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Run the BART algorithm for supervised learning.

bart.Rd

Run the BART algorithm for supervised learning.

Usage

bart(
  X_train,
  y_train,
  W_train = NULL,
  group_ids_train = NULL,
  rfx_basis_train = NULL,
  X_test = NULL,
  W_test = NULL,
  group_ids_test = NULL,
  rfx_basis_test = NULL,
  num_gfr = 5,
  num_burnin = 0,
  num_mcmc = 100,
  params = list()
)

Arguments

X_train

Covariates used to split trees in the ensemble. May be provided either as a dataframe or a matrix. Matrix covariates will be assumed to be all numeric. Covariates passed as a dataframe will be preprocessed based on the variable types (e.g. categorical columns stored as unordered factors will be one-hot encoded, categorical columns stored as ordered factors will passed as integers to the core algorithm, along with the metadata that the column is ordered categorical).

y_train

Outcome to be modeled by the ensemble.

W_train

(Optional) Bases used to define a regression model y ~ W in each leaf of each regression tree. By default, BART assumes constant leaf node parameters, implicitly regressing on a constant basis of ones (i.e. y ~ 1).

group_ids_train

(Optional) Group labels used for an additive random effects model.

rfx_basis_train

(Optional) Basis for "random-slope" regression in an additive random effects model. If group_ids_train is provided with a regression basis, an intercept-only random effects model will be estimated.

X_test

(Optional) Test set of covariates used to define "out of sample" evaluation data. May be provided either as a dataframe or a matrix, but the format of X_test must be consistent with that of X_train.

W_test

(Optional) Test set of bases used to define "out of sample" evaluation data. While a test set is optional, the structure of any provided test set must match that of the training set (i.e. if both X_train and W_train are provided, then a test set must consist of X_test and W_test with the same number of columns).

group_ids_test

(Optional) Test set group labels used for an additive random effects model. We do not currently support (but plan to in the near future), test set evaluation for group labels that were not in the training set.

rfx_basis_test

(Optional) Test set basis for "random-slope" regression in additive random effects model.

num_gfr

Number of "warm-start" iterations run using the grow-from-root algorithm (He and Hahn, 2021). Default: 5.

num_burnin

Number of "burn-in" iterations of the MCMC sampler. Default: 0.

num_mcmc

Number of "retained" iterations of the MCMC sampler. Default: 100.

params

The list of model parameters, each of which has a default value.

1. Global Parameters

  • cutpoint_grid_size Maximum size of the "grid" of potential cutpoints to consider. Default: 100.

  • sigma2_init Starting value of global error variance parameter. Calibrated internally as pct_var_sigma2_init*var((y-mean(y))/sd(y)) if not set.

  • pct_var_sigma2_init Percentage of standardized outcome variance used to initialize global error variance parameter. Default: 1. Superseded by sigma2_init.

  • variance_scale Variance after the data have been scaled. Default: 1.

  • a_global Shape parameter in the IG(a_global, b_global) global error variance model. Default: 0.

  • b_global Scale parameter in the IG(a_global, b_global) global error variance model. Default: 0.

  • random_seed Integer parameterizing the C++ random number generator. If not specified, the C++ random number generator is seeded according to std::random_device.

  • sample_sigma_global Whether or not to update the sigma^2 global error variance parameter based on IG(a_global, b_global). Default: TRUE.

  • keep_burnin Whether or not "burnin" samples should be included in cached predictions. Default FALSE. Ignored if num_mcmc = 0.

  • keep_gfr Whether or not "grow-from-root" samples should be included in cached predictions. Default TRUE. Ignored if num_mcmc = 0.

  • verbose Whether or not to print progress during the sampling loops. Default: FALSE.

2. Mean Forest Parameters

  • num_trees_mean Number of trees in the ensemble for the conditional mean model. Default: 200. If num_trees_mean = 0, the conditional mean will not be modeled using a forest, and the function will only proceed if num_trees_variance > 0.

  • sample_sigma_leaf Whether or not to update the tau leaf scale variance parameter based on IG(a_leaf, b_leaf). Cannot (currently) be set to true if ncol(W_train)>1. Default: FALSE.

2.1. Tree Prior Parameters

  • alpha_mean Prior probability of splitting for a tree of depth 0 in the mean model. Tree split prior combines alpha_mean and beta_mean via alpha_mean*(1+node_depth)^-beta_mean. Default: 0.95.

  • beta_mean Exponent that decreases split probabilities for nodes of depth > 0 in the mean model. Tree split prior combines alpha_mean and beta_mean via alpha_mean*(1+node_depth)^-beta_mean. Default: 2.

  • min_samples_leaf_mean Minimum allowable size of a leaf, in terms of training samples, in the mean model. Default: 5.

  • max_depth_mean Maximum depth of any tree in the ensemble in the mean model. Default: 10. Can be overridden with -1 which does not enforce any depth limits on trees.

2.2. Leaf Model Parameters

  • variable_weights_mean Numeric weights reflecting the relative probability of splitting on each variable in the mean forest. Does not need to sum to 1 but cannot be negative. Defaults to rep(1/ncol(X_train), ncol(X_train)) if not set here.

  • sigma_leaf_init Starting value of leaf node scale parameter. Calibrated internally as 1/num_trees_mean if not set here.

  • a_leaf Shape parameter in the IG(a_leaf, b_leaf) leaf node parameter variance model. Default: 3.

  • b_leaf Scale parameter in the IG(a_leaf, b_leaf) leaf node parameter variance model. Calibrated internally as 0.5/num_trees_mean if not set here.

3. Conditional Variance Forest Parameters

  • num_trees_variance Number of trees in the ensemble for the conditional variance model. Default: 0. Variance is only modeled using a tree / forest if num_trees_variance > 0.

  • variance_forest_init Starting value of root forest prediction in conditional (heteroskedastic) error variance model. Calibrated internally as log(pct_var_variance_forest_init*var((y-mean(y))/sd(y)))/num_trees_variance if not set.

  • pct_var_variance_forest_init Percentage of standardized outcome variance used to initialize global error variance parameter. Default: 1. Superseded by variance_forest_init.

3.1. Tree Prior Parameters

  • alpha_variance Prior probability of splitting for a tree of depth 0 in the variance model. Tree split prior combines alpha_variance and beta_variance via alpha_variance*(1+node_depth)^-beta_variance. Default: 0.95.

  • beta_variance Exponent that decreases split probabilities for nodes of depth > 0 in the variance model. Tree split prior combines alpha_variance and beta_variance via alpha_variance*(1+node_depth)^-beta_variance. Default: 2.

  • min_samples_leaf_variance Minimum allowable size of a leaf, in terms of training samples, in the variance model. Default: 5.

  • max_depth_variance Maximum depth of any tree in the ensemble in the variance model. Default: 10. Can be overridden with -1 which does not enforce any depth limits on trees.

3.2. Leaf Model Parameters

  • variable_weights_variance Numeric weights reflecting the relative probability of splitting on each variable in the variance forest. Does not need to sum to 1 but cannot be negative. Defaults to rep(1/ncol(X_train), ncol(X_train)) if not set here.

  • sigma_leaf_init Starting value of leaf node scale parameter. Calibrated internally as 1/num_trees_mean if not set here.

  • a_forest Shape parameter in the IG(a_forest, b_forest) conditional error variance model (which is only sampled if num_trees_variance > 0). Calibrated internally as num_trees_variance / 1.5^2 + 0.5 if not set.

  • b_forest Scale parameter in the IG(a_forest, b_forest) conditional error variance model (which is only sampled if num_trees_variance > 0). Calibrated internally as num_trees_variance / 1.5^2 if not set.

Value

List of sampling outputs and a wrapper around the sampled forests (which can be used for in-memory prediction on new data, or serialized to JSON on disk).

Examples

n <- 100
p <- 5
X <- matrix(runif(n*p), ncol = p)
f_XW <- (
    ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5) + 
    ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5) + 
    ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5) + 
    ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5)
)
noise_sd <- 1
y <- f_XW + rnorm(n, 0, noise_sd)
test_set_pct <- 0.2
n_test <- round(test_set_pct*n)
n_train <- n - n_test
test_inds <- sort(sample(1:n, n_test, replace = FALSE))
train_inds <- (1:n)[!((1:n) %in% test_inds)]
X_test <- X[test_inds,]
X_train <- X[train_inds,]
y_test <- y[test_inds]
y_train <- y[train_inds]
bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test)
# plot(rowMeans(bart_model$y_hat_test), y_test, xlab = "predicted", ylab = "actual")
# abline(0,1,col="red",lty=3,lwd=3)