def recommend(self, data, top_k, return_preds=False, allow_repeat=False):
# Set model to eval mode
model = self.net.to(self.device)
model.eval()
n_rows = data.shape[0]
idx_list = np.arange(n_rows)
recommendations = np.empty([n_rows, top_k], dtype=np.int64)
all_preds = list()
with torch.no_grad():
for batch_idx in minibatch(idx_list,
batch_size=self.args.valid_batch_size):
batch_data = data[batch_idx]
batch_tensor = sparse2tensor(batch_data).to(self.device)
preds = model(batch_tensor, batch_user=batch_idx, predict=True)
if return_preds:
all_preds.append(preds)
if not allow_repeat:
preds[batch_data.nonzero()] = -np.inf
if top_k > 0:
_, recs = preds.topk(k=top_k, dim=1)
recommendations[batch_idx] = recs.cpu().numpy()
if return_preds:
return recommendations, torch.cat(all_preds, dim=0).cpu()
else:
return recommendations
def compute_adv_grads():
"""Helper function for computing the adversarial gradient."""
# Reset the surrogate model.
sur_args = Bunch(self.args.surrogate)
sur_args.model_load_path = ""
sur_trainer_class = sur_args.model["trainer_class"]
sur_trainer_ = sur_trainer_class(
n_users=self.n_users + self.n_fakes,
n_items=self.n_items,
args=sur_args)
data_tensor = torch.cat(
[sparse2tensor(train_data).to(self.device),
self.fake_tensor.detach().clone().to(self.device)], dim=0)
data_tensor.requires_grad_()
# Train surrogate model.
adv_loss_, adv_grads_ = sur_trainer_.fit_adv(
data_tensor=data_tensor,
epoch_num=sur_args["epochs"],
unroll_steps=self.args.unroll_steps,
n_fakes=self.n_fakes,
target_items=self.target_items
)
return sur_trainer_, adv_loss_, adv_grads_
def train_epoch(self, data):
# Transpose the data first for ItemVAE.
data = data.transpose()
n_rows = data.shape[0]
n_cols = data.shape[1]
idx_list = np.arange(n_rows)
# Set model to training mode.
model = self.net.to(self.device)
model.train()
np.random.shuffle(idx_list)
epoch_loss = 0.0
batch_size = (self.args.batch_size
if self.args.batch_size > 0 else len(idx_list))
for batch_idx in minibatch(idx_list, batch_size=batch_size):
batch_tensor = sparse2tensor(data[batch_idx]).to(self.device)
# Compute loss
outputs = model(batch_tensor)
loss = model.loss(data=batch_tensor, outputs=outputs).sum()
epoch_loss += loss.item()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return epoch_loss
def train_epoch(self, data):
n_rows = data.shape[0]
n_cols = data.shape[1]
idx_list = np.arange(n_rows)
# Set model to training mode.
model = self.net.to(self.device)
model.train()
np.random.shuffle(idx_list)
epoch_loss = 0.0
counter = 0
for batch_idx in minibatch(idx_list, batch_size=self.args.batch_size):
batch_tensor = sparse2tensor(data[batch_idx]).to(self.device)
# Compute loss
outputs = model(batch_tensor, batch_user=batch_idx)
loss = model.loss(data=batch_tensor, outputs=outputs).mean()
epoch_loss += loss.item()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
counter += 1
return epoch_loss / counter
def recommend(self, data, top_k, return_preds=False, allow_repeat=False):
model = self.net
n_rows = data.shape[0]
n_cols = data.shape[1]
idx_list = np.arange(n_rows)
nns_sims = torch.zeros([n_cols, n_cols])
for item in range(n_cols):
topk_sims, topk_nns = model(item_id=item)
nns_sims[item].put_(topk_nns, topk_sims)
recommendations = np.empty([n_rows, top_k], dtype=np.int64)
all_preds = list()
with torch.no_grad():
for batch_idx in minibatch(idx_list,
batch_size=self.args.valid_batch_size):
batch_tensor = sparse2tensor(data[batch_idx])
preds = torch.mm(batch_tensor, nns_sims)
if return_preds:
all_preds.append(preds)
if not allow_repeat:
preds[data[batch_idx].nonzero()] = -np.inf
if top_k > 0:
_, recs = preds.topk(k=top_k, dim=1)
recommendations[batch_idx] = recs.cpu().numpy()
if return_preds:
return recommendations, torch.cat(all_preds, dim=0).cpu()
else:
return recommendations
def train_sgd(self, data):
n_rows = data.shape[0]
n_cols = data.shape[1]
idx_list = np.arange(n_rows)
# Set model to training mode.
model = self.net.to(self.device)
model.train()
np.random.shuffle(idx_list)
epoch_loss = 0.0
batch_size = (self.args.batch_size
if self.args.batch_size > 0 else len(idx_list))
for batch_idx in minibatch(idx_list, batch_size=batch_size):
batch_tensor = sparse2tensor(data[batch_idx]).to(self.device)
# Compute loss
outputs = model(user_id=batch_idx)
loss = mse_loss(data=batch_tensor,
logits=outputs,
weight=self.weight_alpha).sum()
epoch_loss += loss.item()
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return epoch_loss
def train_als(self, data):
model = self.net # A warning will raise if use .to() method here.
P = model.P.detach()
Q = model.Q.detach()
weight_alpha = self.weight_alpha - 1
# Using PyTorch for ALS optimization
# Update P
lamda_eye = torch.eye(self.dim).to(self.device) * self.args.l2
# residual = Q^tQ + lambda*I
residual = torch.mm(Q.t(), Q) + lamda_eye
for user, batch_data in enumerate(data):
# x_u: N x 1
x_u = sparse2tensor(batch_data).to(self.device).t()
# Cu = diagMat(alpha * rating + 1)
cu = batch_data.toarray().squeeze() * weight_alpha + 1
Cu = _array2sparsediag(cu).to(self.device)
Cu_minusI = _array2sparsediag(cu - 1).to(self.device)
# Q^tCuQ + lambda*I = Q^tQ + lambda*I + Q^t(Cu-I)Q
# left hand side
lhs = torch.mm(Q.t(), Cu_minusI.mm(Q)) + residual
# right hand side
rhs = torch.mm(Q.t(), Cu.mm(x_u))
new_p_u = torch.mm(lhs.inverse(), rhs)
model.P[user] = new_p_u.t()
# Update Q
data = data.transpose()
# residual = P^tP + lambda*I
residual = torch.mm(P.t(), P) + lamda_eye
for item, batch_data in enumerate(data):
# x_v: M x 1
x_v = sparse2tensor(batch_data).to(self.device).t()
# Cv = diagMat(alpha * rating + 1)
cv = batch_data.toarray().squeeze() * weight_alpha + 1
Cv = _array2sparsediag(cv).to(self.device)
Cv_minusI = _array2sparsediag(cv - 1).to(self.device)
# left hand side
lhs = torch.mm(P.t(), Cv_minusI.mm(P)) + residual
# right hand side
rhs = torch.mm(P.t(), Cv.mm(x_v))
new_q_v = torch.mm(lhs.inverse(), rhs)
model.Q[item] = new_q_v.t()
return 0
def _initialize(self, train_data):
"""Initialize fake data."""
fake_data = self.init_fake_data(train_data=train_data)
self.fake_tensor = sparse2tensor(fake_data)
self.fake_tensor.requires_grad_()
self.optimizer = optim.SGD([self.fake_tensor],
lr=self.args.adv_lr,
momentum=self.args.adv_momentum)
def recommend(self, data, top_k, return_preds=False, allow_repeat=False):
# Set model to eval mode
model = self.net.to(self.device)
model.eval()
# Transpose the data first for ItemVAE.
data = data.transpose()
n_rows = data.shape[0]
n_cols = data.shape[1]
idx_list = np.arange(n_rows)
recommendations = np.empty([n_cols, top_k], dtype=np.int64)
# Make predictions first, and then sort for top-k.
all_preds = list()
with torch.no_grad():
for batch_idx in minibatch(idx_list,
batch_size=self.args.valid_batch_size):
data_tensor = sparse2tensor(data[batch_idx]).to(self.device)
preds = model(data_tensor)
all_preds.append(preds)
all_preds = torch.cat(all_preds, dim=0).t()
data = data.transpose()
idx_list = np.arange(n_cols)
for batch_idx in minibatch(idx_list,
batch_size=self.args.valid_batch_size):
batch_data = data[batch_idx].toarray()
preds = all_preds[batch_idx]
if not allow_repeat:
preds[batch_data.nonzero()] = -np.inf
if top_k > 0:
_, recs = preds.topk(k=top_k, dim=1)
recommendations[batch_idx] = recs.cpu().numpy()
if return_preds:
return recommendations, all_preds.cpu()
else:
return recommendations
