In brunocarlin/tidy.outliers: What the Package Does (One Line, Title Case)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(tidy.outliers)

The most central aspect of tidy.outliers is to combine it on a workflow to filter outliers out of your training dataset, and to consider the outlier removal process just one of your many dials controlled hyper parameters, you can even 'pool' the score out of multiple outlier methods!

Load tidymodels

library(tidymodels)

Get data

data(ames)


data_split <- ames |>
  mutate(Sale_Price = log10(Sale_Price)) |>
  initial_split(strata = Sale_Price)
ames_train <- training(data_split)
ames_test  <- testing(data_split)

Create the recipe

ames_rec <- 
  recipe(Sale_Price ~ Gr_Liv_Area + Longitude + Latitude, data = ames_train) |> 
  step_log(Gr_Liv_Area, base = 10) |> 
  step_ns(Longitude, deg_free = tune("long df")) |> 
  step_ns(Latitude,  deg_free = tune("lat df")) |> 
  step_outliers_maha(all_numeric(), -all_outcomes()) |>
  step_outliers_lookout(all_numeric(),-contains(r"(.outliers)"),-all_outcomes()) |> 
  step_outliers_remove(contains(r"(.outliers)"),score_dropout = tune("dropout"),aggregation_function = tune("aggregation"))

See the parameters

parameters(ames_rec)

There is already a function for dropouts implemented by dials

ames_param <- 
  ames_rec |> 
  parameters() |> 
  update(
    `long df` = spline_degree(), 
    `lat df` = spline_degree(),
    dropout = dropout(range = c(0.75, 1)),
    aggregation = aggregation() |> value_set(c("mean","weighted_mean"))
  )

Create weighted_mean func

weighted_mean <- function(x) {
  x[[1]] * .75 + x[[2]] * .25
}

Grid Search picks random points

spline_grid <- grid_max_entropy(ames_param, size = 20)
spline_grid

Create a simple model

lin_mod <-
  linear_reg() |>
  set_engine("lm")

Create a simple workflow

wf_tune <- workflow() |>
  add_recipe(ames_rec) |> 
  add_model(lin_mod)

Create training folds

set.seed(2453)
cv_splits <- vfold_cv(ames_train, v = 5, strata = Sale_Price)

Tune the grid

ames_res <- tune_grid(wf_tune, resamples = cv_splits, grid = spline_grid)

estimates <- collect_metrics(ames_res)

rmse_vals <- 
  estimates |> 
  dplyr::filter(.metric == "rmse") |> 
  arrange(mean)
rmse_vals

Plot it

autoplot(ames_res,metric = "rmse")

brunocarlin/tidy.outliers documentation built on Feb. 22, 2023, 8:24 p.m.