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train_Struct2GO.py
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train_Struct2GO.py
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from cProfile import label
from random import shuffle
from re import T
from statistics import mode
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
import pickle
from model.network import SAGNetworkHierarchical,SAGNetworkGlobal
import torch.nn as nn
import torch.optim as optim
import dgl
from dgl.dataloading import GraphDataLoader
import torch.nn.functional as F
from tkinter import _flatten
from sklearn import metrics
from sklearn.metrics import roc_auc_score, roc_curve, auc, precision_score, recall_score, f1_score, average_precision_score
import argparse
import warnings
import datetime
import matplotlib.pyplot as plt
from sklearn.metrics import precision_recall_curve
from data_processing.divide_data import MyDataSet
from model.evaluation import cacul_aupr,calculate_performance
from transformers import get_cosine_schedule_with_warmup
from tqdm import tqdm
import logging
import os
def create_logger(branch_name):
logger = logging.getLogger(branch_name)
handler1 = logging.StreamHandler()
handler2 = logging.FileHandler(filename=os.path.join('log',branch_name+'.log'))
logger.setLevel(logging.DEBUG)
handler1.setLevel(logging.ERROR)
handler2.setLevel(logging.DEBUG)
formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s %(message)s")
handler1.setFormatter(formatter)
handler2.setFormatter(formatter)
logger.addHandler(handler1)
logger.addHandler(handler2)
return logger
warnings.filterwarnings('ignore')
Thresholds = [x/100 for x in range(1,100)]
if __name__ == "__main__":
#参数设置
device = "cuda:7"
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-batch_size', '--batch_size', type=int, default=64, help="the number of the bach size")
parser.add_argument('-learningrate', '--learningrate',type=float,default=1e-4)
parser.add_argument('-dropout', '--dropout',type=float,default=0.5)
parser.add_argument('-branch', '--branch',type=str,default='mf')
parser.add_argument('-labels_num', '--labels_num',type=int,default=328)
args = parser.parse_args()
# 根据选择的标签大类自动填写训练文件路径
train_data_path = 'divided_data/'+args.branch+'_train_dataset'
valid_data_path = 'divided_data/'+args.branch+'_valid_dataset'
label_network_path = 'processed_data/label_'+args.branch+'_network'
logger = create_logger(args.branch)
with open(train_data_path,'rb')as f:
train_dataset = pickle.load(f)
with open(valid_data_path,'rb')as f:
valid_dataset = pickle.load(f)
with open(label_network_path,'rb')as f:
label_network=pickle.load(f)
label_network = label_network.to(device)
# 载入/设置参数
epoch_num = 20
batch_size = args.batch_size
learningrate = args.learningrate
dropout = args.dropout
labels_num = args.labels_num
# 加载数据
train_dataloader = GraphDataLoader(dataset=train_dataset, batch_size = batch_size, drop_last = False, shuffle = True)
valid_dataloader = GraphDataLoader(dataset=valid_dataset, batch_size = batch_size, drop_last = False, shuffle = True)
# TODO: 这里的输入特征应该是one-hot(26)+node2vec(30) = 56
# 但暂时没做特征拼接，先用node2vec特征试试
model = SAGNetworkHierarchical(30, 512, labels_num, num_convs=6, pool_ratio=0.75, dropout=dropout).to(device)
optimizer = optim.Adam(model.parameters(), lr=learningrate)
lr_scheduler = get_cosine_schedule_with_warmup(optimizer, num_warmup_steps=100, num_training_steps=epoch_num*len(train_dataloader))
criterion = nn.CrossEntropyLoss()
best_fscore = 0
best_aupr = 0
best_scores = []
best_score_dict = {}
logger.info('#########'+args.branch+'###########')
logger.info('########start training###########')
for epoch in range(epoch_num):
print("epoch:",epoch)
logger.info("epoch: "+str(epoch))
model.train()
train_loss = 0
print("training")
logger.info("training")
for i,(pids, graphs, labels, seq_feats) in tqdm(enumerate(train_dataloader)):
graphs = graphs.to(device)
seq_feats = seq_feats.to(device)
labels = labels.to(device)
labels = torch.squeeze(labels)
if len(labels.shape)==1:
labels = labels.unsqueeze(0)
optimizer.zero_grad()
logits = model(graphs,seq_feats,label_network)
loss = criterion(logits,labels)
loss.backward()
optimizer.step()
lr_scheduler.step()
# 累加计算平均loss
train_loss+=loss.item()
if i%30 == 29:
logger.info(f'Epoch: {epoch} / {epoch_num}, Step: {i} / {len(train_dataloader)}, Loss(batch): {loss.item()}')
# 每四轮进行一次验证
if epoch%4==3:
model.eval()
print("validating")
logger.info("validating")
valid_loss = 0
pred = []
actual = []
with torch.no_grad():
for i,(pids, graphs, labels, seq_feats) in tqdm(enumerate(valid_dataloader)):
graphs = graphs.to(device)
seq_feats = seq_feats.to(device)
labels = labels.to(device)
labels = torch.squeeze(labels)
if len(labels.shape)==1:
labels = labels.unsqueeze(0)
logits = model(graphs,seq_feats,label_network)
logits = F.sigmoid(logits)
loss = criterion(logits,labels)
valid_loss += loss.item()
pred += logits.tolist()
actual += labels.tolist()
if i%10 == 9:
logger.info(f'Valid Step: {i} / {len(valid_dataloader)}, Loss(batch): {loss.item()}')
fpr, tpr, th = roc_curve(np.array(actual).flatten(), np.array(pred).flatten(), pos_label=1)
auc_score = auc(fpr, tpr)
aupr = cacul_aupr(np.array(actual).flatten(), np.array(pred).flatten())
score_dict = {}
each_best_fcore = 0
each_best_scores = []
for thresh in tqdm(Thresholds):
f_score, precision, recall = calculate_performance(actual, pred, label_network,threshold=thresh)
if f_score >= each_best_fcore:
each_best_fcore = f_score
each_best_scores = [thresh, f_score, recall, precision, auc_score]
scores = [f_score, recall, precision, auc_score]
score_dict[thresh] = scores
if each_best_fcore >= best_fscore:
best_fscore = each_best_fcore
best_scores = each_best_scores
best_score_dict = score_dict
best_aupr = aupr
torch.save(model, 'save_models/bestmodel_{}_{}_{}_{}.pkl'.format(args.branch,batch_size,learningrate,dropout))
thresh, f_score, recall = each_best_scores[0], each_best_scores[1], each_best_scores[2]
precision, auc_score = each_best_scores[3], each_best_scores[4]
logger.info('########valid metric###########')
logger.info('epoch{}, train_loss{}, valid_loss:{}'.format(epoch, train_loss/len(train_dataloader), valid_loss/len(valid_dataloader)))
logger.info('threshold:{}, f_score{}, auc{}, recall{}, precision{}, aupr{}'.format(thresh, f_score, auc_score, recall, precision, aupr))
logger.info('best_fscore: '+str(best_fscore))
logger.info('best_scores[thresh,fmax,recall,precision,auc]: '+str(best_scores))
logger.info('best_aupr: '+str(best_aupr))
logger.info('best_score_dict: '+str(best_score_dict))