| Commit message (Collapse) | Author | Age | Files | Lines |
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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.jaggi@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
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SPARK-1058, Fix Style Errors and Add Scala Style to Spark Build.
Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <scrapcodes@gmail.com>
== Merge branch commits ==
commit 1a8bd1c059b842cb95cc246aaea74a79fec684f4
Author: Prashant Sharma <scrapcodes@gmail.com>
Date: Sun Feb 9 17:39:07 2014 +0530
scala style fixes
commit f91709887a8e0b608c5c2b282db19b8a44d53a43
Author: Patrick Wendell <pwendell@gmail.com>
Date: Fri Jan 24 11:22:53 2014 -0800
Adding scalastyle snapshot
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Version number to 1.0.0-SNAPSHOT
Since 0.9.0-incubating is done and out the door, we shouldn't be building 0.9.0-incubating-SNAPSHOT anymore.
@pwendell
Author: Mark Hamstra <markhamstra@gmail.com>
== Merge branch commits ==
commit 1b00a8a7c1a7f251b4bb3774b84b9e64758eaa71
Author: Mark Hamstra <markhamstra@gmail.com>
Date: Wed Feb 5 09:30:32 2014 -0800
Version number to 1.0.0-SNAPSHOT
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Refactor RDD sampling and add randomSplit to RDD (update)
Replace SampledRDD by PartitionwiseSampledRDD, which accepts a RandomSampler instance as input. The current sample with/without replacement can be easily integrated via BernoulliSampler and PoissonSampler. The benefits are:
1) RDD.randomSplit is implemented in the same way, related to https://github.com/apache/incubator-spark/pull/513
2) Stratified sampling and importance sampling can be implemented in the same manner as well.
Unit tests are included for samplers and RDD.randomSplit.
This should performance better than my previous request where the BernoulliSampler creates many Iterator instances:
https://github.com/apache/incubator-spark/pull/513
Author: Xiangrui Meng <meng@databricks.com>
== Merge branch commits ==
commit e8ce957e5f0a600f2dec057924f4a2ca6adba373
Author: Xiangrui Meng <meng@databricks.com>
Date: Mon Feb 3 12:21:08 2014 -0800
more docs to PartitionwiseSampledRDD
commit fbb4586d0478ff638b24bce95f75ff06f713d43b
Author: Xiangrui Meng <meng@databricks.com>
Date: Mon Feb 3 00:44:23 2014 -0800
move XORShiftRandom to util.random and use it in BernoulliSampler
commit 987456b0ee8612fd4f73cb8c40967112dc3c4c2d
Author: Xiangrui Meng <meng@databricks.com>
Date: Sat Feb 1 11:06:59 2014 -0800
relax assertions in SortingSuite because the RangePartitioner has large variance in this case
commit 3690aae416b2dc9b2f9ba32efa465ba7948477f4
Author: Xiangrui Meng <meng@databricks.com>
Date: Sat Feb 1 09:56:28 2014 -0800
test split ratio of RDD.randomSplit
commit 8a410bc933a60c4d63852606f8bbc812e416d6ae
Author: Xiangrui Meng <meng@databricks.com>
Date: Sat Feb 1 09:25:22 2014 -0800
add a test to ensure seed distribution and minor style update
commit ce7e866f674c30ab48a9ceb09da846d5362ab4b6
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 31 18:06:22 2014 -0800
minor style change
commit 750912b4d77596ed807d361347bd2b7e3b9b7a74
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 31 18:04:54 2014 -0800
fix some long lines
commit c446a25c38d81db02821f7f194b0ce5ab4ed7ff5
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 31 17:59:59 2014 -0800
add complement to BernoulliSampler and minor style changes
commit dbe2bc2bd888a7bdccb127ee6595840274499403
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 31 17:45:08 2014 -0800
switch to partition-wise sampling for better performance
commit a1fca5232308feb369339eac67864c787455bb23
Merge: ac712e4 cf6128f
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 31 16:33:09 2014 -0800
Merge branch 'sample' of github.com:mengxr/incubator-spark into sample
commit cf6128fb672e8c589615adbd3eaa3cbdb72bd461
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 14:40:07 2014 -0800
set SampledRDD deprecated in 1.0
commit f430f847c3df91a3894687c513f23f823f77c255
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 14:38:59 2014 -0800
update code style
commit a8b5e2021a9204e318c80a44d00c5c495f1befb6
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 12:56:27 2014 -0800
move package random to util.random
commit ab0fa2c4965033737a9e3a9bf0a59cbb0df6a6f5
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 12:50:35 2014 -0800
add Apache headers and update code style
commit 985609fe1a55655ad11966e05a93c18c138a403d
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 11:49:25 2014 -0800
add new lines
commit b21bddf29850a2c006a868869b8f91960a029322
Author: Xiangrui Meng <meng@databricks.com>
Date: Sun Jan 26 11:46:35 2014 -0800
move samplers to random.IndependentRandomSampler and add tests
commit c02dacb4a941618e434cefc129c002915db08be6
Author: Xiangrui Meng <meng@databricks.com>
Date: Sat Jan 25 15:20:24 2014 -0800
add RandomSampler
commit 8ff7ba3c5cf1fc338c29ae8b5fa06c222640e89c
Author: Xiangrui Meng <meng@databricks.com>
Date: Fri Jan 24 13:23:22 2014 -0800
init impl of IndependentlySampledRDD
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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 <sowen@cloudera.com>
== Merge branch commits ==
commit 492b13a7469e5a4ed7591ee8e56d8bd7570dfab6
Author: Sean Owen <sowen@cloudera.com>
Date: Mon Jan 27 08:05:25 2014 +0000
Style: spaces around binary operators
commit ce2b5b5a4fefa0356875701f668f01f02ba4d87e
Author: Sean Owen <sowen@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 <sowen@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
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Sparse SVD
# Singular Value Decomposition
Given an *m x n* matrix *A*, compute matrices *U, S, V* such that
*A = U * S * V^T*
There is no restriction on m, but we require n^2 doubles to fit in memory.
Further, n should be less than m.
The decomposition is computed by first computing *A^TA = V S^2 V^T*,
computing svd locally on that (since n x n is small),
from which we recover S and V.
Then we compute U via easy matrix multiplication
as *U = A * V * S^-1*
Only singular vectors associated with the largest k singular values
If there are k such values, then the dimensions of the return will be:
* *S* is *k x k* and diagonal, holding the singular values on diagonal.
* *U* is *m x k* and satisfies U^T*U = eye(k).
* *V* is *n x k* and satisfies V^TV = eye(k).
All input and output is expected in sparse matrix format, 0-indexed
as tuples of the form ((i,j),value) all in RDDs.
# Testing
Tests included. They test:
- Decomposition promise (A = USV^T)
- For small matrices, output is compared to that of jblas
- Rank 1 matrix test included
- Full Rank matrix test included
- Middle-rank matrix forced via k included
# Example Usage
import org.apache.spark.SparkContext
import org.apache.spark.mllib.linalg.SVD
import org.apache.spark.mllib.linalg.SparseMatrix
import org.apache.spark.mllib.linalg.MatrixyEntry
// Load and parse the data file
val data = sc.textFile("mllib/data/als/test.data").map { line =>
val parts = line.split(',')
MatrixEntry(parts(0).toInt, parts(1).toInt, parts(2).toDouble)
}
val m = 4
val n = 4
// recover top 1 singular vector
val decomposed = SVD.sparseSVD(SparseMatrix(data, m, n), 1)
println("singular values = " + decomposed.S.data.toArray.mkString)
# Documentation
Added to docs/mllib-guide.md
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Conflicts:
docs/mllib-guide.md
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Code clean up for mllib
* Removed unnecessary parentheses
* Removed unused imports
* Simplified `filter...size()` to `count ...`
* Removed obsoleted parameters' comments
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