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* 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
* MLI-1 Decision TreesManish Amde2014-04-0117-0/+2188
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Joint work with @hirakendu, @etrain, @atalwalkar and @harsha2010. Key features: + Supports binary classification and regression + Supports gini, entropy and variance for information gain calculation + Supports both continuous and categorical features The algorithm has gone through several development iterations over the last few months leading to a highly optimized implementation. Optimizations include: 1. Level-wise training to reduce passes over the entire dataset. 2. Bin-wise split calculation to reduce computation overhead. 3. Aggregation over partitions before combining to reduce communication overhead. Author: Manish Amde <manish9ue@gmail.com> Author: manishamde <manish9ue@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #79 from manishamde/tree and squashes the following commits: 1e8c704 [Manish Amde] remove numBins field in the Strategy class 7d54b4f [manishamde] Merge pull request #4 from mengxr/dtree f536ae9 [Xiangrui Meng] another pass on code style e1dd86f [Manish Amde] implementing code style suggestions 62dc723 [Manish Amde] updating javadoc and converting helper methods to package private to allow unit testing 201702f [Manish Amde] making some more methods private f963ef5 [Manish Amde] making methods private c487e6a [manishamde] Merge pull request #1 from mengxr/dtree 24500c5 [Xiangrui Meng] minor style updates 4576b64 [Manish Amde] documentation and for to while loop conversion ff363a7 [Manish Amde] binary search for bins and while loop for categorical feature bins 632818f [Manish Amde] removing threshold for classification predict method 2116360 [Manish Amde] removing dummy bin calculation for categorical variables 6068356 [Manish Amde] ensuring num bins is always greater than max number of categories 62c2562 [Manish Amde] fixing comment indentation ad1fc21 [Manish Amde] incorporated mengxr's code style suggestions d1ef4f6 [Manish Amde] more documentation 794ff4d [Manish Amde] minor improvements to docs and style eb8fcbe [Manish Amde] minor code style updates cd2c2b4 [Manish Amde] fixing code style based on feedback 63e786b [Manish Amde] added multiple train methods for java compatability d3023b3 [Manish Amde] adding more docs for nested methods 84f85d6 [Manish Amde] code documentation 9372779 [Manish Amde] code style: max line lenght <= 100 dd0c0d7 [Manish Amde] minor: some docs 0dd7659 [manishamde] basic doc 5841c28 [Manish Amde] unit tests for categorical features f067d68 [Manish Amde] minor cleanup c0e522b [Manish Amde] updated predict and split threshold logic b09dc98 [Manish Amde] minor refactoring 6b7de78 [Manish Amde] minor refactoring and tests d504eb1 [Manish Amde] more tests for categorical features dbb7ac1 [Manish Amde] categorical feature support 6df35b9 [Manish Amde] regression predict logic 53108ed [Manish Amde] fixing index for highest bin e23c2e5 [Manish Amde] added regression support c8f6d60 [Manish Amde] adding enum for feature type b0e3e76 [Manish Amde] adding enum for feature type 154aa77 [Manish Amde] enums for configurations 733d6dd [Manish Amde] fixed tests 02c595c [Manish Amde] added command line parsing 98ec8d5 [Manish Amde] tree building and prediction logic b0eb866 [Manish Amde] added logic to handle leaf nodes 80e8c66 [Manish Amde] working version of multi-level split calculation 4798aae [Manish Amde] added gain stats class dad0afc [Manish Amde] decison stump functionality working 03f534c [Manish Amde] some more tests 0012a77 [Manish Amde] basic stump working 8bca1e2 [Manish Amde] additional code for creating intermediate RDD 92cedce [Manish Amde] basic building blocks for intermediate RDD calculation. untested. cd53eae [Manish Amde] skeletal framework
* [SPARK-1327] GLM needs to check addIntercept for intercept and weightsXiangrui Meng2014-03-267-37/+86
| | | | | | | | | | | | | | | GLM needs to check addIntercept for intercept and weights. The current implementation always uses the first weight as intercept. Added a test for training without adding intercept. JIRA: https://spark-project.atlassian.net/browse/SPARK-1327 Author: Xiangrui Meng <meng@databricks.com> Closes #236 from mengxr/glm and squashes the following commits: bcac1ac [Xiangrui Meng] add two tests to ensure {Lasso, Ridge}.setIntercept will throw an exceptions a104072 [Xiangrui Meng] remove protected to be compatible with 0.9 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
* [SPARK-1212] Adding sparse data support and update KMeansXiangrui Meng2014-03-2316-247/+907
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Continue our discussions from https://github.com/apache/incubator-spark/pull/575 This PR is WIP because it depends on a SNAPSHOT version of breeze. Per previous discussions and benchmarks, I switched to breeze for linear algebra operations. @dlwh and I made some improvements to breeze to keep its performance comparable to the bare-bone implementation, including norm computation and squared distance. This is why this PR needs to depend on a SNAPSHOT version of breeze. @fommil , please find the notice of using netlib-core in `NOTICE`. This is following Apache's instructions on appropriate labeling. I'm going to update this PR to include: 1. Fast distance computation: using `\|a\|_2^2 + \|b\|_2^2 - 2 a^T b` when it doesn't introduce too much numerical error. The squared norms are pre-computed. Otherwise, computing the distance between the center (dense) and a point (possibly sparse) always takes O(n) time. 2. Some numbers about the performance. 3. A released version of breeze. @dlwh, a minor release of breeze will help this PR get merged early. Do you mind sharing breeze's release plan? Thanks! Author: Xiangrui Meng <meng@databricks.com> Closes #117 from mengxr/sparse-kmeans and squashes the following commits: 67b368d [Xiangrui Meng] fix SparseVector.toArray 5eda0de [Xiangrui Meng] update NOTICE 67abe31 [Xiangrui Meng] move ArrayRDDs to mllib.rdd 1da1033 [Xiangrui Meng] remove dependency on commons-math3 and compute EPSILON directly 9bb1b31 [Xiangrui Meng] optimize SparseVector.toArray 226d2cd [Xiangrui Meng] update Java friendly methods in Vectors 238ba34 [Xiangrui Meng] add VectorRDDs with a converter from RDD[Array[Double]] b28ba2f [Xiangrui Meng] add toArray to Vector e69b10c [Xiangrui Meng] remove examples/JavaKMeans.java, which is replaced by mllib/examples/JavaKMeans.java 72bde33 [Xiangrui Meng] clean up code for distance computation 712cb88 [Xiangrui Meng] make Vectors.sparse Java friendly 27858e4 [Xiangrui Meng] update breeze version to 0.7 07c3cf2 [Xiangrui Meng] change Mahout to breeze in doc use a simple lower bound to avoid unnecessary distance computation 6f5cdde [Xiangrui Meng] fix a bug in filtering finished runs 42512f2 [Xiangrui Meng] Merge branch 'master' into sparse-kmeans d6e6c07 [Xiangrui Meng] add predict(RDD[Vector]) to KMeansModel 42b4e50 [Xiangrui Meng] line feed at the end a4ace73 [Xiangrui Meng] Merge branch 'fast-dist' into sparse-kmeans 3ed1a24 [Xiangrui Meng] add doc to BreezeVectorWithSquaredNorm 0107e19 [Xiangrui Meng] update NOTICE 87bc755 [Xiangrui Meng] tuned the KMeans code: changed some for loops to while, use view to avoid copying arrays 0ff8046 [Xiangrui Meng] update KMeans to use fastSquaredDistance f355411 [Xiangrui Meng] add BreezeVectorWithSquaredNorm case class ab74f67 [Xiangrui Meng] add fastSquaredDistance for KMeans 4e7d5ca [Xiangrui Meng] minor style update 07ffaf2 [Xiangrui Meng] add dense/sparse vector data models and conversions to/from breeze vectors use breeze to implement KMeans in order to support both dense and sparse data
* Principal Component AnalysisReza Zadeh2014-03-208-112/+752
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | # Principal Component Analysis Computes the top k principal component coefficients for the m-by-n data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is n-by-k. Each column of the coefficients return matrix contains coefficients for one principal component, and the columns are in descending order of component variance. This function centers the data and uses the singular value decomposition (SVD) algorithm. ## Testing Tests included: * All principal components * Only top k principal components * Dense SVD tests * Dense/sparse matrix tests The results are tested against MATLAB's pca: http://www.mathworks.com/help/stats/pca.html ## Documentation Added to mllib-guide.md ## Example Usage Added to examples directory under SparkPCA.scala Author: Reza Zadeh <rizlar@gmail.com> Closes #88 from rezazadeh/sparkpca and squashes the following commits: e298700 [Reza Zadeh] reformat using IDE 3f23271 [Reza Zadeh] documentation and cleanup b025ab2 [Reza Zadeh] documentation e2667d4 [Reza Zadeh] assertMatrixApproximatelyEquals 3787bb4 [Reza Zadeh] stylin c6ecc1f [Reza Zadeh] docs aa2bbcb [Reza Zadeh] rename sparseToTallSkinnyDense 56975b0 [Reza Zadeh] docs 2df9bde [Reza Zadeh] docs update 8fb0015 [Reza Zadeh] rcond documentation dbf7797 [Reza Zadeh] correct argument number a9f1f62 [Reza Zadeh] documentation 4ce6caa [Reza Zadeh] style changes 9a56a02 [Reza Zadeh] use rcond relative to larget svalue 120f796 [Reza Zadeh] housekeeping 156ff78 [Reza Zadeh] string comprehension 2e1cf43 [Reza Zadeh] rename rcond ea223a6 [Reza Zadeh] many style changes f4002d7 [Reza Zadeh] more docs bd53c7a [Reza Zadeh] proper accumulator a8b5ecf [Reza Zadeh] Don't use for loops 0dc7980 [Reza Zadeh] filter zeros in sparse 6115610 [Reza Zadeh] More documentation 36d51e8 [Reza Zadeh] use JBLAS for UVS^-1 computation bc4599f [Reza Zadeh] configurable rcond 86f7515 [Reza Zadeh] compute per parition, use while 09726b3 [Reza Zadeh] more style changes 4195e69 [Reza Zadeh] private, accumulator 17002be [Reza Zadeh] style changes 4ba7471 [Reza Zadeh] style change f4982e6 [Reza Zadeh] Use dense matrix in example 2828d28 [Reza Zadeh] optimizations: normalize once, use inplace ops 72c9fa1 [Reza Zadeh] rename DenseMatrix to TallSkinnyDenseMatrix, lean f807be9 [Reza Zadeh] fix typo 2d7ccde [Reza Zadeh] Array interface for dense svd and pca cd290fa [Reza Zadeh] provide RDD[Array[Double]] support 398d123 [Reza Zadeh] style change 55abbfa [Reza Zadeh] docs fix ef29644 [Reza Zadeh] bad chnage undo 472566e [Reza Zadeh] all files from old pr 555168f [Reza Zadeh] initial files
* [SPARK-1266] persist factors in implicit ALSXiangrui Meng2014-03-181-56/+89
| | | | | | | | | | | | | | | | | | | | | | In implicit ALS computation, the user or product factor is used twice in each iteration. Caching can certainly help accelerate the computation. I saw the running time decreased by ~70% for implicit ALS on the movielens data. I also made the following changes: 1. Change `YtYb` type from `Broadcast[Option[DoubleMatrix]]` to `Option[Broadcast[DoubleMatrix]]`, so we don't need to broadcast None in explicit computation. 2. Mark methods `computeYtY`, `unblockFactors`, `updateBlock`, and `updateFeatures private`. Users do not need those methods. 3. Materialize the final matrix factors before returning the model. It allows us to clean up other cached RDDs before returning the model. I do not have a better solution here, so I use `RDD.count()`. JIRA: https://spark-project.atlassian.net/browse/SPARK-1266 Author: Xiangrui Meng <meng@databricks.com> Closes #165 from mengxr/als and squashes the following commits: c9676a6 [Xiangrui Meng] add a comment about the last products.persist d3a88aa [Xiangrui Meng] change implicitPrefs match to if ... else ... 63862d6 [Xiangrui Meng] persist factors in implicit ALS
* [SPARK-1260]: faster construction of features with interceptXiangrui Meng2014-03-181-4/+4
| | | | | | | | | | | | | | | The current implementation uses `Array(1.0, features: _*)` to construct a new array with intercept. This is not efficient for big arrays because `Array.apply` uses a for loop that iterates over the arguments. `Array.+:` is a better choice here. Also, I don't see a reason to set initial weights to ones. So I set them to zeros. JIRA: https://spark-project.atlassian.net/browse/SPARK-1260 Author: Xiangrui Meng <meng@databricks.com> Closes #161 from mengxr/sgd and squashes the following commits: b5cfc53 [Xiangrui Meng] set default weights to zeros a1439c2 [Xiangrui Meng] faster construction of features with intercept
* [SPARK-1237, 1238] Improve the computation of YtY for implicit ALSXiangrui Meng2014-03-132-55/+134
| | | | | | | | | | | | | | | | | | | | Computing YtY can be implemented using BLAS's DSPR operations instead of generating y_i y_i^T and then combining them. The latter generates many k-by-k matrices. On the movielens data, this change improves the performance by 10-20%. The algorithm remains the same, verified by computing RMSE on the movielens data. To compare the results, I also added an option to set a random seed in ALS. JIRA: 1. https://spark-project.atlassian.net/browse/SPARK-1237 2. https://spark-project.atlassian.net/browse/SPARK-1238 Author: Xiangrui Meng <meng@databricks.com> Closes #131 from mengxr/als and squashes the following commits: ed00432 [Xiangrui Meng] minor changes d984623 [Xiangrui Meng] minor changes 2fc1641 [Xiangrui Meng] remove commented code 4c7cde2 [Xiangrui Meng] allow specifying a random seed in ALS 200bef0 [Xiangrui Meng] optimize computeYtY and updateBlock
* SPARK-1160: Deprecate toArray in RDDCodingCat2014-03-122-5/+5
| | | | | | | | | | | | | | | | https://spark-project.atlassian.net/browse/SPARK-1160 reported by @mateiz: "It's redundant with collect() and the name doesn't make sense in Java, where we return a List (we can't return an array due to the way Java generics work). It's also missing in Python." In this patch, I deprecated the method and changed the source files using it by replacing toArray with collect() directly Author: CodingCat <zhunansjtu@gmail.com> Closes #105 from CodingCat/SPARK-1060 and squashes the following commits: 286f163 [CodingCat] deprecate in JavaRDDLike ee17b4e [CodingCat] add message and since 2ff7319 [CodingCat] deprecate toArray in RDD
* SPARK-1193. Fix indentation in pom.xmlsSandy Ryza2014-03-071-4/+4
| | | | | | | | Author: Sandy Ryza <sandy@cloudera.com> Closes #91 from sryza/sandy-spark-1193 and squashes the following commits: a878124 [Sandy Ryza] SPARK-1193. Fix indentation in pom.xmls
* SPARK-1121: Include avro for yarn-alpha buildsPatrick Wendell2014-03-021-0/+14
| | | | | | | | | | | | | | | | | This lets us explicitly include Avro based on a profile for 0.23.X builds. It makes me sad how convoluted it is to express this logic in Maven. @tgraves and @sryza curious if this works for you. I'm also considering just reverting to how it was before. The only real problem was that Spark advertised a dependency on Avro even though it only really depends transitively on Avro through other deps. Author: Patrick Wendell <pwendell@gmail.com> Closes #49 from pwendell/avro-build-fix and squashes the following commits: 8d6ee92 [Patrick Wendell] SPARK-1121: Add avro to yarn-alpha profile
* Remove remaining references to incubationPatrick Wendell2014-03-021-2/+2
| | | | | | | | | | This removes some loose ends not caught by the other (incubating -> tlp) patches. @markhamstra this updates the version as you mentioned earlier. Author: Patrick Wendell <pwendell@gmail.com> Closes #51 from pwendell/tlp and squashes the following commits: d553b1b [Patrick Wendell] Remove remaining references to incubation
* Initialized the regVal for first iteration in SGD optimizerDB Tsai2014-03-023-1/+50
| | | | | | | | | | | | | | | | | | Ported from https://github.com/apache/incubator-spark/pull/633 In runMiniBatchSGD, the regVal (for 1st iter) should be initialized as sum of sqrt of weights if it's L2 update; for L1 update, the same logic is followed. It maybe not be important here for SGD since the updater doesn't take the loss as parameter to find the new weights. But it will give us the correct history of loss. However, for LBFGS optimizer we implemented, the correct loss with regVal is crucial to find the new weights. Author: DB Tsai <dbtsai@alpinenow.com> Closes #40 from dbtsai/dbtsai-smallRegValFix and squashes the following commits: 77d47da [DB Tsai] In runMiniBatchSGD, the regVal (for 1st iter) should be initialized as sum of sqrt of weights if it's L2 update; for L1 update, the same logic is followed.
* MLLIB-25: Implicit ALS runs out of memory for moderately large numbers of ↵Sean Owen2014-02-211-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | features There's a step in implicit ALS where the matrix `Yt * Y` is computed. It's computed as the sum of matrices; an f x f matrix is created for each of n user/item rows in a partition. In `ALS.scala:214`: ``` factors.flatMapValues{ case factorArray => factorArray.map{ vector => val x = new DoubleMatrix(vector) x.mmul(x.transpose()) } }.reduceByKeyLocally((a, b) => a.addi(b)) .values .reduce((a, b) => a.addi(b)) ``` Completely correct, but there's a subtle but quite large memory problem here. map() is going to create all of these matrices in memory at once, when they don't need to ever all exist at the same time. For example, if a partition has n = 100000 rows, and f = 200, then this intermediate product requires 32GB of heap. The computation will never work unless you can cough up workers with (more than) that much heap. Fortunately there's a trivial change that fixes it; just add `.view` in there. Author: Sean Owen <sowen@cloudera.com> Closes #629 from srowen/ALSMatrixAllocationOptimization and squashes the following commits: 062cda9 [Sean Owen] Update style per review comments e9a5d63 [Sean Owen] Avoid unnecessary out of memory situation by not simultaneously allocating lots of matrices
* MLLIB-22. Support negative implicit input in ALSSean Owen2014-02-193-21/+52
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | I'm back with another less trivial suggestion for ALS: In ALS for implicit feedback, input values are treated as weights on squared-errors in a loss function (or rather, the weight is a simple function of the input r, like c = 1 + alpha*r). The paper on which it's based assumes that the input is positive. Indeed, if the input is negative, it will create a negative weight on squared-errors, which causes things to go haywire. The optimization will try to make the error in a cell as large possible, and the result is silently bogus. There is a good use case for negative input values though. Implicit feedback is usually collected from signals of positive interaction like a view or like or buy, but equally, can come from "not interested" signals. The natural representation is negative values. The algorithm can be extended quite simply to provide a sound interpretation of these values: negative values should encourage the factorization to come up with 0 for cells with large negative input values, just as much as positive values encourage it to come up with 1. The implications for the algorithm are simple: * the confidence function value must not be negative, and so can become 1 + alpha*|r| * the matrix P should have a value 1 where the input R is _positive_, not merely where it is non-zero. Actually, that's what the paper already says, it's just that we can't assume P = 1 when a cell in R is specified anymore, since it may be negative This in turn entails just a few lines of code change in `ALS.scala`: * `rs(i)` becomes `abs(rs(i))` * When constructing `userXy(us(i))`, it's implicitly only adding where P is 1. That had been true for any us(i) that is iterated over, before, since these are exactly the ones for which P is 1. But now P is zero where rs(i) <= 0, and should not be added I think it's a safe change because: * It doesn't change any existing behavior (unless you're using negative values, in which case results are already borked) * It's the simplest direct extension of the paper's algorithm * (I've used it to good effect in production FWIW) Tests included. I tweaked minor things en route: * `ALS.scala` javadoc writes "R = Xt*Y" when the paper and rest of code defines it as "R = X*Yt" * RMSE in the ALS tests uses a confidence-weighted mean, but the denominator is not actually sum of weights Excuse my Scala style; I'm sure it needs tweaks. Author: Sean Owen <sowen@cloudera.com> Closes #500 from srowen/ALSNegativeImplicitInput and squashes the following commits: cf902a9 [Sean Owen] Support negative implicit input in ALS 953be1c [Sean Owen] Make weighted RMSE in ALS test actually weighted; adjust comment about R = X*Yt
* MLLIB-24: url of "Collaborative Filtering for Implicit Feedback Datasets" ↵Chen Chao2014-02-191-1/+1
| | | | | | | | | | | | | | | in ALS is invalid now url of "Collaborative Filtering for Implicit Feedback Datasets" is invalid now. A new url is provided. http://research.yahoo.com/files/HuKorenVolinsky-ICDM08.pdf Author: Chen Chao <crazyjvm@gmail.com> Closes #619 from CrazyJvm/master and squashes the following commits: a0b54e4 [Chen Chao] change url to IEEE 9e0e9f0 [Chen Chao] correct spell mistale fcfab5d [Chen Chao] wrap line to to fit within 100 chars 590d56e [Chen Chao] url error
* Merge pull request #566 from martinjaggi/copy-MLlib-d.Martin Jaggi2014-02-096-74/+130
| | | | | | | | | | | | | | | | | | | | | | | | | | | | new MLlib documentation for optimization, regression and classification new documentation with tex formulas, hopefully improving usability and reproducibility of the offered MLlib methods. also did some minor changes in the code for consistency. scala tests pass. this is the rebased branch, i deleted the old PR jira: https://spark-project.atlassian.net/browse/MLLIB-19 Author: Martin Jaggi <m.jaggi@gmail.com> Closes #566 and squashes the following commits: 5f0f31e [Martin Jaggi] line wrap at 100 chars 4e094fb [Martin Jaggi] better description of GradientDescent 1d6965d [Martin Jaggi] remove broken url ea569c3 [Martin Jaggi] telling what updater actually does 964732b [Martin Jaggi] lambda R() in documentation a6c6228 [Martin Jaggi] better comments in SGD code for regression b32224a [Martin Jaggi] new optimization documentation d5dfef7 [Martin Jaggi] new classification and regression documentation b07ead6 [Martin Jaggi] correct scaling for MSE loss ba6158c [Martin Jaggi] use d for the number of features bab2ed2 [Martin Jaggi] renaming LeastSquaresGradient
* Merge pull request #557 from ScrapCodes/style. Closes #557.Patrick Wendell2014-02-094-15/+18
| | | | | | | | | | | | | | | | | | | | | SPARK-1058, Fix Style Errors and Add Scala Style to Spark Build. Author: Patrick Wendell <pwendell@gmail.com> Author: Prashant Sharma <scrapcodes@gmail.com> == Merge branch commits == commit 1a8bd1c059b842cb95cc246aaea74a79fec684f4 Author: Prashant Sharma <scrapcodes@gmail.com> Date: Sun Feb 9 17:39:07 2014 +0530 scala style fixes commit f91709887a8e0b608c5c2b282db19b8a44d53a43 Author: Patrick Wendell <pwendell@gmail.com> Date: Fri Jan 24 11:22:53 2014 -0800 Adding scalastyle snapshot
* Merge pull request #542 from markhamstra/versionBump. Closes #542.Mark Hamstra2014-02-081-1/+1
| | | | | | | | | | | | | | | | | | Version number to 1.0.0-SNAPSHOT Since 0.9.0-incubating is done and out the door, we shouldn't be building 0.9.0-incubating-SNAPSHOT anymore. @pwendell Author: Mark Hamstra <markhamstra@gmail.com> == Merge branch commits == commit 1b00a8a7c1a7f251b4bb3774b84b9e64758eaa71 Author: Mark Hamstra <markhamstra@gmail.com> Date: Wed Feb 5 09:30:32 2014 -0800 Version number to 1.0.0-SNAPSHOT
* Merge pull request #528 from mengxr/sample. Closes #528.Xiangrui Meng2014-02-031-2/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Refactor RDD sampling and add randomSplit to RDD (update) Replace SampledRDD by PartitionwiseSampledRDD, which accepts a RandomSampler instance as input. The current sample with/without replacement can be easily integrated via BernoulliSampler and PoissonSampler. The benefits are: 1) RDD.randomSplit is implemented in the same way, related to https://github.com/apache/incubator-spark/pull/513 2) Stratified sampling and importance sampling can be implemented in the same manner as well. Unit tests are included for samplers and RDD.randomSplit. This should performance better than my previous request where the BernoulliSampler creates many Iterator instances: https://github.com/apache/incubator-spark/pull/513 Author: Xiangrui Meng <meng@databricks.com> == Merge branch commits == commit e8ce957e5f0a600f2dec057924f4a2ca6adba373 Author: Xiangrui Meng <meng@databricks.com> Date: Mon Feb 3 12:21:08 2014 -0800 more docs to PartitionwiseSampledRDD commit fbb4586d0478ff638b24bce95f75ff06f713d43b Author: Xiangrui Meng <meng@databricks.com> Date: Mon Feb 3 00:44:23 2014 -0800 move XORShiftRandom to util.random and use it in BernoulliSampler commit 987456b0ee8612fd4f73cb8c40967112dc3c4c2d Author: Xiangrui Meng <meng@databricks.com> Date: Sat Feb 1 11:06:59 2014 -0800 relax assertions in SortingSuite because the RangePartitioner has large variance in this case commit 3690aae416b2dc9b2f9ba32efa465ba7948477f4 Author: Xiangrui Meng <meng@databricks.com> Date: Sat Feb 1 09:56:28 2014 -0800 test split ratio of RDD.randomSplit commit 8a410bc933a60c4d63852606f8bbc812e416d6ae Author: Xiangrui Meng <meng@databricks.com> Date: Sat Feb 1 09:25:22 2014 -0800 add a test to ensure seed distribution and minor style update commit ce7e866f674c30ab48a9ceb09da846d5362ab4b6 Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 31 18:06:22 2014 -0800 minor style change commit 750912b4d77596ed807d361347bd2b7e3b9b7a74 Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 31 18:04:54 2014 -0800 fix some long lines commit c446a25c38d81db02821f7f194b0ce5ab4ed7ff5 Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 31 17:59:59 2014 -0800 add complement to BernoulliSampler and minor style changes commit dbe2bc2bd888a7bdccb127ee6595840274499403 Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 31 17:45:08 2014 -0800 switch to partition-wise sampling for better performance commit a1fca5232308feb369339eac67864c787455bb23 Merge: ac712e4 cf6128f Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 31 16:33:09 2014 -0800 Merge branch 'sample' of github.com:mengxr/incubator-spark into sample commit cf6128fb672e8c589615adbd3eaa3cbdb72bd461 Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 14:40:07 2014 -0800 set SampledRDD deprecated in 1.0 commit f430f847c3df91a3894687c513f23f823f77c255 Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 14:38:59 2014 -0800 update code style commit a8b5e2021a9204e318c80a44d00c5c495f1befb6 Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 12:56:27 2014 -0800 move package random to util.random commit ab0fa2c4965033737a9e3a9bf0a59cbb0df6a6f5 Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 12:50:35 2014 -0800 add Apache headers and update code style commit 985609fe1a55655ad11966e05a93c18c138a403d Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 11:49:25 2014 -0800 add new lines commit b21bddf29850a2c006a868869b8f91960a029322 Author: Xiangrui Meng <meng@databricks.com> Date: Sun Jan 26 11:46:35 2014 -0800 move samplers to random.IndependentRandomSampler and add tests commit c02dacb4a941618e434cefc129c002915db08be6 Author: Xiangrui Meng <meng@databricks.com> Date: Sat Jan 25 15:20:24 2014 -0800 add RandomSampler commit 8ff7ba3c5cf1fc338c29ae8b5fa06c222640e89c Author: Xiangrui Meng <meng@databricks.com> Date: Fri Jan 24 13:23:22 2014 -0800 init impl of IndependentlySampledRDD
* Merge pull request #460 from srowen/RandomInitialALSVectorsSean Owen2014-01-271-1/+9
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Choose initial user/item vectors uniformly on the unit sphere ...rather than within the unit square to possibly avoid bias in the initial state and improve convergence. The current implementation picks the N vector elements uniformly at random from [0,1). This means they all point into one quadrant of the vector space. As N gets just a little large, the vector tend strongly to point into the "corner", towards (1,1,1...,1). The vectors are not unit vectors either. I suggest choosing the elements as Gaussian ~ N(0,1) and normalizing. This gets you uniform random choices on the unit sphere which is more what's of interest here. It has worked a little better for me in the past. This is pretty minor but wanted to warm up suggesting a few tweaks to ALS. Please excuse my Scala, pretty new to it. Author: Sean Owen <sowen@cloudera.com> == Merge branch commits == commit 492b13a7469e5a4ed7591ee8e56d8bd7570dfab6 Author: Sean Owen <sowen@cloudera.com> Date: Mon Jan 27 08:05:25 2014 +0000 Style: spaces around binary operators commit ce2b5b5a4fefa0356875701f668f01f02ba4d87e Author: Sean Owen <sowen@cloudera.com> Date: Sun Jan 19 22:50:03 2014 +0000 Generate factors with all positive components, per discussion in https://github.com/apache/incubator-spark/pull/460 commit b6f7a8a61643a8209e8bc662e8e81f2d15c710c7 Author: Sean Owen <sowen@cloudera.com> Date: Sat Jan 18 15:54:42 2014 +0000 Choose initial user/item vectors uniformly on the unit sphere rather than within the unit square to possibly avoid bias in the initial state and improve convergence
* Merge pull request #315 from rezazadeh/sparsesvdMatei Zaharia2014-01-225-0/+433
|\ | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Sparse SVD # Singular Value Decomposition Given an *m x n* matrix *A*, compute matrices *U, S, V* such that *A = U * S * V^T* There is no restriction on m, but we require n^2 doubles to fit in memory. Further, n should be less than m. The decomposition is computed by first computing *A^TA = V S^2 V^T*, computing svd locally on that (since n x n is small), from which we recover S and V. Then we compute U via easy matrix multiplication as *U = A * V * S^-1* Only singular vectors associated with the largest k singular values If there are k such values, then the dimensions of the return will be: * *S* is *k x k* and diagonal, holding the singular values on diagonal. * *U* is *m x k* and satisfies U^T*U = eye(k). * *V* is *n x k* and satisfies V^TV = eye(k). All input and output is expected in sparse matrix format, 0-indexed as tuples of the form ((i,j),value) all in RDDs. # Testing Tests included. They test: - Decomposition promise (A = USV^T) - For small matrices, output is compared to that of jblas - Rank 1 matrix test included - Full Rank matrix test included - Middle-rank matrix forced via k included # Example Usage import org.apache.spark.SparkContext import org.apache.spark.mllib.linalg.SVD import org.apache.spark.mllib.linalg.SparseMatrix import org.apache.spark.mllib.linalg.MatrixyEntry // Load and parse the data file val data = sc.textFile("mllib/data/als/test.data").map { line => val parts = line.split(',') MatrixEntry(parts(0).toInt, parts(1).toInt, parts(2).toDouble) } val m = 4 val n = 4 // recover top 1 singular vector val decomposed = SVD.sparseSVD(SparseMatrix(data, m, n), 1) println("singular values = " + decomposed.S.data.toArray.mkString) # Documentation Added to docs/mllib-guide.md
| * rename to MatrixSVDReza Zadeh2014-01-171-2/+2
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| * rename to MatrixSVDReza Zadeh2014-01-172-4/+4
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