问题
I generated a model using xgb.train with the "count:poisson" objective function and I get the following error when trying to create the explainer:
Error: Unsupported model type
Lime works when I replace the objective by something else such as reg:logistic.
Is there a way to explain count:poisson in lime? thanks
reproducible example:
library(xgboost)
library(dplyr)
library(caret)
library(insuranceData) # example dataset https://cran.r-project.org/web/packages/insuranceData/insuranceData.pdf
library(lime) # Local Interpretable Model-Agnostic Explanations
set.seed(123)
data(dataCar)
mydb <- dataCar %>% select(clm, exposure, veh_value, veh_body,
veh_age, gender, area, agecat)
label_var <- "clm"
offset_var <- "exposure"
feature_vars <- mydb %>%
select(-one_of(c(label_var, offset_var))) %>%
colnames()
#preparing data for xgboost (one hot encoding of categorical (factor) data
myformula <- paste0( "~", paste0( feature_vars, collapse = " + ") ) %>% as.formula()
dummyFier <- caret::dummyVars(myformula, data=mydb, fullRank = TRUE)
dummyVars.df <- predict(dummyFier,newdata = mydb)
mydb_dummy <- cbind(mydb %>% select(one_of(c(label_var, offset_var))),
dummyVars.df)
rm(myformula, dummyFier, dummyVars.df)
feature_vars_dummy <- mydb_dummy %>% select(-one_of(c(label_var, offset_var))) %>% colnames()
xgbMatrix <- xgb.DMatrix(
data = mydb_dummy %>% select(feature_vars_dummy) %>% as.matrix,
label = mydb_dummy %>% pull(label_var),
missing = "NAN")
#model 1: this does not
myParam <- list(max.depth = 2,
eta = .01,
gamma = 0.001,
objective = 'count:poisson',
eval_metric = "poisson-nloglik")
booster <- xgb.train(
params = myParam,
data = xgbMatrix,
nround = 50)
explainer <- lime(mydb_dummy %>% select(feature_vars_dummy),
model = booster)
explanation <- explain(mydb_dummy %>% select(feature_vars_dummy) %>% head,
explainer,
n_labels = 1,
n_features = 2)
#Error: Unsupported model type
#model 2 : this works
myParam2 <- list(max.depth = 2,
eta = .01,
gamma = 0.001,
objective = 'reg:logistic',
eval_metric = "logloss")
booster2 <- xgb.train(
params = myParam2,
data = xgbMatrix,
nround = 50)
explainer <- lime(mydb_dummy %>% select(feature_vars_dummy),
model = booster)
explanation <- explain(mydb_dummy %>% select(feature_vars_dummy) %>% head,
explainer,
n_features = 2)
plot_features(explanation)
来源:https://stackoverflow.com/questions/49280345/can-the-r-version-of-lime-explain-xgboost-models-with-countpoisson-objective-fu