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* [SPARK-3366][MLLIB]Compute best splits distributively in decision treeqiping.lqp2014-10-035-267/+182
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Currently, all best splits are computed on the driver, which makes the driver a bottleneck for both communication and computation. This PR fix this problem by computed best splits on executors. Instead of send all aggregate stats to the driver node, we can send aggregate stats for a node to a particular executor, using `reduceByKey` operation, then we can compute best split for this node there. Implementation details: Each node now has a nodeStatsAggregator, which save aggregate stats for all features and bins. First use mapPartition to compute node aggregate stats for all nodes in each partition. Then transform node aggregate stats to (nodeIndex, nodeStatsAggregator) pairs and use to `reduceByKey` operation to combine nodeStatsAggregator for the same node. After all stats have been combined, best splits can be computed for each node based on the node aggregate stats. Best split result is collected to driver to construct the decision tree. CC: mengxr manishamde jkbradley, please help me review this, thanks. Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Author: chouqin <liqiping1991@gmail.com> Closes #2595 from chouqin/dt-dist-agg and squashes the following commits: db0d24a [chouqin] fix a minor bug and adjust code a0d9de3 [chouqin] adjust code based on comments 9f201a6 [chouqin] fix bug: statsSize -> allStatsSize a8a7ed0 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-dist-agg f13b346 [chouqin] adjust randomforest comments c32636e [chouqin] adjust code based on comments ac6a505 [chouqin] adjust code based on comments 7bbb787 [chouqin] add comments bdd2a63 [qiping.lqp] fix test suite a75df27 [qiping.lqp] fix test suite b5b0bc2 [qiping.lqp] fix style e76414f [qiping.lqp] fix testsuite 748bd45 [qiping.lqp] fix type-mismatch bug 24eacd8 [qiping.lqp] fix type-mismatch bug 5f63d6c [qiping.lqp] add multiclassification using One-Vs-All strategy 4f56496 [qiping.lqp] fix bug f00fc22 [qiping.lqp] fix bug 532993a [qiping.lqp] Compute best splits distributively in decision tree
* [SPARK-3748] Log thread name in unit test logsReynold Xin2014-10-011-1/+1
| | | | | | | | | | Thread names are useful for correlating failures. Author: Reynold Xin <rxin@apache.org> Closes #2600 from rxin/log4j and squashes the following commits: 83ffe88 [Reynold Xin] [SPARK-3748] Log thread name in unit test logs
* [SPARK-3751] [mllib] DecisionTree: example update + print optionsJoseph K. Bradley2014-10-012-13/+31
| | | | | | | | | | | | | | | | | | | | | | | | | | DecisionTreeRunner functionality additions: * Allow user to pass in a test dataset * Do not print full model if the model is too large. As part of this, modify DecisionTreeModel and RandomForestModel to allow printing less info. Proposed updates: * toString: prints model summary * toDebugString: prints full model (named after RDD.toDebugString) Similar update to Python API: * __repr__() now prints a model summary * toDebugString() now prints the full model CC: mengxr chouqin manishamde codedeft Small update (whomever can take a look). Thanks! Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2604 from jkbradley/dtrunner-update and squashes the following commits: b2b3c60 [Joseph K. Bradley] re-added python sql doc test, temporarily removed before 07b1fae [Joseph K. Bradley] repr() now prints a model summary toDebugString() now prints the full model 1d0d93d [Joseph K. Bradley] Updated DT and RF to print less when toString is called. Added toDebugString for verbose printing. 22eac8c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update e007a95 [Joseph K. Bradley] Updated DecisionTreeRunner to accept a test dataset.
* [SPARK-3701][MLLIB] update python linalg api and small fixesXiangrui Meng2014-09-301-4/+4
| | | | | | | | | | | | | | | | | | | 1. doc updates 2. simple checks on vector dimensions 3. use column major for matrices davies jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #2548 from mengxr/mllib-py-clean and squashes the following commits: 6dce2df [Xiangrui Meng] address comments 116b5db [Xiangrui Meng] use np.dot instead of array.dot 75f2fcc [Xiangrui Meng] fix python style fefce00 [Xiangrui Meng] better check of vector size with more tests 067ef71 [Xiangrui Meng] majored -> major ef853f9 [Xiangrui Meng] update python linalg api and small fixes
* [MLlib] [SPARK-2885] DIMSUM: All-pairs similarityReza Zadeh2014-09-294-5/+251
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | # All-pairs similarity via DIMSUM Compute all pairs of similar vectors using brute force approach, and also DIMSUM sampling approach. Laying down some notation: we are looking for all pairs of similar columns in an m x n RowMatrix whose entries are denoted a_ij, with the i’th row denoted r_i and the j’th column denoted c_j. There is an oversampling parameter labeled ɣ that should be set to 4 log(n)/s to get provably correct results (with high probability), where s is the similarity threshold. The algorithm is stated with a Map and Reduce, with proofs of correctness and efficiency in published papers [1] [2]. The reducer is simply the summation reducer. The mapper is more interesting, and is also the heart of the scheme. As an exercise, you should try to see why in expectation, the map-reduce below outputs cosine similarities. ![dimsumv2](https://cloud.githubusercontent.com/assets/3220351/3807272/d1d9514e-1c62-11e4-9f12-3cfdb1d78b3a.png) [1] Bosagh-Zadeh, Reza and Carlsson, Gunnar (2013), Dimension Independent Matrix Square using MapReduce, arXiv:1304.1467 http://arxiv.org/abs/1304.1467 [2] Bosagh-Zadeh, Reza and Goel, Ashish (2012), Dimension Independent Similarity Computation, arXiv:1206.2082 http://arxiv.org/abs/1206.2082 # Testing Tests for all invocations included. Added L1 and L2 norm computation to MultivariateStatisticalSummary since it was needed. Added tests for both of them. Author: Reza Zadeh <rizlar@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #1778 from rezazadeh/dimsumv2 and squashes the following commits: 404c64c [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2 4eb71c6 [Reza Zadeh] Add excludes for normL1 and normL2 ee8bd65 [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2 976ddd4 [Reza Zadeh] Broadcast colMags. Avoid div by zero. 3467cff [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2 aea0247 [Reza Zadeh] Allow large thresholds to promote sparsity 9fe17c0 [Xiangrui Meng] organize imports 2196ba5 [Xiangrui Meng] Merge branch 'rezazadeh-dimsumv2' into dimsumv2 254ca08 [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2 f2947e4 [Xiangrui Meng] some optimization 3c4cf41 [Xiangrui Meng] Merge branch 'master' into rezazadeh-dimsumv2 0e4eda4 [Reza Zadeh] Use partition index for RNG 251bb9c [Reza Zadeh] Documentation 25e9d0d [Reza Zadeh] Line length for style fb296f6 [Reza Zadeh] renamed to normL1 and normL2 3764983 [Reza Zadeh] Documentation e9c6791 [Reza Zadeh] New interface and documentation 613f261 [Reza Zadeh] Column magnitude summary 75a0b51 [Reza Zadeh] Use Ints instead of Longs in the shuffle 0f12ade [Reza Zadeh] Style changes eb1dc20 [Reza Zadeh] Use Double.PositiveInfinity instead of Double.Max f56a882 [Reza Zadeh] Remove changes to MultivariateOnlineSummarizer dbc55ba [Reza Zadeh] Make colMagnitudes a method in RowMatrix 41e8ece [Reza Zadeh] style changes 139c8e1 [Reza Zadeh] Syntax changes 029aa9c [Reza Zadeh] javadoc and new test 75edb25 [Reza Zadeh] All tests passing! 05e59b8 [Reza Zadeh] Add test 502ce52 [Reza Zadeh] new interface 654c4fb [Reza Zadeh] default methods 3726ca9 [Reza Zadeh] Remove MatrixAlgebra 6bebabb [Reza Zadeh] remove changes to MatrixSuite 5b8cd7d [Reza Zadeh] Initial files
* [SPARK-1545] [mllib] Add Random ForestsJoseph K. Bradley2014-09-2813-492/+1353
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR adds RandomForest to MLlib. The implementation is basic, and future performance optimizations will be important. (Note: RFs = Random Forests.) # Overview ## RandomForest * trains multiple trees at once to reduce the number of passes over the data * allows feature subsets at each node * uses a queue of nodes instead of fixed groups for each level This implementation is based an implementation by manishamde and the [Alpine Labs Sequoia Forest](https://github.com/AlpineNow/SparkML2) by codedeft (in particular, the TreePoint, BaggedPoint, and node queue implementations). Thank you for your inputs! ## Testing Correctness: This has been tested for correctness with the test suites and with DecisionTreeRunner on example datasets. Performance: This has been performance tested using [this branch of spark-perf](https://github.com/jkbradley/spark-perf/tree/rfs). Results below. ### Regression tests for DecisionTree Summary: For training 1 tree, there are small regressions, especially from feature subsampling. In the table below, each row is a single (random) dataset. The 2 different sets of result columns are for 2 different RF implementations: * (numTrees): This is from an earlier commit, after implementing RandomForest to train multiple trees at once. It does not include any code for feature subsampling. * (feature subsets): This is from this current PR's code, after implementing feature subsampling. These tests were to identify regressions in DecisionTree, so they are training 1 tree with all of the features (i.e., no feature subsampling). These were run on an EC2 cluster with 15 workers, training 1 tree with maxDepth = 5 (= 6 levels). Speedup values < 1 indicate slowdowns from the old DecisionTree implementation. numInstances | numFeatures | runtime (sec) | speedup | runtime (sec) | speedup ---- | ---- | ---- | ---- | ---- | ---- | | (numTrees) | (numTrees) | (feature subsets) | (feature subsets) 20000 | 100 | 4.051 | 1.044433473 | 4.478 | 0.9448414471 20000 | 500 | 8.472 | 1.104461756 | 9.315 | 1.004508857 20000 | 1500 | 19.354 | 1.05854087 | 20.863 | 0.9819776638 20000 | 3500 | 43.674 | 1.072033704 | 45.887 | 1.020332556 200000 | 100 | 4.196 | 1.171830315 | 4.848 | 1.014232673 200000 | 500 | 8.926 | 1.082791844 | 9.771 | 0.989151571 200000 | 1500 | 20.58 | 1.068415938 | 22.134 | 0.9934038131 200000 | 3500 | 48.043 | 1.075203464 | 52.249 | 0.9886505005 2000000 | 100 | 4.944 | 1.01355178 | 5.796 | 0.8645617667 2000000 | 500 | 11.11 | 1.016831683 | 12.482 | 0.9050632911 2000000 | 1500 | 31.144 | 1.017852556 | 35.274 | 0.8986789136 2000000 | 3500 | 79.981 | 1.085382778 | 101.105 | 0.8586123337 20000000 | 100 | 8.304 | 0.9270231214 | 9.073 | 0.8484514494 20000000 | 500 | 28.174 | 1.083268262 | 34.236 | 0.8914592826 20000000 | 1500 | 143.97 | 0.9579634646 | 159.275 | 0.8659111599 ### Tests for forests I have run other tests with numTrees=10 and with sqrt(numFeatures), and those indicate that multi-model training and feature subsets can speed up training for forests, especially when training deeper trees. # Details on specific classes ## Changes to DecisionTree * Main train() method is now in RandomForest. * findBestSplits() is no longer needed. (It split levels into groups, but we now use a queue of nodes.) * Many small changes to support RFs. (Note: These methods should be moved to RandomForest.scala in a later PR, but are in DecisionTree.scala to make code comparison easier.) ## RandomForest * Main train() method is from old DecisionTree. * selectNodesToSplit: Note that it selects nodes and feature subsets jointly to track memory usage. ## RandomForestModel * Stores an Array[DecisionTreeModel] * Prediction: * For classification, most common label. For regression, mean. * We could support other methods later. ## examples/.../DecisionTreeRunner * This now takes numTrees and featureSubsetStrategy, to support RFs. ## DTStatsAggregator * 2 types of functionality (w/ and w/o subsampling features): These require different indexing methods. (We could treat both as subsampling, but this is less efficient DTStatsAggregator is now abstract, and 2 child classes implement these 2 types of functionality. ## impurities * These now take instance weights. ## Node * Some vals changed to vars. * This is unfortunately a public API change (DeveloperApi). This could be avoided by creating a LearningNode struct, but would be awkward. ## RandomForestSuite Please let me know if there are missing tests! ## BaggedPoint This wraps TreePoint and holds bootstrap weights/counts. # Design decisions * BaggedPoint: BaggedPoint is separate from TreePoint since it may be useful for other bagging algorithms later on. * RandomForest public API: What options should be easily supported by the train* methods? Should ALL options be in the Java-friendly constructors? Should there be a constructor taking Strategy? * Feature subsampling options: What options should be supported? scikit-learn supports the same options, except for "onethird." One option would be to allow users to specific fractions ("0.1"): the current options could be supported, and any unrecognized values would be parsed as Doubles in [0,1]. * Splits and bins are computed before bootstrapping, so all trees use the same discretization. * One queue, instead of one queue per tree. CC: mengxr manishamde codedeft chouqin Please let me know if you have suggestions---thanks! Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Author: chouqin <liqiping1991@gmail.com> Closes #2435 from jkbradley/rfs-new and squashes the following commits: c694174 [Joseph K. Bradley] Fixed typo cc59d78 [Joseph K. Bradley] fixed imports e25909f [Joseph K. Bradley] Simplified node group maps. Specifically, created NodeIndexInfo to store node index in agg and feature subsets, and no longer create extra maps in findBestSplits fbe9a1e [Joseph K. Bradley] Changed default featureSubsetStrategy to be sqrt for classification, onethird for regression. Updated docs with references. ef7c293 [Joseph K. Bradley] Updates based on code review. Most substantial changes: * Simplified DTStatsAggregator * Made RandomForestModel.trees public * Added test for regression to RandomForestSuite 593b13c [Joseph K. Bradley] Fixed bug in metadata for computing log2(num features). Now it checks >= 1. a1a08df [Joseph K. Bradley] Removed old comments 866e766 [Joseph K. Bradley] Changed RandomForestSuite randomized tests to use multiple fixed random seeds. ff8bb96 [Joseph K. Bradley] removed usage of null from RandomForest and replaced with Option bf1a4c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new 6b79c07 [Joseph K. Bradley] Added RandomForestSuite, and fixed small bugs, style issues. d7753d4 [Joseph K. Bradley] Added numTrees and featureSubsetStrategy to DecisionTreeRunner (to support RandomForest). Fixed bugs so that RandomForest now runs. 746d43c [Joseph K. Bradley] Implemented feature subsampling. Tested DecisionTree but not RandomForest. 6309d1d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new. Added RandomForestModel.toString b7ae594 [Joseph K. Bradley] Updated docs. Small fix for bug which does not cause errors: No longer allocate unused child nodes for leaf nodes. 121c74e [Joseph K. Bradley] Basic random forests are implemented. Random features per node not yet implemented. Test suite not implemented. 325d18a [Joseph K. Bradley] Merge branch 'chouqin-dt-preprune' into rfs-new 4ef9bf1 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new 61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy. a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 6da8571 [Joseph K. Bradley] RFs partly implemented, not done yet eddd1eb [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new 5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1 0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune f1d11d1 [chouqin] fix typo c7ebaf1 [chouqin] fix typo 39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py 306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training. 0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1 2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code: efcc736 [qiping.lqp] fix bug 10b8012 [qiping.lqp] fix style 6728fad [qiping.lqp] minor fix: remove empty lines bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune cadd569 [qiping.lqp] add api docs 46b891f [qiping.lqp] fix bug e72c7e4 [qiping.lqp] add comments 845c6fa [qiping.lqp] fix style f195e83 [qiping.lqp] fix style 987cbf4 [qiping.lqp] fix bug ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
* [SPARK-3614][MLLIB] Add minimumOccurence filtering to IDFRJ Nowling2014-09-263-5/+88
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR for [SPARK-3614](https://issues.apache.org/jira/browse/SPARK-3614) adds functionality for filtering out terms which do not appear in at least a minimum number of documents. This is implemented using a minimumOccurence parameter (default 0). When terms' document frequencies are less than minimumOccurence, their IDFs are set to 0, just like when the DF is 0. As a result, the TF-IDFs for the terms are found to be 0, as if the terms were not present in the documents. This PR makes the following changes: * Add a minimumOccurence parameter to the IDF and DocumentFrequencyAggregator classes. * Create a parameter-less constructor for IDF with a default minimumOccurence value of 0 to remain backwards-compatibility with the original IDF API. * Sets the IDFs to 0 for terms which DFs are less than minimumOccurence * Add tests to the Spark IDFSuite and Java JavaTfIdfSuite test suites * Updated the MLLib Feature Extraction programming guide to describe the new feature Author: RJ Nowling <rnowling@gmail.com> Closes #2494 from rnowling/spark-3614-idf-filter and squashes the following commits: 0aa3c63 [RJ Nowling] Fix identation e6523a8 [RJ Nowling] Remove unnecessary toDouble's from IDFSuite bfa82ec [RJ Nowling] Add space after if 30d20b3 [RJ Nowling] Add spaces around equals signs 9013447 [RJ Nowling] Add space before division operator 79978fc [RJ Nowling] Remove unnecessary semi-colon 40fd70c [RJ Nowling] Change minimumOccurence to minDocFreq in code and docs 47850ab [RJ Nowling] Changed minimumOccurence to Int from Long 9fb4093 [RJ Nowling] Remove unnecessary lines from IDF class docs 1fc09d8 [RJ Nowling] Add backwards-compatible constructor to DocumentFrequencyAggregator 1801fd2 [RJ Nowling] Fix style errors in IDF.scala 6897252 [RJ Nowling] Preface minimumOccurence members with val to make them final and immutable a200bab [RJ Nowling] Remove unnecessary else statement 4b974f5 [RJ Nowling] Remove accidentally-added import from testing c0cc643 [RJ Nowling] Add minimumOccurence filtering to IDF
* [SPARK-1484][MLLIB] Warn when running an iterative algorithm on uncached data.Aaron Staple2014-09-254-25/+83
| | | | | | | | | | | | | | | | | | | Add warnings to KMeans, GeneralizedLinearAlgorithm, and computeSVD when called with input data that is not cached. KMeans is implemented iteratively, and I believe that GeneralizedLinearAlgorithm’s current optimizers are iterative and its future optimizers are also likely to be iterative. RowMatrix’s computeSVD is iterative against an RDD when run in DistARPACK mode. ALS and DecisionTree are iterative as well, but they implement RDD caching internally so do not require a warning. I added a warning to GeneralizedLinearAlgorithm rather than inside its optimizers, where the iteration actually occurs, because internally GeneralizedLinearAlgorithm maps its input data to an uncached RDD before passing it to an optimizer. (In other words, the warning would be printed for every GeneralizedLinearAlgorithm run, regardless of whether its input is cached, if the warning were in GradientDescent or other optimizer.) I assume that use of an uncached RDD by GeneralizedLinearAlgorithm is intentional, and that the mapping there (adding label, intercepts and scaling) is a lightweight operation. Arguably a user calling an optimizer such as GradientDescent will be knowledgable enough to cache their data without needing a log warning, so lack of a warning in the optimizers may be ok. Some of the documentation examples making use of these iterative algorithms did not cache their training RDDs (while others did). I updated the examples to always cache. I also fixed some (unrelated) minor errors in the documentation examples. Author: Aaron Staple <aaron.staple@gmail.com> Closes #2347 from staple/SPARK-1484 and squashes the following commits: bd49701 [Aaron Staple] Address review comments. ab2d4a4 [Aaron Staple] Disable warnings on python code path. a7a0f99 [Aaron Staple] Change code comments per review comments. 7cca1dc [Aaron Staple] Change warning message text. c77e939 [Aaron Staple] [SPARK-1484][MLLIB] Warn when running an iterative algorithm on uncached data. 3b6c511 [Aaron Staple] Minor doc example fixes.
* [SPARK-3491] [MLlib] [PySpark] use pickle to serialize data in MLlibDavies Liu2014-09-194-342/+214
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Currently, we serialize the data between JVM and Python case by case manually, this cannot scale to support so many APIs in MLlib. This patch will try to address this problem by serialize the data using pickle protocol, using Pyrolite library to serialize/deserialize in JVM. Pickle protocol can be easily extended to support customized class. All the modules are refactored to use this protocol. Known issues: There will be some performance regression (both CPU and memory, the serialized data increased) Author: Davies Liu <davies.liu@gmail.com> Closes #2378 from davies/pickle_mllib and squashes the following commits: dffbba2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into pickle_mllib 810f97f [Davies Liu] fix equal of matrix 032cd62 [Davies Liu] add more type check and conversion for user_product bd738ab [Davies Liu] address comments e431377 [Davies Liu] fix cache of rdd, refactor 19d0967 [Davies Liu] refactor Picklers 2511e76 [Davies Liu] cleanup 1fccf1a [Davies Liu] address comments a2cc855 [Davies Liu] fix tests 9ceff73 [Davies Liu] test size of serialized Rating 44e0551 [Davies Liu] fix cache a379a81 [Davies Liu] fix pickle array in python2.7 df625c7 [Davies Liu] Merge commit '154d141' into pickle_mllib 154d141 [Davies Liu] fix autobatchedpickler 44736d7 [Davies Liu] speed up pickling array in Python 2.7 e1d1bfc [Davies Liu] refactor 708dc02 [Davies Liu] fix tests 9dcfb63 [Davies Liu] fix style 88034f0 [Davies Liu] rafactor, address comments 46a501e [Davies Liu] choose batch size automatically df19464 [Davies Liu] memorize the module and class name during pickleing f3506c5 [Davies Liu] Merge branch 'master' into pickle_mllib 722dd96 [Davies Liu] cleanup _common.py 0ee1525 [Davies Liu] remove outdated tests b02e34f [Davies Liu] remove _common.py 84c721d [Davies Liu] Merge branch 'master' into pickle_mllib 4d7963e [Davies Liu] remove muanlly serialization 6d26b03 [Davies Liu] fix tests c383544 [Davies Liu] classification f2a0856 [Davies Liu] mllib/regression d9f691f [Davies Liu] mllib/util cccb8b1 [Davies Liu] mllib/tree 8fe166a [Davies Liu] Merge branch 'pickle' into pickle_mllib aa2287e [Davies Liu] random f1544c4 [Davies Liu] refactor clustering 52d1350 [Davies Liu] use new protocol in mllib/stat b30ef35 [Davies Liu] use pickle to serialize data for mllib/recommendation f44f771 [Davies Liu] enable tests about array 3908f5c [Davies Liu] Merge branch 'master' into pickle c77c87b [Davies Liu] cleanup debugging code 60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
* [SPARK-3418] Sparse Matrix support (CCS) and additional native BLAS ↵Burak2014-09-187-9/+831
| | | | | | | | | | | | | | | | | | | | | | | | | | | operations added Local `SparseMatrix` support added in Compressed Column Storage (CCS) format in addition to Level-2 and Level-3 BLAS operations such as dgemv and dgemm respectively. BLAS doesn't support sparse matrix operations, therefore support for `SparseMatrix`-`DenseMatrix` multiplication and `SparseMatrix`-`DenseVector` implementations have been added. I will post performance comparisons in the comments momentarily. Author: Burak <brkyvz@gmail.com> Closes #2294 from brkyvz/SPARK-3418 and squashes the following commits: 88814ed [Burak] Hopefully fixed MiMa this time 47e49d5 [Burak] really fixed MiMa issue f0bae57 [Burak] [SPARK-3418] Fixed MiMa compatibility issues (excluded from check) 4b7dbec [Burak] 9/17 comments addressed 7af2f83 [Burak] sealed traits Vector and Matrix d3a8a16 [Burak] [SPARK-3418] Squashed missing alpha bug. 421045f [Burak] [SPARK-3418] New code review comments addressed f35a161 [Burak] [SPARK-3418] Code review comments addressed and multiplication further optimized 2508577 [Burak] [SPARK-3418] Fixed one more style issue d16e8a0 [Burak] [SPARK-3418] Fixed style issues and added documentation for methods 204a3f7 [Burak] [SPARK-3418] Fixed failing Matrix unit test 6025297 [Burak] [SPARK-3418] Fixed Scala-style errors dc7be71 [Burak] [SPARK-3418][MLlib] Matrix unit tests expanded with indexing and updating d2d5851 [Burak] [SPARK-3418][MLlib] Sparse Matrix support and additional native BLAS operations added
* [SPARK-3516] [mllib] DecisionTree: Add minInstancesPerNode, minInfoGain ↵qiping.lqp2014-09-155-11/+13
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | params to example and Python API Added minInstancesPerNode, minInfoGain params to: * DecisionTreeRunner.scala example * Python API (tree.py) Also: * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes CC: mengxr Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Author: chouqin <liqiping1991@gmail.com> Closes #2349 from jkbradley/chouqin-dt-preprune and squashes the following commits: 61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy. a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune 19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune f1d11d1 [chouqin] fix typo c7ebaf1 [chouqin] fix typo 39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py 0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1 efcc736 [qiping.lqp] fix bug 10b8012 [qiping.lqp] fix style 6728fad [qiping.lqp] minor fix: remove empty lines bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune cadd569 [qiping.lqp] add api docs 46b891f [qiping.lqp] fix bug e72c7e4 [qiping.lqp] add comments 845c6fa [qiping.lqp] fix style f195e83 [qiping.lqp] fix style 987cbf4 [qiping.lqp] fix bug ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
* [MLlib] Update SVD documentation in IndexedRowMatrixReza Zadeh2014-09-151-8/+4
| | | | | | | | | | | Updating this to reflect the newest SVD via ARPACK Author: Reza Zadeh <rizlar@gmail.com> Closes #2389 from rezazadeh/irmdocs and squashes the following commits: 7fa1313 [Reza Zadeh] Update svd docs 715da25 [Reza Zadeh] Updated computeSVD documentation IndexedRowMatrix
* [SPARK-3396][MLLIB] Use SquaredL2Updater in LogisticRegressionWithSGDChristoph Sawade2014-09-152-4/+42
| | | | | | | | | | | | | SimpleUpdater ignores the regularizer, which leads to an unregularized LogReg. To enable the common L2 regularizer (and the corresponding regularization parameter) for logistic regression the SquaredL2Updater has to be used in SGD (see, e.g., [SVMWithSGD]) Author: Christoph Sawade <christoph@sawade.me> Closes #2398 from BigCrunsh/fix-regparam-logreg and squashes the following commits: 0820c04 [Christoph Sawade] Use SquaredL2Updater in LogisticRegressionWithSGD
* [SPARK-3160] [SPARK-3494] [mllib] DecisionTree: eliminate pre-allocated ↵Joseph K. Bradley2014-09-127-256/+268
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | nodes, parentImpurities arrays. Memory calc bug fix. This PR includes some code simplifications and re-organization which will be helpful for implementing random forests. The main changes are that the nodes and parentImpurities arrays are no longer pre-allocated in the main train() method. Also added 2 bug fixes: * maxMemoryUsage calculation * over-allocation of space for bins in DTStatsAggregator for unordered features. Relation to RFs: * Since RFs will be deeper and will therefore be more likely sparse (not full trees), it could be a cost savings to avoid pre-allocating a full tree. * The associated re-organization also reduces bookkeeping, which will make RFs easier to implement. * The return code doneTraining may be generalized to include cases such as nodes ready for local training. Details: No longer pre-allocate parentImpurities array in main train() method. * parentImpurities values are now stored in individual nodes (in Node.stats.impurity). * These were not really needed. They were used in calculateGainForSplit(), but they can be calculated anyways using parentNodeAgg. No longer using Node.build since tree structure is constructed on-the-fly. * Did not eliminate since it is public (Developer) API. Marked as deprecated. Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training. * Moved tree construction from main train() method into findBestSplitsPerGroup() since there is no need to keep the (split, gain) array for an entire level of nodes. Only one element of that array is needed at a time, so we do not the array. findBestSplits() now returns 2 items: * rootNode (newly created root node on first iteration, same root node on later iterations) * doneTraining (indicating if all nodes at that level were leafs) Updated DecisionTreeSuite. Notes: * Improved test "Second level node building with vs. without groups" ** generateOrderedLabeledPoints() modified so that it really does require 2 levels of internal nodes. * Related update: Added Node.deepCopy (private[tree]), used for test suite CC: mengxr Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2341 from jkbradley/dt-spark-3160 and squashes the following commits: 07dd1ee [Joseph K. Bradley] Fixed overflow bug with computing maxMemoryUsage in DecisionTree. Also fixed bug with over-allocating space in DTStatsAggregator for unordered features. debe072 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1 0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160 1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training. 2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
* [SPARK-2207][SPARK-3272][MLLib]Add minimum information gain and minimum ↵qiping.lqp2014-09-107-36/+213
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | instances per node as training parameters for decision tree. These two parameters can act as early stop rules to do pre-pruning. When a split cause cause left or right child to have less than `minInstancesPerNode` or has less information gain than `minInfoGain`, current node will not be split by this split. When there is no possible splits that satisfy requirements, there is no useful information gain stats, but we still need to calculate the predict value for current node. So I separated calculation of predict from calculation of information gain, which can also save computation when the number of possible splits is large. Please see [SPARK-3272](https://issues.apache.org/jira/browse/SPARK-3272) for more details. CC: mengxr manishamde jkbradley, please help me review this, thanks. Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Author: chouqin <liqiping1991@gmail.com> Closes #2332 from chouqin/dt-preprune and squashes the following commits: f1d11d1 [chouqin] fix typo c7ebaf1 [chouqin] fix typo 39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test 0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1 efcc736 [qiping.lqp] fix bug 10b8012 [qiping.lqp] fix style 6728fad [qiping.lqp] minor fix: remove empty lines bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune cadd569 [qiping.lqp] add api docs 46b891f [qiping.lqp] fix bug e72c7e4 [qiping.lqp] add comments 845c6fa [qiping.lqp] fix style f195e83 [qiping.lqp] fix style 987cbf4 [qiping.lqp] fix bug ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
* [SPARK-3443][MLLIB] update default values of tree:Xiangrui Meng2014-09-083-21/+11
| | | | | | | | | | | | | | | | | | Adjust the default values of decision tree, based on the memory requirement discussed in https://github.com/apache/spark/pull/2125 : 1. maxMemoryInMB: 128 -> 256 2. maxBins: 100 -> 32 3. maxDepth: 4 -> 5 (in some example code) jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #2322 from mengxr/tree-defaults and squashes the following commits: cda453a [Xiangrui Meng] fix tests 5900445 [Xiangrui Meng] update comments 8c81831 [Xiangrui Meng] update default values of tree:
* [SPARK-3086] [SPARK-3043] [SPARK-3156] [mllib] DecisionTree aggregation ↵Joseph K. Bradley2014-09-0811-1248/+1322
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | improvements Summary: 1. Variable numBins for each feature [SPARK-3043] 2. Reduced data reshaping in aggregation [SPARK-3043] 3. Choose ordering for ordered categorical features adaptively [SPARK-3156] 4. Changed nodes to use 1-indexing [SPARK-3086] 5. Small clean-ups Note: This PR looks bigger than it is since I moved several functions from inside findBestSplitsPerGroup to outside of it (to make it clear what was being serialized in the aggregation). Speedups: This update helps most when many features use few bins but a few features use many bins. Some example results on speedups with 2M examples, 3.5K features (15-worker EC2 cluster): * Example where old code was reasonably efficient (1/2 continuous, 1/4 binary, 1/4 20-category): 164.813 --> 116.491 sec * Example where old code wasted many bins (1/10 continuous, 81/100 binary, 9/100 20-category): 128.701 --> 39.334 sec Details: (1) Variable numBins for each feature [SPARK-3043] DecisionTreeMetadata now computes a variable numBins for each feature. It also tracks numSplits. (2) Reduced data reshaping in aggregation [SPARK-3043] Added DTStatsAggregator, a wrapper around the aggregate statistics array for easy but efficient indexing. * Added ImpurityAggregator and ImpurityCalculator classes, to make DecisionTree code more oblivious to the type of impurity. * Design note: I originally tried creating Impurity classes which stored data and storing the aggregates in an Array[Array[Array[Impurity]]]. However, this led to significant slowdowns, perhaps because of overhead in creating so many objects. The aggregate statistics are never reshaped, and cumulative sums are computed in-place. Updated the layout of aggregation functions. The update simplifies things by (1) dividing features into ordered/unordered (instead of ordered/unordered/continuous) and (2) making use of the DTStatsAggregator for indexing. For this update, the following functions were refactored: * updateBinForOrderedFeature * updateBinForUnorderedFeature * binaryOrNotCategoricalBinSeqOp * multiclassWithCategoricalBinSeqOp * regressionBinSeqOp The above 5 functions were replaced with: * orderedBinSeqOp * someUnorderedBinSeqOp Other changes: * calculateGainForSplit now treats all feature types the same way. * Eliminated extractLeftRightNodeAggregates. (3) Choose ordering for ordered categorical features adaptively [SPARK-3156] Updated binsToBestSplit(): * This now computes cumulative sums of stats for ordered features. * For ordered categorical features, it chooses an ordering for categories. (This uses to be done by findSplitsBins.) * Uses iterators to shorten code and avoid building an Array[Array[InformationGainStats]]. Side effects: * In findSplitsBins: A sample of the data is only taken for data with continuous features. It is not needed for data with only categorical features. * In findSplitsBins: splits and bins are no longer pre-computed for ordered categorical features since they are not needed. * TreePoint binning is simpler for categorical features. (4) Changed nodes to use 1-indexing [SPARK-3086] Nodes used to be indexed from 0. Now they are indexed from 1. Node indexing functions are now collected in object Node (Node.scala). (5) Small clean-ups Eliminated functions extractNodeInfo() and extractInfoForLowerLevels() to reduce duplicate code. Eliminated InvalidBinIndex since it is no longer used. CC: mengxr manishamde Please let me know if you have thoughts on this—thanks! Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2125 from jkbradley/dt-opt3alt and squashes the following commits: 42c192a [Joseph K. Bradley] Merge branch 'rfs' into dt-opt3alt d3cc46b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt 00e4404 [Joseph K. Bradley] optimization for TreePoint construction (pre-computing featureArity and isUnordered as arrays) 425716c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs a2acea5 [Joseph K. Bradley] Small optimizations based on profiling aa4e4df [Joseph K. Bradley] Updated DTStatsAggregator with bug fix (nodeString should not be multiplied by statsSize) 4651154 [Joseph K. Bradley] Changed numBins semantics for unordered features. * Before: numBins = numSplits = (1 << k - 1) - 1 * Now: numBins = 2 * numSplits = 2 * [(1 << k - 1) - 1] * This also involved changing the semantics of: ** DecisionTreeMetadata.numUnorderedBins() 1e3b1c7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt 1485fcc [Joseph K. Bradley] Made some DecisionTree methods private. 92f934f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt e676da1 [Joseph K. Bradley] Updated documentation for DecisionTree 37ca845 [Joseph K. Bradley] Fixed problem with how DecisionTree handles ordered categorical features. 105f8ab [Joseph K. Bradley] Removed commented-out getEmptyBinAggregates from DecisionTree 062c31d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt 6d32ccd [Joseph K. Bradley] In DecisionTree.binsToBestSplit, changed loops to iterators to shorten code. 807cd00 [Joseph K. Bradley] Finished DTStatsAggregator, a wrapper around the aggregate statistics for easy but hopefully efficient indexing. Modified old ImpurityAggregator classes and renamed them ImpurityCalculator; added ImpurityAggregator classes which work with DTStatsAggregator but do not store data. Unit tests all succeed. f2166fd [Joseph K. Bradley] still working on DTStatsAggregator 92f7118 [Joseph K. Bradley] Added partly written DTStatsAggregator fd8df30 [Joseph K. Bradley] Moved some aggregation helpers outside of findBestSplitsPerGroup d7c53ee [Joseph K. Bradley] Added more doc for ImpurityAggregator a40f8f1 [Joseph K. Bradley] Changed nodes to be indexed from 1. Tests work. 95cad7c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3 5f94342 [Joseph K. Bradley] Added treeAggregate since not yet merged from master. Moved node indexing functions to Node. 61c4509 [Joseph K. Bradley] Fixed bugs from merge: missing DT timer call, and numBins setting. Cleaned up DT Suite some. 3ba7166 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3 b314659 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3 9c83363 [Joseph K. Bradley] partial merge but not done yet 45f7ea7 [Joseph K. Bradley] partial merge, not yet done 5fce635 [Joseph K. Bradley] Merge branch 'dt-opt2' into dt-opt3 26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used. Removed debugging println calls in DecisionTree.scala. 356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2 430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs. d036089 [Joseph K. Bradley] Print timing info to logDebug. e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private 8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 dd4d3aa [Joseph K. Bradley] Mid-process in bug fix: bug for binary classification with categorical features * Bug: Categorical features were all treated as ordered for binary classification. This is possible but would require the bin ordering to be determined on-the-fly after the aggregation. Currently, the ordering is determined a priori and fixed for all splits. * (Temp) Fix: Treat low-arity categorical features as unordered for binary classification. * Related change: I removed most tests for isMulticlass in the code. I instead test metadata for whether there are unordered features. * Status: The bug may be fixed, but more testing needs to be done. 438a660 [Joseph K. Bradley] removed subsampling for mnist8m from DT 86e217f [Joseph K. Bradley] added cache to DT input e3c84cc [Joseph K. Bradley] Added stuff fro mnist8m to D T Runner 51ef781 [Joseph K. Bradley] Fixed bug introduced by last commit: Variance impurity calculation was incorrect since counts were swapped accidentally fd65372 [Joseph K. Bradley] Major changes: * Created ImpurityAggregator classes, rather than old aggregates. * Feature split/bin semantics are based on ordered vs. unordered ** E.g.: numSplits = numBins for all unordered features, and numSplits = numBins - 1 for all ordered features. * numBins can differ for each feature c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt 0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree 3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging) f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing 511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
* [SPARK-3397] Bump pom.xml version number of master branch to 1.2.0-SNAPSHOTGuoQiang Li2014-09-061-1/+1
| | | | | | | | Author: GuoQiang Li <witgo@qq.com> Closes #2268 from witgo/SPARK-3397 and squashes the following commits: eaf913f [GuoQiang Li] Bump pom.xml version number of master branch to 1.2.0-SNAPSHOT
* [SPARK-3372] [MLlib] MLlib doesn't pass maven build / checkstyle due to ↵Kousuke Saruta2014-09-031-2/+2
| | | | | | | | | | multi-byte character contained in Gradient.scala Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2248 from sarutak/SPARK-3372 and squashes the following commits: 73a28b8 [Kousuke Saruta] Replaced UTF-8 hyphen with ascii hyphen
* [MLlib] Squash bug in IndexedRowMatrixReza Zadeh2014-09-021-1/+1
| | | | | | | | | | Kill this bug fast before it does damage. Author: Reza Zadeh <rizlar@gmail.com> Closes #2224 from rezazadeh/indexrmbug and squashes the following commits: 53386d6 [Reza Zadeh] Squash bug in IndexedRowMatrix
* [SPARK-2495][MLLIB] make KMeans constructor publicXiangrui Meng2014-08-251-1/+1
| | | | | | | | | | to re-construct k-means models freeman-lab Author: Xiangrui Meng <meng@databricks.com> Closes #2112 from mengxr/public-constructors and squashes the following commits: 18d53a9 [Xiangrui Meng] make KMeans constructor public
* [SPARK-3142][MLLIB] output shuffle data directly in Word2VecXiangrui Meng2014-08-191-11/+12
| | | | | | | | | | Sorry I didn't realize this in #2043. Ishiihara Author: Xiangrui Meng <meng@databricks.com> Closes #2049 from mengxr/more-w2v and squashes the following commits: 050b1c5 [Xiangrui Meng] output shuffle data directly
* [HOTFIX][Streaming][MLlib] use temp folder for checkpointXiangrui Meng2014-08-191-6/+0
| | | | | | | | | | | | or Jenkins will complain about no Apache header in checkpoint files. tdas rxin Author: Xiangrui Meng <meng@databricks.com> Closes #2046 from mengxr/tmp-checkpoint and squashes the following commits: 0d3ec73 [Xiangrui Meng] remove ssc.stop 9797843 [Xiangrui Meng] change checkpointDir to lazy val 89964ab [Xiangrui Meng] use temp folder for checkpoint
* [SPARK-3130][MLLIB] detect negative values in naive BayesXiangrui Meng2014-08-192-5/+51
| | | | | | | | | | | because NB treats feature values as term frequencies. jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #2038 from mengxr/nb-neg and squashes the following commits: 52c37c3 [Xiangrui Meng] address comments 65f892d [Xiangrui Meng] detect negative values in nb
* [MLLIB] minor update to word2vecXiangrui Meng2014-08-191-10/+8
| | | | | | | | | | | very minor update Ishiihara Author: Xiangrui Meng <meng@databricks.com> Closes #2043 from mengxr/minor-w2v and squashes the following commits: be649fd [Xiangrui Meng] remove map because we only need append eccefcc [Xiangrui Meng] minor updates to word2vec
* [SPARK-3136][MLLIB] Create Java-friendly methods in RandomRDDsXiangrui Meng2014-08-193-294/+334
| | | | | | | | | | | | | Though we don't use default argument for methods in RandomRDDs, it is still not easy for Java users to use because the output type is either `RDD[Double]` or `RDD[Vector]`. Java users should expect `JavaDoubleRDD` and `JavaRDD[Vector]`, respectively. We should create dedicated methods for Java users, and allow default arguments in Scala methods in RandomRDDs, to make life easier for both Java and Scala users. This PR also contains documentation for random data generation. brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #2041 from mengxr/stat-doc and squashes the following commits: fc5eedf [Xiangrui Meng] add missing comma ffde810 [Xiangrui Meng] address comments aef6d07 [Xiangrui Meng] add doc for random data generation b99d94b [Xiangrui Meng] add java-friendly methods to RandomRDDs
* [SPARK-3128][MLLIB] Use streaming test suite for StreamingLRfreeman2014-08-192-54/+74
| | | | | | | | | | | | | | | | | | | Refactored tests for streaming linear regression to use existing streaming test utilities. Summary of changes: - Made ``mllib`` depend on tests from ``streaming`` - Rewrote accuracy and convergence tests to use ``setupStreams`` and ``runStreams`` - Added new test for the accuracy of predictions generated by ``predictOnValue`` These tests should run faster, be easier to extend/maintain, and provide a reference for new tests. mengxr tdas Author: freeman <the.freeman.lab@gmail.com> Closes #2037 from freeman-lab/streamingLR-predict-tests and squashes the following commits: e851ca7 [freeman] Fixed long lines 50eb0bf [freeman] Refactored tests to use streaming test tools 32c43c2 [freeman] Added test for prediction
* [SPARK-3108][MLLIB] add predictOnValues to StreamingLR and fix predictOnXiangrui Meng2014-08-181-6/+25
| | | | | | | | | | | | It is useful in streaming to allow users to carry extra data with the prediction, for monitoring the prediction error for example. freeman-lab Author: Xiangrui Meng <meng@databricks.com> Closes #2023 from mengxr/predict-on-values and squashes the following commits: cac47b8 [Xiangrui Meng] add classtag 2821b3b [Xiangrui Meng] use mapValues 0925efa [Xiangrui Meng] add predictOnValues to StreamingLR and fix predictOn
* [SPARK-2850] [SPARK-2626] [mllib] MLlib stats examples + small fixesJoseph K. Bradley2014-08-184-10/+33
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Added examples for statistical summarization: * Scala: StatisticalSummary.scala ** Tests: correlation, MultivariateOnlineSummarizer * python: statistical_summary.py ** Tests: correlation (since MultivariateOnlineSummarizer has no Python API) Added examples for random and sampled RDDs: * Scala: RandomAndSampledRDDs.scala * python: random_and_sampled_rdds.py * Both test: ** RandomRDDGenerators.normalRDD, normalVectorRDD ** RDD.sample, takeSample, sampleByKey Added sc.stop() to all examples. CorrelationSuite.scala * Added 1 test for RDDs with only 1 value RowMatrix.scala * numCols(): Added check for numRows = 0, with error message. * computeCovariance(): Added check for numRows <= 1, with error message. Python SparseVector (pyspark/mllib/linalg.py) * Added toDense() function python/run-tests script * Added stat.py (doc test) CC: mengxr dorx Main changes were examples to show usage across APIs. Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1878 from jkbradley/mllib-stats-api-check and squashes the following commits: ea5c047 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check dafebe2 [Joseph K. Bradley] Bug fixes for examples SampledRDDs.scala and sampled_rdds.py: Check for division by 0 and for missing key in maps. 8d1e555 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check 60c72d9 [Joseph K. Bradley] Fixed stat.py doc test to work for Python versions printing nan or NaN. b20d90a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check 4e5d15e [Joseph K. Bradley] Changed pyspark/mllib/stat.py doc tests to use NaN instead of nan. 32173b7 [Joseph K. Bradley] Stats examples update. c8c20dc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check cf70b07 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check 0b7cec3 [Joseph K. Bradley] Small updates based on code review. Renamed statistical_summary.py to correlations.py ab48f6e [Joseph K. Bradley] RowMatrix.scala * numCols(): Added check for numRows = 0, with error message. * computeCovariance(): Added check for numRows <= 1, with error message. 65e4ebc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check 8195c78 [Joseph K. Bradley] Added examples for random and sampled RDDs: * Scala: RandomAndSampledRDDs.scala * python: random_and_sampled_rdds.py * Both test: ** RandomRDDGenerators.normalRDD, normalVectorRDD ** RDD.sample, takeSample, sampleByKey 064985b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check ee918e9 [Joseph K. Bradley] Added examples for statistical summarization: * Scala: StatisticalSummary.scala ** Tests: correlation, MultivariateOnlineSummarizer * python: statistical_summary.py ** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
* [mllib] DecisionTree: treeAggregate + Python example bug fixJoseph K. Bradley2014-08-181-1/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Small DecisionTree updates: * Changed main DecisionTree aggregate to treeAggregate. * Fixed bug in python example decision_tree_runner.py with missing argument (since categoricalFeaturesInfo is no longer an optional argument for trainClassifier). * Fixed same bug in python doc tests, and added tree.py to doc tests. CC: mengxr Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #2015 from jkbradley/dt-opt2 and squashes the following commits: b5114fa [Joseph K. Bradley] Fixed python tree.py doc test (extra newline) 8e4665d [Joseph K. Bradley] Added tree.py to python doc tests. Fixed bug from missing categoricalFeaturesInfo argument. b7b2922 [Joseph K. Bradley] Fixed bug in python example decision_tree_runner.py with missing argument. Changed main DecisionTree aggregate to treeAggregate. 85bbc1f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2 66d076f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2 a0ed0da [Joseph K. Bradley] Renamed DTMetadata to DecisionTreeMetadata. Small doc updates. 3726d20 [Joseph K. Bradley] Small code improvements based on code review. ac0b9f8 [Joseph K. Bradley] Small updates based on code review. Main change: Now using << instead of math.pow. db0d773 [Joseph K. Bradley] scala style fix 6a38f48 [Joseph K. Bradley] Added DTMetadata class for cleaner code 931a3a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2 797f68a [Joseph K. Bradley] Fixed DecisionTreeSuite bug for training second level. Needed to update treePointToNodeIndex with groupShift. f40381c [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2 5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint 6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory. 2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used. Removed debugging println calls in DecisionTree.scala. 356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2 430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs. d036089 [Joseph K. Bradley] Print timing info to logDebug. e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private 8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt 0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree 3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging) f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing 511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
* [MLlib] Remove transform(dataset: RDD[String]) from Word2Vec public APILiquan Pei2014-08-181-9/+0
| | | | | | | | | | | mengxr Remove transform(dataset: RDD[String]) from public API. Author: Liquan Pei <liquanpei@gmail.com> Closes #2010 from Ishiihara/Word2Vec-api and squashes the following commits: 17b1031 [Liquan Pei] remove transform(dataset: RDD[String]) from public API
* [SPARK-3097][MLlib] Word2Vec performance improvementLiquan Pei2014-08-171-15/+35
| | | | | | | | | | | | | | | | mengxr Please review the code. Adding weights in reduceByKey soon. Only output model entry for words appeared in the partition before merging and use reduceByKey to combine model. In general, this implementation is 30s or so faster than implementation using big array. Author: Liquan Pei <liquanpei@gmail.com> Closes #1932 from Ishiihara/Word2Vec-improve2 and squashes the following commits: d5377a9 [Liquan Pei] use syn0Global and syn1Global to represent model cad2011 [Liquan Pei] bug fix for synModify array out of bound 083aa66 [Liquan Pei] update synGlobal in place and reduce synOut size 9075e1c [Liquan Pei] combine syn0Global and syn1Global to synGlobal aa2ab36 [Liquan Pei] use reduceByKey to combine models
* [SPARK-3087][MLLIB] fix col indexing bug in chi-square and add a check for ↵Xiangrui Meng2014-08-173-17/+59
| | | | | | | | | | | | number of distinct values There is a bug determining the column index. dorx Author: Xiangrui Meng <meng@databricks.com> Closes #1997 from mengxr/chisq-index and squashes the following commits: 8fc2ab2 [Xiangrui Meng] fix col indexing bug and add a check for number of distinct values
* [SPARK-3042] [mllib] DecisionTree Filter top-down instead of bottom-upJoseph K. Bradley2014-08-169-630/+615
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | DecisionTree needs to match each example to a node at each iteration. It currently does this with a set of filters very inefficiently: For each example, it examines each node at the current level and traces up to the root to see if that example should be handled by that node. Fix: Filter top-down using the partly built tree itself. Major changes: * Eliminated Filter class, findBinsForLevel() method. * Set up node parent links in main loop over levels in train(). * Added predictNodeIndex() for filtering top-down. * Added DTMetadata class Other changes: * Pre-compute set of unorderedFeatures. Notes for following expected PR based on [https://issues.apache.org/jira/browse/SPARK-3043]: * The unorderedFeatures set will next be stored in a metadata structure to simplify function calls (to store other items such as the data in strategy). I've done initial tests indicating that this speeds things up, but am only now running large-scale ones. CC: mengxr manishamde chouqin Any comments are welcome---thanks! Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1975 from jkbradley/dt-opt2 and squashes the following commits: a0ed0da [Joseph K. Bradley] Renamed DTMetadata to DecisionTreeMetadata. Small doc updates. 3726d20 [Joseph K. Bradley] Small code improvements based on code review. ac0b9f8 [Joseph K. Bradley] Small updates based on code review. Main change: Now using << instead of math.pow. db0d773 [Joseph K. Bradley] scala style fix 6a38f48 [Joseph K. Bradley] Added DTMetadata class for cleaner code 931a3a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2 797f68a [Joseph K. Bradley] Fixed DecisionTreeSuite bug for training second level. Needed to update treePointToNodeIndex with groupShift. f40381c [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2 5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint 6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory. 2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used. Removed debugging println calls in DecisionTree.scala. 356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2 430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs. d036089 [Joseph K. Bradley] Print timing info to logDebug. e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private 8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt 0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree 3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging) f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing 511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
* [SPARK-3077][MLLIB] fix some chisq-testXiangrui Meng2014-08-162-13/+17
| | | | | | | | | | | | | - promote nullHypothesis field in ChiSqTestResult to TestResult. Every test should have a null hypothesis - correct null hypothesis statement for independence test - p-value: 0.01 -> 0.1 Author: Xiangrui Meng <meng@databricks.com> Closes #1982 from mengxr/fix-chisq and squashes the following commits: 5f0de02 [Xiangrui Meng] make ChiSqTestResult constructor package private bc74ea1 [Xiangrui Meng] update chisq-test
* [SPARK-3081][MLLIB] rename RandomRDDGenerators to RandomRDDsXiangrui Meng2014-08-163-12/+12
| | | | | | | | | | | | `RandomRDDGenerators` means factory for `RandomRDDGenerator`. However, its methods return RDDs but not RDDGenerators. So a more proper (and shorter) name would be `RandomRDDs`. dorx brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #1979 from mengxr/randomrdds and squashes the following commits: b161a2d [Xiangrui Meng] rename RandomRDDGenerators to RandomRDDs
* [SPARK-3048][MLLIB] add LabeledPoint.parse and remove loadStreamingLabeledPointsXiangrui Meng2014-08-165-22/+9
| | | | | | | | | | | | | | | | | Move `parse()` from `LabeledPointParser` to `LabeledPoint` and make it public. This breaks binary compatibility only when a user uses synthesized methods like `tupled` and `curried`, which is rare. `LabeledPoint.parse` is more consistent with `Vectors.parse`, which is why `LabeledPointParser` is not preferred. freeman-lab tdas Author: Xiangrui Meng <meng@databricks.com> Closes #1952 from mengxr/labelparser and squashes the following commits: c818fb2 [Xiangrui Meng] merge master ce20e6f [Xiangrui Meng] update mima excludes b386b8d [Xiangrui Meng] fix tests 2436b3d [Xiangrui Meng] add parse() to LabeledPoint
* [SPARK-3001][MLLIB] Improve Spearman's correlationXiangrui Meng2014-08-151-78/+42
| | | | | | | | | | | | | | | | | | | | | | The current implementation requires sorting individual columns, which could be done with a global sort. result on a 32-node cluster: m | n | prev | this ---|---|-------|----- 1000000 | 50 | 55s | 9s 10000000 | 50 | 97s | 76s 1000000 | 100 | 119s | 15s Author: Xiangrui Meng <meng@databricks.com> Closes #1917 from mengxr/spearman and squashes the following commits: 4d5d262 [Xiangrui Meng] remove unused import 85c48de [Xiangrui Meng] minor updates a048d0c [Xiangrui Meng] remove cache and set a limit to cachedIds b98bb18 [Xiangrui Meng] add comments 0846e07 [Xiangrui Meng] first version
* [SPARK-3078][MLLIB] Make LRWithLBFGS API consistent with othersXiangrui Meng2014-08-154-49/+29
| | | | | | | | | | | | | Should ask users to set parameters through the optimizer. dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #1973 from mengxr/lr-lbfgs and squashes the following commits: e3efbb1 [Xiangrui Meng] fix tests 21b3579 [Xiangrui Meng] fix method name 641eea4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into lr-lbfgs 456ab7c [Xiangrui Meng] update LRWithLBFGS
* [SPARK-3022] [SPARK-3041] [mllib] Call findBins once per level + unordered ↵Joseph K. Bradley2014-08-155-207/+449
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | feature bug fix DecisionTree improvements: (1) TreePoint representation to avoid binning multiple times (2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features (3) Timing for DecisionTree internals Details: (1) TreePoint representation to avoid binning multiple times [https://issues.apache.org/jira/browse/SPARK-3022] Added private[tree] TreePoint class for representing binned feature values. The input RDD of LabeledPoint is converted to the TreePoint representation initially and then cached. This avoids the previous problem of re-computing bins multiple times. (2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features [https://issues.apache.org/jira/browse/SPARK-3041] isSampleValid used to treat unordered categorical features incorrectly: It treated the bins as if indexed by featured values, rather than by subsets of values/categories. * exhibited for unordered features (multi-class classification with categorical features of low arity) * Fix: Index bins correctly for unordered categorical features. (3) Timing for DecisionTree internals Added tree/impl/TimeTracker.scala class which is private[tree] for now, for timing key parts of DT code. Prints timing info via logDebug. CC: mengxr manishamde chouqin Very similar update, with one bug fix. Many apologies for the conflicting update, but I hope that a few more optimizations I have on the way (which depend on this update) will prove valuable to you: SPARK-3042 and SPARK-3043 Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #1950 from jkbradley/dt-opt1 and squashes the following commits: 5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint 6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory. 2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs. d036089 [Joseph K. Bradley] Print timing info to logDebug. e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private 8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1 0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree 3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging) f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing 511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
* [SPARK-2979][MLlib] Improve the convergence rate by minimizing the condition ↵DB Tsai2014-08-143-4/+126
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | number In theory, the scale of your inputs are irrelevant to logistic regression. You can "theoretically" multiply X1 by 1E6 and the estimate for β1 will adjust accordingly. It will be 1E-6 times smaller than the original β1, due to the invariance property of MLEs. However, during the optimization process, the convergence (rate) depends on the condition number of the training dataset. Scaling the variables often reduces this condition number, thus improving the convergence rate. Without reducing the condition number, some training datasets mixing the columns with different scales may not be able to converge. GLMNET and LIBSVM packages perform the scaling to reduce the condition number, and return the weights in the original scale. See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf Here, if useFeatureScaling is enabled, we will standardize the training features by dividing the variance of each column (without subtracting the mean to densify the sparse vector), and train the model in the scaled space. Then we transform the coefficients from the scaled space to the original scale as GLMNET and LIBSVM do. Currently, it's only enabled in LogisticRegressionWithLBFGS. Author: DB Tsai <dbtsai@alpinenow.com> Closes #1897 from dbtsai/dbtsai-feature-scaling and squashes the following commits: f19fc02 [DB Tsai] Added more comments 1d85289 [DB Tsai] Improve the convergence rate by minimize the condition number in LOR with LBFGS
* [SPARK-2995][MLLIB] add ALS.setIntermediateRDDStorageLevelXiangrui Meng2014-08-131-15/+30
| | | | | | | | | | | | | As mentioned in SPARK-2465, using `MEMORY_AND_DISK_SER` for user/product in/out links together with `spark.rdd.compress=true` can help reduce the space requirement by a lot, at the cost of speed. It might be useful to add this option so people can run ALS on much bigger datasets. Another option for the method name is `setIntermediateRDDStorageLevel`. Author: Xiangrui Meng <meng@databricks.com> Closes #1913 from mengxr/als-storagelevel and squashes the following commits: d942017 [Xiangrui Meng] rename to setIntermediateRDDStorageLevel 7550029 [Xiangrui Meng] add ALS.setIntermediateDataStorageLevel
* [MLLIB] use Iterator.fill instead of Array.fillXiangrui Meng2014-08-131-5/+5
| | | | | | | | | | Iterator.fill uses less memory Author: Xiangrui Meng <meng@databricks.com> Closes #1930 from mengxr/rand-gen-iter and squashes the following commits: 24178ca [Xiangrui Meng] use Iterator.fill instead of Array.fill
* [SPARK-2993] [MLLib] colStats (wrapper around ↵Doris Xin2014-08-124-260/+309
| | | | | | | | | | | | | | | | | | MultivariateStatisticalSummary) in Statistics For both Scala and Python. The ser/de util functions were moved out of `PythonMLLibAPI` and into their own object to avoid creating the `PythonMLLibAPI` object inside of `MultivariateStatisticalSummarySerialized`, which is then referenced inside of a method in `PythonMLLibAPI`. `MultivariateStatisticalSummarySerialized` was created to serialize the `Vector` fields in `MultivariateStatisticalSummary`. Author: Doris Xin <doris.s.xin@gmail.com> Closes #1911 from dorx/colStats and squashes the following commits: 77b9924 [Doris Xin] developerAPI tag de9cbbe [Doris Xin] reviewer comments and moved more ser/de 459faba [Doris Xin] colStats in Statistics for both Scala and Python
* [MLlib] Correctly set vectorSize and alphaLiquan Pei2014-08-121-13/+12
| | | | | | | | | | | mengxr Correctly set vectorSize and alpha in Word2Vec training. Author: Liquan Pei <liquanpei@gmail.com> Closes #1900 from Ishiihara/Word2Vec-bugfix and squashes the following commits: 85f64f2 [Liquan Pei] correctly set vectorSize and alpha
* [SPARK-2923][MLLIB] Implement some basic BLAS routinesXiangrui Meng2014-08-116-65/+428
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Having some basic BLAS operations implemented in MLlib can help simplify the current implementation and improve some performance. Tested on my local machine: ~~~ bin/spark-submit --class org.apache.spark.examples.mllib.BinaryClassification \ examples/target/scala-*/spark-examples-*.jar --algorithm LR --regType L2 \ --regParam 1.0 --numIterations 1000 ~/share/data/rcv1.binary/rcv1_train.binary ~~~ 1. before: ~1m 2. after: ~30s CC: jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #1849 from mengxr/ml-blas and squashes the following commits: ba583a2 [Xiangrui Meng] exclude Vector.copy a4d7d2f [Xiangrui Meng] Merge branch 'master' into ml-blas 6edeab9 [Xiangrui Meng] address comments 940bdeb [Xiangrui Meng] rename MLlibBLAS to BLAS c2a38bc [Xiangrui Meng] enhance dot tests 4cfaac4 [Xiangrui Meng] add apache header 48d01d2 [Xiangrui Meng] add tests for zeros and copy 3b882b1 [Xiangrui Meng] use blas.scal in gradient 735eb23 [Xiangrui Meng] remove d from BLAS routines d2d7d3c [Xiangrui Meng] update gradient and lbfgs 7f78186 [Xiangrui Meng] add zeros to Vectors; add dscal and dcopy to BLAS 14e6645 [Xiangrui Meng] add ddot cbb8273 [Xiangrui Meng] add daxpy test 07db0bb [Xiangrui Meng] Merge branch 'master' into ml-blas e8c326d [Xiangrui Meng] axpy
* [SPARK-2934][MLlib] Adding LogisticRegressionWithLBFGS InterfaceDB Tsai2014-08-112-4/+136
| | | | | | | | | | | | | | | for training with LBFGS Optimizer which will converge faster than SGD. Author: DB Tsai <dbtsai@alpinenow.com> Closes #1862 from dbtsai/dbtsai-lbfgs-lor and squashes the following commits: aa84b81 [DB Tsai] small change f852bcd [DB Tsai] Remove duplicate method f119fdc [DB Tsai] Formatting 97776aa [DB Tsai] address more feedback 85b4a91 [DB Tsai] address feedback 3cf50c2 [DB Tsai] LogisticRegressionWithLBFGS interface
* [SPARK-2515][mllib] Chi Squared testDoris Xin2014-08-114-0/+512
| | | | | | | | | | | | | | | | | | | | | | | | | Author: Doris Xin <doris.s.xin@gmail.com> Closes #1733 from dorx/chisquare and squashes the following commits: cafb3a7 [Doris Xin] fixed p-value for extreme case. d286783 [Doris Xin] Merge branch 'master' into chisquare e95e485 [Doris Xin] reviewer comments. 7dde711 [Doris Xin] ChiSqTestResult renaming and changed to Class 80d03e2 [Doris Xin] Reviewer comments. c39eeb5 [Doris Xin] units passed with updated API e90d90a [Doris Xin] Merge branch 'master' into chisquare 7eea80b [Doris Xin] WIP d64c2fb [Doris Xin] Merge branch 'master' into chisquare 5686082 [Doris Xin] facelift bc7eb2e [Doris Xin] unit passed; still need docs and some refactoring 50703a5 [Doris Xin] merge master 4e4e361 [Doris Xin] WIP e6b83f3 [Doris Xin] reviewer comments 3d61582 [Doris Xin] input names 706d436 [Doris Xin] Added API for RDD[Vector] 6598379 [Doris Xin] API and code structure. ff17423 [Doris Xin] WIP
* [SPARK-1997][MLLIB] update breeze to 0.9Xiangrui Meng2014-08-083-4/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 0.9 dependences (this version doesn't depend on scalalogging and I excluded commons-math3 from its transitive dependencies): ~~~ +-org.scalanlp:breeze_2.10:0.9 [S] +-com.github.fommil.netlib:core:1.1.2 +-com.github.rwl:jtransforms:2.4.0 +-net.sf.opencsv:opencsv:2.3 +-net.sourceforge.f2j:arpack_combined_all:0.1 +-org.scalanlp:breeze-macros_2.10:0.3.1 [S] | +-org.scalamacros:quasiquotes_2.10:2.0.0 [S] | +-org.slf4j:slf4j-api:1.7.5 +-org.spire-math:spire_2.10:0.7.4 [S] +-org.scalamacros:quasiquotes_2.10:2.0.0 [S] | +-org.spire-math:spire-macros_2.10:0.7.4 [S] +-org.scalamacros:quasiquotes_2.10:2.0.0 [S] ~~~ Closes #1749 CC: witgo avati Author: Xiangrui Meng <meng@databricks.com> Closes #1857 from mengxr/breeze-0.9 and squashes the following commits: 7fc16b6 [Xiangrui Meng] don't know why but exclude a private method for mima dcc502e [Xiangrui Meng] update breeze to 0.9
* [SPARK-2852][MLLIB] Separate model from IDF/StandardScaler algorithmsXiangrui Meng2014-08-074-129/+121
| | | | | | | | | | | | | | | | | This is part of SPARK-2828: 1. separate IDF model from IDF algorithm (which generates a model) 2. separate StandardScaler model from StandardScaler CC: dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #1814 from mengxr/feature-api-update and squashes the following commits: 40d863b [Xiangrui Meng] move mean and variance to model 48a0fff [Xiangrui Meng] separate Model from StandardScaler algorithm 89f3486 [Xiangrui Meng] update IDF to separate Model from Algorithm
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