def __init__(self, config):
self.config = config # model configuration
self._metron_s = MetronAtK(top_k=config['top_k'])
self._metron_t = MetronAtK(top_k=config['top_k'])
self._writer = SummaryWriter(log_dir='runs/{}'.format(config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
# explicit feedback
# self.crit = torch.nn.MSELoss()
# implicit feedback
self.crit = torch.nn.BCELoss()
def __init__(self, config):
super(MatrixFactorization, self).__init__()
self.config = config
self._metron = MetronAtK(top_k= config['topk'])
self.num_users = config['num_users']
self.num_items = config['num_items']
self.latent_dim = config['latent_dim']
self.embedding_user = torch.nn.Embedding(num_embeddings=self.num_users, embedding_dim=self.latent_dim)
self.embedding_item = torch.nn.Embedding(num_embeddings=self.num_items, embedding_dim=self.latent_dim)
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK(top_k=10)
self.opt = use_optimizer(self.model, config)
self.model_name = config['model']
# explicit feedback
# self.crit = torch.nn.MSELoss()
# implicit feedback
if self.model_name == 'MF':
self.crit = torch.nn.MSELoss()
else:
self.crit = torch.nn.BCELoss()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK()
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
if not config['implicit']:
# explicit feedback
self.crit = torch.nn.MSELoss()
else:
# implicit feedback
self.crit = torch.nn.BCEWithLogitsLoss()
def __init__(self, config):
self.config = config # model configuration
self._metron = MetronAtK(top_k=10)
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # tensorboard writer
self._writer.add_text('config', str(config), 0)
self.opt = use_optimizer(self.model, config)
self.crit = torch.nn.BCELoss()
self.mse = torch.nn.MSELoss()
self.sparse = False
if config['friend_item_matrix'].split(".")[-1] == "npz":
self.friend_item_matrix = scipy.sparse.load_npz(
config['friend_item_matrix'])
self.sparse = True
else:
self.friend_item_matrix = np.load(config['friend_item_matrix'])
def __init__(self, config):
super(GMF, self).__init__()
self.config = config
self._metron = MetronAtK(top_k= config['topk'])
self.crit = torch.nn.BCEWithLogitsLoss()
self.num_users = config['num_users']
self.num_items = config['num_items']
self.latent_dim = config['latent_dim']
self.embedding_user = torch.nn.Embedding(num_embeddings=self.num_users, embedding_dim=self.latent_dim)
self.embedding_item = torch.nn.Embedding(num_embeddings=self.num_items, embedding_dim=self.latent_dim)
self.affine_output = torch.nn.Linear(in_features=self.latent_dim, out_features=1)
self.bpr = config['BPR_loss']
self.logsigmoid = torch.nn.LogSigmoid()
def __init__(self, config):
"""
Function to initialize the engine
:param config: configuration dictionary
"""
self.config = config # model configuration
self._metron = MetronAtK(top_k=10) # Metrics for Top-10
self._writer = SummaryWriter(log_dir='runs/{}'.format(
config['alias'])) # Tensorboard Writer
self._writer.add_text('config', str(config),
0) # String output for Tensorboard Writer
self.opt = use_optimizer(self.model, config) # set optimizer
# self.crit = torch.nn.MSELoss() # mean squared error loss for explicit feedback
self.crit = torch.nn.BCELoss(
) # binary cross entropy loss for implicit feedback
def __init__(self):
self._metron = MetronAtK(top_k=10)
