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* [SPARK-15523][ML][MLLIB] Update JPMML to 1.2.15Villu Ruusmann2016-05-261-7/+3
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? See https://issues.apache.org/jira/browse/SPARK-15523 This PR replaces PR #13293. It's isolated to a new branch, and contains some more squashed changes. ## How was this patch tested? 1. Executed `mvn clean package` in `mllib` directory 2. Executed `dev/test-dependencies.sh --replace-manifest` in the root directory. Author: Villu Ruusmann <villu.ruusmann@gmail.com> Closes #13297 from vruusmann/update-jpmml.
* [SPARK-15543][SQL] Rename DefaultSources to make them more self-describingReynold Xin2016-05-252-3/+7
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch renames various DefaultSources to make their names more self-describing. The choice of "DefaultSource" was from the days when we did not have a good way to specify short names. They are now named: - LibSVMFileFormat - CSVFileFormat - JdbcRelationProvider - JsonFileFormat - ParquetFileFormat - TextFileFormat Backward compatibility is maintained through aliasing. ## How was this patch tested? Updated relevant test cases too. Author: Reynold Xin <rxin@databricks.com> Closes #13311 from rxin/SPARK-15543.
* Log warnings for numIterations * miniBatchFraction < 1.0Gio Borje2016-05-251-0/+5
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add a warning log for the case that `numIterations * miniBatchFraction <1.0` during gradient descent. If the product of those two numbers is less than `1.0`, then not all training examples will be used during optimization. To put this concretely, suppose that `numExamples = 100`, `miniBatchFraction = 0.2` and `numIterations = 3`. Then, 3 iterations will occur each sampling approximately 6 examples each. In the best case, each of the 6 examples are unique; hence 18/100 examples are used. This may be counter-intuitive to most users and led to the issue during the development of another Spark ML model: https://github.com/zhengruifeng/spark-libFM/issues/11. If a user actually does not require the training data set, it would be easier and more intuitive to use `RDD.sample`. ## How was this patch tested? `build/mvn -DskipTests clean package` build succeeds Author: Gio Borje <gborje@linkedin.com> Closes #13265 from Hydrotoast/master.
* [SPARK-15500][DOC][ML][PYSPARK] Remove default value in Param doc field in ALSNick Pentreath2016-05-251-2/+2
| | | | | | | | | | | | Remove "Default: MEMORY_AND_DISK" from `Param` doc field in ALS storage level params. This fixes up the output of `explainParam(s)` so that default values are not displayed twice. We can revisit in the case that [SPARK-15130](https://issues.apache.org/jira/browse/SPARK-15130) moves ahead with adding defaults in some way to PySpark param doc fields. Tests N/A. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13277 from MLnick/SPARK-15500-als-remove-default-storage-param.
* [MINOR][MLLIB][STREAMING][SQL] Fix typoslfzCarlosC2016-05-252-2/+2
| | | | | | | | | | fixed typos for source code for components [mllib] [streaming] and [SQL] None and obvious. Author: lfzCarlosC <lfz.carlos@gmail.com> Closes #13298 from lfzCarlosC/master.
* [SPARK-15442][ML][PYSPARK] Add 'relativeError' param to PySpark ↵Nick Pentreath2016-05-241-5/+8
| | | | | | | | | | | | | | | | | | QuantileDiscretizer This PR adds the `relativeError` param to PySpark's `QuantileDiscretizer` to match Scala. Also cleaned up a duplication of `numBuckets` where the param is both a class and instance attribute (I removed the instance attr to match the style of params throughout `ml`). Finally, cleaned up the docs for `QuantileDiscretizer` to reflect that it now uses `approxQuantile`. ## How was this patch tested? A little doctest and built API docs locally to check HTML doc generation. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13228 from MLnick/SPARK-15442-py-relerror-param.
* [SPARK-15339][ML] ML 2.0 QA: Scala APIs and code audit for regressionYanbo Liang2016-05-195-47/+58
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? * ```GeneralizedLinearRegression``` API docs enhancement. * The default value of ```GeneralizedLinearRegression``` ```linkPredictionCol``` is not set rather than empty. This will consistent with other similar params such as ```weightCol``` * Make some methods more private. * Fix a minor bug of LinearRegression. * Fix some other issues. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13129 from yanboliang/spark-15339.
* [SPARK-15075][SPARK-15345][SQL] Clean up SparkSession builder and propagate ↵Reynold Xin2016-05-191-1/+1
| | | | | | | | | | | | | | | | config options to existing sessions if specified ## What changes were proposed in this pull request? Currently SparkSession.Builder use SQLContext.getOrCreate. It should probably the the other way around, i.e. all the core logic goes in SparkSession, and SQLContext just calls that. This patch does that. This patch also makes sure config options specified in the builder are propagated to the existing (and of course the new) SparkSession. ## How was this patch tested? Updated tests to reflect the change, and also introduced a new SparkSessionBuilderSuite that should cover all the branches. Author: Reynold Xin <rxin@databricks.com> Closes #13200 from rxin/SPARK-15075.
* [SPARK-15296][MLLIB] Refactor All Java Tests that use SparkSessionSandeep Singh2016-05-1958-1147/+206
| | | | | | | | | | | | ## What changes were proposed in this pull request? Refactor All Java Tests that use SparkSession, to extend SharedSparkSesion ## How was this patch tested? Existing Tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #13101 from techaddict/SPARK-15296.
* [MINOR][ML][PYSPARK] ml.evaluation Scala and Python API syncYanbo Liang2016-05-191-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? ```ml.evaluation``` Scala and Python API sync. ## How was this patch tested? Only API docs change, no new tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13195 from yanboliang/evaluation-doc.
* [SPARK-15341][DOC][ML] Add documentation for "model.write" to clarify ↵Yanbo Liang2016-05-195-2/+23
| | | | | | | | | | | | | | | "summary" was not saved ## What changes were proposed in this pull request? Currently in ```model.write```, we don't save ```summary```(if applicable). We should add documentation to clarify it. We fixed the incorrect link ```[[MLWriter]]``` to ```[[org.apache.spark.ml.util.MLWriter]]``` BTW. ## How was this patch tested? Documentation update, no unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13131 from yanboliang/spark-15341.
* [SPARK-15414][MLLIB] Make the mllib,ml linalg type conversion APIs publicSandeep Singh2016-05-192-18/+42
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Open up APIs for converting between new, old linear algebra types (in spark.mllib.linalg): `Sparse`/`Dense` X `Vector`/`Matrices` `.asML` and `.fromML` ## How was this patch tested? Existing Tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #13202 from techaddict/SPARK-15414.
* [SPARK-15361][ML] ML 2.0 QA: Scala APIs audit for ml.clusteringYanbo Liang2016-05-194-21/+43
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Audit Scala API for ml.clustering. Fix some wrong API documentations and update outdated one. ## How was this patch tested? Existing unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13148 from yanboliang/spark-15361.
* [SPARK-15411][ML] Add @since to ml.stat.MultivariateOnlineSummarizer.scalaDB Tsai2016-05-191-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add since to ml.stat.MultivariateOnlineSummarizer.scala ## How was this patch tested? unit tests Author: DB Tsai <dbt@netflix.com> Closes #13197 from dbtsai/cleanup.
* [SPARK-15292][ML] ML 2.0 QA: Scala APIs audit for classificationYanbo Liang2016-05-192-35/+91
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Audit Scala API for classification, almost all issues were related ```MultilayerPerceptronClassifier``` in this section. * Fix one wrong param getter function: ```getOptimizer``` -> ```getSolver``` * Add missing setter function for ```solver``` and ```stepSize```. * Make ```GD``` solver take effect. * Update docs, annotations and fix other minor issues. ## How was this patch tested? Existing unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13076 from yanboliang/spark-15292.
* [SPARK-15362][ML] Make spark.ml KMeansModel load backwards compatibleYanbo Liang2016-05-191-5/+17
| | | | | | | | | | | | ## What changes were proposed in this pull request? [SPARK-14646](https://issues.apache.org/jira/browse/SPARK-14646) makes ```KMeansModel``` store the cluster centers one per row. ```KMeansModel.load()``` method needs to be updated in order to load models saved with Spark 1.6. ## How was this patch tested? Since ```save/load``` is ```Experimental``` for 1.6, I think offline test for backwards compatibility is enough. Author: Yanbo Liang <ybliang8@gmail.com> Closes #13149 from yanboliang/spark-15362.
* [DOC][MINOR] ml.feature Scala and Python API syncBryan Cutler2016-05-194-6/+10
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? I reviewed Scala and Python APIs for ml.feature and corrected discrepancies. ## How was this patch tested? Built docs locally, ran style checks Author: Bryan Cutler <cutlerb@gmail.com> Closes #13159 from BryanCutler/ml.feature-api-sync.
* [SPARK-15192][SQL] null check for SparkSession.createDataFrameWenchen Fan2016-05-181-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds null check in `SparkSession.createDataFrame`, so that we can make sure the passed in rows matches the given schema. ## How was this patch tested? new tests in `DatasetSuite` Author: Wenchen Fan <wenchen@databricks.com> Closes #13008 from cloud-fan/row-encoder.
* [SPARK-14891][ML] Add schema validation for ALSNick Pentreath2016-05-183-18/+143
| | | | | | | | | | | | | This PR adds schema validation to `ml`'s ALS and ALSModel. Currently, no schema validation was performed as `transformSchema` was never called in `ALS.fit` or `ALSModel.transform`. Furthermore, due to no schema validation, if users passed in Long (or Float etc) ids, they would be silently cast to Int with no warning or error thrown. With this PR, ALS now supports all numeric types for `user`, `item`, and `rating` columns. The rating column is cast to `Float` and the user and item cols are cast to `Int` (as is the case currently) - however for user/item, the cast throws an error if the value is outside integer range. Behavior for rating col is unchanged (as it is not an issue). ## How was this patch tested? New test cases in `ALSSuite`. Author: Nick Pentreath <nickp@za.ibm.com> Closes #12762 from MLnick/SPARK-14891-als-validate-schema.
* [SPARK-15346][MLLIB] Reduce duplicate computation in picking initial pointsDLucky2016-05-181-6/+10
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | mateiz srowen I state that the contribution is my original work and that I license the work to the project under the project's open source license There's some format problems with my last PR, with HyukjinKwon 's help I read the guidance, re-check my code and PR, then run the tests, finally re-submit the PR request here. The related JIRA issue though marked as resolved, this change may relate to it I think. ## Proposed Change After picking each new initial centers, it's unnecessary to compute the distances between all the points and the old ones. Instead this change keeps the distance between all the points and their closest centers, and compare to the distance of them with the new center then update them. ## Test result One can find an easy test way in (https://issues.apache.org/jira/browse/SPARK-6706) I test the KMeans++ method for a small dataset with 16k points, and the whole KMeans|| with a large one with 240k points. The data has 4096 features and I tunes K from 100 to 500. The test environment was on my 4 machine cluster, I also tested a 3M points data on a larger cluster with 25 machines and got similar results, which I would not draw the detail curve. The result of the first two exps are shown below ### Local KMeans++ test: Dataset:4m_ini_center Data_size:16234 Dimension:4096 Lloyd's Iteration = 10 The y-axis is time in sec, the x-axis is tuning the K. ![image](https://cloud.githubusercontent.com/assets/10915169/15175831/d0c92b82-179a-11e6-8b68-4e165fc2fdff.png) ![local_total](https://cloud.githubusercontent.com/assets/10915169/15175957/6b21c3b0-179b-11e6-9741-66dfe4e23eb7.jpg) ### On a larger dataset An improve show in the graph but not commit in this file: In this experiment I also have an improvement for calculation in normalization data (the distance is convert to the cosine distance). As if the data is normalized into (0,1), one improvement in the original vesion for util.MLUtils.fastSauaredDistance would have no effect (the precisionBound 2.0 * EPSILON * sumSquaredNorm / (normDiff * normDiff + EPSILON) will never less then precision in this case). Therefore I design an early terminal method when comparing two distance (used for findClosest). But I don't include this improve in this file, you may only refer to the curves without "normalize" for comparing the results. Dataset:4k24 Data_size:243960 Dimension:4096 Normlize Enlarge Initialize Lloyd's_Iteration NO 1 3 5 YES 10000 3 5 Notice: the normlized data is enlarged to ensure precision The cost time: x-for value of K, y-for time in sec ![4k24_total](https://cloud.githubusercontent.com/assets/10915169/15176635/9a54c0bc-179e-11e6-81c5-238e0c54bce2.jpg) SE for unnormalized data between two version, to ensure the correctness ![4k24_unnorm_se](https://cloud.githubusercontent.com/assets/10915169/15176661/b85dabc8-179e-11e6-9269-fe7d2101dd48.jpg) Here is the SE between normalized data just for reference, it's also correct. ![4k24_norm_se](https://cloud.githubusercontent.com/assets/10915169/15176742/1fbde940-179f-11e6-8290-d24b0dd4a4f7.jpg) Author: DLucky <mouendless@gmail.com> Closes #13133 from mouendless/patch-2.
* [SPARK-15322][MLLIB][CORE][SQL] update deprecate accumulator usage into ↵WeichenXu2016-05-183-9/+10
| | | | | | | | | | | | | | | | accumulatorV2 in spark project ## What changes were proposed in this pull request? I use Intellj-IDEA to search usage of deprecate SparkContext.accumulator in the whole spark project, and update the code.(except those test code for accumulator method itself) ## How was this patch tested? Exisiting unit tests Author: WeichenXu <WeichenXu123@outlook.com> Closes #13112 from WeichenXu123/update_accuV2_in_mllib.
* [SPARK-15171][SQL] Remove the references to deprecated method ↵Sean Zhong2016-05-183-8/+8
| | | | | | | | | | | | | | | | | dataset.registerTempTable ## What changes were proposed in this pull request? Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`. ## How was this patch tested? This PR only changes the unit test code, examples, and comments. It should be safe. This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged. Author: Sean Zhong <seanzhong@databricks.com> Closes #13098 from clockfly/spark-15171-remove-deprecation.
* [SPARK-14615][ML] Use the new ML Vector and Matrix in the ML pipeline based ↵DB Tsai2016-05-17152-345/+1007
| | | | | | | | | | | | | | | | | | algorithms ## What changes were proposed in this pull request? Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis. ## How was this patch tested? Unit tests Author: DB Tsai <dbt@netflix.com> Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Xiangrui Meng <meng@databricks.com> Closes #12627 from dbtsai/SPARK-14615-NewML.
* [MINOR][DOCS] Replace remaining 'sqlContext' in ScalaDoc/JavaDoc.Dongjoon Hyun2016-05-171-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? According to the recent change, this PR replaces all the remaining `sqlContext` usage with `spark` in ScalaDoc/JavaDoc (.scala/.java files) except `SQLContext.scala`, `SparkPlan.scala', and `DatasetHolder.scala`. ## How was this patch tested? Manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #13125 from dongjoon-hyun/minor_doc_sparksession.
* [SPARK-15290][BUILD] Move annotations, like @Since / @DeveloperApi, into ↵Sean Owen2016-05-171-1/+1
| | | | | | | | | | | | | | | | | | spark-tags ## What changes were proposed in this pull request? (See https://github.com/apache/spark/pull/12416 where most of this was already reviewed and committed; this is just the module structure and move part. This change does not move the annotations into test scope, which was the apparently problem last time.) Rename `spark-test-tags` -> `spark-tags`; move common annotations like `Since` to `spark-tags` ## How was this patch tested? Jenkins tests. Author: Sean Owen <sowen@cloudera.com> Closes #13074 from srowen/SPARK-15290.
* [MINOR] Fix TyposZheng RuiFeng2016-05-152-33/+36
| | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,Rename matrix args in BreezeUtil to upper to match the doc 2,Fix several typos in ML and SQL ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13078 from zhengruifeng/fix_ann.
* [SPARK-15096][ML] LogisticRegression MultiClassSummarizer numClasses can ↵wm624@hotmail.com2016-05-142-1/+5
| | | | | | | | | | | | | | | | | | fail if no valid labels are found ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) Throw better exception when numClasses is empty and empty.max is thrown. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Add a new unit test, which calls histogram with empty numClasses. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #12969 from wangmiao1981/logisticR.
* [SPARK-15267][SQL] Refactor options for JDBC and ORC data sources and change ↵hyukjinkwon2016-05-131-1/+1
| | | | | | | | | | | | | | | | | | | | | | default compression for ORC ## What changes were proposed in this pull request? Currently, Parquet, JSON and CSV data sources have a class for thier options, (`ParquetOptions`, `JSONOptions` and `CSVOptions`). It is convenient to manage options for sources to gather options into a class. Currently, `JDBC`, `Text`, `libsvm` and `ORC` datasources do not have this class. This might be nicer if these options are in a unified format so that options can be added and This PR refactors the options in Spark internal data sources adding new classes, `OrcOptions`, `TextOptions`, `JDBCOptions` and `LibSVMOptions`. Also, this PR change the default compression codec for ORC from `NONE` to `SNAPPY`. ## How was this patch tested? Existing tests should cover this for refactoring and unittests in `OrcHadoopFsRelationSuite` for changing the default compression codec for ORC. Author: hyukjinkwon <gurwls223@gmail.com> Closes #13048 from HyukjinKwon/SPARK-15267.
* [SPARK-14900][ML] spark.ml classification metrics should include accuracywm624@hotmail.com2016-05-133-17/+24
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? (Please fill in changes proposed in this fix) Add accuracy to MulticlassMetrics class and add corresponding code in MulticlassClassificationEvaluator. ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Scala Unit tests in ml.evaluation Author: wm624@hotmail.com <wm624@hotmail.com> Closes #12882 from wangmiao1981/accuracy.
* [SPARK-13961][ML] spark.ml ChiSqSelector and RFormula should support other ↵BenFradet2016-05-1319-27/+53
| | | | | | | | | | | | | | | | numeric types for label ## What changes were proposed in this pull request? Made ChiSqSelector and RFormula accept all numeric types for label ## How was this patch tested? Unit tests Author: BenFradet <benjamin.fradet@gmail.com> Closes #12467 from BenFradet/SPARK-13961.
* [SPARK-15181][ML][PYSPARK] Python API for GLR summaries.sethah2016-05-131-9/+9
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds a python API for generalized linear regression summaries (training and test). This helps provide feature parity for Python GLMs. ## How was this patch tested? Added a unit test to `pyspark.ml.tests` Author: sethah <seth.hendrickson16@gmail.com> Closes #12961 from sethah/GLR_summary.
* [SPARK-15171][SQL] Deprecate registerTempTable and add dataset.createTempViewSean Zhong2016-05-121-2/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView. ## How was this patch tested? Unit tests. Author: Sean Zhong <seanzhong@databricks.com> Closes #12945 from clockfly/spark-15171.
* [SPARK-15268][SQL] Make JavaTypeInference work with UDTRegistrationLiang-Chi Hsieh2016-05-111-0/+16
| | | | | | | | | | | | | ## What changes were proposed in this pull request? We have a private `UDTRegistration` API to register user defined type. Currently `JavaTypeInference` can't work with it. So `SparkSession.createDataFrame` from a bean class will not correctly infer the schema of the bean class. ## How was this patch tested? `VectorUDTSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13046 from viirya/fix-udt-registry-javatypeinference.
* [SPARK-15037][SQL][MLLIB] Use SparkSession instead of SQLContext in ↵Sandeep Singh2016-05-10114-1037/+1283
| | | | | | | | | | | | | | | Scala/Java TestSuites ## What changes were proposed in this pull request? Use SparkSession instead of SQLContext in Scala/Java TestSuites as this PR already very big working Python TestSuites in a diff PR. ## How was this patch tested? Existing tests Author: Sandeep Singh <sandeep@techaddict.me> Closes #12907 from techaddict/SPARK-15037.
* [SPARK-15172][ML] Explicitly tell user initial coefficients is ignored when ↵dding32016-05-091-2/+3
| | | | | | | | | | | | | | size mismatch happened in LogisticRegression ## What changes were proposed in this pull request? Explicitly tell user initial coefficients is ignored if its size doesn't match expected size in LogisticRegression ## How was this patch tested? local build Author: dding3 <dingding@dingding-ubuntu.sh.intel.com> Closes #12948 from dding3/master.
* [SPARK-14814][MLLIB] API: Java compatibility, docsYuhao Yang2016-05-092-2/+12
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-14814 fix a java compatibility function in mllib DecisionTreeModel. As synced in jira, other compatibility issues don't need fixes. ## How was this patch tested? existing ut Author: Yuhao Yang <hhbyyh@gmail.com> Closes #12971 from hhbyyh/javacompatibility.
* [SPARK-15211][SQL] Select features column from LibSVMRelation causes failureLiang-Chi Hsieh2016-05-092-1/+10
| | | | | | | | | | | | | ## What changes were proposed in this pull request? We need to use `requiredSchema` in `LibSVMRelation` to project the fetch required columns when loading data from this data source. Otherwise, when users try to select `features` column, it will cause failure. ## How was this patch tested? `LibSVMRelationSuite`. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #12986 from viirya/fix-libsvmrelation.
* [SPARK-14050][ML] Add multiple languages support and additional methods for ↵Burak Köse2016-05-0617-71/+2561
| | | | | | | | | | | | | | | | | | | | | | | | | Stop Words Remover ## What changes were proposed in this pull request? This PR continues the work from #11871 with the following changes: * load English stopwords as default * covert stopwords to list in Python * update some tests and doc ## How was this patch tested? Unit tests. Closes #11871 cc: burakkose srowen Author: Burak Köse <burakks41@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Author: Burak KOSE <burakks41@gmail.com> Closes #12843 from mengxr/SPARK-14050.
* [HOTFIX] Fix MLUtils compileAndrew Or2016-05-051-1/+1
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* [SPARK-15152][DOC][MINOR] Scaladoc and Code style ImprovementsJacek Laskowski2016-05-052-6/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Minor doc and code style fixes ## How was this patch tested? local build Author: Jacek Laskowski <jacek@japila.pl> Closes #12928 from jaceklaskowski/SPARK-15152.
* [SPARK-15106][PYSPARK][ML] Add PySpark package doc for ML component & remove ↵Holden Karau2016-05-052-2/+2
| | | | | | | | | | | | | | | | "BETA" ## What changes were proposed in this pull request? Copy the package documentation from Scala/Java to Python for ML package and remove beta tags. Not super sure if we want to keep the BETA tag but since we are making it the default it seems like probably the time to remove it (happy to put it back in if we want to keep it BETA). ## How was this patch tested? Python documentation built locally as HTML and text and verified output. Author: Holden Karau <holden@us.ibm.com> Closes #12883 from holdenk/SPARK-15106-add-pyspark-package-doc-for-ml.
* [SPARK-14844][ML] Add setFeaturesCol and setPredictionCol to KMeansM…Dominik Jastrzębski2016-05-042-0/+23
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Introduction of setFeaturesCol and setPredictionCol methods to KMeansModel in ML library. ## How was this patch tested? By running KMeansSuite. Author: Dominik Jastrzębski <dominik.jastrzebski@codilime.com> Closes #12609 from dominik-jastrzebski/master.
* [SPARK-14237][SQL] De-duplicate partition value appending logic in various ↵Cheng Lian2016-05-041-16/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | buildReader() implementations ## What changes were proposed in this pull request? Currently, various `FileFormat` data sources share approximately the same code for partition value appending. This PR tries to eliminate this duplication. A new method `buildReaderWithPartitionValues()` is added to `FileFormat` with a default implementation that appends partition values to `InternalRow`s produced by the reader function returned by `buildReader()`. Special data sources like Parquet, which implements partition value appending inside `buildReader()` because of the vectorized reader, and the Text data source, which doesn't support partitioning, override `buildReaderWithPartitionValues()` and simply delegate to `buildReader()`. This PR brings two benefits: 1. Apparently, it de-duplicates partition value appending logic 2. Now the reader function returned by `buildReader()` is only required to produce `InternalRow`s rather than `UnsafeRow`s if the data source doesn't override `buildReaderWithPartitionValues()`. Because the safe-to-unsafe conversion is also performed while appending partition values. This makes 3rd-party data sources (e.g. spark-avro) easier to implement since they no longer need to access private APIs involving `UnsafeRow`. ## How was this patch tested? Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #12866 from liancheng/spark-14237-simplify-partition-values-appending.
* [SPARK-14973][ML] The CrossValidator and TrainValidationSplit miss the seed ↵yinxusen2016-05-035-18/+30
| | | | | | | | | | | | | | | | | | when saving and loading ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14973 Add seed support when saving/loading of CrossValidator and TrainValidationSplit. ## How was this patch tested? Spark unit test. Author: yinxusen <yinxusen@gmail.com> Closes #12825 from yinxusen/SPARK-14973.
* [SPARK-6717][ML] Clear shuffle files after checkpointing in ALSHolden Karau2016-05-032-44/+194
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When ALS is run with a checkpoint interval, during the checkpoint materialize the current state and cleanup the previous shuffles (non-blocking). ## How was this patch tested? Existing ALS unit tests, new ALS checkpoint cleanup unit tests added & shuffle files checked after ALS w/checkpointing run. Author: Holden Karau <holden@us.ibm.com> Author: Holden Karau <holden@pigscanfly.ca> Closes #11919 from holdenk/SPARK-6717-clear-shuffle-files-after-checkpointing-in-ALS.
* [SPARK-14931][ML][PYTHON] Mismatched default values between pipelines in ↵Xusen Yin2016-05-016-28/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Spark and PySpark - update ## What changes were proposed in this pull request? This PR is an update for [https://github.com/apache/spark/pull/12738] which: * Adds a generic unit test for JavaParams wrappers in pyspark.ml for checking default Param values vs. the defaults in the Scala side * Various fixes for bugs found * This includes changing classes taking weightCol to treat unset and empty String Param values the same way. Defaults changed: * Scala * LogisticRegression: weightCol defaults to not set (instead of empty string) * StringIndexer: labels default to not set (instead of empty array) * GeneralizedLinearRegression: * maxIter always defaults to 25 (simpler than defaulting to 25 for a particular solver) * weightCol defaults to not set (instead of empty string) * LinearRegression: weightCol defaults to not set (instead of empty string) * Python * MultilayerPerceptron: layers default to not set (instead of [1,1]) * ChiSqSelector: numTopFeatures defaults to 50 (instead of not set) ## How was this patch tested? Generic unit test. Manually tested that unit test by changing defaults and verifying that broke the test. Author: Joseph K. Bradley <joseph@databricks.com> Author: yinxusen <yinxusen@gmail.com> Closes #12816 from jkbradley/yinxusen-SPARK-14931.
* [SPARK-15030][ML][SPARKR] Support formula in spark.kmeans in SparkRYanbo Liang2016-04-306-20/+50
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? * ```RFormula``` supports empty response variable like ```~ x + y```. * Support formula in ```spark.kmeans``` in SparkR. * Fix some outdated docs for SparkR. ## How was this patch tested? Unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #12813 from yanboliang/spark-15030.
* [SPARK-14952][CORE][ML] Remove methods that were deprecated in 1.6.0Herman van Hovell2016-04-302-6/+0
| | | | | | | | | | | | | | | | | | #### What changes were proposed in this pull request? This PR removes three methods the were deprecated in 1.6.0: - `PortableDataStream.close()` - `LinearRegression.weights` - `LogisticRegression.weights` The rationale for doing this is that the impact is small and that Spark 2.0 is a major release. #### How was this patch tested? Compilation succeded. Author: Herman van Hovell <hvanhovell@questtec.nl> Closes #12732 from hvanhovell/SPARK-14952.
* [SPARK-14653][ML] Remove json4s from mllib-localXiangrui Meng2016-04-302-0/+103
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR moves Vector.toJson/fromJson to ml.linalg.VectorEncoder under mllib/ to keep mllib-local's dependency minimal. The json encoding is used by Params. So we still need this feature in SPARK-14615, where we will switch to ml.linalg in spark.ml APIs. ## How was this patch tested? Copied existing unit tests over. cc; dbtsai Author: Xiangrui Meng <meng@databricks.com> Closes #12802 from mengxr/SPARK-14653.
* [SPARK-13289][MLLIB] Fix infinite distances between word vectors in ↵Junyang2016-04-302-10/+29
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Word2VecModel ## What changes were proposed in this pull request? This PR fixes the bug that generates infinite distances between word vectors. For example, Before this PR, we have ``` val synonyms = model.findSynonyms("who", 40) ``` will give the following results: ``` to Infinity and Infinity that Infinity with Infinity ``` With this PR, the distance between words is a value between 0 and 1, as follows: ``` scala> model.findSynonyms("who", 10) res0: Array[(String, Double)] = Array((Harvard-educated,0.5253688097000122), (ex-SAS,0.5213794708251953), (McMutrie,0.5187736749649048), (fellow,0.5166833400726318), (businessman,0.5145374536514282), (American-born,0.5127736330032349), (British-born,0.5062344074249268), (gray-bearded,0.5047978162765503), (American-educated,0.5035858750343323), (mentored,0.49849334359169006)) scala> model.findSynonyms("king", 10) res1: Array[(String, Double)] = Array((queen,0.6787897944450378), (prince,0.6786158084869385), (monarch,0.659771203994751), (emperor,0.6490438580513), (goddess,0.643266499042511), (dynasty,0.635733425617218), (sultan,0.6166239380836487), (pharaoh,0.6150713562965393), (birthplace,0.6143025159835815), (empress,0.6109727025032043)) scala> model.findSynonyms("queen", 10) res2: Array[(String, Double)] = Array((princess,0.7670737504959106), (godmother,0.6982434988021851), (raven-haired,0.6877717971801758), (swan,0.684934139251709), (hunky,0.6816608309745789), (Titania,0.6808111071586609), (heroine,0.6794036030769348), (king,0.6787897944450378), (diva,0.67848801612854), (lip-synching,0.6731793284416199)) ``` ### There are two places changed in this PR: - Normalize the word vector to avoid overflow when calculating inner product between word vectors. This also simplifies the distance calculation, since the word vectors only need to be normalized once. - Scale the learning rate by number of iteration, to be consistent with Google Word2Vec implementation ## How was this patch tested? Use word2vec to train text corpus, and run model.findSynonyms() to get the distances between word vectors. Author: Junyang <fly.shenjy@gmail.com> Author: flyskyfly <fly.shenjy@gmail.com> Closes #11812 from flyjy/TVec.
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