Re-initialize a forest model (tracking data structures) from a specific forest in a ForestContainer

Re-initialize a forest model (tracking data structures) from a specific forest in a ForestContainer

Usage

resetForestModel(forest_model, forest, dataset, residual, is_mean_model)

Arguments

forest_model: Forest model with tracking data structures
forest: Forest from which to re-initialize forest model
dataset: Training dataset object
residual: Residual which will also be updated
is_mean_model: Whether the model being updated is a conditional mean model

Value

None

Examples

n <- 100
p <- 10
num_trees <- 100
leaf_dimension <- 1
is_leaf_constant <- TRUE
is_exponentiated <- FALSE
alpha <- 0.95
beta <- 2.0
min_samples_leaf <- 2
max_depth <- 10
feature_types <- as.integer(rep(0, p))
leaf_model <- 0
sigma2 <- 1.0
leaf_scale <- as.matrix(1.0)
variable_weights <- rep(1/p, p)
a_forest <- 1
b_forest <- 1
cutpoint_grid_size <- 100
X <- matrix(runif(n*p), ncol = p)
forest_dataset <- createForestDataset(X)
y <- -5 + 10*(X[,1] > 0.5) + rnorm(n)
outcome <- createOutcome(y)
rng <- createCppRNG(1234)
global_model_config <- createGlobalModelConfig(global_error_variance=sigma2)
forest_model_config <- createForestModelConfig(feature_types=feature_types, 
                                               num_trees=num_trees, num_observations=n, 
                                               num_features=p, alpha=alpha, beta=beta, 
                                               min_samples_leaf=min_samples_leaf, 
                                               max_depth=max_depth, 
                                               variable_weights=variable_weights, 
                                               cutpoint_grid_size=cutpoint_grid_size, 
                                               leaf_model_type=leaf_model, 
                                               leaf_model_scale=leaf_scale)
forest_model <- createForestModel(forest_dataset, forest_model_config, global_model_config)
active_forest <- createForest(num_trees, leaf_dimension, is_leaf_constant, is_exponentiated)
forest_samples <- createForestSamples(num_trees, leaf_dimension, 
                                      is_leaf_constant, is_exponentiated)
active_forest$prepare_for_sampler(forest_dataset, outcome, forest_model, 0, 0.)
forest_model$sample_one_iteration(
    forest_dataset, outcome, forest_samples, active_forest, 
    rng, forest_model_config, global_model_config, 
    keep_forest = TRUE, gfr = FALSE
)
resetActiveForest(active_forest, forest_samples, 0)
resetForestModel(forest_model, active_forest, forest_dataset, outcome, TRUE)