TabNet Regressor#

pytorch_tabnet package initialization.

class pytorch_tabnet.TabNetRegressor(n_d: int = 8, n_a: int = 8, n_steps: int = 3, gamma: float = 1.3, cat_idxs: List[int] = <factory>, cat_dims: List[int] = <factory>, cat_emb_dim: int | 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: Any = <class 'torch.optim.adam.Adam'>, optimizer_params: Dict = <factory>, scheduler_fn: Any = None, scheduler_params: Dict = <factory>, mask_type: str = 'sparsemax', input_dim: int = None, output_dim: int = None, device_name: str = 'auto', n_shared_decoder: int = 1, n_indep_decoder: int = 1, grouped_features: List[List[int]] = <factory>, compile_backend: str = '')[source]

Bases: TabSupervisedModel

TabNet model for regression tasks.

compute_loss(y_pred: Tensor, y_true: Tensor, w: Tensor | None = None) → Tensor[source]

Compute the loss for regression.

Parameters:

  • y_pred (torch.Tensor) – Network output.

  • y_true (torch.Tensor) – True values.

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

Returns:

Loss value.

Return type:

torch.Tensor

output_dim: int = None
predict_func(outputs: ndarray) → ndarray[source]

Return regression outputs as predictions.

Parameters:

outputs (np.ndarray) – Network outputs.

Returns:

Regression predictions.

Return type:

np.ndarray

prepare_target(y: ndarray) → ndarray[source]

Return the input as target for regression.

Parameters:

y (np.ndarray) – Target array.

Returns:

Same as input.

Return type:

np.ndarray

set_fit_request(*, X_train: bool | None | str = '$UNCHANGED$', batch_size: bool | None | str = '$UNCHANGED$', callbacks: bool | None | str = '$UNCHANGED$', compute_importance: bool | None | str = '$UNCHANGED$', drop_last: bool | None | str = '$UNCHANGED$', eval_metric: bool | None | str = '$UNCHANGED$', eval_name: bool | None | str = '$UNCHANGED$', eval_set: bool | None | str = '$UNCHANGED$', from_unsupervised: 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 = '$UNCHANGED$', virtual_batch_size: bool | None | str = '$UNCHANGED$', warm_start: bool | None | str = '$UNCHANGED$', weights: bool | None | str = '$UNCHANGED$', y_train: bool | None | str = '$UNCHANGED$') → TabNetRegressor

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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.

  • compute_importance (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for compute_importance 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_metric (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for eval_metric 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.

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

  • 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.

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

Returns:

self – The updated object.

Return type:

object

stack_batches(list_y_true: List[Tensor], list_y_score: List[Tensor]) → Tuple[Tensor, Tensor][source]

Stack batches of true and predicted values for regression.

Parameters:

  • list_y_true (List[torch.Tensor]) – List of true values for each batch.

  • list_y_score (List[torch.Tensor]) – List of predicted values for each batch.

Returns:

Stacked true values and predicted values.

Return type:

Tuple[torch.Tensor, torch.Tensor]

update_fit_params(X_train: ndarray, y_train: ndarray, eval_set: List[Tuple[ndarray, ndarray]], weights: bool | ndarray) → None[source]

Update fit parameters for regression.

Parameters:

  • X_train (np.ndarray) – Training data.

  • y_train (np.ndarray) – Training targets.

  • eval_set (list) – List of evaluation sets.

  • weights (bool or np.ndarray) – Sample weights.

Raises:

ValueError – If y_train does not have 2 dimensions.

Example#

from pytorch_tabnet import TabNetRegressor
import numpy as np
X = np.random.rand(100, 10)
y = np.random.rand(100, 1)
reg = TabNetRegressor()
reg.fit(X_train=X, y_train=y)
preds = reg.predict(X)
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