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.tf1.legacy import ImplicitModelTrainer
from openrec.tf1.legacy.utils import ImplicitDataset
from openrec.tf1.legacy.recommenders import VisualCML
from openrec.tf1.legacy.utils.evaluators import AUC, Recall
from openrec.tf1.legacy.utils.samplers import PairwiseSampler
from config import sess_config
import dataloader
raw_data = dataloader.load_amazon_book()
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')
