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')