Test frame as tibble - wrong calculations for variable importance and confusing warning messages

[kpmakowski]

If you do have test frame as tibble (easy to get when using tidyverse)

For calculation of variable importance you get the same values for full_model and all variables except baseline, which is obviously wrong.
variable dropout_loss label
1 full_model 284.9159 lm
2 construction.year 284.9159 lm
3 surface 284.9159 lm
4 floor 284.9159 lm
5 no.rooms 284.9159 lm
6 district 284.9159 lm
7 baseline 1261.6643 lm

For single_variable calculations you get following warning (only), however output is of limited value.
Warning message:
In if (class(explainer$data[, variable]) == "factor" & type != "factor") { :
the condition has length > 1 and only the first element will be used

Casting tibble to regular data.frame solves the issue. Having training data as tibble seems not to have an impact on calculations at all.

`apartmentsTest_tibble <- apartmentsTest %>% as_tibble()

model_liniowy <- lm(m2.price ~ construction.year + surface + floor + no.rooms + district, data = apartments)

explainer_lm <- explain(model_liniowy, data = apartmentsTest_tibble[,2:6], y = apartmentsTest_tibble$m2.price)

vi_lm <- variable_importance(explainer_lm, loss_function = loss_root_mean_square)
vi_lm

sv_lm <- single_variable(explainer_lm, variable = "construction.year", type = "pdp")`

[pbiecek]

Thanks for pointing this out, will fix soon

[pbiecek]

fixed in 59e4295