def hybrid_forward(
self,
F,
data: Tensor,
observed_indicator: Tensor,
scale: Optional[Tensor],
rep_params: List[Tensor],
**kwargs,
) -> Tuple[Tensor, Tensor, List[Tensor]]:
data_np = data.asnumpy()
observed_indicator_np = observed_indicator.astype("int32").asnumpy()
if scale is None:
# Even though local binning implicitly scales the data, we still return the scale as an input to the model.
scale = F.expand_dims(
F.sum(data * observed_indicator, axis=-1)
/ F.sum(observed_indicator, axis=-1),
-1,
)
bin_centers_hyb = np.ones((len(data), self.num_bins)) * (-1)
bin_edges_hyb = np.ones((len(data), self.num_bins + 1)) * (-1)
# Every time series needs to be binned individually
for i in range(len(data_np)):
# Identify observed data points.
data_loc = data_np[i]
observed_indicator_loc = observed_indicator_np[i]
data_obs_loc = data_loc[observed_indicator_loc == 1]
if data_obs_loc.size > 0:
# Calculate time series specific bin centers and edges.
if self.is_quantile:
bin_centers_loc = np.quantile(
data_obs_loc, np.linspace(0, 1, self.num_bins)
)
else:
bin_centers_loc = np.linspace(
np.min(data_obs_loc),
np.max(data_obs_loc),
self.num_bins,
)
bin_centers_hyb[i] = ensure_binning_monotonicity(
bin_centers_loc
)
bin_edges_hyb[i] = bin_edges_from_bin_centers(
bin_centers_hyb[i]
)
# Bin the time series.
data_obs_loc_binned = np.digitize(
data_obs_loc, bins=bin_edges_hyb[i], right=False
)
else:
data_obs_loc_binned = []
# Write the binned time series back into the data array.
data_loc[observed_indicator_loc == 1] = data_obs_loc_binned
data_np[i] = data_loc
else:
bin_centers_hyb = rep_params[0].asnumpy()
bin_edges_hyb = rep_params[1].asnumpy()
bin_edges_hyb = np.repeat(
bin_edges_hyb,
len(data_np) // len(bin_edges_hyb),
axis=0,
)
bin_centers_hyb = np.repeat(
bin_centers_hyb,
len(data_np) // len(bin_centers_hyb),
axis=0,
)
for i in range(len(data_np)):
data_loc = data_np[i]
observed_indicator_loc = observed_indicator_np[i]
data_obs_loc = data_loc[observed_indicator_loc == 1]
# Bin the time series based on previously computed bin edges.
data_obs_loc_binned = np.digitize(
data_obs_loc, bins=bin_edges_hyb[i], right=False
)
data_loc[observed_indicator_loc == 1] = data_obs_loc_binned
data_np[i] = data_loc
bin_centers_hyb = F.array(bin_centers_hyb)
bin_edges_hyb = F.array(bin_edges_hyb)
data = mx.nd.array(data_np)
return data, scale, [bin_centers_hyb, bin_edges_hyb]
def process_dynamic_cat(self, F, feature: Tensor) -> Tensor:
return self.embed_dynamic(feature.astype(self.dtype))
def hybrid_forward(
self,
F,
feat_static_cat: Tensor,
past_target: Tensor,
past_observed_values: Tensor,
past_time_feat: Tensor,
future_time_feat: Tensor,
scale: Tensor,
) -> Tensor:
"""
Computes the training loss for the wavenet model.
Parameters
----------
F
feat_static_cat
Static categorical features: (batch_size, num_cat_features)
past_target
Past target: (batch_size, receptive_field)
past_observed_values
Observed value indicator for the past target: (batch_size,
receptive_field)
past_time_feat
Past time features: (batch_size, num_time_features,
receptive_field)
future_time_feat
Future time features: (batch_size, num_time_features, pred_length)
scale
scale of the time series: (batch_size, 1)
Returns
-------
Tensor
Prediction samples with shape (batch_size, num_samples,
pred_length)
"""
def blow_up(u):
"""
Expand to (batch_size x num_samples)
"""
return F.repeat(u, repeats=self.num_samples, axis=0)
past_target = past_target.astype("int32")
full_features = self.get_full_features(
F,
feat_static_cat=feat_static_cat,
past_observed_values=past_observed_values,
past_time_feat=past_time_feat,
future_time_feat=future_time_feat,
future_observed_values=None,
scale=scale,
)
# To compute queues for the first step, we need features from
# -self.pred_length - self.receptive_field + 1 to -self.pred_length + 1
features_end_ix = (-self.pred_length +
1 if self.pred_length > 1 else None)
queues = self.get_initial_conv_queues(
F,
past_target=F.slice_axis(past_target,
begin=-self.receptive_field,
end=None,
axis=-1),
features=F.slice_axis(
full_features,
begin=-self.pred_length - self.receptive_field + 1,
end=features_end_ix,
axis=-1,
),
)
queues = [blow_up(queue) for queue in queues]
res = F.slice_axis(past_target, begin=-2, end=None, axis=-1)
res = blow_up(res)
for n in range(self.pred_length):
# Generate one-step ahead predictions. The input consists of target
# and features corresponding to the last two time steps.
current_target = F.slice_axis(res, begin=-2, end=None, axis=-1)
current_features = F.slice_axis(
full_features,
begin=self.receptive_field + n - 1,
end=self.receptive_field + n + 1,
axis=-1,
)
embedding = self.target_feature_embedding(
F,
target=current_target,
features=blow_up(current_features),
)
# (batch_size, 1, num_bins) where 1 corresponds to the time axis.
unnormalized_outputs, queues = self.base_net(
F, embedding, one_step_prediction=True, queues=queues)
if self.temperature > 0:
# (batch_size, 1, num_bins) where 1 corresponds to the time
# axis.
probs = F.softmax(unnormalized_outputs / self.temperature,
axis=-1)
# (batch_size, 1)
y = F.sample_multinomial(probs)
else:
# (batch_size, 1)
y = F.argmax(unnormalized_outputs, axis=-1)
y = y.astype("int32")
res = F.concat(res, y, num_args=2, dim=-1)
samples = F.slice_axis(res, begin=-self.pred_length, end=None, axis=-1)
samples = samples.reshape(shape=(-1, self.num_samples,
self.pred_length))
samples = self.post_transform(samples)
samples = F.broadcast_mul(scale.expand_dims(axis=1), samples)
return samples
def process_static_cat(self, F, feature: Tensor) -> Tensor:
feature = self.embed_static(feature.astype(self.dtype))
return F.tile(feature.expand_dims(axis=1), reps=(1, self.T, 1))
