How to get best params after tuning by pyspark.ml.tuning.TrainValidationSplit?

Question

I\'m trying to tune the hyper-parameters of a Spark (PySpark) ALS model by TrainValidationSplit.

It works well, but I want to know which co

Answer 1

You can access best model using bestModel property of the TrainValidationSplitModel:

best_model = model.bestModel

Rank can be accessed directly using rank property of the ALSModel:

best_model.rank

10

Getting maximum number of iterations requires a bit more trickery:

(best_model
    ._java_obj     # Get Java object
    .parent()      # Get parent (ALS estimator)
    .getMaxIter()) # Get maxIter

10