This package implements the methodology described in the paper
- Linero, A.R. and Yang, Y. (2017). Bayesian tree ensembles that adapt to smoothness and sparsity. (In preapration)
The package can be installed with the devtools package:
library(devtools)
install_github("theodds/SoftBART")Note that if you are on OSX, you may need to first run the following commands from the terminal:
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /
The package is designed to mirror the functionality of the BayesTree package. The function softbart is the primary function, and is used in essentially the same manner as the bart function in BayesTree.
The following is a minimal example on "Friedman's example":
## Load library
library(SoftBart)
## Functions used to generate fake data
set.seed(1234)
f_fried <- function(x) 10 * sin(pi * x[,1] * x[,2]) + 20 * (x[,3] - 0.5)^2 +
10 * x[,4] + 5 * x[,5]
gen_data <- function(n_train, n_test, P, sigma) {
X <- matrix(runif(n_train * P), nrow = n_train)
mu <- f_fried(X)
X_test <- matrix(runif(n_test * P), nrow = n_test)
mu_test <- f_fried(X_test)
Y <- mu + sigma * rnorm(n_train)
Y_test <- mu_test + sigma * rnorm(n_test)
return(list(X = X, Y = Y, mu = mu, X_test = X_test, Y_test = Y_test, mu_test = mu_test))
}
## Simiulate dataset
sim_data <- gen_data(250, 100, 50, 1)
## Fit the model
fit <- softbart(X = sim_data$X, Y = sim_data$Y, X_test = sim_data$X_test,
hypers = Hypers(sim_data$X, sim_data$Y, num_tree = 50, temperature = 1),
opts = Opts(num_burn = 5000, num_save = 5000, update_tau = TRUE))
## Plot the fit (note: interval estimates are not prediction intervals,
## so they do not cover the predictions at the nominal rate)
plot(fit)
## Look at posterior model inclusion probabilities for each predictor.
posterior_probs <- function(fit) colMeans(fit$var_counts > 0)
plot(posterior_probs(fit),
col = ifelse(posterior_probs(fit) > 0.5, muted("blue"), muted("green")),
pch = 20)
rmse <- function(x,y) sqrt(mean((x-y)^2))
rmse(fit$y_hat_test_mean, sim_data$mu_test)
rmse(fit$y_hat_train_mean, sim_data$mu)In more complex settings, one may wish to incorporate the SoftBART model as a component within a larger model. In this case, it is possible to construct a SoftBART object within R and do a single Gibbs sampling update.
hypers <- Hypers(sim_data$X, sim_data$Y)
opts <- Opts()
forest <- MakeForest(hypers, opts)
mu_hat <- forest$do_gibbs(sim_data$X, sim_data$Y, sim_data$X_test, opts$num_burn)
mu_hat <- forest$do_gibbs(sim_data$X, sim_data$Y, sim_data$X_test, opts$num_save)The do_gibbs function takes as input the data used to do the update, an additional set of points at which to predict, and the number of iterations to run the sampler. By default, the probability vector s will not be updated until at least num_burn / 2 iterations have been run. This can be checked by calling forest$num_gibbs. The s vector itself can be obtained by calling forest$get_s(), and in the future we will add features to deal with other components as well. The code above first burns in and then samples from the posterior, and is essentially equivalent to using softbart. WARNING: if you are going to do this, you need to preprocess X and X_test by hand, so that all values lie in [0,1]. The softbart function, in addition to doing the sampling, also preprocesses X and X_test by applying a quantile transformation.
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