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

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
}

SHAP Observation Plot

Description

This Function plots the given contributions for a single observation, and demonstrates how the model arrived at the prediction for the given observation.

Usage

observation_plot(
  variable_values,
  shap_values,
  expected_value,
  names = NULL,
  num_vars = 10,
  fill_colors = c("#A54657", "#0D3B66"),
  connect_color = "#849698",
  expected_color = "#849698",
  predicted_color = "#EE964B",
  title = "Individual Observation Explanation",
  font_family = "Times New Roman"
)

Arguments

Details

This function allows the user to pass a sing row from a data frame of SHAP values and variable values along with an expected model output and it returns a ggplot object displaying a specific map of the effect of Variable value on SHAP value. It is created with {ggplot2}, and the returned value is a {ggplot2} object that can be modified for given themes/colors.

Please note that for the variable_values and shap_values arguments, both of which are data frames, the columns must be in the same order. This is essential in assuring that the variable values and labels are matched to the correct shap values.

Value

A {ggplot2} object

Examples

if (interactive()) {
library(mshap)
library(ggplot2)

# Generate fake data
set.seed(18)
dat <- data.frame(
  age = runif(1000, min = 0, max = 20),
  prop_domestic = runif(1000),
  model = sample(c(0, 1), 1000, replace = TRUE),
  maintain = rexp(1000, .01) + 200
)
shap <- data.frame(
  age = rexp(1000, 1/dat$age) * (-1)^(rbinom(1000, 1, dat$prop_domestic)),
  prop_domestic = -200 * rnorm(100, dat$prop_domestic, 0.02) + 100,
  model = ifelse(dat$model == 0, rnorm(1000, -50, 30), rnorm(1000, 50, 30)),
  maintain = (rnorm(1000, dat$maintain, 100) - 400) * 0.2
)
expected_value <- 1000

# A Basic sumary plot
summary_plot(
  variable_values = dat,
  shap_values = shap
)

# A Customized summary plot
summary_plot(
  variable_values = dat,
  shap_values = shap,
  legend.position = "bottom",
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  colorscale = c("blue", "purple", "red"),
  font_family = "Arial",
  title = "A Custom Title"
)

# A basic observation plot
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value
)

# A Customized Observation plot
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value,
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  font_family = "Arial",
  title = "A Custom Title",
  fill_colors = c("red", "blue"),
  connect_color = "black",
  expected_color = "purple",
  predicted_color = "yellow"
)

# Add elements to the returned object
# see vignette("mshap_plots") for more information
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value,
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  font_family = "Arial",
  title = "A Custom Title"
) +
  geom_label(
    aes(y = 950, x = 4, label = "This is a really big bar!"),
    color = "#FFFFFF",
    fill = NA
  ) +
  theme(
    plot.background = element_rect(fill = "grey"),
    panel.background = element_rect(fill = "lightyellow")
  )
}

SHAP Summary Plot

Description

A Function for obtaining a beeswarm plot, similar to the summary plot in the {shap} python package.

Usage

summary_plot(
  variable_values,
  shap_values,
  names = NULL,
  num_vars = 10,
  colorscale = c("#A54657", "#FAF0CA", "#0D3B66"),
  legend.position = c(0.8, 0.2),
  font_family = "Times New Roman",
  title = "SHAP Value Summary"
)

Arguments

Details

This function allows the user to pass a data frame of SHAP values and variable values and returns a ggplot object displaying a general summary of the effect of Variable level on SHAP value by variable. It is created with {ggbeeswarm}, and the returned value is a {ggplot2} object that can be modified for given themes/colors.

Please note that for the variable_values and shap_values arguments, both of which are data frames, the columns must be in the same order. This is essential in assuring that the variable values and labels are matched to the correct shap values.

Value

A {ggplot2} object

Examples

if (interactive()) {
library(mshap)
library(ggplot2)

# Generate fake data
set.seed(18)
dat <- data.frame(
  age = runif(1000, min = 0, max = 20),
  prop_domestic = runif(1000),
  model = sample(c(0, 1), 1000, replace = TRUE),
  maintain = rexp(1000, .01) + 200
)
shap <- data.frame(
  age = rexp(1000, 1/dat$age) * (-1)^(rbinom(1000, 1, dat$prop_domestic)),
  prop_domestic = -200 * rnorm(100, dat$prop_domestic, 0.02) + 100,
  model = ifelse(dat$model == 0, rnorm(1000, -50, 30), rnorm(1000, 50, 30)),
  maintain = (rnorm(1000, dat$maintain, 100) - 400) * 0.2
)
expected_value <- 1000

# A Basic sumary plot
summary_plot(
  variable_values = dat,
  shap_values = shap
)

# A Customized summary plot
summary_plot(
  variable_values = dat,
  shap_values = shap,
  legend.position = "bottom",
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  colorscale = c("blue", "purple", "red"),
  font_family = "Arial",
  title = "A Custom Title"
)

# A basic observation plot
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value
)

# A Customized Observation plot
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value,
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  font_family = "Arial",
  title = "A Custom Title",
  fill_colors = c("red", "blue"),
  connect_color = "black",
  expected_color = "purple",
  predicted_color = "yellow"
)

# Add elements to the returned object
# see vignette("mshap_plots") for more information
observation_plot(
  variable_values = dat[1,],
  shap_values = shap[1,],
  expected_value = expected_value,
  names = c("Age", "% Domestic", "Model", "Maintenence Hours"),
  font_family = "Arial",
  title = "A Custom Title"
) +
  geom_label(
    aes(y = 950, x = 4, label = "This is a really big bar!"),
    color = "#FFFFFF",
    fill = NA
  ) +
  theme(
    plot.background = element_rect(fill = "grey"),
    panel.background = element_rect(fill = "lightyellow")
  )
}

Select variables with a function

Description

This selection helper selects the variables for which a function returns TRUE.

Usage

where(fn)

Arguments

Value

A selection of columns