mshap {mshap}

mSHAP

Description

A function for calculation SHAP values of two-part models.

Usage

mshap(shap_1, shap_2, ex_1, ex_2, shap_1_names = NULL, shap_2_names = NULL)

Arguments

shap_1, shap_2

The SHAP values that will be multiplied together. They may be matrices or data frames, and up to one may be a list where each element is a matrix or data frame (this is necessary when one of the models is a multinomial classifier, for instance). Each data frame or matrix here must have the same number of rows, and if there are different numbers of columns or the columns are not the same, then ⁠shap_*_names⁠ must be specified.

ex_1, ex_2

The expected values of the models across the training set. If one of the arguments ⁠shap_*⁠ is a list, then the corresponding ⁠ex_*⁠ argument must be a vector (or array) of the same length as the list.

shap_1_names, shap_2_names

The character vector containing the names of the columns in shap_1 and shap_2, respectively. These must be in the same order as the columns themselves. If a list is passed to one of the ⁠shap_*⁠ arguments, it does NOT affect the corresponding ⁠shap_*_names⁠ argument, which will still be a single character vector.

Details

This function allows the user to input the SHAP values for two separate models (along with the expected values), and mSHAP then outputs the SHAP values of the two model predictions multiplied together.

An included feature of the function is the ability to pass data frames that do not have the same number of columns. Say for instance that one model benefits from a certain variable but the other does not. As long as the ⁠shap_*_names⁠ arguments are supplied, the function will automatically add a column of 0's for missing variables in either data frame (matrix). This corresponds to a SHAP value of 0, which of course is accurate if the variable was not included in the model.

Value

A list containing the multiplied SHAP values and the expected value. Or, in the case of a list passed as one of the ⁠shap_*⁠ augments, a list of lists where each element corresponds to the same element in the list passed to ⁠shap_*⁠.

Examples

if (interactive()) {
shap1 <- data.frame(
  age = runif(1000, -5, 5),
  income = runif(1000, -5, 5),
  married = runif(1000, -5, 5),
  sex = runif(1000, -5, 5)
)
shap2 <- list(
  data.frame(
    age = runif(1000, -5, 5),
    income = runif(1000, -5, 5),
    married = runif(1000, -5, 5),
    sex = runif(1000, -5, 5)
  ),
  data.frame(
    age = runif(1000, -5, 5),
    income = runif(1000, -5, 5),
    married = runif(1000, -5, 5),
    sex = runif(1000, -5, 5)
  ),
  data.frame(
    age = runif(1000, -5, 5),
    income = runif(1000, -5, 5),
    married = runif(1000, -5, 5),
    sex = runif(1000, -5, 5)
  )
)

ex1 <- 3
ex2 <- c(4, 5, 6)

# Case where both models have a single output
res1 <- mshap(
  shap_1 = shap1,
  shap_2 = shap2[[1]],
  ex_1 = ex1,
  ex_2 = ex2[1]
)
View(res1$shap_vals)
res1$expected_value

# Case where one of your models has multiple outputs that are explained
res2 <- mshap(
  shap_1 = shap1,
  shap_2 = shap2,
  ex_1 = ex1,
  ex_2 = ex2
)
View(res2[[1]]$shap_vals)
res2[[1]]$expected_value

# Case where the models have different variables
res3 <- mshap(
  shap_1 = shap1,
  shap_2 = shap2,
  ex_1 = ex1,
  ex_2 = ex2,
  shap_1_names = c("Age", "Income", "Married", "Sex"),
  shap_2_names = c("Age", "Income", "Children", "American")
)
# Note how there are now 6 columns of SHAP values, since there are 6
# distinct variables
View(res3[[1]]$shap_vals)
res3[[1]]$expected_value
}