def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [
("_factors", "factors", "factors", 10, None, None),
("_hidden_neurons", "hidden_neurons", "hidden_neurons", "(64,32)",
lambda x: list(make_tuple(x)),
lambda x: self._batch_remove(str(x), " []").replace(",", "-")),
("_hidden_activations", "hidden_activations", "hidden_activations",
"('relu','relu')", lambda x: list(make_tuple(x)),
lambda x: self._batch_remove(str(x), " []").replace(",", "-")),
("_learning_rate", "lr", "lr", 0.001, None, None),
("_l_w", "reg", "reg", 0.1, None, None)
]
self.autoset_params()
if self._batch_size < 1:
self._batch_size = self._data.transactions
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._i_items_set = list(range(self._num_items))
self._sampler = pws.Sampler(self._data.i_train_dict)
self._model = NeuralFactorizationMachineModel(
self._num_users, self._num_items, self._factors,
tuple(
m
for m in zip(self._hidden_neurons, self._hidden_activations)),
self._l_w, self._learning_rate)
def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [("_factors", "factors", "factors", 10, None,
None),
("_learning_rate", "lr", "lr", 0.001, None, None),
("_l_w", "reg", "reg", 0.1, None, None)]
self.autoset_params()
if self._batch_size < 1:
self._batch_size = self._data.transactions
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._i_items_set = list(range(self._num_items))
self._sampler = pws.Sampler(self._data.i_train_dict)
self._model = FactorizationMachineModel(self._num_users,
self._num_items, self._factors,
self._l_w, self._learning_rate)
def __init__(self, data, config, params, *args, **kwargs):
self._random = np.random
self._params_list = [
("_factors", "factors", "factors", 10, None, None),
("_learning_rate", "lr", "lr", 0.001, None, None),
("_l_w", "reg", "reg", 0.1, None, None),
("_simplify", "mf_simplification", "mf", False, None, None)
]
self.autoset_params()
if self._batch_size < 1:
self._batch_size = self._data.transactions
self._ratings = self._data.train_dict
self._sp_i_train = self._data.sp_i_train
self._i_items_set = list(range(self._num_items))
if ((not self._simplify)
and hasattr(self._data, "nfeatures")) and (hasattr(
self._data, "side_information_data")) and (hasattr(
self._data.side_information_data, "feature_map")):
self._nfeatures = self._data.nfeatures
self._item_array = self.get_item_fragment()
else:
self._nfeatures = 0
self._field_dims = [self._num_users, self._num_items, self._nfeatures]
self._sampler = pws.Sampler(self._data.i_train_dict,
self._data.sp_i_train_ratings)
self._model = FactorizationMachineModel(self._num_users,
self._num_items,
self._nfeatures, self._factors,
self._l_w, self._learning_rate)