BaseGBT

Abstract base class for all GBRL models. It defines the core API and shared logic for managing GBT learners, including training steps, gradient handling, SHAP value computation, device control, saving/loading, and visualization utilities.

class gbrl.models.base.BaseGBT[source]

Bases: ABC

copy() → BaseGBT[source]: Copy class instance

export_learner(filename: str, modelname: str = None) → None[source]

Exports learner model as a C-header file

Parameters:: filename (str) – Absolute path and name of exported filename.

fit(*args, **kwargs) → float | Tuple[float, ...][source]

Fit multiple iterations (as in supervised learning)

Returns:: final loss per learner over all examples.
Return type:: Union[float, Tuple[float, …]

get_device() → str | Tuple[str, str][source]

Returns GBRL device/devices per learner

Returns:: GBRL device per model
Return type:: Union[str, Tuple[str, str]]

get_iteration() → int | Tuple[int, ...][source]

Returns:: number of boosting iterations per learner.
Return type:: Union[int, Tuple[int, …]]

get_num_trees(*args, **kwargs) → int | Tuple[int, ...][source]

Returns number of trees in the ensemble per learner

Returns:: number of trees in the ensemble
Return type:: Union[int, Tuple[int, …]]

get_params() → Tuple[ndarray, ndarray][source]

Returns predicted model parameters and their respective gradients

Returns:: Tuple[np.ndarray, np.ndarray]

get_schedule_learning_rates() → float | Tuple[float, ...][source]: Gets learning rate values for optimizers according to schedule of ensemble. Constant schedule - no change in values. Linear schedule - learning rate value accordign to number of trees in the ensemble. :returns: learning rate schedule per optimizer. :rtype: Union[float, Tuple[float, …]]

get_total_iterations() → int[source]

Returns:: total number of boosting iterations (sum of actor and critic if they are not shared otherwise equals get_iteration())
Return type:: int

classmethod load_learner()[source]

Loads a BaseGBT model from a file.

Parameters:

load_name (str) – Full path to the saved model file.
device (str) – Device to load the model onto (‘cpu’ or ‘cuda’).

Returns:

Loaded ParametricActor model.

Return type:

BaseGBT

plot_tree(tree_idx: int, filename: str, *args, **kwargs) → None[source]

Plots tree using (only works if GBRL was compiled with graphviz)

Parameters:

tree_idx (int) – tree index to plot
filename (str) – .png filename to save

print_tree(tree_idx: int, *args, **kwargs) → None[source]

Prints tree information

Parameters:: tree_idx (int) – tree index to print

save_learner(save_path: str) → None[source]

Saves model to file

Parameters:: filename (str) – Absolute path and name of save filename.

set_bias(*args, **kwargs) → None[source]: Sets GBRL bias

set_device(device: str)[source]

Sets GBRL device (either cpu or cuda)

Parameters:: device (str) – choices are [‘cpu’, ‘cuda’]

set_feature_weights(feature_weights: ndarray | Tensor) → None[source]

Sets GBRL feature_weights

Parameters:: feature_weights (NumericalData)

shap(features: ndarray | Tensor, *args, **kwargs) → ndarray | Tuple[ndarray, ndarray][source]

Calculates SHAP values for the entire ensemble: Implementation based on - https://github.com/yupbank/linear_tree_shap. See Linear TreeShap, Yu et al, 2023, https://arxiv.org/pdf/2209.08192.

Parameters:: features (NumericalData)
Returns:: SHAP values of shap [n_samples, number of input features, number of outputs]. The output is a tuple of SHAP values per model only in the case of a separate actor-critic model.
Return type:: Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]

abstractmethod step(*args, **kwargs) → None[source]: Perform a boosting step (fits a single tree on the gradients)

tree_shap(tree_idx: int, features: ndarray | Tensor, *args, **kwargs) → ndarray | Tuple[ndarray, ndarray][source]

Calculates SHAP values for a single tree: Implementation based on - https://github.com/yupbank/linear_tree_shap. See Linear TreeShap, Yu et al, 2023, https://arxiv.org/pdf/2209.08192.

Parameters:

tree_idx (int) – tree index
features (NumericalData)

Returns:

SHAP values of shap [n_samples, number of input features, number of outputs]. The output is a tuple of SHAP values per model only in the case of a separate actor-critic model.

Return type:

Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]