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module neuralforecast.models.kan

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class KANLinear

KANLinear

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method __init__

__init__(
    in_features,
    out_features,
    grid_size=5,
    spline_order=3,
    scale_noise=0.1,
    scale_base=1.0,
    scale_spline=1.0,
    enable_standalone_scale_spline=True,
    base_activation=<class 'torch.nn.modules.activation.SiLU'>,
    grid_eps=0.02,
    grid_range=[-1, 1]
)

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property scaled_spline_weight

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method b_splines

b_splines(x: Tensor)
Compute the B-spline bases for the given input tensor. Args:
  • x (torch.Tensor): Input tensor of shape (batch_size, in_features).
Returns:
  • torch.Tensor: B-spline bases tensor of shape (batch_size, in_features, grid_size + spline_order).

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method curve2coeff

curve2coeff(x: Tensor, y: Tensor)
Compute the coefficients of the curve that interpolates the given points. Args:
  • x (torch.Tensor): Input tensor of shape (batch_size, in_features).
  • y (torch.Tensor): Output tensor of shape (batch_size, in_features, out_features).
Returns:
  • torch.Tensor: Coefficients tensor of shape (out_features, in_features, grid_size + spline_order).

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method forward

forward(x: Tensor)

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method regularization_loss

regularization_loss(regularize_activation=1.0, regularize_entropy=1.0)

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method reset_parameters

reset_parameters()

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method update_grid

update_grid(x: Tensor, margin=0.01)

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class KAN

KAN Simple Kolmogorov-Arnold Network (KAN). This network uses the Kolmogorov-Arnold approximation theorem, where splines are learned to approximate more complex functions. Unlike the MLP, the non-linear function are learned at the edges, and the nodes simply sum the different learned functions. Args:
  • h (int): forecast horizon.
  • input_size (int): considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
  • grid_size (int): number of intervals used by the splines to approximate the function.
  • spline_order (int): order of the B-splines.
  • scale_noise (float): regularization coefficient for the splines.
  • scale_base (float): scaling coefficient for the base function.
  • scale_spline (float): scaling coefficient for the splines.
  • enable_standalone_scale_spline (bool): whether each spline is scaled individually.
  • grid_eps (float): used for numerical stability.
  • grid_range (list): range of the grid used for spline approximation.
  • n_hidden_layers (int): number of hidden layers for the KAN.
  • hidden_size (int or list): number of units for each hidden layer of the KAN. If an integer, all hidden layers will have the same size. Use a list to specify the size of each hidden layer.
  • stat_exog_list (str list): static exogenous columns.
  • hist_exog_list (str list): historic exogenous columns.
  • futr_exog_list (str list): future exogenous columns.
  • exclude_insample_y (bool): the model skips the autoregressive features y[t-input_size:t] if True.
  • loss (PyTorch module): instantiated train loss class from losses collection.
  • valid_loss (PyTorch module): instantiated valid loss class from losses collection.
  • max_steps (int): maximum number of training steps.
  • learning_rate (float): Learning rate between (0, 1).
  • num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps.
  • early_stop_patience_steps (int): Number of validation iterations before early stopping.
  • val_check_steps (int): Number of training steps between every validation loss check.
  • batch_size (int): number of different series in each batch.
  • valid_batch_size (int): number of different series in each validation and test batch, if None uses batch_size.
  • windows_batch_size (int): number of windows to sample in each training batch, default uses all.
  • inference_windows_batch_size (int): number of windows to sample in each inference batch, -1 uses all.
  • start_padding_enabled (bool): if True, the model will pad the time series with zeros at the beginning, by input size.
  • training_data_availability_threshold (Union[float, List[float]]): minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies [insample_fraction, outsample_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior).
  • step_size (int): step size between each window of temporal data.
  • scaler_type (str): type of scaler for temporal inputs normalization see temporal scalers.
  • random_seed (int): random_seed for pytorch initializer and numpy generators.
  • drop_last_loader (bool): if True TimeSeriesDataLoader drops last non-full batch.
  • alias (str): optional, Custom name of the model.
  • optimizer (Subclass of ‘torch.optim.Optimizer’): optional, user specified optimizer instead of the default choice (Adam).
  • optimizer_kwargs (dict): optional, list of parameters used by the user specified optimizer.
  • dataloader_kwargs (dict): optional, list of parameters passed into the PyTorch Lightning dataloader by the TimeSeriesDataLoader.
  • **trainer_kwargs (int): keyword trainer arguments inherited from PyTorch Lighning’s trainer.
References:

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method __init__

__init__(
    h,
    input_size,
    grid_size: int = 5,
    spline_order: int = 3,
    scale_noise: float = 0.1,
    scale_base: float = 1.0,
    scale_spline: float = 1.0,
    enable_standalone_scale_spline: bool = True,
    grid_eps: float = 0.02,
    grid_range: list = [-1, 1],
    n_hidden_layers: int = 1,
    hidden_size: Union[int, list] = 512,
    stat_exog_list=None,
    hist_exog_list=None,
    futr_exog_list=None,
    exclude_insample_y=False,
    loss=MAE(),
    valid_loss=None,
    max_steps: int = 1000,
    learning_rate: float = 0.001,
    num_lr_decays: int = -1,
    early_stop_patience_steps: int = -1,
    val_check_steps: int = 100,
    batch_size: int = 32,
    valid_batch_size: Optional[int] = None,
    windows_batch_size=1024,
    inference_windows_batch_size=-1,
    start_padding_enabled=False,
    training_data_availability_threshold=0.0,
    step_size: int = 1,
    scaler_type: str = 'identity',
    random_seed: int = 1,
    drop_last_loader: bool = False,
    alias: Optional[str] = None,
    optimizer=None,
    optimizer_kwargs=None,
    dataloader_kwargs=None,
    **trainer_kwargs
)

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property automatic_optimization

If set to False you are responsible for calling .backward(), .step(), .zero_grad().

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property current_epoch

The current epoch in the Trainer, or 0 if not attached.

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property device

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property device_mesh

Strategies like ModelParallelStrategy will create a device mesh that can be accessed in the :meth:~pytorch_lightning.core.hooks.ModelHooks.configure_model hook to parallelize the LightningModule.

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property dtype

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property example_input_array

The example input array is a specification of what the module can consume in the :meth:forward method. The return type is interpreted as follows:
  • Single tensor: It is assumed the model takes a single argument, i.e., model.forward(model.example_input_array)
  • Tuple: The input array should be interpreted as a sequence of positional arguments, i.e., model.forward(*model.example_input_array)
  • Dict: The input array represents named keyword arguments, i.e., model.forward(**model.example_input_array)

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property fabric

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property global_rank

The index of the current process across all nodes and devices.

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property global_step

Total training batches seen across all epochs. If no Trainer is attached, this property is 0.

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property hparams

The collection of hyperparameters saved with :meth:save_hyperparameters. It is mutable by the user. For the frozen set of initial hyperparameters, use :attr:hparams_initial. Returns: Mutable hyperparameters dictionary

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property hparams_initial

The collection of hyperparameters saved with :meth:save_hyperparameters. These contents are read-only. Manual updates to the saved hyperparameters can instead be performed through :attr:hparams. Returns:
  • AttributeDict: immutable initial hyperparameters

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property local_rank

The index of the current process within a single node.

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property logger

Reference to the logger object in the Trainer.

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property loggers

Reference to the list of loggers in the Trainer.

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property on_gpu

Returns True if this model is currently located on a GPU. Useful to set flags around the LightningModule for different CPU vs GPU behavior.

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property strict_loading

Determines how Lightning loads this model using .load_state_dict(..., strict=model.strict_loading).

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property trainer

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method forward

forward(windows_batch, update_grid=False)

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method regularization_loss

regularization_loss(regularize_activation=1.0, regularize_entropy=1.0)