def train_loader(
dataset: ListDataset,
prediction_interval_length: float,
context_interval_length: float,
is_train: bool = True,
override_args: dict = None,
) -> DataLoader:
if override_args is None:
override_args = {}
splitter = ContinuousTimeInstanceSplitter(
future_interval_length=prediction_interval_length,
past_interval_length=context_interval_length,
train_sampler=ContinuousTimeUniformSampler(num_instances=10),
)
kwargs: Dict[str, Any] = dict(
dataset=dataset,
transform=splitter,
batch_size=10,
stack_fn=partial(batchify,
ctx=mx.cpu(),
dtype=np.float32,
variable_length=True),
)
kwargs.update(override_args)
if is_train:
return TrainDataLoader(num_batches_per_epoch=22,
num_workers=None,
**kwargs)
else:
return InferenceDataLoader(num_workers=None, **kwargs)
def create_transformation(self) -> Transformation:
return Chain([
ContinuousTimeInstanceSplitter(
past_interval_length=self.context_interval_length,
future_interval_length=self.prediction_interval_length,
train_sampler=ContinuousTimeUniformSampler(
num_instances=self.num_training_instances),
),
RenameFields({
"past_target": "target",
"past_valid_length": "valid_length",
}),
])
def get_predictor(**kwargs) -> PointProcessGluonPredictor:
default_kwargs = dict(
input_names=["past_target", "past_valid_length"],
prediction_net=MockTPPPredictionNet(
prediction_interval_length=5.0),
batch_size=128,
prediction_interval_length=5.0,
freq="H",
ctx=mx.cpu(),
input_transform=ContinuousTimeInstanceSplitter(
1, 5, ContinuousTimeUniformSampler(num_instances=5)),
)
default_kwargs.update(**kwargs)
return PointProcessGluonPredictor(**default_kwargs)
def train_loader(
dataset: ListDataset,
prediction_interval_length: float,
context_interval_length: float,
is_train: bool = True,
override_args: dict = None,
) -> Iterable[DataBatch]:
if override_args is None:
override_args = {}
if is_train:
sampler = ContinuousTimeUniformSampler(
num_instances=10,
min_past=context_interval_length,
min_future=prediction_interval_length,
)
else:
sampler = ContinuousTimePredictionSampler(
min_past=context_interval_length)
splitter = ContinuousTimeInstanceSplitter(
future_interval_length=prediction_interval_length,
past_interval_length=context_interval_length,
instance_sampler=sampler,
freq=dataset.freq,
)
kwargs = dict(
dataset=dataset,
transform=splitter,
batch_size=10,
stack_fn=partial(batchify, dtype=np.float32, variable_length=True),
)
kwargs.update(override_args)
if is_train:
return itertools.islice(
TrainDataLoader(num_workers=None, **kwargs), NUM_BATCHES)
else:
return InferenceDataLoader(**kwargs)
def _create_instance_splitter(self, mode: str):
assert mode in ["training", "validation", "test"]
instance_sampler = {
"training": ContinuousTimeUniformSampler(
num_instances=self.num_training_instances,
min_past=self.context_interval_length,
min_future=self.prediction_interval_length,
),
"validation": ContinuousTimePredictionSampler(
allow_empty_interval=True,
min_past=self.context_interval_length,
min_future=self.prediction_interval_length,
),
"test": ContinuousTimePredictionSampler(
min_past=self.context_interval_length,
allow_empty_interval=False,
),
}[mode]
assert isinstance(instance_sampler, ContinuousTimePointSampler)
return Chain(
[
ContinuousTimeInstanceSplitter(
past_interval_length=self.context_interval_length,
future_interval_length=self.prediction_interval_length,
instance_sampler=instance_sampler,
),
RenameFields(
{
"past_target": "target",
"past_valid_length": "valid_length",
}
),
]
)
