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KANLinear
KANLinear
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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. Parameters:
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=False, 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=loss, instantiated valid loss class from
losses
collection.max_steps: int=1000, maximum number of training steps.learning_rate: float=1e-3, Learning rate between (0, 1).num_lr_decays: int=-1, Number of learning rate decays, evenly
distributed across max_steps.early_stop_patience_steps: int=-1,
Number of validation iterations before early stopping.val_check_steps: int=100, Number of training steps between every
validation loss check.batch_size: int=32, number of different
series in each batch.valid_batch_size: int=None, number of
different series in each validation and test batch, if None uses
batch_size.windows_batch_size: int=1024, number of windows to
sample in each training batch, default uses all.inference_windows_batch_size: int=-1, number of windows to sample in
each inference batch, -1 uses all.start_padding_enabled:
bool=False, if True, the model will pad the time series with zeros at
the beginning, by input size.step_size: int=1, step size between
each window of temporal data.scaler_type: str=‘identity’, type of
scaler for temporal inputs normalization see temporal
scalers.random_seed: int=1, random_seed for pytorch initializer and numpy
generators.drop_last_loader: bool=False, 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
- Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max Tegmark. “KAN: Kolmogorov-Arnold Networks”*
KAN.fit
*Fit. The
fit method, optimizes the neural network’s weights using the
initialization parameters (learning_rate, windows_batch_size, …) and
the loss function as defined during the initialization. Within fit
we use a PyTorch Lightning Trainer that inherits the initialization’s
self.trainer_kwargs, to customize its inputs, see PL’s trainer
arguments.
The method is designed to be compatible with SKLearn-like classes and in
particular to be compatible with the StatsForecast library.
By default the model is not saving training checkpoints to protect
disk memory, to get them change enable_checkpointing=True in
__init__.
Parameters:dataset: NeuralForecast’s
TimeSeriesDataset,
see
documentation.val_size: int, validation size for temporal cross-validation.random_seed: int=None, random_seed for pytorch initializer and numpy
generators, overwrites model.__init__’s.test_size: int, test
size for temporal cross-validation.*
KAN.predict
*Predict. Neural network prediction with PL’s
Trainer execution of
predict_step.
Parameters:dataset: NeuralForecast’s
TimeSeriesDataset,
see
documentation.test_size: int=None, test size for temporal cross-validation.step_size: int=1, Step size between each window.random_seed:
int=None, random_seed for pytorch initializer and numpy generators,
overwrites model.__init__’s.quantiles: list of floats,
optional (default=None), target quantiles to predict. **data_module_kwargs: PL’s TimeSeriesDataModule args, see
documentation.*