midnight

The ‘midnight’ package implements a ‘parsnip’ engine for the ‘midr’ package, allowing users to seamlessly fit, tune, and evaluate MID (Maximum Interpretation Decomposition) models with ‘tidymodels’ workflows. Development and augmentation of the package are driven by research from the “Moonlight Seminar 2025”, a collaborative study group of actuaries from the Institute of Actuaries of Japan focused on advancing the practical applications of interpretable models.

Installation

You can install the development version of midnight from GitHub with:

# install.packages("pak")
pak::pak("ryo-asashi/midnight")

Fit MID Surrogate Models using ‘parsnip’

This is a basic example which shows you how to solve a common problem:

library(parsnip)
library(midr)
library(midnight)
library(khroma)
library(ggplot2)
library(gridExtra)
theme_set(theme_midr())
# create a first-order mid surrogate model
mid_spec_1 <- mid_surrogate() %>%
  set_mode("regression") %>%
  set_engine("midr", verbosity = 3)
mid_spec_1
#> mid surrogate Model Specification (regression)
#> 
#> Engine-Specific Arguments:
#>   verbosity = 3
#> 
#> Computational engine: midr
# fit the model
mid_1 <- mid_spec_1 %>%
  fit_xy(x = airquality[, -1], y = airquality[1])
mid_1
#> parsnip model object
#> 
#> 
#> Call:
#> interpret(x = x, y = y, weights = weights, k = k, lambda = penalty,
#>  verbosity = ..1)
#> 
#> Intercept: 42.099
#> 
#> Main Effects:
#> 5 main effect terms
#> 
#> Uninterpreted Variation Ratio: 0.028441
# create a second-order mid surrogate model via "custom formula"
mid_spec_2 <- mid_surrogate(
  params_main = 50, params_inter = 5, penalty = .9,
  custom_formula = Ozone ~ .^2
) %>%
  set_mode("regression") %>%
  set_engine("midr", verbosity = 3)
# fit the model
mid_2 <- mid_spec_2 %>%
  fit_xy(x = airquality[, -1], y = airquality[1])
mid_2
#> parsnip model object
#> 
#> 
#> Call:
#> interpret(formula = Ozone ~ .^2, data = data, weights = weights,
#>  verbosity = ..1, k = k, lambda = penalty)
#> 
#> Intercept: 42.099
#> 
#> Main Effects:
#> 5 main effect terms
#> 
#> Interactions:
#> 10 interaction terms
#> 
#> Uninterpreted Variation Ratio: 0.073086

grid.arrange(nrow = 2,
 ggmid(mid.importance(mid_2$fit), theme = "muted"),
 ggmid(mid_2$fit, "Temp", main.effects = TRUE),
 ggmid(mid_2$fit, "Wind", main.effects = TRUE),
 ggmid(mid_2$fit, "Temp:Wind", main.effects = TRUE, theme = "tokyo") 
)