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* [SPARK-12869] Implemented an improved version of the toIndexedRowMatrixFokko Driesprong2016-04-141-2/+29
| | | | | | | | | | | | | | Hi guys, I've implemented an improved version of the `toIndexedRowMatrix` function on the `BlockMatrix`. I needed this for a project, but would like to share it with the rest of the community. In the case of dense matrices, it can increase performance up to 19 times: https://github.com/Fokko/BlockMatrixToIndexedRowMatrix If there are any questions or suggestions, please let me know. Keep up the good work! Cheers. Author: Fokko Driesprong <f.driesprong@catawiki.nl> Author: Fokko Driesprong <fokko@driesprongen.nl> Closes #10839 from Fokko/master.
* [SPARK-14565][ML] RandomForest should use parseInt and parseDouble for ↵Yong Tang2016-04-141-2/+2
| | | | | | | | | | | | | | | | | feature subset size instead of regexes ## What changes were proposed in this pull request? This fix tries to change RandomForest's supported strategies from using regexes to using parseInt and parseDouble, for the purpose of robustness and maintainability. ## How was this patch tested? Existing tests passed. Author: Yong Tang <yong.tang.github@outlook.com> Closes #12360 from yongtang/SPARK-14565.
* [SPARK-14375][ML] Unit test for spark.ml KMeansSummaryYanbo Liang2016-04-131-1/+17
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? * Modify ```KMeansSummary.clusterSizes``` method to make it robust to empty clusters. * Add unit test for spark.ml ```KMeansSummary```. * Add Since tag. ## How was this patch tested? unit tests. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12254 from yanboliang/spark-14375.
* [SPARK-14461][ML] GLM training summaries should provide solverYanbo Liang2016-04-131-0/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? GLM training summaries should provide solver. ## How was this patch tested? Unit tests. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12253 from yanboliang/spark-14461.
* [SPARK-10386][MLLIB] PrefixSpanModel supports save/loadYanbo Liang2016-04-132-0/+68
| | | | | | | | | | ```PrefixSpanModel``` supports ```save/load```. It's similar with #9267. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #10664 from yanboliang/spark-10386.
* [SPARK-13783][ML] Model export/import for spark.ml: GBTsYanbo Liang2016-04-132-40/+33
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? * Added save/load for ```GBTClassifier/GBTClassificationModel/GBTRegressor/GBTRegressionModel```. * Meanwhile, I modified ```EnsembleModelReadWrite.saveImpl/loadImpl``` to support save/load ```treeWeights```. ## How was this patch tested? Adds standard unit tests for GBT save/load. cc jkbradley GayathriMurali Author: Yanbo Liang <ybliang8@gmail.com> Closes #12230 from yanboliang/spark-13783.
* [SPARK-14563][ML] use a random table name instead of __THIS__ in SQLTransformerXiangrui Meng2016-04-121-0/+10
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use a random table name instead of `__THIS__` in SQLTransformer, and add a test for `transformSchema`. The problems of using `__THIS__` are: * It doesn't work under HiveContext (in Spark 1.6) * Race conditions ## How was this patch tested? * Manual test with HiveContext. * Added a unit test for `transformSchema` to improve coverage. cc: yhuai Author: Xiangrui Meng <meng@databricks.com> Closes #12330 from mengxr/SPARK-14563.
* [SPARK-13322][ML] AFTSurvivalRegression supports feature standardizationYanbo Liang2016-04-121-0/+22
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? AFTSurvivalRegression should support feature standardization, it will improve the convergence rate. Test the convergence rate on the [Ovarian](https://stat.ethz.ch/R-manual/R-devel/library/survival/html/ovarian.html) data which is standard data comes with Survival library in R, * without standardization(before this PR) -> 74 iterations. * with standardization(after this PR) -> 38 iterations. But after this fix, with or without ```standardization``` will converge to the same solution. It means that ```standardization = false``` will run the same code route as ```standardization = true```. Because if the features are not standardized at all, it will result convergency issue when the features have very different scales. This behavior is the same as ML [```LinearRegression``` and ```LogisticRegression```](https://issues.apache.org/jira/browse/SPARK-8522). See more discussion about this topic at #11247. cc mengxr ## How was this patch tested? unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11365 from yanboliang/spark-13322.
* [SPARK-3724][ML] RandomForest: More options for feature subset size.Yong Tang2016-04-123-0/+74
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR tries to support more options for feature subset size in RandomForest implementation. Previously, RandomForest only support "auto", "all", "sort", "log2", "onethird". This PR tries to support any given value to allow model search. In this PR, `featureSubsetStrategy` could be passed with: a) a real number in the range of `(0.0-1.0]` that represents the fraction of the number of features in each subset, b) an integer number (`>0`) that represents the number of features in each subset. ## How was this patch tested? Two tests `JavaRandomForestClassifierSuite` and `JavaRandomForestRegressorSuite` have been updated to check the additional options for params in this PR. An additional test has been added to `org.apache.spark.mllib.tree.RandomForestSuite` to cover the cases in this PR. Author: Yong Tang <yong.tang.github@outlook.com> Closes #11989 from yongtang/SPARK-3724.
* [MINOR][ML] Fixed MLlib build warningsJoseph K. Bradley2016-04-121-0/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fixes to eliminate warnings during package and doc builds. ## How was this patch tested? Existing unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #12263 from jkbradley/warning-cleanups.
* [SPARK-14298][ML][MLLIB] Add unit test for EM LDA disable checkpointingYanbo Liang2016-04-111-0/+11
| | | | | | | | | | | | | ## What changes were proposed in this pull request? This is follow up for #12089, add unit test for EM LDA which test disable checkpointing when set ```checkpointInterval = -1```. ## How was this patch tested? unit test. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12286 from yanboliang/spark-14298-followup.
* [SPARK-13600][MLLIB] Use approxQuantile from DataFrame stats in ↵Oliver Pierson2016-04-111-75/+40
| | | | | | | | | | | | | QuantileDiscretizer ## What changes were proposed in this pull request? QuantileDiscretizer can return an unexpected number of buckets in certain cases. This PR proposes to fix this issue and also refactor QuantileDiscretizer to use approxQuantiles from DataFrame stats functions. ## How was this patch tested? QuantileDiscretizerSuite unit tests (some existing tests will change or even be removed in this PR) Author: Oliver Pierson <ocp@gatech.edu> Closes #11553 from oliverpierson/SPARK-13600.
* [SPARK-14500] [ML] Accept Dataset[_] instead of DataFrame in MLlib APIsXiangrui Meng2016-04-1117-36/+50
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR updates MLlib APIs to accept `Dataset[_]` as input where `DataFrame` was the input type. This PR doesn't change the output type. In Java, `Dataset[_]` maps to `Dataset<?>`, which includes `Dataset<Row>`. Some implementations were changed in order to return `DataFrame`. Tests and examples were updated. Note that this is a breaking change for subclasses of Transformer/Estimator. Lol, we don't have to rename the input argument, which has been `dataset` since Spark 1.2. TODOs: - [x] update MiMaExcludes (seems all covered by explicit filters from SPARK-13920) - [x] Python - [x] add a new test to accept Dataset[LabeledPoint] - [x] remove unused imports of Dataset ## How was this patch tested? Exiting unit tests with some modifications. cc: rxin jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #12274 from mengxr/SPARK-14500.
* [SPARK-14497][ML] Use top instead of sortBy() to get top N frequent words as ↵fwang12016-04-101-3/+4
| | | | | | | | | | | | | | | dict in ConutVectorizer ## What changes were proposed in this pull request? Replace sortBy() with top() to calculate the top N frequent words as dictionary. ## How was this patch tested? existing unit tests. The terms with same TF would be sorted in descending order. The test would fail if hardcode the terms with same TF the dictionary like "c", "d"... Author: fwang1 <desperado.wf@gmail.com> Closes #12265 from lionelfeng/master.
* [SPARK-14392][ML] CountVectorizer Estimator should include binary toggle Paramwm624@hotmail.com2016-04-091-3/+16
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? CountVectorizerModel has a binary toggle param. This PR is to add binary toggle param for estimator CountVectorizer. As discussed in the JIRA, instead of adding a param into CountVerctorizer, I moved the binary param to CountVectorizerParams. Therefore, the estimator inherits the binary param. ## How was this patch tested? Add a new test case, which fits the model with binary flag set to true and then check the trained model's all non-zero counts is set to 1.0. All tests in CounterVectorizerSuite.scala are passed. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #12200 from wangmiao1981/binary_param.
* [SPARK-13048][ML][MLLIB] keepLastCheckpoint option for LDA EM optimizerJoseph K. Bradley2016-04-073-9/+47
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The EMLDAOptimizer should generally not delete its last checkpoint since that can cause failures when DistributedLDAModel methods are called (if any partitions need to be recovered from the checkpoint). This PR adds a "deleteLastCheckpoint" option which defaults to false. This is a change in behavior from Spark 1.6, in that the last checkpoint will not be removed by default. This involves adding the deleteLastCheckpoint option to both spark.ml and spark.mllib, and modifying PeriodicCheckpointer to support the option. This also: * Makes MLlibTestSparkContext extend TempDirectory and set the checkpointDir to tempDir * Updates LibSVMRelationSuite because of a name conflict with "tempDir" (and fixes a bug where it failed to delete a temp directory) * Adds a MIMA exclude for DistributedLDAModel constructor, which is already ```private[clustering]``` ## How was this patch tested? Added 2 new unit tests to spark.ml LDASuite, which calls into spark.mllib. Author: Joseph K. Bradley <joseph@databricks.com> Closes #12166 from jkbradley/emlda-save-checkpoint.
* [SPARK-14134][CORE] Change the package name used for shading classes.Marcelo Vanzin2016-04-061-1/+1
| | | | | | | | | | | | | | | The current package name uses a dash, which is a little weird but seemed to work. That is, until a new test tried to mock a class that references one of those shaded types, and then things started failing. Most changes are just noise to fix the logging configs. For reference, SPARK-8815 also raised this issue, although at the time it did not cause any issues in Spark, so it was not addressed. Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #11941 from vanzin/SPARK-14134.
* [SPARK-12382][ML] Remove mllib GBT implementation and wrap mlsethah2016-04-063-45/+87
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch removes the implementation of gradient boosted trees in mllib/tree/GradientBoostedTrees.scala and changes mllib GBTs to call the implementation in spark.ML. Primary changes: * Removed `boost` method in mllib GradientBoostedTrees.scala * Created new test suite GradientBoostedTreesSuite in ML, which contains unit tests that were specific to GBT internals from mllib Other changes: * Added an `updatePrediction` method in GradientBoostedTrees package. This method is added to provide consistency for methods that build predictions from boosted models. There are several methods that hard code the method of predicting as: sum_{i=1}^{numTrees} (treePrediction*treeWeight). Calling this function ensures that test methods that check accuracy use the same prediction method that the algorithm uses during training * Added methods that were previously only used in testing, but were public methods, to GradientBoostedTrees. This includes `computeError` (previously part of `Loss` trait) and `evaluateEachIteration`. These are used in the new spark.ML unit tests. They are left in mllib as well so as to not break the API. ## How was this patch tested? Existing unit tests which compare ML and MLlib ensure that mllib GBTs have not changed. Only a single unit test was moved to ML, which verifies that `runWithValidation` performs as expected. Author: sethah <seth.hendrickson16@gmail.com> Closes #12050 from sethah/SPARK-12382.
* [SPARK-13538][ML] Add GaussianMixture to MLZheng RuiFeng2016-04-061-0/+133
| | | | | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13538 ## What changes were proposed in this pull request? Add GaussianMixture and GaussianMixtureModel to ML package ## How was this patch tested? unit tests and manual tests were done. Local Scalastyle checks passed. Author: Zheng RuiFeng <ruifengz@foxmail.com> Author: Ruifeng Zheng <ruifengz@foxmail.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #11419 from zhengruifeng/mlgmm.
* [SPARK-14386][ML] Changed spark.ml ensemble trees methods to return concrete ↵Joseph K. Bradley2016-04-041-1/+1
| | | | | | | | | | | | | | | | | | | | types ## What changes were proposed in this pull request? In spark.ml, GBT and RandomForest expose the trait DecisionTreeModel in the trees method, but they should not since it is a private trait (and not ready to be made public). It will also be more useful to users if we return the concrete types. This PR: return concrete types The MIMA checks appear to be OK with this change. ## How was this patch tested? Existing unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #12158 from jkbradley/hide-dtm.
* [SPARK-13784][ML] Persistence for RandomForestClassifier, RandomForestRegressorJoseph K. Bradley2016-04-042-41/+37
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? **Main change**: Added save/load for RandomForestClassifier, RandomForestRegressor (implementation details below) Modified numTrees method (*deprecation*) * Goal: Use default implementations of unit tests which assume Estimators and Models share the same set of Params. * What this PR does: Moves method numTrees outside of trait TreeEnsembleModel. Adds it to GBT and RF Models. Deprecates it in RF Models in favor of new method getNumTrees. In Spark 2.1, we can have RF Models include Param numTrees. Minor items * Fixes bugs in GBTClassificationModel, GBTRegressionModel fromOld methods where they assign the wrong old UID. **Implementation details** * Split DecisionTreeModelReadWrite.loadTreeNodes into 2 methods in order to reuse some code for ensembles. * Added EnsembleModelReadWrite object with save/load implementations usable for RFs and GBTs * These store all trees' nodes in a single DataFrame, and all trees' metadata in a second DataFrame. * Split trait RandomForestParams into parts in order to add more Estimator Params to RF models * Split DefaultParamsWriter.saveMetadata into two methods to allow ensembles to store sub-models' metadata in a single DataFrame. Same for DefaultParamsReader.loadMetadata ## How was this patch tested? Adds standard unit tests for RF save/load Author: Joseph K. Bradley <joseph@databricks.com> Author: GayathriMurali <gayathri.m.softie@gmail.com> Closes #12118 from jkbradley/GayathriMurali-SPARK-13784.
* [SPARK-14355][BUILD] Fix typos in Exception/Testcase/Comments and static ↵Dongjoon Hyun2016-04-036-10/+10
| | | | | | | | | | | | | | | | | | | | | analysis results ## What changes were proposed in this pull request? This PR contains the following 5 types of maintenance fix over 59 files (+94 lines, -93 lines). - Fix typos(exception/log strings, testcase name, comments) in 44 lines. - Fix lint-java errors (MaxLineLength) in 6 lines. (New codes after SPARK-14011) - Use diamond operators in 40 lines. (New codes after SPARK-13702) - Fix redundant semicolon in 5 lines. - Rename class `InferSchemaSuite` to `CSVInferSchemaSuite` in CSVInferSchemaSuite.scala. ## How was this patch tested? Manual and pass the Jenkins tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12139 from dongjoon-hyun/SPARK-14355.
* [SPARK-14308][ML][MLLIB] Remove unused mllib tree classes and move private ↵sethah2016-04-013-3/+2
| | | | | | | | | | | | | | | | | | | | | | classes to ML ## What changes were proposed in this pull request? Decision tree helper classes will be migrated to ML. This patch moves those internal classes that are not part of the public API and removes ones that are no longer used, after [SPARK-12183](https://github.com/apache/spark/pull/11855). No functional changes are made. Details: * Bin.scala is removed as the ML implementation does not require bins * mllib NodeIdCache is removed. It was only used by the mllib implementation previously, which no longer exists * mllib TreePoint is removed. It was only used by the mllib implementation previously, which no longer exists * BaggedPoint, DTStatsAggregator, DecisionTreeMetadata, BaggedPointSuite and TimeTracker are all moved to ML. ## How was this patch tested? No functional changes are made. Existing unit tests ensure behavior is unchanged. Author: sethah <seth.hendrickson16@gmail.com> Closes #12097 from sethah/cleanup_mllib_tree.
* [SPARK-7425][ML] spark.ml Predictor should support other numeric types for labelBenFradet2016-04-0116-18/+242
| | | | | | | | Currently, the Predictor abstraction expects the input labelCol type to be DoubleType, but we should support other numeric types. This will involve updating the PredictorParams.validateAndTransformSchema method. Author: BenFradet <benjamin.fradet@gmail.com> Closes #10355 from BenFradet/SPARK-7425.
* [SPARK-11262][ML] Unit test for gradient, loss layers, memory management for ↵Alexander Ulanov2016-03-314-7/+106
| | | | | | | | | | | | | | | | | | | | | multilayer perceptron 1.Implement LossFunction trait and implement squared error and cross entropy loss with it 2.Implement unit test for gradient and loss 3.Implement InPlace trait and in-place layer evaluation 4.Refactor interface for ActivationFunction 5.Update of Layer and LayerModel interfaces 6.Fix random weights assignment 7.Implement memory allocation by MLP model instead of individual layers These features decreased the memory usage and increased flexibility of internal API. Author: Alexander Ulanov <nashb@yandex.ru> Author: avulanov <avulanov@gmail.com> Closes #9229 from avulanov/mlp-refactoring.
* [SPARK-11892][ML] Model export/import for spark.ml: OneVsRestXusen Yin2016-03-311-2/+66
| | | | | | | | | | | | | | | | # What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-11892 Add save/load for spark ml.OneVsRest and its model. Also add OneVsRest and OneVsRestModel in MetaAlgorithmReadWrite. # How was this patch tested? Test with Scala unit test. Author: Xusen Yin <yinxusen@gmail.com> Closes #9934 from yinxusen/SPARK-11892.
* [SPARK-13782][ML] Model export/import for spark.ml: BisectingKMeansYuhao Yang2016-03-312-1/+39
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-13782 Model export/import for BisectingKMeans in spark.ml and mllib ## How was this patch tested? unit tests Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11933 from hhbyyh/bisectingsave.
* [SPARK-14164][MLLIB] Improve input layer validation of ↵Dongjoon Hyun2016-03-311-0/+17
| | | | | | | | | | | | | | | | | | | | | | MultilayerPerceptronClassifier ## What changes were proposed in this pull request? This issue improves an input layer validation and adds related testcases to MultilayerPerceptronClassifier. ```scala - // TODO: how to check ALSO that all elements are greater than 0? - ParamValidators.arrayLengthGt(1) + (t: Array[Int]) => t.forall(ParamValidators.gt(0)) && t.length > 1 ``` ## How was this patch tested? Pass the Jenkins tests including the new testcases. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11964 from dongjoon-hyun/SPARK-14164.
* [SPARK-11507][MLLIB] add compact in Matrices fromBreezeYuhao Yang2016-03-301-0/+12
| | | | | | | | | | | | | | | | | | | | | jira: https://issues.apache.org/jira/browse/SPARK-11507 "In certain situations when adding two block matrices, I get an error regarding colPtr and the operation fails. External issue URL includes full error and code for reproducing the problem." root cause: colPtr.last does NOT always equal to values.length in breeze SCSMatrix, which fails the require in SparseMatrix. easy step to repro: ``` val m1: BM[Double] = new CSCMatrix[Double] (Array (1.0, 1, 1), 3, 3, Array (0, 1, 2, 3), Array (0, 1, 2) ) val m2: BM[Double] = new CSCMatrix[Double] (Array (1.0, 2, 2, 4), 3, 3, Array (0, 0, 2, 4), Array (1, 2, 1, 2) ) val sum = m1 + m2 Matrices.fromBreeze(sum) ``` Solution: By checking the code in [CSCMatrix](https://github.com/scalanlp/breeze/blob/28000a7b901bc3cfbbbf5c0bce1d0a5dda8281b0/math/src/main/scala/breeze/linalg/CSCMatrix.scala), CSCMatrix in breeze can have extra zeros in the end of data array. Invoking compact will make sure it aligns with the require of SparseMatrix. This should add limited overhead as the actual compact operation is only performed when necessary. Author: Yuhao Yang <hhbyyh@gmail.com> Closes #9520 from hhbyyh/matricesFromBreeze.
* [SPARK-14181] TrainValidationSplit should have HasSeedXusen Yin2016-03-301-0/+4
| | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-14181 TrainValidationSplit should have HasSeed for the random split of RDD. I also changed the random split from the RDD function to the DataFrame function. Author: Xusen Yin <yinxusen@gmail.com> Closes #11985 from yinxusen/SPARK-14181.
* [SPARK-13963][ML] Adding binary toggle param to HashingTFBryan Cutler2016-03-292-1/+35
| | | | | | | | | | | | ## What changes were proposed in this pull request? Adding binary toggle parameter to ml.feature.HashingTF, as well as mllib.feature.HashingTF since the former wraps this functionality. This parameter, if true, will set non-zero valued term counts to 1 to transform term count features to binary values that are well suited for discrete probability models. ## How was this patch tested? Added unit tests for ML and MLlib Author: Bryan Cutler <cutlerb@gmail.com> Closes #11832 from BryanCutler/binary-param-HashingTF-SPARK-13963.
* [SPARK-11730][ML] Add feature importances for GBTs.sethah2016-03-283-3/+51
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Now that GBTs have been moved to ML, they can use the implementation of feature importance for random forests. This patch simply adds a `featureImportances` attribute to `GBTClassifier` and `GBTRegressor` and adds tests for each. GBT feature importances here simply average the feature importances for each tree in its ensemble. This follows the implementation from scikit-learn. This method is also suggested by J Friedman in [this paper](https://statweb.stanford.edu/~jhf/ftp/trebst.pdf). ## How was this patch tested? Unit tests were added to `GBTClassifierSuite` and `GBTRegressorSuite` to validate feature importances. Author: sethah <seth.hendrickson16@gmail.com> Closes #11961 from sethah/SPARK-11730.
* [SPARK-11893] Model export/import for spark.ml: TrainValidationSplitXusen Yin2016-03-281-2/+43
| | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-11893 jkbradley In order to share read/write with `TrainValidationSplit`, I move the `SharedReadWrite` out of `CrossValidator` into a new trait `SharedReadWrite` in the tunning package. To reduce the repeated tests, I move the complex tests from `CrossValidatorSuite` to `SharedReadWriteSuite`, and create a fake validator called `MyValidator` to test the shared code. With `SharedReadWrite`, potential newly added `Validator` can share the read/write common part, and only need to implement their extra params save/load. Author: Xusen Yin <yinxusen@gmail.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #9971 from yinxusen/SPARK-11893.
* [SPARK-14187][MLLIB] Fix incorrect use of binarySearch in SparseMatrixChenliang Xu2016-03-281-0/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Fix incorrect use of binarySearch in SparseMatrix ## How was this patch tested? Unit test added. Author: Chenliang Xu <chexu@groupon.com> Closes #11992 from luckyrandom/SPARK-14187.
* [SPARK-14159][ML] Fixed bug in StringIndexer + related issue in RFormulaJoseph K. Bradley2016-03-251-0/+13
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? StringIndexerModel.transform sets the output column metadata to use name inputCol. It should not. Fixing this causes a problem with the metadata produced by RFormula. Fix in RFormula: I added the StringIndexer columns to prefixesToRewrite, and I modified VectorAttributeRewriter to find and replace all "prefixes" since attributes collect multiple prefixes from StringIndexer + Interaction. Note that "prefixes" is no longer accurate since internal strings may be replaced. ## How was this patch tested? Unit test which failed before this fix. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11965 from jkbradley/StringIndexer-fix.
* [SPARK-11871] Add save/load for MLPCXusen Yin2016-03-241-6/+37
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-11871 Add save/load for MLPC ## How was this patch tested? Test with Scala unit test Author: Xusen Yin <yinxusen@gmail.com> Closes #9854 from yinxusen/SPARK-11871.
* [SPARK-12183][ML][MLLIB] Remove mllib tree implementation, and wrap spark.ml oneJoseph K. Bradley2016-03-233-552/+435
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Primary change: * Removed spark.mllib.tree.DecisionTree implementation of tree and forest learning. * spark.mllib now calls the spark.ml implementation. * Moved unit tests (of tree learning internals) from spark.mllib to spark.ml as needed. ml.tree.DecisionTreeModel * Added toOld and made ```private[spark]```, implemented for Classifier and Regressor in subclasses. These methods now use OldInformationGainStats.invalidInformationGainStats for LeafNodes in order to mimic the spark.mllib implementation. ml.tree.Node * Added ```private[tree] def deepCopy```, used by unit tests Copied developer comments from spark.mllib implementation to spark.ml one. Moving unit tests * Tree learning internals were tested by spark.mllib.tree.DecisionTreeSuite, or spark.mllib.tree.RandomForestSuite. * Those tests were all moved to spark.ml.tree.impl.RandomForestSuite. The order in the file + the test names are the same, so you should be able to compare them by opening them in 2 windows side-by-side. * I made minimal changes to each test to allow it to run. Each test makes the same checks as before, except for a few removed assertions which were checking irrelevant values. * No new unit tests were added. * mllib.tree.DecisionTreeSuite: I removed some checks of splits and bins which were not relevant to the unit tests they were in. Those same split calculations were already being tested in other unit tests, for each dataset type. **Changes of behavior** (to be noted in SPARK-13448 once this PR is merged) * spark.ml.tree.impl.RandomForest: Rather than throwing an error when maxMemoryInMB is set to too small a value (to split any node), we now allow 1 node to be split, even if its memory requirements exceed maxMemoryInMB. This involved removing the maxMemoryPerNode check in RandomForest.run, as well as modifying selectNodesToSplit(). Once this PR is merged, I will note the change of behavior on SPARK-13448. * spark.mllib.tree.DecisionTree: When a tree only has one node (root = leaf node), the "stats" field will now be empty, rather than being set to InformationGainStats.invalidInformationGainStats. This does not remove information from the tree, and it will save a bit of storage. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11855 from jkbradley/remove-mllib-tree-impl.
* [SPARK-13952][ML] Add random seed to GBTsethah2016-03-233-4/+8
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? `GBTClassifier` and `GBTRegressor` should use random seed for reproducible results. Because of the nature of current unit tests, which compare GBTs in ML and GBTs in MLlib for equality, I also added a random seed to MLlib GBT algorithm. I made alternate constructors in `mllib.tree.GradientBoostedTrees` to accept a random seed, but left them as private so as to not change the API unnecessarily. ## How was this patch tested? Existing unit tests verify that functionality did not change. Other ML algorithms do not seem to have unit tests that directly test the functionality of random seeding, but reproducibility with seeding for GBTs is effectively verified in existing tests. I can add more tests if needed. Author: sethah <seth.hendrickson16@gmail.com> Closes #11903 from sethah/SPARK-13952.
* [SPARK-14035][MLLIB] Make error message more verbose for mllib NaiveBayesSuiteJoseph K. Bradley2016-03-231-10/+18
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Print more info about failed NaiveBayesSuite tests which have exhibited flakiness. ## How was this patch tested? Ran locally with incorrect check to cause failure. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11858 from jkbradley/naive-bayes-bug-log.
* [SPARK-14011][CORE][SQL] Enable `LineLength` Java checkstyle ruleDongjoon Hyun2016-03-213-3/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? [Spark Coding Style Guide](https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide) has 100-character limit on lines, but it's disabled for Java since 11/09/15. This PR enables **LineLength** checkstyle again. To help that, this also introduces **RedundantImport** and **RedundantModifier**, too. The following is the diff on `checkstyle.xml`. ```xml - <!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places --> - <!-- <module name="LineLength"> <property name="max" value="100"/> <property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/> </module> - --> <module name="NoLineWrap"/> <module name="EmptyBlock"> <property name="option" value="TEXT"/> -167,5 +164,7 </module> <module name="CommentsIndentation"/> <module name="UnusedImports"/> + <module name="RedundantImport"/> + <module name="RedundantModifier"/> ``` ## How was this patch tested? Currently, `lint-java` is disabled in Jenkins. It needs a manual test. After passing the Jenkins tests, `dev/lint-java` should passes locally. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11831 from dongjoon-hyun/SPARK-14011.
* [SPARK-10788][MLLIB][ML] Remove duplicate bins for decision treessethah2016-03-171-0/+4
| | | | | | | | | | | | | | | | | | Decision trees in spark.ml (RandomForest.scala) communicate twice as much data as needed for unordered categorical features. Here's an example. Say there are 3 categories A, B, C. We consider 3 splits: * A vs. B, C * A, B vs. C * A, C vs. B Currently, we collect statistics for each of the 6 subsets of categories (3 * 2 = 6). However, we could instead collect statistics for the 3 subsets on the left-hand side of the 3 possible splits: A and A,B and A,C. If we also have stats for the entire node, then we can compute the stats for the 3 subsets on the right-hand side of the splits. In pseudomath: stats(B,C) = stats(A,B,C) - stats(A). This patch adds a parent stats array to the `DTStatsAggregator` so that the right child stats do not need to be stored. The right child stats are computed by subtracting left child stats from the parent stats for unordered categorical features. Author: sethah <seth.hendrickson16@gmail.com> Closes #9474 from sethah/SPARK-10788.
* [SPARK-13761][ML] Remove remaining uses of validateParamsJoseph K. Bradley2016-03-174-23/+11
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Cleanups from [https://github.com/apache/spark/pull/11620]: remove remaining uses of validateParams, and put functionality into transformSchema ## How was this patch tested? Existing unit tests, modified to check using transformSchema instead of validateParams Author: Joseph K. Bradley <joseph@databricks.com> Closes #11790 from jkbradley/SPARK-13761-cleanup.
* [SPARK-11891] Model export/import for RFormula and RFormulaModelXusen Yin2016-03-171-1/+39
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-11891 Author: Xusen Yin <yinxusen@gmail.com> Closes #9884 from yinxusen/SPARK-11891.
* [SPARK-13928] Move org.apache.spark.Logging into ↵Wenchen Fan2016-03-175-6/+10
| | | | | | | | | | | | | | | | org.apache.spark.internal.Logging ## What changes were proposed in this pull request? Logging was made private in Spark 2.0. If we move it, then users would be able to create a Logging trait themselves to avoid changing their own code. ## How was this patch tested? existing tests. Author: Wenchen Fan <wenchen@databricks.com> Closes #11764 from cloud-fan/logger.
* [SPARK-13629][ML] Add binary toggle Param to CountVectorizerYuhao Yang2016-03-171-1/+18
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? It would be handy to add a binary toggle Param to CountVectorizer, as in the scikit-learn one: http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html If set, then all non-zero counts will be set to 1. ## How was this patch tested? unit tests Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11536 from hhbyyh/cvToggle.
* [SPARK-13761][ML] Deprecate validateParamsYuhao Yang2016-03-163-13/+17
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Deprecate validateParams() method here: https://github.com/apache/spark/blob/035d3acdf3c1be5b309a861d5c5beb803b946b5e/mllib/src/main/scala/org/apache/spark/ml/param/params.scala#L553 Move all functionality in overridden methods to transformSchema(). Check docs to make sure they indicate complex Param interaction checks should be done in transformSchema. ## How was this patch tested? unit tests Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11620 from hhbyyh/depreValid.
* [SPARK-13927][MLLIB] add row/column iterator to local matricesXiangrui Meng2016-03-161-0/+13
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add row/column iterator to local matrices to simplify tasks like BlockMatrix => RowMatrix conversion. It handles dense and sparse matrices properly. ## How was this patch tested? Unit tests on sparse and dense matrix. cc: dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #11757 from mengxr/SPARK-13927.
* [SPARK-11888][ML] Decision tree persistence in spark.mlJoseph K. Bradley2016-03-1615-38/+107
| | | | | | | | | | | | | | | | | | | | ### What changes were proposed in this pull request? Made these MLReadable and MLWritable: DecisionTreeClassifier, DecisionTreeClassificationModel, DecisionTreeRegressor, DecisionTreeRegressionModel * The shared implementation is in treeModels.scala * I use case classes to create a DataFrame to save, and I use the Dataset API to parse loaded files. Other changes: * Made CategoricalSplit.numCategories public (to use in persistence) * Fixed a bug in DefaultReadWriteTest.testEstimatorAndModelReadWrite, where it did not call the checkModelData function passed as an argument. This caused an error in LDASuite, which I fixed. ### How was this patch tested? Persistence is tested via unit tests. For each algorithm, there are 2 non-trivial trees (depth 2). One is built with continuous features, and one with categorical; this ensures that both types of splits are tested. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11581 from jkbradley/dt-io.
* [SPARK-13613][ML] Provide ignored tests to export test dataset into CSV formatYanbo Liang2016-03-164-33/+97
| | | | | | | | | | | | ## What changes were proposed in this pull request? Provide ignored test cases to export the test dataset into CSV format in ```LinearRegressionSuite```, ```LogisticRegressionSuite```, ```AFTSurvivalRegressionSuite``` and ```GeneralizedLinearRegressionSuite```, so users can validate the training accuracy compared with R's glm, glmnet and survival package. cc mengxr ## How was this patch tested? The test suite is ignored, but I have enabled all these cases offline and it works as expected. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11463 from yanboliang/spark-13613.
* [SPARK-13894][SQL] SqlContext.range return type from DataFrame to DataSetCheng Hao2016-03-161-1/+1
| | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-13894 Change the return type of the `SQLContext.range` API from `DataFrame` to `Dataset`. ## How was this patch tested? No additional unit test required. Author: Cheng Hao <hao.cheng@intel.com> Closes #11730 from chenghao-intel/range.
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