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Posted to issues@spark.apache.org by "Andrew Crosby (JIRA)" <ji...@apache.org> on 2018/10/01 16:18:00 UTC
[jira] [Commented] (SPARK-23537) Logistic Regression without
standardization
[ https://issues.apache.org/jira/browse/SPARK-23537?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16634263#comment-16634263 ]
Andrew Crosby commented on SPARK-23537:
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The different results for standardization=True vs standardization=False are to be expected. The resason for this difference is that the two settings lead to different effective regularization strengths. With standardization=True, the regularization is applied to the scaled model coefficients. Whereas, with standardization=False, the regularization is applied to the unscaled model coefficients.
As it's implemented in Spark, the features actually get scaled regardless of whether standardization is set to true or false, but when standardization=False the strength of the regularization in the scaled space is altered to account for this. See the comment at [https://github.com/apache/spark/blob/a802c69b130b69a35b372ffe1b01289577f6fafb/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala#L685.]
As an aside, your results show a very slow rate of convergence when standardization is set to false. I believe this to be an issue caused by the continued application of feature scaling when standardization=False which can lead to very large gradients from the regularization terms in the solver. I've recently raised SPARK-25544 to cover this issue.
> 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: 2.0.2, 2.2.1
> Reporter: Jordi
> Priority: Major
> Attachments: non-standardization.log, standardization.log
>
>
> 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, using the hashing trick (2^20 sparse vector).
> 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:java}
> 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 significantly 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:java}
> 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:java}
> 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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