GBTModel

Generic GBT Model for supervised learning. Can be used independently of reinforcement learning pipelines to perform standard regression or classification with gradient-boosted trees using custom optimizers and loss functions.

class gbrl.models.gbt.GBTModel(tree_struct: Dict, input_dim: int, output_dim: int, optimizers: Dict | List[Dict], params: Dict = {}, verbose: int = 0, device: str = 'cpu')[source]

Bases: BaseGBT

General class for gradient boosting trees

copy() → GBTModel[source]

Copy class instance

Returns:

copy of current instance. The actual type will be the type of the subclass that calls this method.

Return type:

GradientBoostingTrees

fit(X: ndarray | Tensor, targets: ndarray | Tensor, iterations: int, shuffle: bool = True, loss_type: str = 'MultiRMSE') → float[source]

Fit multiple iterations (as in supervised learning)

Parameters:

  • X (NumericalData) – inputs

  • targets (NumericalData) – targets

  • iterations (int) – number of boosting iterations

  • shuffle (bool, optional) – Shuffle dataset. Defaults to True.

  • loss_type (str, optional) – Loss to use (only MultiRMSE is currently implemented ). Defaults to ‘MultiRMSE’.

Returns:

final loss over all examples.

Return type:

float

classmethod load_learner(load_name: str, device: str) → GBTModel[source]

Loads GBRL model from a file

Parameters:

load_name (str) – full path to file name

Returns:

GBRL instance

plot_tree(tree_idx: int, filename: str) → 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) → None[source]

Prints tree information

Parameters:

tree_idx (int) – tree index to print

set_bias(bias: ndarray | Tensor) → None[source]

Sets GBRL bias

Parameters:

bias (NumericalData)

set_bias_from_targets(targets: ndarray | Tensor) → None[source]

Sets bias as mean of targets

Parameters:

targets (NumericalData) – Targets

step(X: ndarray | Tensor | None = None, grad: ndarray | Tensor | None = None, max_grad_norm: float | None = None) → None[source]

Perform a boosting step (fits a single tree on the gradients)

Parameters:

  • X (NumericalData) – inputs

  • max_grad_norm (float, optional) – perform gradient clipping by norm. Defaults to None.

  • grad (Optional[NumericalData], optional) – manually calculated gradients. Defaults to None.