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Refactor model parameters into "config" objects to future-proof low-level interface #135

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Refactor model parameters into "config" objects to future-proof low-level interface #135

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e274b1f
Refactoring out the "pre-initialized" parameter in the custom sampler
andrewherren Feb 3, 2025
a372b21
Updated vignette
andrewherren Feb 3, 2025
57c7d26
Refactored R / cpp11 interface
andrewherren Feb 3, 2025
f424604
Updated BART and BCF to use modelconfig
andrewherren Feb 3, 2025
7d5b111
Updated vignettes and docs
andrewherren Feb 4, 2025
9595947
Fixed typo
andrewherren Feb 4, 2025
7cc1963
Updated interface to use separate "global" and "forest" model configs
andrewherren Feb 4, 2025
86dd8d8
Fixed example code
andrewherren Feb 4, 2025
95610df
Fixed bug in code example
andrewherren Feb 4, 2025
0c7c0e1
Updated BCF's use of the new interface
andrewherren Feb 4, 2025
8ea864f
Updated documentation and vignettes
andrewherren Feb 4, 2025
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2 changes: 2 additions & 0 deletions NAMESPACE
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,9 @@ export(createCppRNG)
export(createForest)
export(createForestDataset)
export(createForestModel)
export(createForestModelConfig)
export(createForestSamples)
export(createGlobalModelConfig)
export(createOutcome)
export(createPreprocessorFromJson)
export(createPreprocessorFromJsonString)
Expand Down
59 changes: 43 additions & 16 deletions R/bart.R
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Original file line number Diff line number Diff line change
Expand Up @@ -540,11 +540,22 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train

# Sampling data structures
feature_types <- as.integer(feature_types)
global_model_config <- createGlobalModelConfig(global_error_variance=current_sigma2)
if (include_mean_forest) {
forest_model_mean <- createForestModel(forest_dataset_train, feature_types, num_trees_mean, nrow(X_train), alpha_mean, beta_mean, min_samples_leaf_mean, max_depth_mean)
forest_model_config_mean <- createForestModelConfig(feature_types=feature_types, num_trees=num_trees_mean, num_features=ncol(X_train),
num_observations=nrow(X_train), variable_weights=variable_weights_mean, leaf_dimension=leaf_dimension,
alpha=alpha_mean, beta=beta_mean, min_samples_leaf=min_samples_leaf_mean, max_depth=max_depth_mean,
leaf_model_type=leaf_model_mean_forest, leaf_model_scale=current_leaf_scale,
cutpoint_grid_size=cutpoint_grid_size)
forest_model_mean <- createForestModel(forest_dataset_train, forest_model_config_mean, global_model_config)
}
if (include_variance_forest) {
forest_model_variance <- createForestModel(forest_dataset_train, feature_types, num_trees_variance, nrow(X_train), alpha_variance, beta_variance, min_samples_leaf_variance, max_depth_variance)
forest_model_config_variance <- createForestModelConfig(feature_types=feature_types, num_trees=num_trees_variance, num_features=ncol(X_train),
num_observations=nrow(X_train), variable_weights=variable_weights_variance, leaf_dimension=1,
alpha=alpha_variance, beta=beta_variance, min_samples_leaf=min_samples_leaf_variance,
max_depth=max_depth_variance, leaf_model_type=leaf_model_variance_forest,
cutpoint_grid_size=cutpoint_grid_size)
forest_model_variance <- createForestModel(forest_dataset_train, forest_model_config_variance, global_model_config)
}

# Container of forest samples
Expand Down Expand Up @@ -601,11 +612,13 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
if (requires_basis) init_values_mean_forest <- rep(0., ncol(leaf_basis_train))
else init_values_mean_forest <- 0.
active_forest_mean$prepare_for_sampler(forest_dataset_train, outcome_train, forest_model_mean, leaf_model_mean_forest, init_values_mean_forest)
active_forest_mean$adjust_residual(forest_dataset_train, outcome_train, forest_model_mean, requires_basis, F)
}

# Initialize the leaves of each tree in the variance forest
if (include_variance_forest) {
active_forest_variance$prepare_for_sampler(forest_dataset_train, outcome_train, forest_model_variance, leaf_model_variance_forest, variance_forest_init)

}

# Run GFR (warm start) if specified
Expand All @@ -624,26 +637,28 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train

if (include_mean_forest) {
forest_model_mean$sample_one_iteration(
forest_dataset_train, outcome_train, forest_samples_mean, active_forest_mean,
rng, feature_types, leaf_model_mean_forest, current_leaf_scale, variable_weights_mean,
a_forest, b_forest, current_sigma2, cutpoint_grid_size, keep_forest = keep_sample, gfr = T, pre_initialized = T
forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_mean,
active_forest = active_forest_mean, rng = rng, forest_model_config = forest_model_config_mean,
global_model_config = global_model_config, keep_forest = keep_sample, gfr = T
)
}
if (include_variance_forest) {
forest_model_variance$sample_one_iteration(
forest_dataset_train, outcome_train, forest_samples_variance, active_forest_variance,
rng, feature_types, leaf_model_variance_forest, current_leaf_scale, variable_weights_variance,
a_forest, b_forest, current_sigma2, cutpoint_grid_size, keep_forest = keep_sample, gfr = T, pre_initialized = T
forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_variance,
active_forest = active_forest_variance, rng = rng, forest_model_config = forest_model_config_variance,
global_model_config = global_model_config, keep_forest = keep_sample, gfr = T
)
}
if (sample_sigma_global) {
current_sigma2 <- sampleGlobalErrorVarianceOneIteration(outcome_train, forest_dataset_train, rng, a_global, b_global)
if (keep_sample) global_var_samples[sample_counter] <- current_sigma2
global_model_config$update_global_error_variance(current_sigma2)
}
if (sample_sigma_leaf) {
leaf_scale_double <- sampleLeafVarianceOneIteration(active_forest_mean, rng, a_leaf, b_leaf)
current_leaf_scale <- as.matrix(leaf_scale_double)
if (keep_sample) leaf_scale_samples[sample_counter] <- leaf_scale_double
forest_model_config_mean$update_leaf_model_scale(current_leaf_scale)
}
if (has_rfx) {
rfx_model$sample_random_effect(rfx_dataset_train, outcome_train, rfx_tracker_train, rfx_samples, keep_sample, current_sigma2, rng)
Expand All @@ -663,6 +678,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
if (sample_sigma_leaf) {
leaf_scale_double <- leaf_scale_samples[forest_ind + 1]
current_leaf_scale <- as.matrix(leaf_scale_double)
forest_model_config_mean$update_leaf_model_scale(current_leaf_scale)
}
}
if (include_variance_forest) {
Expand All @@ -673,14 +689,18 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
resetRandomEffectsModel(rfx_model, rfx_samples, forest_ind, sigma_alpha_init)
resetRandomEffectsTracker(rfx_tracker_train, rfx_model, rfx_dataset_train, outcome_train, rfx_samples)
}
if (sample_sigma_global) current_sigma2 <- global_var_samples[forest_ind + 1]
if (sample_sigma_global) {
current_sigma2 <- global_var_samples[forest_ind + 1]
global_model_config$update_global_error_variance(current_sigma2)
}
} else if (has_prev_model) {
if (include_mean_forest) {
resetActiveForest(active_forest_mean, previous_forest_samples_mean, previous_model_warmstart_sample_num - 1)
resetForestModel(forest_model_mean, active_forest_mean, forest_dataset_train, outcome_train, TRUE)
if (sample_sigma_leaf && (!is.null(previous_leaf_var_samples))) {
leaf_scale_double <- previous_leaf_var_samples[previous_model_warmstart_sample_num]
current_leaf_scale <- as.matrix(leaf_scale_double)
forest_model_config_mean$update_leaf_model_scale(current_leaf_scale)
}
}
if (include_variance_forest) {
Expand All @@ -696,6 +716,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
if (sample_sigma_global) {
if (!is.null(previous_global_var_samples)) {
current_sigma2 <- previous_global_var_samples[previous_model_warmstart_sample_num]
global_model_config$update_global_error_variance(current_sigma2)
}
}
} else {
Expand All @@ -705,6 +726,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
resetForestModel(forest_model_mean, active_forest_mean, forest_dataset_train, outcome_train, TRUE)
if (sample_sigma_leaf) {
current_leaf_scale <- as.matrix(sigma_leaf_init)
forest_model_config_mean$update_leaf_model_scale(current_leaf_scale)
}
}
if (include_variance_forest) {
Expand All @@ -717,7 +739,10 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train
sigma_xi_init, sigma_xi_shape, sigma_xi_scale)
rootResetRandomEffectsTracker(rfx_tracker_train, rfx_model, rfx_dataset_train, outcome_train)
}
if (sample_sigma_global) current_sigma2 <- sigma2_init
if (sample_sigma_global) {
current_sigma2 <- sigma2_init
global_model_config$update_global_error_variance(current_sigma2)
}
}
for (i in (num_gfr+1):num_samples) {
is_mcmc <- i > (num_gfr + num_burnin)
Expand Down Expand Up @@ -746,26 +771,28 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train

if (include_mean_forest) {
forest_model_mean$sample_one_iteration(
forest_dataset_train, outcome_train, forest_samples_mean, active_forest_mean,
rng, feature_types, leaf_model_mean_forest, current_leaf_scale, variable_weights_mean,
a_forest, b_forest, current_sigma2, cutpoint_grid_size, keep_forest = keep_sample, gfr = F, pre_initialized = T
forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_mean,
active_forest = active_forest_mean, rng = rng, forest_model_config = forest_model_config_mean,
global_model_config = global_model_config, keep_forest = keep_sample, gfr = F
)
}
if (include_variance_forest) {
forest_model_variance$sample_one_iteration(
forest_dataset_train, outcome_train, forest_samples_variance, active_forest_variance,
rng, feature_types, leaf_model_variance_forest, current_leaf_scale, variable_weights_variance,
a_forest, b_forest, current_sigma2, cutpoint_grid_size, keep_forest = keep_sample, gfr = F, pre_initialized = T
forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_variance,
active_forest = active_forest_variance, rng = rng, forest_model_config = forest_model_config_variance,
global_model_config = global_model_config, keep_forest = keep_sample, gfr = F
)
}
if (sample_sigma_global) {
current_sigma2 <- sampleGlobalErrorVarianceOneIteration(outcome_train, forest_dataset_train, rng, a_global, b_global)
if (keep_sample) global_var_samples[sample_counter] <- current_sigma2
global_model_config$update_global_error_variance(current_sigma2)
}
if (sample_sigma_leaf) {
leaf_scale_double <- sampleLeafVarianceOneIteration(active_forest_mean, rng, a_leaf, b_leaf)
current_leaf_scale <- as.matrix(leaf_scale_double)
if (keep_sample) leaf_scale_samples[sample_counter] <- leaf_scale_double
forest_model_config_mean$update_leaf_model_scale(current_leaf_scale)
}
if (has_rfx) {
rfx_model$sample_random_effect(rfx_dataset_train, outcome_train, rfx_tracker_train, rfx_samples, keep_sample, current_sigma2, rng)
Expand Down
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