In Boosting Assembler wrapping each estimator into a subroutine causes a performance degradation

I’ve recalled the real motivation behind not wrapping every individual estimator into its own subroutine - generation of many nested function calls leads to a performance degradation in Java. The observed difference reaches 4x for larger models (eg. XGBoost with 1000 estimators). The basic test I created (sorry about Scala):

@ import com.github.m2cgen.ModelOld
import com.github.m2cgen.ModelOld

@ import com.github.m2cgen.ModelNew
import com.github.m2cgen.ModelNew

@ def nextRandomData(): Array[Double] = (0 until 4).map(_ => Random.nextDouble).toArray
defined function nextRandomData

@ def testScore: Unit = {
    val start = System.currentTimeMillis()
    (0 until 100000).foreach(_ => <ModelNew|ModelOld>.score(nextRandomData))
    println("Runtime: " + (System.currentTimeMillis() - start).toString)
  }

Results for ModelOld:

@ testScore
Runtime: 2973

For ModelNew:

@ testScore
Runtime: 10747

The test model has been trained using the sklearn.datasets.load_iris() dataset. Classifier has been created as following:

model = XGBClassifier(n_estimators=1000)

In the attached archive I included the following:

1. ModelNew.java - java code generated with the most recent master.
2. ModelOld.java - java code generated with the release 0.5.0 version.
3. Models.jar - the jar containing both compiled sources.
4. xgboost_model2 - the trained estimator in Pickle format.

CC: @StrikerRUS FYI

[StrikerRUS]

Can we close this?

[izeigerman]

Yes, absolutely. Thank you!