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
import os
import sys
sys.path.append(os.getcwd())
from openrec.legacy import ImplicitModelTrainer
from openrec.legacy.utils import ImplicitDataset
from openrec.legacy.recommenders import ConcatVisualBPR
from openrec.legacy.utils.evaluators import AUC
from openrec.legacy.utils.samplers import PairwiseSampler
from config import sess_config
import dataloader
raw_data = dataloader.load_tradesy()
batch_size = 1000
test_batch_size = 100
item_serving_size = 1000
display_itr = 10000
train_dataset = ImplicitDataset(raw_data['train_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Train')
val_dataset = ImplicitDataset(raw_data['val_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Val')
test_dataset = ImplicitDataset(raw_data['test_data'],
raw_data['max_user'],
raw_data['max_item'],
name='Test')