topepo

library(tidymodels)
library(doMC)

# ------------------------------------------------------------------------------

tidymodels_prefer()
theme_set(theme_bw())
options(pillar.advice = FALSE, pillar.min_title_chars = Inf)
registerDoMC(cores = parallel::detectCores())

topepo

# Allow fuser functions to work in tidymodels
# See https://stackoverflow.com/questions/75871678/how-can-i-pass-an-extra-variable-to-a-tidymodels-fit-function

# Create a function that can parse a formula that uses a special (non-existant)
# `groups()` function. This returns a list of character vectors that has the 
# columns and their roles based on the formula. It works with '.'. 

fuser_variables <- function(f, data) {
  cl <- match.call()
  trms <- terms(f, data = data, specials = "groups")

topepo

extract_code <- function(file) {
  # TODO we could put in an argument for the pattern 
  # or something more sophisticated...
  target <- tempfile()
  knitr::purl(file, output = target, quiet = TRUE)
  code <- readLines(target)
  code <- gsub("^##.*", "", code)
  r_file <- gsub("[Rq]md$", "R", file)
  writeLines(code, r_file)
  invisible(TRUE)

topepo

#' Check to see if a repo has remote dependencies.
#' @param repos A character vector of repos to query (e.g. `tidyverse/dplyr`). 
#' @param hard_depend Should only depends and imports be included in the query.
#' Using `FALSE` will include suggests and others. 
#' @details Packages listed in `Config/Needs/*` will not be returned. 
#' Requires dplyr, pak, and purrr. 
#' @return A tibble with columns for the repo and any github repos that are
#' listed as dependencies. 
#' @examples
#' tests <- paste0("tidymodels/", c("broom", "parsnip", "recipes"))

topepo

library(tidymodels)
tidymodels_prefer()
theme_set(theme_bw())

library(doMC)
registerDoMC(cores = 20)

# ------------------------------------------------------------------------------

data(ad_data)

topepo

library(tidymodels)
library(rules)
tidymodels_prefer()
theme_set(theme_bw())

library(doMC)
registerDoMC(cores = 20)

# ------------------------------------------------------------------------------

topepo

suppressPackageStartupMessages(library(tidymodels))
library(furrr)

plan(multisession, workers = 20)

data(cells)

set.seed(1)
split <- initial_split(cells, prop = .95)
cell_train <- training(split)

topepo

library(tidymodels)
library(patchwork)
library(doMC)
registerDoMC(cores = 20)

theme_set(theme_bw() + theme(legend.position = "top")) 

# ------------------------------------------------------------------------------

url <- "https://github.com/topepo/cars/raw/master/2019_07_03_city/car_data_splits.RData"

topepo

# pak::pak("tidymodels/parsnip@logistic-liblinear")

library(AppliedPredictiveModeling)
library(tidymodels)
theme_set(theme_bw())

# ------------------------------------------------------------------------------

lr_pull <- function(pen, eng = "glmnet", dat, ...) {
  logistic_reg(penalty = pen, ...) %>%

topepo

library(tidymodels)
library(plsmod)
library(DALEX)

theme_set(theme_bw())

## ── Attaching packages ───────────────────────────────── tidymodels 0.1.1 ──
## ✓ broom     0.7.0      ✓ recipes   0.1.13
## ✓ dials     0.0.8      ✓ rsample   0.0.7 
## ✓ dplyr     1.0.0      ✓ tibble    3.0.3