gluonts.mx.model.simple_feedforward package#

class gluonts.mx.model.simple_feedforward.SimpleFeedForwardEstimator(prediction_length: int, sampling: bool = True, trainer: Trainer = gluonts.mx.trainer._base.Trainer(add_default_callbacks=True, callbacks=None, clip_gradient=10.0, ctx=None, epochs=100, hybridize=True, init='xavier', learning_rate=0.001, num_batches_per_epoch=50, weight_decay=1e-08), num_hidden_dimensions: Optional[List[int]] = None, context_length: Optional[int] = None, distr_output: DistributionOutput = gluonts.mx.distribution.student_t.StudentTOutput(), imputation_method: Optional[MissingValueImputation] = None, batch_normalization: bool = False, mean_scaling: bool = True, num_parallel_samples: int = 100, train_sampler: Optional[InstanceSampler] = None, validation_sampler: Optional[InstanceSampler] = None, batch_size: int = 32)[source]#

Bases: GluonEstimator

SimpleFeedForwardEstimator shows how to build a simple MLP model predicting the next target time-steps given the previous ones.

Given that we want to define a gluon model trainable by SGD, we inherit the parent class GluonEstimator that handles most of the logic for fitting a neural-network.

We thus only have to define:

How the data is transformed before being fed to our model:
```
def create_transformation(self) -> Transformation
```

How the training happens:

def create_training_network(self) -> HybridBlock

how the predictions can be made for a batch given a trained network:

def create_predictor(
     self,
     transformation: Transformation,
     trained_net: HybridBlock,
) -> Predictor

Parameters

prediction_length (int) – Length of the prediction horizon
trainer – Trainer object to be used (default: Trainer())
num_hidden_dimensions – Number of hidden nodes in each layer (default: [40, 40])
context_length – Number of time units that condition the predictions (default: None, in which case context_length = prediction_length)
distr_output – Distribution to fit (default: StudentTOutput())
batch_normalization – Whether to use batch normalization (default: False)
mean_scaling – Scale the network input by the data mean and the network output by its inverse (default: True)
num_parallel_samples – Number of evaluation samples per time series to increase parallelism during inference. This is a model optimization that does not affect the accuracy (default: 100)
train_sampler – Controls the sampling of windows during training.
validation_sampler – Controls the sampling of windows during validation.
batch_size – The size of the batches to be used training and prediction.

create_predictor(transformation, trained_network)[source]#

Create and return a predictor object.

Parameters

transformation – Transformation to be applied to data before it goes into the model.
module – A trained HybridBlock object.

Returns

A predictor wrapping a HybridBlock used for inference.

Return type

Predictor

create_training_data_loader(data: Dataset, **kwargs) → Iterable[Dict[str, Any]][source]#

Create a data loader for training purposes.

Parameters: data – Dataset from which to create the data loader.
Returns: The data loader, i.e. and iterable over batches of data.
Return type: DataLoader

create_training_network() → HybridBlock[source]#

Create and return the network used for training (i.e., computing the loss).

Returns: The network that computes the loss given input data.
Return type: HybridBlock

create_transformation() → Transformation[source]#

Create and return the transformation needed for training and inference.

Returns: The transformation that will be applied entry-wise to datasets, at training and inference time.
Return type: Transformation

create_validation_data_loader(data: Dataset, **kwargs) → Iterable[Dict[str, Any]][source]#

Create a data loader for validation purposes.

Parameters: data – Dataset from which to create the data loader.
Returns: The data loader, i.e. and iterable over batches of data.
Return type: DataLoader