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From sro...@apache.org
Subject spark git commit: [SPARK-6496] [MLLIB] GeneralizedLinearAlgorithm.run(input, initialWeights) should initialize numFeatures
Date Wed, 25 Mar 2015 17:06:10 GMT
Repository: spark
Updated Branches:
  refs/heads/branch-1.3 8e4e2e3f8 -> 2be4255a0


[SPARK-6496] [MLLIB] GeneralizedLinearAlgorithm.run(input, initialWeights) should initialize
numFeatures

In GeneralizedLinearAlgorithm ```numFeatures``` is default to -1, we need to update it to
correct value when we call run() to train a model.
```LogisticRegressionWithLBFGS.run(input)``` works well, but when we call ```LogisticRegressionWithLBFGS.run(input,
initialWeights)``` to train multiclass classification model, it will throw exception due to
the numFeatures is not updated.
In this PR, we just update numFeatures at the beginning of GeneralizedLinearAlgorithm.run(input,
initialWeights) and add test case.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #5167 from yanboliang/spark-6496 and squashes the following commits:

8131c48 [Yanbo Liang] LogisticRegressionWithLBFGS.run(input, initialWeights) should initialize
numFeatures

(cherry picked from commit 10c78607b2724f5a64b0cdb966e9c5805f23919b)
Signed-off-by: Sean Owen <sowen@cloudera.com>


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2be4255a
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2be4255a
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2be4255a

Branch: refs/heads/branch-1.3
Commit: 2be4255a05e7a1548f51b02f6bf62507f1c3414b
Parents: 8e4e2e3
Author: Yanbo Liang <ybliang8@gmail.com>
Authored: Wed Mar 25 17:05:56 2015 +0000
Committer: Sean Owen <sowen@cloudera.com>
Committed: Wed Mar 25 17:06:04 2015 +0000

----------------------------------------------------------------------
 .../spark/mllib/regression/GeneralizedLinearAlgorithm.scala    | 4 ++++
 .../spark/mllib/classification/LogisticRegressionSuite.scala   | 6 ++++++
 2 files changed, 10 insertions(+)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/2be4255a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
----------------------------------------------------------------------
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
index 7c66e8c..9a2751a 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala
@@ -196,6 +196,10 @@ abstract class GeneralizedLinearAlgorithm[M <: GeneralizedLinearModel]
    */
   def run(input: RDD[LabeledPoint], initialWeights: Vector): M = {
 
+    if (numFeatures < 0) {
+      numFeatures = input.map(_.features.size).first()
+    }
+
     if (input.getStorageLevel == StorageLevel.NONE) {
       logWarning("The input data is not directly cached, which may hurt performance if its"
         + " parent RDDs are also uncached.")

http://git-wip-us.apache.org/repos/asf/spark/blob/2be4255a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
----------------------------------------------------------------------
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
index aaa81da..a26c528 100644
--- a/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -425,6 +425,12 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext
with M
 
     val model = lr.run(testRDD)
 
+    val numFeatures = testRDD.map(_.features.size).first()
+    val initialWeights = Vectors.dense(new Array[Double]((numFeatures + 1) * 2))
+    val model2 = lr.run(testRDD, initialWeights)
+
+    LogisticRegressionSuite.checkModelsEqual(model, model2)
+
     /**
      * The following is the instruction to reproduce the model using R's glmnet package.
      *


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