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train.py
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train.py
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#coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as TFn
import numpy as np
from tqdm import tqdm
import dgl
import time
import argparse
from data_processor.data_statistics import data_statistics
from utils.tools import get_time_dif, set_seed, data_describe, dataloaders, datasets, path_check
from utils.logger import Logger
from utils.optim import fix_weight_decay
from utils.metric import metrics
from utils import RunRecordManager
import data_processor.yoochoose_dataset as yoochoose
import data_processor.jdata_dataset as jdata
from graph.graph_construction import *
from graph.collate import gnn_collate_fn
from IAGNN import IAGNN
import pretty_errors
MODEL_NAME = 'DAGCN'
#@torchsnooper.snoop()
def train(args, model, optimizer, scheduler, device, iters, args_filter,
item_cates):
model_name = args.model_name
start_time = time.time()
total_batch = 0 # 记录进行到多少batch
dev_best_loss, best_acc = float('inf'), 0
STEP_SIZE = 200
last_improve = 0 # 记录上次验证集loss下降的batch
loss_list = []
exp_setting = '-'.join('{}:{}'.format(k, v) for k, v in vars(args).items()
if k in args_filter)
Log = Logger(fn='./logs/{}-{}-{:.0f}.log'.format(model_name, args.dataset,
start_time))
Log.log(exp_setting)
record_manager = RunRecordManager(args.db)
record_manager.start_run(model_name, start_time, args)
item_cates = torch.from_numpy(np.array(item_cates)).to(device) #[all]
for epoch in range(args.epochs):
print('Epoch [{}/{}]'.format(epoch + 1, args.epochs))
L = nn.CrossEntropyLoss(
reduce='none') # reduce=('none' if args.weight_loss else 'mean')
_loss = 0
for i, (bgs, label, next) in enumerate(iters['train']):
model.train()
outputs, embeddings, session_length = model.forward(
bgs.to(device), next.to(device))
# print(outputs)
# break
item_catess = item_cates.view(1, -1).expand_as(outputs)
mask = torch.where(item_catess == next.to(device),
torch.ones_like(item_catess),
torch.zeros_like(item_catess)) # [bs,all]
mask = torch.cat([mask[:, 1:], mask[:, 0].view(-1, 1)], dim=1)
outputs = outputs * mask # [bs,all]
label = label.to(device)
model.zero_grad()
y = (label - 1).squeeze()
# cosine_loss = L_cos(h_all, c_all, target=y)
loss = L(outputs, y) #- 0.1 * cosine_loss
# loss_corr=model.corr_loss(embeddings,session_length)
# # print(loss); print(loss_corr)
# loss+=loss_corr*args.beta
loss_list.append(loss.item())
loss.backward()
optimizer.step()
if total_batch % STEP_SIZE == 0:
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.6}, Time: {2} {3}'
_loss = np.mean(loss_list)
Log.log(
msg.format(total_batch, _loss, time_dif, '*'))
loss_list = []
total_batch += 1
print('performance on test set....')
scheduler.step()
infos = "\n"
metrics = {}
for key in iters:
if key == 'test':
# acc=0;continue
acc, info, m = evaluate_topk(args, model, iters[key],
item_cates, device, 20, key)
metrics[key] = m
infos += info
elif key != 'train':
acc_l, info_l, m_l = evaluate_topk(args,
model,
iters[key],
item_cates,
device,
20,
key,
observe=False)
metrics[key] = m_l
infos += info_l
infos += "\n"
msg = f'epoch[{epoch + 1}] :{infos}'
for test_set_name, m in metrics.items():
for top_k, v in m.items():
record_manager.update_best(model_name, start_time,
epoch + 1, test_set_name, top_k,
v['acc'], v['mrr'], v['ndcg'],
_loss)
if acc > best_acc:
best_acc = acc
Log.log(msg, red=True)
last_improve = 0
if args.save_flag:
torch.save(model.state_dict(),
'./ckpt/{}_epoch{}.ckpt'.format(exp_setting, epoch))
else:
Log.log(msg, red=False)
last_improve += 1
if last_improve >= args.patience:
Log.log('Early stop: No more improvement')
break
# try to release gpu memory hold by validation/test set
# torch.cuda.empty_cache()
def evaluate_topk(args,
model,
data_iter,
item_cates,
device,
anchor=20,
des='',
observe=False):
model.eval()
res = {'5': [], '10': [], '20': [], '50': []}
ret_metrics = {}
labels = []
acc_anchor = 0
with torch.no_grad():
with tqdm(total=(data_iter.__len__()), desc='Predicting',
leave=False) as p:
for i, (bgs, label, next) in (enumerate(data_iter)):
# print(datas)
outputs, _, _ = model.forward(bgs.to(device), next.to(device))
item_catess = item_cates.view(1, -1).expand_as(outputs)
mask = torch.where(item_catess == next.to(device),
torch.ones_like(item_catess),
torch.zeros_like(item_catess)) # [bs,all]
mask = torch.cat([mask[:, 1:], mask[:, 0].view(-1, 1)], dim=1)
outputs = outputs * mask # [bs,all]
for k in res:
res[k].append(outputs.topk(int(k))[1].cpu())
labels.append(label)
p.update(1)
labels = np.concatenate(labels) # .flatten()
labels = labels - 1
if observe:
graphs = dgl.unbatch(bgs)
length = min(20, len(graphs))
for i in range(length):
print(graphs[i].nodes['i'].data['id'])
print(label[0:length])
sm = outputs.topk(int(20))[1].cpu()[0:length].numpy() + 1
for i in range(length):
print(sm[i].tolist())
print(des)
msg = des + '\n'
for k in res:
acc, mrr, ndcg = metrics(res[k], labels)
print("Top{} : acc {} , mrr {}, ndcg {}".format(k, acc, mrr, ndcg))
msg += 'Top-{} acc:{:.3f}, mrr:{:.4f}, ndcg:{:.4f} \n'.format(
k, acc * 100, mrr * 100, ndcg * 100)
if int(k) == anchor:
acc_anchor = acc
ret_metrics[k] = {'acc': acc, 'mrr': mrr, 'ndcg': ndcg}
return acc_anchor, msg, ret_metrics
path_check(['./logs', './ckpt'])
argparser = argparse.ArgumentParser('CDSBR')
argparser.add_argument('--model_name', default='IAGNN', type=str, help='model name')
argparser.add_argument('--seed', default=422, type=int, help='random seed')
argparser.add_argument('--emb_size',
default=128,
type=int,
help='embedding size')
argparser.add_argument('--gpu', default=0, type=int, help='gpu id')
# data related setting
argparser.add_argument('--max_length',
default=10,
type=int,
help='max session length')
argparser.add_argument('--dataset',
default='jdata_cd',
help='dataset=[yc_BT_16|jdata_cd]')
# train related setting
argparser.add_argument('--batch', default=512, type=int, help='batch size')
argparser.add_argument('--epochs', default=10, type=int, help='total epochs')
argparser.add_argument('--patience',
default=3,
type=int,
help='early stopping patience')
argparser.add_argument('--lr', default=0.003, type=float, help='learning rate')
argparser.add_argument('--lr_step', default=3, type=int, help='lr decay step')
argparser.add_argument('--lr_gama',
default=0.1,
type=float,
help='lr decay gama')
argparser.add_argument('--save_flag',
default=False,
type=bool,
help='save checkpoint or not')
argparser.add_argument('--debug',
default=False,
type=bool,
help='cpu mode for debug')
# dropout related setting
argparser.add_argument('--fdrop', default=0.2, type=float, help='feature drop')
argparser.add_argument('--adrop',
default=0.0,
type=float,
help='attention drop')
# model ralated setting
argparser.add_argument('--GL', default=3, type=int, help='gnn layers')
argparser.add_argument('--vinitial', default='id', help='id/mean/max/sum/gru')
argparser.add_argument('--graph_feature_select',
default='gated',
help='last/gated/mean')
argparser.add_argument('--pooling',
default='cnext',
help='ilast/imean/cmean/cnext/input')
argparser.add_argument('--cluster_type',
default='mean',
help='mean/max/last/mean+')
argparser.add_argument('--predictor',
default='cosine',
help='cosine/bicosine/bilinear/matmul')
argparser.add_argument('--add_loss',
default=False,
type=bool,
help='add corr losss or not')
argparser.add_argument('--beta',
default=10.0,
type=float,
help='corr loss weight')
argparser.add_argument('--tao',
default=1.0,
type=float,
help='weight for softmax') #需要调参
# model comments
argparser.add_argument('--comment',
default='None',
type=str,
help='other introduction')
argparser.add_argument('--statistics',
action='store_true',
help='show data statistics')
# record result
argparser.add_argument('--db',
default='sqlite',
type=str,
choices=['sqlite', 'mysql'],
help='record the result to sqlite or mysql database.')
args = argparser.parse_args()
print(args)
args_filter = [
'dataset', 'GL', 'predictor', 'add_loss', 'beta', 'tao'
'batch', 'lr', 'lr_step', 'emb_size', 'fdrop', 'adrop', 'max_length',
'comment'
] # recording hyper-parameters
device = torch.device('cuda:' + str(args.gpu) if torch.cuda.is_available()
and args.debug == False and args.gpu >= 0 else 'cpu')
if args.dataset.startswith('yc_BT'):
data = yoochoose
elif args.dataset.startswith('jd'):
data = jdata
elif args.dataset.startswith('digi'):
data = yoochoose
path = '../dataset/'
# modes=["train" ,"test" ,"test_buy" ]
all_data, max_vid, item_cates = data.load_cd_data(
path + args.dataset, type='aug', test_length=True,
highfreq_only=True) # type='aug','common'
if args.statistics:
data_statistics(all_data)
print(max_vid)
data_describe(dataset=args.dataset, datas=all_data)
set_seed(args.seed)
collate_fn = gnn_collate_fn(seq_to_SSL_graph)
all_data, num_class = datasets(all_data, data.TBVSessionDataset,
args.max_length, max_vid)
iters = dataloaders(datas=all_data, batch=args.batch, collate=collate_fn)
model = IAGNN(num_class,
args.emb_size,
num_layers=args.GL,
device=device,
batch_norm=True,
add_loss=args.add_loss,
feat_drop=args.fdrop,
attention_drop=args.adrop,
tao=args.tao,
vinitial_type=args.vinitial,
graph_feature_select=args.graph_feature_select,
pooling_type=args.pooling,
predictor_type=args.predictor).to(device)
optimizer = torch.optim.AdamW(fix_weight_decay(model),
lr=args.lr,
weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_step,
gamma=args.lr_gama)
train(args, model, optimizer, scheduler, device, iters, args_filter,
item_cates)