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Preprocessing for use in shiny app

Source: R/1_cheem_lists.r
cheem_ls.Rd

Performs the preprocessing steps needs to supply the plot functions global_view() and radial_cheem_tour() used in the shiny app. The user need to supply the attributions and predictions. For help getting started with this see vignette("getting-started-with-cheem").

Usage

cheem_ls(
  x,
  y = NULL,
  attr_df,
  pred = NULL,
  class = NULL,
  label = "label",
  verbose = getOption("verbose")
)

Arguments

x

The explanatory variables of the model.

y

The target variable of the model.

attr_df

A data frame or matrix of (n, p) local explanation attributions.

pred

A (n, 1) vector, the predictions from the associated model.

class

Optional, (n, 1) vector, a variable to group points by. This can be the same as or different from y, the target variable.

label

Optionally provide a character label to store reminder text for the type of model and local explanation used. Defaults to "label".

verbose

Logical, if start time and run duration should be printed. Defaults to getOption("verbose").

Value

A list of data.frames needed for the shiny application.

See also

global_view() radial_cheem_tour() radial_cheem_tour()

Other cheem preprocessing: subset_cheem()

Examples

library(cheem)

## Classification setup
X    <- spinifex::penguins_na.rm[, 1:4]
Y    <- spinifex::penguins_na.rm$species
clas <- spinifex::penguins_na.rm$species

## Bring your own attributions and predictions, 
## for help review the vignette or package down site;
if(FALSE){
  vignette("getting-started-with-cheem")
  browseURL("https://nspyrison.github.io/cheem/articles/getting-started-with-cheem.html")
}

## Cheem
peng_chm <- cheem_ls(X, Y, penguin_xgb_shap, penguin_xgb_pred, clas,
                     label = "Penguins, xgb, shapviz")

## Save for use with shiny app (expects an rds file)
if(FALSE){ ## Don't accidentally save.
  saveRDS(peng_chm, "./chm_peng_xgb_shapviz.rds")
  run_app() ## Select the saved rds file from the data dropdown.
}

## Cheem visuals
if(interactive()){
  prim <- 1
  comp <- 2
  global_view(peng_chm, primary_obs = prim, comparison_obs = comp)
  bas <- sug_basis(penguin_xgb_shap, prim, comp)
  mv  <- sug_manip_var(penguin_xgb_shap, primary_obs = prim, comp)
  ggt <- radial_cheem_tour(peng_chm, basis = bas, manip_var = mv)
  animate_plotly(ggt)
}

## Regression example
library(cheem)

## Regression setup
dat  <- amesHousing2018_NorthAmes
X    <- dat[, 1:9]
Y    <- dat$SalePrice
clas <- dat$SubclassMS

#' ## Bring your own attributions and predictions, 
## for help review the vignette or package down site;
if(FALSE){
  vignette("getting-started-with-cheem")
  browseURL("https://nspyrison.github.io/cheem/articles/getting-started-with-cheem.html")
}

## Cheem list
ames_rf_chm <- cheem_ls(X, Y, ames_rf_shap, ames_rf_pred, clas,
                        label = "North Ames, RF, treeshap")

## Save for use with shiny app (expects an rds file)
if(FALSE){ ## Don't accidentally save.
  saveRDS(ames_rf_chm, "./chm_NAmes_rf_tshap.rds")
  run_app() ## Select the saved rds file from the data drop down.
}

## Cheem visuals
if(interactive()){
  prim <- 1
  comp <- 2
  global_view(ames_rf_chm, primary_obs = prim, comparison_obs = comp)
  bas <- sug_basis(ames_rf_shap, prim, comp)
  mv  <- sug_manip_var(ames_rf_shap, primary_obs = prim, comp)
  ggt <- radial_cheem_tour(ames_rf_chm, basis = bas, manip_var = mv)
  animate_plotly(ggt)
}