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* [SPARK-4749] [mllib]: Allow initializing KMeans clusters using a seednate.crosswhite2015-01-213-9/+66
| | | | | | | | | | | | | | | | | | | This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark Author: nate.crosswhite <nate.crosswhite@stresearch.com> Author: nxwhite-str <nxwhite-str@users.noreply.github.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3610 from nxwhite-str/master and squashes the following commits: a2ebbd3 [nxwhite-str] Merge pull request #1 from mengxr/SPARK-4749-kmeans-seed 7668124 [Xiangrui Meng] minor updates f8d5928 [nate.crosswhite] Addressing PR issues 277d367 [nate.crosswhite] Merge remote-tracking branch 'upstream/master' 9156a57 [nate.crosswhite] Merge remote-tracking branch 'upstream/master' 5d087b4 [nate.crosswhite] Adding KMeans train with seed and Scala unit test 616d111 [nate.crosswhite] Merge remote-tracking branch 'upstream/master' 35c1884 [nate.crosswhite] Add kmeans initial seed to pyspark API
* [MLlib] [SPARK-5301] Missing conversions and operations on IndexedRowMatrix ↵Reza Zadeh2015-01-214-0/+35
| | | | | | | | | | | | | | | | | | | and CoordinateMatrix * Transpose is missing from CoordinateMatrix (this is cheap to compute, so it should be there) * IndexedRowMatrix should be convertable to CoordinateMatrix (conversion added) Tests for both added. Author: Reza Zadeh <reza@databricks.com> Closes #4089 from rezazadeh/matutils and squashes the following commits: ec5238b [Reza Zadeh] Array -> Iterator to avoid temp array 3ce0b5d [Reza Zadeh] Array -> Iterator bbc907a [Reza Zadeh] Use 'i' for index, and zipWithIndex cb10ae5 [Reza Zadeh] remove unnecessary import a7ae048 [Reza Zadeh] Missing linear algebra utilities
* [SPARK-5186] [MLLIB] Vector.equals and Vector.hashCode are very inefficientYuhao Yang2015-01-202-3/+70
| | | | | | | | | | | | | | | | | | | | | | JIRA Issue: https://issues.apache.org/jira/browse/SPARK-5186 Currently SparseVector is using the inherited equals from Vector, which will create a full-size array for even the sparse vector. The pull request contains a specialized equals optimization that improves on both time and space. 1. The implementation will be consistent with the original. Especially it will keep equality comparison between SparseVector and DenseVector. Author: Yuhao Yang <hhbyyh@gmail.com> Author: Yuhao Yang <yuhao@yuhaodevbox.sh.intel.com> Closes #3997 from hhbyyh/master and squashes the following commits: 0d9d130 [Yuhao Yang] function name change and ut update 93f0d46 [Yuhao Yang] unify sparse vs dense vectors 985e160 [Yuhao Yang] improve locality for equals bdf8789 [Yuhao Yang] improve equals and rewrite hashCode for Vector a6952c3 [Yuhao Yang] fix scala style for comments 50abef3 [Yuhao Yang] fix ut for sparse vector with explicit 0 f41b135 [Yuhao Yang] iterative equals for sparse vector 5741144 [Yuhao Yang] Specialized equals for SparseVector
* SPARK-5019 [MLlib] - GaussianMixtureModel exposes instances of ↵Travis Galoppo2015-01-203-30/+25
| | | | | | | | | | | | | MultivariateGauss... This PR modifies GaussianMixtureModel to expose instances of MutlivariateGaussian rather than separate mean and covariance arrays. Author: Travis Galoppo <tjg2107@columbia.edu> Closes #4088 from tgaloppo/spark-5019 and squashes the following commits: 3ef6c7f [Travis Galoppo] In GaussianMixtureModel: Changed name of weight, gaussian to weights, gaussians. Other sources modified accordingly. 091e8da [Travis Galoppo] SPARK-5019 - GaussianMixtureModel exposes instances of MultivariateGaussian rather than mean/covariance matrices
* [SPARK-5282][mllib]: RowMatrix easily gets int overflow in the memory size ↵Yuhao Yang2015-01-191-2/+2
| | | | | | | | | | | | | | | warning JIRA: https://issues.apache.org/jira/browse/SPARK-5282 fix the possible int overflow in the memory computation warning Author: Yuhao Yang <hhbyyh@gmail.com> Closes #4069 from hhbyyh/addscStop and squashes the following commits: e54e5c8 [Yuhao Yang] change to MB based number 7afac23 [Yuhao Yang] 5282: fix int overflow in the warning
* [SPARK-5193][SQL] Remove Spark SQL Java-specific API.Reynold Xin2015-01-165-89/+24
| | | | | | | | | | | | | | | | | | | | | | | | | After the following patches, the main (Scala) API is now usable for Java users directly. https://github.com/apache/spark/pull/4056 https://github.com/apache/spark/pull/4054 https://github.com/apache/spark/pull/4049 https://github.com/apache/spark/pull/4030 https://github.com/apache/spark/pull/3965 https://github.com/apache/spark/pull/3958 Author: Reynold Xin <rxin@databricks.com> Closes #4065 from rxin/sql-java-api and squashes the following commits: b1fd860 [Reynold Xin] Fix Mima 6d86578 [Reynold Xin] Ok one more attempt in fixing Python... e8f1455 [Reynold Xin] Fix Python again... 3e53f91 [Reynold Xin] Fixed Python. 83735da [Reynold Xin] Fix BigDecimal test. e9f1de3 [Reynold Xin] Use scala BigDecimal. 500d2c4 [Reynold Xin] Fix Decimal. ba3bfa2 [Reynold Xin] Updated javadoc for RowFactory. c4ae1c5 [Reynold Xin] [SPARK-5193][SQL] Remove Spark SQL Java-specific API.
* [SPARK-5123][SQL] Reconcile Java/Scala API for data types.Reynold Xin2015-01-139-9/+13
| | | | | | | | | | | | | | Having two versions of the data type APIs (one for Java, one for Scala) requires downstream libraries to also have two versions of the APIs if the library wants to support both Java and Scala. I took a look at the Scala version of the data type APIs - it can actually work out pretty well for Java out of the box. As part of the PR, I created a sql.types package and moved all type definitions there. I then removed the Java specific data type API along with a lot of the conversion code. This subsumes https://github.com/apache/spark/pull/3925 Author: Reynold Xin <rxin@databricks.com> Closes #3958 from rxin/SPARK-5123-datatype-2 and squashes the following commits: 66505cc [Reynold Xin] [SPARK-5123] Expose only one version of the data type APIs (i.e. remove the Java-specific API).
* SPARK-5018 [MLlib] [WIP] Make MultivariateGaussian publicTravis Galoppo2015-01-114-41/+75
| | | | | | | | | | | | | | | | | | | | | Moving MutlivariateGaussian from private[mllib] to public. The class uses Breeze vectors internally, so this involves creating a public interface using MLlib vectors and matrices. This initial commit provides public construction, accessors for mean/covariance, density and log-density. Other potential methods include entropy and sample generation. Author: Travis Galoppo <tjg2107@columbia.edu> Closes #3923 from tgaloppo/spark-5018 and squashes the following commits: 2b15587 [Travis Galoppo] Style correction b4121b4 [Travis Galoppo] Merge remote-tracking branch 'upstream/master' into spark-5018 e30a100 [Travis Galoppo] Made mu, sigma private[mllib] members of MultivariateGaussian Moved MultivariateGaussian (and test suite) from stat.impl to stat.distribution (required updates in GaussianMixture{EM,Model}.scala) Marked MultivariateGaussian as @DeveloperApi Fixed style error 9fa3bb7 [Travis Galoppo] Style improvements 91a5fae [Travis Galoppo] Rearranged equation for part of density function 8c35381 [Travis Galoppo] Fixed accessor methods to match member variable names. Modified calculations to avoid log(pow(x,y)) calculations 0943dc4 [Travis Galoppo] SPARK-5018 4dee9e1 [Travis Galoppo] SPARK-5018
* [SPARK-4406] [MLib] FIX: Validate k in SVDMechCoder2015-01-094-1/+19
| | | | | | | | | | | Raise exception when k is non-positive in SVD Author: MechCoder <manojkumarsivaraj334@gmail.com> Closes #3945 from MechCoder/spark-4406 and squashes the following commits: 64e6d2d [MechCoder] TST: Add better test errors and messages 12dae73 [MechCoder] [SPARK-4406] FIX: Validate k in SVD
* [SPARK-5015] [mllib] Random seed for GMM + make test suite deterministicJoseph K. Bradley2015-01-092-13/+27
| | | | | | | | | | | | | | | | | | Issues: * From JIRA: GaussianMixtureEM uses randomness but does not take a random seed. It should take one as a parameter. * This also makes the test suite flaky since initialization can fail due to stochasticity. Fix: * Add random seed * Use it in test suite CC: mengxr tgaloppo Author: Joseph K. Bradley <joseph@databricks.com> Closes #3981 from jkbradley/gmm-seed and squashes the following commits: f0df4fd [Joseph K. Bradley] Added seed parameter to GMM. Updated test suite to use seed to prevent flakiness
* [SPARK-5145][Mllib] Add BLAS.dsyr and use it in GaussianMixtureEMLiang-Chi Hsieh2015-01-093-4/+73
| | | | | | | | | | | This pr uses BLAS.dsyr to replace few implementations in GaussianMixtureEM. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #3949 from viirya/blas_dsyr and squashes the following commits: 4e4d6cf [Liang-Chi Hsieh] Add unit test. Rename function name, modify doc and style. 3f57fd2 [Liang-Chi Hsieh] Add BLAS.dsyr and use it in GaussianMixtureEM.
* [SPARK-4048] Enhance and extend hadoop-provided profile.Marcelo Vanzin2015-01-081-5/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This change does a few things to make the hadoop-provided profile more useful: - Create new profiles for other libraries / services that might be provided by the infrastructure - Simplify and fix the poms so that the profiles are only activated while building assemblies. - Fix tests so that they're able to run when the profiles are activated - Add a new env variable to be used by distributions that use these profiles to provide the runtime classpath for Spark jobs and daemons. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #2982 from vanzin/SPARK-4048 and squashes the following commits: 82eb688 [Marcelo Vanzin] Add a comment. eb228c0 [Marcelo Vanzin] Fix borked merge. 4e38f4e [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 9ef79a3 [Marcelo Vanzin] Alternative way to propagate test classpath to child processes. 371ebee [Marcelo Vanzin] Review feedback. 52f366d [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 83099fc [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 7377e7b [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 322f882 [Marcelo Vanzin] Fix merge fail. f24e9e7 [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 8b00b6a [Marcelo Vanzin] Merge branch 'master' into SPARK-4048 9640503 [Marcelo Vanzin] Cleanup child process log message. 115fde5 [Marcelo Vanzin] Simplify a comment (and make it consistent with another pom). e3ab2da [Marcelo Vanzin] Fix hive-thriftserver profile. 7820d58 [Marcelo Vanzin] Fix CliSuite with provided profiles. 1be73d4 [Marcelo Vanzin] Restore flume-provided profile. d1399ed [Marcelo Vanzin] Restore jetty dependency. 82a54b9 [Marcelo Vanzin] Remove unused profile. 5c54a25 [Marcelo Vanzin] Fix HiveThriftServer2Suite with *-provided profiles. 1fc4d0b [Marcelo Vanzin] Update dependencies for hive-thriftserver. f7b3bbe [Marcelo Vanzin] Add snappy to hadoop-provided list. 9e4e001 [Marcelo Vanzin] Remove duplicate hive profile. d928d62 [Marcelo Vanzin] Redirect child stderr to parent's log. 4d67469 [Marcelo Vanzin] Propagate SPARK_DIST_CLASSPATH on Yarn. 417d90e [Marcelo Vanzin] Introduce "SPARK_DIST_CLASSPATH". 2f95f0d [Marcelo Vanzin] Propagate classpath to child processes during testing. 1adf91c [Marcelo Vanzin] Re-enable maven-install-plugin for a few projects. 284dda6 [Marcelo Vanzin] Rework the "hadoop-provided" profile, add new ones.
* [SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to P...RJ Nowling2015-01-081-0/+88
| | | | | | | | | | | | | | ...ySpark MLlib This is a follow up to PR3680 https://github.com/apache/spark/pull/3680 . Author: RJ Nowling <rnowling@gmail.com> Closes #3955 from rnowling/spark4891 and squashes the following commits: 1236a01 [RJ Nowling] Fix Python style issues 7a01a78 [RJ Nowling] Fix Python style issues 174beab [RJ Nowling] [SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to PySpark MLlib
* SPARK-5148 [MLlib] Make usersOut/productsOut storagelevel in ALS configurableFernando Otero (ZeoS)2015-01-082-2/+43
| | | | | | | | | | | Author: Fernando Otero (ZeoS) <fotero@gmail.com> Closes #3953 from zeitos/storageLevel and squashes the following commits: 0f070b9 [Fernando Otero (ZeoS)] fix imports 6869e80 [Fernando Otero (ZeoS)] fix comment length 90c9f7e [Fernando Otero (ZeoS)] fix comment length 18a992e [Fernando Otero (ZeoS)] changing storage level
* [SPARK-5116][MLlib] Add extractor for SparseVector and DenseVectorShuo Xiang2015-01-076-51/+57
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Add extractor for SparseVector and DenseVector in MLlib to save some code while performing pattern matching on Vectors. For example, previously we may use: vec match { case dv: DenseVector => val values = dv.values ... case sv: SparseVector => val indices = sv.indices val values = sv.values val size = sv.size ... } with extractor it is: vec match { case DenseVector(values) => ... case SparseVector(size, indices, values) => ... } Author: Shuo Xiang <shuoxiangpub@gmail.com> Closes #3919 from coderxiang/extractor and squashes the following commits: 359e8d5 [Shuo Xiang] merge master ca5fc3e [Shuo Xiang] merge master 0b1e190 [Shuo Xiang] use extractor for vectors in RowMatrix.scala e961805 [Shuo Xiang] use extractor for vectors in StandardScaler.scala c2bbdaf [Shuo Xiang] use extractor for vectors in IDFscala 8433922 [Shuo Xiang] use extractor for vectors in NaiveBayes.scala and Normalizer.scala d83c7ca [Shuo Xiang] use extractor for vectors in Vectors.scala 5523dad [Shuo Xiang] Add extractor for SparseVector and DenseVector
* [SPARK-5128][MLLib] Add common used log1pExp API in MLUtilsDB Tsai2015-01-074-21/+37
| | | | | | | | | | | | | | | | | | When `x` is positive and large, computing `math.log(1 + math.exp(x))` will lead to arithmetic overflow. This will happen when `x > 709.78` which is not a very large number. It can be addressed by rewriting the formula into `x + math.log1p(math.exp(-x))` when `x > 0`. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3915 from dbtsai/mathutil and squashes the following commits: bec6a84 [DB Tsai] remove empty line 3239541 [DB Tsai] revert part of patch into another PR 23144f3 [DB Tsai] doc 49f3658 [DB Tsai] temp 6c29ed3 [DB Tsai] formating f8447f9 [DB Tsai] address another overflow issue in gradientMultiplier in LOR gradient code 64eefd0 [DB Tsai] first commit
* [SPARK-5099][Mllib] Simplify logistic loss functionLiang-Chi Hsieh2015-01-061-3/+9
| | | | | | | | | | | | | | | | This is a minor pr where I think that we can simply take minus of `margin`, instead of subtracting `margin`. Mathematically, they are equal. But the modified equation is the common form of logistic loss function and so more readable. It also computes more accurate value as some quick tests show. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #3899 from viirya/logit_func and squashes the following commits: 91a3860 [Liang-Chi Hsieh] Modified for comment. 0aa51e4 [Liang-Chi Hsieh] Further simplified. 72a295e [Liang-Chi Hsieh] Revert LogLoss back and add more considerations in Logistic Loss. a3f83ca [Liang-Chi Hsieh] Fix a bug. 2bc5712 [Liang-Chi Hsieh] Simplify loss function.
* [SPARK-5050][Mllib] Add unit test for sqdistLiang-Chi Hsieh2015-01-062-3/+33
| | | | | | | | | | | | | | Related to #3643. Follow the previous suggestion to add unit test for `sqdist` in `VectorsSuite`. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #3869 from viirya/sqdist_test and squashes the following commits: fb743da [Liang-Chi Hsieh] Modified for comment and fix bug. 90a08f3 [Liang-Chi Hsieh] Modified for comment. 39a3ca6 [Liang-Chi Hsieh] Take care of special case. b789f42 [Liang-Chi Hsieh] More proper unit test with random sparsity pattern. c36be68 [Liang-Chi Hsieh] Add unit test for sqdist.
* SPARK-5017 [MLlib] - Use SVD to compute determinant and inverse of ↵Travis Galoppo2015-01-062-11/+142
| | | | | | | | | | | | | | | | | | | | covariance matrix MultivariateGaussian was calling both pinv() and det() on the covariance matrix, effectively performing two matrix decompositions. Both values are now computed using the singular value decompositon. Both the pseudo-inverse and the pseudo-determinant are used to guard against singular matrices. Author: Travis Galoppo <tjg2107@columbia.edu> Closes #3871 from tgaloppo/spark-5017 and squashes the following commits: 383b5b3 [Travis Galoppo] MultivariateGaussian - minor optimization in density calculation a5b8bc5 [Travis Galoppo] Added additional points to tests in test suite. Fixed comment in MultivariateGaussian 629d9d0 [Travis Galoppo] Moved some test values from var to val. dc3d0f7 [Travis Galoppo] Catch potential exception calculating pseudo-determinant. Style improvements. d448137 [Travis Galoppo] Added test suite for MultivariateGaussian, including test for degenerate case. 1989be0 [Travis Galoppo] SPARK-5017 - Fixed to use SVD to compute determinant and inverse of covariance matrix. Previous code called both pinv() and det(), effectively performing two matrix decompositions. Additionally, the pinv() implementation in Breeze is known to fail for singular matrices. b4415ea [Travis Galoppo] Merge branch 'spark-5017' of https://github.com/tgaloppo/spark into spark-5017 6f11b6d [Travis Galoppo] SPARK-5017 - Use SVD to compute determinant and inverse of covariance matrix. Code was calling both det() and pinv(), effectively performing two matrix decompositions. Futhermore, Breeze pinv() currently fails for singular matrices. fd9784c [Travis Galoppo] SPARK-5017 - Use SVD to compute determinant and inverse of covariance matrix
* SPARK-4159 [CORE] Maven build doesn't run JUnit test suitesSean Owen2015-01-062-13/+2
| | | | | | | | | | | | | | | | | | This PR: - Reenables `surefire`, and copies config from `scalatest` (which is itself an old fork of `surefire`, so similar) - Tells `surefire` to test only Java tests - Enables `surefire` and `scalatest` for all children, and in turn eliminates some duplication. For me this causes the Scala and Java tests to be run once each, it seems, as desired. It doesn't affect the SBT build but works for Maven. I still need to verify that all of the Scala tests and Java tests are being run. Author: Sean Owen <sowen@cloudera.com> Closes #3651 from srowen/SPARK-4159 and squashes the following commits: 2e8a0af [Sean Owen] Remove specialized SPARK_HOME setting for REPL, YARN tests as it appears to be obsolete 12e4558 [Sean Owen] Append to unit-test.log instead of overwriting, so that both surefire and scalatest output is preserved. Also standardize/correct comments a bit. e6f8601 [Sean Owen] Reenable Java tests by reenabling surefire with config cloned from scalatest; centralize test config in the parent
* SPARK-5020 [MLlib] GaussianMixtureModel.predictMembership() should take an ↵Travis Galoppo2014-12-311-7/+2
| | | | | | | | | | | | | | | RDD only Removed unnecessary parameters to predictMembership() CC: jkbradley Author: Travis Galoppo <tjg2107@columbia.edu> Closes #3854 from tgaloppo/spark-5020 and squashes the following commits: 1bf4669 [Travis Galoppo] renamed predictMembership() to predictSoft() 0f1d96e [Travis Galoppo] SPARK-5020 - Removed superfluous parameters from predictMembership()
* SPARK-4547 [MLLIB] OOM when making bins in BinaryClassificationMetricsSean Owen2014-12-312-3/+92
| | | | | | | | | | | | | | | | Now that I've implemented the basics here, I'm less convinced there is a need for this change, somehow. Callers can downsample before or after. Really the OOM is not in the ROC curve code, but in code that might `collect()` it for local analysis. Still, might be useful to down-sample since the ROC curve probably never needs millions of points. This is a first pass. Since the `(score,label)` are already grouped and sorted, I think it's sufficient to just take every Nth such pair, in order to downsample by a factor of N? this is just like retaining every Nth point on the curve, which I think is the goal. All of the data is still used to build the curve of course. What do you think about the API, and usefulness? Author: Sean Owen <sowen@cloudera.com> Closes #3702 from srowen/SPARK-4547 and squashes the following commits: 1d34d05 [Sean Owen] Indent and reorganize numBins scaladoc 692d825 [Sean Owen] Change handling of large numBins, make 2nd consturctor instead of optional param, style change a03610e [Sean Owen] Add downsamplingFactor to BinaryClassificationMetrics
* [SPARK-4797] Replace breezeSquaredDistanceLiang-Chi Hsieh2014-12-313-8/+100
| | | | | | | | | | | | | | | | | | | | This PR replaces slow breezeSquaredDistance. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #3643 from viirya/faster_squareddistance and squashes the following commits: f28b275 [Liang-Chi Hsieh] Move the implementation to linalg.Vectors and rename as sqdist. 0bc48ee [Liang-Chi Hsieh] Merge branch 'master' into faster_squareddistance ba34422 [Liang-Chi Hsieh] Fix bug. 91849d0 [Liang-Chi Hsieh] Modified for comment. 44a65ad [Liang-Chi Hsieh] Modified for comments. 35db395 [Liang-Chi Hsieh] Fix bug and some modifications for comments. f4f5ebb [Liang-Chi Hsieh] Follow BLAS.dot pattern to replace intersect, diff with while-loop. a36e09f [Liang-Chi Hsieh] Use while-loop to replace foreach for better performance. d3e0628 [Liang-Chi Hsieh] Make the methods private. dd415bc [Liang-Chi Hsieh] Consider different cases of SparseVector and DenseVector. 13669db [Liang-Chi Hsieh] Replace breezeSquaredDistance.
* [SPARK-4998][MLlib]delete the "train" functionLiu Jiongzhou2014-12-301-7/+0
| | | | | | | | | | | | | To make the functions with the same in "object" effective, specially when using java reflection. As the "train" function defined in "class DecisionTree" will hide the functions with the same name in "object DecisionTree". JIRA[SPARK-4998] Author: Liu Jiongzhou <ljzzju@163.com> Closes #3836 from ljzzju/master and squashes the following commits: 4e13133 [Liu Jiongzhou] [MLlib]delete the "train" function
* [Spark-4995] Replace Vector.toBreeze.activeIterator with foreachActiveJakub Dubovsky2014-12-303-6/+8
| | | | | | | | | | | | | | | New foreachActive method of vector was introduced by SPARK-4431 as more efficient alternative to vector.toBreeze.activeIterator. There are some parts of codebase where it was not yet replaced. dbtsai Author: Jakub Dubovsky <dubovsky@avast.com> Closes #3846 from james64/SPARK-4995-foreachActive and squashes the following commits: 3eb7e37 [Jakub Dubovsky] Scalastyle fix 32fe6c6 [Jakub Dubovsky] activeIterator removed - IndexedRowMatrix.toBreeze 47a4777 [Jakub Dubovsky] activeIterator removed in RowMatrix.toBreeze 90a7d98 [Jakub Dubovsky] activeIterator removed in MLUtils.saveAsLibSVMFile
* [SPARK-4972][MLlib] Updated the scala doc for lasso and ridge regression for ↵DB Tsai2014-12-292-2/+2
| | | | | | | | | | | | the change of LeastSquaresGradient In #SPARK-4907, we added factor of 2 into the LeastSquaresGradient. We updated the scala doc for lasso and ridge regression here. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3808 from dbtsai/doc and squashes the following commits: ec3c989 [DB Tsai] first commit
* Added setMinCount to Word2Vec.scalaganonp2014-12-292-5/+12
| | | | | | | | | | | | | | | | | Wanted to customize the private minCount variable in the Word2Vec class. Added a method to do so. Author: ganonp <ganonp@gmail.com> Closes #3693 from ganonp/my-custom-spark and squashes the following commits: ad534f2 [ganonp] made norm method public 5110a6f [ganonp] Reorganized 854958b [ganonp] Fixed Indentation for setMinCount 12ed8f9 [ganonp] Update Word2Vec.scala 76bdf5a [ganonp] Update Word2Vec.scala ffb88bb [ganonp] Update Word2Vec.scala 5eb9100 [ganonp] Added setMinCount to Word2Vec.scala
* SPARK-4156 [MLLIB] EM algorithm for GMMsTravis Galoppo2014-12-295-1/+450
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Implementation of Expectation-Maximization for Gaussian Mixture Models. This is my maiden contribution to Apache Spark, so I apologize now if I have done anything incorrectly; having said that, this work is my own, and I offer it to the project under the project's open source license. Author: Travis Galoppo <tjg2107@columbia.edu> Author: Travis Galoppo <travis@localhost.localdomain> Author: tgaloppo <tjg2107@columbia.edu> Author: FlytxtRnD <meethu.mathew@flytxt.com> Closes #3022 from tgaloppo/master and squashes the following commits: aaa8f25 [Travis Galoppo] MLUtils: changed privacy of EPSILON from [util] to [mllib] 709e4bf [Travis Galoppo] fixed usage line to include optional maxIterations parameter acf1fba [Travis Galoppo] Fixed parameter comment in GaussianMixtureModel Made maximum iterations an optional parameter to DenseGmmEM 9b2fc2a [Travis Galoppo] Style improvements Changed ExpectationSum to a private class b97fe00 [Travis Galoppo] Minor fixes and tweaks. 1de73f3 [Travis Galoppo] Removed redundant array from array creation 578c2d1 [Travis Galoppo] Removed unused import 227ad66 [Travis Galoppo] Moved prediction methods into model class. 308c8ad [Travis Galoppo] Numerous changes to improve code cff73e0 [Travis Galoppo] Replaced accumulators with RDD.aggregate 20ebca1 [Travis Galoppo] Removed unusued code 42b2142 [Travis Galoppo] Added functionality to allow setting of GMM starting point. Added two cluster test to testing suite. 8b633f3 [Travis Galoppo] Style issue 9be2534 [Travis Galoppo] Style issue d695034 [Travis Galoppo] Fixed style issues c3b8ce0 [Travis Galoppo] Merge branch 'master' of https://github.com/tgaloppo/spark Adds predict() method 2df336b [Travis Galoppo] Fixed style issue b99ecc4 [tgaloppo] Merge pull request #1 from FlytxtRnD/predictBranch f407b4c [FlytxtRnD] Added predict() to return the cluster labels and membership values 97044cf [Travis Galoppo] Fixed style issues dc9c742 [Travis Galoppo] Moved MultivariateGaussian utility class e7d413b [Travis Galoppo] Moved multivariate Gaussian utility class to mllib/stat/impl Improved comments 9770261 [Travis Galoppo] Corrected a variety of style and naming issues. 8aaa17d [Travis Galoppo] Added additional train() method to companion object for cluster count and tolerance parameters. 676e523 [Travis Galoppo] Fixed to no longer ignore delta value provided on command line e6ea805 [Travis Galoppo] Merged with master branch; update test suite with latest context changes. Improved cluster initialization strategy. 86fb382 [Travis Galoppo] Merge remote-tracking branch 'upstream/master' 719d8cc [Travis Galoppo] Added scala test suite with basic test c1a8e16 [Travis Galoppo] Made GaussianMixtureModel class serializable Modified sum function for better performance 5c96c57 [Travis Galoppo] Merge remote-tracking branch 'upstream/master' c15405c [Travis Galoppo] SPARK-4156
* [SPARK-4409][MLlib] Additional Linear Algebra UtilsBurak Yavuz2014-12-294-43/+868
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Addition of a very limited number of local matrix manipulation and generation methods that would be helpful in the further development for algorithms on top of BlockMatrix (SPARK-3974), such as Randomized SVD, and Multi Model Training (SPARK-1486). The proposed methods for addition are: For `Matrix` - map: maps the values in the matrix with a given function. Produces a new matrix. - update: the values in the matrix are updated with a given function. Occurs in place. Factory methods for `DenseMatrix`: - *zeros: Generate a matrix consisting of zeros - *ones: Generate a matrix consisting of ones - *eye: Generate an identity matrix - *rand: Generate a matrix consisting of i.i.d. uniform random numbers - *randn: Generate a matrix consisting of i.i.d. gaussian random numbers - *diag: Generate a diagonal matrix from a supplied vector *These methods already exist in the factory methods for `Matrices`, however for cases where we require a `DenseMatrix`, you constantly have to add `.asInstanceOf[DenseMatrix]` everywhere, which makes the code "dirtier". I propose moving these functions to factory methods for `DenseMatrix` where the putput will be a `DenseMatrix` and the factory methods for `Matrices` will call these functions directly and output a generic `Matrix`. Factory methods for `SparseMatrix`: - speye: Identity matrix in sparse format. Saves a ton of memory when dimensions are large, especially in Multi Model Training, where each row requires being multiplied by a scalar. - sprand: Generate a sparse matrix with a given density consisting of i.i.d. uniform random numbers. - sprandn: Generate a sparse matrix with a given density consisting of i.i.d. gaussian random numbers. - diag: Generate a diagonal matrix from a supplied vector, but is memory efficient, because it just stores the diagonal. Again, very helpful in Multi Model Training. Factory methods for `Matrices`: - Include all the factory methods given above, but return a generic `Matrix` rather than `SparseMatrix` or `DenseMatrix`. - horzCat: Horizontally concatenate matrices to form one larger matrix. Very useful in both Multi Model Training, and for the repartitioning of BlockMatrix. - vertCat: Vertically concatenate matrices to form one larger matrix. Very useful for the repartitioning of BlockMatrix. The names for these methods were selected from MATLAB Author: Burak Yavuz <brkyvz@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3319 from brkyvz/SPARK-4409 and squashes the following commits: b0354f6 [Burak Yavuz] [SPARK-4409] Incorporated mengxr's code 04c4829 [Burak Yavuz] Merge pull request #1 from mengxr/SPARK-4409 80cfa29 [Xiangrui Meng] minor changes ecc937a [Xiangrui Meng] update sprand 4e95e24 [Xiangrui Meng] simplify fromCOO implementation 10a63a6 [Burak Yavuz] [SPARK-4409] Fourth pass of code review f62d6c7 [Burak Yavuz] [SPARK-4409] Modified genRandMatrix 3971c93 [Burak Yavuz] [SPARK-4409] Third pass of code review 75239f8 [Burak Yavuz] [SPARK-4409] Second pass of code review e4bd0c0 [Burak Yavuz] [SPARK-4409] Modified horzcat and vertcat 65c562e [Burak Yavuz] [SPARK-4409] Hopefully fixed Java Test d8be7bc [Burak Yavuz] [SPARK-4409] Organized imports 065b531 [Burak Yavuz] [SPARK-4409] First pass after code review a8120d2 [Burak Yavuz] [SPARK-4409] Finished updates to API according to SPARK-4614 f798c82 [Burak Yavuz] [SPARK-4409] Updated API according to SPARK-4614 c75f3cd [Burak Yavuz] [SPARK-4409] Added JavaAPI Tests, and fixed a couple of bugs d662f9d [Burak Yavuz] [SPARK-4409] Modified according to remote repo 83dfe37 [Burak Yavuz] [SPARK-4409] Scalastyle error fixed a14c0da [Burak Yavuz] [SPARK-4409] Initial commit to add methods
* [SPARK-4608][Streaming] Reorganize StreamingContext implicit to improve API ↵zsxwing2014-12-252-2/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | convenience There is only one implicit function `toPairDStreamFunctions` in `StreamingContext`. This PR did similar reorganization like [SPARK-4397](https://issues.apache.org/jira/browse/SPARK-4397). Compiled the following codes with Spark Streaming 1.1.0 and ran it with this PR. Everything is fine. ```Scala import org.apache.spark._ import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ object StreamingApp { def main(args: Array[String]) { val conf = new SparkConf().setMaster("local[2]").setAppName("FileWordCount") val ssc = new StreamingContext(conf, Seconds(10)) val lines = ssc.textFileStream("/some/path") val words = lines.flatMap(_.split(" ")) val pairs = words.map(word => (word, 1)) val wordCounts = pairs.reduceByKey(_ + _) wordCounts.print() ssc.start() ssc.awaitTermination() } } ``` Author: zsxwing <zsxwing@gmail.com> Closes #3464 from zsxwing/SPARK-4608 and squashes the following commits: aa6d44a [zsxwing] Fix a copy-paste error f74c190 [zsxwing] Merge branch 'master' into SPARK-4608 e6f9cc9 [zsxwing] Update the docs 27833bb [zsxwing] Remove `import StreamingContext._` c15162c [zsxwing] Reorganize StreamingContext implicit to improve API convenience
* SPARK-4297 [BUILD] Build warning fixes omnibusSean Owen2014-12-241-0/+2
| | | | | | | | | | There are a number of warnings generated in a normal, successful build right now. They're mostly Java unchecked cast warnings, which can be suppressed. But there's a grab bag of other Scala language warnings and so on that can all be easily fixed. The forthcoming PR fixes about 90% of the build warnings I see now. Author: Sean Owen <sowen@cloudera.com> Closes #3157 from srowen/SPARK-4297 and squashes the following commits: 8c9e469 [Sean Owen] Suppress unchecked cast warnings, and several other build warning fixes
* [SPARK-4907][MLlib] Inconsistent loss and gradient in LeastSquaresGradient ↵DB Tsai2014-12-222-8/+8
| | | | | | | | | | | | | | | | | | | | compared with R In most of the academic paper and algorithm implementations, people use L = 1/2n ||A weights-y||^2 instead of L = 1/n ||A weights-y||^2 for least-squared loss. See Eq. (1) in http://web.stanford.edu/~hastie/Papers/glmnet.pdf Since MLlib uses different convention, this will result different residuals and all the stats properties will be different from GLMNET package in R. The model coefficients will be still the same under this change. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3746 from dbtsai/lir and squashes the following commits: 19c2e85 [DB Tsai] make stepsize twice to converge to the same solution 0b2c29c [DB Tsai] first commit
* [SPARK-4728][MLLib] Add exponential, gamma, and log normal sampling to MLlib ↵RJ Nowling2014-12-185-4/+622
| | | | | | | | | | | | | | | | | | | | | | da... ...ta generators This patch adds: * Exponential, gamma, and log normal generators that wrap Apache Commons math3 to the private API * Functions for generating exponential, gamma, and log normal RDDs and vector RDDs * Tests for the above Author: RJ Nowling <rnowling@gmail.com> Closes #3680 from rnowling/spark4728 and squashes the following commits: 455f50a [RJ Nowling] Add tests for exponential, gamma, and log normal samplers to JavaRandomRDDsSuite 3e1134a [RJ Nowling] Fix val/var, unncessary creation of Distribution objects when setting seeds, and import line longer than line wrap limits 58f5b97 [RJ Nowling] Fix bounds in tests so they scale with variance, not stdev 84fd98d [RJ Nowling] Add more values for testing distributions. 9f96232 [RJ Nowling] [SPARK-4728] Add exponential, gamma, and log normal sampling to MLlib data generators
* [SPARK-4887][MLlib] Fix a bad unittest in LogisticRegressionSuiteDB Tsai2014-12-181-3/+4
| | | | | | | | | | | | | | | | The original test doesn't make sense since if you step in, the lossSum is already NaN, and the coefficients are diverging. That's because the step size is too large for SGD, so it doesn't work. The correct behavior is that you should get smaller coefficients than the one without regularization. Comparing the values using 20000.0 relative error doesn't make sense as well. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3735 from dbtsai/mlortestfix and squashes the following commits: b1a3c42 [DB Tsai] first commit
* [SPARK-4494][mllib] IDFModel.transform() add support for single vectorYuu ISHIKAWA2014-12-152-54/+86
| | | | | | | | | | | | | | I improved `IDFModel.transform` to allow using a single vector. [[SPARK-4494] IDFModel.transform() add support for single vector - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-4494) Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #3603 from yu-iskw/idf and squashes the following commits: 256ff3d [Yuu ISHIKAWA] Fix typo a3bf566 [Yuu ISHIKAWA] - Fix typo - Optimize import order - Aggregate the assertion tests - Modify `IDFModel.transform` API for pyspark d25e49b [Yuu ISHIKAWA] Add the implementation of `IDFModel.transform` for a term frequency vector
* [FIX][DOC] Fix broken links in ml-guide.mdXiangrui Meng2014-12-041-1/+0
| | | | | | | | | | | | and some minor changes in ScalaDoc. Author: Xiangrui Meng <meng@databricks.com> Closes #3601 from mengxr/SPARK-4575-fix and squashes the following commits: c559768 [Xiangrui Meng] minor code update ce94da8 [Xiangrui Meng] Java Bean -> JavaBean 0b5c182 [Xiangrui Meng] fix links in ml-guide
* [SPARK-4575] [mllib] [docs] spark.ml pipelines doc + bug fixesJoseph K. Bradley2014-12-046-18/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Documentation: * Added ml-guide.md, linked from mllib-guide.md * Updated mllib-guide.md with small section pointing to ml-guide.md Examples: * CrossValidatorExample * SimpleParamsExample * (I copied these + the SimpleTextClassificationPipeline example into the ml-guide.md) Bug fixes: * PipelineModel: did not use ParamMaps correctly * UnaryTransformer: issues with TypeTag serialization (Thanks to mengxr for that fix!) CC: mengxr shivaram etrain Documentation for Pipelines: I know the docs are not complete, but the goal is to have enough to let interested people get started using spark.ml and to add more docs once the package is more established/complete. Author: Joseph K. Bradley <joseph@databricks.com> Author: jkbradley <joseph.kurata.bradley@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3588 from jkbradley/ml-package-docs and squashes the following commits: d393b5c [Joseph K. Bradley] fixed bug in Pipeline (typo from last commit). updated examples for CV and Params for spark.ml c38469c [Joseph K. Bradley] Updated ml-guide with CV examples 99f88c2 [Joseph K. Bradley] Fixed bug in PipelineModel.transform* with usage of params. Updated CrossValidatorExample to use more training examples so it is less likely to get a 0-size fold. ea34dc6 [jkbradley] Merge pull request #4 from mengxr/ml-package-docs 3b83ec0 [Xiangrui Meng] replace TypeTag with explicit datatype 41ad9b1 [Joseph K. Bradley] Added examples for spark.ml: SimpleParamsExample + Java version, CrossValidatorExample + Java version. CrossValidatorExample not working yet. Added programming guide for spark.ml, but need to add CrossValidatorExample to it once CrossValidatorExample works.
* [SPARK-4580] [SPARK-4610] [mllib] [docs] Documentation for tree ensembles + ↵Joseph K. Bradley2014-12-049-71/+71
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | DecisionTree API fix Major changes: * Added programming guide sections for tree ensembles * Added examples for tree ensembles * Updated DecisionTree programming guide with more info on parameters * **API change**: Standardized the tree parameter for the number of classes (for classification) Minor changes: * Updated decision tree documentation * Updated existing tree and tree ensemble examples * Use train/test split, and compute test error instead of training error. * Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix) Note: I know this is a lot of lines, but most is covered by: * Programming guide sections for gradient boosting and random forests. (The changes are probably best viewed by generating the docs locally.) * New examples (which were copied from the programming guide) * The "numClasses" renaming I have run all examples and relevant unit tests. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #3461 from jkbradley/ensemble-docs and squashes the following commits: 70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide 8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide 6fab846 [Joseph K. Bradley] small fixes based on review b9f8576 [Joseph K. Bradley] updated decision tree doc 375204c [Joseph K. Bradley] fixed python style 2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file. added header. Fixed small bug in same example in the programming guide. 706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small c76c823 [Joseph K. Bradley] added migration guide for mllib abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder 07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification). cdfdfbc [Joseph K. Bradley] added examples for GBT 6372a2b [Joseph K. Bradley] updated decision tree examples to use random split. tested all of them. ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide. still need to update their examples
* [SPARK-4717][MLlib] Optimize BLAS library to avoid de-reference multiple ↵DB Tsai2014-12-031-39/+60
| | | | | | | | | | | | | | | times in loop Have a local reference to `values` and `indices` array in the `Vector` object so JVM can locate the value with one operation call. See `SPARK-4581` for similar optimization, and the bytecode analysis. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3577 from dbtsai/blasopt and squashes the following commits: 62d38c4 [DB Tsai] formating 0316cef [DB Tsai] first commit
* [SPARK-4708][MLLib] Make k-mean runs two/three times faster with ↵DB Tsai2014-12-035-68/+70
| | | | | | | | | | | | | | | | | | | | | | | | | | | | dense/sparse sample Note that the usage of `breezeSquaredDistance` in `org.apache.spark.mllib.util.MLUtils.fastSquaredDistance` is in the critical path, and `breezeSquaredDistance` is slow. We should replace it with our own implementation. Here is the benchmark against mnist8m dataset. Before DenseVector: 70.04secs SparseVector: 59.05secs With this PR DenseVector: 30.58secs SparseVector: 21.14secs Author: DB Tsai <dbtsai@alpinenow.com> Closes #3565 from dbtsai/kmean and squashes the following commits: 08bc068 [DB Tsai] restyle de24662 [DB Tsai] address feedback b185a77 [DB Tsai] cleanup 4554ddd [DB Tsai] first commit
* [SPARK-4611][MLlib] Implement the efficient vector normDB Tsai2014-12-024-6/+79
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The vector norm in breeze is implemented by `activeIterator` which is known to be very slow. In this PR, an efficient vector norm is implemented, and with this API, `Normalizer` and `k-means` have big performance improvement. Here is the benchmark against mnist8m dataset. a) `Normalizer` Before DenseVector: 68.25secs SparseVector: 17.01secs With this PR DenseVector: 12.71secs SparseVector: 2.73secs b) `k-means` Before DenseVector: 83.46secs SparseVector: 61.60secs With this PR DenseVector: 70.04secs SparseVector: 59.05secs Author: DB Tsai <dbtsai@alpinenow.com> Closes #3462 from dbtsai/norm and squashes the following commits: 63c7165 [DB Tsai] typo 0c3637f [DB Tsai] add import org.apache.spark.SparkContext._ back 6fa616c [DB Tsai] address feedback 9b7cb56 [DB Tsai] move norm to static method 0b632e6 [DB Tsai] kmeans dbed124 [DB Tsai] style c1a877c [DB Tsai] first commit
* [SPARK-4614][MLLIB] Slight API changes in Matrix and MatricesXiangrui Meng2014-11-262-11/+59
| | | | | | | | | | | | | Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes. brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #3468 from mengxr/SPARK-4614 and squashes the following commits: 3b0e4e2 [Xiangrui Meng] add mima excludes 6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
* [SPARK-4604][MLLIB] make MatrixFactorizationModel publicXiangrui Meng2014-11-252-3/+81
| | | | | | | | | | | | User could construct an MF model directly. I added a note about the performance. Author: Xiangrui Meng <meng@databricks.com> Closes #3459 from mengxr/SPARK-4604 and squashes the following commits: f64bcd3 [Xiangrui Meng] organize imports ed08214 [Xiangrui Meng] check preconditions and unit tests a624c12 [Xiangrui Meng] make MatrixFactorizationModel public
* [SPARK-4583] [mllib] LogLoss for GradientBoostedTrees fix + doc updatesJoseph K. Bradley2014-11-256-72/+146
| | | | | | | | | | | | | | | | | | | | | | | Currently, the LogLoss used by GradientBoostedTrees has 2 issues: * the gradient (and therefore loss) does not match that used by Friedman (1999) * the error computation uses 0/1 accuracy, not log loss This PR updates LogLoss. It also adds some doc for boosting and forests. I tested it on sample data and made sure the log loss is monotonically decreasing with each boosting iteration. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Closes #3439 from jkbradley/gbt-loss-fix and squashes the following commits: cfec17e [Joseph K. Bradley] removed forgotten temp comments a27eb6d [Joseph K. Bradley] corrections to last log loss commit ed5da2c [Joseph K. Bradley] updated LogLoss (boosting) for numerical stability 5e52bff [Joseph K. Bradley] * Removed the 1/2 from SquaredError. This also required updating the test suite since it effectively doubles the gradient and loss. * Added doc for developers within RandomForest. * Small cleanup in test suite (generating data only once) e57897a [Joseph K. Bradley] Fixed LogLoss for GradientBoostedTrees, and updated doc for losses, forests, and boosting
* [SPARK-4581][MLlib] Refactorize StandardScaler to improve the transformation ↵DB Tsai2014-11-251-20/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | performance The following optimizations are done to improve the StandardScaler model transformation performance. 1) Covert Breeze dense vector to primitive vector to reduce the overhead. 2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector. 3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call. 4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to make the codebase cleaner. Benchmark with mnist8m dataset: Before, DenseVector withMean and withStd: 50.97secs DenseVector withMean and withoutStd: 42.11secs DenseVector withoutMean and withStd: 8.75secs SparseVector withoutMean and withStd: 5.437secs With this PR, DenseVector withMean and withStd: 5.76secs DenseVector withMean and withoutStd: 5.28secs DenseVector withoutMean and withStd: 5.30secs SparseVector withoutMean and withStd: 1.27secs Note that without the local reference copy of `factor` and `shift` arrays, the runtime is almost three time slower. DenseVector withMean and withStd: 18.15secs DenseVector withMean and withoutStd: 18.05secs DenseVector withoutMean and withStd: 18.54secs SparseVector withoutMean and withStd: 2.01secs The following code, ```scala while (i < size) { values(i) = (values(i) - shift(i)) * factor(i) i += 1 } ``` will generate the bytecode ``` L13 LINENUMBER 106 L13 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] [] ILOAD 7 ILOAD 6 IF_ICMPGE L14 L15 LINENUMBER 107 L15 ALOAD 5 ILOAD 7 ALOAD 5 ILOAD 7 DALOAD ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D ILOAD 7 DALOAD DSUB ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ILOAD 7 DALOAD DMUL DASTORE L16 LINENUMBER 108 L16 ILOAD 7 ICONST_1 IADD ISTORE 7 GOTO L13 ``` , while with the local reference of the `shift` and `factor` arrays, the bytecode will be ``` L14 LINENUMBER 107 L14 ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ASTORE 9 L15 LINENUMBER 108 L15 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] [] ILOAD 8 ILOAD 7 IF_ICMPGE L16 L17 LINENUMBER 109 L17 ALOAD 6 ILOAD 8 ALOAD 6 ILOAD 8 DALOAD ALOAD 2 ILOAD 8 DALOAD DSUB ALOAD 9 ILOAD 8 DALOAD DMUL DASTORE L18 LINENUMBER 110 L18 ILOAD 8 ICONST_1 IADD ISTORE 8 GOTO L15 ``` You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3435 from dbtsai/standardscaler and squashes the following commits: 85885a9 [DB Tsai] revert to have lazy in shift array. daf2b06 [DB Tsai] Address the feedback cdb5cef [DB Tsai] small change 9c51eef [DB Tsai] style fc795e4 [DB Tsai] update 5bffd3d [DB Tsai] first commit
* [SPARK-4526][MLLIB]GradientDescent get a wrong gradient value according to ↵GuoQiang Li2014-11-251-19/+26
| | | | | | | | | | | | | | | | | the gradient formula. This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed. cc mengxr Author: GuoQiang Li <witgo@qq.com> Closes #3399 from witgo/GradientDescent and squashes the following commits: 13cb228 [GuoQiang Li] review commit 668ab66 [GuoQiang Li] Double to Long b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0 0b5c3e3 [GuoQiang Li] Minor fix 12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.
* [SPARK-4596][MLLib] Refactorize Normalizer to make code cleanerDB Tsai2014-11-251-10/+19
| | | | | | | | | | | | | | | In this refactoring, the performance will be slightly increased due to removing the overhead from breeze vector. The bottleneck is still in breeze norm which is implemented by activeIterator. This inefficiency of breeze norm will be addressed in next PR. At least, this PR makes the code more consistent in the codebase. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3446 from dbtsai/normalizer and squashes the following commits: e20a2b9 [DB Tsai] first commit
* [SPARK-4582][MLLIB] get raw vectors for further processing in Word2Vectkaessmann2014-11-241-0/+7
| | | | | | | | | | | | | | | | | | | | | This is #3309 for the master branch. e.g. clustering Author: tkaessmann <tobias.kaessmanns24.com> Closes #3309 from tkaessmann/branch-1.2 and squashes the following commits: e3a3142 [tkaessmann] changes the comment for getVectors 58d3d83 [tkaessmann] removes sign from comment a5be213 [tkaessmann] fixes getVectors to fit code guidelines 3782fa9 [tkaessmann] get raw vectors for further processing Author: tkaessmann <tobias.kaessmann@s24.com> Closes #3437 from mengxr/SPARK-4582 and squashes the following commits: 6c666b4 [tkaessmann] get raw vectors for further processing in Word2Vec
* [SPARK-4562] [MLlib] speedup vectorDavies Liu2014-11-241-8/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array. It also improve the serialization of DenseVector. Before this change: trial | trainingTime | testTime -------|--------|-------- 0 | 5.126 | 1.786 1 |2.698 |1.693 After the change: trial | trainingTime | testTime -------|--------|-------- 0 |4.692 |0.554 1 |2.307 |0.525 This could partially fix the performance regression during test. Author: Davies Liu <davies@databricks.com> Closes #3420 from davies/ser2 and squashes the following commits: 0e1e6f3 [Davies Liu] fix tests 426f5db [Davies Liu] impove toArray() 44707ec [Davies Liu] add name for ISO-8859-1 fa7d791 [Davies Liu] address comments 1cfb137 [Davies Liu] handle zero sparse vector 2548ee2 [Davies Liu] fix tests 9e6389d [Davies Liu] bugfix 470f702 [Davies Liu] speed up DenseMatrix f0d3c40 [Davies Liu] speedup SparseVector ef6ce70 [Davies Liu] speed up dense vector
* [SPARK-4431][MLlib] Implement efficient foreachActive for dense and sparse ↵DB Tsai2014-11-213-72/+105
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | vector Previously, we were using Breeze's activeIterator to access the non-zero elements in dense/sparse vector. Due to the overhead, we switched back to native `while loop` in #SPARK-4129. However, #SPARK-4129 requires de-reference the dv.values/sv.values in each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer, we're using Breeze's dense vector to store the partial stats, and this is very expensive compared with using primitive scala array. In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse vector operation which makes codebase easier to maintain. Breeze dense vector is replaced by primitive array to reduce the overhead further. Benchmarking with mnist8m dataset on single JVM with first 200 samples loaded in memory, and repeating 5000 times. Before change: Sparse Vector - 30.02 Dense Vector - 38.27 With this PR: Sparse Vector - 6.29 Dense Vector - 11.72 Author: DB Tsai <dbtsai@alpinenow.com> Closes #3288 from dbtsai/activeIterator and squashes the following commits: 844b0e6 [DB Tsai] formating 03dd693 [DB Tsai] futher performance tunning. 1907ae1 [DB Tsai] address feedback 98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation. c0cbd5a [DB Tsai] fix a bug 6441f92 [DB Tsai] Finished SPARK-4431
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