Fairmodels produce issue for output variable as protected variable.

[Nehagupta90]

Good day everyone

I am using the output variable of my data (isKilled= yes/no) as a protected variable and “yes” as a priviliged value. But every time I run the code, I just get a few metrics calculated and get the output as shown at the end.

Metric calculation : 2/12 metrics calculated for all models ( 10 NA created )

I used the following code, so could you please suggest where is the problem :

data = readARFF("apns.arff") na.omit(data)

index= sample(1:nrow(data), 0.7*nrow(data)) train= data[index,] test= data[-index,]

task = TaskClassif$new("data", backend = train, target = "isKilled")

learner= lrn("classif.randomForest", predict_type = "prob")

model= learner$train(task ) explainer = explain_mlr3(model, data = test[,-15], y = as.numeric(test$isKilled)-1, label="RF")

prot <- ifelse(test$isKilled == 'no', 1, 0) privileged <- '1' privileged %in% as.factor(prot)

fc= fairness_check(explainer, protected = prot, privileged = privileged)

plot(fc) msfc <- metric_scores(fc) plot(msfc) msfc$metric_scores_data

Output are as below:

msfc$metric_scores_data score subgroup metric model 1 NA 0 TPR RF 2 0.90967742 1 TPR RF 5 0.00000000 0 PPV RF 6 1.00000000 1 PPV RF 11 0.06214689 0 FPR RF 12 NA 1 FPR RF 19 0.06214689 0 STP RF 20 0.90967742 1 STP RF 21 0.93785311 0 ACC RF 22 0.90967742 1 ACC RF

[jakwisn]

Probably division by zero occurs, therefore you get NA. In order to correctly compute metrics, the confusion matrix has to be somewhat populated. Additionally, if there is a metric with a score of zero in the metric scores plot, it won't be shown in the fairness check it produces NAN to not show false information.

[Nehagupta90]

Thank you jakub.

So what would be the solution? How can I avoid the divide by zero problem?

Thanks

On Monday, January 24, 2022, Jakub Wiśniewski @.***> wrote:

Probably division by zero occurs, therefore you get NA. In order to correctly compute metrics, the confusion matrix has to be somewhat populated. Additionally, if there is a metric with a score of zero in the metric scores plot, it won't be shown in the fairness check it produces NAN to not show false information.

— Reply to this email directly, view it on GitHub https://github.com/ModelOriented/fairmodels/issues/45#issuecomment-1020647620, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN2ZZ2OOX2BGB7CAOT4NDZTUXXM3NANCNFSM5MVTR4GQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

You are receiving this because you authored the thread.Message ID: @.***>

[jakwisn]

To get metrics without NA the predictor must make some mistakes. In case of this one it seems that in some metrics (like PPV) there are no true positives in one subgroup and no false positives in other. The fact that you use y as protected might further deepen the problem. Can you show what you get when you run fc$groups_confusion_matrices?

[Nehagupta90]

Thank you Jakub

If I run this, I get the following:

fc$groups_confusion_matrices $RF $0 NULL

$1 NULL

attr(,"class") [1] "group_matrices"

On Tue, Jan 25, 2022 at 10:55 AM Jakub Wiśniewski @.***> wrote:

To get metrics without NA the predictor must make some mistakes. In case of this one it seems that in some metrics (like PPV) there are no true positives in one subgroup and no false positives in other. The fact that you use y as protected might further deepen the problem. Can you show what you get when you run fc$groups_confusion_matrices?

— Reply to this email directly, view it on GitHub https://github.com/ModelOriented/fairmodels/issues/45#issuecomment-1021004058, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN2ZZ2PFGEL2VV7CDQX6GVDUXZXPZANCNFSM5MVTR4GQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

You are receiving this because you authored the thread.Message ID: @.***>

[Nehagupta90]

I think the problem occur when we use the output variable as a protected variable. I used another dataset with output variable (Y/N values) and it gives the same problem.

msfc$metric_scores_data score subgroup metric model 1 NA 0 TPR RF 2 0.8421053 1 TPR RF 5 0.0000000 0 PPV RF 6 1.0000000 1 PPV RF 11 0.1055901 0 FPR RF 12 NA 1 FPR RF 19 0.1055901 0 STP RF 20 0.8421053 1 STP RF 21 0.8944099 0 ACC RF 22 0.8421053 1 ACC RF

On Tue, Jan 25, 2022 at 3:28 PM Neha gupta @.***> wrote:

Thank you Jakub

If I run this, I get the following:

fc$groups_confusion_matrices $RF $0 NULL

$1 NULL

attr(,"class") [1] "group_matrices"

On Tue, Jan 25, 2022 at 10:55 AM Jakub Wiśniewski < @.***> wrote:

To get metrics without NA the predictor must make some mistakes. In case of this one it seems that in some metrics (like PPV) there are no true positives in one subgroup and no false positives in other. The fact that you use y as protected might further deepen the problem. Can you show what you get when you run fc$groups_confusion_matrices?

— Reply to this email directly, view it on GitHub https://github.com/ModelOriented/fairmodels/issues/45#issuecomment-1021004058, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN2ZZ2PFGEL2VV7CDQX6GVDUXZXPZANCNFSM5MVTR4GQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

You are receiving this because you authored the thread.Message ID: @.***>

[jakwisn]

Yes, this is surely it, but I am surprised that you did not got values for confusion matrices. This is unexpected behaviour and it might be a bug. Is it possible to link the dataset? Could you also list the packages that you used, and please provide version of fairmodels and DALEX. Thanks for bringing it up

[Nehagupta90]

Thanks Jakub again.

I attached the dataset.

I am using the following libraries:

library(farff) library(DALEX) version ‘2.2.1’ library(DALEXtra) library(fairmodels) version ‘1.1.0’ library(mlr3) version ‘0.11.0’

On Tue, Jan 25, 2022 at 10:06 PM Jakub Wiśniewski @.***> wrote:

Yes, this is surely it, but I am surprised that you did not got values for confusion matrices. This is unexpected behaviour and it might be a bug. Is it possible to link the dataset, and packages that you used, and please provide version of fairmodels and DALEX. Thanks

— Reply to this email directly, view it on GitHub https://github.com/ModelOriented/fairmodels/issues/45#issuecomment-1021608622, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN2ZZ2PFGHOTWIHJ6C2QOKTUX4GGDANCNFSM5MVTR4GQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

You are receiving this because you authored the thread.Message ID: @.***>

[jakwisn]

Hi, can't see the dataset, can you somehow link it? Thanks