The goal of rinfa is to provide Rust bindings for parsnip model specifications.
You can install the development version of rinfa from GitHub with:
# install.packages("devtools")
devtools::install_github("simonpcouch/rinfa")x <- matrix(rnorm(3e7), ncol = 3, dimnames = list(NULL, paste0("X", 1:3)))
y <- rnorm(1e7)
dat <- cbind(as.data.frame(x), y)
# the usual formula interface
system.time(
lm(y ~ ., dat)
)
#> user system elapsed
#> 1.097 0.189 1.292
system.time(
# lm()'s speedy friend (still from base R)
.lm.fit(x, y)
)
#> user system elapsed
#> 0.321 0.022 0.344
# rinfa's implementation
library(rinfa)
system.time({
.linfa_linear_reg(x, y)
})
#> user system elapsed
#> 0.115 0.046 0.161To use rinfa with tidymodels, set the modeling engine to "linfa":
# using the formula interface:
linfa_fit <- fit(linear_reg(engine = "linfa"), y ~ ., dat)
# using the (more performant, in this case) XY interface:
linfa_fit_xy <- fit_xy(linear_reg(engine = "linfa"), x = x, y = y)The rinfa package provides an additional engine "linfa" for the models
in the following table:
| model | engine | mode |
|---|---|---|
| decision_tree | linfa | classification |
| linear_reg | linfa | regression |
| logistic_reg | linfa | classification |
| multinom_reg | linfa | classification |
| naive_Bayes | linfa | classification |
| svm_linear | linfa | classification |
To read more about the design of rinfa, see
R/README.md.
