Handling NAs

[belariow]

Hi there,

Lime currently does not seem to support NAs in data. Here's an example:

library(caret)
library(lime)

set.seed(123)

x = as.data.frame(matrix(rnorm(100*10), ncol=10))
x$V1 = ifelse(x$V2 > 0, NA, x$V1) # introduce random NAs in V1
y = round(runif(100))
y = as.factor(y)
levels(y) = c("no", "yes")
data = cbind(x, target = y)

fitControl <- trainControl(method = "repeatedcv",
                           number = 10,
                           repeats = 1,
                           allowParallel = TRUE,
                           classProbs = TRUE,
                           summaryFunction = twoClassSummary)

XGBModel = train(target ~ ., 
                 data = data,
                 trControl = fitControl, 
                 method = "xgbTree",
                 search = "random", 
                 metric = "ROC",
                 na.action = na.pass) # force XGB to take NAs into account

prediction = predict.train(XGBModel, data, na.action = na.pass, type = "prob") # works fine

explain = lime(data, XGBModel, bin_continuous = T, n_permutations = 1000) # error

# Error in quantile.default(x[[i]], seq(0, 1, length.out = n_bins + 1)) : 
#   missing values and NaN's not allowed if 'na.rm' is FALSE

explain = lime(na.omit(data), XGBModel, bin_continuous = T, n_permutations = 1000) # works fine

It would be great if NAs could be handled like XGB does.

Thanks guys for your work!

[thomasp85]

It seems to be an easy fix. Just to be clear, you don't want to put any emphasis on the distribution of NA in a feature? What I mean by this is that when doing the permutations of the data it draws from the distribution of the training data - this distribution should just be calculated while ignoring NA-values, right?

[thomasp85]

Fixed in 2d80643