def create_predictor(
self, transformation: Transformation,
trained_network: HybridBlock) -> RepresentableBlockPredictor:
prediction_network = SelfAttentionPredictionNetwork(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_hidden=self.model_dim,
m_ffn=self.ffn_dim_multiplier,
n_head=self.num_heads,
n_layers=self.num_layers,
n_output=self.num_outputs,
cardinalities=self.cardinalities,
kernel_sizes=self.kernel_sizes,
dist_enc=self.distance_encoding,
pre_ln=self.pre_layer_norm,
dropout=self.dropout,
temperature=self.temperature,
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation,
prediction_net=prediction_network,
batch_size=self.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
forecast_generator=QuantileForecastGenerator(
quantiles=[str(q) for q in prediction_network.quantiles], ),
)
def create_predictor(
self,
transformation: transform.Transformation,
trained_network: Seq2SeqTrainingNetwork,
) -> Predictor:
# todo: this is specific to quantile output
quantile_strs = [
Quantile.from_float(quantile).name for quantile in self.quantiles
]
prediction_splitter = self._create_instance_splitter("test")
prediction_network = Seq2SeqPredictionNetwork(
embedder=trained_network.embedder,
scaler=trained_network.scaler,
encoder=trained_network.encoder,
enc2dec=trained_network.enc2dec,
decoder=trained_network.decoder,
quantile_output=trained_network.quantile_output,
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation + prediction_splitter,
prediction_net=prediction_network,
batch_size=self.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
forecast_generator=QuantileForecastGenerator(quantile_strs),
)
def create_predictor(
self, transformation: Transformation,
trained_network: HybridBlock) -> RepresentableBlockPredictor:
prediction_network = TemporalFusionTransformerPredictionNetwork(
context_length=self.context_length,
prediction_length=self.prediction_length,
d_var=self.variable_dim,
d_hidden=self.hidden_dim,
n_head=self.num_heads,
n_output=self.num_outputs,
d_past_feat_dynamic_real=list(
self.past_dynamic_feature_dims.values()),
c_past_feat_dynamic_cat=list(
self.past_dynamic_cardinalities.values()),
d_feat_dynamic_real=[1] * len(self.time_features) +
list(self.dynamic_feature_dims.values()),
c_feat_dynamic_cat=list(self.dynamic_cardinalities.values()),
d_feat_static_real=list(self.static_feature_dims.values()),
c_feat_static_cat=list(self.static_cardinalities.values()),
dropout=self.dropout_rate,
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
forecast_generator=QuantileForecastGenerator(
quantiles=[str(q) for q in prediction_network.quantiles], ),
)
def create_predictor(
self,
transformation: Transformation,
trained_network: ForkingSeq2SeqNetworkBase,
) -> Predictor:
# this is specific to quantile output
quantile_strs = [
Quantile.from_float(quantile).name
for quantile in self.quantile_output.quantiles
]
prediction_network = ForkingSeq2SeqPredictionNetwork(
encoder=trained_network.encoder,
enc2dec=trained_network.enc2dec,
decoder=trained_network.decoder,
quantile_output=trained_network.quantile_output,
context_length=self.context_length,
cardinality=self.cardinality,
embedding_dimension=self.embedding_dimension,
scaling=self.scaling,
dtype=self.dtype,
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
forecast_generator=QuantileForecastGenerator(quantile_strs),
)
def create_predictor(
self,
transformation: Transformation,
trained_network: ForkingSeq2SeqNetworkBase,
) -> Predictor:
quantile_strs = (
[
Quantile.from_float(quantile).name
for quantile in self.quantile_output.quantiles
]
if self.quantile_output is not None
else None
)
prediction_splitter = self._create_instance_splitter("test")
prediction_network_class = (
ForkingSeq2SeqPredictionNetwork
if self.quantile_output is not None
else ForkingSeq2SeqDistributionPredictionNetwork
)
prediction_network = prediction_network_class(
encoder=trained_network.encoder,
enc2dec=trained_network.enc2dec,
decoder=trained_network.decoder,
quantile_output=trained_network.quantile_output,
distr_output=trained_network.distr_output,
context_length=self.context_length,
num_forking=self.num_forking,
cardinality=self.cardinality,
embedding_dimension=self.embedding_dimension,
scaling=self.scaling,
scaling_decoder_dynamic_feature=self.scaling_decoder_dynamic_feature,
dtype=self.dtype,
)
copy_parameters(trained_network, prediction_network)
return RepresentableBlockPredictor(
input_transform=transformation + prediction_splitter,
prediction_net=prediction_network,
batch_size=self.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
forecast_generator=(
QuantileForecastGenerator(quantile_strs)
if quantile_strs is not None
else DistributionForecastGenerator(self.distr_output)
),
)
def create_predictor(
self,
transformation: Transformation,
trained_network: TemporalFusionTransformerTrainingNetwork,
device: torch.device,
) -> Predictor:
prediction_network = TemporalFusionTransformerPredictionNetwork(
context_length=self.context_length,
prediction_length=self.prediction_length,
variable_dim=self.variable_dim,
embed_dim=self.embed_dim,
num_heads=self.num_heads,
num_outputs=self.num_outputs,
dropout=self.dropout_rate,
d_past_feat_dynamic_real=_default_feat_args(
list(self.past_dynamic_feature_dims.values())),
c_past_feat_dynamic_cat=_default_feat_args(
list(self.past_dynamic_cardinalities.values())),
# +1 is for Age Feature
d_feat_dynamic_real=_default_feat_args(
[1] * (len(self.time_features) + 1) +
list(self.dynamic_feature_dims.values())),
c_feat_dynamic_cat=_default_feat_args(
list(self.dynamic_cardinalities.values())),
d_feat_static_real=_default_feat_args(
list(self.static_feature_dims.values()), ),
c_feat_static_cat=_default_feat_args(
list(self.static_cardinalities.values()), ),
).to(device)
copy_parameters(trained_network, prediction_network)
input_names = get_module_forward_input_names(prediction_network)
prediction_splitter = self.create_instance_splitter("test")
return PyTorchPredictor(
input_transform=transformation + prediction_splitter,
input_names=input_names,
prediction_net=prediction_network,
batch_size=self.trainer.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
device=device,
forecast_generator=QuantileForecastGenerator(
quantiles=[str(q) for q in prediction_network.quantiles], ),
)