[jira] [Created] (SPARK-23537) Logistic Regression without standardization

["Jordi (JIRA)" <ji...@apache.org>]

Jordi created SPARK-23537:
-----------------------------

             Summary: Logistic Regression without standardization
                 Key: SPARK-23537
                 URL: https://issues.apache.org/jira/browse/SPARK-23537
             Project: Spark
          Issue Type: Bug
          Components: ML, Optimizer
    Affects Versions: 2.2.1
            Reporter: Jordi


I'm trying to train a Logistic Regression model, using Spark 2.2.1. I prefer to not use standardization since all my features are binary.

I trained two models to compare results, I've been expecting to end with two similar models since it seems that internally the optimizer performs standardization and "de-standardization" (when it's deactivated) in order to improve the convergence.

Here you have the code I used:
{code}
val lr = new org.apache.spark.ml.classification.LogisticRegression()
        .setRegParam(0.05)
        .setElasticNetParam(0.0)
        .setFitIntercept(true)
        .setMaxIter(5000)
        .setStandardization(false)

val model = lr.fit(data)
{code}
The results are disturbing me, I end with two quite different models.

Standardization:

Training time: 8min.
Iterations: 37
Intercept: -4.386090107224499
Max weight: 4.724752299455218
Min weight: -3.560570478164854
Mean weight: -0.049325201841722795
l1 norm: 116710.39522171849
l2 norm: 402.2581552373957
Non zero weights: 128084
Non zero ratio: 0.12215042114257812

Last 10 LBFGS Val and Grand Norms:
{code}
18/02/27 17:14:45 INFO LBFGS: Val and Grad Norm: 0.430740 (rel: 8.00e-07) 0.000559057
18/02/27 17:14:50 INFO LBFGS: Val and Grad Norm: 0.430740 (rel: 3.94e-07) 0.000267527
18/02/27 17:14:54 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 2.62e-07) 0.000205888
18/02/27 17:14:59 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 1.36e-07) 0.000144173
18/02/27 17:15:04 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 7.74e-08) 0.000140296
18/02/27 17:15:09 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 1.52e-08) 0.000122709
18/02/27 17:15:13 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 1.78e-08) 3.08789e-05
18/02/27 17:15:18 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 2.66e-09) 2.23806e-05
18/02/27 17:15:23 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 4.31e-09) 1.47422e-05
18/02/27 17:15:28 INFO LBFGS: Val and Grad Norm: 0.430739 (rel: 9.17e-10) 2.37442e-05
{code}
No standardization:

Training time: 7h 14 min.
Iterations: 4992
Intercept: -4.216690468849263
Max weight: 0.41930559767624725
Min weight: -0.5949182537565524
Mean weight: -1.2659769019012E-6
l1 norm: 14.262025330648694
l2 norm: 1.2508777025612263
Non zero weights: 128955
Non zero ratio: 0.12298107147216797

Last 10 LBFGS Val and Grand Norms:
{code}
18/02/28 00:28:56 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 2.17e-07) 0.217581
18/02/28 00:29:01 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.88e-07) 0.185812
18/02/28 00:29:06 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.33e-07) 0.214570
18/02/28 00:29:11 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 8.62e-08) 0.489464
18/02/28 00:29:16 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.90e-07) 0.178448
18/02/28 00:29:21 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 7.91e-08) 0.172527
18/02/28 00:29:26 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.38e-07) 0.189389
18/02/28 00:29:31 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.13e-07) 0.480678
18/02/28 00:29:36 INFO LBFGS: Val and Grad Norm: 0.559320 (rel: 1.75e-07) 0.184529
18/02/28 00:29:41 INFO LBFGS: Val and Grad Norm: 0.559319 (rel: 8.90e-08) 0.154329
{code}

Am I missing something?



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