[Experimental] Convert xgboost to rpf objects

[mnwright]

Adds a function to convert xgboost to rpf objects. Not very fast, it seems the bottleneck is get_leaf_bounds(), which could be implemented in C++.

Example:

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")

[jemus42]

Wondering if glex should provide a purify S3 generic and a method for xgb, while this package can define the method for itself and just use it to call its internal method in C++.

Not that it matters much for the experimental part but I'd prefer to not have xgboost as an Imports here, maybe a Suggests but, well, might as well use glex for that functionality in any case.

[mnwright]

I agree. The C++ part (set_model) should stay here and the rest go to glex (or somewhere else). Just didn't want to have to two PRs.

[jemus42]

Forest serialization is now done in #63