TabNet Pretrainer

pytorch_tabnet package initialization.

class pytorch_tabnet.TabNetPretrainer(n_d: int = 8, n_a: int = 8, n_steps: int = 3, gamma: float = 1.3, cat_idxs: ~typing.List[int] = <factory>, cat_dims: ~typing.List[int] = <factory>, cat_emb_dim: int | ~typing.List[int] = 1, n_independent: int = 2, n_shared: int = 2, epsilon: float = 1e-15, momentum: float = 0.02, lambda_sparse: float = 0.001, seed: int = 0, clip_value: int = 1, verbose: int = 1, optimizer_fn: ~typing.Any = <class 'torch.optim.adam.Adam'>, optimizer_params: ~typing.Dict = <factory>, scheduler_fn: ~typing.Any = None, scheduler_params: ~typing.Dict = <factory>, mask_type: str = 'sparsemax', input_dim: int = None, output_dim: ~typing.List[int] | int = None, device_name: str = 'auto', n_shared_decoder: int = 1, n_indep_decoder: int = 1, grouped_features: ~typing.List[~typing.List[int]] = <factory>, compile_backend: str = '')[source]

Bases: TabModel

Abstract base class for TabNet pretraining models.

compute_loss(output: Tensor, embedded_x: Tensor, obf_vars: Tensor, w: Tensor | None = None) → Tensor[source]

Compute the unsupervised loss for pretraining.

Parameters:

  • output (torch.Tensor) – Network output.

  • embedded_x (torch.Tensor) – Embedded input.

  • obf_vars (torch.Tensor) – Obfuscated variables mask.

  • w (Optional[torch.Tensor]) – Optional sample weights.

Returns:

Loss value.

Return type:

torch.Tensor

fit(X_train: ndarray, eval_set: List[ndarray | List[ndarray]] | None = None, eval_name: List[str] | None = None, loss_fn: Callable | None = None, pretraining_ratio: float = 0.5, weights: int | ndarray = 0, max_epochs: int = 100, patience: int = 10, batch_size: int = 1024, virtual_batch_size: int = 128, num_workers: int = 0, drop_last: bool = True, callbacks: List[Callable] | None = None, pin_memory: bool = True, warm_start: bool = False, *args: List, **kwargs: Dict) → None[source]

Train the TabNet pretrainer model.

Parameters:

  • X_train (np.ndarray) – Train set to reconstruct in self supervision

  • eval_set (list of np.array) – List of evaluation set

  • eval_name (list of str) – List of eval set names.

  • loss_fn (callable or None) – PyTorch loss function

  • pretraining_ratio (float) – Percentage of features to mask for reconstruction

  • weights (int or np.ndarray) – Sampling weights for each example

  • max_epochs (int) – Maximum number of epochs

  • patience (int) – Early stopping patience

  • batch_size (int) – Training batch size

  • virtual_batch_size (int) – Batch size for Ghost Batch Normalization

  • num_workers (int) – Number of workers for DataLoader

  • drop_last (bool) – Whether to drop last batch

  • callbacks (list of callable) – Custom callbacks

  • pin_memory (bool) – Whether to use pinned memory

  • warm_start (bool) – Whether to warm start from previous fit

  • *args (list) – Additional arguments

  • **kwargs (dict) – Additional keyword arguments

predict(X: ndarray) → tuple[ndarray, ndarray][source]

Predict outputs and embeddings for a batch.

Parameters:

X (np.ndarray or scipy.sparse.csr_matrix) – Input data.

Returns:

Predictions and embedded inputs.

Return type:

tuple

set_fit_request(*, X_train: bool | None | str = '$UNCHANGED$', batch_size: bool | None | str = '$UNCHANGED$', callbacks: bool | None | str = '$UNCHANGED$', drop_last: bool | None | str = '$UNCHANGED$', eval_name: bool | None | str = '$UNCHANGED$', eval_set: bool | None | str = '$UNCHANGED$', loss_fn: bool | None | str = '$UNCHANGED$', max_epochs: bool | None | str = '$UNCHANGED$', num_workers: bool | None | str = '$UNCHANGED$', patience: bool | None | str = '$UNCHANGED$', pin_memory: bool | None | str = '$UNCHANGED$', pretraining_ratio: bool | None | str = '$UNCHANGED$', virtual_batch_size: bool | None | str = '$UNCHANGED$', warm_start: bool | None | str = '$UNCHANGED$', weights: bool | None | str = '$UNCHANGED$') → TabNetPretrainer

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

  • X_train (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_train parameter in fit.

  • batch_size (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for batch_size parameter in fit.

  • callbacks (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for callbacks parameter in fit.

  • drop_last (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for drop_last parameter in fit.

  • eval_name (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_name parameter in fit.

  • eval_set (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_set parameter in fit.

  • loss_fn (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for loss_fn parameter in fit.

  • max_epochs (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for max_epochs parameter in fit.

  • num_workers (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for num_workers parameter in fit.

  • patience (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for patience parameter in fit.

  • pin_memory (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for pin_memory parameter in fit.

  • pretraining_ratio (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for pretraining_ratio parameter in fit.

  • virtual_batch_size (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for virtual_batch_size parameter in fit.

  • warm_start (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for warm_start parameter in fit.

  • weights (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for weights parameter in fit.

Returns:

self – The updated object.

Return type:

object

stack_batches(list_output: List[Tensor], list_embedded_x: List[Tensor], list_obfuscation: List[Tensor]) → Tuple[Tensor, Tensor, Tensor][source]

Stack batches of outputs, embeddings, and obfuscations.

Parameters:

  • list_output (List[torch.Tensor]) – List of outputs.

  • list_embedded_x (List[torch.Tensor]) – List of embedded inputs.

  • list_obfuscation (List[torch.Tensor]) – List of obfuscation masks.

Returns:

Stacked outputs, embeddings, and obfuscations.

Return type:

tuple

update_fit_params(weights: ndarray) → None[source]

Update fit parameters for pretraining.

Parameters:

weights (np.ndarray) – Sample weights.

Example

from pytorch_tabnet.pretraining import TabNetPretrainer
import numpy as np
X = np.random.rand(100, 10)
pretrainer = TabNetPretrainer()
pretrainer.fit(X_train=X)