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. See Notes for a mapping from model type to valid forest types. |
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. |
Notes
Mapping from model type to forest types:
- 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 aForestSamplesobject. This is the default value of this