def test_unique(self):
N = 100
nsampled = 50
RND = 100
sampler = LogUniformSampler(N)
histogram = [0 for idx in range(N)]
all_values = [idx for idx in range(N)]
sample_ids, expected, sample_freq = sampler.sample(
nsampled, np.asarray(all_values, dtype=np.int32))
sample_set = set(sample_ids)
self.assertEqual(len(sample_set), nsampled)
for rnd in range(RND):
sample_ids, true_freq, sample_freq = sampler.sample(
nsampled, np.asarray(all_values, dtype=np.int32))
for idx in range(N):
self.assertTrue(
EXPECT_NEAR(expected[idx], true_freq[idx],
expected[idx] * 0.5))
for idx in range(nsampled):
histogram[sample_ids[idx]] += 1
for idx in range(N):
average_count = histogram[idx] / RND
self.assertTrue(EXPECT_NEAR(expected[idx], average_count, 0.2))
class SampledSoftmax(nn.Module):
def __init__(self, ntokens, nsampled, nhid, tied_weight):
super(SampledSoftmax, self).__init__()
# Parameters
self.ntokens = ntokens
self.nsampled = nsampled
self.sampler = LogUniformSampler(self.ntokens)
self.params = nn.Linear(nhid, ntokens)
if tied_weight is not None:
self.params.weight = tied_weight
else:
util.initialize(self.params, self.ntokens)
self.params.bias.data.fill_(0)
def forward(self, inputs, labels, train=False):
if train:
sample_values = self.sampler.sample(self.nsampled, labels.data.cpu().numpy())
return self.sampled(inputs, labels, sample_values)
else:
return self.full(inputs, labels)
def sampled(self, inputs, labels, sample_values):
assert(inputs.data.get_device() == labels.data.get_device())
device_id = labels.data.get_device()
batch_size, d = inputs.size()
sample_ids, true_freq, sample_freq = sample_values
# sample ids according to word distribution - Unique
sample_ids = Variable(torch.LongTensor(sample_ids), requires_grad=False).cuda(device_id)
true_freq = Variable(torch.FloatTensor(true_freq), requires_grad=False).cuda(device_id)
sample_freq = Variable(torch.FloatTensor(sample_freq), requires_grad=False).cuda(device_id)
# gather true labels - weights and frequencies
true_weights = self.params.weight[labels.data, :]
true_bias = self.params.bias[labels.data]
# gather sample ids - weights and frequencies
sample_weights = self.params.weight[sample_ids.data, :]
sample_bias = self.params.bias[sample_ids.data]
# calculate logits
true_logits = torch.sum(torch.mul(inputs, true_weights), dim=1) + true_bias
sample_logits = torch.matmul(inputs, torch.t(sample_weights)) + sample_bias
# perform correction
true_logits = true_logits.sub(torch.log(true_freq))
sample_logits = sample_logits.sub(torch.log(sample_freq))
# return logits and new_labels
logits = torch.cat((torch.unsqueeze(true_logits, dim=1), sample_logits), dim=1)
new_targets = Variable(torch.zeros(batch_size).long(), requires_grad=False).cuda(device_id)
return logits, new_targets
def full(self, inputs, labels):
return self.params(inputs), labels
class SampledSoftmax(nn.Module):
def __init__(self, ntokens, nsampled, nhid, device):
super(SampledSoftmax, self).__init__()
# Parameters
self.ntokens = ntokens
self.nsampled = nsampled
self.device = device
#
self.sampler = LogUniformSampler(self.ntokens)
#
self.weight = nn.Parameter(torch.Tensor(ntokens, nhid))
self.reset_parameters()
def reset_parameters(self):
stdv = math.sqrt(6.0 / (self.weight.size(0) + self.weight.size(1)))
self.weight.data.uniform_(-stdv, stdv)
def forward(self, inputs, labels):
# sample ids according to word distribution - Unique
sample_values = self.sampler.sample(self.nsampled,
labels.data.cpu().numpy())
return self.sampled(inputs,
labels,
sample_values,
remove_accidental_match=True)
"""@Dai Quoc Nguyen: Implement the sampled softmax loss function as described in the paper
On Using Very Large Target Vocabulary for Neural Machine Translation https://www.aclweb.org/anthology/P15-1001/"""
def sampled(self,
inputs,
labels,
sample_values,
remove_accidental_match=False):
assert (inputs.data.get_device() == labels.data.get_device())
batch_size, d = inputs.size()
sample_ids, true_freq, sample_freq = sample_values
sample_ids = Variable(torch.LongTensor(sample_ids)).to(self.device)
# gather true labels
true_weights = torch.index_select(self.weight, 0, labels)
# gather sample ids
sample_weights = torch.index_select(self.weight, 0, sample_ids)
# calculate logits
true_logits = torch.exp(
torch.sum(torch.mul(inputs, true_weights), dim=1))
sample_logits = torch.exp(torch.matmul(inputs,
torch.t(sample_weights)))
logits = -torch.log(true_logits / torch.sum(sample_logits, dim=1))
return logits
def test_AccidentalMatch(self):
np.random.seed(1000)
num_classes = 5
batch_size = 3
nsampled = 4
nhid = 10
labels = np.random.randint(low=0, high=num_classes, size=batch_size)
(weights, biases, hidden_acts, sampled_vals, exp_logits,
exp_labels) = self._GenerateTestData(num_classes=num_classes,
dim=nhid,
batch_size=batch_size,
num_true=1,
labels=labels,
sampled=[1, 0, 2, 3],
subtract_log_q=True)
ss = model.SampledSoftmax(num_classes,
nsampled,
nhid,
tied_weight=None)
ss.params.weight.data = torch.from_numpy(weights)
ss.params.bias.data = torch.from_numpy(biases)
ss.params.cuda()
hidden_acts = Variable(torch.from_numpy(hidden_acts)).cuda()
labels = Variable(torch.LongTensor(labels)).cuda()
sampler = LogUniformSampler(nsampled)
sampled_values = sampler.sample(nsampled, labels.data.cpu().numpy())
sample_ids, true_freq, sample_freq = sampled_values
logits, new_targets = ss.sampled(hidden_acts,
labels,
sampled_values,
remove_accidental_match=True)
criterion = nn.CrossEntropyLoss()
loss = criterion(logits.view(-1, nsampled + 1), new_targets)
np_logits = logits.data.cpu().numpy()
for row in range(batch_size):
label = labels[row]
for col in range(nsampled):
if sample_ids[col] == label:
self.assertTrue(
EXPECT_NEAR(np.exp(np_logits[row, col + 1]), 0, 1e-4))
class SampledSoftmax(nn.Module):
def __init__(self, ntokens, nsampled, nhid, tied_weight):
super(SampledSoftmax, self).__init__()
# Parameters
self.ntokens = ntokens
self.nsampled = nsampled
self.sampler = LogUniformSampler(self.ntokens)
self.params = nn.Linear(nhid, ntokens)
if tied_weight is not None:
self.params.weight = tied_weight
else:
util.initialize(self.params.weight)
def forward(self, inputs, labels):
if self.training:
# sample ids according to word distribution - Unique
sample_values = self.sampler.sample(self.nsampled,
labels.data.cpu().numpy())
return self.sampled(inputs,
labels,
sample_values,
remove_accidental_match=True)
else:
return self.full(inputs, labels)
def sampled(self,
inputs,
labels,
sample_values,
remove_accidental_match=False):
assert (inputs.data.get_device() == labels.data.get_device())
device_id = labels.data.get_device()
batch_size, d = inputs.size()
sample_ids, true_freq, sample_freq = sample_values
sample_ids = Variable(torch.LongTensor(sample_ids)).cuda(device_id)
true_freq = Variable(torch.FloatTensor(true_freq)).cuda(device_id)
sample_freq = Variable(torch.FloatTensor(sample_freq)).cuda(device_id)
# gather true labels - weights and frequencies
true_weights = torch.index_select(self.params.weight, 0, labels)
true_bias = torch.index_select(self.params.bias, 0, labels)
# gather sample ids - weights and frequencies
sample_weights = torch.index_select(self.params.weight, 0, sample_ids)
sample_bias = torch.index_select(self.params.bias, 0, sample_ids)
# calculate logits
true_logits = torch.sum(torch.mul(inputs, true_weights),
dim=1) + true_bias
sample_logits = torch.matmul(inputs,
torch.t(sample_weights)) + sample_bias
# remove true labels from sample set
if remove_accidental_match:
acc_hits = self.sampler.accidental_match(
labels.data.cpu().numpy(),
sample_ids.data.cpu().numpy())
acc_hits = list(zip(*acc_hits))
sample_logits[acc_hits] = -1e37
# perform correction
true_logits = true_logits.sub(torch.log(true_freq))
sample_logits = sample_logits.sub(torch.log(sample_freq))
# return logits and new_labels
logits = torch.cat(
(torch.unsqueeze(true_logits, dim=1), sample_logits), dim=1)
new_targets = Variable(torch.zeros(batch_size).long()).cuda(device_id)
return logits, new_targets
def full(self, inputs, labels):
return self.params(inputs), labels
class Trainer(object):
def __init__(self, log, model, beam_size, train_data, eval_data, optim,
use_cuda, loss_func, cate_loss_func, topk, input_size, args):
self.m_log = log
self.model = model
self.train_data = train_data
self.eval_data = eval_data
self.optim = optim
self.m_loss_func = loss_func
self.topk = topk
self.evaluation = Evaluation(self.m_log,
self.model,
self.m_loss_func,
use_cuda,
self.topk,
warm_start=args.warm_start)
self.device = torch.device('cuda' if use_cuda else 'cpu')
self.args = args
self.m_cate_loss_func = cate_loss_func
### early stopping
self.m_patience = args.patience
self.m_best_recall = 0.0
self.m_best_mrr = 0.0
self.m_early_stop = False
self.m_counter = 0
self.m_best_cate_recall = 0.0
self.m_best_cate_mrr = 0.0
self.m_batch_iter = 0
#### sample negative items
self.m_sampler = LogUniformSampler(input_size)
self.m_nsampled = args.negative_num
self.m_remove_match = True
self.m_teacher_forcing_ratio = 2.0
self.m_beam_size = beam_size
self.m_teacher_forcing_flag = True
self.m_logsoftmax = nn.LogSoftmax(dim=1)
def saveModel(self, epoch, loss, recall, mrr):
checkpoint = {
'model': self.model.state_dict(),
'args': self.args,
'epoch': epoch,
'optim': self.optim,
'loss': loss,
'recall': recall,
'mrr': mrr
}
model_name = os.path.join(self.args.checkpoint_dir, "model_best.pt")
torch.save(checkpoint, model_name)
def train(self, start_epoch, end_epoch, batch_size, start_time=None):
if start_time is None:
self.start_time = time.time()
else:
self.start_time = start_time
### start training
for epoch in range(start_epoch, end_epoch + 1):
msg = "*" * 10 + str(epoch) + "*" * 5
self.m_log.addOutput2IO(msg)
print("teaching", self.m_teacher_forcing_flag)
st = time.time()
### an epoch
train_mixture_loss, train_loss, train_cate_loss = self.train_epoch(
epoch, batch_size)
### evaluate model on train dataset or validation dateset
mixture_loss, loss, recall, mrr, cate_loss, cate_recall, cate_mrr = self.evaluation.eval(
self.train_data, batch_size, "train")
print("train", train_loss)
print("mix", mixture_loss)
print("loss", loss)
print("recall", recall)
print("mrr", mrr)
msg = "train Epoch: {}, train loss: {:.4f}, mixture loss: {:.4f}, loss: {:.4f}, recall: {:.4f}, mrr: {:.4f}, cate_loss: {:.4f}, cate_recall: {:.4f}, cate_mrr: {:.4f}, time: {}".format(
epoch, train_mixture_loss, mixture_loss, loss, recall, mrr,
cate_loss, cate_recall, cate_mrr,
time.time() - st)
self.m_log.addOutput2IO(msg)
self.m_log.addScalar2Tensorboard("train_mixture_loss",
train_mixture_loss, epoch)
self.m_log.addScalar2Tensorboard("mixture_loss", mixture_loss,
epoch)
self.m_log.addScalar2Tensorboard("train_loss_eval", loss, epoch)
self.m_log.addScalar2Tensorboard("train_recall", recall, epoch)
self.m_log.addScalar2Tensorboard("train_mrr", mrr, epoch)
self.m_log.addScalar2Tensorboard("train_cate_loss_eval", cate_loss,
epoch)
self.m_log.addScalar2Tensorboard("train_cate_recall", cate_recall,
epoch)
self.m_log.addScalar2Tensorboard("train_cate_mrr", cate_mrr, epoch)
if self.m_best_cate_recall == 0:
self.m_best_cate_recall = cate_recall
elif self.m_best_cate_recall >= cate_recall:
self.m_teacher_forcing_flag = False
# self.m_teacher_forcing_flag = True
else:
self.m_best_cate_recall = cate_recall
### evaluate model on test dataset
mixture_loss, loss, recall, mrr, cate_loss, cate_recall, cate_mrr = self.evaluation.eval(
self.eval_data, batch_size, "test")
msg = "Epoch: {}, mixture loss: {:.4f}, loss: {:.4f}, recall: {:.4f}, mrr: {:.4f}, cate_loss: {:.4f}, cate_recall: {:.4f}, cate_mrr: {:.4f}, time: {}".format(
epoch, mixture_loss, loss, recall, mrr, cate_loss, cate_recall,
cate_mrr,
time.time() - st)
self.m_log.addOutput2IO(msg)
self.m_log.addScalar2Tensorboard("test_mixture_loss", mixture_loss,
epoch)
self.m_log.addScalar2Tensorboard("test_loss", loss, epoch)
self.m_log.addScalar2Tensorboard("test_recall", recall, epoch)
self.m_log.addScalar2Tensorboard("test_mrr", mrr, epoch)
self.m_log.addScalar2Tensorboard("test_cate_loss", cate_loss,
epoch)
self.m_log.addScalar2Tensorboard("test_cate_recall", cate_recall,
epoch)
self.m_log.addScalar2Tensorboard("test_cate_mrr", cate_mrr, epoch)
if self.m_best_recall == 0:
self.m_best_recall = recall
self.saveModel(epoch, loss, recall, mrr)
elif self.m_best_recall > recall:
self.m_counter += 1
if self.m_counter > self.m_patience:
break
msg = "early stop counter " + str(self.m_counter)
self.m_log.addOutput2IO(msg)
else:
self.m_best_recall = recall
self.m_best_mrr = mrr
self.saveModel(epoch, loss, recall, mrr)
self.m_counter = 0
msg = "best recall: " + str(
self.m_best_recall) + "\t best mrr: \t" + str(self.m_best_mrr)
self.m_log.addOutput2IO(msg)
def train_epoch(self, epoch, batch_size):
self.model.train()
losses = []
cate_losses = []
mixture_losses = []
def reset_hidden(hidden, mask):
"""Helper function that resets hidden state when some sessions terminate"""
if len(mask) != 0:
hidden[:, mask, :] = 0
return hidden
dataloader = self.train_data
for x_long_cate_action_batch, x_long_cate_batch, mask_long_cate_action_batch, mask_long_cate_batch, max_long_cate_actionNum_batch, max_long_cateNum_batch, pad_x_long_cate_actionNum_batch, x_long_cateNum_batch, x_short_action_batch, x_short_cate_batch, mask_short_action_batch, pad_x_short_actionNum_batch, y_action_batch, y_cate_batch, y_action_idx_batch in dataloader:
### negative samples
sample_values = self.m_sampler.sample(self.m_nsampled,
y_action_batch)
sample_ids, true_freq, sample_freq = sample_values
### whether excluding current pos sample from negative samples
if self.m_remove_match:
acc_hits = self.m_sampler.accidental_match(
y_action_batch, sample_ids)
acc_hits = list(zip(*acc_hits))
### cates of long-range actions
x_long_cate_batch = x_long_cate_batch.to(self.device)
### mask of cates of long-range actions
mask_long_cate_batch = mask_long_cate_batch.to(self.device)
### items of long-range actions
x_long_cate_action_batch = x_long_cate_action_batch.to(self.device)
### mask of items of long-range actions
mask_long_cate_action_batch = mask_long_cate_action_batch.to(
self.device)
### items of short-range actions
x_short_action_batch = x_short_action_batch.to(self.device)
### mask of items of short-range actions
mask_short_action_batch = mask_short_action_batch.to(self.device)
### cate of short-range actions
x_short_cate_batch = x_short_cate_batch.to(self.device)
### items of target action
y_action_batch = y_action_batch.to(self.device)
### cates of target action
y_cate_batch = y_cate_batch.to(self.device)
### idx of target action in seq
y_action_idx_batch = y_action_idx_batch.to(self.device)
# batch_size = x_batch.size(0)
self.optim.zero_grad()
seq_cate_short_input = self.model.m_cateNN(
x_short_cate_batch, mask_short_action_batch,
pad_x_short_actionNum_batch, "train")
logit_cate_short = self.model.m_cateNN.m_cate_h2o(
seq_cate_short_input)
pred_item_prob = None
if self.m_teacher_forcing_flag:
seq_cate_input, seq_short_input = self.model.m_itemNN(
x_long_cate_action_batch, x_long_cate_batch,
mask_long_cate_action_batch, mask_long_cate_batch,
max_long_cate_actionNum_batch, max_long_cateNum_batch,
pad_x_long_cate_actionNum_batch, x_long_cateNum_batch,
x_short_action_batch, mask_short_action_batch,
pad_x_short_actionNum_batch, y_cate_batch, "train")
mixture_output = torch.cat((seq_cate_input, seq_short_input),
dim=1)
fc_output = self.model.fc(mixture_output)
sampled_logit_batch, sampled_target_batch = self.model.m_ss(
fc_output, y_action_batch, sample_ids, true_freq,
sample_freq, acc_hits, self.device, self.m_remove_match)
sampled_prob_batch = self.m_logsoftmax(sampled_logit_batch)
pred_item_prob = sampled_prob_batch
else:
log_prob_cate_short = self.m_logsoftmax(logit_cate_short)
log_prob_cate_short, pred_cate_index = torch.topk(
log_prob_cate_short, self.m_beam_size, dim=-1)
pred_cate_index = pred_cate_index.detach()
log_prob_cate_short = log_prob_cate_short
for beam_index in range(self.m_beam_size):
pred_cate_beam = pred_cate_index[:, beam_index]
prob_cate_beam = log_prob_cate_short[:, beam_index]
seq_cate_input, seq_short_input = self.model.m_itemNN(
x_long_cate_action_batch, x_long_cate_batch,
mask_long_cate_action_batch, mask_long_cate_batch,
max_long_cate_actionNum_batch, max_long_cateNum_batch,
pad_x_long_cate_actionNum_batch, x_long_cateNum_batch,
x_short_action_batch, mask_short_action_batch,
pad_x_short_actionNum_batch, pred_cate_beam, "train")
mixture_output = torch.cat(
(seq_cate_input, seq_short_input), dim=1)
fc_output = self.model.fc(mixture_output)
sampled_logit_batch, sampled_target_batch = self.model.m_ss(
fc_output, y_action_batch, sample_ids, true_freq,
sample_freq, acc_hits, self.device,
self.m_remove_match)
sampled_prob_batch = self.m_logsoftmax(sampled_logit_batch)
if pred_item_prob is None:
pred_item_prob = sampled_prob_batch + prob_cate_beam.reshape(
-1, 1)
pred_item_prob = pred_item_prob.unsqueeze(-1)
else:
pred_item_prob_beam = sampled_prob_batch + prob_cate_beam.reshape(
-1, 1)
pred_item_prob_beam = pred_item_prob_beam.unsqueeze(-1)
pred_item_prob = torch.cat(
(pred_item_prob, pred_item_prob_beam), dim=-1)
pred_item_prob = torch.logsumexp(pred_item_prob, dim=-1)
loss_batch = self.m_loss_func(pred_item_prob, sampled_target_batch,
"prob")
losses.append(loss_batch.item())
cate_loss_batch = self.m_cate_loss_func(logit_cate_short,
y_cate_batch, "logit")
cate_losses.append(cate_loss_batch.item())
mixture_loss_batch = loss_batch + cate_loss_batch
mixture_losses.append(mixture_loss_batch.item())
mixture_loss_batch.backward()
max_norm = 5.0
self.m_batch_iter += 1
torch.nn.utils.clip_grad_norm(self.model.parameters(), max_norm)
self.optim.step()
mean_mixture_losses = np.mean(mixture_losses)
mean_losses = np.mean(losses)
mean_cate_losses = np.mean(cate_losses)
return mean_mixture_losses, mean_losses, mean_cate_losses
p = log_uniform_distribution(N)
start_time = time.time()
torch.multinomial(p, num_samples, replacement=True)
end_time = time.time()
print("non_unique multinomial cuda", end_time - start_time)
start_time = time.time()
torch.multinomial(p, 100)
end_time = time.time()
print("unique multinomial cuda", end_time - start_time)
sampler = LogUniformSampler(N)
labels = np.random.choice(N, batch_size)
start_time = time.time()
sample_id, true_freq, sample_freq = sampler.sample(num_samples, labels)
end_time = time.time()
print("unique log_uniform c++", end_time - start_time)
"""
sampler = LogUniformSampler()
start_time = time.time()
sample_id = sampler.sample(N, num_samples, unique=True, labels=labels.tolist())
end_time = time.time()
print("unique no_accidental_hits log_uniform c++", end_time - start_time)
label_set = set(labels.tolist())
for idx in sample_id:
assert(idx not in label_set)
"""
class Evaluation(object):
def __init__(self,
log,
model,
loss_func,
use_cuda,
input_size,
k=20,
warm_start=5):
self.model = model
self.m_loss_func = loss_func
self.topk = k
self.warm_start = warm_start
self.device = torch.device('cuda' if use_cuda else 'cpu')
self.m_log = log
beam_size = 5
self.m_beam_size = beam_size
self.m_sampler = LogUniformSampler(input_size)
self.m_nsampled = 100
self.m_remove_match = True
print("evaluation is based on sampled 100", self.m_nsampled)
def eval(self, eval_data, batch_size, train_test_flag):
self.model.eval()
mixture_losses = []
losses = []
recalls = []
mrrs = []
weights = []
cate_losses = []
cate_recalls = []
cate_mrrs = []
cate_weights = []
dataloader = eval_data
with torch.no_grad():
total_test_num = []
for x_long_cate_action_batch, x_long_cate_batch, mask_long_cate_action_batch, mask_long_cate_batch, max_long_cate_actionNum_batch, max_long_cateNum_batch, pad_x_long_cate_actionNum_batch, x_long_cateNum_batch, x_short_action_batch, x_short_cate_batch, mask_short_action_batch, pad_x_short_actionNum_batch, y_action_batch, y_cate_batch, y_action_idx_batch in dataloader:
###speed evaluation for train dataset
if train_test_flag == "train":
eval_flag = random.randint(1, 101)
if eval_flag != 10:
continue
sample_values = self.m_sampler.sample(self.m_nsampled,
y_action_batch)
sample_ids, true_freq, sample_freq = sample_values
### whether excluding current pos sample from negative samples
if self.m_remove_match:
acc_hits = self.m_sampler.accidental_match(
y_action_batch, sample_ids)
acc_hits = list(zip(*acc_hits))
x_long_cate_action_batch = x_long_cate_action_batch.to(
self.device)
mask_long_cate_action_batch = mask_long_cate_action_batch.to(
self.device)
x_long_cate_batch = x_long_cate_batch.to(self.device)
mask_long_cate_batch = mask_long_cate_batch.to(self.device)
x_short_action_batch = x_short_action_batch.to(self.device)
mask_short_action_batch = mask_short_action_batch.to(
self.device)
x_short_cate_batch = x_short_cate_batch.to(self.device)
y_action_batch = y_action_batch.to(self.device)
y_cate_batch = y_cate_batch.to(self.device)
warm_start_mask = (y_action_idx_batch >= self.warm_start)
### cateNN
seq_cate_short_input = self.model.m_cateNN(
x_short_cate_batch, mask_short_action_batch,
pad_x_short_actionNum_batch, "test")
logit_cate_short = self.model.m_cateNN.m_cate_h2o(
seq_cate_short_input)
### retrieve top k predicted categories
prob_cate_short = F.softmax(logit_cate_short, dim=-1)
cate_prob_beam, cate_id_beam = prob_cate_short.topk(
dim=1, k=self.m_beam_size)
item_prob_flag = False
for beam_index in range(self.m_beam_size):
### for each category, predict item, then mix predictions for rec
cate_id_beam_batch = cate_id_beam[:, beam_index]
cate_id_beam_batch = cate_id_beam_batch.reshape(-1, 1)
seq_cate_input, seq_short_input = self.model.m_itemNN(
x_long_cate_action_batch, x_long_cate_batch,
mask_long_cate_action_batch, mask_long_cate_batch,
max_long_cate_actionNum_batch, max_long_cateNum_batch,
pad_x_long_cate_actionNum_batch, x_long_cateNum_batch,
x_short_action_batch, mask_short_action_batch,
pad_x_short_actionNum_batch, cate_id_beam_batch,
"test")
mixture_output = torch.cat(
(seq_cate_input, seq_short_input), dim=1)
output_batch = self.model.fc(mixture_output)
### sampled_logit_batch
sampled_logit_batch, sampled_target_batch = self.model.m_ss(
output_batch, y_action_batch, sample_ids, true_freq,
sample_freq, acc_hits, self.device,
self.m_remove_match)
### batch_size*voc_size
prob_batch = F.softmax(sampled_logit_batch, dim=-1)
## batch_size*1
cate_prob_batch = cate_prob_beam[:, beam_index]
item_prob_batch = prob_batch * cate_prob_batch.reshape(
-1, 1)
# if not item_prob:
if not item_prob_flag:
item_prob_flag = True
item_prob = item_prob_batch
else:
item_prob += item_prob_batch
### evaluate cate prediction
cate_loss_batch = self.m_loss_func(logit_cate_short,
y_cate_batch, "logit")
cate_losses.append(cate_loss_batch.item())
cate_topk = 5
cate_recall_batch, cate_mrr_batch = evaluate(logit_cate_short,
y_cate_batch,
warm_start_mask,
k=cate_topk)
cate_weights.append(int(warm_start_mask.int().sum()))
cate_recalls.append(cate_recall_batch)
cate_mrrs.append(cate_mrr_batch)
### evaluate item prediction
recall_batch, mrr_batch = evaluate(item_prob,
y_action_batch,
warm_start_mask,
k=self.topk)
weights.append(int(warm_start_mask.int().sum()))
recalls.append(recall_batch)
mrrs.append(mrr_batch)
total_test_num.append(y_action_batch.view(-1).size(0))
mean_mixture_losses = 0.0
mean_losses = 0.0
mean_recall = np.average(recalls, weights=weights)
mean_mrr = np.average(mrrs, weights=weights)
mean_cate_losses = np.mean(cate_losses)
mean_cate_recall = np.average(cate_recalls, weights=cate_weights)
mean_cate_mrr = np.average(cate_mrrs, weights=cate_weights)
msg = "total_test_num" + str(np.sum(total_test_num))
self.m_log.addOutput2IO(msg)
return mean_mixture_losses, mean_losses, mean_recall, mean_mrr, mean_cate_losses, mean_cate_recall, mean_cate_mrr