plot.ceteris_paribus_2d_explainer: Plot Ceteris Paribus 2D Explanations

In ingredients: Effects and Importances of Model Ingredients

Plot Ceteris Paribus 2D Explanations

Description

This function plots What-If Plots for a single prediction / observation.

Usage

## S3 method for class 'ceteris_paribus_2d_explainer'
plot(
  x,
  ...,
  facet_ncol = NULL,
  add_raster = TRUE,
  add_contour = TRUE,
  bins = 3,
  add_observation = TRUE,
  pch = "+",
  size = 6
)

Arguments

x	a ceteris paribus explainer produced with the ceteris_paribus_2d() function
...	currently will be ignored
facet_ncol	number of columns for the facet_wrap
add_raster	if TRUE then geom_raster will be added to present levels with diverging colors
add_contour	if TRUE then geom_contour will be added to present contours
bins	number of contours to be added
add_observation	if TRUE then geom_point will be added to present observation that is explained
pch	character, symbol used to plot observations
size	numeric, size of individual datapoints

Value

a ggplot2 object

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

library("DALEX")
library("ingredients")
library("ranger")


apartments_rf_model <- ranger(m2.price ~., data = apartments)

explainer_rf <- explain(apartments_rf_model,
                        data = apartments_test[,-1],
                        y = apartments_test[,1],
                        verbose = FALSE)

new_apartment <- apartments_test[1,]
new_apartment

wi_rf_2d <- ceteris_paribus_2d(explainer_rf, observation = new_apartment)
head(wi_rf_2d)

plot(wi_rf_2d)
plot(wi_rf_2d, add_contour = FALSE)
plot(wi_rf_2d, add_observation = FALSE)
plot(wi_rf_2d, add_raster = FALSE)

# HR data
model <- ranger(status ~ gender + age + hours + evaluation + salary, data = HR,
                probability = TRUE)

pred1 <- function(m, x)   predict(m, x)$predictions[,1]

explainer_rf_fired <- explain(model,
                              data = HR[,1:5],
                              y = as.numeric(HR$status == "fired"),
                              predict_function = pred1,
                              label = "fired")
new_emp <- HR[1,]
new_emp

wi_rf_2d <- ceteris_paribus_2d(explainer_rf_fired, observation = new_emp)
head(wi_rf_2d)

plot(wi_rf_2d)

ingredients documentation built on Jan. 15, 2023, 5:09 p.m.