Calculating log-loss for the trained model in Spark ML

[Abhishek Anand <ab...@gmail.com>]

I am building a ML pipeline for logistic regression.

val lr = new LogisticRegression()

lr.setMaxIter(100).setRegParam(0.001)

val pipeline = new
Pipeline().setStages(Array(geoDimEncoder,clientTypeEncoder,
               devTypeDimIdEncoder,pubClientIdEncoder,tmpltIdEncoder,
               hourEncoder,assembler,lr))

val model = pipeline.fit(trainingDF)

Now, when the model is trained, I want to see the value
the probabilities for the training set and compute certain
validation parameters like log-loss. But, I am unable to find
this using "model".

The only thing I could find is

model.transform(testDF).select(....)

Cannot I get the metrics using the trained set for training set validation ?

Thanks !!