Error running multinomial regression on a dataset with a field having constant value

[kundan kumar]

I am running the sample multinomial regression code given in spark docs
(Version 2.2.0)


LogisticRegression lr = new
LogisticRegression().setMaxIter(100).setRegParam(0.3).setElasticNetParam(0.8);
LogisticRegressionModel lrModel = lr.fit(training);

But in the dataset I am adding a constant field where all the values are
same.

Now, I get an error saying

2018-03-11 15:42:58,835 [main] ERROR OWLQN  - Failure! Resetting history:
breeze.optimize.NaNHistory:
2018-03-11 15:42:58,922 [main] INFO  OWLQN  - Step Size: 1.000
2018-03-11 15:42:58,938 [main] INFO  OWLQN  - Val and Grad Norm: NaN (rel:
NaN) NaN
2018-03-11 15:42:58,940 [main] INFO  OWLQN  - Converged because max
iterations reached


Without the constant field in the dataset everything works fine.

Please help me understand what is the reason behind this error. When I run
a binary logistic regression code it runs fine even if there are constant
values in a field.

Do I really need to get rod of constant field from my dataset while running
multinomial regression.

Is it a bug or this is expected ??


Thanks !!
Kundan