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.
"""
user_ids, item_ids = _predict_process_ids(user_ids, item_ids,
self.num_items, False)
return np.random.random(size=len(item_ids))
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, 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.
a[a > 0.5] = 1
a[a <= 0.5] = 0
"""
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)
totQuery = int(user_ids.size()[0])
# pdb.Pdb.complete = rlcompleter.Completer(locals()).complete
# pdb.set_trace()
uniqItems = np.unique(item_ids.data.numpy())
totUniqItems = uniqItems.shape[0]
pred = []
user_ids = user_ids.data.numpy()
item_ids = item_ids.data.numpy()
# pdb.Pdb.complete =rlcompleter.Completer(locals()).complete
# pdb.set_trace()
for qID in range(totQuery):
u = int(user_ids[qID])
i1 = int(item_ids[qID])
items2 = np.unique(np.random.randint(0, totUniqItems, size=self.k_sample))
t1 = torch.LongTensor([u, i1])
t1 = t1.repeat(items2.shape[0], 1)
t2 = torch.LongTensor([items2])
t2 = torch.transpose(t2, 0, 1)
t3 = torch.cat((t1, t2), 1)
y = self._net(t3)
# new change
y = y.data
y[y > 0.5] = 1.0
y[y <= 0.5] = 0.0
i1_score = (y.sum()/self.k_sample)*5
pred.append(int(i1_score))
# old
# pred.append(y.mean().data[0])
# pdb.Pdb.complete =rlcompleter.Completer(locals()).complete
# pdb.set_trace()
return np.array(pred)