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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