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train_test.py
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from model.KRED import KREDModel
from model.KRED import Softmax_BCELoss
import torch
from torch import optim, nn
from trainer.trainer import Trainer
from base.base_data_loader import *
from torch.utils.data import Dataset, DataLoader, RandomSampler
from utils.metrics import *
from utils.util import *
class NewsDataset(Dataset):
def __init__(self, dic_data, transform=None):
self.dic_data = dic_data
self.transform = transform
def __len__(self):
return len(self.dic_data['label'])
def __getitem__(self, idx):
if torch.is_tensor(idx):
idx = idx.tolist()
sample = {'item1': self.dic_data['item1'][idx], 'item2': self.dic_data['item2'][idx], 'label': self.dic_data['label'][idx]}
return sample
def multi_task_training(config, data):
user_history_dict, entity_embedding, relation_embedding, entity_adj, relation_adj, doc_feature_dict, entity_num, position_num, type_num, user2item_train, user2item_test, vert_train, vert_test, local_train, local_test, pop_train, pop_test, item2item_train, item2item_test = data
train_data_u2i = NewsDataset(user2item_train)
train_sampler_u2i = RandomSampler(train_data_u2i)
train_dataloader_u2i = DataLoader(train_data_u2i, sampler=train_sampler_u2i, batch_size=config['data_loader']['batch_size'],
collate_fn=my_collate_fn, pin_memory=False)
train_data_vert = NewsDataset(vert_train)
train_sampler_vert = RandomSampler(train_data_vert)
train_dataloader_vert = DataLoader(train_data_vert, sampler=train_sampler_vert, batch_size=config['data_loader']['batch_size'],
pin_memory=False)
train_data_pop = NewsDataset(pop_train)
train_sampler_pop = RandomSampler(train_data_pop)
train_dataloader_pop = DataLoader(train_data_pop, sampler=train_sampler_pop, batch_size=config['data_loader']['batch_size'],
pin_memory=False)
train_data_i2i = NewsDataset(item2item_train)
train_sampler_i2i = RandomSampler(train_data_i2i)
train_dataloader_i2i = DataLoader(train_data_i2i, sampler=train_sampler_i2i, batch_size=config['data_loader']['batch_size'],
pin_memory=False)
device, deviceids = prepare_device(config['n_gpu'])
model = KREDModel(config, user_history_dict, doc_feature_dict, entity_embedding, relation_embedding, entity_adj,
relation_adj, entity_num, position_num, type_num).to(device)()
pretrain_epoch = 0
while (pretrain_epoch < 5):
model.train()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=config['optimizer']['lr'], weight_decay=0)
total_loss_vert = 0
model.train()
for step, batch in enumerate(train_dataloader_vert):
out = model(batch['item1'], batch['item2'], "vert_classify")[1]
loss = criterion(out, torch.tensor(batch['label']).to(device)())
total_loss_vert = total_loss_vert + loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch {} loss {}'.format(pretrain_epoch, total_loss_vert))
total_loss_pop = 0
model.train()
for step, batch in enumerate(train_dataloader_pop):
out = model(batch['item1'], batch['item2'], "pop_predict")[3]
loss = criterion(out, torch.tensor(batch['label']).to(device)())
total_loss_pop = total_loss_pop + loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch {} loss {}'.format(pretrain_epoch, total_loss_pop))
criterion = Softmax_BCELoss(config)
total_loss_i2i = 0
model.train()
for step, batch in enumerate(train_dataloader_i2i):
out = model(batch['item1'], batch['item2'], "item2item")[4]
loss = criterion(out, torch.stack(batch['label']).float().to(device)())
total_loss_i2i = total_loss_i2i + loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch {} loss {}'.format(pretrain_epoch, total_loss_i2i))
optimizer = optim.Adam(model.parameters(), lr=config['optimizer']['lr'], weight_decay=args.l2_regular)
total_loss_u2i = 0
model.train()
for step, batch in enumerate(train_dataloader_u2i):
batch = real_batch(batch)
out = model(batch['item1'], batch['item2'], "user2item")[0]
loss = criterion(out, torch.tensor(batch['label']).to(device)())
total_loss_u2i = total_loss_u2i + loss
optimizer.zero_grad()
loss.backward()
optimizer.step()
print('epoch {} loss {}'.format(pretrain_epoch, total_loss_u2i))
pretrain_epoch = pretrain_epoch + 1
if config['trainer']['task'] == "user2item":
criterion = Softmax_BCELoss(config)
train_data_loader = train_dataloader_u2i
elif config['trainer']['task'] == "item2item":
criterion = Softmax_BCELoss(config)
train_data_loader = train_dataloader_i2i
elif config['trainer']['task'] == "vert_classify":
criterion = nn.CrossEntropyLoss()
train_data_loader = train_dataloader_vert
elif config['trainer']['task'] == "pop_predict":
criterion = nn.CrossEntropyLoss()
train_data_loader = train_dataloader_pop
else:
print("Error: task name error.")
trainer = Trainer(config, model, criterion, optimizer, device, train_data_loader, data[-1])
trainer.train()
def single_task_training(config, data):
user_history_dict, entity_embedding, relation_embedding, entity_adj, relation_adj, doc_feature_dict, entity_num, position_num, type_num, train_data, test_data = data
if config['trainer']['task'] == "user2item":
train_data_u2i = NewsDataset(train_data)
train_sampler_u2i = RandomSampler(train_data_u2i)
train_dataloader_u2i = DataLoader(train_data_u2i, sampler=train_sampler_u2i,
batch_size=config['data_loader']['batch_size'],
collate_fn=my_collate_fn, pin_memory=False)
criterion = Softmax_BCELoss(config)
train_data_loader = train_dataloader_u2i
elif config['trainer']['task'] == "item2item":
train_data_i2i = NewsDataset(train_data)
train_sampler_i2i = RandomSampler(train_data_i2i)
train_dataloader_i2i = DataLoader(train_data_i2i, sampler=train_sampler_i2i,
batch_size=config['data_loader']['batch_size'],
pin_memory=False)
criterion = Softmax_BCELoss(config)
train_data_loader = train_dataloader_i2i
elif config['trainer']['task'] == "vert_classify":
train_data_vert = NewsDataset(train_data)
train_sampler_vert = RandomSampler(train_data_vert)
train_dataloader_vert = DataLoader(train_data_vert, sampler=train_sampler_vert,
batch_size=config['data_loader']['batch_size'],
pin_memory=False)
criterion = nn.CrossEntropyLoss()
train_data_loader = train_dataloader_vert
elif config['trainer']['task'] == "pop_predict":
train_data_pop = NewsDataset(train_data)
train_sampler_pop = RandomSampler(train_data_pop)
train_dataloader_pop = DataLoader(train_data_pop, sampler=train_sampler_pop,
batch_size=config['data_loader']['batch_size'],
pin_memory=False)
criterion = nn.CrossEntropyLoss()
train_data_loader = train_dataloader_pop
else:
print("Error: task name error.")
if config['trainer']['adressa_adaptation']:
device, deviceids = prepare_device(config['n_gpu'])
else:
device = 'mps'
model = KREDModel(config, user_history_dict, doc_feature_dict, entity_embedding, relation_embedding, entity_adj,
relation_adj, entity_num, position_num, type_num, device).to(device)
optimizer = optim.Adam(model.parameters(), lr=config['optimizer']['lr'], weight_decay=0)
trainer = Trainer(config, model, criterion, optimizer, device, train_data_loader, data[-1])
trainer.train()
def testing(test_data, config):
if config['trainer']['task'] == "user2item":
task_index = 0
elif config['trainer']['task'] == "item2item":
task_index = 4
elif config['trainer']['task'] == "vert_classify":
task_index = 1
elif config['trainer']['task'] == "pop_predict":
task_index = 3
model = torch.load('./out/saved/models/KRED/checkpoint.pt')
model.eval()
y_pred = []
start_list = list(range(0, len(test_data['label']), config['data_loader']['batch_size']))
for start in start_list:
if start + config['data_loader']['batch_size'] <= len(test_data['label']):
end = start + config['data_loader']['batch_size']
else:
end = len(test_data['label'])
out = model(test_data['item1'][start:end], test_data['item2'][start:end],
config['data_loader']['batch_size'])[
task_index].cpu().data.numpy()
y_pred.extend(out)
truth = test_data['label']
score = evaluate(y_pred, truth, test_data, config['trainer']['task'])
def testing_base_model(test_data, config):
"""
:param test_data: test dataset to evaluate the model
:param config: configuration settings
:return auc_score, ndcg_score: the two scores selected for performance evaluation
Performs testing using the model in evaluation mode on the test dataset
"""
print("Model testing:")
if config['trainer']['task'] == "user2item":
task_index = 0
elif config['trainer']['task'] == "item2item":
task_index = 4
elif config['trainer']['task'] == "vert_classify":
task_index = 1
elif config['trainer']['task'] == "pop_predict":
task_index = 3
model = torch.load('./out/saved/models/KRED/checkpoint.pt')
model.eval()
y_pred = []
start_list = list(range(0, len(test_data['label']), config['data_loader']['batch_size']))
for start in start_list:
if start + config['data_loader']['batch_size'] <= len(test_data['label']):
end = start + config['data_loader']['batch_size']
else:
end = len(test_data['label'])
out = model(test_data['item1'][start:end], test_data['item2'][start:end],
config['data_loader']['batch_size'])[task_index].cpu().data.numpy()
y_pred.extend(out)
truth = test_data['label']
auc_score, ndcg_score = evaluate_base_model(y_pred, truth, test_data, config['trainer']['task'])
return auc_score, ndcg_score
def testing_movies(test_data, config):
"""
:param test_data: test dataset to evaluate the model
:param config: configuration settings
:return auc_score, ndcg_score: the two scores selected for performance evaluation
Performs testing using the model in evaluation mode on the test dataset
"""
print("Model testing:")
if config['trainer']['task'] == "user2item":
task_index = 0
elif config['trainer']['task'] == "item2item":
task_index = 4
elif config['trainer']['task'] == "vert_classify":
task_index = 1
elif config['trainer']['task'] == "pop_predict":
task_index = 3
model = torch.load('./out/saved/models/KRED/checkpoint.pt')
model.eval()
y_pred = []
start_list = list(range(0, len(test_data['label']), config['data_loader']['batch_size']))
for start in start_list:
if start + config['data_loader']['batch_size'] <= len(test_data['label']):
end = start + config['data_loader']['batch_size']
else:
end = len(test_data['label'])
out = model(test_data['item1'][start:end], test_data['item2'][start:end],
config['data_loader']['batch_size'])[
task_index].cpu().data.numpy()
y_pred.extend(out)
truth = test_data['label']
auc_score, ndcg_score = evaluate_movies(y_pred, truth, test_data, config['trainer']['task'])
return auc_score, ndcg_score
def testing_adressa(test_data, config):
"""
:param test_data: test dataset to evaluate the model
:param config: configuration settings
:return auc_score, ndcg_score: the two scores selected for performance evaluation
Performs testing using the model in evaluation mode on the test dataset
"""
print("Model testing:")
if config['trainer']['task'] == "user2item":
task_index = 0
elif config['trainer']['task'] == "item2item":
task_index = 4
elif config['trainer']['task'] == "vert_classify":
task_index = 1
elif config['trainer']['task'] == "pop_predict":
task_index = 3
model = torch.load('./out/saved/models/KRED/checkpoint.pt')
model.eval()
y_pred = []
start_list = list(range(0, len(test_data['label']), config['data_loader']['batch_size']))
for start in start_list:
if start + config['data_loader']['batch_size'] <= len(test_data['label']):
end = start + config['data_loader']['batch_size']
else:
end = len(test_data['label'])
out = model(test_data['item1'][start:end], test_data['item2'][start:end],
config['data_loader']['batch_size'])[task_index].cpu().data.numpy()
y_pred.extend(out)
truth = test_data['label']
auc_score, ndcg_score = evaluate_base_model(y_pred, truth, test_data, config['trainer']['task'])
return auc_score, ndcg_score