individual_surrogate_model yields incorrect results because column names are mixed up

[FrieseWoudloper]

The function transform_to_interpretable returns a data frame with the wrong column names. I'm using the latest localModel version from Github (0.5).

Test case:

library(archivist)
library(DALEX)
library(DALEXtra)
library(randomForest)

titanic  <- archivist::aread("pbiecek/models/27e5c")
new_observation <- archivist::aread("pbiecek/models/e3596")
titanic_rf  <- archivist::aread("pbiecek/models/4e0fc")

x <- explain(model = titanic_rf, 
                    data = titanic[, -9],
                    y = titanic$survived == "yes", 
                    label = "Random Forest",
                    verbose = FALSE)
seed <- 1

I then run this code (taken from individual_surrogate_model function):

  x$data <- x$data[, intersect(colnames(x$data), colnames(new_observation)), drop = F]
  predicted_names <- assign_target_names(x)

  # Create interpretable features
  feature_representations_full <- get_feature_representations(x, new_observation,
                                                              predicted_names, seed)
  encoded_data <- transform_to_interpretable(x, new_observation,
                                             feature_representations_full)

Contents of encoded_data:

          class       gender      age                           sibsp    parch     fare
1 gender = male     baseline baseline embarked = Belfast, Southampton baseline baseline
2 gender = male age <= 15.36 baseline embarked = Belfast, Southampton baseline baseline
3 gender = male     baseline baseline embarked = Belfast, Southampton baseline baseline

     parch     fare embarked
1 baseline baseline baseline
2 baseline baseline baseline
3 baseline baseline baseline

The column names are not correct: class should be gender, gender should be age, etcetera.

I think this causes the counterintuitive LIME explanation for Johnny D's prediction from the random forest model found in section 9.6.2 of the book Explanatory Model Analysis:

http://ema.drwhy.ai/ema_files/figure-html/limeExplLocalModelTitanic-1.png

This problem is caused by a line of code in the function transform_to_interpretable:

transform_to_interpretable <- function(x, new_observation,
                                       feature_representations) {
  encoded_data <- as.data.frame(lapply(feature_representations,
                                       function(x) x[[1]]))
  colnames(encoded_data) <- intersect(colnames(new_observation),
                                       colnames(x$data))
  encoded_data
}

This solution works for my test case, but might not work for all cases:

transform_to_interpretable <- function(x, new_observation,
                                       feature_representations) {
  encoded_data <- as.data.frame(lapply(feature_representations,
                                       function(x) x[[1]]))
  colnames(encoded_data) <- colnames(x$data)
  encoded_data
}

in which case the new_observation argument is obsolete.

[FrieseWoudloper]

The problem arrises when the ordering of columns in the dataframe of the explainer containing the instances, is different from that in the dataframe containing the new observation.

[pbiecek]

Thank you for fixing this problem!