def task_pos_weights(self, indices):
"""Get weights for positive samples on each task
This should only be used when all tasks are binary classification.
It's quite common that the number of positive samples and the number of
negative samples are significantly different for binary classification.
To compensate for the class imbalance issue, we can weight each datapoint
in loss computation.
In particular, for each task we will set the weight of negative samples
to be 1 and the weight of positive samples to be the number of negative
samples divided by the number of positive samples.
Parameters
----------
indices : 1D LongTensor
The function will compute the weights on the data subset specified by
the indices, e.g. the indices for the training set.
Returns
-------
Tensor of dtype float32 and shape (T)
Weight of positive samples on all tasks
"""
task_pos_weights = torch.ones(self.labels.shape[1])
num_pos = F.sum(self.labels[indices], dim=0)
num_indices = F.sum(self.mask[indices], dim=0)
task_pos_weights[num_pos > 0] = ((num_indices - num_pos) / num_pos)[num_pos > 0]
return task_pos_weights
def forward_test(self, pos_g, neg_g, logs, gpu_id=-1):
"""Do the forward and generate ranking results.
Parameters
----------
pos_g : DGLGraph
Graph holding positive edges.
neg_g : DGLGraph
Graph holding negative edges.
logs : List
Where to put results in.
gpu_id : int
Which gpu to accelerate the calculation. if -1 is provided, cpu is used.
"""
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, False)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id,
False)
self.score_func.prepare(pos_g, gpu_id, False)
batch_size = pos_g.number_of_edges()
pos_scores = self.predict_score(pos_g)
pos_scores = reshape(pos_scores, batch_size, -1)
neg_scores = self.predict_neg_score(
pos_g,
neg_g,
to_device=cuda,
gpu_id=gpu_id,
trace=False,
neg_deg_sample=self.args.neg_deg_sample_eval)
neg_scores = reshape(neg_scores, batch_size, -1)
# We need to filter the positive edges in the negative graph.
if self.args.eval_filter:
filter_bias = reshape(neg_g.edata['bias'], batch_size, -1)
if gpu_id >= 0:
filter_bias = cuda(filter_bias, gpu_id)
# find all indices where it is not false negative sample
mask = filter_bias != -1
# To compute the rank of a positive edge among all negative edges,
# we need to know how many negative edges have higher scores than
# the positive edge.
for i in range(batch_size):
if self.args.eval_filter:
# select all the true negative samples where its score >= positive sample
ranking = F.asnumpy(
F.sum(masked_select(neg_scores[i] >= pos_scores[i],
mask[i]),
dim=0) + 1)
else:
ranking = F.asnumpy(
F.sum(neg_scores[i] >= pos_scores[i], dim=0) + 1)
logs.append({
'MRR': 1.0 / ranking,
'MR': float(ranking),
'HITS@1': 1.0 if ranking <= 1 else 0.0,
'HITS@3': 1.0 if ranking <= 3 else 0.0,
'HITS@10': 1.0 if ranking <= 10 else 0.0
})
def forward_test(self, pos_g, neg_g, logs, gpu_id=-1):
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, False)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id,
False)
batch_size = pos_g.number_of_edges()
pos_scores = self.predict_score(pos_g)
pos_scores = reshape(logsigmoid(pos_scores), batch_size, -1)
neg_scores = self.predict_neg_score(pos_g,
neg_g,
to_device=cuda,
gpu_id=gpu_id,
trace=False)
neg_scores = reshape(logsigmoid(neg_scores), batch_size, -1)
# We need to filter the positive edges in the negative graph.
filter_bias = reshape(neg_g.edata['bias'], batch_size, -1)
if self.args.gpu >= 0:
filter_bias = cuda(filter_bias, self.args.gpu)
neg_scores += filter_bias
# To compute the rank of a positive edge among all negative edges,
# we need to know how many negative edges have higher scores than
# the positive edge.
rankings = F.sum(neg_scores > pos_scores, dim=1) + 1
rankings = F.asnumpy(rankings)
for i in range(batch_size):
ranking = rankings[i]
logs.append({
'MRR': 1.0 / ranking,
'MR': float(ranking),
'HITS@1': 1.0 if ranking <= 1 else 0.0,
'HITS@3': 1.0 if ranking <= 3 else 0.0,
'HITS@10': 1.0 if ranking <= 10 else 0.0
})
def pairwise_squared_distance(x):
"""
x : (n_samples, n_points, dims)
return : (n_samples, n_points, n_points)
"""
x2s = F.sum(x * x, -1, True)
# assuming that __matmul__ is always implemented (true for PyTorch, MXNet and Chainer)
return x2s + F.swapaxes(x2s, -1, -2) - 2 * x @ F.swapaxes(x, -1, -2)
def forward(self, pos_g, neg_g, gpu_id=-1):
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, True)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id, True)
self.score_func.prepare(pos_g, gpu_id, True)
pos_score = self.predict_score(pos_g)
pos_score = logsigmoid(pos_score)
if gpu_id >= 0:
neg_score = self.predict_neg_score(pos_g,
neg_g,
to_device=cuda,
gpu_id=gpu_id,
trace=True)
else:
neg_score = self.predict_neg_score(pos_g, neg_g, trace=True)
neg_score = reshape(neg_score, -1, neg_g.neg_sample_size)
# Adversarial sampling
if self.args.neg_adversarial_sampling:
neg_score = F.sum(
F.softmax(neg_score * self.args.adversarial_temperature,
dim=1).detach() * logsigmoid(-neg_score),
dim=1)
else:
neg_score = F.mean(logsigmoid(-neg_score), dim=1)
# subsampling weight
# TODO: add subsampling to new sampler
if self.args.non_uni_weight:
subsampling_weight = pos_g.edata['weight']
pos_score = (pos_score *
subsampling_weight).sum() / subsampling_weight.sum()
neg_score = (neg_score *
subsampling_weight).sum() / subsampling_weight.sum()
else:
pos_score = pos_score.mean()
neg_score = neg_score.mean()
# compute loss
loss = -(pos_score + neg_score) / 2
log = {
'pos_loss': -get_scalar(pos_score),
'neg_loss': -get_scalar(neg_score),
'loss': get_scalar(loss)
}
# regularization: TODO(zihao)
#TODO: only reg ent&rel embeddings. other params to be added.
if self.args.regularization_coef > 0.0 and self.args.regularization_norm > 0:
coef, nm = self.args.regularization_coef, self.args.regularization_norm
reg = coef * (norm(self.entity_emb.curr_emb(), nm) +
norm(self.relation_emb.curr_emb(), nm))
log['regularization'] = get_scalar(reg)
loss = loss + reg
return loss, log
def _weight_balancing(self):
"""Perform re-balancing for each task.
It's quite common that the number of positive samples and the
number of negative samples are significantly different. To compensate
for the class imbalance issue, we can weight each datapoint in
loss computation.
In particular, for each task we will set the weight of negative samples
to be 1 and the weight of positive samples to be the number of negative
samples divided by the number of positive samples.
If weight balancing is performed, one attribute will be affected:
* self._task_pos_weights is set, which is a list of positive sample weights
for each task.
"""
num_pos = F.sum(self.labels, dim=0)
num_indices = F.sum(self.mask, dim=0)
self._task_pos_weights = (num_indices - num_pos) / num_pos
def forward_test_wikikg(self,
query,
ans,
candidate,
mode,
logs,
gpu_id=-1):
"""Do the forward and generate ranking results.
Parameters
----------
query : Tensor
input head and relation for test or valid
ans : Tenseor
the correct tail entity index
cadidate : Tensor
negative sampled tail entity
"""
scores = self.predict_score_wikikg(query,
candidate,
mode,
to_device=cuda,
gpu_id=gpu_id,
trace=False)
if mode == "Valid":
batch_size = query.shape[0]
neg_scores = reshape(scores, batch_size, -1)
for i in range(batch_size):
ranking = F.asnumpy(
F.sum(neg_scores[i] >= neg_scores[i][ans[i]], dim=0) + 1)
logs.append({
'MRR': 1.0 / ranking,
'MR': float(ranking),
'HITS@1': 1.0 if ranking <= 1 else 0.0,
'HITS@3': 1.0 if ranking <= 3 else 0.0,
'HITS@10': 1.0 if ranking <= 10 else 0.0
})
else:
argsort = F.argsort(scores, dim=1, descending=True)
logs.append(argsort[:, :10])
def _weight_balancing(self):
num_pos = F.sum(self.label_list, dim=0)
num_indices = F.sum(self.mask_list, dim=0)
self._task_pos_weights = (num_indices - num_pos) / num_pos
def extended_jaccard_func(x, y):
score = F.sum(x * y, dim=0)
x = F.sum(x * x, dim=0)
y = F.sum(y * y, dim=0)
return score / (x + y - score)
def dot_func(x, y):
return F.sum(x * y, dim=0)
def l2_func(x, y):
score = x - y
return -F.sum(score * score, dim=0) ** (1/2)
def cosine_func(x, y):
score = F.sum(x * y, dim=0)
x_norm2 = F.sum(x * x, dim=0) ** (1/2)
y_norm2 = F.sum(y * y, dim=0) ** (1/2)
return score / (x_norm2 * y_norm2)
def forward(self, pos_g, neg_g, gpu_id=-1):
"""Do the forward.
Parameters
----------
pos_g : DGLGraph
Graph holding positive edges.
neg_g : DGLGraph
Graph holding negative edges.
gpu_id : int
Which gpu to accelerate the calculation. if -1 is provided, cpu is used.
Returns
-------
tensor
loss value
dict
loss info
"""
pos_g.ndata['emb'] = self.entity_emb(pos_g.ndata['id'], gpu_id, True)
pos_g.edata['emb'] = self.relation_emb(pos_g.edata['id'], gpu_id, True)
self.score_func.prepare(pos_g, gpu_id, True)
pos_score = self.predict_score(pos_g)
pos_score = logsigmoid(pos_score)
if gpu_id >= 0:
neg_score = self.predict_neg_score(
pos_g,
neg_g,
to_device=cuda,
gpu_id=gpu_id,
trace=True,
neg_deg_sample=self.args.neg_deg_sample)
else:
neg_score = self.predict_neg_score(
pos_g,
neg_g,
trace=True,
neg_deg_sample=self.args.neg_deg_sample)
neg_score = reshape(neg_score, -1, neg_g.neg_sample_size)
# Adversarial sampling
if self.args.neg_adversarial_sampling:
neg_score = F.sum(
F.softmax(neg_score * self.args.adversarial_temperature,
dim=1).detach() * logsigmoid(-neg_score),
dim=1)
else:
neg_score = F.mean(logsigmoid(-neg_score), dim=1)
# subsampling weight
# TODO: add subsampling to new sampler
#if self.args.non_uni_weight:
# subsampling_weight = pos_g.edata['weight']
# pos_score = (pos_score * subsampling_weight).sum() / subsampling_weight.sum()
# neg_score = (neg_score * subsampling_weight).sum() / subsampling_weight.sum()
#else:
pos_score = pos_score.mean()
neg_score = neg_score.mean()
# compute loss
loss = -(pos_score + neg_score) / 2
log = {
'pos_loss': -get_scalar(pos_score),
'neg_loss': -get_scalar(neg_score),
'loss': get_scalar(loss)
}
# regularization: TODO(zihao)
#TODO: only reg ent&rel embeddings. other params to be added.
if self.args.regularization_coef > 0.0 and self.args.regularization_norm > 0:
coef, nm = self.args.regularization_coef, self.args.regularization_norm
reg = coef * (norm(self.entity_emb.curr_emb(), nm) +
norm(self.relation_emb.curr_emb(), nm))
log['regularization'] = get_scalar(reg)
loss = loss + reg
return loss, log
