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baseline_train_mf_ood.py
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baseline_train_mf_ood.py
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from minigpt4.models.rec_model import MatrixFactorization
from torch.utils.data.dataset import Dataset
from torch.utils.data.dataloader import DataLoader
import pandas as pd
import numpy as np
import torch.optim
from sklearn.metrics import roc_auc_score
import torch.nn as nn
import torch.nn.functional as F
import omegaconf
import random
import os
from minigpt4.tasks import base_task
import time
import numpy as np
def uAUC_me(user, predict, label):
if not isinstance(predict,np.ndarray):
predict = np.array(predict)
if not isinstance(label,np.ndarray):
label = np.array(label)
predict = predict.squeeze()
label = label.squeeze()
start_time = time.time()
u, inverse, counts = np.unique(user,return_inverse=True,return_counts=True) # sort in increasing
index = np.argsort(inverse)
candidates_dict = {}
k = 0
total_num = 0
only_one_interaction = 0
computed_u = []
for u_i in u:
start_id,end_id = total_num, total_num+counts[k]
u_i_counts = counts[k]
index_ui = index[start_id:end_id]
if u_i_counts ==1:
only_one_interaction += 1
total_num += counts[k]
k += 1
continue
# print(index_ui, predict.shape)
candidates_dict[u_i] = [predict[index_ui], label[index_ui]]
total_num += counts[k]
k+=1
print("only one interaction users:",only_one_interaction)
auc=[]
only_one_class = 0
for ui,pre_and_true in candidates_dict.items():
pre_i,label_i = pre_and_true
try:
ui_auc = roc_auc_score(label_i,pre_i)
auc.append(ui_auc)
computed_u.append(ui)
except:
only_one_class += 1
# print("only one class")
auc_for_user = np.array(auc)
print("computed user:", auc_for_user.shape[0], "can not users:", only_one_class)
uauc = auc_for_user.mean()
print("uauc for validation Cost:", time.time()-start_time,'uauc:', uauc)
return uauc, computed_u, auc_for_user
class early_stoper(object):
def __init__(self,ref_metric='valid_auc', incerase =True,patience=20) -> None:
self.ref_metric = ref_metric
self.best_metric = None
self.increase = incerase
self.reach_count = 0
self.patience= patience
# self.metrics = None
def _registry(self,metrics):
self.best_metric = metrics
def update(self, metrics):
if self.best_metric is None:
self._registry(metrics)
return True
else:
if self.increase and metrics[self.ref_metric] > self.best_metric[self.ref_metric]:
self.best_metric = metrics
self.reach_count = 0
return True
elif not self.increase and metrics[self.ref_metric] < self.best_metric[self.ref_metric]:
self.best_metric = metrics
self.reach_count = 0
return True
else:
self.reach_count += 1
return False
def is_stop(self):
if self.reach_count>=self.patience:
return True
else:
return False
# set random seed
def run_a_trail(train_config,log_file=None, save_mode=False,save_file=None,need_train=True,warm_or_cold=None):
seed=2023
random.seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# load dataset
# data_dir = "/home/zyang/LLM/MiniGPT-4/dataset/ml-100k/"
# data_dir = "/home/sist/zyang/LLM/datasets/ml-1m/"
data_dir = "/data/zyang/datasets/ml-1m/"
train_data = pd.read_pickle(data_dir+"train_ood2.pkl")[['uid','iid','label']].values
valid_data = pd.read_pickle(data_dir+"valid_ood2.pkl")[['uid','iid','label']].values
test_data = pd.read_pickle(data_dir+"test_ood2.pkl")[['uid','iid','label']].values
# train_config={
# "lr": 1e-2,
# "wd": 1e-4,
# "epoch": 5000,
# "eval_epoch":1,
# "patience":50,
# "batch_size":1024
# }
user_num = max(train_data[:,0].max(), valid_data[:,0].max(), test_data[:,0].max()) + 1
item_num = max(train_data[:,1].max(), valid_data[:,1].max(), test_data[:,1].max()) + 1
# if warm_or_cold is not None:
# if warm_or_cold == 'warm':
# test_data = pd.read_pickle(data_dir+"test_ood2.pkl")[['uid','iid','label', 'not_cold']]
# test_data = test_data[test_data['not_cold'].isin([1])][['uid','iid','label']].values
# print("warm data size:", test_data.shape[0])
# # pass
# else:
# test_data = pd.read_pickle(data_dir+"test_ood2.pkl")[['uid','iid','label', 'not_cold']]
# test_data = test_data[test_data['not_cold'].isin([0])][['uid','iid','label']].values
# print("cold data size:", test_data.shape[0])
# # pass
if warm_or_cold is not None:
if warm_or_cold == 'warm':
test_data = pd.read_pickle(data_dir+"test_warm_cold_ood2.pkl")[['uid','iid','label', 'warm']]
test_data = test_data[test_data['warm'].isin([1])][['uid','iid','label']].values
print("warm data size:", test_data.shape[0])
# pass
else:
test_data = pd.read_pickle(data_dir+"test_warm_cold_ood2.pkl")[['uid','iid','label', 'cold']]
test_data = test_data[test_data['cold'].isin([1])][['uid','iid','label']].values
print("cold data size:", test_data.shape[0])
# pass
print("user nums:", user_num, "item nums:", item_num)
mf_config={
"user_num": int(user_num),
"item_num": int(item_num),
"embedding_size": int(train_config['embedding_size'])
}
mf_config = omegaconf.OmegaConf.create(mf_config)
train_data_loader = DataLoader(train_data, batch_size = train_config['batch_size'], shuffle=True)
valid_data_loader = DataLoader(valid_data, batch_size = train_config['batch_size'], shuffle=False)
test_data_loader = DataLoader(test_data, batch_size = train_config['batch_size'], shuffle=False)
model = MatrixFactorization(mf_config).cuda()
opt = torch.optim.Adam(model.parameters(),lr=train_config['lr'],weight_decay=train_config['wd'])
early_stop = early_stoper(ref_metric='valid_auc',incerase=True,patience=train_config['patience'])
# trainig part
criterion = nn.BCEWithLogitsLoss()
if not need_train:
model.load_state_dict(torch.load(save_file))
model.eval()
pre=[]
label = []
users = []
for batch_id,batch_data in enumerate(valid_data_loader):
batch_data = batch_data.cuda()
ui_matching = model(batch_data[:,0].long(),batch_data[:,1].long())
users.extend(batch_data[:,0].cpu().numpy())
pre.extend(ui_matching.detach().cpu().numpy())
label.extend(batch_data[:,-1].cpu().numpy())
valid_auc = roc_auc_score(label,pre)
valid_uauc, _, _ = uAUC_me(users, pre, label)
label = np.array(label)
pre = np.array(pre)
thre = 0.1
pre[pre>=thre] = 1
pre[pre<thre] =0
val_acc = (label==pre).mean()
pre=[]
label = []
users = []
for batch_id,batch_data in enumerate(test_data_loader):
batch_data = batch_data.cuda()
ui_matching = model(batch_data[:,0].long(),batch_data[:,1].long())
pre.extend(ui_matching.detach().cpu().numpy())
label.extend(batch_data[:,-1].cpu().numpy())
users.extend(batch_data[:,0].cpu().numpy())
test_auc = roc_auc_score(label,pre)
test_uauc, _, _ = uAUC_me(users, pre, label)
print("valid_auc:{}, valid_uauc:{}, test_auc:{}, test_uauc:{}, acc: {}".format(valid_auc, valid_uauc, test_auc, test_uauc, val_acc))
return
for epoch in range(train_config['epoch']):
model.train()
for bacth_id, batch_data in enumerate(train_data_loader):
batch_data = batch_data.cuda()
ui_matching = model(batch_data[:,0].long(),batch_data[:,1].long())
loss = criterion(ui_matching,batch_data[:,-1].float())
opt.zero_grad()
loss.backward()
opt.step()
if epoch% train_config['eval_epoch']==0:
model.eval()
pre=[]
label = []
users = []
for batch_id,batch_data in enumerate(valid_data_loader):
batch_data = batch_data.cuda()
ui_matching = model(batch_data[:,0].long(),batch_data[:,1].long())
users.extend(batch_data[:,0].cpu().numpy())
pre.extend(ui_matching.detach().cpu().numpy())
label.extend(batch_data[:,-1].cpu().numpy())
valid_auc = roc_auc_score(label,pre)
valid_uauc, _, _ = uAUC_me(users, pre, label)
pre=[]
label = []
users = []
for batch_id,batch_data in enumerate(test_data_loader):
batch_data = batch_data.cuda()
ui_matching = model(batch_data[:,0].long(),batch_data[:,1].long())
users.extend(batch_data[:,0].cpu().numpy())
pre.extend(ui_matching.detach().cpu().numpy())
label.extend(batch_data[:,-1].cpu().numpy())
test_auc = roc_auc_score(label,pre)
test_uauc, _, _ = uAUC_me(users, pre, label)
updated = early_stop.update({'valid_auc':valid_auc, 'valid_uauc':valid_uauc,'test_auc':test_auc, 'test_uauc':test_uauc, 'epoch':epoch})
if updated and save_mode:
torch.save(model.state_dict(),save_file)
print("epoch:{}, valid_auc:{}, test_auc:{}, early_count:{}".format(epoch, valid_auc, test_auc, early_stop.reach_count))
if early_stop.is_stop():
print("early stop is reached....!")
# print("best results:", early_stop.best_metric)
break
if epoch>500 and early_stop.best_metric[early_stop.ref_metric] < 0.52:
print("training reaches to 500 epoch but the valid_auc is still less than 0.55")
break
print("train_config:", train_config,"\nbest result:",early_stop.best_metric)
if log_file is not None:
print("train_config:", train_config, "best result:", early_stop.best_metric, file=log_file)
log_file.flush()
# if __name__=='__main__':
# # lr_ = [1e-1,1e-2,1e-3]
# lr_=[1e-1]
# dw_ = [1e-2,1e-3,1e-4,1e-5,1e-6,1e-7]
# # embedding_size_ = [32, 64, 128, 156, 512]
# embedding_size_ = [64,128,256]
# try:
# f = open("0913ml1m-ood-v2-rec_mf_search_lr"+str(lr_[0])+".log",'rw+')
# except:
# f = open("0913ml1m-ood-v2-rec_mf_search_lr"+str(lr_[0])+".log",'w+')
# for lr in lr_:
# for wd in dw_:
# for embedding_size in embedding_size_:
# train_config={
# 'lr': lr,
# 'wd': wd,
# 'embedding_size': embedding_size,
# "epoch": 5000,
# "eval_epoch":1,
# "patience":100,
# "batch_size":2048
# }
# print(train_config)
# run_a_trail(train_config=train_config, log_file=f, save_mode=False)
# f.close()
# {'lr': 0.001, 'wd': 0.0001, 'embedding_size': 256, 'epoch': 5000, 'eval_epoch': 1, 'patience': 100, 'batch_size': 2048},
# {'valid_auc': 0.6760080227104877, 'valid_uauc': 0.6191863368703151, 'test_auc': 0.6482002627476354, 'test_uauc': 0.636100123360848, 'epoch': 465}
# save version....
# if __name__=='__main__':
# # lr_ = [1e-1,1e-2,1e-3]
# lr_=[1e-3] #1e-2
# dw_ = [1e-4]
# # embedding_size_ = [32, 64, 128, 156, 512]
# embedding_size_ = [256]
# save_path = "/data/zyang/LLM/PretrainedModels/mf/"
# # save_path = "/home/sist/zyang/LLM/PretrainedModels/mf/"
# # try:
# # f = open("rec_mf_search_lr"+str(lr_[0])+".log",'rw+')
# # except:
# # f = open("rec_mf_search_lr"+str(lr_[0])+".log",'w+')
# f=None
# for lr in lr_:
# for wd in dw_:
# for embedding_size in embedding_size_:
# train_config={
# 'lr': lr,
# 'wd': wd,
# 'embedding_size': embedding_size,
# "epoch": 5000,
# "eval_epoch":1,
# "patience":100,
# "batch_size":2048
# }
# print(train_config)
# save_path += "0912_ml1m_oodv2_best_model_d" + str(embedding_size)+ 'lr-'+ str(lr) + "wd"+str(wd) + ".pth"
# print("save path: ", save_path)
# run_a_trail(train_config=train_config, log_file=f, save_mode=True,save_file=save_path)
# f.close()
#### /data/zyang/LLM/PretrainedModels/mf/best_model_d128.pth
# with prtrain version:
if __name__=='__main__':
# lr_ = [1e-1,1e-2,1e-3]
lr_=[1e-3] #1e-2
dw_ = [1e-4]
# embedding_size_ = [32, 64, 128, 156, 512]
embedding_size_ = [256]
save_path = "/data/zyang/LLM/PretrainedModels/mf/"
# try:
# f = open("rec_mf_search_lr"+str(lr_[0])+".log",'rw+')
# except:
# f = open("rec_mf_search_lr"+str(lr_[0])+".log",'w+')
f=None
for lr in lr_:
for wd in dw_:
for embedding_size in embedding_size_:
train_config={
'lr': lr,
'wd': wd,
'embedding_size': embedding_size,
"epoch": 5000,
"eval_epoch":1,
"patience":50,
"batch_size":1024
}
print(train_config)
# save_path = "/data/zyang/LLM/PretrainedModels/mf/0912_ml100k_oodv2_best_model_d64lr-0.001wd0.0001.pth"
save_path = "/data/zyang/LLM/PretrainedModels/mf/0912_ml1m_oodv2_best_model_d256lr-0.001wd0.0001.pth"
# if os.path.exists(save_path + "0912_ml100k_oodv2_best_model_d" + str(embedding_size)+ 'lr-'+ str(lr) + "wd"+str(wd) + ".pth"):
# save_path += "0912_ml100k_oodv2_best_model_d" + str(embedding_size)+ 'lr-'+ str(lr) + "wd"+str(wd) + ".pth"
# print(save_path)
# else:
# save_path += "best_model_d" + str(embedding_size) + ".pth"
run_a_trail(train_config=train_config, log_file=f, save_mode=False,save_file=save_path,need_train=False,warm_or_cold='warm')
if f is not None:
f.close()