marcelrobeer / contrastiveexplanation Goto Github PK

Does this package support Xgboost?

Does the model supports categorical feature types for lgbm?
I got an error when running with specified categorical features.

I wanted to ask if you considered to create a DOI for the Contrastive Explanation. This allows researchers to reference a version of the package with ease and can be done with Zenodo for example. There is also great integration between Zenodo, which creates a new DOI and a persistent copy of the repository for each release, and Github. You can find instructions on how to create a DOI here from official Github docs: https://docs.github.com/en/repositories/archiving-a-github-repository/referencing-and-citing-content
This step helps with visibility of this repository and therefore making your research software more used.

On a similar note, it also helps researchers to know how to correctly cite the software. I see that you already added this in the readme but Github also offers the citation file format which also shows up on the top right if used:

You can find more information about it here: https://citation-file-format.github.io/

I would be happy to help with this if you have any questions.

Hi, I was wondering if the Clustering algorithm is supported or not. I see you mentioned it in the README but looking at the code, I can't find it anywhere.
Thanks :)

Hi,

When I run the exp.explain_instance_domain(model.predict_proba, sample) , I get different output everytime.
After every run, a different column is given as output.

Is there a way I can get constant results?

Hi

I have been trying your approach for a regression problem with categorical features. I receive an explanation in form:

The model predicted 123 because sales < 1445 and not month ( dummy example)

month is a categorical variable with values "1", "2", .. "12".

What does it mean " and not month" then ?

Thank you

I am trying to exactly recreate the example from the README for the Iris-dataset. Unforunately, when running .explain_instance_domain(model.predict_proba, sample) I get the following output:

[F] Picked foil "1" using foil selection strategy "second"
[D] Obtaining neighborhood data
C:\Users\dsemkoandrosenko\contrastive_explanation\contrastive_explanation.py:264: UserWarning: Could not find a difference between fact "setosa" and foil "versicolor"
warnings.warn(f'Could not find a difference between fact '
"The model predicted 'setosa' instead of 'versicolor' because ''"

I get the same issue with every single other sample, and even every other dataset I try. What could be the issue?

Versions:
Windows 10
Python: 3.7.4
Scikit-Learn: 0.21.3
Numpy: 1.18.2

Hi, thank you for the great package. I would like to know is there a way to specify which features to change? For example I would like to see what I need to change only for specific features?

Thank you

Running the example notebook (1.3a block) I get the following error:

ValueError: blocks must be 2-D

Any idea how to fix it?

Does the current implementation support only binary-valued categorical features?

Because I tried with the adult income dataset which has many multi-value categorical and continuous features (https://archive.ics.uci.edu/ml/datasets/adult)
and got output like these:

"The model predicted '<=50k' instead of '>50k' because 'hours_per_week <= 42.832 and not occupation and age <= 34.95 and not education and hours_per_week <= 57.892'"

Here, education and occupation are not binary features - they have many levels.

This is a great package. Do you plan to support Tensorflow or Pytorch in the future?

running the example notebook (Contrastive explanation - example usage), the line exp.explain_instance_domain(model.predict_proba, sample)
gives the following error: