ForestModel {stochtree} | R Documentation |
Class that defines and samples a forest model
Description
Hosts the C++ data structures needed to sample an ensemble of decision trees, and exposes functionality to run a forest sampler (using either MCMC or the grow-from-root algorithm).
Public fields
tracker_ptr
External pointer to a C++ ForestTracker class
tree_prior_ptr
External pointer to a C++ TreePrior class
Methods
Public methods
Method new()
Create a new ForestModel object.
Usage
ForestModel$new( forest_dataset, feature_types, num_trees, n, alpha, beta, min_samples_leaf, max_depth = -1 )
Arguments
forest_dataset
ForestDataset
object, used to initialize forest sampling data structuresfeature_types
Feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_trees
Number of trees in the forest being sampled
n
Number of observations in
forest_dataset
alpha
Root node split probability in tree prior
beta
Depth prior penalty in tree prior
min_samples_leaf
Minimum number of samples in a tree leaf
max_depth
Maximum depth that any tree can reach
Returns
A new ForestModel
object.
Method sample_one_iteration()
Run a single iteration of the forest sampling algorithm (MCMC or GFR)
Usage
ForestModel$sample_one_iteration( forest_dataset, residual, forest_samples, active_forest, rng, forest_model_config, global_model_config, keep_forest = TRUE, gfr = TRUE )
Arguments
forest_dataset
Dataset used to sample the forest
residual
Outcome used to sample the forest
forest_samples
Container of forest samples
active_forest
"Active" forest updated by the sampler in each iteration
rng
Wrapper around C++ random number generator
forest_model_config
ForestModelConfig object containing forest model parameters and settings
global_model_config
GlobalModelConfig object containing global model parameters and settings
keep_forest
(Optional) Whether the updated forest sample should be saved to
forest_samples
. Default:TRUE
.gfr
(Optional) Whether or not the forest should be sampled using the "grow-from-root" (GFR) algorithm. Default:
TRUE
.
Method propagate_basis_update()
Propagates basis update through to the (full/partial) residual by iteratively (a) adding back in the previous prediction of each tree, (b) recomputing predictions for each tree (caching on the C++ side), (c) subtracting the new predictions from the residual.
This is useful in cases where a basis (for e.g. leaf regression) is updated outside of a tree sampler (as with e.g. adaptive coding for binary treatment BCF). Once a basis has been updated, the overall "function" represented by a tree model has changed and this should be reflected through to the residual before the next sampling loop is run.
Usage
ForestModel$propagate_basis_update(dataset, outcome, active_forest)
Arguments
dataset
ForestDataset
object storing the covariates and bases for a given forestoutcome
Outcome
object storing the residuals to be updated based on forest predictionsactive_forest
"Active" forest updated by the sampler in each iteration
Method propagate_residual_update()
Update the current state of the outcome (i.e. partial residual) data by subtracting the current predictions of each tree.
This function is run after the Outcome
class's update_data
method, which overwrites the partial residual with an entirely new stream of outcome data.
Usage
ForestModel$propagate_residual_update(residual)
Arguments
residual
Outcome used to sample the forest
Returns
None
Method update_alpha()
Update alpha in the tree prior
Usage
ForestModel$update_alpha(alpha)
Arguments
alpha
New value of alpha to be used
Returns
None
Method update_beta()
Update beta in the tree prior
Usage
ForestModel$update_beta(beta)
Arguments
beta
New value of beta to be used
Returns
None
Method update_min_samples_leaf()
Update min_samples_leaf in the tree prior
Usage
ForestModel$update_min_samples_leaf(min_samples_leaf)
Arguments
min_samples_leaf
New value of min_samples_leaf to be used
Returns
None
Method update_max_depth()
Update max_depth in the tree prior
Usage
ForestModel$update_max_depth(max_depth)
Arguments
max_depth
New value of max_depth to be used
Returns
None