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Posted to reviews@spark.apache.org by mengxr <gi...@git.apache.org> on 2014/03/19 03:25:01 UTC
[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
GitHub user mengxr opened a pull request:
https://github.com/apache/spark/pull/175
[SPARK-1273] MLlib bug fixes, improvements, and doc updates for v0.9.1
Cherry-picked a few MLlib commits that are bug fixes, optimization, or doc updates for the v0.9.1 release.
JIRA: https://spark-project.atlassian.net/browse/SPARK-1273
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/mengxr/spark branch-0.9
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/175.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 #175
----
commit 2512e67ab84aa7a4f2e4c1a7ecae8870a3453c7c
Author: Andrew Tulloch <an...@tullo.ch>
Date: 2014-01-19T17:51:00Z
LocalSparkContext for MLlib
commit 05649855853cb51c53aa8875306a8f80975e61ed
Author: Andrew Tulloch <an...@tullo.ch>
Date: 2014-01-21T13:34:59Z
Fixed import order
commit a26ac906a7739262d67c9fd75d849f2b067b4287
Author: Sean Owen <so...@cloudera.com>
Date: 2014-01-27T19:15:51Z
Merge pull request #460 from srowen/RandomInitialALSVectors
Choose initial user/item vectors uniformly on the unit sphere
...rather than within the unit square to possibly avoid bias in the initial state and improve convergence.
The current implementation picks the N vector elements uniformly at random from [0,1). This means they all point into one quadrant of the vector space. As N gets just a little large, the vector tend strongly to point into the "corner", towards (1,1,1...,1). The vectors are not unit vectors either.
I suggest choosing the elements as Gaussian ~ N(0,1) and normalizing. This gets you uniform random choices on the unit sphere which is more what's of interest here. It has worked a little better for me in the past.
This is pretty minor but wanted to warm up suggesting a few tweaks to ALS.
Please excuse my Scala, pretty new to it.
Author: Sean Owen <so...@cloudera.com>
== Merge branch commits ==
commit 492b13a7469e5a4ed7591ee8e56d8bd7570dfab6
Author: Sean Owen <so...@cloudera.com>
Date: Mon Jan 27 08:05:25 2014 +0000
Style: spaces around binary operators
commit ce2b5b5a4fefa0356875701f668f01f02ba4d87e
Author: Sean Owen <so...@cloudera.com>
Date: Sun Jan 19 22:50:03 2014 +0000
Generate factors with all positive components, per discussion in https://github.com/apache/incubator-spark/pull/460
commit b6f7a8a61643a8209e8bc662e8e81f2d15c710c7
Author: Sean Owen <so...@cloudera.com>
Date: Sat Jan 18 15:54:42 2014 +0000
Choose initial user/item vectors uniformly on the unit sphere rather than within the unit square to possibly avoid bias in the initial state and improve convergence
commit f27441a1213459912acf4252e5e5736fb7cb54de
Author: Chen Chao <cr...@gmail.com>
Date: 2014-02-20T06:06:35Z
MLLIB-24: url of "Collaborative Filtering for Implicit Feedback Datasets" in ALS is invalid now
url of "Collaborative Filtering for Implicit Feedback Datasets" is invalid now. A new url is provided. http://research.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf
Author: Chen Chao <cr...@gmail.com>
Closes #619 from CrazyJvm/master and squashes the following commits:
a0b54e4 [Chen Chao] change url to IEEE
9e0e9f0 [Chen Chao] correct spell mistale
fcfab5d [Chen Chao] wrap line to to fit within 100 chars
590d56e [Chen Chao] url error
commit 6340a182c5dec27ef67c4385d93e3ab5681bc5d4
Author: Sean Owen <so...@cloudera.com>
Date: 2014-02-20T07:44:53Z
MLLIB-22. Support negative implicit input in ALS
I'm back with another less trivial suggestion for ALS:
In ALS for implicit feedback, input values are treated as weights on squared-errors in a loss function (or rather, the weight is a simple function of the input r, like c = 1 + alpha*r). The paper on which it's based assumes that the input is positive. Indeed, if the input is negative, it will create a negative weight on squared-errors, which causes things to go haywire. The optimization will try to make the error in a cell as large possible, and the result is silently bogus.
There is a good use case for negative input values though. Implicit feedback is usually collected from signals of positive interaction like a view or like or buy, but equally, can come from "not interested" signals. The natural representation is negative values.
The algorithm can be extended quite simply to provide a sound interpretation of these values: negative values should encourage the factorization to come up with 0 for cells with large negative input values, just as much as positive values encourage it to come up with 1.
The implications for the algorithm are simple:
* the confidence function value must not be negative, and so can become 1 + alpha*|r|
* the matrix P should have a value 1 where the input R is _positive_, not merely where it is non-zero. Actually, that's what the paper already says, it's just that we can't assume P = 1 when a cell in R is specified anymore, since it may be negative
This in turn entails just a few lines of code change in `ALS.scala`:
* `rs(i)` becomes `abs(rs(i))`
* When constructing `userXy(us(i))`, it's implicitly only adding where P is 1. That had been true for any us(i) that is iterated over, before, since these are exactly the ones for which P is 1. But now P is zero where rs(i) <= 0, and should not be added
I think it's a safe change because:
* It doesn't change any existing behavior (unless you're using negative values, in which case results are already borked)
* It's the simplest direct extension of the paper's algorithm
* (I've used it to good effect in production FWIW)
Tests included.
I tweaked minor things en route:
* `ALS.scala` javadoc writes "R = Xt*Y" when the paper and rest of code defines it as "R = X*Yt"
* RMSE in the ALS tests uses a confidence-weighted mean, but the denominator is not actually sum of weights
Excuse my Scala style; I'm sure it needs tweaks.
Author: Sean Owen <so...@cloudera.com>
Closes #500 from srowen/ALSNegativeImplicitInput and squashes the following commits:
cf902a9 [Sean Owen] Support negative implicit input in ALS
953be1c [Sean Owen] Make weighted RMSE in ALS test actually weighted; adjust comment about R = X*Yt
commit 5ff70e9f0e5224496a5b18f807b7e6f50abbe474
Author: Xiangrui Meng <me...@databricks.com>
Date: 2014-03-13T07:43:19Z
[SPARK-1237, 1238] Improve the computation of YtY for implicit ALS (picked for v0.9.1)
Computing YtY can be implemented using BLAS's DSPR operations instead of generating y_i y_i^T and then combining them. The latter generates many k-by-k matrices. On the movielens data, this change improves the performance by 10-20%. The algorithm remains the same, verified by computing RMSE on the movielens data.
To compare the results, I also added an option to set a random seed in ALS.
JIRA:
1. https://spark-project.atlassian.net/browse/SPARK-1237
2. https://spark-project.atlassian.net/browse/SPARK-1238
Author: Xiangrui Meng <me...@databricks.com>
Closes #131 from mengxr/als and squashes the following commits:
ed00432 [Xiangrui Meng] minor changes
d984623 [Xiangrui Meng] minor changes
2fc1641 [Xiangrui Meng] remove commented code
4c7cde2 [Xiangrui Meng] allow specifying a random seed in ALS
200bef0 [Xiangrui Meng] optimize computeYtY and updateBlock
Conflicts:
mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala
commit 7ee9bc71dfdd9cdc87991279f17106db9ec582da
Author: Xiangrui Meng <me...@databricks.com>
Date: 2014-03-18T22:14:13Z
[SPARK-1260]: faster construction of features with intercept
The current implementation uses `Array(1.0, features: _*)` to construct a new array with intercept. This is not efficient for big arrays because `Array.apply` uses a for loop that iterates over the arguments. `Array.+:` is a better choice here.
Also, I don't see a reason to set initial weights to ones. So I set them to zeros.
JIRA: https://spark-project.atlassian.net/browse/SPARK-1260
Author: Xiangrui Meng <me...@databricks.com>
Closes #161 from mengxr/sgd and squashes the following commits:
b5cfc53 [Xiangrui Meng] set default weights to zeros
a1439c2 [Xiangrui Meng] faster construction of features with intercept
commit 2fe1e01224d746d9acab9c2098c4f38202aa066a
Author: Martin Jaggi <m....@gmail.com>
Date: 2014-02-09T23:19:50Z
Merge pull request #566 from martinjaggi/copy-MLlib-d. (picked for v0.9.1)
new MLlib documentation for optimization, regression and classification
new documentation with tex formulas, hopefully improving usability and reproducibility of the offered MLlib methods.
also did some minor changes in the code for consistency. scala tests pass.
this is the rebased branch, i deleted the old PR
jira:
https://spark-project.atlassian.net/browse/MLLIB-19
Author: Martin Jaggi <m....@gmail.com>
Closes #566 and squashes the following commits:
5f0f31e [Martin Jaggi] line wrap at 100 chars
4e094fb [Martin Jaggi] better description of GradientDescent
1d6965d [Martin Jaggi] remove broken url
ea569c3 [Martin Jaggi] telling what updater actually does
964732b [Martin Jaggi] lambda R() in documentation
a6c6228 [Martin Jaggi] better comments in SGD code for regression
b32224a [Martin Jaggi] new optimization documentation
d5dfef7 [Martin Jaggi] new classification and regression documentation
b07ead6 [Martin Jaggi] correct scaling for MSE loss
ba6158c [Martin Jaggi] use d for the number of features
bab2ed2 [Martin Jaggi] renaming LeastSquaresGradient
Conflicts:
docs/_layouts/global.html
docs/mllib-classification-regression.md
docs/mllib-optimization.md
mllib/src/main/scala/org/apache/spark/mllib/optimization/GradientDescent.scala
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38125704
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38125610
Jenkins, test this please.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr closed the pull request at:
https://github.com/apache/spark/pull/175
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38011671
@tdas
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
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https://github.com/apache/spark/pull/175#issuecomment-38012695
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/175#discussion_r10735173
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -61,22 +62,26 @@ class LogisticGradient extends Gradient {
}
/**
- * Compute gradient and loss for a Least-squared loss function.
+ * Compute gradient and loss for a Least-squared loss function, as used in linear regression.
+ * This is correct for the averaged least squares loss function (mean squared error)
+ * L = 1/n ||A weights-y||^2
+ * See also the documentation for the precise formulation.
*/
-class SquaredGradient extends Gradient {
+class LeastSquaresGradient extends Gradient {
--- End diff --
Thanks for the catch!
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38134818
@mengxr Can you please close this PR? This has been merged, but Github doesnt seem to realize that.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38011693
Jenkins, test this please.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38143842
Sure. Thanks for helping review the changes!
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38012590
Does Jenkins only test the master branch?
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/175#discussion_r10735088
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala ---
@@ -537,8 +620,12 @@ object ALS {
* @param lambda regularization factor (recommended: 0.01)
*/
def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
+<<<<<<< HEAD
: MatrixFactorizationModel =
{
+=======
+ : MatrixFactorizationModel = {
--- End diff --
Merge not done properly!!!
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/175#discussion_r10776709
--- Diff: mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala ---
@@ -0,0 +1,23 @@
+package org.apache.spark.mllib.util
--- End diff --
Please add Apache header on this.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/175#discussion_r10735050
--- Diff: mllib/src/main/scala/org/apache/spark/mllib/optimization/Gradient.scala ---
@@ -61,22 +62,26 @@ class LogisticGradient extends Gradient {
}
/**
- * Compute gradient and loss for a Least-squared loss function.
+ * Compute gradient and loss for a Least-squared loss function, as used in linear regression.
+ * This is correct for the averaged least squares loss function (mean squared error)
+ * L = 1/n ||A weights-y||^2
+ * See also the documentation for the precise formulation.
*/
-class SquaredGradient extends Gradient {
+class LeastSquaresGradient extends Gradient {
--- End diff --
Changes in the publicly visible class names are API changes!
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38125705
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by tdas <gi...@git.apache.org>.
Github user tdas commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38122145
Jenkins, test this.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by mengxr <gi...@git.apache.org>.
Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/175#discussion_r10777438
--- Diff: mllib/src/test/scala/org/apache/spark/mllib/util/LocalSparkContext.scala ---
@@ -0,0 +1,23 @@
+package org.apache.spark.mllib.util
--- End diff --
Done.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38012697
One or more automated tests failed
Refer to this link for build results: https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/13256/
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38012657
Merged build triggered.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38122466
Merged build triggered.
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[GitHub] spark pull request: [SPARK-1273] MLlib bug fixes, improvements, an...
Posted by AmplabJenkins <gi...@git.apache.org>.
Github user AmplabJenkins commented on the pull request:
https://github.com/apache/spark/pull/175#issuecomment-38122467
Merged build started.
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