join(
args.save_dir, "%s_%s_%d" % (args.dataset, args.tatic, args.nhop)
)
log_dir = os.path.join(
args.log_dir, "%s_%s_%d" % (args.dataset, args.tatic, args.nhop)
)
if args.branch != "both":
save_dir += "_" + args.branch
log_dir += "_" + args.branch
if args.tag != "":
save_dir += "_" + args.tag
log_dir += "_" + args.tag
# Make save dir if it doesn't exist
if not os.path.isdir(save_dir):
os.system("mkdir " + save_dir)
if not os.path.isdir(log_dir):
os.system("mkdir " + log_dir)
# Read data. data is stored in format of dictionary. Each key has information about protein-ligand complex.
with open(args.train_keys, "rb") as fp:
train_keys = pickle.load(fp)
with open(args.test_keys, "rb") as fp:
test_keys = pickle.load(fp)
# Print simple statistics about dude data and pdbbind data
print(f"Number of train data: {len(train_keys)}")
print(f"Number of test data: {len(test_keys)}")
# Initialize model
# if args.ngpu > 0:
# cmd = utils.set_cuda_visible_device(args.ngpu)
# os.environ["CUDA_VISIBLE_DEVICES"] = cmd[:-1]
model = gnn(args)
print(
"Number of parameters: ",
sum(p.numel() for p in model.parameters() if p.requires_grad),
)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = utils.initialize_model(model, device, load_save_file=args.ckpt)
# Train and test dataset
train_dataset = BaseDataset(
train_keys, data_path, embedding_dim=args.embedding_dim)
test_dataset = BaseDataset(
test_keys, data_path, embedding_dim=args.embedding_dim)
# num_train_iso = len([0 for k in train_keys if 'iso' in k])
# num_train_non = len([0 for k in train_keys if 'non' in k])
# train_weights = [1/num_train_iso if 'iso' in k else 1/num_train_non for k in train_keys]
# train_sampler = UnderSampler(train_weights, len(train_weights), replacement=True)
train_dataloader = DataLoader(
train_dataset,
args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
# sampler = train_sampler
)
test_dataloader = DataLoader(
test_dataset,
args.batch_size,
shuffle=False,
num_workers=args.num_workers,
collate_fn=collate_fn,
)
# Optimizer
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# Loss function
loss_fn = nn.BCELoss()
# Logging file
log_file = open(
os.path.join(log_dir, "%s_trace.csv" % args.dataset), "w", encoding="utf-8"
)
log_file.write(
"epoch,train_losses,test_losses,train_roc,test_roc,train_time,test_time\n"
)
for epoch in range(num_epochs):
print("EPOCH", epoch)
st = time.time()
# Collect losses of each iteration
train_losses = []
test_losses = []
# Collect true label of each iteration
train_true = []
test_true = []
# Collect predicted label of each iteration
train_pred = []
test_pred = []
model.train()
for sample in tqdm(train_dataloader):
model.zero_grad()
H, A1, A2, M, S, Y, V, _ = sample
H, A1, A2, M, S, Y, V = (
H.to(device),
A1.to(device),
A2.to(device),
M.to(device),
S.to(device),
Y.to(device),
V.to(device),
)
# Train neural network
pred, attn_loss = model(
X=(H, A1, A2, V), attn_masking=(M, S), training=True
)
loss = loss_fn(pred, Y) + attn_loss
loss.backward()
optimizer.step()
# Print loss at the end of tqdm bar
tqdm.write("Loss: %.4f" % loss.data.cpu().item())
# Collect loss, true label and predicted label
train_losses.append(loss.data.cpu().item())
train_true.append(Y.data.cpu().numpy())
train_pred.append(pred.data.cpu().numpy())
model.eval()
st_eval = time.time()
for sample in tqdm(test_dataloader):
H, A1, A2, M, S, Y, V, _ = sample
H, A1, A2, M, S, Y, V = (
H.to(device),
A1.to(device),
A2.to(device),
M.to(device),
S.to(device),
Y.to(device),
V.to(device),
)
# Test neural network
pred, attn_loss = model(
X=(H, A1, A2, V), attn_masking=(M, S), training=True
)
loss = loss_fn(pred, Y) + attn_loss
# Collect loss, true label and predicted label
test_losses.append(loss.data.cpu().item())
test_true.append(Y.data.cpu().numpy())
test_pred.append(pred.data.cpu().numpy())
end = time.time()
train_losses = np.mean(np.array(train_losses))
test_losses = np.mean(np.array(test_losses))
train_pred = np.concatenate(train_pred, 0)
test_pred = np.concatenate(test_pred, 0)
train_true = np.concatenate(train_true, 0)
test_true = np.concatenate(test_true, 0)
train_roc = roc_auc_score(train_true, train_pred)
test_roc = roc_auc_score(test_true, test_pred)
print(
"%d,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f"
% (
epoch,
train_losses,
test_losses,
train_roc,
test_roc,
st_eval - st,
end - st_eval,
)
)
log_file.write(
"%d,%.3f,%.3f,%.3f,%.3f,%.3f,%.3f\n"
% (
epoch,
train_losses,
test_losses,
train_roc,
test_roc,
st_eval - st,
end - st_eval,
)
)
log_file.flush()
name = save_dir + "/save_" + str(epoch) + ".pt"
torch.save(model.state_dict(), name)
log_file.close()
if __name__ == "__main__":
args = parser.parse_args()
print(args)
now = time.localtime()
s = "%04d-%02d-%02d %02d:%02d:%02d" % (
now.tm_year,
now.tm_mon,
now.tm_mday,
now.tm_hour,
now.tm_min,
now.tm_sec,
)
print(s)
main(args)