summary_plot: SHAP Summary Plot
In mshap: Multiplicative SHAP Values for Two-Part Models

Description Usage Arguments Details Value Examples

View source: R/summary_plot.R

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

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"
)

`variable_values`	A data frame of the values of the variables that caused the given SHAP values, generally will be the same data frame or matrix that was passed to the model for prediction.
`shap_values`	A data frame of shap values, either returned by `mshap()` or obtained from the python `{shap}` module.
`names`	A character vector of variable names, corresponding to the order of the columns in both `variable_values` and `shap_values`. If `NULL` (default), then the column names of the `variable_values` are taken as `names`.
`num_vars`	An integer specifying the number of variables to show in the plot, defaults to the 10 most important.
`colorscale`	The color scale used for the color of the plot. It should be a character vector of length three, with the low color first, the middle color second, and the high color third. These can be hex color codes or colors recognized by `{ggplot2}`.
`legend.position`	The position of the legend. See `?ggplot2::theme` for more information.
`font_family`	A character string specifying the family of the text on the plot. Defaults to Times New Roman.
`title`	A character string specifying the title of the plot.

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.

A {ggplot2} object

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")
  )
}