---
title: "Adding models to butcher"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Adding models to butcher}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(butcher)
```

If you come across any model objects that should be subject to butchering, but does not exist in our current repository as listed [here](https://www.tidymodels.org/find/parsnip/), please consider becoming a contributor to this package! For any first-timers, this is great place to start as we've created templates that make this process as seamless as possible. 

Let's say our new model object, of class `blob`, was generated from a `R` package called `blobber`. If you want to add axe methods for this class, first clone butcher onto your local computer and open up RStudio (see `usethis::create_from_github("tidymodels/butcher")` for an automated way to do this). After you have opened RStudio and are in the `butcher` RStudio Project, run: 

```{r, eval = FALSE}
> new_model_butcher(model_class = "blob", package_name = "blobber")
```

You'll get the following console messages:

```{r, eval = FALSE}
✔ Setting active project to 'path_to_butcher_package'
✔ Adding 'blobber' to Suggests field in DESCRIPTION
● Use `requireNamespace("blobber", quietly = TRUE)` to test if package is installed
● Then directly refer to functons like `blobber::fun()` (replacing `fun()`).

ℹ Writing skeleton files
✔ Writing 'R/blob.R'
✔ Writing 'tests/testthat/test-blob.R'
● Modify 'R/blob.R'
● Modify 'tests/testthat/test-blob.R'
```

`new_model_butcher()` leverages `usethis` to: 

1. Add the new `blobber` modeling package under `Suggests` in the `butcher` package description file.
2. Generate a skeleton file under the `R` directory with all possible axe methods for `blob`.
3. Generate an associated test file under `tests/testthat` to test new `blob` axe methods.

As shown by the `R` scripts attached to other model objects that exist in this package, *not all* axe generics are used. In fact, if you take a look at the `elnet.R` script, the only component of the model object fit from the package `glmnet` that might be worth axing is the `call`. To help target what is worth removing from `blob`, we recommend first beginning with `butcher::weigh()` to identify which parts of the model object take up the most memory.

```{r, eval = FALSE}
> weigh(fitted_blob_object)
# A tibble: 25 x 2
   object            size
   <chr>            <dbl>
 1 terms         4.01    
 2 qr.qr         0.00666 
 3 residuals     0.00286 
 4 fitted.values 0.00286 
 5 effects       0.0014  
 6 coefficients  0.00109 
 7 call          0.000728
 8 model.mpg     0.000304
 9 model.cyl     0.000304
10 model.disp    0.000304
# … with 15 more rows
```

In this example, the fitted model objected generated from `blobber` has a `terms` component that is taking 4.01 Mb. From here, you can examine the structure of this terms component by leveraging `lobstr::sxp(fitted_blob_object$terms)` or simply running `utils::str(fitted_blob_object$terms)`. If you are looking to hunt for a specific component like the environment, fitted values, training data, controls or the call object, take a look at `butcher::locate()`. 

Perhaps from our model object, `blob`, we find that the `call` is the only piece worth axing (or replacing). The `R/blob.R` skeleton would be completed by putting a placeholder for the original call. 

```{r, eval = FALSE}
#' Axing a blob.
#'
#' blob model objects are created from the blobber package. They are
#' generally leveraged for classification ... insert anything relevant 
#' ... This is where all the blob specific documentation lies.
#'
#' @param x Model object.
#' @param verbose Print information each time an axe method is executed
#'  that notes how much memory is released and what functions are
#'  disabled. Default is \code{TRUE}.
#' @param ... Any additional arguments related to axing.
#'
#' @return Axed model object.
#'
#' @name axe-blob
NULL

#' Remove the call.
#'
#' @rdname axe-blob
#' @export
axe_call.blob <- function(x, verbose = TRUE, ...) {
  old <- x
  x <- exchange(x, "call", call("dummy_call"))
  if (verbose) {
    assess_object(
      old, 
      x,
      disabled = c("print", "summary")
    )
  }
  add_butcher_class(x)
}
```

Here we assign the current blob object `x` to the variable `old` as a means to evaluate the memory released once `axe_call()` is executed on the original model object. Next, we actually `exchange()` the current call with a dummy call of a (hopefully) smaller size. We also include `assess_object()` with the additional string parameter of `disabled` so console messages will be printed out, alerting users of any downstream functions would be affected by axing the call. Since the original model object is fundamentally different, we attach an additional `butcher_blob` class by calling `add_butcher_class()` at the end of each axe method. Once the axe methods are set, we then have a skeleton file `tests/testthat/test-blob.R` to aid in any unit testing. 

## Recap

Adding a new model object to butcher:

1) Run `new_model_butcher(model_class = "blob", package_name = "blobber")`
2) Use butcher helper functions `butcher::weigh()` and `butcher::locate()` to decide what to axe
3) Finalize edits to `R/blob.R` and `tests/testthat/test-blob.R` 
4) Make a pull request! 

