[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[asfgit]

Github user asfgit closed the pull request at:

https://github.com/apache/spark/pull/11179


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-187464865
  
Merged into master. Thanks!


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185644709
  
LGTM, thanks @NarineK 
ping @mengxr Could you take a look when you have time? 
[SPARK-13322](https://issues.apache.org/jira/browse/SPARK-13322) depends on 
this PR.   


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r53295432
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -437,23 +437,25 @@ object AFTSurvivalRegressionModel extends 
MLReadable[AFTSurvivalRegressionModel]
 private class AFTAggregator(parameters: BDV[Double], fitIntercept: Boolean)
   extends Serializable {
 
-  // beta is the intercept and regression coefficients to the covariates
-  private val beta = parameters.slice(1, parameters.length)
+  // the regression coefficients to the covariates
+  private val coefficients = parameters.slice(2, parameters.length)
+  private val intercept = parameters.valueAt(1)
   // sigma is the scale parameter of the AFT model
   private val sigma = math.exp(parameters(0))
 
   private var totalCnt: Long = 0L
   private var lossSum = 0.0
-  private var gradientBetaSum = BDV.zeros[Double](beta.length)
+  private var gradientCoefficientSum = 
BDV.zeros[Double](coefficients.length)
+  private var gradientInterceptSum = 0.0
   private var gradientLogSigmaSum = 0.0
 
   def count: Long = totalCnt
 
   def loss: Double = if (totalCnt == 0) 1.0 else lossSum / totalCnt
 
-  // Here we optimize loss function over beta and log(sigma)
+  // Here we optimize loss function over coefficients, intercept and 
log(sigma)
   def gradient: BDV[Double] = BDV.vertcat(BDV(Array(gradientLogSigmaSum / 
totalCnt.toDouble)),
-gradientBetaSum/totalCnt.toDouble)
+BDV(Array(gradientInterceptSum/totalCnt.toDouble)), 
gradientCoefficientSum/totalCnt.toDouble)
--- End diff --

Vector ```vertcat``` may be time-consuming, we can fix this issue use 
pre-assigned array. But it also involves other changes which are beyond the 
scope of this PR. I will fix this when solving 
[SPARK-13322](https://issues.apache.org/jira/browse/SPARK-13322), this PR is 
ready to merge, thanks.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[AmplabJenkins]

Github user AmplabJenkins commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185628760
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[AmplabJenkins]

Github user AmplabJenkins commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185628757
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185628462
  
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 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185605905
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[NarineK]

Github user NarineK commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185599578
  
Fixed the comment, thnx @yanboliang 


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-185090545
  
LGTM except minor issue, thanks!


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r53131871
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -437,23 +437,25 @@ object AFTSurvivalRegressionModel extends 
MLReadable[AFTSurvivalRegressionModel]
 private class AFTAggregator(parameters: BDV[Double], fitIntercept: Boolean)
   extends Serializable {
 
-  // beta is the intercept and regression coefficients to the covariates
-  private val beta = parameters.slice(1, parameters.length)
+  // the regression coefficients to the covariates
+  private val coefficients = parameters.slice(2, parameters.length)
+  private val intercept = parameters.valueAt(1)
   // sigma is the scale parameter of the AFT model
   private val sigma = math.exp(parameters(0))
 
   private var totalCnt: Long = 0L
   private var lossSum = 0.0
-  private var gradientBetaSum = BDV.zeros[Double](beta.length)
+  private var gradientCoefficientSum = 
BDV.zeros[Double](coefficients.length)
+  private var gradientInterceptSum = 0.0
   private var gradientLogSigmaSum = 0.0
 
   def count: Long = totalCnt
 
   def loss: Double = if (totalCnt == 0) 1.0 else lossSum / totalCnt
 
-  // Here we optimize loss function over beta and log(sigma)
+  // Here we optimize loss function over coefficients and log(sigma)
--- End diff --

```beta``` means ```coefficients and intercept```, so here should be 
```coefficients, intercept and log(sigma)``` in the annotation.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[NarineK]

Github user NarineK commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-184524951
  
Thank you for the review comments, @yanboliang 
I've added your suggestions. Let me know if you have more comments.



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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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https://github.com/apache/spark/pull/11179#issuecomment-184515193
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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https://github.com/apache/spark/pull/11179#issuecomment-184515192
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-184515101
  
**[Test build #51339 has 
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 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

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https://github.com/apache/spark/pull/11179#issuecomment-184508061
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-184112663
  
@NarineK Thanks for working on this issue, it looks good overall and I left 
some inline comments.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r52871118
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -481,8 +481,10 @@ private class AFTAggregator(parameters: BDV[Double], 
fitIntercept: Boolean)
 assert(!lossSum.isInfinity,
   s"AFTAggregator loss sum is infinity. Error for unknown reason.")
 
-gradientBetaSum += xi * (delta - math.exp(epsilon)) / sigma
-gradientLogSigmaSum += delta + (delta - math.exp(epsilon)) * epsilon
+val delta_expeps = delta - math.exp(epsilon)
--- End diff --

```delta_expeps``` -> ```deltaMinusExpEps```, we use the camel case 
convention.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r52870569
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -464,15 +467,12 @@ private class AFTAggregator(parameters: BDV[Double], 
fitIntercept: Boolean)
*/
   def add(data: AFTPoint): this.type = {
 
-// TODO: Don't create a new xi vector each time.
-val xi = if (fitIntercept) {
-  Vectors.dense(Array(1.0) ++ data.features.toArray).toBreeze
-} else {
-  Vectors.dense(Array(0.0) ++ data.features.toArray).toBreeze
-}
+val fitInterceptFlag = if (fitIntercept) 1.0 else 0.0
--- End diff --

```fitInterceptFlag``` -> ```interceptFlag```. ```fitInterceptFlag``` is 
more like a name of boolean variable.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r52870230
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -453,7 +455,8 @@ private class AFTAggregator(parameters: BDV[Double], 
fitIntercept: Boolean)
 
   // Here we optimize loss function over beta and log(sigma)
   def gradient: BDV[Double] = BDV.vertcat(BDV(Array(gradientLogSigmaSum / 
totalCnt.toDouble)),
-gradientBetaSum/totalCnt.toDouble)
+BDV.vertcat(BDV(Array(gradientInterceptSum/totalCnt.toDouble)),
+gradientBetaSum/totalCnt.toDouble))
--- End diff --

Use ```BDV.vertcat(BDV(Array(gradientLogSigmaSum / totalCnt.toDouble)), 
BDV(Array(gradientInterceptSum/totalCnt.toDouble)), 
gradientBetaSum/totalCnt.toDouble)``` to combine three parts directly.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[yanboliang]

Github user yanboliang commented on a diff in the pull request:

https://github.com/apache/spark/pull/11179#discussion_r52869386
  
--- Diff: 
mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala 
---
@@ -438,13 +438,15 @@ private class AFTAggregator(parameters: BDV[Double], 
fitIntercept: Boolean)
   extends Serializable {
 
   // beta is the intercept and regression coefficients to the covariates
--- End diff --

Please update the doc, beta is only the regression coefficients after your 
change. 


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-183191566
  
@yanboliang Could you take a look?


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[NarineK]

GitHub user NarineK opened a pull request:

https://github.com/apache/spark/pull/11179

[SPARK-13295] [ ML, MLlib ] AFTSurvivalRegression.AFTAggregator 
improvements - Avoids creating new instances of arrays/vectors for each record

As also mentioned/marked by TODO in AFTAggregator.AFTAggregator.add(data: 
AFTPoint) a new array is being created for intercept value and it is being 
concatenated
with another array which contains the betas, the resulted Array is being 
converted into a Dense vector which in it's turn is being converted into breeze 
vector.
This is expensive and not necessarily beautiful.

I've tried to solve above mentioned problem by simple algebraic 
decompositions - keeping and treating intercept independently.

Please let me know what do you think and if you have any questions.

Thanks,
Narine


You can merge this pull request into a Git repository by running:

$ git pull https://github.com/NarineK/spark survivaloptim

Alternatively you can review and apply these changes as the patch at:

https://github.com/apache/spark/pull/11179.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

This closes #11179


commit 8d443e9d7cd4b8b4cf7a4e14bec8287b7db6aff7
Author: Narine Kokhlikyan 
Date:   2016-02-12T02:42:08Z

Initial commit - AFTSurvivalRegression improvements




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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[mengxr]

Github user mengxr commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-183191594
  
ok to test


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-183197817
  
**[Test build #51173 has 
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-183205820
  
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 * This patch passes all tests.
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 * This patch adds no public classes.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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https://github.com/apache/spark/pull/11179#issuecomment-183206177
  
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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

[SparkQA]

Github user SparkQA commented on the pull request:

https://github.com/apache/spark/pull/11179#issuecomment-183193865
  
**[Test build #51170 has 
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 * This patch passes all tests.
 * This patch merges cleanly.
 * This patch adds no public classes.


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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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[GitHub] spark pull request: [SPARK-13295] [ ML, MLlib ] AFTSurvivalRegress...

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