def forward(
self,
feat_static_cat: torch.Tensor,
feat_static_real: torch.Tensor,
past_time_feat: torch.Tensor,
past_target: torch.Tensor,
past_observed_values: torch.Tensor,
future_time_feat: torch.Tensor,
future_target: torch.Tensor,
future_observed_values: torch.Tensor,
) -> torch.Tensor:
distr = self.distribution(
feat_static_cat=feat_static_cat,
feat_static_real=feat_static_real,
past_time_feat=past_time_feat,
past_target=past_target,
past_observed_values=past_observed_values,
future_time_feat=future_time_feat,
future_target=future_target,
future_observed_values=future_observed_values,
)
# put together target sequence
# (batch_size, seq_len, *target_shape)
target = torch.cat(
(
past_target[
:, self.history_length - self.context_length :, ...
],
future_target,
),
dim=1,
)
# (batch_size, seq_len)
loss = -distr.log_prob(target)
# return loss
# (batch_size, seq_len, *target_shape)
observed_values = torch.cat(
(
past_observed_values[
:, self.history_length - self.context_length :, ...
],
future_observed_values,
),
dim=1,
)
# mask the loss at one time step iff one or more observations is missing in the target dimensions
# (batch_size, seq_len)
loss_weights = (
observed_values
if (len(self.target_shape) == 0)
else observed_values.min(dim=-1, keepdim=False)
)
weighted_loss = weighted_average(loss, weights=loss_weights)
return loss
def forward(
self,
past_target: torch.Tensor,
past_observed_values: torch.Tensor,
future_target: torch.Tensor,
future_observed_values: torch.Tensor,
past_feat_dynamic_real: torch.Tensor,
past_feat_dynamic_cat: torch.Tensor,
feat_dynamic_real: torch.Tensor,
feat_dynamic_cat: torch.Tensor,
feat_static_real: torch.Tensor,
feat_static_cat: torch.Tensor,
) -> torch.Tensor:
(
past_covariates,
future_covariates,
static_covariates,
offset,
scale,
) = self._preprocess(
past_target,
past_observed_values,
past_feat_dynamic_real,
past_feat_dynamic_cat,
feat_dynamic_real,
feat_dynamic_cat,
feat_static_real,
feat_static_cat,
)
preds = super().forward(
past_observed_values,
past_covariates,
future_covariates,
static_covariates,
)
preds = self._postprocess(preds, offset, scale)
loss = self.loss(future_target, preds)
loss = weighted_average(loss, future_observed_values)
return loss.mean()
def forward(
self,
target_dimension_indicator: torch.Tensor,
past_time_feat: torch.Tensor,
past_target_cdf: torch.Tensor,
past_observed_values: torch.Tensor,
past_is_pad: torch.Tensor,
future_time_feat: torch.Tensor,
future_target_cdf: torch.Tensor,
future_observed_values: torch.Tensor,
) -> Tuple[torch.Tensor, ...]:
"""
Computes the loss for training DeepVAR, all inputs tensors representing
time series have NTC layout.
Parameters
----------
target_dimension_indicator
Indices of the target dimension (batch_size, target_dim)
past_time_feat
Dynamic features of past time series (batch_size, history_length,
num_features)
past_target_cdf
Past marginal CDF transformed target values (batch_size,
history_length, target_dim)
past_observed_values
Indicator whether or not the values were observed (batch_size,
history_length, target_dim)
past_is_pad
Indicator whether the past target values have been padded
(batch_size, history_length)
future_time_feat
Future time features (batch_size, prediction_length, num_features)
future_target_cdf
Future marginal CDF transformed target values (batch_size,
prediction_length, target_dim)
future_observed_values
Indicator whether or not the future values were observed
(batch_size, prediction_length, target_dim)
Returns
-------
distr
Loss with shape (batch_size, 1)
likelihoods
Likelihoods for each time step
(batch_size, context + prediction_length, 1)
distr_args
Distribution arguments (context + prediction_length,
number_of_arguments)
"""
seq_len = self.context_length + self.prediction_length
# unroll the decoder in "training mode", i.e. by providing future data
# as well
rnn_outputs, _, scale, _, inputs = self.unroll_encoder(
past_time_feat=past_time_feat,
past_target_cdf=past_target_cdf,
past_observed_values=past_observed_values,
past_is_pad=past_is_pad,
future_time_feat=future_time_feat,
future_target_cdf=future_target_cdf,
target_dimension_indicator=target_dimension_indicator,
)
# put together target sequence
# (batch_size, seq_len, target_dim)
target = torch.cat(
(past_target_cdf[:, -self.context_length:,
...], future_target_cdf),
dim=1,
)
# assert_shape(target, (-1, seq_len, self.target_dim))
distr, distr_args = self.distr(rnn_outputs=rnn_outputs, scale=scale)
# we sum the last axis to have the same shape for all likelihoods
# (batch_size, subseq_length, 1)
likelihoods = -distr.log_prob(target).unsqueeze(-1)
# assert_shape(likelihoods, (-1, seq_len, 1))
past_observed_values = torch.min(past_observed_values,
1 - past_is_pad.unsqueeze(-1))
# (batch_size, subseq_length, target_dim)
observed_values = torch.cat(
(
past_observed_values[:, -self.context_length:, ...],
future_observed_values,
),
dim=1,
)
# mask the loss at one time step if one or more observations is missing
# in the target dimensions (batch_size, subseq_length, 1)
loss_weights, _ = observed_values.min(dim=-1, keepdim=True)
# assert_shape(loss_weights, (-1, seq_len, 1))
loss = weighted_average(likelihoods, weights=loss_weights, dim=1)
# assert_shape(loss, (-1, -1, 1))
self.distribution = distr
return (loss.mean(), likelihoods) + distr_args