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train.py
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train.py
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import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from dgllife.model import model_zoo
from dgllife.utils import smiles_to_bigraph
from dgllife.utils import EarlyStopping, Meter
from dgllife.utils import AttentiveFPAtomFeaturizer
from dgllife.utils import AttentiveFPBondFeaturizer
import dgl
import random
import argparse
from dgllife.data import MoleculeCSVDataset
if torch.cuda.is_available():
print('use GPU')
device = 'cuda'
else:
print('use CPU')
device = 'cpu'
# 设置全局随机种子
import os
import random
import numpy as np
seed = 42
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.manual_seed(seed) # 为CPU设置随机种子
torch.cuda.manual_seed(seed) # 为当前GPU设置随机种子
torch.cuda.manual_seed_all(seed)
def set_random_seed(seed=42):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
def collate_molgraphs(data):
assert len(data[0]) in [3, 4], \
'Expect the tuple to be of length 3 or 4, got {:d}'.format(len(data[0]))
if len(data[0]) == 3:
smiles, graphs, labels = map(list, zip(*data))
masks = None
else:
smiles, graphs, labels, masks = map(list, zip(*data))
bg = dgl.batch(graphs)
bg.set_n_initializer(dgl.init.zero_initializer)
bg.set_e_initializer(dgl.init.zero_initializer)
labels = torch.stack(labels, dim=0)
if masks is None:
masks = torch.ones(labels.shape)
else:
masks = torch.stack(masks, dim=0)
return smiles, bg, labels, masks
atom_featurizer = AttentiveFPAtomFeaturizer(atom_data_field='hv')
bond_featurizer = AttentiveFPBondFeaturizer(bond_data_field='he')
n_feats = atom_featurizer.feat_size('hv')
e_feats = bond_featurizer.feat_size('he')
def load_data(data,target1='5NTP',target2='6QU7',prec='HTVS'):
dataset = MoleculeCSVDataset(data,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=atom_featurizer,
edge_featurizer= bond_featurizer,
smiles_column='SMILES',
cache_file_path='train_cache.bin',
task_names=[f'{target1}_{prec}',f'{target2}_{prec}'],
load=False,init_mask=True,n_jobs=8
)
return dataset
def run_a_train_epoch(n_epochs, epoch, model, data_loader, loss_criterion, optimizer):
model.train()
losses = []
train_meter = Meter()
for batch_id, batch_data in enumerate(data_loader):
batch_data
smiles, bg, labels, masks = batch_data
bg=bg.to(device)
labels = labels.to(device)
masks = masks.to(device)
n_feats = bg.ndata.pop('hv').to(device)
e_feats = bg.edata.pop('he').to(device)
prediction = model(bg, n_feats, e_feats)
loss = (loss_criterion(prediction, labels) * (masks != 0).float()).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_meter.update(prediction, labels, masks)
losses.append(loss.data.item())
total_r2 = np.mean(train_meter.compute_metric('r2'))
total_loss = np.mean(losses)
if epoch % 10 == 0:
print('epoch {:d}/{:d}, training_r2 {:.4f}, training_loss {:.4f}'.format(epoch + 1, n_epochs, total_r2,total_loss))
return total_r2, total_loss
def run_an_eval_epoch(n_epochs, model, data_loader,loss_criterion):
model.eval()
val_losses=[]
eval_meter = Meter()
with torch.no_grad():
for batch_id, batch_data in enumerate(data_loader):
smiles, bg, labels, masks = batch_data
bg = bg.to(device)
labels = labels.to(device)
masks = masks.to(device)
n_feats = bg.ndata.pop('hv').to(device)
e_feats = bg.edata.pop('he').to(device)
vali_prediction = model(bg, n_feats, e_feats)
val_loss = (loss_criterion(vali_prediction, labels) * (masks != 0).float()).mean()
val_loss=val_loss.detach().cpu().numpy()
val_losses.append(val_loss)
eval_meter.update(vali_prediction, labels, masks)
total_score = np.mean(eval_meter.compute_metric('r2'))
total_loss = np.mean(val_losses)
return total_score, total_loss
def train(csv_list,model_name,target1,target2,prec):
df_list=[]
for csv in csv_list.split(','):
df_list.append(pd.read_csv(csv))
df=pd.concat(df_list)
smiles_set=set()
ind_list=[]
for i, smiles in enumerate(df['SMILES']):
if smiles not in smiles_set:
smiles_set.add(smiles)
ind_list.append(i)
print(len(ind_list))
val_list=random.sample(ind_list,len(ind_list)//10)
train_list=[]
for i in ind_list:
if i not in val_list:
train_list.append(i)
dc_listings1=df.iloc[train_list]
# dc_listings1 = pd.concat([df2,df3,df.iloc[train_list]])
# dc_listings1=dc_listings1.iloc[:len(dc_listings1)//5]
dc_listings2 = df.iloc[val_list]
train_datasets = load_data(dc_listings1,target1,target2,prec)
val_datasets = load_data(dc_listings2,target1,target2,prec)
train_loader = DataLoader(train_datasets, batch_size=256,shuffle=True,
collate_fn=collate_molgraphs)
vali_loader = DataLoader(val_datasets,batch_size=256,shuffle=True,
collate_fn=collate_molgraphs)
model = model_zoo.AttentiveFPPredictor(node_feat_size=n_feats,
edge_feat_size=e_feats,
num_layers=2,
num_timesteps=1,
graph_feat_size=300,
n_tasks=2,
dropout=0.3
)
model = model.to(device)
#Train
loss_fn = nn.MSELoss(reduction='none')
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay = 0.000001
)
stopper = EarlyStopping(mode='higher', patience=20)
n_epochs = 501
for e in range(n_epochs):
score = run_a_train_epoch(n_epochs, e, model, train_loader, loss_fn, optimizer)
val_score = run_an_eval_epoch(n_epochs, model, vali_loader,loss_fn)
early_stop = stopper.step(val_score[0], model)
if val_score[0]==stopper.best_score:
torch.save(model.state_dict(), model_name)
if e % 10 == 0:
print('epoch {:d}/{:d}, validation {} {:.4f}, validation {} {:.4f}, best validation {} {:.4f}'.format(
e + 1, n_epochs, 'r2', val_score[0], 'loss', val_score[-1],
'r2', stopper.best_score))
if early_stop:
break
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--csv')
parser.add_argument('--model')
parser.add_argument('--target1',default='5NTP')
parser.add_argument('--target2',default='6QU7')
parser.add_argument('--prec',default='XP')
args = parser.parse_args()
train(args.csv,args.model,args.target1,args.target2,args.prec)