xai examples codes

xai.R

library("rms")
library("randomForest")
library("gbm")
library("DALEX")

set.seed(1)
library("DALEXtra")
library("lime")

#https://medium.com/analytics-vidhya/a-beginners-guide-to-learning-r-with-the-titanic-dataset-a630bc5495a8
#https://ema.drwhy.ai/breakDown.html#advanced-use-of-the-predict_parts-function

titanicdataset = read.csv("/home/deniz/Masaüstü/yükseklisans/ayz/code/train.csv", na.strings = "")

View(titanicdataset)
 ##############################################BREAKDOWN PLOTS FOR ADDİTİVE ATTRİBUTES ####################################################################
##modellerin okunması 
titanic_imputed <- archivist::aread("pbiecek/models/27e5c")
titanic_rf <- archivist:: aread("pbiecek/models/4e0fc")
(henry <- archivist::aread("pbiecek/models/a6538"))
##modellerin okunması 



explain_rf <- DALEX::explain(model = titanic_rf,  
                             data = titanic_imputed[, -9],
                             y = titanic_imputed$survived == "yes", 
                             label = "Random Forest")


bd_rf <- predict_parts(explainer = explain_rf,
                       new_observation = henry,
                       type = "break_down")
bd_rf 


plot(bd_rf)


bd_rf_order <- predict_parts(explainer = explain_rf,
                             new_observation = henry, 
                             type = "break_down",
                             order = c("class", "age", "gender", "fare", 
                                       "parch", "sibsp", "embarked"))
plot(bd_rf_order, max_features = 3) 



bd_rf_distr <- predict_parts(explainer = explain_rf,
                             new_observation = henry, 
                             type = "break_down",
                             order = c("age", "class", "fare", "gender", 
                                       "embarked", "sibsp", "parch"),
                             keep_distributions = TRUE)
plot(bd_rf_distr, plot_distributions = TRUE) 




##############################################BREAKDOWN PLOTS FOR ADDİTİVE ATTRİBUTES ####################################################################




############################################# #BREAKDOWN PLOTS FOR Intereaction s####################################################################

explain_rf <- DALEX::explain(model = titanic_rf,  
                             data = titanic_imputed[, -9],
                             y = titanic_imputed$survived == "yes", 
                             label = "Random Forest")


bd_rf <- predict_parts(explainer = explain_rf,
                       new_observation = henry,
                       type = "break_down_interactions")
bd_rf


plot(bd_rf)


############################################# #BREAKDOWN PLOTS FOR Intereaction s####################################################################

############################################# SHAPLEY ADDTİVE ####################################################################




shap_henry <- predict_parts(explainer = explain_rf, 
                            new_observation = henry, 
                            type = "shap",
                            B = 25)






shap_henry

plot(shap_henry)



############################################# SHAPLEY ADDTİVE ####################################################################



############################################# LİME  ####################################################################


set.seed(1)
library("DALEXtra")
library("lime")
model_type.dalex_explainer <- DALEXtra::model_type.dalex_explainer
predict_model.dalex_explainer <- DALEXtra::predict_model.dalex_explainer

johnny_d <- archivist:: aread("pbiecek/models/e3596")
titanic_rf_exp <- DALEX::explain(model = titanic_rf,  
                                 data = titanic_imputed[, -9],
                                 y = titanic_imputed$survived == "yes", 
                                 label = "Random Forest")

lime_johnny <- predict_surrogate(explainer = titanic_rf_exp, 
                                 new_observation = johnny_d, 
                                 n_features = 3, 
                                 n_permutations = 1000,
                                 type = "lime")



(as.data.frame(lime_johnny))


plot(lime_johnny)

############################################# LİME  ####################################################################