Fastshap summary plot - Error: can't combine <double> and <factor<919a3>>

Question

I'm trying to get a summary plot using fastshap explain function as in the code below.

p_function_G<- function(object, newdata)
  caret::predict.train(object,
                       newdata = 
                       newdata,
                       type = "prob")[,"AntiSocial"] # select G class




# Calculate the Shapley values
#
# boostFit: is a caret model using catboost algorithm
# trainset: is the dataset used for bulding the caret model.
#   The dataset contains 4 categories W,G,R,GM
#    corresponding to 4 diferent animal  behaviors
library(caret)

shap_values_G <- fastshap::explain(xgb_fit,
                  X = game_train,                   
                  pred_wrapper = 
                  p_function_G,
                  nsim = 50,
                  newdata= game_train[which(game_test=="AntiSocial"),])
) 

However I'm getting error

Error in 'stop_vctrs()': can't combine latitude and gender <factor<919a3>>

What's the way out?

Answer 1

I see that you are adapting code from Julia Silge's Predict ratings for board games Tutorial. The original code used SHAPforxgboost for generating SHAP values, but you're using the fastshap package.

Because Shapley explanations are only recently starting to gain traction, there aren't very many standard data formats. fastshap does not like tidyverse tibbles, it only takes matrices or matrix-likes.

The error occurs because, by default, fastshap attempts to convert the tibble to a matrix. But this fails, because matrices can only have one type (f.x. either double or factor, not both).

I also ran into a similar issue and found that you can solve this by passing the X parameter as a data.frame. I don't have access to your full code but you could you try replacing the shap_values_G code-block as so:

shap_values_G <- fastshap::explain(xgb_fit,
                  X = game_train,                   
                  pred_wrapper = 
                  p_function_G,
                  nsim = 50,
                  newdata= as.data.frame(game_train[which(game_test=="AntiSocial"),]))
) 

Wrap newdata with as.data.frame. This converts the tibble to a dataframe and so shouldn't upset fastshap.