class TemporalFusionTransformerNetwork(HybridBlock):
@validated()
def __init__(
self,
context_length: int,
prediction_length: int,
d_var: int,
d_hidden: int,
n_head: int,
n_output: int,
d_past_feat_dynamic_real: List[int],
c_past_feat_dynamic_cat: List[int],
d_feat_dynamic_real: List[int],
c_feat_dynamic_cat: List[int],
d_feat_static_real: List[int],
c_feat_static_cat: List[int],
dropout: float = 0.0,
**kwargs,
):
super(TemporalFusionTransformerNetwork, self).__init__(**kwargs)
self.context_length = context_length
self.prediction_length = prediction_length
self.d_var = d_var
self.d_hidden = d_hidden
self.n_head = n_head
self.n_output = n_output
self.quantiles = sum(
[[i / 10, 1.0 - i / 10] for i in range(1, (n_output + 1) // 2)],
[0.5],
)
self.normalize_eps = 1e-5
self.d_past_feat_dynamic_real = d_past_feat_dynamic_real
self.c_past_feat_dynamic_cat = c_past_feat_dynamic_cat
self.d_feat_dynamic_real = d_feat_dynamic_real
self.c_feat_dynamic_cat = c_feat_dynamic_cat
self.d_feat_static_real = d_feat_static_real
self.c_feat_static_cat = c_feat_static_cat
self.n_past_feat_dynamic = len(self.d_past_feat_dynamic_real) + len(
self.c_past_feat_dynamic_cat
)
self.n_feat_dynamic = len(self.d_feat_dynamic_real) + len(
self.c_feat_dynamic_cat
)
self.n_feat_static = len(self.d_feat_static_real) + len(
self.c_feat_static_cat
)
with self.name_scope():
self.target_proj = nn.Dense(
units=self.d_var,
in_units=1,
flatten=False,
prefix=f"target_projection_",
)
if self.d_past_feat_dynamic_real:
self.past_feat_dynamic_proj = FeatureProjector(
feature_dims=self.d_past_feat_dynamic_real,
embedding_dims=[self.d_var]
* len(self.d_past_feat_dynamic_real),
prefix="past_feat_dynamic_",
)
else:
self.past_feat_dynamic_proj = None
if self.c_past_feat_dynamic_cat:
self.past_feat_dynamic_embed = FeatureEmbedder(
cardinalities=self.c_past_feat_dynamic_cat,
embedding_dims=[self.d_var]
* len(self.c_past_feat_dynamic_cat),
prefix="past_feat_dynamic_",
)
else:
self.past_feat_dynamic_embed = None
if self.d_feat_dynamic_real:
self.feat_dynamic_proj = FeatureProjector(
feature_dims=self.d_feat_dynamic_real,
embedding_dims=[self.d_var]
* len(self.d_feat_dynamic_real),
prefix="feat_dynamic_",
)
else:
self.feat_dynamic_proj = None
if self.c_feat_dynamic_cat:
self.feat_dynamic_embed = FeatureEmbedder(
cardinalities=self.c_feat_dynamic_cat,
embedding_dims=[self.d_var] * len(self.c_feat_dynamic_cat),
prefix="feat_dynamic_",
)
else:
self.feat_dynamic_embed = None
if self.d_feat_static_real:
self.feat_static_proj = FeatureProjector(
feature_dims=self.d_feat_static_real,
embedding_dims=[self.d_var] * len(self.d_feat_static_real),
prefix="feat_static_",
)
else:
self.feat_static_proj = None
if self.c_feat_static_cat:
self.feat_static_embed = FeatureEmbedder(
cardinalities=self.c_feat_static_cat,
embedding_dims=[self.d_var] * len(self.c_feat_static_cat),
prefix="feat_static_",
)
else:
self.feat_static_embed = None
self.static_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_feat_static,
dropout=dropout,
)
self.ctx_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_past_feat_dynamic + self.n_feat_dynamic + 1,
add_static=True,
dropout=dropout,
)
self.tgt_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_feat_dynamic,
add_static=True,
dropout=dropout,
)
self.selection = GatedResidualNetwork(
d_hidden=self.d_var,
dropout=dropout,
)
self.enrichment = GatedResidualNetwork(
d_hidden=self.d_var,
dropout=dropout,
)
self.state_h = GatedResidualNetwork(
d_hidden=self.d_var,
d_output=self.d_hidden,
dropout=dropout,
)
self.state_c = GatedResidualNetwork(
d_hidden=self.d_var,
d_output=self.d_hidden,
dropout=dropout,
)
self.temporal_encoder = TemporalFusionEncoder(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_input=self.d_var,
d_hidden=self.d_hidden,
)
self.temporal_decoder = TemporalFusionDecoder(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_hidden=self.d_hidden,
d_var=self.d_var,
n_head=self.n_head,
dropout=dropout,
)
self.output = QuantileOutput(quantiles=self.quantiles)
self.output_proj = self.output.get_quantile_proj()
self.loss = self.output.get_loss()
def _preprocess(
self,
F,
past_target: Tensor,
past_observed_values: Tensor,
past_feat_dynamic_real: Tensor,
past_feat_dynamic_cat: Tensor,
feat_dynamic_real: Tensor,
feat_dynamic_cat: Tensor,
feat_static_real: Tensor,
feat_static_cat: Tensor,
):
obs = F.broadcast_mul(past_target, past_observed_values)
count = F.sum(past_observed_values, axis=1, keepdims=True)
offset = F.broadcast_div(
F.sum(obs, axis=1, keepdims=True),
count + self.normalize_eps,
)
scale = F.broadcast_div(
F.sum(obs ** 2, axis=1, keepdims=True),
count + self.normalize_eps,
)
scale = F.broadcast_sub(scale, offset ** 2)
scale = F.sqrt(scale)
past_target = F.broadcast_div(
F.broadcast_sub(past_target, offset),
scale + self.normalize_eps,
)
past_target = F.expand_dims(past_target, axis=-1)
past_covariates = []
future_covariates = []
static_covariates: List[Tensor] = []
proj = self.target_proj(past_target)
past_covariates.append(proj)
if self.past_feat_dynamic_proj is not None:
projs = self.past_feat_dynamic_proj(past_feat_dynamic_real)
past_covariates.extend(projs)
if self.past_feat_dynamic_embed is not None:
embs = self.past_feat_dynamic_embed(past_feat_dynamic_cat)
past_covariates.extend(embs)
if self.feat_dynamic_proj is not None:
projs = self.feat_dynamic_proj(feat_dynamic_real)
for proj in projs:
ctx_proj = F.slice_axis(
proj, axis=1, begin=0, end=self.context_length
)
tgt_proj = F.slice_axis(
proj, axis=1, begin=self.context_length, end=None
)
past_covariates.append(ctx_proj)
future_covariates.append(tgt_proj)
if self.feat_dynamic_embed is not None:
embs = self.feat_dynamic_embed(feat_dynamic_cat)
for emb in embs:
ctx_emb = F.slice_axis(
emb, axis=1, begin=0, end=self.context_length
)
tgt_emb = F.slice_axis(
emb, axis=1, begin=self.context_length, end=None
)
past_covariates.append(ctx_emb)
future_covariates.append(tgt_emb)
if self.feat_static_proj is not None:
projs = self.feat_static_proj(feat_static_real)
static_covariates.extend(projs)
if self.feat_static_embed is not None:
embs = self.feat_static_embed(feat_static_cat)
static_covariates.extend(embs)
return (
past_covariates,
future_covariates,
static_covariates,
offset,
scale,
)
def _postprocess(
self,
F,
preds: Tensor,
offset: Tensor,
scale: Tensor,
) -> Tensor:
offset = F.expand_dims(offset, axis=-1)
scale = F.expand_dims(scale, axis=-1)
preds = F.broadcast_add(
F.broadcast_mul(preds, (scale + self.normalize_eps)),
offset,
)
return preds
def _forward(
self,
F,
past_observed_values: Tensor,
past_covariates: Tensor,
future_covariates: Tensor,
static_covariates: Tensor,
):
static_var, _ = self.static_selector(static_covariates)
c_selection = self.selection(static_var).expand_dims(axis=1)
c_enrichment = self.enrichment(static_var).expand_dims(axis=1)
c_h = self.state_h(static_var)
c_c = self.state_c(static_var)
ctx_input, _ = self.ctx_selector(past_covariates, c_selection)
tgt_input, _ = self.tgt_selector(future_covariates, c_selection)
encoding = self.temporal_encoder(ctx_input, tgt_input, [c_h, c_c])
decoding = self.temporal_decoder(
encoding, c_enrichment, past_observed_values
)
preds = self.output_proj(decoding)
return preds
def __init__(
self,
context_length: int,
prediction_length: int,
d_hidden: int,
m_ffn: int,
n_head: int,
n_layers: int,
n_output: int,
cardinalities: List[int],
kernel_sizes: Optional[List[int]],
dist_enc: Optional[str],
pre_ln: bool,
dropout: float,
temperature: float,
normalizer_eps: float = 1e-5,
**kwargs,
):
super().__init__(**kwargs)
if kernel_sizes is None or len(kernel_sizes) == 0:
self.kernel_sizes = (1, )
else:
self.kernel_sizes = kernel_sizes
self.context_length = context_length
self.prediction_length = prediction_length
self.d_hidden = d_hidden
assert (n_output % 2 == 1) and (n_output <= 9)
self.quantiles = sum(
([i / 10, 1.0 - i / 10] for i in range(1, (n_output + 1) // 2)),
[0.5],
)
self.normalizer_eps = normalizer_eps
with self.name_scope():
self._blocks = []
for layer in range(n_layers):
block = SelfAttentionBlock(
d_hidden=self.d_hidden,
m_ffn=m_ffn,
kernel_sizes=self.kernel_sizes,
n_head=n_head,
dist_enc=dist_enc,
pre_ln=pre_ln,
dropout=dropout,
temperature=temperature,
)
self.register_child(block=block, name=f"block_{layer+1}")
self._blocks.append(block)
self.target_proj = nn.Dense(
units=self.d_hidden,
in_units=1,
use_bias=True,
flatten=False,
weight_initializer=init.Xavier(),
prefix="target_proj_",
)
self.covar_proj = nn.Dense(
units=self.d_hidden,
use_bias=True,
flatten=False,
weight_initializer=init.Xavier(),
prefix="covar_proj_",
)
if cardinalities:
self.embedder = FeatureEmbedder(
cardinalities=cardinalities,
embedding_dims=[self.d_hidden] * len(cardinalities),
prefix="embedder_",
)
self.output = QuantileOutput(quantiles=self.quantiles)
self.output_proj = self.output.get_quantile_proj()
self.loss = self.output.get_loss()
class SelfAttentionNetwork(HybridBlock):
@validated()
def __init__(
self,
context_length: int,
prediction_length: int,
d_hidden: int,
m_ffn: int,
n_head: int,
n_layers: int,
n_output: int,
cardinalities: List[int],
kernel_sizes: Optional[List[int]],
dist_enc: Optional[str],
pre_ln: bool,
dropout: float,
temperature: float,
normalizer_eps: float = 1e-5,
**kwargs,
):
super().__init__(**kwargs)
if kernel_sizes is None or len(kernel_sizes) == 0:
self.kernel_sizes = (1, )
else:
self.kernel_sizes = kernel_sizes
self.context_length = context_length
self.prediction_length = prediction_length
self.d_hidden = d_hidden
assert (n_output % 2 == 1) and (n_output <= 9)
self.quantiles = sum(
([i / 10, 1.0 - i / 10] for i in range(1, (n_output + 1) // 2)),
[0.5],
)
self.normalizer_eps = normalizer_eps
with self.name_scope():
self._blocks = []
for layer in range(n_layers):
block = SelfAttentionBlock(
d_hidden=self.d_hidden,
m_ffn=m_ffn,
kernel_sizes=self.kernel_sizes,
n_head=n_head,
dist_enc=dist_enc,
pre_ln=pre_ln,
dropout=dropout,
temperature=temperature,
)
self.register_child(block=block, name=f"block_{layer+1}")
self._blocks.append(block)
self.target_proj = nn.Dense(
units=self.d_hidden,
in_units=1,
use_bias=True,
flatten=False,
weight_initializer=init.Xavier(),
prefix="target_proj_",
)
self.covar_proj = nn.Dense(
units=self.d_hidden,
use_bias=True,
flatten=False,
weight_initializer=init.Xavier(),
prefix="covar_proj_",
)
if cardinalities:
self.embedder = FeatureEmbedder(
cardinalities=cardinalities,
embedding_dims=[self.d_hidden] * len(cardinalities),
prefix="embedder_",
)
self.output = QuantileOutput(quantiles=self.quantiles)
self.output_proj = self.output.get_quantile_proj()
self.loss = self.output.get_loss()
def _preprocess(
self,
F,
past_target: Tensor,
past_observed_values: Tensor,
past_is_pad: Tensor,
past_feat_dynamic_real: Tensor,
past_feat_dynamic_cat: Tensor,
future_target: Tensor,
future_feat_dynamic_real: Tensor,
future_feat_dynamic_cat: Tensor,
feat_static_real: Tensor,
feat_static_cat: Tensor,
) -> Tuple[Tensor, Optional[Tensor], Tensor, Tensor, Optional[Tensor],
Tensor, Tensor, ]:
obs = past_target * past_observed_values
count = F.sum(past_observed_values, axis=1, keepdims=True)
offset = F.sum(obs, axis=1,
keepdims=True) / (count + self.normalizer_eps)
scale = F.sum(obs**2, axis=1,
keepdims=True) / (count + self.normalizer_eps)
scale = scale - offset**2
scale = scale.sqrt()
past_target = (past_target - offset) / (scale + self.normalizer_eps)
if future_target is not None:
future_target = (future_target - offset) / (scale +
self.normalizer_eps)
def _assemble_covariates(
feat_dynamic_real: Tensor,
feat_dynamic_cat: Tensor,
feat_static_real: Tensor,
feat_static_cat: Tensor,
is_past: bool,
) -> Tensor:
covariates = []
if feat_dynamic_real.shape[-1] > 0:
covariates.append(feat_dynamic_real)
if feat_static_real.shape[-1] > 0:
covariates.append(
feat_static_real.expand_dims(axis=1).repeat(
axis=1,
repeats=self.context_length
if is_past else self.prediction_length,
))
if len(covariates) > 0:
covariates = F.concat(*covariates, dim=-1)
covariates = self.covar_proj(covariates)
else:
covariates = None
categories = []
if feat_dynamic_cat.shape[-1] > 0:
categories.append(feat_dynamic_cat)
if feat_static_cat.shape[-1] > 0:
categories.append(
feat_static_cat.expand_dims(axis=1).repeat(
axis=1,
repeats=self.context_length
if is_past else self.prediction_length,
))
if len(categories) > 0:
categories = F.concat(*categories, dim=-1)
embeddings = self.embedder(categories)
embeddings = F.reshape(embeddings,
shape=(0, 0, -4, self.d_hidden,
-1)).sum(axis=-1)
if covariates is not None:
covariates = covariates + embeddings
else:
covariates = embeddings
else:
pass
return covariates
past_covariates = _assemble_covariates(
past_feat_dynamic_real,
past_feat_dynamic_cat,
feat_static_real,
feat_static_cat,
is_past=True,
)
future_covariates = _assemble_covariates(
future_feat_dynamic_real,
future_feat_dynamic_cat,
feat_static_real,
feat_static_cat,
is_past=False,
)
past_observed_values = F.broadcast_logical_and(
past_observed_values,
F.logical_not(past_is_pad),
)
return (
past_target,
past_covariates,
past_observed_values,
future_target,
future_covariates,
offset,
scale,
)
def _postprocess(self, F, preds: Tensor, offset: Tensor,
scale: Tensor) -> Tensor:
offset = F.expand_dims(offset, axis=-1)
scale = F.expand_dims(scale, axis=-1)
preds = preds * (scale + self.normalizer_eps) + offset
return preds
def _forward_step(
self,
F,
horizon: int,
target: Tensor,
covars: Optional[Tensor],
mask: Tensor,
) -> Tensor:
target = F.expand_dims(target, axis=-1)
mask = F.expand_dims(mask, axis=-1)
value = self.target_proj(target)
if covars is not None:
value = value + covars
for block in self._blocks:
value = block(value, mask)
value = F.slice_axis(value, axis=1, begin=-horizon, end=None)
preds = self.output_proj(value)
return preds
def __init__(
self,
prediction_length: int,
freq: str,
context_length: Optional[int] = None,
decoder_mlp_dim_seq: List[int] = None,
trainer: Trainer = Trainer(),
quantiles: Optional[List[float]] = None,
distr_output: Optional[DistributionOutput] = None,
scaling: bool = False,
scaling_decoder_dynamic_feature: bool = False,
) -> None:
assert (prediction_length >
0), f"Invalid prediction length: {prediction_length}."
assert decoder_mlp_dim_seq is None or all(
d > 0 for d in decoder_mlp_dim_seq
), "Elements of `mlp_hidden_dimension_seq` should be > 0"
assert quantiles is None or all(
0 <= d <= 1 for d in
quantiles), "Elements of `quantiles` should be >= 0 and <= 1"
self.decoder_mlp_dim_seq = (decoder_mlp_dim_seq if decoder_mlp_dim_seq
is not None else [30])
self.quantiles = (quantiles if
(quantiles is not None) or (distr_output is not None)
else [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
# `use_static_feat` and `use_dynamic_feat` always True because network
# always receives input; either from the input data or constants
encoder = RNNEncoder(
mode="gru",
hidden_size=50,
num_layers=1,
bidirectional=True,
prefix="encoder_",
use_static_feat=True,
use_dynamic_feat=True,
)
decoder = ForkingMLPDecoder(
dec_len=prediction_length,
final_dim=self.decoder_mlp_dim_seq[-1],
hidden_dimension_sequence=self.decoder_mlp_dim_seq[:-1],
prefix="decoder_",
)
quantile_output = (QuantileOutput(self.quantiles)
if self.quantiles else None)
super().__init__(
encoder=encoder,
decoder=decoder,
quantile_output=quantile_output,
distr_output=distr_output,
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
trainer=trainer,
scaling=scaling,
scaling_decoder_dynamic_feature=scaling_decoder_dynamic_feature,
)
def __init__(
self,
freq: str,
prediction_length: int,
context_length: Optional[int] = None,
use_past_feat_dynamic_real: bool = False,
use_feat_dynamic_real: bool = False,
use_feat_static_cat: bool = False,
cardinality: List[int] = None,
embedding_dimension: List[int] = None,
add_time_feature: bool = True,
add_age_feature: bool = False,
enable_encoder_dynamic_feature: bool = True,
enable_decoder_dynamic_feature: bool = True,
seed: Optional[int] = None,
decoder_mlp_dim_seq: Optional[List[int]] = None,
channels_seq: Optional[List[int]] = None,
dilation_seq: Optional[List[int]] = None,
kernel_size_seq: Optional[List[int]] = None,
use_residual: bool = True,
quantiles: Optional[List[float]] = None,
distr_output: Optional[DistributionOutput] = None,
trainer: Trainer = Trainer(),
scaling: bool = False,
scaling_decoder_dynamic_feature: bool = False,
num_forking: Optional[int] = None,
) -> None:
assert (distr_output is None) or (quantiles is None)
assert (prediction_length >
0), f"Invalid prediction length: {prediction_length}."
assert decoder_mlp_dim_seq is None or all(
d > 0 for d in decoder_mlp_dim_seq
), "Elements of `mlp_hidden_dimension_seq` should be > 0"
assert channels_seq is None or all(
d > 0
for d in channels_seq), "Elements of `channels_seq` should be > 0"
assert dilation_seq is None or all(
d > 0
for d in dilation_seq), "Elements of `dilation_seq` should be > 0"
# TODO: add support for kernel size=1
assert kernel_size_seq is None or all(
d > 1 for d in
kernel_size_seq), "Elements of `kernel_size_seq` should be > 0"
assert quantiles is None or all(
0 <= d <= 1 for d in
quantiles), "Elements of `quantiles` should be >= 0 and <= 1"
self.decoder_mlp_dim_seq = (decoder_mlp_dim_seq if decoder_mlp_dim_seq
is not None else [30])
self.channels_seq = (channels_seq
if channels_seq is not None else [30, 30, 30])
self.dilation_seq = (dilation_seq
if dilation_seq is not None else [1, 3, 9])
self.kernel_size_seq = (kernel_size_seq
if kernel_size_seq is not None else [7, 3, 3])
self.quantiles = (quantiles if
(quantiles is not None) or (distr_output is not None)
else [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
assert (len(self.channels_seq) == len(self.dilation_seq) == len(
self.kernel_size_seq)), (
f"mismatch CNN configurations: {len(self.channels_seq)} vs. "
f"{len(self.dilation_seq)} vs. {len(self.kernel_size_seq)}")
if seed:
np.random.seed(seed)
mx.random.seed(seed, trainer.ctx)
# `use_static_feat` and `use_dynamic_feat` always True because network
# always receives input; either from the input data or constants
encoder = HierarchicalCausalConv1DEncoder(
dilation_seq=self.dilation_seq,
kernel_size_seq=self.kernel_size_seq,
channels_seq=self.channels_seq,
use_residual=use_residual,
use_static_feat=True,
use_dynamic_feat=True,
prefix="encoder_",
)
decoder = ForkingMLPDecoder(
dec_len=prediction_length,
final_dim=self.decoder_mlp_dim_seq[-1],
hidden_dimension_sequence=self.decoder_mlp_dim_seq[:-1],
prefix="decoder_",
)
quantile_output = (QuantileOutput(self.quantiles)
if self.quantiles else None)
super().__init__(
encoder=encoder,
decoder=decoder,
quantile_output=quantile_output,
distr_output=distr_output,
freq=freq,
prediction_length=prediction_length,
context_length=context_length,
use_past_feat_dynamic_real=use_past_feat_dynamic_real,
use_feat_dynamic_real=use_feat_dynamic_real,
use_feat_static_cat=use_feat_static_cat,
enable_encoder_dynamic_feature=enable_encoder_dynamic_feature,
enable_decoder_dynamic_feature=enable_decoder_dynamic_feature,
cardinality=cardinality,
embedding_dimension=embedding_dimension,
add_time_feature=add_time_feature,
add_age_feature=add_age_feature,
trainer=trainer,
scaling=scaling,
scaling_decoder_dynamic_feature=scaling_decoder_dynamic_feature,
num_forking=num_forking,
)
def __init__(
self,
context_length: int,
prediction_length: int,
d_var: int,
d_hidden: int,
n_head: int,
n_output: int,
d_past_feat_dynamic_real: List[int],
c_past_feat_dynamic_cat: List[int],
d_feat_dynamic_real: List[int],
c_feat_dynamic_cat: List[int],
d_feat_static_real: List[int],
c_feat_static_cat: List[int],
dropout: float = 0.0,
**kwargs,
):
super(TemporalFusionTransformerNetwork, self).__init__(**kwargs)
self.context_length = context_length
self.prediction_length = prediction_length
self.d_var = d_var
self.d_hidden = d_hidden
self.n_head = n_head
self.n_output = n_output
self.quantiles = sum(
[[i / 10, 1.0 - i / 10] for i in range(1, (n_output + 1) // 2)],
[0.5],
)
self.normalize_eps = 1e-5
self.d_past_feat_dynamic_real = d_past_feat_dynamic_real
self.c_past_feat_dynamic_cat = c_past_feat_dynamic_cat
self.d_feat_dynamic_real = d_feat_dynamic_real
self.c_feat_dynamic_cat = c_feat_dynamic_cat
self.d_feat_static_real = d_feat_static_real
self.c_feat_static_cat = c_feat_static_cat
self.n_past_feat_dynamic = len(self.d_past_feat_dynamic_real) + len(
self.c_past_feat_dynamic_cat)
self.n_feat_dynamic = len(self.d_feat_dynamic_real) + len(
self.c_feat_dynamic_cat)
self.n_feat_static = len(self.d_feat_static_real) + len(
self.c_feat_static_cat)
with self.name_scope():
self.target_proj = nn.Dense(
units=self.d_var,
in_units=1,
flatten=False,
prefix=f"target_projection_",
)
if self.d_past_feat_dynamic_real:
self.past_feat_dynamic_proj = FeatureProjector(
feature_dims=self.d_past_feat_dynamic_real,
embedding_dims=[self.d_var] *
len(self.d_past_feat_dynamic_real),
prefix="past_feat_dynamic_",
)
else:
self.past_feat_dynamic_proj = None
if self.c_past_feat_dynamic_cat:
self.past_feat_dynamic_embed = FeatureEmbedder(
cardinalities=self.c_past_feat_dynamic_cat,
embedding_dims=[self.d_var] *
len(self.c_past_feat_dynamic_cat),
prefix="past_feat_dynamic_",
)
else:
self.past_feat_dynamic_embed = None
if self.d_feat_dynamic_real:
self.feat_dynamic_proj = FeatureProjector(
feature_dims=self.d_feat_dynamic_real,
embedding_dims=[self.d_var] *
len(self.d_feat_dynamic_real),
prefix="feat_dynamic_",
)
else:
self.feat_dynamic_proj = None
if self.c_feat_dynamic_cat:
self.feat_dynamic_embed = FeatureEmbedder(
cardinalities=self.c_feat_dynamic_cat,
embedding_dims=[self.d_var] * len(self.c_feat_dynamic_cat),
prefix="feat_dynamic_",
)
else:
self.feat_dynamic_embed = None
if self.d_feat_static_real:
self.feat_static_proj = FeatureProjector(
feature_dims=self.d_feat_static_real,
embedding_dims=[self.d_var] * len(self.d_feat_static_real),
prefix="feat_static_",
)
else:
self.feat_static_proj = None
if self.c_feat_static_cat:
self.feat_static_embed = FeatureEmbedder(
cardinalities=self.c_feat_static_cat,
embedding_dims=[self.d_var] * len(self.c_feat_static_cat),
prefix="feat_static_",
)
else:
self.feat_static_embed = None
self.static_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_feat_static,
dropout=dropout,
)
self.ctx_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_past_feat_dynamic + self.n_feat_dynamic + 1,
add_static=True,
dropout=dropout,
)
self.tgt_selector = VariableSelectionNetwork(
d_hidden=self.d_var,
n_vars=self.n_feat_dynamic,
add_static=True,
dropout=dropout,
)
self.selection = GatedResidualNetwork(
d_hidden=self.d_var,
dropout=dropout,
)
self.enrichment = GatedResidualNetwork(
d_hidden=self.d_var,
dropout=dropout,
)
self.state_h = GatedResidualNetwork(
d_hidden=self.d_var,
d_output=self.d_hidden,
dropout=dropout,
)
self.state_c = GatedResidualNetwork(
d_hidden=self.d_var,
d_output=self.d_hidden,
dropout=dropout,
)
self.temporal_encoder = TemporalFusionEncoder(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_input=self.d_var,
d_hidden=self.d_hidden,
)
self.temporal_decoder = TemporalFusionDecoder(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_hidden=self.d_hidden,
d_var=self.d_var,
n_head=self.n_head,
dropout=dropout,
)
self.output = QuantileOutput(quantiles=self.quantiles)
self.output_proj = self.output.get_quantile_proj()
self.loss = self.output.get_loss()