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Procedure.py
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Procedure.py
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'''
Created on Mar 1, 2020
Pytorch Implementation of LightGCN in
Xiangnan He et al. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
@author: Jianbai Ye ([email protected])
Design training and test process
'''
import os
import joblib
from os.path import join as pjoin
import world
import numpy as np
import torch
import utils
import dataloader
from pprint import pprint
from utils import timer
from time import time
from tqdm import tqdm
import model
import multiprocessing
from sklearn.metrics import roc_auc_score
from validate_submission import *
CORES = multiprocessing.cpu_count() // 2
def BPR_train_original(dataset, recommend_model, loss_class, epoch, neg_k=1, w=None):
Recmodel = recommend_model
Recmodel.train()
bpr: utils.BPRLoss = loss_class
with timer(name="Sample"):
S = utils.UniformSample_original(dataset)
users = torch.Tensor(S[:, 0]).long()
posItems = torch.Tensor(S[:, 1]).long()
negItems = torch.Tensor(S[:, 2]).long()
users = users.to(world.device)
posItems = posItems.to(world.device)
negItems = negItems.to(world.device)
users, posItems, negItems = utils.shuffle(users, posItems, negItems)
total_batch = len(users) // world.config['bpr_batch_size'] + 1
aver_loss = 0.
for (batch_i,
(batch_users,
batch_pos,
batch_neg)) in enumerate(utils.minibatch(users,
posItems,
negItems,
batch_size=world.config['bpr_batch_size'])):
cri = bpr.stageOne(batch_users, batch_pos, batch_neg)
aver_loss += cri
if world.tensorboard:
w.add_scalar(f'BPRLoss/BPR', cri, epoch * int(len(users) / world.config['bpr_batch_size']) + batch_i)
aver_loss = aver_loss / total_batch
time_info = timer.dict()
timer.zero()
return f"loss{aver_loss:.3f}-{time_info}"
def test_one_batch(X):
sorted_items = X[0].numpy()
groundTrue = X[1]
r = utils.getLabel(groundTrue, sorted_items)
pre, recall, ndcg = [], [], []
for k in world.topks:
ret = utils.RecallPrecision_ATk(groundTrue, r, k)
pre.append(ret['precision'])
recall.append(ret['recall'])
ndcg.append(utils.NDCGatK_r(groundTrue,r,k))
return {'recall':np.array(recall),
'precision':np.array(pre),
'ndcg':np.array(ndcg)}
def save(ids_valid_data, ids_test_data, data_path, description='test',testing=False):
testing_des = 'testing_' if testing else ''
pkl_path = pjoin(data_path, f'{testing_des}{description}_score.pkl')
print('saving ====> {}'.format(pkl_path))
joblib.dump([ids_valid_data, ids_test_data], pkl_path)
def predict(model, data_path, epoch, seed='2020', testing=False, description=''):
market_name = os.path.split(data_path)[-1]
print('===> market: {}'.format(market_name))
print('===> predict:')
id_seriral_pkl_path = pjoin(data_path, 'id_seriral.pkl')
pkl_arr = joblib.load(id_seriral_pkl_path)
ids_valid_data, ids_test_data = pkl_arr
# predict
def predict_dot_product_score(df, model, description=''):
# TODO: device
users = torch.from_numpy(df['userId'].values).to('cuda:0')
items = torch.from_numpy(df['itemId'].values).to('cuda:0')
# print()
model.eval()
test_batch_s = 20480
batch_number = len(users) // test_batch_s + 1 if len(users) % test_batch_s else 0
scores , uembs, iembs = [], [], []
with torch.no_grad():
for i in range(batch_number):
score, uemb, iemb = model.xmrec_test(users[i*test_batch_s:(i+1)*test_batch_s], \
items[i*test_batch_s:(i+1)*test_batch_s])
scores.append(score)
uembs.append(uemb)
iembs.append(iemb)
scores = torch.cat(scores, dim=0)
df[f'{description}_score'] = scores.squeeze().detach().cpu().numpy()
return df
ids_valid_data = predict_dot_product_score(ids_valid_data, model, description=description)
ids_test_data = predict_dot_product_score(ids_test_data, model, description=description)
# reverse_ids
def return_idx(df, itemIdsmap, userIdsmap):
df['userId'] = userIdsmap.id2idx(df['userId'])
df['itemId'] = itemIdsmap.id2idx(df['itemId'])
return df
print('====> load IdsMap')
itemIdsMap = joblib.load(pjoin(data_path, "itemIdsMap.pkl"))
userIdsMap = joblib.load(pjoin(data_path, "userIdsMap.pkl"))
ids_valid_data = return_idx(ids_valid_data, itemIdsMap, userIdsMap)
ids_test_data = return_idx(ids_test_data, itemIdsMap, userIdsMap)
# cal score from groudtruth.
offline_scores(ids_valid_data, f'{description}_score',data_path, market_name)
# save score as features for lightgbm
return ids_valid_data, ids_test_data
def Test(dataset, Recmodel, epoch, w=None, multicore=0):
u_batch_size = world.config['test_u_batch_size']
dataset: utils.BasicDataset
testDict: dict = dataset.testDict
Recmodel: model.LightGCN
# eval mode with no dropout
Recmodel = Recmodel.eval()
max_K = max(world.topks)
if multicore == 1:
pool = multiprocessing.Pool(CORES)
results = {'precision': np.zeros(len(world.topks)),
'recall': np.zeros(len(world.topks)),
'ndcg': np.zeros(len(world.topks))}
with torch.no_grad():
users = list(testDict.keys())
try:
assert u_batch_size <= len(users) / 10
except AssertionError:
print(f"test_u_batch_size is too big for this dataset, try a small one {len(users) // 10}")
users_list = []
rating_list = []
groundTrue_list = []
# auc_record = []
# ratings = []
total_batch = len(users) // u_batch_size + 1
for batch_users in utils.minibatch(users, batch_size=u_batch_size):
allPos = dataset.getUserPosItems(batch_users)
groundTrue = [testDict[u] for u in batch_users]
batch_users_gpu = torch.Tensor(batch_users).long()
batch_users_gpu = batch_users_gpu.to(world.device)
rating = Recmodel.getUsersRating(batch_users_gpu)
#rating = rating.cpu()
exclude_index = []
exclude_items = []
for range_i, items in enumerate(allPos):
exclude_index.extend([range_i] * len(items))
exclude_items.extend(items)
rating[exclude_index, exclude_items] = -(1<<10)
_, rating_K = torch.topk(rating, k=max_K)
rating = rating.cpu().numpy()
# aucs = [
# utils.AUC(rating[i],
# dataset,
# test_data) for i, test_data in enumerate(groundTrue)
# ]
# auc_record.extend(aucs)
del rating
users_list.append(batch_users)
rating_list.append(rating_K.cpu())
groundTrue_list.append(groundTrue)
assert total_batch == len(users_list)
X = zip(rating_list, groundTrue_list)
if multicore == 1:
pre_results = pool.map(test_one_batch, X)
else:
pre_results = []
for x in X:
pre_results.append(test_one_batch(x))
scale = float(u_batch_size/len(users))
for result in pre_results:
results['recall'] += result['recall']
results['precision'] += result['precision']
results['ndcg'] += result['ndcg']
results['recall'] /= float(len(users))
results['precision'] /= float(len(users))
results['ndcg'] /= float(len(users))
# results['auc'] = np.mean(auc_record)
if world.tensorboard:
w.add_scalars(f'Test/Recall@{world.topks}',
{str(world.topks[i]): results['recall'][i] for i in range(len(world.topks))}, epoch)
w.add_scalars(f'Test/Precision@{world.topks}',
{str(world.topks[i]): results['precision'][i] for i in range(len(world.topks))}, epoch)
w.add_scalars(f'Test/NDCG@{world.topks}',
{str(world.topks[i]): results['ndcg'][i] for i in range(len(world.topks))}, epoch)
if multicore == 1:
pool.close()
print(results)
return results