gluonts.torch.model.wavenet package

class gluonts.torch.model.wavenet.WaveNet(pred_length: int, bin_values: List[float], num_residual_channels: int, num_skip_channels: int, dilation_depth: int, num_stacks: int, num_feat_dynamic_real: int = 1, num_feat_static_real: int = 1, cardinality: List[int] = [1], embedding_dimension: int = 5, num_parallel_samples: int = 100, temperature: float = 1.0, use_log_scale_feature: bool = True)

Bases: torch.nn.modules.module.Module

The WaveNet model.

Parameters

• pred_length – Prediction length.

• bin_values – List of bin values.

• num_residual_channels – Number of residual channels.

• num_skip_channels – Number of skip channels.

• dilation_depth – The depth of the dilated convolution.

• num_stacks – The number of dilation stacks.

• num_feat_dynamic_real – The number of dynamic real features, by default 1

• optional – The number of dynamic real features, by default 1

• num_feat_static_real – The number of static real features, by default 1

• optional – The number of static real features, by default 1

• cardinality – The cardinalities of static categorical features, by default [1]

• optional – The cardinalities of static categorical features, by default [1]

• embedding_dimension – The dimension of the embeddings for categorical features, by default 5

• optional – The dimension of the embeddings for categorical features, by default 5

• num_parallel_samples – The number of parallel samples to generate during inference. This parameter is only used in inference mode, by default 100

• optional – The number of parallel samples to generate during inference. This parameter is only used in inference mode, by default 100

• temperature – Temparature used for sampling from the output softmax distribution, by default 1.0

• optional – Temparature used for sampling from the output softmax distribution, by default 1.0

base_net(inputs: torch.Tensor, queues: Optional[List[torch.Tensor]] = None) → Tuple[torch.Tensor, List[torch.Tensor]]

Forward pass through the WaveNet.

Parameters

• inputs – A tensor of inputs Shape: (batch_size, num_residual_channels, sequence_length)

• queues – Convolutional queues containing past computations. This speeds up predictions and must be provided during prediction mode. See [Paine et al., 2016] for details, by default None

• optional – Convolutional queues containing past computations. This speeds up predictions and must be provided during prediction mode. See [Paine et al., 2016] for details, by default None

• al. ([Paine et) – arXiv preprint arXiv:1611.09482 (2016).

• algorithm." (2016] "Fast wavenet generation) – arXiv preprint arXiv:1611.09482 (2016).

Returns

• A tensor containing the unnormalized outputs of the network of

• shape (batch_size, pred_length, num_bins) and a list containing the

• convolutional queues for each layer. The queue corresponding to

• layer `l` has shape ((batch_size, num_residual_channels, 2^l).)

forward(feat_static_cat: torch.Tensor, feat_static_real: torch.Tensor, past_target: torch.Tensor, past_observed_values: torch.Tensor, past_time_feat: torch.Tensor, future_time_feat: torch.Tensor, scale: torch.Tensor, prediction_length: Optional[int] = None, num_parallel_samples: Optional[int] = None, temperature: Optional[float] = None) → torch.Tensor

Generate predictions from the WaveNet model.

Parameters

• feat_static_cat – Static categorical features: (batch_size, num_cat_features)

• feat_static_real – Static real-valued features: (batch_size, num_feat_static_real)

• past_target – Past target: (batch_size, receptive_field)

• past_observed_values – Observed value indicator for the past target: (batch_size, receptive_field)

• past_time_feat – Past time features: (batch_size, num_time_features, receptive_field)

• future_time_feat – Future time features: (batch_size, num_time_features, pred_length)

• scale – Scale of the time series: (batch_size, 1)

• prediction_length – Time length of the samples to generate. If not provided, use self.prediction_length.

• num_parallel_samples – Number of samples to generate. If not provided, use self.num_parallel_samples.

• temperature – Temperature to use in generating samples. If not provided, use self.temperature.

Return type

Predictions with shape (batch_size, num_parallel_samples, pred_length)

get_full_features(feat_static_cat: torch.Tensor, feat_static_real: torch.Tensor, past_observed_values: torch.Tensor, past_time_feat: torch.Tensor, future_time_feat: torch.Tensor, future_observed_values: Optional[torch.Tensor], scale: torch.Tensor) → torch.Tensor

Prepares the inputs for the network by repeating static feature and concatenating it with time features and observed value indicator.

Parameters

• feat_static_cat – Static categorical features: (batch_size, num_cat_features)

• feat_static_real – Static real-valued features: (batch_size, num_feat_static_real)

• past_observed_values – Observed value indicator for the past target: (batch_size, receptive_field)

• past_time_feat – Past time features: (batch_size, num_time_features, receptive_field)

• future_time_feat – Future time features: (batch_size, num_time_features, pred_length)

• future_observed_values – Observed value indicator for the future target: (batch_size, pred_length). This will be set to all ones, if not provided (e.g., during inference)

• scale – scale of the time series: (batch_size, 1)

Returns

• A tensor containing all the features ready to be passed through the

• network.

• Shape ((batch_size, num_features, receptive_field + pred_length))

static get_receptive_field(dilation_depth: int, num_stacks: int) → int

loss(feat_static_cat: torch.Tensor, feat_static_real: torch.Tensor, past_target: torch.Tensor, past_observed_values: torch.Tensor, past_time_feat: torch.Tensor, future_time_feat: torch.Tensor, future_target: torch.Tensor, future_observed_values: torch.Tensor, scale: torch.Tensor) → torch.Tensor

Computes the training loss for the wavenet model.

Parameters

• feat_static_cat – Static categorical features: (batch_size, num_cat_features)

• feat_static_real – Static real-valued features: (batch_size, num_feat_static_real)

• past_target – Past target: (batch_size, receptive_field)

• past_observed_values – Observed value indicator for the past target: (batch_size, receptive_field)

• past_time_feat – Past time features: (batch_size, num_time_features, receptive_field)

• future_time_feat – Future time features: (batch_size, num_time_features, pred_length)

• future_target – Target on which the loss is computed: (batch_size, pred_length)

• future_observed_values – Observed value indicator for the future target: (batch_size, pred_length). This will be set to all ones, if not provided (e.g., during inference)

• scale – Scale of the time series: (batch_size, 1)

Return type

Loss tensor with shape (batch_size, pred_length)

target_feature_embedding(target: torch.Tensor, features: torch.Tensor) → torch.Tensor

Provides a joint embedding for the target and features.

Parameters

• target – Full target of shape (batch_size, sequence_length)

• features – Full features of shape (batch_size, num_features, sequence_length)

Returns

• A tensor containing a joint embedding of target and features.

• Shape ((batch_size, n_residue, sequence_length))

training: bool

class gluonts.torch.model.wavenet.WaveNetEstimator(freq: str, prediction_length: int, num_bins: int = 1024, num_residual_channels: int = 24, num_skip_channels: int = 32, dilation_depth: Optional[int] = None, num_stacks: int = 1, temperature: float = 1.0, num_feat_dynamic_real: int = 0, num_feat_static_cat: int = 0, num_feat_static_real: int = 0, cardinality: List[int] = [1], seasonality: Optional[int] = None, embedding_dimension: int = 5, use_log_scale_feature: bool = True, time_features: Optional[List[Callable[[pandas.core.indexes.period.PeriodIndex], numpy.ndarray]]] = None, lr: float = 0.001, weight_decay: float = 1e-08, train_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, validation_sampler: Optional[gluonts.transform.sampler.InstanceSampler] = None, batch_size: int = 32, num_batches_per_epoch: int = 50, num_parallel_samples: int = 100, negative_data: bool = False, trainer_kwargs: Optional[Dict[str, Any]] = None)

Bases: gluonts.torch.model.estimator.PyTorchLightningEstimator

create_lightning_module() → lightning.pytorch.core.module.LightningModule

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

pl.LightningModule

create_predictor(transformation: gluonts.transform._base.Transformation, module: gluonts.torch.model.wavenet.lightning_module.WaveNetLightningModule) → gluonts.torch.model.predictor.PyTorchPredictor

Create and return a predictor object.

Parameters

• transformation – Transformation to be applied to data before it goes into the model.

• module – A trained pl.LightningModule object.

Returns

A predictor wrapping a nn.Module used for inference.

Return type

Predictor

create_training_data_loader(data: gluonts.dataset.Dataset, module: gluonts.torch.model.wavenet.lightning_module.WaveNetLightningModule, shuffle_buffer_length: Optional[int] = None, **kwargs) → Iterable

Create a data loader for training purposes.

Parameters

• data – Dataset from which to create the data loader.

• module – The pl.LightningModule object that will receive the batches from the data loader.

Returns

The data loader, i.e. and iterable over batches of data.

Return type

Iterable

create_transformation() → gluonts.transform._base.Transformation

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: gluonts.dataset.Dataset, module: gluonts.torch.model.wavenet.lightning_module.WaveNetLightningModule, **kwargs) → Iterable

Create a data loader for validation purposes.

Parameters

• data – Dataset from which to create the data loader.

• module – The pl.LightningModule object that will receive the batches from the data loader.

Returns

The data loader, i.e. and iterable over batches of data.

Return type

Iterable

lead_time: int

prediction_length: int

class gluonts.torch.model.wavenet.WaveNetLightningModule(model_kwargs: dict, lr: float = 0.001, weight_decay: float = 1e-08)

Bases: lightning.pytorch.core.module.LightningModule

LightningModule wrapper over WaveNet.

Parameters

• model_kwargs – Keyword arguments to pass to WaveNet.

• lr – Learning rate, by default 1e-3

• optional – Learning rate, by default 1e-3

• weight_decay – Weight decay, by default 1e-8

• optional – Weight decay, by default 1e-8

configure_optimizers()

Returns the optimizer to use.

forward(*args, **kwargs)

Same as torch.nn.Module.forward().

Parameters

• *args – Whatever you decide to pass into the forward method.

• **kwargs – Keyword arguments are also possible.

Returns

Your model’s output

training_step(batch, batch_idx: int)

Execute training step.

validation_step(batch, batch_idx: int)

Execute validation step.

Submodules

• gluonts.torch.model.wavenet.estimator module
• gluonts.torch.model.wavenet.lightning_module module
• gluonts.torch.model.wavenet.module module