fit calibrators at fit.container()

[simonpcouch]

Closes #9.

Before this PR, interface was:

 library(container)
 library(modeldata)
 library(probably)
#> 
#> Attaching package: 'probably'
#> The following objects are masked from 'package:base':
#> 
#>     as.factor, as.ordered
 library(tibble)

 # create example data
 set.seed(1)
 dat <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))

 dat
#> # A tibble: 100 × 2
#>         y y_pred
#>     <dbl>  <dbl>
#>  1 -0.626 -0.934
#>  2  0.184  0.134
#>  3 -0.836 -1.33 
#>  4  1.60   0.956
#>  5  0.330 -0.490
#>  6 -0.820  1.36 
#>  7  0.487  0.960
#>  8  0.738  1.28 
#>  9  0.576  0.672
#> 10 -0.305  1.53 
#> # ℹ 90 more rows

 # calibrate numeric predictions
 reg_cal <- cal_estimate_linear(dat, truth = y, estimate = y_pred)

 # specify calibration
 reg_ctr <-
   container(mode = "regression") %>%
   adjust_numeric_calibration(reg_cal)

 # "train" container
 reg_ctr_trained <- fit(reg_ctr, dat, outcome = y, estimate = y_pred)

 predict(reg_ctr, dat)
#> # A tibble: 100 × 2
#>         y  y_pred
#>     <dbl>   <dbl>
#>  1 -0.626 -0.257 
#>  2  0.184  0.303 
#>  3 -0.836 -0.512 
#>  4  1.60   0.479 
#>  5  0.330  0.0108
#>  6 -0.820  0.523 
#>  7  0.487  0.480 
#>  8  0.738  0.516 
#>  9  0.576  0.438 
#> 10 -0.305  0.536 
#> # ℹ 90 more rows

^{Created on 2024-04-26 with reprex v2.1.0}

With this PR:

library(container)
library(modeldata)
library(probably)
#> 
#> Attaching package: 'probably'
#> The following objects are masked from 'package:base':
#> 
#>     as.factor, as.ordered
library(tibble)

# create example data
set.seed(1)
dat <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))

dat
#> # A tibble: 100 × 2
#>         y y_pred
#>     <dbl>  <dbl>
#>  1 -0.626 -0.934
#>  2  0.184  0.134
#>  3 -0.836 -1.33 
#>  4  1.60   0.956
#>  5  0.330 -0.490
#>  6 -0.820  1.36 
#>  7  0.487  0.960
#>  8  0.738  1.28 
#>  9  0.576  0.672
#> 10 -0.305  1.53 
#> # ℹ 90 more rows

# specify calibration
reg_ctr <-
  container(mode = "regression") %>%
  adjust_numeric_calibration(type = "linear")

# train container
reg_ctr_trained <- fit(reg_ctr, dat, outcome = y, estimate = y_pred)

predict(reg_ctr_trained, dat)
#> # A tibble: 100 × 2
#>         y  y_pred
#>     <dbl>   <dbl>
#>  1 -0.626 -0.257 
#>  2  0.184  0.303 
#>  3 -0.836 -0.512 
#>  4  1.60   0.479 
#>  5  0.330  0.0108
#>  6 -0.820  0.523 
#>  7  0.487  0.480 
#>  8  0.738  0.516 
#>  9  0.576  0.438 
#> 10 -0.305  0.536 
#> # ℹ 90 more rows

^{Created on 2024-04-26 with reprex v2.1.0}

Will be followed up by workflows PR updates (tidymodels/workflows#225) and a tune PR later today.

[simonpcouch]

There are a couple gaps in this logic depending on when/how different variants of the type are supplied. Will sit tight on closing those up until we address #13.

[simonpcouch]

When putting together #13, ultimately felt that this should have a different argument than the same one supplied to container(type). I'd propose method, but will wait to Replace All until there's more feedback here.

[hfrick]

Nice one