def get_raw_data(dataset_name):
raw_data = None
if dataset_name == "amazon_book":
raw_data = dataloader.load_amazon_book()
elif dataset_name == "citeulike":
raw_data = dataloader.load_citeulike()
elif dataset_name == "tradesy":
raw_data = dataloader.load_tradesy()
else:
print("Bad dataset name.")
exit()
return raw_data
from openrec import ModelTrainer
from openrec.utils import Dataset
from BPR import BPR
from openrec.utils.evaluators import AUC
from openrec.utils.samplers import RandomPairwiseSampler
from openrec.utils.samplers import EvaluationSampler
import dataloader
raw_data = dataloader.load_citeulike()
dim_embed = 100
total_iter = 10000
batch_size = 1000
eval_iter = 10000
save_iter = eval_iter
train_dataset = Dataset(raw_data['train_data'], raw_data['total_users'], raw_data['total_items'], name='Train')
val_dataset = Dataset(raw_data['val_data'], raw_data['total_users'], raw_data['total_items'], name='Val', num_negatives=500)
test_dataset = Dataset(raw_data['test_data'], raw_data['total_users'], raw_data['total_items'], name='Test', num_negatives=500)
train_sampler = RandomPairwiseSampler(batch_size=batch_size, dataset=train_dataset, num_process=5)
val_sampler = EvaluationSampler(batch_size=batch_size, dataset=val_dataset)
test_sampler = EvaluationSampler(batch_size=batch_size, dataset=test_dataset)
bpr_model = BPR(batch_size=batch_size, total_users=train_dataset.total_users(), total_items=train_dataset.total_items(),
l2_reg=0.01,
dim_user_embed=dim_embed, dim_item_embed=dim_embed, save_model_dir='bpr_recommender/', train=True, serve=True)
model_trainer = ModelTrainer(model=bpr_model)
auc_evaluator = AUC()
model_trainer.train(total_iter=total_iter, eval_iter=eval_iter, save_iter=save_iter, train_sampler=train_sampler,
from openrec.tf2.data import Dataset
from openrec.tf2.recommenders import BPR
from openrec.tf2.metrics import AUC, NDCG, Recall, DictMean
from tqdm.auto import tqdm
import tensorflow as tf
import numpy as np
from tensorflow.keras import optimizers
import dataloader
raw_data = dataloader.load_citeulike('../dataset/')
dim_embed = 50
total_iter = int(1e5)
batch_size = 1000
eval_interval = 1000
save_interval = eval_interval
train_dataset = Dataset(raw_data=raw_data['train_data'],
total_users=raw_data['total_users'],
total_items=raw_data['total_items'])
val_dataset = Dataset(raw_data=raw_data['val_data'],
total_users=raw_data['total_users'],
total_items=raw_data['total_items'])
bpr_model = BPR(total_users=raw_data['total_users'],
total_items=raw_data['total_items'],
dim_user_embed=dim_embed,
dim_item_embed=dim_embed)
optimizer = optimizers.Adam()
