Error in check_explainer(explainer): For Python support, use precalculate=True in Explainer init

[vsteiger]

Error in check_explainer(explainer): For Python support, use precalculate=True in Explainer init

[vsteiger]

Hi @hbaniecki

explainer1$data[1, , drop=FALSE] runs smoothly and prints the first subject in the sample

For:

explainer1$predict_function(explainer1$model, explainer1$data)
explainer1$predict_function(explainer1$model, explainer1$data[1, ])
explainer1$predict_function(explainer1$model, explainer1$data[1, , drop=FALSE])

We get an error, that we have encountered in Python originally but solved it via formatting:

Error in py_call_impl(callable, dots$args, dots$keywords) :
IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

Regarding the example data due to company restrictions we are not allowed to share any of the data itself

[hbaniecki]

Hi @vsteiger,

1. Does the full example code work for you?
2. For your use-case:
   a) in Python: what is the verbose output of an Explainer?
   
   b) in Python: can you access explainer.y_hat and explainer.residuals?
   c) in R: can you access explainer$y_hat and explainer$residuals?
   

   modelStudio/R/modelStudio.R

   Lines 655 to 659 in 91da18c

   	if ("dalex._explainer.object.Explainer" %in% class(explainer)) {
   	if (is.null(explainer$y_hat) || is.null(explainer$residuals))
   	stop('For Python support, use precalculate=True in Explainer init')
   	class(explainer) <- c('explainer', class(explainer))
   	}

[vsteiger]

Hi @hbaniecki

We found the error in the explainer building step in python.
We've corrected this and now the verbose output in Python looks clean.

We continued in R:

explainer1 <- py_load_object("explainer_scikitlearn_precalculateTrue_v02.pickle", pickle = "pickle")

class(explainer1)

"explainer" "explainer" "explainer" "dalex._explainer.object.Explainer" "python.builtin.object"

In R we can access both explainer.y_hat and explainer.residuals and they are not NULL:

is.null(explainer1$residuals)
[1] FALSE

> is.null(explainer1$y_hat)
[1] FALSE

Now, when running modelStudio(explainer1, B = 5) on the explainer file, we got this error message:

new_observation argument is NULL. new_observation_n observations needed to calculate local explanations are taken from the data.

Error in modelStudio.explainer(explainer1, B = 5) :

explainer$predict_function returns an error when executed on new_observation[1,, drop = FALSE]

[hbaniecki]

@vsteiger great!

what do you get from explainer$data[1, , drop=FALSE] ?

explainer$predict_function(explainer$model, explainer$data)
explainer$predict_function(explainer$model, explainer$data[1, ])
explainer$predict_function(explainer$model, explainer$data[1, , drop=FALSE])

Just for context, I ran the example from documentation https://modelstudio.drwhy.ai/articles/ms-r-python-examples.html and it runs OK for me with the same software versions.

So maybe to solve this I would need to have exemplary data/model made by you where the error occurs.

[hbaniecki]

Yes, but it could be synthetically generated data from numpy working on a changed model in sklearn, just to reproduce the error.