def _batch_setup(model: LightFM,
item_chunks: Dict[int, np.ndarray],
item_features: Union[None, sp.csr_matrix] = None,
user_features: Union[None, sp.csr_matrix] = None,
n_process: int = 1):
global _item_repr, _user_repr
global _item_repr_biases, _user_repr_biases
global _pool
global _item_chunks
if item_features is None:
n_items = len(model.item_biases)
item_features = sp.identity(n_items, dtype=CYTHON_DTYPE, format='csr')
if user_features is None:
n_users = len(model.user_biases)
user_features = sp.identity(n_users, dtype=CYTHON_DTYPE, format='csr')
n_users = user_features.shape[0]
user_features = model._construct_user_features(n_users, user_features)
_user_repr, _user_repr_biases = _precompute_representation(
features=user_features,
feature_embeddings=model.user_embeddings,
feature_biases=model.user_biases,
)
n_items = item_features.shape[0]
item_features = model._construct_item_features(n_items, item_features)
_item_repr, _item_repr_biases = _precompute_representation(
features=item_features,
feature_embeddings=model.item_embeddings,
feature_biases=model.item_biases,
)
_item_repr = _item_repr.T
_item_chunks = item_chunks
_clean_pool()
# Pool creation should go last
if n_process > 1:
_pool = mp.Pool(processes=n_process)
