ceterisParibus (version 0.4.2)

plot.ceteris_paribus_explainer: Plot Ceteris Paribus Explanations

Description

Function 'plot.ceteris_paribus_explainer' plots Ceteris Paribus Plots for selected observations. Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs.

Usage

# S3 method for ceteris_paribus_explainer
plot(
  x,
  ...,
  size = 1,
  alpha = 0.3,
  color = "black",
  size_points = 2,
  alpha_points = 1,
  color_points = color,
  size_rugs = 0.5,
  alpha_rugs = 1,
  color_rugs = color,
  size_residuals = 1,
  alpha_residuals = 1,
  color_residuals = color,
  only_numerical = TRUE,
  show_profiles = TRUE,
  show_observations = TRUE,
  show_rugs = FALSE,
  show_residuals = FALSE,
  aggregate_profiles = NULL,
  as.gg = FALSE,
  facet_ncol = NULL,
  selected_variables = NULL
)

Value

a ggplot2 object

Arguments

x

a ceteris paribus explainer produced with function `ceteris_paribus()`

...

other explainers that shall be plotted together

size

a numeric. Size of lines to be plotted

alpha

a numeric between 0 and 1. Opacity of lines

color

a character. Either name of a color or name of a variable that should be used for coloring

size_points

a numeric. Size of points to be plotted

alpha_points

a numeric between 0 and 1. Opacity of points

color_points

a character. Either name of a color or name of a variable that should be used for coloring

size_rugs

a numeric. Size of rugs to be plotted

alpha_rugs

a numeric between 0 and 1. Opacity of rugs

color_rugs

a character. Either name of a color or name of a variable that should be used for coloring

size_residuals

a numeric. Size of line and points to be plotted for residuals

alpha_residuals

a numeric between 0 and 1. Opacity of points and lines for residuals

color_residuals

a character. Either name of a color or name of a variable that should be used for coloring for residuals

only_numerical

a logical. If TRUE then only numerical variables will be plotted. If FALSE then only categorical variables will be plotted.

show_profiles

a logical. If TRUE then profiles will be plotted. Either individual or aggregate (see `aggregate_profiles`)

show_observations

a logical. If TRUE then individual observations will be marked as points

show_rugs

a logical. If TRUE then individual observations will be marked as rugs

show_residuals

a logical. If TRUE then residuals will be plotted as a line ended with a point

aggregate_profiles

function. If NULL (default) then individual profiles will be plotted. If a function (e.g. mean or median) then profiles will be aggregated and only the aggregate profile will be plotted

as.gg

if TRUE then returning plot will have gg class

facet_ncol

number of columns for the `facet_wrap()`

selected_variables

if not NULL then only `selected_variables` will be presented

Examples

library("DALEX")
 if (FALSE) {
library("randomForest")
set.seed(59)

apartments_rf_model <- randomForest(m2.price ~ construction.year + surface + floor +
      no.rooms + district, data = apartments)

explainer_rf <- explain(apartments_rf_model,
      data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)

apartments_small <- apartmentsTest[1:20,]
apartments_small_1 <- apartmentsTest[1,]
apartments_small_2 <- select_sample(apartmentsTest, n = 20)
apartments_small_3 <- select_neighbours(apartmentsTest, apartments_small_1, n = 20)

cp_rf <- ceteris_paribus(explainer_rf, apartments_small)
cp_rf_1 <- ceteris_paribus(explainer_rf, apartments_small_1)
cp_rf_2 <- ceteris_paribus(explainer_rf, apartments_small_2)
cp_rf_3 <- ceteris_paribus(explainer_rf, apartments_small_3)
cp_rf

cp_rf_y <- ceteris_paribus(explainer_rf, apartments_small, y = apartments_small$m2.price)
cp_rf_y1 <- ceteris_paribus(explainer_rf, apartments_small_1, y = apartments_small_1$m2.price)
cp_rf_y2 <- ceteris_paribus(explainer_rf, apartments_small_2, y = apartments_small_2$m2.price)
cp_rf_y3 <- ceteris_paribus(explainer_rf, apartments_small_3, y = apartments_small_3$m2.price)

plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE,
               show_residuals = TRUE, color = "black",
               alpha = 0.3, alpha_points = 1, alpha_residuals = 0.5,
               size_points = 2, size_rugs = 0.5)

plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE,
               show_residuals = TRUE, color = "black",
               selected_variables = c("construction.year", "surface"),
               alpha = 0.3, alpha_points = 1, alpha_residuals = 0.5,
               size_points = 2, size_rugs = 0.5)

plot(cp_rf_y1, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
               show_residuals = TRUE, alpha = 0.5, size_points = 3,
               alpha_points = 1, size_rugs = 0.5)

plot(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
               alpha = 0.2, alpha_points = 1, size_rugs = 0.5)

plot(cp_rf_y3, show_profiles = TRUE, show_rugs = TRUE,
               show_residuals = TRUE, alpha = 0.2, color_residuals = "orange", size_rugs = 0.5)

plot(cp_rf_y, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE, size_rugs = 0.5,
               show_residuals = TRUE, alpha = 0.5, color = "surface", as.gg = TRUE) +
               scale_color_gradient(low = "darkblue", high = "darkred")

plot(cp_rf_y1, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
               show_residuals = TRUE, alpha = 0.5, color = "surface", size_points = 3)

plot(cp_rf_y2, show_profiles = TRUE, show_observations = TRUE, show_rugs = TRUE,
               size = 0.5, alpha = 0.5, color = "surface")

plot(cp_rf_y, show_profiles = TRUE, show_rugs = TRUE, size_rugs = 0.5,
               show_residuals = FALSE, aggregate_profiles = mean, color = "darkblue")
}