def predict(self, sequences, item_ids=None):
"""
Make predictions: given a sequence of interactions, predict
the next item in the sequence.
Parameters
----------
sequences: array, (1 x max_sequence_length)
Array containing the indices of the items in the sequence.
item_ids: array (num_items x 1), optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: array
Predicted scores for all items in item_ids.
"""
self._net.train(False)
if self._representation != "mylstm":
sequences = np.atleast_2d(sequences)
if item_ids is None:
item_ids = np.arange(self._num_items).reshape(-1, 1)
self._check_input(item_ids)
self._check_input(sequences)
sequences = torch.from_numpy(
sequences.astype(np.int64).reshape(1, -1))
item_ids = torch.from_numpy(item_ids.astype(np.int64))
sequence_var = gpu(sequences, self._use_cuda)
item_var = gpu(item_ids, self._use_cuda)
_, sequence_representations = self._net.user_representation(
sequence_var)
size = (len(item_var), ) + sequence_representations.size()[1:]
out = self._net(
sequence_representations.expand(*size).unsqueeze(2), item_var)
return cpu(out).detach().numpy().flatten()
else:
sequences = torch.from_numpy(sequences.astype(np.int64))
sequence_var = gpu(sequences, self._use_cuda)
sequence_representations, _ = self._net.user_representation(
sequence_var)
#print("s",sequence_representations.size())
out = self._net(sequence_representations, 0)
#print("o",out.size())
return cpu(out).detach().numpy()
def predict(self, user_ids, item_ids=None):
"""
Make predictions: given a user id, compute the recommendation
scores for items.
Parameters
----------
user_ids: int or array
If int, will predict the recommendation scores for this
user for all items in item_ids. If an array, will predict
scores for all (user, item) pairs defined by user_ids and
item_ids.
item_ids: array, optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: np.array
Predicted scores for all items in item_ids.
"""
self._check_input(user_ids, item_ids, allow_items_none=True)
self._net.train(False)
user_ids, item_ids = _predict_process_ids(user_ids, item_ids,
self._num_items,
self._use_cuda)
out = self._net(user_ids, item_ids)
return cpu(out).detach().numpy().flatten()
def predict(self, sequences, item_ids=None):
"""
Make predictions: given a sequence of interactions, predict
the next item in the sequence.
Parameters
----------
sequences: array, (1 x max_sequence_length)
Array containing the indices of the items in the sequence.
item_ids: array (num_items x 1), optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: array
Predicted scores for all items in item_ids.
"""
self._net.train(False)
sequences = np.atleast_2d(sequences)
if item_ids is None:
item_ids = np.arange(self._num_items).reshape(-1, 1)
self._check_input(item_ids)
self._check_input(sequences)
sequences = torch.from_numpy(sequences.astype(np.int64).reshape(1, -1))
item_ids = torch.from_numpy(item_ids.astype(np.int64))
sequence_var = Variable(gpu(sequences, self._use_cuda))
item_var = Variable(gpu(item_ids, self._use_cuda))
_, sequence_representations = self._net.user_representation(
sequence_var)
out = self._net(sequence_representations.repeat(len(item_var), 1),
item_var)
return cpu(out.data).numpy().flatten()
def predict(self, user_ids, item_ids=None):
"""
Make predictions: given a user id, compute the recommendation
scores for items.
Parameters
----------
user_ids: int or array
If int, will predict the recommendation scores for this
user for all items in item_ids. If an array, will predict
scores for all (user, item) pairs defined by user_ids and
item_ids.
item_ids: array, optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: np.array
Predicted scores for all items in item_ids.
"""
if item_ids is None:
item_ids = np.arange(self._num_items)
if isinstance(user_ids, int):
user_ids = np.repeat(user_ids, len(item_ids))
self._check_input(user_ids, item_ids)
user_ids = torch.from_numpy(user_ids.reshape(-1, 1).astype(np.int64))
item_ids = torch.from_numpy(item_ids.reshape(-1, 1).astype(np.int64))
user_var = Variable(gpu(user_ids, self._use_cuda))
item_var = Variable(gpu(item_ids, self._use_cuda))
out = self._net(user_var, item_var)
return cpu(out.data).numpy().flatten()
def predict(self, sequences, item_ids=None):
"""
Make predictions: given a sequence of interactions, predict
the next item in the sequence.
Parameters
----------
sequences: array, (1 x max_sequence_length)
Array containing the indices of the items in the sequence.
item_ids: array (num_items x 1), optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: array
Predicted scores for all items in item_ids.
"""
self._net.train(False)
sequences = np.atleast_2d(sequences)
if item_ids is None:
item_ids = np.arange(self._num_items).reshape(-1, 1)
sequences = torch.from_numpy(sequences.astype(np.int64).reshape(1, -1))
item_ids = torch.from_numpy(item_ids.astype(np.int64))
sequence_var = Variable(gpu(sequences, self._use_cuda))
item_var = Variable(gpu(item_ids, self._use_cuda))
_, sequence_representations = self._net.user_representation(sequence_var)
out = self._net(sequence_representations.repeat(len(item_var), 1),
item_var)
return cpu(out.data).numpy().flatten()
def predict(self, user_ids, item_ids=None):
"""
Make predictions: given a user id, compute the recommendation
scores for items.
Parameters
----------
user_ids: int or array
If int, will predict the recommendation scores for this
user for all items in item_ids. If an array, will predict
scores for all (user, item) pairs defined by user_ids and
item_ids.
item_ids: array, optional
Array containing the item ids for which prediction scores
are desired. If not supplied, predictions for all items
will be computed.
Returns
-------
predictions: np.array
Predicted scores for all items in item_ids.
"""
if item_ids is None:
item_ids = np.arange(self._num_items)
if isinstance(user_ids, int):
user_ids = np.repeat(user_ids, len(item_ids))
user_ids = torch.from_numpy(user_ids.reshape(-1, 1).astype(np.int64))
item_ids = torch.from_numpy(item_ids.reshape(-1, 1).astype(np.int64))
user_var = Variable(gpu(user_ids, self._use_cuda))
item_var = Variable(gpu(item_ids, self._use_cuda))
out = self._net(user_var, item_var)
return cpu(out.data).numpy().flatten()
