kernel.compute_forest_max_leaf_index

kernel.compute_forest_max_leaf_index(
    model_object,
    forest_type=None,
    forest_inds=None,
)

Compute and return the largest possible leaf index computable by compute_forest_leaf_indices for the forests in a designated forest sample container.

Parameters

Name	Type	Description	Default
model_object	BARTModel, BCFModel, or ForestContainer	Object corresponding to a BART / BCF model with at least one forest sample, or a low-level `ForestContainer` object.	required
forest_type	str	Which forest to use from `model_object`. Valid inputs depend on the model type, and whether or not a given forest was sampled in that model. * BART * `'mean'`: `'mean'`: Extracts leaf indices for the mean forest * `'variance'`: Extracts leaf indices for the variance forest * BCF * `'prognostic'`: Extracts leaf indices for the prognostic forest * `'treatment'`: Extracts leaf indices for the treatment effect forest * `'variance'`: Extracts leaf indices for the variance forest * ForestContainer * `NULL`: It is not necessary to disambiguate when this function is called directly on a `ForestSamples` object. This is the default value of this	`None`
forest_inds	int or np.ndarray	Indices of the forest sample(s) for which to compute max leaf indices. If not provided, this function will return max leaf indices for every sample of a forest. This function uses 0-indexing, so the first forest sample corresponds to `forest_num = 0`, and so on.	`None`

Returns

Name	Type	Description
	Numpy array containing the largest possible leaf index computable by `compute_forest_leaf_indices` for the forests in a designated forest sample container.