def __init__(self,
n_factors=10,
model=None,
sparse=False,
n_iter=10,
loss=None,
l2=0.0,
learning_rate=1e-2,
optimizer_func=None,
batch_size=None,
random_state=None,
use_cuda=False,
device_id=None,
logger=None,
n_jobs=0,
pin_memory=False,
verbose=False,
early_stopping=False,
n_iter_no_change=10,
tol=1e-4,
stopping=False):
super(FM, self).__init__()
self._no_improvement_count = 0
self.n_factors = n_factors
self.n_iter = n_iter
self.batch_size = batch_size
self.learning_rate = learning_rate
self.l2 = l2
self._model = model
self._sparse = sparse
self._random_state = random_state or np.random.RandomState()
self.use_cuda = use_cuda
self._optimizer_func = optimizer_func
self._loss_func = loss or torch.nn.MSELoss()
self._logger = logger or Logger()
self._n_jobs = n_jobs
self._optimizer = None
self._dataset = None
self._sparse = sparse
self._n_items = None
self._n_users = None
self._pin_memory = pin_memory
self._disable = not verbose
self._early_stopping = early_stopping
self._n_iter_no_change = n_iter_no_change
self._tol = tol
self._stopping = stopping
if device_id is not None and not self.use_cuda:
raise ValueError("use_cuda flag must be true")
self._device_id = device_id
set_seed(self._random_state.randint(-10 ** 8, 10 ** 8),
cuda=self.use_cuda)
def __init__(self,
n_factors=10,
model=None,
sparse=False,
n_iter=10,
loss=None,
l2=0.0,
learning_rate=1e-2,
optimizer_func=None,
batch_size=None,
random_state=None,
use_cuda=False,
logger=None,
n_jobs=0):
super(FM, self).__init__()
self._n_factors = n_factors
self._model = model
self._n_iter = n_iter
self._sparse = sparse
self._batch_size = batch_size
self._random_state = random_state or np.random.RandomState()
self._use_cuda = use_cuda
self._l2 = l2
self._learning_rate = learning_rate
self._optimizer_func = optimizer_func
self._loss_func = loss or torch.nn.MSELoss()
self._logger = logger or Logger()
self._n_jobs = n_jobs
self._optimizer = None
self._dataset = None
self._sparse = sparse
self._initialized = False
set_seed(self._random_state.randint(-10 ** 8, 10 ** 8),
cuda=self._use_cuda)