plotD3_feature_importance: Plot Feature Importance Objects in D3 with r2d3 Package.

In ingredients: Effects and Importances of Model Ingredients

Plot Feature Importance Objects in D3 with r2d3 Package.

Description

Function plotD3.feature_importance_explainer plots dropouts for variables used in the model. It uses output from feature_importance function that corresponds to permutation based measure of feature importance. Variables are sorted in the same order in all panels. The order depends on the average drop out loss. In different panels variable contributions may not look like sorted if variable importance is different in different models.

Usage

## S3 method for class 'feature_importance_explainer'
plotD3(
  x,
  ...,
  max_vars = NULL,
  show_boxplots = TRUE,
  bar_width = 12,
  split = "model",
  scale_height = FALSE,
  margin = 0.15,
  chart_title = "Feature importance"
)

Arguments

x	a feature importance explainer produced with the feature_importance() function
...	other explainers that shall be plotted together
max_vars	maximum number of variables that shall be presented for for each model. By default NULL which means all variables
show_boxplots	logical if TRUE (default) boxplot will be plotted to show permutation data.
bar_width	width of bars in px. By default 12px
split	either "model" or "feature" determines the plot layout
scale_height	a logical. If TRUE, the height of plot scales with window size. By default it's FALSE
margin	extend x axis domain range to adjust the plot. Usually value between 0.1 and 0.3, by default it's 0.15
chart_title	a character. Set custom title

Value

a r2d3 object.

References

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

Examples

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

lm_model <- lm(m2.price ~., data = apartments)
explainer_lm <- explain(lm_model,
                        data = apartments[,-1],
                        y = apartments[,1],
                        verbose = FALSE)

fi_lm <- feature_importance(explainer_lm,
      loss_function = DALEX::loss_root_mean_square, B = 1)

head(fi_lm)
plotD3(fi_lm)


library("ranger")

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

explainer_rf <- explain(rf_model,
                        data = apartments[,-1],
                        y = apartments[,1],
                        label = "ranger forest",
                        verbose = FALSE)

fi_rf <- feature_importance(explainer_rf, loss_function = DALEX::loss_root_mean_square)

head(fi_rf)
plotD3(fi_lm, fi_rf)

plotD3(fi_lm, fi_rf, split = "feature")

plotD3(fi_lm, fi_rf, max_vars = 3, bar_width = 16, scale_height = TRUE)
plotD3(fi_lm, fi_rf, max_vars = 3, bar_width = 16, split = "feature", scale_height = TRUE)
plotD3(fi_lm, margin = 0.2)

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