inst/doc/simulation.R

In iBART: Iterative Bayesian Additive Regression Trees Descriptor Selection Method

## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## -----------------------------------------------------------------------------
set.seed(123)
# Allocate 10GB of memory for Java. Must be called before library(iBART)
options(java.parameters = "-Xmx10g") 
library(iBART)

## ----iBART--------------------------------------------------------------------
#### Simulation Parameters ####
n <- 250 # Change n to 100 here to reproduce result in Supplementary Materials A.2.3
p <- 10  # Number of primary features

#### Generate Data ####
X <- matrix(runif(n * p, min = -1, max = 1), nrow = n, ncol = p)
colnames(X) <- paste("x.", seq(from = 1, to = p, by = 1), sep = "")
y <- 15 * (exp(X[, 1]) - exp(X[, 2]))^2 + 20 * sin(pi * X[, 3] * X[, 4]) + rnorm(n, mean = 0, sd = 0.5)

## ----iBART short--------------------------------------------------------------
#### iBART ####
iBART_sim <- iBART(X = X, y = y,
                   head = colnames(X),
                   num_burn_in = 5000,                   # lower value for faster run
                   num_iterations_after_burn_in = 1000,  # lower value for faster run
                   num_permute_samples = 20,             # lower value for faster run
                   opt = c("unary"), # only apply unary operators after base iteration
                   sin_cos = TRUE,
                   apply_pos_opt_on_neg_x = FALSE,
                   Lzero = TRUE,
                   K = 4,
                   standardize = FALSE,
                   seed = 123)

## ----iBART result-------------------------------------------------------------
# iBART selected descriptors
iBART_sim$descriptor_names

# iBART model
coef(iBART_sim$iBART_model, s = "lambda.min")

## ----iBART full, eval=FALSE---------------------------------------------------
#  iBART_sim <- iBART(X = X, y = y,
#                     head = colnames(X),
#                     opt = c("unary", "binary", "unary"),
#                     sin_cos = TRUE,
#                     apply_pos_opt_on_neg_x = FALSE,
#                     Lzero = TRUE,
#                     K = 4,
#                     standardize = FALSE,
#                     seed = 123)

## ----load result--------------------------------------------------------------
load("../data/iBART_sim.rda")                 # load full result

iBART_sim$descriptor_names                    # iBART selected descriptors
coef(iBART_sim$iBART_model, s = "lambda.min") # iBART model

## ----cor----------------------------------------------------------------------
f1_true <- (exp(X[,1]) - exp(X[,2]))^2
f1_cor <- abs(exp(X[,1]) - exp(X[,2]))
cor(f1_true, f1_cor)

## ----size_plot, fig.width=7, fig.height=3.5-----------------------------------
library(ggplot2)
df_dim <- data.frame(dim = c(iBART_sim$iBART_sel_size, iBART_sim$iBART_gen_size),
                     iter = rep(0:3, 2),
                     type = rep(c("Selected", "Generated"), each = 4))
ggplot(df_dim, aes(x = iter, y = dim, colour = type, group = type)) +
  theme(text = element_text(size = 15), legend.title = element_blank()) +
  geom_line(size = 1) +
  geom_point(size = 3, shape = 21, fill = "white") +
  geom_text(data = df_dim, aes(label = dim, y = dim + 10, group = type),
            position = position_dodge(0), size = 5, colour = "blue") +
  labs(x = "Iteration", y = "Number of descriptors") +
  scale_x_continuous(breaks = c(0, 1, 2, 3))

## ----sessioninfo--------------------------------------------------------------
sessionInfo()