#' @title Prepare everything the prediction model needs
model_init <- function(){
is_package_not_installed <- function(pkg) !pkg %in% rownames(installed.packages())
if(is_package_not_installed("catboost")){
message("Installing catboost; this may take a few minutes")
os <- Sys.info()[['sysname']]
ver <- "0.18"
github_slug <- 'https://github.com/catboost/catboost/releases/download/'
url <- paste0(github_slug, "v", ver, "/catboost-R-", os, "-", ver, ".tgz")
remotes::install_url(
url,
args = c("--no-multiarch", "--no-test-load"),
dependencies = TRUE, upgrade = "never"
)
# remotes::install_github('catboost/catboost', subdir = 'catboost/R-package')
}
predict_function <- function(model_object, new_data){
catboost::catboost.predict(model = model_object, pool = new_data) %>%
as.data.frame(stringsAsFactors = FALSE) %>%
dplyr::rename("fit" = ".") %>%
purrr::map_df(link_function)
}
link_function <- function(x){ # 1 <= x <= 3
minmax <- function(x, a, b) pmin(pmax(x, a), b)
normalize <- function(x) if(max(x) == min(x)) x else (x - min(x)) / (max(x) - min(x))
scale <- function(x) if(isTRUE(x %>% sd() > 0)) base::scale(x) else base::scale(x, TRUE, FALSE)
y <- x %>% minmax(1, 3) %>% scale() %>% normalize()
y <- y * 2 + 1
as.vector(y)
}
model_config <- config::get(file = file.path(model_path, "model_config.yml"), use_parent = FALSE)
list2env(model_config, envir = parent.frame())
assign("predict_function", predict_function, envir = parent.frame())
assign("link_function", link_function, envir = parent.frame())
return(invisible())
}
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