library(randomPlantedForest)
library(xgboost)
library(ggplot2)
library(patchwork)

# Fit xgboost
x <- as.matrix(mtcars[, -1])
y <- mtcars$mpg
xg <- xgboost(data = x[1:26, ], label = y[1:26],
              params = list(max_depth = 4, eta = .1),
              nrounds = 10, verbose = 0)

# Convert
xgrpf <- convert_xgboost_rpf(xg, x, y)

# Prediction seems to be fairly similar
max(abs(predict(xg, x) - predict(xgrpf, x)))

library(glex)
xg_g <- glex(xg, x = x)
rpf_g <- glex(xgrpf, x = as.data.frame(x))

# Intercept is different
xg_g$intercept - rpf_g$intercept

# Components mostly differ in intercept
autoplot(xg_g, "wt") + autoplot(rpf_g, "wt")
autoplot(xg_g, "cyl") + autoplot(rpf_g, "cyl")