Using lime() on xgboost object

[martinju]

Hi, and thank you for an excellent package!

I am trying to apply the lime package to a model fitted with xgboost (using the original xgboost package), but the lime function does not seem to accept the input format even if the predict function works fine.

Example using both xgbDmatrix and a regular matrix

x = matrix(rnorm(100*10),ncol=10)
y = rnorm(100)

xgbDMatrix.obj<- xgb.DMatrix(data=x,
label = y)

mod = xgb.train(data = xgbDMatrix.obj,nrounds = 100) # Variant 1, using xgbDMatrix format of data input
#predict(mod,x,type="prob") # works fine
explain <- lime(x=x,model=mod) # Throws error

mod = xgboost(data = x,label = y,nrounds = 100) # Variant 2 using a regular matrix + vector as data input
#predict(mod,x,type="prob") # works fine
explain <- lime(x=x,model=mod) # Throws error

In the readme you mention manually building a predict function.
If that is the solution here, could you please provide some guidelines on how to do that?

[thomasp85]

Thanks for the report – I'll take a look. Keep in mind that lime is still in early development and any error could potentially be a bug on my part and not due to anything you have done. I'll be in touch

[thomasp85]

Two things: currently lime only works with classifiers not regressors. Also, on my system I get an error when running predict, complaining about type being an unknown argument

[martinju]

Thanks for taking a look at this!

1. OK, let's do a classification example instead:
   x = matrix(rnorm(100*10),ncol=10)
   y = round(runif(100))

xgbDMatrix.obj<- xgb.DMatrix(data=x,label = y)
mod = xgb.train(data = xgbDMatrix.obj,nrounds = 100,objective = "binary:logistic") # Variant 1, using xgbDMatrix format of data input
predict(mod,xgbDMatrix.obj,type="prob") # works fine
explain <- lime(x=xgbDMatrix.obj,model=mod) # Throws error
predict(mod,x,type="prob") # works fine
explain <- lime(x=x.obj,model=mod) # Throws error

mod = xgboost(data = x,label = y,nrounds = 100,objective = "binary:logistic") # Variant 2 using a regular matrix + vector as data input
predict(mod,x,type="prob") # works fine
explain <- lime(x=x,model=mod) # Throws error

2. I do not get an error when using predict with type="prob" (I am using xgboost 0.6-4). Anyhow, the predict function gives probabilities by default for classification problems, so doing leaving out type="prob" gives the same on my computer.

[thomasp85]

Sorry for letting this hang - I was pulled away by higher priority stuff.

Several problems in your example:

lime takes data.frame input in order to ensure that variables are named. I might add a default coercion to data.frame which would make matrices work, but still unsure whether this is a good idea.

The predict function does not return a data.frame containing the probabilities of each class, which is the required predict output...

I would generally recommend using xgboost through the caret package as this is the target API lime is coded up against (soon to be joined be mlr)

[martinju]

Thank you for getting back to me.

So I tried running this from caret instead, and I got something up and running. However, when moving to real data I ran into some problems:

1: it seems that lime(x=x,...) cannot handle when x contains missing data. It throws the 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

A workaround is to remove the missing data before passing it lime, but as there is indeed information in the missing data which was used during model training, that is not an optimal solution.

2: even without missing data, I get an error when using bin_continuous=T:

Error in cut.default(x[[i]], unique(bin_cuts[[i]]), labels = FALSE, include.lowest = TRUE) :
invalid number of intervals

Everything works fine if if put bin_continuous=F with no missing data.

3: When re-runinng the explain-function which is the output from lime(), I get slightly different results every time. I guess this is due to the randomness in the permutations. Is it possible to somewhere control the number of permutations used? I guess that could resolve that issue with the expense of an increase in the running time.

4: Finally if you could point out how I might create my own prediction based on native xgboost, that would be excellent (the caret wrapper does not support all of xgboost's features). Do I need to overwrite the prediction function of the xgboost class?

Thank you so much for your support so far!

[thomasp85]

1. I'm unsure how binning should work with missing values, so for now I'm ok with that failing (should prob have a better error message though)
2. You may have stumbled upon a bug - can you make a minimal example?
3. The permutations are random which is why you get different results. To "control" randomness you simply set the seed in R prior to running the explanation (e.g. set.seed(1))
4. The only requirements for a model to be lime compliant is that the input data is a data.frame and that the model has a predict method that outputs a data frame of class probabilities (one class per column) when called with type='prob'

[martinju]

1. OK. Maybe you could let the NA's become a separate category when binning? Or you may simply remove them when binning, although that throws away some information. That is anyway better than having to delete all rows including at least one NA-value. In an extreme situation that may throw away all your data even if you can easily fit a model to those data.
2. I just realized this error was caused by including an intercept the model. Here is an example:

library(lime)
library(caret)

set.seed(123)
x = as.data.frame(matrix(rnorm(100*10),ncol=10))
x = cbind(V0=1,x,V11=sample(c(0,1),size = 10,replace = T),V12=sample(c(0,1),size = 10,replace = T))
y = round(runif(100))
y.factor <- as.factor(y)
levels(y.factor) <- c("no","yes")

fitControl <- trainControl(method="none",
verboseIter = TRUE,
classProbs = TRUE,
allowParallel = TRUE)

caretFit.glm <- train(y=y.factor,
x=x,
method = "glm",
trControl = fitControl)

explain <- lime(x=x,model=caretFit.glm,bin_continuous = T) # Throws error

3. Yes, I understand this. I would like to be able to manually set how many permutations/samples are done as part of the algorithm (more samples means more stable results across different seeds).
4. Thank you, I will try to overwrite xgboost's prediction function then.

[thomasp85]

1. That might be a possible approach - I'll think about it a bit
2. Thanks
3. You always have control over the number of permutations using the n_permtations argument in the explainer. And you can control randomness by setting the seed. Unsure what else you need?
4. Let me know if there are any problems

[martinju]

Thank you! Regarding 3 I wasn't aware of the n_permutations parameter. That is exactly what I was looking for :) What is the default value?

[thomasp85]

5000

I'll try to improve the docs on this in the future. In general R does not have good support for documenting functions created by other functions...

[martinju]

Thanks!

[negyn]

Hi, thank you for LIME package, it is so interesting for me.
I have some questions, it seems to me that the current version of LIME package in R doesn't support the text and image data, is it right? so, except random forest what other kinds of classifier does it support?

[pommedeterresautee]

@negyn #6