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BiTransDPI
提交 2a4d6f3f 作者: 朱学凯
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add some change

上级 a43ff66c
正在显示 包含 2073 行增加 和 5 行删除
.ipynb_checkpoints/eval-checkpoint.py 0 → 100644
import numpy as np
import re
def eval_result(pred, label):
pred = np.array(pred)
label = np.array(label)
num = len(pred)
diff = pred - label
mse = np.sum(np.power(diff, 2)) / num
rmse = np.sqrt(mse)
pearson_co = np.corrcoef(pred, label)
return rmse, pearson_co
def eval(pred_path, label_path):
with open(pred_path, 'r') as f:
pred = f.readlines()
pred = [float(i.strip()) for i in pred]
with open(label_path, 'r') as f:
label = f.readlines()
label = [float(i.strip()) for i in label]
remse, r_mat = eval_result(pred, label)
r = r_mat[0, 1]
save_path = pred_path.replace('test.txt', 'eval_results')
with open(save_path, 'w') as f:
f.write('RMSE : {} ; Pearson Correlation Coefficient : {}'.format(remse, r))
print('RMSE : {} ; Pearson Correlation Coefficient : {}'.format(remse, r))
if __name__ == '__main__':
with open('pre_test.sh', 'r') as f:
pred_dir = f.readline()
pred_dir = pred_dir.split()[5].split('/')[-1]
pred_result = './predict/{}/test.txt'.format(pred_dir)
test_label_path = './data/test_ic50'
eval(pred_result, test_label_path)
.ipynb_checkpoints/pre_test-checkpoint.sh
CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --output=./predict/lr-1e-5-batch-32-e-10-layer3-0417-add-type-ids-and-mask-step-24711 --config=./config/config_layer_3.json --init=./model/lr-1e-5-batch-32-e-10-layer3-0417-add-type-ids-and-mask/epoch-2-step-24711-loss-0.9765299144620636.pth --do_eval=True CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --b=64 --output=./predict/lr-1e-5-batch-32-e-10-layer6-0420-step-74133 --config=./config/config_layer_6.json --init=./model/lr-1e-5-batch-32-e-10-layer6-0420/epoch-8-step-74133-loss-0.8989318833651185.pth --do_eval=True
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.ipynb_checkpoints/run_interaction-checkpoint.py 0 → 100644
from argparse import ArgumentParser
from dataset import Data_Encoder
import torch
from torch.utils.data import DataLoader
from configuration_bert import BertConfig
from modeling_bert import BertAffinityModel
from torch.utils.tensorboard import SummaryWriter
import os
from tqdm import tqdm
torch.set_default_tensor_type(torch.DoubleTensor)
def get_task(task_name):
if task_name.lower() == 'train':
df_train = {"sps": './data/train_sps',
"smile": './data/train_smile',
"affinity": './data/train_ic50',
}
tokenizer_config = {"vocab_file": './config/vocab.txt',
"vocab_pair": './config/drug_codes_chembl.txt',
"begin_id": '[CLS]',
"separate_id": "[SEP]",
"max_len": 256
}
return df_train, tokenizer_config
elif task_name.lower() == 'test':
df_test = {"sps": './data/test_sps',
"smile": './data/test_smile',
"affinity": './data/test_ic50',
}
tokenizer_config = {"vocab_file": './config/vocab.txt',
"vocab_pair": './config/drug_codes_chembl.txt',
"begin_id": '[CLS]',
"separate_id": "[SEP]",
"max_len": 256
}
return df_test, tokenizer_config
def train(args, model, dataset):
data_loder_para = {'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
}
data_generator = DataLoader(dataset, **data_loder_para)
model.train()
opt = torch.optim.Adam(model.parameters(), lr=args.lr)
loss_fct = torch.nn.MSELoss()
writer = SummaryWriter('./log/' + args.savedir)
num_step = args.epochs * len(data_generator)
step = 0
save_step = num_step // 10
# detect GPU
if torch.cuda.is_available():
model.cuda()
# print(model)
print('epoch num : {}'.format(args.epochs))
print('step num : {}'.format(num_step))
print('batch size : {}'.format(args.batch_size))
print('learning rate : {}'.format(args.lr))
print('begin training')
# training
for epoch in range(args.epochs):
for i, (input, token_type_ids, input_mask, affinity) in enumerate(data_generator):
# use cuda
# input model
if torch.cuda.is_available():
pred_affinity = model(input_ids=input.cuda(), token_type_ids=token_type_ids.cuda(), attention_mask=input_mask.cuda())
loss = loss_fct(pred_affinity, affinity.cuda().unsqueeze(-1))
else:
pred_affinity = model(input_ids=input, token_type_ids=token_type_ids, attention_mask=input_mask)
loss = loss_fct(pred_affinity, affinity.unsqueeze(-1))
step += 1
writer.add_scalar('loss', loss, global_step=step)
# Update gradient
opt.zero_grad()
loss.backward()
opt.step()
# if (i % 100 == 0):
print('Training at Epoch ' + str(epoch + 1) + ' step ' + str(step) + ' with loss ' + str(
loss.cpu().detach().numpy()))
# save
if epoch >= 1 and step % save_step == 0:
save_path = './model/' + args.savedir + '/'
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(model.state_dict(), save_path + 'epoch-{}-step-{}-loss-{}.pth'.format(epoch, step, loss))
print('training over')
writer.close()
def test(args, model, dataset):
data_loder_para = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.workers,
}
data_generator = DataLoader(dataset, **data_loder_para)
with torch.no_grad():
if torch.cuda.is_available():
model.load_state_dict(torch.load(args.init), strict=True)
else:
model.load_state_dict(torch.load(args.init, map_location=torch.device('cpu')), strict=True)
model.eval()
if not os.path.exists(args.output):
os.mkdir(args.output)
result = args.output + '/' + '{}.txt'.format(args.task)
print('begin predicting')
with open(result, 'w') as f:
for i, (input, token_type_ids, input_mask, affinity) in enumerate(tqdm(data_generator)):
if torch.cuda.is_available():
model.cuda()
pred_affinity = model(input_ids=input.cuda(), token_type_ids=token_type_ids.cuda(),
attention_mask=input_mask.cuda())
else:
pred_affinity = model(input_ids=input, token_type_ids=token_type_ids, attention_mask=input_mask)
pred_affinity = pred_affinity.cpu().numpy()
for res in range(args.batch_size):
pred = pred_affinity[res, :][0]
f.write(str(pred) + '\n')
if args.do_eval:
os.system('python eval.py')
def main(args):
# load data
data_file, tokenizer_config = get_task(args.task)
dataset = Data_Encoder(data_file, tokenizer_config)
# creat model
print('------------------creat model---------------------------')
config = BertConfig.from_pretrained(args.config)
model = BertAffinityModel(config)
print('model name : BertAffinity')
print('task name : {}'.format(args.task))
if args.task == 'train':
train(args, model, dataset)
elif args.task in ['test']:
test(args, model, dataset)
if __name__ == '__main__':
# get parameter
parser = ArgumentParser(description='BertAffinity')
parser.add_argument('-b', '--batch-size', default=8, type=int,
metavar='N',
help='mini-batch size (default: 16), this is the total '
'batch size of all GPUs on the current node when '
'using Data Parallel or Distributed Data Parallel')
parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('--epochs', default=50, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--task', choices=['train', 'test', 'channel', 'ER', 'GPCR', 'kinase'],
default='train', type=str, metavar='TASK',
help='Task name. Could be train, test, channel, ER, GPCR, kinase.')
parser.add_argument('--lr', '--learning-rate', default=1e-5, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--config', default='./config/config.json', type=str, help='model config file path')
# parser.add_argument('--log', default='training_log', type=str, help='training log')
parser.add_argument('--savedir', default='train', type=str, help='log and model save path')
# parser.add_argument('--device', default='0', type=str, help='name of GPU')
parser.add_argument('--init', default='model', type=str, help='init checkpoint')
parser.add_argument('--output', default='predict', type=str, help='result save path')
# parser.add_argument('--shuffle', default=True, type=str, help='shuffle data')
parser.add_argument('--do_eval', default=False, type=bool, help='do eval')
args = parser.parse_args()
# local test
# args.task = 'train'
# args.savedir = 'local_test_train'
# args.epochs = 10
# args.lr = 1e-5
# args.config = './config/config_layer_3.json'
# args.task = 'test'
# args.init = './model/lr-1e-5-batch-32-e-10-layer3-0417-add-type-ids-and-mask/epoch-9-step-82370-loss-0.8841055645024439.pth'
# args.output = './predict/test'
# args.config = './config/config_layer_3.json'
main(args)
data/.ipynb_checkpoints/test_ic50-checkpoint 0 → 100755
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eval.py
...@@ -32,7 +32,7 @@ def eval(pred_path, label_path): ...@@ -32,7 +32,7 @@ def eval(pred_path, label_path):
if __name__ == '__main__': if __name__ == '__main__':
with open('pre_test.sh', 'r') as f: with open('pre_test.sh', 'r') as f:
pred_dir = f.readline() pred_dir = f.readline()
pred_dir = pred_dir.split()[4].split('/')[-1] pred_dir = pred_dir.split()[5].split('/')[-1]
pred_result = './predict/{}/test.txt'.format(pred_dir) pred_result = './predict/{}/test.txt'.format(pred_dir)
test_label_path = './data/test_ic50' test_label_path = './data/test_ic50'
eval(pred_result, test_label_path) eval(pred_result, test_label_path)
log/experment_recode/.ipynb_checkpoints/layer_6_batch_32-checkpoint.txt 0 → 100644
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log/experment_recode/.ipynb_checkpoints/layer_9_batch_32-checkpoint.txt 0 → 100644
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log/experment_recode/layer_6_batch_32.txt 0 → 100644
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log/experment_recode/layer_9_batch_32.txt 0 → 100644
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pre_test.sh
CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --output=./predict/lr-1e-5-batch-32-e-10-layer3-0417-add-type-ids-and-mask-step-24711 --config=./config/config_layer_3.json --init=./model/lr-1e-5-batch-32-e-10-layer3-0417-add-type-ids-and-mask/epoch-2-step-24711-loss-0.9765299144620636.pth --do_eval=True CUDA_VISIBLE_DEVICES=1 python run_interaction.py --task=test --b=64 --output=./predict/lr-1e-5-batch-32-e-10-layer6-0420-step-74133 --config=./config/config_layer_6.json --init=./model/lr-1e-5-batch-32-e-10-layer6-0420/epoch-8-step-74133-loss-0.8989318833651185.pth --do_eval=True
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predict/lr-1e-5-batch-32-e-10-layer6-0420-step-74133/test.txt 0 → 100644
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predict/lr-1e-5-batch-32-e-10-layer6-0420-step-82370/.ipynb_checkpoints/eval_results-checkpoint 0 → 100644
RMSE : 1.0240501538228575 ; Pearson Correlation Coefficient : 0.7363279193240799
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predict/lr-1e-5-batch-32-e-10-layer6-0420-step-82370/eval_results 0 → 100644
RMSE : 1.0240501538228575 ; Pearson Correlation Coefficient : 0.7363279193240799
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predict/lr-1e-5-batch-32-e-10-layer6-0420-step-82370/test.txt 0 → 100644
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predict/lr-1e-5-batch-32-e-10-layer9-0420-step-57659/test.txt 0 → 100644
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predict/lr-1e-5-batch-32-e-10-layer9-0420-step-65896/eval_results 0 → 100644
RMSE : 1.0055980079639106 ; Pearson Correlation Coefficient : 0.7307091262603383
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predict/lr-1e-5-batch-32-e-10-layer9-0420-step-65896/test.txt 0 → 100644
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predict/lr-1e-5-batch-64-e-10-layer12-0413-step-22475/.ipynb_checkpoints/test-checkpoint.txt 0 → 100644
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predict/lr-1e-5-batch-64-e-10-layer3-0413-step-40672/.ipynb_checkpoints/test-checkpoint.txt 0 → 100644
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-20595/eval_results 0 → 100644
RMSE : 1.1229353121987031 ; Pearson Correlation Coefficient : 0.6521638915583137
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-20595/test.txt 0 → 100644
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-28833/eval_results 0 → 100644
RMSE : 1.1092552623206697 ; Pearson Correlation Coefficient : 0.6740378152907927
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-28833/test.txt 0 → 100644
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-41190/.ipynb_checkpoints/eval_results-checkpoint 0 → 100644
RMSE : 1.0635720063061838 ; Pearson Correlation Coefficient : 0.698596143686921
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-41190/eval_results 0 → 100644
RMSE : 1.0635720063061838 ; Pearson Correlation Coefficient : 0.698596143686921
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predict/lr-1e-5-batch-64-e-10-layer3-0417-add-type-ids-and-mask-step-41190/test.txt 0 → 100644
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