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* [SPARK-2163] class LBFGS optimize with Double tolerance instead of IntGang Bai2014-06-202-1/+35
| | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-2163 This pull request includes the change for **[SPARK-2163]**: * Changed the convergence tolerance parameter from type `Int` to type `Double`. * Added types for vars in `class LBFGS`, making the style consistent with `class GradientDescent`. * Added associated test to check that optimizing via `class LBFGS` produces the same results as via calling `runLBFGS` from `object LBFGS`. This is a very minor change but it will solve the problem in my implementation of a regression model for count data, where I make use of LBFGS for parameter estimation. Author: Gang Bai <me@baigang.net> Closes #1104 from BaiGang/fix_int_tol and squashes the following commits: cecf02c [Gang Bai] Changed setConvergenceTol'' to specify tolerance with a parameter of type Double. For the reason and the problem caused by an Int parameter, please check https://issues.apache.org/jira/browse/SPARK-2163. Added a test in LBFGSSuite for validating that optimizing via class LBFGS produces the same results as calling runLBFGS from object LBFGS. Keep the indentations and styles correct.
* Squishing a typo bug before it causes real harmDoris Xin2014-06-181-1/+1
| | | | | | | | | | in updateNumRows method in RowMatrix Author: Doris Xin <doris.s.xin@gmail.com> Closes #1125 from dorx/updateNumRows and squashes the following commits: 8564aef [Doris Xin] Squishing a typo bug before it causes real harm
* SPARK-2085: [MLlib] Apply user-specific regularization instead of uniform ↵Shuo Xiang2014-06-121-1/+7
| | | | | | | | | | | | | | regularization in ALS The current implementation of ALS takes a single regularization parameter and apply it on both of the user factors and the product factors. This kind of regularization can be less effective while user number is significantly larger than the number of products (and vice versa). For example, if we have 10M users and 1K product, regularization on user factors will dominate. Following the discussion in [this thread](http://apache-spark-user-list.1001560.n3.nabble.com/possible-bug-in-Spark-s-ALS-implementation-tt2567.html#a2704), the implementation in this PR will regularize each factor vector by #ratings * lambda. Author: Shuo Xiang <sxiang@twitter.com> Closes #1026 from coderxiang/als-reg and squashes the following commits: 93dfdb4 [Shuo Xiang] Merge remote-tracking branch 'upstream/master' into als-reg b98f19c [Shuo Xiang] merge latest master 52c7b58 [Shuo Xiang] Apply user-specific regularization instead of uniform regularization in Alternating Least Squares (ALS)
* [SPARK-1672][MLLIB] Separate user and product partitioning in ALSTor Myklebust2014-06-112-83/+141
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Some clean up work following #593. 1. Allow to set different number user blocks and number product blocks in `ALS`. 2. Update `MovieLensALS` to reflect the change. Author: Tor Myklebust <tmyklebu@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #1014 from mengxr/SPARK-1672 and squashes the following commits: 0e910dd [Xiangrui Meng] change private[this] to private[recommendation] 36420c7 [Xiangrui Meng] set exclusion rules for ALS 9128b77 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-1672 294efe9 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-1672 9bab77b [Xiangrui Meng] clean up add numUserBlocks and numProductBlocks to MovieLensALS 84c8e8c [Xiangrui Meng] Merge branch 'master' into SPARK-1672 d17a8bf [Xiangrui Meng] merge master a4925fd [Tor Myklebust] Style. bd8a75c [Tor Myklebust] Merge branch 'master' of github.com:apache/spark into alsseppar 021f54b [Tor Myklebust] Separate user and product blocks. dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go. 23d6f91 [Tor Myklebust] Stop making the partitioner configurable. 495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark 674933a [Tor Myklebust] Fix style. 40edc23 [Tor Myklebust] Fix missing space. f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach. 5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'. 36a0f43 [Tor Myklebust] Make the partitioner private. d872b09 [Tor Myklebust] Add negative id ALS test. df27697 [Tor Myklebust] Support custom partitioners. Currently we use the same partitioner for users and products. c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing. c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
* Resolve scalatest warnings during buildwitgo2014-06-103-6/+6
| | | | | | | | Author: witgo <witgo@qq.com> Closes #1032 from witgo/ShouldMatchers and squashes the following commits: 7ebf34c [witgo] Resolve scalatest warnings during build
* Remove compile-scoped junit dependency.Marcelo Vanzin2014-06-051-0/+8
| | | | | | | | | | | | | This avoids having junit classes showing up in the assembly jar. I verified that only test classes in the jtransforms package use junit. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #794 from vanzin/junit-dep-exclusion and squashes the following commits: 274e1c2 [Marcelo Vanzin] Remove junit from assembly in sbt build also. ad950be [Marcelo Vanzin] Remove compile-scoped junit dependency.
* [SPARK-2029] Bump pom.xml version number of master branch to 1.1.0-SNAPSHOT.Takuya UESHIN2014-06-051-1/+1
| | | | | | | | Author: Takuya UESHIN <ueshin@happy-camper.st> Closes #974 from ueshin/issues/SPARK-2029 and squashes the following commits: e19e8f4 [Takuya UESHIN] Bump version number to 1.1.0-SNAPSHOT.
* [SPARK-1752][MLLIB] Standardize text format for vectors and labeled pointsXiangrui Meng2014-06-0413-20/+449
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | We should standardize the text format used to represent vectors and labeled points. The proposed formats are the following: 1. dense vector: `[v0,v1,..]` 2. sparse vector: `(size,[i0,i1],[v0,v1])` 3. labeled point: `(label,vector)` where "(..)" indicates a tuple and "[...]" indicate an array. `loadLabeledPoints` is added to pyspark's `MLUtils`. I didn't add `loadVectors` to pyspark because `RDD.saveAsTextFile` cannot stringify dense vectors in the proposed format automatically. `MLUtils#saveLabeledData` and `MLUtils#loadLabeledData` are deprecated. Users should use `RDD#saveAsTextFile` and `MLUtils#loadLabeledPoints` instead. In Scala, `MLUtils#loadLabeledPoints` is compatible with the format used by `MLUtils#loadLabeledData`. CC: @mateiz, @srowen Author: Xiangrui Meng <meng@databricks.com> Closes #685 from mengxr/labeled-io and squashes the following commits: 2d1116a [Xiangrui Meng] make loadLabeledData/saveLabeledData deprecated since 1.0.1 297be75 [Xiangrui Meng] change LabeledPoint.parse to LabeledPointParser.parse to maintain binary compatibility d6b1473 [Xiangrui Meng] Merge branch 'master' into labeled-io 56746ea [Xiangrui Meng] replace # by . 623a5f0 [Xiangrui Meng] merge master f06d5ba [Xiangrui Meng] add docs and minor updates 640fe0c [Xiangrui Meng] throw SparkException 5bcfbc4 [Xiangrui Meng] update test to add scientific notations e86bf38 [Xiangrui Meng] remove NumericTokenizer 050fca4 [Xiangrui Meng] use StringTokenizer 6155b75 [Xiangrui Meng] merge master f644438 [Xiangrui Meng] remove parse methods based on eval from pyspark a41675a [Xiangrui Meng] python loadLabeledPoint uses Scala's implementation ce9a475 [Xiangrui Meng] add deserialize_labeled_point to pyspark with tests e9fcd49 [Xiangrui Meng] add serializeLabeledPoint and tests aea4ae3 [Xiangrui Meng] minor updates 810d6df [Xiangrui Meng] update tokenizer/parser implementation 7aac03a [Xiangrui Meng] remove Scala parsers c1885c1 [Xiangrui Meng] add headers and minor changes b0c50cb [Xiangrui Meng] add customized parser d731817 [Xiangrui Meng] style update 63dc396 [Xiangrui Meng] add loadLabeledPoints to pyspark ea122b5 [Xiangrui Meng] Merge branch 'master' into labeled-io cd6c78f [Xiangrui Meng] add __str__ and parse to LabeledPoint a7a178e [Xiangrui Meng] add stringify to pyspark's Vectors 5c2dbfa [Xiangrui Meng] add parse to pyspark's Vectors 7853f88 [Xiangrui Meng] update pyspark's SparseVector.__str__ e761d32 [Xiangrui Meng] make LabelPoint.parse compatible with the dense format used before v1.0 and deprecate loadLabeledData and saveLabeledData 9e63a02 [Xiangrui Meng] add loadVectors and loadLabeledPoints 19aa523 [Xiangrui Meng] update toString and add parsers for Vectors and LabeledPoint
* [MLLIB] set RDD names in ALSNeville Li2014-06-041-5/+11
| | | | | | | | | | | This is very useful when debugging & fine tuning jobs with large data sets. Author: Neville Li <neville@spotify.com> Closes #966 from nevillelyh/master and squashes the following commits: 6747764 [Neville Li] [MLLIB] use string interpolation for RDD names 3b15d34 [Neville Li] [MLLIB] set RDD names in ALS
* Fixed a typoDB Tsai2014-06-031-1/+1
| | | | | | | | | | in RowMatrix.scala Author: DB Tsai <dbtsai@dbtsai.com> Closes #959 from dbtsai/dbtsai-typo and squashes the following commits: fab0e0e [DB Tsai] Fixed typo
* [SPARK-1942] Stop clearing spark.driver.port in unit testsSyed Hashmi2014-06-039-9/+0
| | | | | | | | | | | | | | | | | | | stop resetting spark.driver.port in unit tests (scala, java and python). Author: Syed Hashmi <shashmi@cloudera.com> Author: CodingCat <zhunansjtu@gmail.com> Closes #943 from syedhashmi/master and squashes the following commits: 885f210 [Syed Hashmi] Removing unnecessary file (created by mergetool) b8bd4b5 [Syed Hashmi] Merge remote-tracking branch 'upstream/master' b895e59 [Syed Hashmi] Revert "[SPARK-1784] Add a new partitioner" 57b6587 [Syed Hashmi] Revert "[SPARK-1784] Add a balanced partitioner" 1574769 [Syed Hashmi] [SPARK-1942] Stop clearing spark.driver.port in unit tests 4354836 [Syed Hashmi] Revert "SPARK-1686: keep schedule() calling in the main thread" fd36542 [Syed Hashmi] [SPARK-1784] Add a balanced partitioner 6668015 [CodingCat] SPARK-1686: keep schedule() calling in the main thread 4ca94cc [Syed Hashmi] [SPARK-1784] Add a new partitioner
* [SPARK-1553] Alternating nonnegative least-squaresTor Myklebust2014-06-024-14/+300
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This pull request includes a nonnegative least-squares solver (NNLS) tailored to the kinds of small-scale problems that come up when training matrix factorisation models by alternating nonnegative least-squares (ANNLS). The method used for the NNLS subproblems is based on the classical method of projected gradients. There is a modification where, if the set of active constraints has not changed since the last iteration, a conjugate gradient step is considered and possibly rejected in favour of the gradient; this improves convergence once the optimal face has been located. The NNLS solver is in `org.apache.spark.mllib.optimization.NNLSbyPCG`. Author: Tor Myklebust <tmyklebu@gmail.com> Closes #460 from tmyklebu/annls and squashes the following commits: 79bc4b5 [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark into annls 199b0bc [Tor Myklebust] Make the ctor private again and use the builder pattern. 7fbabf1 [Tor Myklebust] Cleanup matrix math in NNLSSuite. 65ef7f2 [Tor Myklebust] Make ALS's ctor public and remove a couple of "convenience" wrappers. 2d4f3cb [Tor Myklebust] Cleanup. 0cb4481 [Tor Myklebust] Drop the iteration limit from 40k to max(400,20n). e2a01d1 [Tor Myklebust] Create a workspace object for NNLS to cut down on memory allocations. b285106 [Tor Myklebust] Clean up NNLS test cases. 9c820b6 [Tor Myklebust] Tweak variable names. 8a1a436 [Tor Myklebust] Describe the problem and add a reference to Polyak's paper. 5345402 [Tor Myklebust] Style fixes that got eaten. ac673bd [Tor Myklebust] More safeguards against numerical ridiculousness. c288b6a [Tor Myklebust] Finish moving the NNLS solver. 9a82fa6 [Tor Myklebust] Fix scalastyle moanings. 33bf4f2 [Tor Myklebust] Fix missing space. 89ea0a8 [Tor Myklebust] Hack ALSSuite to support NNLS testing. f5dbf4d [Tor Myklebust] Teach ALS how to use the NNLS solver. 6cb563c [Tor Myklebust] Tests for the nonnegative least squares solver. a68ac10 [Tor Myklebust] A nonnegative least-squares solver.
* SPARK-1925: Replace '&' with '&&'zsxwing2014-05-261-2/+2
| | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-1925 Author: zsxwing <zsxwing@gmail.com> Closes #879 from zsxwing/SPARK-1925 and squashes the following commits: 5cf5a6d [zsxwing] SPARK-1925: Replace '&' with '&&'
* Update LBFGSSuite.scalabaishuo(白硕)2014-05-231-2/+2
| | | | | | | | | | the same reason as https://github.com/apache/spark/pull/588 Author: baishuo(白硕) <vc_java@hotmail.com> Closes #815 from baishuo/master and squashes the following commits: 6876c1e [baishuo(白硕)] Update LBFGSSuite.scala
* [SPARK-1741][MLLIB] add predict(JavaRDD) to RegressionModel, ↵Xiangrui Meng2014-05-156-2/+76
| | | | | | | | | | | | | | | | | | | ClassificationModel, and KMeans `model.predict` returns a RDD of Scala primitive type (Int/Double), which is recognized as Object in Java. Adding predict(JavaRDD) could make life easier for Java users. Added tests for KMeans, LinearRegression, and NaiveBayes. Will update examples after https://github.com/apache/spark/pull/653 gets merged. cc: @srowen Author: Xiangrui Meng <meng@databricks.com> Closes #670 from mengxr/predict-javardd and squashes the following commits: b77ccd8 [Xiangrui Meng] Merge branch 'master' into predict-javardd 43caac9 [Xiangrui Meng] add predict(JavaRDD) to RegressionModel, ClassificationModel, and KMeans
* Package docsPrashant Sharma2014-05-143-0/+69
| | | | | | | | | | | | | | This is a few changes based on the original patch by @scrapcodes. Author: Prashant Sharma <prashant.s@imaginea.com> Author: Patrick Wendell <pwendell@gmail.com> Closes #785 from pwendell/package-docs and squashes the following commits: c32b731 [Patrick Wendell] Changes based on Prashant's patch c0463d3 [Prashant Sharma] added eof new line ce8bf73 [Prashant Sharma] Added eof new line to all files. 4c35f2e [Prashant Sharma] SPARK-1563 Add package-info.java and package.scala files for all packages that appear in docs
* [SPARK-1696][MLLIB] use alpha in dense dsprXiangrui Meng2014-05-141-1/+1
| | | | | | | | | | It doesn't affect existing code because only `alpha = 1.0` is used in the code. Author: Xiangrui Meng <meng@databricks.com> Closes #778 from mengxr/mllib-dspr-fix and squashes the following commits: a37402e [Xiangrui Meng] use alpha in dense dspr
* SPARK-1791 - SVM implementation does not use threshold parameterAndrew Tulloch2014-05-132-1/+38
| | | | | | | | | | | | | | | | | | | | | | | | Summary: https://issues.apache.org/jira/browse/SPARK-1791 Simple fix, and backward compatible, since - anyone who set the threshold was getting completely wrong answers. - anyone who did not set the threshold had the default 0.0 value for the threshold anyway. Test Plan: Unit test added that is verified to fail under the old implementation, and pass under the new implementation. Reviewers: CC: Author: Andrew Tulloch <andrew@tullo.ch> Closes #725 from ajtulloch/SPARK-1791-SVM and squashes the following commits: 770f55d [Andrew Tulloch] SPARK-1791 - SVM implementation does not use threshold parameter
* SPARK-1798. Tests should clean up temp filesSean Owen2014-05-122-14/+5
| | | | | | | | | | | | | | | | | | | | Three issues related to temp files that tests generate – these should be touched up for hygiene but are not urgent. Modules have a log4j.properties which directs the unit-test.log output file to a directory like `[module]/target/unit-test.log`. But this ends up creating `[module]/[module]/target/unit-test.log` instead of former. The `work/` directory is not deleted by "mvn clean", in the parent and in modules. Neither is the `checkpoint/` directory created under the various external modules. Many tests create a temp directory, which is not usually deleted. This can be largely resolved by calling `deleteOnExit()` at creation and trying to call `Utils.deleteRecursively` consistently to clean up, sometimes in an `@After` method. _If anyone seconds the motion, I can create a more significant change that introduces a new test trait along the lines of `LocalSparkContext`, which provides management of temp directories for subclasses to take advantage of._ Author: Sean Owen <sowen@cloudera.com> Closes #732 from srowen/SPARK-1798 and squashes the following commits: 5af578e [Sean Owen] Try to consistently delete test temp dirs and files, and set deleteOnExit() for each b21b356 [Sean Owen] Remove work/ and checkpoint/ dirs with mvn clean bdd0f41 [Sean Owen] Remove duplicate module dir in log4j.properties output path for tests
* Bug fix of sparse vector conversionFunes2014-05-082-1/+14
| | | | | | | | | | | | | | | Fixed a small bug caused by the inconsistency of index/data array size and vector length. Author: Funes <tianshaocun@gmail.com> Author: funes <tianshaocun@gmail.com> Closes #661 from funes/bugfix and squashes the following commits: edb2b9d [funes] remove unused import 75dced3 [Funes] update test case d129a66 [Funes] Add test for sparse breeze by vector builder 64e7198 [Funes] Copy data only when necessary b85806c [Funes] Bug fix of sparse vector conversion
* [SPARK-1157][MLlib] Bug fix: lossHistory should exclude rejection steps, and ↵DB Tsai2014-05-082-48/+30
| | | | | | | | | | | | | | remove miniBatch Getting the lossHistory from Breeze's API which already excludes the rejection steps in line search. Also, remove the miniBatch in LBFGS since those quasi-Newton methods approximate the inverse of Hessian. It doesn't make sense if the gradients are computed from a varying objective. Author: DB Tsai <dbtsai@alpinenow.com> Closes #582 from dbtsai/dbtsai-lbfgs-bug and squashes the following commits: 9cc6cf9 [DB Tsai] Removed the miniBatch in LBFGS. 1ba6a33 [DB Tsai] Formatting the code. d72c679 [DB Tsai] Using Breeze's states to get the loss.
* SPARK-1544 Add support for deep decision trees.Manish Amde2014-05-073-23/+170
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | @etrain and I came with a PR for arbitrarily deep decision trees at the cost of multiple passes over the data at deep tree levels. To summarize: 1) We take a parameter that indicates the amount of memory users want to reserve for computation on each worker (and 2x that at the driver). 2) Using that information, we calculate two things - the maximum depth to which we train as usual (which is, implicitly, the maximum number of nodes we want to train in parallel), and the size of the groups we should use in the case where we exceed this depth. cc: @atalwalkar, @hirakendu, @mengxr Author: Manish Amde <manish9ue@gmail.com> Author: manishamde <manish9ue@gmail.com> Author: Evan Sparks <sparks@cs.berkeley.edu> Closes #475 from manishamde/deep_tree and squashes the following commits: 968ca9d [Manish Amde] merged master 7fc9545 [Manish Amde] added docs ce004a1 [Manish Amde] minor formatting b27ad2c [Manish Amde] formatting 426bb28 [Manish Amde] programming guide blurb 8053fed [Manish Amde] more formatting 5eca9e4 [Manish Amde] grammar 4731cda [Manish Amde] formatting 5e82202 [Manish Amde] added documentation, fixed off by 1 error in max level calculation cbd9f14 [Manish Amde] modified scala.math to math dad9652 [Manish Amde] removed unused imports e0426ee [Manish Amde] renamed parameter 718506b [Manish Amde] added unit test 1517155 [Manish Amde] updated documentation 9dbdabe [Manish Amde] merge from master 719d009 [Manish Amde] updating user documentation fecf89a [manishamde] Merge pull request #6 from etrain/deep_tree 0287772 [Evan Sparks] Fixing scalastyle issue. 2f1e093 [Manish Amde] minor: added doc for maxMemory parameter 2f6072c [manishamde] Merge pull request #5 from etrain/deep_tree abc5a23 [Evan Sparks] Parameterizing max memory. 50b143a [Manish Amde] adding support for very deep trees
* Update GradientDescentSuite.scalabaishuo(白硕)2014-05-071-3/+3
| | | | | | | | | | | | use more faster way to construct an array Author: baishuo(白硕) <vc_java@hotmail.com> Closes #588 from baishuo/master and squashes the following commits: 45b95fb [baishuo(白硕)] Update GradientDescentSuite.scala c03b61c [baishuo(白硕)] Update GradientDescentSuite.scala b666d27 [baishuo(白硕)] Update GradientDescentSuite.scala
* SPARK-1727. Correct small compile errors, typos, and markdown issues in ↵Sean Owen2014-05-061-6/+6
| | | | | | | | | | | | | | | | | | (primarly) MLlib docs While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs. Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown. Author: Sean Owen <sowen@cloudera.com> Closes #653 from srowen/SPARK-1727 and squashes the following commits: 6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count 8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output) 99966a9 [Sean Owen] Update issue tracker URL in docs 23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak) 8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
* [SPARK-1594][MLLIB] Cleaning up MLlib APIs and guideXiangrui Meng2014-05-0534-320/+381
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Final pass before the v1.0 release. * Remove `VectorRDDs` * Move `BinaryClassificationMetrics` from `evaluation.binary` to `evaluation` * Change default value of `addIntercept` to false and allow to add intercept in Ridge and Lasso. * Clean `DecisionTree` package doc and test suite. * Mark model constructors `private[spark]` * Rename `loadLibSVMData` to `loadLibSVMFile` and hide `LabelParser` from users. * Add `saveAsLibSVMFile`. * Add `appendBias` to `MLUtils`. Author: Xiangrui Meng <meng@databricks.com> Closes #524 from mengxr/mllib-cleaning and squashes the following commits: 295dc8b [Xiangrui Meng] update loadLibSVMFile doc 1977ac1 [Xiangrui Meng] fix doc of appendBias 649fcf0 [Xiangrui Meng] rename loadLibSVMData to loadLibSVMFile; hide LabelParser from user APIs 54b812c [Xiangrui Meng] add appendBias a71e7d0 [Xiangrui Meng] add saveAsLibSVMFile d976295 [Xiangrui Meng] Merge branch 'master' into mllib-cleaning b7e5cec [Xiangrui Meng] remove some experimental annotations and make model constructors private[mllib] 9b02b93 [Xiangrui Meng] minor code style update a593ddc [Xiangrui Meng] fix python tests fc28c18 [Xiangrui Meng] mark more classes experimental f6cbbff [Xiangrui Meng] fix Java tests 0af70b0 [Xiangrui Meng] minor 6e139ef [Xiangrui Meng] Merge branch 'master' into mllib-cleaning 94e6dce [Xiangrui Meng] move BinaryLabelCounter and BinaryConfusionMatrixImpl to evaluation.binary df34907 [Xiangrui Meng] clean DecisionTreeSuite to use LocalSparkContext c81807f [Xiangrui Meng] set the default value of AddIntercept to false 03389c0 [Xiangrui Meng] allow to add intercept in Ridge and Lasso c66c56f [Xiangrui Meng] move tree md to package object doc a2695df [Xiangrui Meng] update guide for BinaryClassificationMetrics 9194f4c [Xiangrui Meng] move BinaryClassificationMetrics one level up 1c1a0e3 [Xiangrui Meng] remove VectorRDDs because it only contains one function that is not necessary for us to maintain
* [SPARK-1646] Micro-optimisation of ALSTor Myklebust2014-04-291-5/+17
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This change replaces some Scala `for` and `foreach` constructs with `while` constructs. There may be a slight performance gain on the order of 1-2% when training an ALS model. I trained an ALS model on the Movielens 10M-rating dataset repeatedly both with and without these changes. All 7 runs in both columns were done in a Scala `for` loop like this: for (iter <- 0 to 10) { val before = System.currentTimeMillis() val model = ALS.train(rats, 20, 10) val after = System.currentTimeMillis() println("%d ms".format(after-before)) println("rmse %g".format(computeRmse(model, rats, numRatings))) } The timings were done on a multiuser machine, and I stopped one set of timings after 7 had been completed. It would be nice if somebody with dedicated hardware could confirm my timings. After Before 121980 ms 122041 ms 117069 ms 117127 ms 115332 ms 117523 ms 115381 ms 117402 ms 114635 ms 116550 ms 114140 ms 114076 ms 112993 ms 117200 ms Ratios are about 1.0005, 1.0005, 1.019, 1.0175, 1.01671, 0.99944, and 1.03723. I therefore suspect these changes make for a slight performance gain on the order of 1-2%. Author: Tor Myklebust <tmyklebu@gmail.com> Closes #568 from tmyklebu/alsopt and squashes the following commits: 5ded80f [Tor Myklebust] Fix style. 79595ff [Tor Myklebust] Fix style error. 4ef0313 [Tor Myklebust] Merge branch 'master' of github.com:apache/spark into alsopt 114fb74 [Tor Myklebust] Turn some 'for' loops into 'while' loops. dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go. 23d6f91 [Tor Myklebust] Stop making the partitioner configurable. 495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark 674933a [Tor Myklebust] Fix style. 40edc23 [Tor Myklebust] Fix missing space. f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach. 5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'. 36a0f43 [Tor Myklebust] Make the partitioner private. d872b09 [Tor Myklebust] Add negative id ALS test. df27697 [Tor Myklebust] Support custom partitioners. Currently we use the same partitioner for users and products. c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing. c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
* [SPARK-1636][MLLIB] Move main methods to examplesXiangrui Meng2014-04-299-304/+7
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | * `NaiveBayes` -> `SparseNaiveBayes` * `KMeans` -> `DenseKMeans` * `SVMWithSGD` and `LogisticRegerssionWithSGD` -> `BinaryClassification` * `ALS` -> `MovieLensALS` * `LinearRegressionWithSGD`, `LassoWithSGD`, and `RidgeRegressionWithSGD` -> `LinearRegression` * `DecisionTree` -> `DecisionTreeRunner` `scopt` is used for parsing command-line parameters. `scopt` has MIT license and it only depends on `scala-library`. Example help message: ~~~ BinaryClassification: an example app for binary classification. Usage: BinaryClassification [options] <input> --numIterations <value> number of iterations --stepSize <value> initial step size, default: 1.0 --algorithm <value> algorithm (SVM,LR), default: LR --regType <value> regularization type (L1,L2), default: L2 --regParam <value> regularization parameter, default: 0.1 <input> input paths to labeled examples in LIBSVM format ~~~ Author: Xiangrui Meng <meng@databricks.com> Closes #584 from mengxr/mllib-main and squashes the following commits: 7b58c60 [Xiangrui Meng] minor 6e35d7e [Xiangrui Meng] make imports explicit and fix code style c6178c9 [Xiangrui Meng] update TS PCA/SVD to use new spark-submit 6acff75 [Xiangrui Meng] use scopt for DecisionTreeRunner be86069 [Xiangrui Meng] use main instead of extending App b3edf68 [Xiangrui Meng] move DecisionTree's main method to examples 8bfaa5a [Xiangrui Meng] change NaiveBayesParams to Params fe23dcb [Xiangrui Meng] remove main from KMeans and add DenseKMeans as an example 67f4448 [Xiangrui Meng] remove main methods from linear regression algorithms and add LinearRegression example b066bbc [Xiangrui Meng] remove main from ALS and add MovieLensALS example b040f3b [Xiangrui Meng] change BinaryClassificationParams to Params 577945b [Xiangrui Meng] remove unused imports from NB 3d299bc [Xiangrui Meng] remove main from LR/SVM and add an example app for binary classification f70878e [Xiangrui Meng] remove main from NaiveBayes and add an example NaiveBayes app 01ec2cd [Xiangrui Meng] Merge branch 'master' into mllib-main 9420692 [Xiangrui Meng] add scopt to examples dependencies
* Improved build configurationwitgo2014-04-281-14/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | 1, Fix SPARK-1441: compile spark core error with hadoop 0.23.x 2, Fix SPARK-1491: maven hadoop-provided profile fails to build 3, Fix org.scala-lang: * ,org.apache.avro:* inconsistent versions dependency 4, A modified on the sql/catalyst/pom.xml,sql/hive/pom.xml,sql/core/pom.xml (Four spaces formatted into two spaces) Author: witgo <witgo@qq.com> Closes #480 from witgo/format_pom and squashes the following commits: 03f652f [witgo] review commit b452680 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom bee920d [witgo] revert fix SPARK-1629: Spark Core missing commons-lang dependence 7382a07 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom 6902c91 [witgo] fix SPARK-1629: Spark Core missing commons-lang dependence 0da4bc3 [witgo] merge master d1718ed [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom e345919 [witgo] add avro dependency to yarn-alpha 77fad08 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom 62d0862 [witgo] Fix org.scala-lang: * inconsistent versions dependency 1a162d7 [witgo] Merge branch 'master' of https://github.com/apache/spark into format_pom 934f24d [witgo] review commit cf46edc [witgo] exclude jruby 06e7328 [witgo] Merge branch 'SparkBuild' into format_pom 99464d2 [witgo] fix maven hadoop-provided profile fails to build 0c6c1fc [witgo] Fix compile spark core error with hadoop 0.23.x 6851bec [witgo] Maintain consistent SparkBuild.scala, pom.xml
* [Fix #79] Replace Breakable For Loops By While LoopsSandeep2014-04-231-29/+31
| | | | | | | | | | Author: Sandeep <sandeep@techaddict.me> Closes #503 from techaddict/fix-79 and squashes the following commits: e3f6746 [Sandeep] Style changes 07a4f6b [Sandeep] for loop to While loop 0a6d8e9 [Sandeep] Breakable for loop to While loop
* [SPARK-1506][MLLIB] Documentation improvements for MLlib 1.0Xiangrui Meng2014-04-221-0/+100
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Preview: http://54.82.240.23:4000/mllib-guide.html Table of contents: * Basics * Data types * Summary statistics * Classification and regression * linear support vector machine (SVM) * logistic regression * linear linear squares, Lasso, and ridge regression * decision tree * naive Bayes * Collaborative Filtering * alternating least squares (ALS) * Clustering * k-means * Dimensionality reduction * singular value decomposition (SVD) * principal component analysis (PCA) * Optimization * stochastic gradient descent * limited-memory BFGS (L-BFGS) Author: Xiangrui Meng <meng@databricks.com> Closes #422 from mengxr/mllib-doc and squashes the following commits: 944e3a9 [Xiangrui Meng] merge master f9fda28 [Xiangrui Meng] minor 9474065 [Xiangrui Meng] add alpha to ALS examples 928e630 [Xiangrui Meng] initialization_mode -> initializationMode 5bbff49 [Xiangrui Meng] add imports to labeled point examples c17440d [Xiangrui Meng] fix python nb example 28f40dc [Xiangrui Meng] remove localhost:4000 369a4d3 [Xiangrui Meng] Merge branch 'master' into mllib-doc 7dc95cc [Xiangrui Meng] update linear methods 053ad8a [Xiangrui Meng] add links to go back to the main page abbbf7e [Xiangrui Meng] update ALS argument names 648283e [Xiangrui Meng] level down statistics 14e2287 [Xiangrui Meng] add sample libsvm data and use it in guide 8cd2441 [Xiangrui Meng] minor updates 186ab07 [Xiangrui Meng] update section names 6568d65 [Xiangrui Meng] update toc, level up lr and svm 162ee12 [Xiangrui Meng] rename section names 5c1e1b1 [Xiangrui Meng] minor 8aeaba1 [Xiangrui Meng] wrap long lines 6ce6a6f [Xiangrui Meng] add summary statistics to toc 5760045 [Xiangrui Meng] claim beta cc604bf [Xiangrui Meng] remove classification and regression 92747b3 [Xiangrui Meng] make section titles consistent e605dd6 [Xiangrui Meng] add LIBSVM loader f639674 [Xiangrui Meng] add python section to migration guide c82ffb4 [Xiangrui Meng] clean optimization 31660eb [Xiangrui Meng] update linear algebra and stat 0a40837 [Xiangrui Meng] first pass over linear methods 1fc8271 [Xiangrui Meng] update toc 906ed0a [Xiangrui Meng] add a python example to naive bayes 5f0a700 [Xiangrui Meng] update collaborative filtering 656d416 [Xiangrui Meng] update mllib-clustering 86e143a [Xiangrui Meng] remove data types section from main page 8d1a128 [Xiangrui Meng] move part of linear algebra to data types and add Java/Python examples d1b5cbf [Xiangrui Meng] merge master 72e4804 [Xiangrui Meng] one pass over tree guide 64f8995 [Xiangrui Meng] move decision tree guide to a separate file 9fca001 [Xiangrui Meng] add first version of linear algebra guide 53c9552 [Xiangrui Meng] update dependencies f316ec2 [Xiangrui Meng] add migration guide f399f6c [Xiangrui Meng] move linear-algebra to dimensionality-reduction 182460f [Xiangrui Meng] add guide for naive Bayes 137fd1d [Xiangrui Meng] re-organize toc a61e434 [Xiangrui Meng] update mllib's toc
* [SPARK-1281] Improve partitioning in ALSTor Myklebust2014-04-222-23/+54
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ALS was using HashPartitioner and explicit uses of `%` together. Further, the naked use of `%` meant that, if the number of partitions corresponded with the stride of arithmetic progressions appearing in user and product ids, users and products could be mapped into buckets in an unfair or unwise way. This pull request: 1) Makes the Partitioner an instance variable of ALS. 2) Replaces the direct uses of `%` with calls to a Partitioner. 3) Defines an anonymous Partitioner that scrambles the bits of the object's hashCode before reducing to the number of present buckets. This pull request does not make the partitioner user-configurable. I'm not all that happy about the way I did (1). It introduces an icky lifetime issue and dances around it by nulling something. However, I don't know a better way to make the partitioner visible everywhere it needs to be visible. Author: Tor Myklebust <tmyklebu@gmail.com> Closes #407 from tmyklebu/master and squashes the following commits: dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go. 23d6f91 [Tor Myklebust] Stop making the partitioner configurable. 495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark 674933a [Tor Myklebust] Fix style. 40edc23 [Tor Myklebust] Fix missing space. f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach. 5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'. 36a0f43 [Tor Myklebust] Make the partitioner private. d872b09 [Tor Myklebust] Add negative id ALS test. df27697 [Tor Myklebust] Support custom partitioners. Currently we use the same partitioner for users and products. c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing. c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
* [Fix #274] Document + fix annotation usagesAndrew Or2014-04-212-6/+3
| | | | | | | | | | | | | | | ... so that we don't follow an unspoken set of forbidden rules for adding **@AlphaComponent**, **@DeveloperApi**, and **@Experimental** annotations in the code. In addition, this PR (1) removes unnecessary `:: * ::` tags, (2) adds missing `:: * ::` tags, and (3) removes annotations for internal APIs. Author: Andrew Or <andrewor14@gmail.com> Closes #470 from andrewor14/annotations-fix and squashes the following commits: 92a7f42 [Andrew Or] Document + fix annotation usages
* [SPARK-1535] ALS: Avoid the garbage-creating ctor of DoubleMatrixTor Myklebust2014-04-191-2/+11
| | | | | | | | | | | `new DoubleMatrix(double[])` creates a garbage `double[]` of the same length as its argument and immediately throws it away. This pull request avoids that constructor in the ALS code. Author: Tor Myklebust <tmyklebu@gmail.com> Closes #442 from tmyklebu/foo2 and squashes the following commits: 2784fc5 [Tor Myklebust] Mention that this is probably fixed as of jblas 1.2.4; repunctuate. a09904f [Tor Myklebust] Helper function for wrapping Array[Double]'s with DoubleMatrix's.
* SPARK-1357 (addendum). More Experimental items in MLlibSean Owen2014-04-183-0/+10
| | | | | | | | | | | | Per discussion, this is my suggestion to make ALS Rating, ClassificationModel, RegressionModel experimental for now, to reserve the right to possibly change after 1.0. See what you think of this much. Author: Sean Owen <sowen@cloudera.com> Closes #372 from srowen/SPARK-1357Addendum and squashes the following commits: 17cf1ea [Sean Owen] Remove (another) blank line after ":: Experimental ::" 6800e4c [Sean Owen] Remove blank line after ":: Experimental ::" b3a88d2 [Sean Owen] Make ALS Rating, ClassificationModel, RegressionModel experimental for now, to reserve the right to possibly change after 1.0
* SPARK-1483: Rename minSplits to minPartitions in public APIsCodingCat2014-04-181-6/+6
| | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-1483 From the original JIRA: " The parameter name is part of the public API in Scala and Python, since you can pass named parameters to a method, so we should name it to this more descriptive term. Everywhere else we refer to "splits" as partitions." - @mateiz Author: CodingCat <zhunansjtu@gmail.com> Closes #430 from CodingCat/SPARK-1483 and squashes the following commits: 4b60541 [CodingCat] deprecate defaultMinSplits ba2c663 [CodingCat] Rename minSplits to minPartitions in public APIs
* SPARK-1310: Start adding k-fold cross validation to MLLib [adds kFold to ↵Holden Karau2014-04-162-0/+60
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | MLUtils & fixes bug in BernoulliSampler] Author: Holden Karau <holden@pigscanfly.ca> Closes #18 from holdenk/addkfoldcrossvalidation and squashes the following commits: 208db9b [Holden Karau] Fix a bad space e84f2fc [Holden Karau] Fix the test, we should be looking at the second element instead 6ddbf05 [Holden Karau] swap training and validation order 7157ae9 [Holden Karau] CR feedback 90896c7 [Holden Karau] New line 150889c [Holden Karau] Fix up error messages in the MLUtilsSuite 2cb90b3 [Holden Karau] Fix the names in kFold c702a96 [Holden Karau] Fix imports in MLUtils e187e35 [Holden Karau] Move { up to same line as whenExecuting(random) in RandomSamplerSuite.scala c5b723f [Holden Karau] clean up 7ebe4d5 [Holden Karau] CR feedback, remove unecessary learners (came back during merge mistake) and insert an empty line bb5fa56 [Holden Karau] extra line sadness 163c5b1 [Holden Karau] code review feedback 1.to -> 1 to and folds -> numFolds 5a33f1d [Holden Karau] Code review follow up. e8741a7 [Holden Karau] CR feedback b78804e [Holden Karau] Remove cross validation [TODO in another pull request] 91eae64 [Holden Karau] Consolidate things in mlutils 264502a [Holden Karau] Add a test for the bug that was found with BernoulliSampler not copying the complement param dd0b737 [Holden Karau] Wrap long lines (oops) c0b7fa4 [Holden Karau] Switch FoldedRDD to use BernoulliSampler and PartitionwiseSampledRDD 08f8e4d [Holden Karau] Fix BernoulliSampler to respect complement a751ec6 [Holden Karau] Add k-fold cross validation to MLLib
* [WIP] SPARK-1430: Support sparse data in Python MLlibMatei Zaharia2014-04-153-50/+132
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type. On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models. Some to-do items left: - [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector. - [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling. - [x] Explain how to use these in the Python MLlib docs. CC @mengxr, @joshrosen Author: Matei Zaharia <matei@databricks.com> Closes #341 from mateiz/py-ml-update and squashes the following commits: d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge b9f97a3 [Matei Zaharia] Fix test 1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python 88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs 37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script. a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights 74eefe7 [Matei Zaharia] Added LabeledPoint class in Python 889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict a5d6426 [Matei Zaharia] Add linalg.py to run-tests script 0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data 2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data 154f45d [Matei Zaharia] Update docs, name some magic values 881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
* Decision Tree documentation for MLlib programming guideManish Amde2014-04-151-0/+569
| | | | | | | | | | | | | | | | | | | | | | | | | | | | Added documentation for user to use the decision tree algorithms for classification and regression in Spark 1.0 release. Apart from a general review, I need specific input on the following: * I had to move a lot of the existing documentation under the *linear methods* umbrella to accommodate decision trees. I wonder if there is a better way to organize the programming guide given we are so close to the release. * I have not looked closely at pyspark but I am wondering new mllib algorithms are automatically plugged in or do we need to some extra work to call mllib functions from pyspark. I will add to the pyspark examples based upon the advice I get. cc: @mengxr, @hirakendu, @etrain, @atalwalkar Author: Manish Amde <manish9ue@gmail.com> Closes #402 from manishamde/tree_doc and squashes the following commits: 022485a [Manish Amde] more documentation 865826e [Manish Amde] minor: grammar dbb0e5e [Manish Amde] minor improvements to text b9ef6c4 [Manish Amde] basic decision tree code examples 6e297d7 [Manish Amde] added subsections f427e84 [Manish Amde] renaming sections 9c0c4be [Manish Amde] split candidate 6925275 [Manish Amde] impurity and information gain 94fd2f9 [Manish Amde] more reorg b93125c [Manish Amde] more subsection reorg 3ecb2ad [Manish Amde] minor text addition 1537dd3 [Manish Amde] added placeholders and some doc d06511d [Manish Amde] basic skeleton
* [SPARK-1157][MLlib] L-BFGS Optimizer based on Breeze's implementation.DB Tsai2014-04-153-14/+480
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR uses Breeze's L-BFGS implement, and Breeze dependency has already been introduced by Xiangrui's sparse input format work in SPARK-1212. Nice work, @mengxr ! When use with regularized updater, we need compute the regVal and regGradient (the gradient of regularized part in the cost function), and in the currently updater design, we can compute those two values by the following way. Let's review how updater works when returning newWeights given the input parameters. w' = w - thisIterStepSize * (gradient + regGradient(w)) Note that regGradient is function of w! If we set gradient = 0, thisIterStepSize = 1, then regGradient(w) = w - w' As a result, for regVal, it can be computed by val regVal = updater.compute( weights, new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2 and for regGradient, it can be obtained by val regGradient = weights.sub( updater.compute(weights, new DoubleMatrix(initialWeights.length, 1), 1, 1, regParam)._1) The PR includes the tests which compare the result with SGD with/without regularization. We did a comparison between LBFGS and SGD, and often we saw 10x less steps in LBFGS while the cost of per step is the same (just computing the gradient). The following is the paper by Prof. Ng at Stanford comparing different optimizers including LBFGS and SGD. They use them in the context of deep learning, but worth as reference. http://cs.stanford.edu/~jngiam/papers/LeNgiamCoatesLahiriProchnowNg2011.pdf Author: DB Tsai <dbtsai@alpinenow.com> Closes #353 from dbtsai/dbtsai-LBFGS and squashes the following commits: 984b18e [DB Tsai] L-BFGS Optimizer based on Breeze's implementation. Also fixed indentation issue in GradientDescent optimizer.
* SPARK-1488. Resolve scalac feature warnings during buildSean Owen2014-04-143-0/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | For your consideration: scalac currently notes a number of feature warnings during compilation: ``` [warn] there were 65 feature warning(s); re-run with -feature for details ``` Warnings are like: ``` [warn] /Users/srowen/Documents/spark/core/src/main/scala/org/apache/spark/SparkContext.scala:1261: implicit conversion method rddToPairRDDFunctions should be enabled [warn] by making the implicit value scala.language.implicitConversions visible. [warn] This can be achieved by adding the import clause 'import scala.language.implicitConversions' [warn] or by setting the compiler option -language:implicitConversions. [warn] See the Scala docs for value scala.language.implicitConversions for a discussion [warn] why the feature should be explicitly enabled. [warn] implicit def rddToPairRDDFunctions[K: ClassTag, V: ClassTag](rdd: RDD[(K, V)]) = [warn] ^ ``` scalac is suggesting that it's just best practice to explicitly enable certain language features by importing them where used. This PR simply adds the imports it suggests (and squashes one other Java warning along the way). This leaves just deprecation warnings in the build. Author: Sean Owen <sowen@cloudera.com> Closes #404 from srowen/SPARK-1488 and squashes the following commits: 8598980 [Sean Owen] Quiet scalac warnings about language features by explicitly importing language features. 39bc831 [Sean Owen] Enable -feature in scalac to emit language feature warnings
* [WIP] [SPARK-1328] Add vector statisticsXusen Yin2014-04-115-76/+230
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | As with the new vector system in MLlib, we find that it is good to add some new APIs to precess the `RDD[Vector]`. Beside, the former implementation of `computeStat` is not stable which could loss precision, and has the possibility to cause `Nan` in scientific computing, just as said in the [SPARK-1328](https://spark-project.atlassian.net/browse/SPARK-1328). APIs contain: * rowMeans(): RDD[Double] * rowNorm2(): RDD[Double] * rowSDs(): RDD[Double] * colMeans(): Vector * colMeans(size: Int): Vector * colNorm2(): Vector * colNorm2(size: Int): Vector * colSDs(): Vector * colSDs(size: Int): Vector * maxOption((Vector, Vector) => Boolean): Option[Vector] * minOption((Vector, Vector) => Boolean): Option[Vector] * rowShrink(): RDD[Vector] * colShrink(): RDD[Vector] This is working in process now, and some more APIs will add to `LabeledPoint`. Moreover, the implicit declaration will move from `MLUtils` to `MLContext` later. Author: Xusen Yin <yinxusen@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #268 from yinxusen/vector-statistics and squashes the following commits: d61363f [Xusen Yin] rebase to latest master 16ae684 [Xusen Yin] fix minor error and remove useless method 10cf5d3 [Xusen Yin] refine some return type b064714 [Xusen Yin] remove computeStat in MLUtils cbbefdb [Xiangrui Meng] update multivariate statistical summary interface and clean tests 4eaf28a [Xusen Yin] merge VectorRDDStatistics into RowMatrix 48ee053 [Xusen Yin] fix minor error e624f93 [Xusen Yin] fix scala style error 1fba230 [Xusen Yin] merge while loop together 69e1f37 [Xusen Yin] remove lazy eval, and minor memory footprint 548e9de [Xusen Yin] minor revision 86522c4 [Xusen Yin] add comments on functions dc77e38 [Xusen Yin] test sparse vector RDD 18cf072 [Xusen Yin] change def to lazy val to make sure that the computations in function be evaluated only once f7a3ca2 [Xusen Yin] fix the corner case of maxmin 967d041 [Xusen Yin] full revision with Aggregator class 138300c [Xusen Yin] add new Aggregator class 1376ff4 [Xusen Yin] rename variables and adjust code 4a5c38d [Xusen Yin] add scala doc, refine code and comments 036b7a5 [Xusen Yin] fix the bug of Nan occur f6e8e9a [Xusen Yin] add sparse vectors test 4cfbadf [Xusen Yin] fix bug of min max 4e4fbd1 [Xusen Yin] separate seqop and combop out as independent functions a6d5a2e [Xusen Yin] rewrite for only computing non-zero elements 3980287 [Xusen Yin] rename variables 62a2c3e [Xusen Yin] use axpy and in-place if possible 9a75ebd [Xusen Yin] add case class to wrap return values d816ac7 [Xusen Yin] remove useless APIs c4651bb [Xusen Yin] remove row-wise APIs and refine code 1338ea1 [Xusen Yin] all-in-one version test passed cc65810 [Xusen Yin] add parallel mean and variance 9af2e95 [Xusen Yin] refine the code style ad6c82d [Xusen Yin] add shrink test e09d5d2 [Xusen Yin] add scala docs and refine shrink method 8ef3377 [Xusen Yin] pass all tests 28cf060 [Xusen Yin] fix error of column means 54b19ab [Xusen Yin] add new API to shrink RDD[Vector] 8c6c0e1 [Xusen Yin] add basic statistics
* [SPARK-1225, 1241] [MLLIB] Add AreaUnderCurve and BinaryClassificationMetricsXiangrui Meng2014-04-119-0/+671
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR implements a generic version of `AreaUnderCurve` using the `RDD.sliding` implementation from https://github.com/apache/spark/pull/136 . It also contains refactoring of https://github.com/apache/spark/pull/160 for binary classification evaluation. Author: Xiangrui Meng <meng@databricks.com> Closes #364 from mengxr/auc and squashes the following commits: a05941d [Xiangrui Meng] replace TP/FP/TN/FN by their full names 3f42e98 [Xiangrui Meng] add (0, 0), (1, 1) to roc, and (0, 1) to pr fb4b6d2 [Xiangrui Meng] rename Evaluator to Metrics and add more metrics b1b7dab [Xiangrui Meng] fix code styles 9dc3518 [Xiangrui Meng] add tests for BinaryClassificationEvaluator ca31da5 [Xiangrui Meng] remove PredictionAndResponse 3d71525 [Xiangrui Meng] move binary evalution classes to evaluation.binary 8f78958 [Xiangrui Meng] add PredictionAndResponse dda82d5 [Xiangrui Meng] add confusion matrix aa7e278 [Xiangrui Meng] add initial version of binary classification evaluator 221ebce [Xiangrui Meng] add a new test to sliding a920865 [Xiangrui Meng] Merge branch 'sliding' into auc a9b250a [Xiangrui Meng] move sliding to mllib cab9a52 [Xiangrui Meng] use last for the last element db6cb30 [Xiangrui Meng] remove unnecessary toSeq 9916202 [Xiangrui Meng] change RDD.sliding return type to RDD[Seq[T]] 284d991 [Xiangrui Meng] change SlidedRDD to SlidingRDD c1c6c22 [Xiangrui Meng] add AreaUnderCurve 65461b2 [Xiangrui Meng] Merge branch 'sliding' into auc 5ee6001 [Xiangrui Meng] add TODO d2a600d [Xiangrui Meng] add sliding to rdd
* Remove Unnecessary Whitespace'sSandeep2014-04-102-3/+3
| | | | | | | | | | stack these together in a commit else they show up chunk by chunk in different commits. Author: Sandeep <sandeep@techaddict.me> Closes #380 from techaddict/white_space and squashes the following commits: b58f294 [Sandeep] Remove Unnecessary Whitespace's
* [SPARK-1357 (fix)] remove empty line after :: DeveloperApi/Experimental ::Xiangrui Meng2014-04-0933-71/+21
| | | | | | | | | | Remove empty line after :: DeveloperApi/Experimental :: in comments to make the original doc show up in the preview of the generated html docs. Thanks @andrewor14 ! Author: Xiangrui Meng <meng@databricks.com> Closes #373 from mengxr/api and squashes the following commits: 9c35bdc [Xiangrui Meng] remove the empty line after :: DeveloperApi/Experimental ::
* [SPARK-1357] [MLLIB] Annotate developer and experimental APIsXiangrui Meng2014-04-0942-122/+355
| | | | | | | | | | | | | | | | | | | | | Annotate developer and experimental APIs in MLlib. Author: Xiangrui Meng <meng@databricks.com> Closes #298 from mengxr/api and squashes the following commits: 13390e8 [Xiangrui Meng] Merge branch 'master' into api dc4cbb3 [Xiangrui Meng] mark distribute matrices experimental 6b9f8e2 [Xiangrui Meng] add Experimental annotation 8773d0d [Xiangrui Meng] add DeveloperApi annotation da31733 [Xiangrui Meng] update developer and experimental tags 555e0fe [Xiangrui Meng] Merge branch 'master' into api ef1a717 [Xiangrui Meng] mark some constructors private add default parameters to JavaDoc 00ffbcc [Xiangrui Meng] update tree API annotation 0b674fa [Xiangrui Meng] mark decision tree APIs 86b9e34 [Xiangrui Meng] one pass over APIs of GLMs, NaiveBayes, and ALS f21d862 [Xiangrui Meng] Merge branch 'master' into api 2b133d6 [Xiangrui Meng] intial annotation of developer and experimental apis
* [SPARK-1390] Refactoring of matrices backed by RDDsXiangrui Meng2014-04-0818-992/+1152
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This is to refactor interfaces for matrices backed by RDDs. It would be better if we have a clear separation of local matrices and those backed by RDDs. Right now, we have 1. `org.apache.spark.mllib.linalg.SparseMatrix`, which is a wrapper over an RDD of matrix entries, i.e., coordinate list format. 2. `org.apache.spark.mllib.linalg.TallSkinnyDenseMatrix`, which is a wrapper over RDD[Array[Double]], i.e. row-oriented format. We will see naming collision when we introduce local `SparseMatrix`, and the name `TallSkinnyDenseMatrix` is not exact if we switch to `RDD[Vector]` from `RDD[Array[Double]]`. It would be better to have "RDD" in the class name to suggest that operations may trigger jobs. The proposed names are (all under `org.apache.spark.mllib.linalg.rdd`): 1. `RDDMatrix`: trait for matrices backed by one or more RDDs 2. `CoordinateRDDMatrix`: wrapper of `RDD[(Long, Long, Double)]` 3. `RowRDDMatrix`: wrapper of `RDD[Vector]` whose rows do not have special ordering 4. `IndexedRowRDDMatrix`: wrapper of `RDD[(Long, Vector)]` whose rows are associated with indices The current code also introduces local matrices. Author: Xiangrui Meng <meng@databricks.com> Closes #296 from mengxr/mat and squashes the following commits: 24d8294 [Xiangrui Meng] fix for groupBy returning Iterable bfc2b26 [Xiangrui Meng] merge master 8e4f1f5 [Xiangrui Meng] Merge branch 'master' into mat 0135193 [Xiangrui Meng] address Reza's comments 03cd7e1 [Xiangrui Meng] add pca/gram to IndexedRowMatrix add toBreeze to DistributedMatrix for test simplify tests b177ff1 [Xiangrui Meng] address Matei's comments be119fe [Xiangrui Meng] rename m/n to numRows/numCols for local matrix add tests for matrices b881506 [Xiangrui Meng] rename SparkPCA/SVD to TallSkinnyPCA/SVD e7d0d4a [Xiangrui Meng] move IndexedRDDMatrixRow to IndexedRowRDDMatrix 0d1491c [Xiangrui Meng] fix test errors a85262a [Xiangrui Meng] rename RDDMatrixRow to IndexedRDDMatrixRow b8b6ac3 [Xiangrui Meng] Remove old code 4cf679c [Xiangrui Meng] port pca to RowRDDMatrix, and add multiply and covariance 7836e2f [Xiangrui Meng] initial refactoring of matrices backed by RDDs
* [SPARK-1434] [MLLIB] change labelParser from anonymous function to traitXiangrui Meng2014-04-084-25/+97
| | | | | | | | | | | | | | | | This is a patch to address @mateiz 's comment in https://github.com/apache/spark/pull/245 MLUtils#loadLibSVMData uses an anonymous function for the label parser. Java users won't like it. So I make a trait for LabelParser and provide two implementations: binary and multiclass. Author: Xiangrui Meng <meng@databricks.com> Closes #345 from mengxr/label-parser and squashes the following commits: ac44409 [Xiangrui Meng] use singleton objects for label parsers 3b1a7c6 [Xiangrui Meng] add tests for label parsers c2e571c [Xiangrui Meng] rename LabelParser.apply to LabelParser.parse use extends for singleton 11c94e0 [Xiangrui Meng] add return types 7f8eb36 [Xiangrui Meng] change labelParser from annoymous function to trait
* Spark 1271: Co-Group and Group-By should pass Iterable[X]Holden Karau2014-04-083-8/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Author: Holden Karau <holden@pigscanfly.ca> Closes #242 from holdenk/spark-1320-cogroupandgroupshouldpassiterator and squashes the following commits: f289536 [Holden Karau] Fix bad merge, should have been Iterable rather than Iterator 77048f8 [Holden Karau] Fix merge up to master d3fe909 [Holden Karau] use toSeq instead 7a092a3 [Holden Karau] switch resultitr to resultiterable eb06216 [Holden Karau] maybe I should have had a coffee first. use correct import for guava iterables c5075aa [Holden Karau] If guava 14 had iterables 2d06e10 [Holden Karau] Fix Java 8 cogroup tests for the new API 11e730c [Holden Karau] Fix streaming tests 66b583d [Holden Karau] Fix the core test suite to compile 4ed579b [Holden Karau] Refactor from iterator to iterable d052c07 [Holden Karau] Python tests now pass with iterator pandas 3bcd81d [Holden Karau] Revert "Try and make pickling list iterators work" cd1e81c [Holden Karau] Try and make pickling list iterators work c60233a [Holden Karau] Start investigating moving to iterators for python API like the Java/Scala one. tl;dr: We will have to write our own iterator since the default one doesn't pickle well 88a5cef [Holden Karau] Fix cogroup test in JavaAPISuite for streaming a5ee714 [Holden Karau] oops, was checking wrong iterator e687f21 [Holden Karau] Fix groupbykey test in JavaAPISuite of streaming ec8cc3e [Holden Karau] Fix test issues\! 4b0eeb9 [Holden Karau] Switch cast in PairDStreamFunctions fa395c9 [Holden Karau] Revert "Add a join based on the problem in SVD" ec99e32 [Holden Karau] Revert "Revert this but for now put things in list pandas" b692868 [Holden Karau] Revert 7e533f7 [Holden Karau] Fix the bug 8a5153a [Holden Karau] Revert me, but we have some stuff to debug b4e86a9 [Holden Karau] Add a join based on the problem in SVD c4510e2 [Holden Karau] Revert this but for now put things in list pandas b4e0b1d [Holden Karau] Fix style issues 71e8b9f [Holden Karau] I really need to stop calling size on iterators, it is the path of sadness. b1ae51a [Holden Karau] Fix some of the types in the streaming JavaAPI suite. Probably still needs more work 37888ec [Holden Karau] core/tests now pass 249abde [Holden Karau] org.apache.spark.rdd.PairRDDFunctionsSuite passes 6698186 [Holden Karau] Revert "I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy" fe992fe [Holden Karau] hmmm try and fix up basic operation suite 172705c [Holden Karau] Fix Java API suite caafa63 [Holden Karau] I think this might be a bad rabbit hole. Started work to make CoGroupedRDD use iterator and then went crazy 88b3329 [Holden Karau] Fix groupbykey to actually give back an iterator 4991af6 [Holden Karau] Fix some tests be50246 [Holden Karau] Calling size on an iterator is not so good if we want to use it after 687ffbc [Holden Karau] This is the it compiles point of replacing Seq with Iterator and JList with JIterator in the groupby and cogroup signatures
* SPARK-1387. Update build plugins, avoid plugin version warning, centralize ↵Sean Owen2014-04-061-1/+1
| | | | | | | | | | | | | | | | | | | | versions Another handful of small build changes to organize and standardize a bit, and avoid warnings: - Update Maven plugin versions for good measure - Since plugins need maven 3.0.4 already, require it explicitly (<3.0.4 had some bugs anyway) - Use variables to define versions across dependencies where they should move in lock step - ... and make this consistent between Maven/SBT OK, I also updated the JIRA URL while I was at it here. Author: Sean Owen <sowen@cloudera.com> Closes #291 from srowen/SPARK-1387 and squashes the following commits: 461eca1 [Sean Owen] Couldn't resist also updating JIRA location to new one c2d5cc5 [Sean Owen] Update plugins and Maven version; use variables consistently across Maven/SBT to define dependency versions that should stay in step.
* [SPARK-1212, Part II] Support sparse data in MLlibXiangrui Meng2014-04-0238-576/+915
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In PR https://github.com/apache/spark/pull/117, we added dense/sparse vector data model and updated KMeans to support sparse input. This PR is to replace all other `Array[Double]` usage by `Vector` in generalized linear models (GLMs) and Naive Bayes. Major changes: 1. `LabeledPoint` becomes `LabeledPoint(Double, Vector)`. 2. Methods that accept `RDD[Array[Double]]` now accept `RDD[Vector]`. We cannot support both in an elegant way because of type erasure. 3. Mark 'createModel' and 'predictPoint' protected because they are not for end users. 4. Add libSVMFile to MLContext. 5. NaiveBayes can accept arbitrary labels (introducing a breaking change to Python's `NaiveBayesModel`). 6. Gradient computation no longer creates temp vectors. 7. Column normalization and centering are removed from Lasso and Ridge because the operation will densify the data. Simple feature transformation can be done before training. TODO: 1. ~~Use axpy when possible.~~ 2. ~~Optimize Naive Bayes.~~ Author: Xiangrui Meng <meng@databricks.com> Closes #245 from mengxr/vector and squashes the following commits: eb6e793 [Xiangrui Meng] move libSVMFile to MLUtils and rename to loadLibSVMData c26c4fc [Xiangrui Meng] update DecisionTree to use RDD[Vector] 11999c7 [Xiangrui Meng] Merge branch 'master' into vector f7da54b [Xiangrui Meng] add minSplits to libSVMFile da25e24 [Xiangrui Meng] revert the change to default addIntercept because it might change the behavior of existing code without warning 493f26f [Xiangrui Meng] Merge branch 'master' into vector 7c1bc01 [Xiangrui Meng] add a TODO to NB b9b7ef7 [Xiangrui Meng] change default value of addIntercept to false b01df54 [Xiangrui Meng] allow to change or clear threshold in LR and SVM 4addc50 [Xiangrui Meng] merge master 4ca5b1b [Xiangrui Meng] remove normalization from Lasso and update tests f04fe8a [Xiangrui Meng] remove normalization from RidgeRegression and update tests d088552 [Xiangrui Meng] use static constructor for MLContext 6f59eed [Xiangrui Meng] update libSVMFile to determine number of features automatically 3432e84 [Xiangrui Meng] update NaiveBayes to support sparse data 0f8759b [Xiangrui Meng] minor updates to NB b11659c [Xiangrui Meng] style update 78c4671 [Xiangrui Meng] add libSVMFile to MLContext f0fe616 [Xiangrui Meng] add a test for sparse linear regression 44733e1 [Xiangrui Meng] use in-place gradient computation e981396 [Xiangrui Meng] use axpy in Updater db808a1 [Xiangrui Meng] update JavaLR example befa592 [Xiangrui Meng] passed scala/java tests 75c83a4 [Xiangrui Meng] passed test compile 1859701 [Xiangrui Meng] passed compile 834ada2 [Xiangrui Meng] optimized MLUtils.computeStats update some ml algorithms to use Vector (cont.) 135ab72 [Xiangrui Meng] merge glm 0e57aa4 [Xiangrui Meng] update Lasso and RidgeRegression to parse the weights correctly from GLM mark createModel protected mark predictPoint protected d7f629f [Xiangrui Meng] fix a bug in GLM when intercept is not used 3f346ba [Xiangrui Meng] update some ml algorithms to use Vector
generated by cgit v1.2.3 (git 2.39.1) at 2024-08-14 02:08:22 +0000