def get_predictor(self, input_transform, batch_size=32, device=None):
return PyTorchPredictor(
prediction_length=self.prediction_length,
freq=self.freq,
input_names=["past_target"],
prediction_net=self,
batch_size=batch_size,
input_transform=input_transform,
forecast_generator=DistributionForecastGenerator(
self.distr_output),
device=None,
)
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, trained_network):
prediction_splitter = self._create_instance_splitter("test")
if self.sampling is True:
prediction_network = SimpleFeedForwardSamplingNetwork(
num_hidden_dimensions=self.num_hidden_dimensions,
prediction_length=self.prediction_length,
context_length=self.context_length,
distr_output=self.distr_output,
batch_normalization=self.batch_normalization,
mean_scaling=self.mean_scaling,
params=trained_network.collect_params(),
num_parallel_samples=self.num_parallel_samples,
)
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,
)
else:
prediction_network = SimpleFeedForwardDistributionNetwork(
num_hidden_dimensions=self.num_hidden_dimensions,
prediction_length=self.prediction_length,
context_length=self.context_length,
distr_output=self.distr_output,
batch_normalization=self.batch_normalization,
mean_scaling=self.mean_scaling,
params=trained_network.collect_params(),
num_parallel_samples=self.num_parallel_samples,
)
return RepresentableBlockPredictor(
input_transform=transformation + prediction_splitter,
prediction_net=prediction_network,
batch_size=self.batch_size,
forecast_generator=DistributionForecastGenerator(
self.distr_output
),
freq=self.freq,
prediction_length=self.prediction_length,
ctx=self.trainer.ctx,
)
def create_predictor(
self,
transformation: Transformation,
module,
) -> PyTorchPredictor:
prediction_splitter = self._create_instance_splitter(module, "test")
return PyTorchPredictor(
input_transform=transformation + prediction_splitter,
input_names=PREDICTION_INPUT_NAMES,
prediction_net=module.model,
forecast_generator=DistributionForecastGenerator(
self.distr_output),
batch_size=self.batch_size,
freq=self.freq,
prediction_length=self.prediction_length,
device=torch.device(
"cuda" if torch.cuda.is_available() else "cpu"),
)