Code Snippets in R

We use the titanic dataset and a random forest model.

library("DALEX")
library("randomForest")
titanic_imputed <- archivist::aread("pbiecek/models/27e5c")
titanic_rf <- archivist::aread("pbiecek/models/4e0fc")
explainer_rf <- DALEX::explain(model = titanic_rf,  
                               data = titanic_imputed[, -9],
                               y = titanic_imputed$survived, 
                               label = "Random Forest")

Partial-dependence profiles

library("ggplot2")
pdp_rf <- model_profile(explainer = explainer_rf, variables = "age")
plot(pdp_rf) +  ggtitle("Partial-dependence profile for age")

  • Only need to supplier explainer and variable arguments
  • Optional argument N allows you to vary the sample size used for calculation, default is 100
  • We can specify specific grouping variables if creating grouped PDP
  • We can also create clustered PDP by specifying the k argument for number of clusters. Uses hierarchical clustering under the hood.

We can include CP profiles (i.e. an ICE plot) with an additional argument to plot():

plot(pdp_rf, geom = "profiles") + 
    ggtitle("Ceteris-paribus and partial-dependence profiles for age")

Clustered partial-dependence profiles

This uses hclust() function:

pdp_rf_clust <- model_profile(explainer = explainer_rf, 
                              variables = "age", k = 3)

plot(pdp_rf_clust, geom = "profiles") + 
    ggtitle("Clustered partial-dependence profiles for age")

Grouped partial dependence profiles {-}

Below we group by gender:

pdp_rf_gender <- model_profile(explainer = explainer_rf, 
                               variables = "age", groups = "gender")

plot(pdp_rf_gender, geom = "profiles") + 
    ggtitle("Partial-dependence profiles for age, grouped by gender")

Contrastive partial-dependence profiles

library("rms")
titanic_lmr <- archivist::aread("pbiecek/models/58b24")
explainer_lmr <- DALEX::explain(model = titanic_lmr, 
                                data = titanic_imputed[, -9],
                                y = titanic_imputed$survived, 
                                label = "Logistic Regression")

pdp_lmr <- model_profile(explainer = explainer_lmr, variables = "age")
pdp_rf <- model_profile(explainer = explainer_rf, variables = "age")

plot(pdp_rf, pdp_lmr) +
    ggtitle("Partial-dependence profiles for age for two models")