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* [SPARK-13244][SQL] Migrates DataFrame to DatasetCheng Lian2016-03-1029-99/+118
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`. Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`). There are several noticeable API changes related to those returning arrays: 1. `collect`/`take` - Old APIs in class `DataFrame`: ```scala def collect(): Array[Row] def take(n: Int): Array[Row] ``` - New APIs in class `Dataset[T]`: ```scala def collect(): Array[T] def take(n: Int): Array[T] def collectRows(): Array[Row] def takeRows(n: Int): Array[Row] ``` Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side. Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here). 1. `randomSplit` - Old APIs in class `DataFrame`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] def randomSplit(weights: Array[Double]): Array[DataFrame] ``` - New APIs in class `Dataset[T]`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] def randomSplit(weights: Array[Double]): Array[Dataset[T]] ``` Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one. 1. `groupBy` Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`. Other noticeable changes: 1. Dataset always do eager analysis now We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`. ## How was this patch tested? Existing tests do the work. ## TODO - [ ] Fix all tests - [ ] Re-enable MiMA check - [ ] Update ScalaDoc (`since`, `group`, and example code) Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Closes #11443 from liancheng/ds-to-df.
* [SPARK-3854][BUILD] Scala style: require spaces before `{`.Dongjoon Hyun2016-03-1011-15/+15
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since the opening curly brace, '{', has many usages as discussed in [SPARK-3854](https://issues.apache.org/jira/browse/SPARK-3854), this PR adds a ScalaStyle rule to prevent '){' pattern for the following majority pattern and fixes the code accordingly. If we enforce this in ScalaStyle from now, it will improve the Scala code quality and reduce review time. ``` // Correct: if (true) { println("Wow!") } // Incorrect: if (true){ println("Wow!") } ``` IntelliJ also shows new warnings based on this. ## How was this patch tested? Pass the Jenkins ScalaStyle test. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11637 from dongjoon-hyun/SPARK-3854.
* [SPARK-11108][ML] OneHotEncoder should support other numeric typessethah2016-03-102-3/+35
| | | | | | | | | | | | | | Adding support for other numeric types: * Integer * Short * Long * Float * Decimal Author: sethah <seth.hendrickson16@gmail.com> Closes #9777 from sethah/SPARK-11108.
* [SPARK-11861][ML] Add feature importances for decision treessethah2016-03-0910-27/+126
| | | | | | | | This patch adds an API entry point for single decision tree feature importances. Author: sethah <seth.hendrickson16@gmail.com> Closes #9912 from sethah/SPARK-11861.
* [SPARK-13615][ML] GeneralizedLinearRegression supports save/loadYanbo Liang2016-03-092-10/+96
| | | | | | | | | | | | ## What changes were proposed in this pull request? ```GeneralizedLinearRegression``` supports ```save/load```. cc mengxr ## How was this patch tested? unit test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11465 from yanboliang/spark-13615.
* [SPARK-13702][CORE][SQL][MLLIB] Use diamond operator for generic instance ↵Dongjoon Hyun2016-03-098-12/+12
| | | | | | | | | | | | | | | | | | | | | | | | | creation in Java code. ## What changes were proposed in this pull request? In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator. ``` - final ArrayList<Product2<Object, Object>> dataToWrite = - new ArrayList<Product2<Object, Object>>(); + final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>(); ``` Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this. ## How was this patch tested? Manual. Pass the existing tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11541 from dongjoon-hyun/SPARK-13702.
* [ML] testEstimatorAndModelReadWrite should call checkModelDataYanbo Liang2016-03-082-1/+5
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Although we defined ```checkModelData``` in [```read/write``` test](https://github.com/apache/spark/blob/master/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala#L994) of ML estimators/models and pass it to ```testEstimatorAndModelReadWrite```, ```testEstimatorAndModelReadWrite``` omits to call ```checkModelData``` to check the equality of model data. So actually we did not run the check of model data equality for all test cases currently, we should fix it. BTW, fix the bug of LDA read/write test which did not set ```docConcentration```. This bug should have failed test, but it does not complain because we did not run ```checkModelData``` actually. cc jkbradley mengxr ## How was this patch tested? No new unit test, should pass the exist ones. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11513 from yanboliang/ml-check-model-data.
* [SPARK-13715][MLLIB] Remove last usages of jblas in testsSean Owen2016-03-087-118/+106
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Remove last usage of jblas, in tests ## How was this patch tested? Jenkins tests -- the same ones that are being modified. Author: Sean Owen <sowen@cloudera.com> Closes #11560 from srowen/SPARK-13715.
* [SPARK-13665][SQL] Separate the concerns of HadoopFsRelationMichael Armbrust2016-03-072-76/+67
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | `HadoopFsRelation` is used for reading most files into Spark SQL. However today this class mixes the concerns of file management, schema reconciliation, scan building, bucketing, partitioning, and writing data. As a result, many data sources are forced to reimplement the same functionality and the various layers have accumulated a fair bit of inefficiency. This PR is a first cut at separating this into several components / interfaces that are each described below. Additionally, all implementations inside of Spark (parquet, csv, json, text, orc, svmlib) have been ported to the new API `FileFormat`. External libraries, such as spark-avro will also need to be ported to work with Spark 2.0. ### HadoopFsRelation A simple `case class` that acts as a container for all of the metadata required to read from a datasource. All discovery, resolution and merging logic for schemas and partitions has been removed. This an internal representation that no longer needs to be exposed to developers. ```scala case class HadoopFsRelation( sqlContext: SQLContext, location: FileCatalog, partitionSchema: StructType, dataSchema: StructType, bucketSpec: Option[BucketSpec], fileFormat: FileFormat, options: Map[String, String]) extends BaseRelation ``` ### FileFormat The primary interface that will be implemented by each different format including external libraries. Implementors are responsible for reading a given format and converting it into `InternalRow` as well as writing out an `InternalRow`. A format can optionally return a schema that is inferred from a set of files. ```scala trait FileFormat { def inferSchema( sqlContext: SQLContext, options: Map[String, String], files: Seq[FileStatus]): Option[StructType] def prepareWrite( sqlContext: SQLContext, job: Job, options: Map[String, String], dataSchema: StructType): OutputWriterFactory def buildInternalScan( sqlContext: SQLContext, dataSchema: StructType, requiredColumns: Array[String], filters: Array[Filter], bucketSet: Option[BitSet], inputFiles: Array[FileStatus], broadcastedConf: Broadcast[SerializableConfiguration], options: Map[String, String]): RDD[InternalRow] } ``` The current interface is based on what was required to get all the tests passing again, but still mixes a couple of concerns (i.e. `bucketSet` is passed down to the scan instead of being resolved by the planner). Additionally, scans are still returning `RDD`s instead of iterators for single files. In a future PR, bucketing should be removed from this interface and the scan should be isolated to a single file. ### FileCatalog This interface is used to list the files that make up a given relation, as well as handle directory based partitioning. ```scala trait FileCatalog { def paths: Seq[Path] def partitionSpec(schema: Option[StructType]): PartitionSpec def allFiles(): Seq[FileStatus] def getStatus(path: Path): Array[FileStatus] def refresh(): Unit } ``` Currently there are two implementations: - `HDFSFileCatalog` - based on code from the old `HadoopFsRelation`. Infers partitioning by recursive listing and caches this data for performance - `HiveFileCatalog` - based on the above, but it uses the partition spec from the Hive Metastore. ### ResolvedDataSource Produces a logical plan given the following description of a Data Source (which can come from DataFrameReader or a metastore): - `paths: Seq[String] = Nil` - `userSpecifiedSchema: Option[StructType] = None` - `partitionColumns: Array[String] = Array.empty` - `bucketSpec: Option[BucketSpec] = None` - `provider: String` - `options: Map[String, String]` This class is responsible for deciding which of the Data Source APIs a given provider is using (including the non-file based ones). All reconciliation of partitions, buckets, schema from metastores or inference is done here. ### DataSourceAnalysis / DataSourceStrategy Responsible for analyzing and planning reading/writing of data using any of the Data Source APIs, including: - pruning the files from partitions that will be read based on filters. - appending partition columns* - applying additional filters when a data source can not evaluate them internally. - constructing an RDD that is bucketed correctly when required* - sanity checking schema match-up and other analysis when writing. *In the future we should do that following: - Break out file handling into its own Strategy as its sufficiently complex / isolated. - Push the appending of partition columns down in to `FileFormat` to avoid an extra copy / unvectorization. - Use a custom RDD for scans instead of `SQLNewNewHadoopRDD2` Author: Michael Armbrust <michael@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Closes #11509 from marmbrus/fileDataSource.
* [SPARK-13036][SPARK-13318][SPARK-13319] Add save/load for feature.pyXusen Yin2016-03-042-3/+45
| | | | | | | | | | Add save/load for feature.py. Meanwhile, add save/load for `ElementwiseProduct` in Scala side and fix a bug of missing `setDefault` in `VectorSlicer` and `StopWordsRemover`. In this PR I ignore the `RFormula` and `RFormulaModel` because its Scala implementation is pending in https://github.com/apache/spark/pull/9884. I'll add them in this PR if https://github.com/apache/spark/pull/9884 gets merged first. Or add a follow-up JIRA for `RFormula`. Author: Xusen Yin <yinxusen@gmail.com> Closes #11203 from yinxusen/SPARK-13036.
* [SPARK-13646][MLLIB] QuantileDiscretizer counts dataset twice in get…Abou Haydar Elias2016-03-041-1/+1
| | | | | | | | | | | ## What changes were proposed in this pull request? It avoids counting the dataframe twice. Author: Abou Haydar Elias <abouhaydar.elias@gmail.com> Author: Elie A <abouhaydar.elias@gmail.com> Closes #11491 from eliasah/quantile-discretizer-patch.
* [MINOR] Fix typos in comments and testcase name of codeDongjoon Hyun2016-03-031-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes typos in comments and testcase name of code. ## How was this patch tested? manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11481 from dongjoon-hyun/minor_fix_typos_in_code.
* [MINOR][ML][DOC] Remove duplicated periods at the end of some sharedParamYanbo Liang2016-03-032-15/+15
| | | | | | | | | | | | ## What changes were proposed in this pull request? Remove duplicated periods at the end of some sharedParams in ScalaDoc, such as [here](https://github.com/apache/spark/pull/11344/files#diff-9edc669edcf2c0c7cf1efe4a0a57da80L367) cc mengxr srowen ## How was this patch tested? Documents change, no test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11344 from yanboliang/shared-cleanup.
* [SPARK-13583][CORE][STREAMING] Remove unused imports and add checkstyle ruleDongjoon Hyun2016-03-0334-44/+18
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time. This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers. ## How was this patch tested? ``` ./dev/lint-java ./build/sbt compile ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11438 from dongjoon-hyun/SPARK-13583.
* [SPARK-13423][WIP][CORE][SQL][STREAMING] Static analysis fixes for 2.xSean Owen2016-03-0327-55/+61
| | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly: - Inner class should be static - Mismatched hashCode/equals - Overflow in compareTo - Unchecked warnings - Misuse of assert, vs junit.assert - get(a) + getOrElse(b) -> getOrElse(a,b) - Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions - Dead code - tailrec - exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count - reduce(_+_) -> sum map + flatten -> map The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places. ## How was the this patch tested? Existing Jenkins unit tests. Author: Sean Owen <sowen@cloudera.com> Closes #11292 from srowen/SPARK-13423.
* [SPARK-12811][ML] Estimator for Generalized Linear Models(GLMs)Yanbo Liang2016-03-014-4/+1094
| | | | | | | | | | Estimator for Generalized Linear Models(GLMs) which will be solved by IRLS. cc mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #11136 from yanboliang/spark-12811.
* [SPARK-13506][MLLIB] Fix the wrong parameter in R code comment in ↵Zheng RuiFeng2016-02-291-1/+2
| | | | | | | | | | | | | | | | | | AssociationRulesSuite JIRA: https://issues.apache.org/jira/browse/SPARK-13506 ## What changes were proposed in this pull request? just chang R Snippet Comment in AssociationRulesSuite ## How was this patch tested? unit test passsed Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #11387 from zhengruifeng/ars.
* [SPARK-13545][MLLIB][PYSPARK] Make MLlib LogisticRegressionWithLBFGS's ↵Yanbo Liang2016-02-291-0/+4
| | | | | | | | | | | | | | | | | default parameters consistent in Scala and Python ## What changes were proposed in this pull request? * The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.) * BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route. * Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly. cc mengxr dbtsai ## How was this patch tested? No new tests, it should pass all current tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11424 from yanboliang/spark-13545.
* [SPARK-12634][PYSPARK][DOC] PySpark tree parameter desc to consistent formatBryan Cutler2016-02-264-132/+140
| | | | | | | | | | | Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module. closes #10601 Author: Bryan Cutler <cutlerb@gmail.com> Author: vijaykiran <mail@vijaykiran.com> Closes #11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
* [SPARK-13457][SQL] Removes DataFrame RDD operationsCheng Lian2016-02-2750-99/+117
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is another try of PR #11323. This PR removes DataFrame RDD operations except for `foreach` and `foreachPartitions` (they are actions rather than transformations). Original calls are now replaced by calls to methods of `DataFrame.rdd`. PR #11323 was reverted because it introduced a regression: both `DataFrame.foreach` and `DataFrame.foreachPartitions` wrap underlying RDD operations with `withNewExecutionId` to track Spark jobs. But they are removed in #11323. ## How was the this patch tested? No extra tests are added. Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #11388 from liancheng/remove-df-rdd-ops.
* [SPARK-13028] [ML] Add MaxAbsScaler to ML.feature as a transformerYuhao Yang2016-02-252-0/+246
| | | | | | | | | | | | | | | | | | | | | | jira: https://issues.apache.org/jira/browse/SPARK-13028 MaxAbsScaler works in a very similar way as MinMaxScaler, but scales in a way that the training data lies within the range [-1, 1] by dividing through the largest maximum value in each feature. The motivation to use this scaling includes robustness to very small standard deviations of features and preserving zero entries in sparse data. Unlike StandardScaler and MinMaxScaler, MaxAbsScaler does not shift/center the data, and thus does not destroy any sparsity. Something similar from sklearn: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MaxAbsScaler.html#sklearn.preprocessing.MaxAbsScaler Author: Yuhao Yang <hhbyyh@gmail.com> Closes #10939 from hhbyyh/maxabs and squashes the following commits: fd8bdcd [Yuhao Yang] add tag and some optimization on fit 648fced [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs 75bebc2 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs cb10bb6 [Yuhao Yang] remove minmax 91ef8f3 [Yuhao Yang] ut added 8ab0747 [Yuhao Yang] Merge remote-tracking branch 'upstream/master' into maxabs a9215b5 [Yuhao Yang] max abs scaler
* [SPARK-12874][ML] ML StringIndexer does not protect itself from column name ↵Yu ISHIKAWA2016-02-252-0/+12
| | | | | | | | | | | | | | | | duplication ## What changes were proposed in this pull request? ML StringIndexer does not protect itself from column name duplication. We should still improve a way to validate a schema of `StringIndexer` and `StringIndexerModel`. However, it would be great to fix at another issue. ## How was this patch tested? unit test Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #11370 from yu-iskw/SPARK-12874.
* Revert "[SPARK-13457][SQL] Removes DataFrame RDD operations"Davies Liu2016-02-2549-116/+98
| | | | This reverts commit 157fe64f3ecbd13b7286560286e50235eecfe30e.
* [SPARK-13457][SQL] Removes DataFrame RDD operationsCheng Lian2016-02-2549-98/+116
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR removes DataFrame RDD operations. Original calls are now replaced by calls to methods of `DataFrame.rdd`. ## How was the this patch tested? No extra tests are added. Existing tests should do the work. Author: Cheng Lian <lian@databricks.com> Closes #11323 from liancheng/remove-df-rdd-ops.
* [SPARK-13490][ML] ML LinearRegression should cache standardization param valueYanbo Liang2016-02-251-1/+2
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Like #11027 for ```LogisticRegression```, ```LinearRegression``` with L1 regularization should also cache the value of the ```standardization``` rather than re-fetching it from the ```ParamMap``` for every OWLQN iteration. cc srowen ## How was this patch tested? No extra tests are added. It should pass all existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11367 from yanboliang/spark-13490.
* [SPARK-13444][MLLIB] QuantileDiscretizer chooses bad splits on large DataFramesOliver Pierson2016-02-252-2/+29
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Change line 113 of QuantileDiscretizer.scala to `val requiredSamples = math.max(numBins * numBins, 10000.0)` so that `requiredSamples` is a `Double`. This will fix the division in line 114 which currently results in zero if `requiredSamples < dataset.count` ## How was the this patch tested? Manual tests. I was having a problems using QuantileDiscretizer with my a dataset and after making this change QuantileDiscretizer behaves as expected. Author: Oliver Pierson <ocp@gatech.edu> Author: Oliver Pierson <opierson@umd.edu> Closes #11319 from oliverpierson/SPARK-13444.
* [SPARK-13011] K-means wrapper in SparkRXusen Yin2016-02-232-3/+94
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-13011 Author: Xusen Yin <yinxusen@gmail.com> Closes #11124 from yinxusen/SPARK-13011.
* [SPARK-13338][ML] Allow setting 'degree' parameter to 1 for PolynomialExpansionGrzegorz Chilkiewicz2016-02-232-33/+41
| | | | | | Author: Grzegorz Chilkiewicz <grzegorz.chilkiewicz@codilime.com> Closes #11216 from grzegorz-chilkiewicz/master.
* [SPARK-13355][MLLIB] replace GraphImpl.fromExistingRDDs by Graph.applyXiangrui Meng2016-02-221-2/+1
| | | | | | | | `GraphImpl.fromExistingRDDs` expects preprocessed vertex RDD as input. We call it in LDA without validating this requirement. So it might introduce errors. Replacing it by `Graph.apply` would be safer and more proper because it is a public API. The tests still pass. So maybe it is safe to use `fromExistingRDDs` here (though it doesn't seem so based on the implementation) or the test cases are special. jkbradley ankurdave Author: Xiangrui Meng <meng@databricks.com> Closes #11226 from mengxr/SPARK-13355.
* [SPARK-13429][MLLIB] Unify Logistic Regression convergence tolerance of ML & ↵Yanbo Liang2016-02-222-10/+10
| | | | | | | | | | | | | | MLlib ## What changes were proposed in this pull request? In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```. cc dbtsai ## How was the this patch tested? unit tests Author: Yanbo Liang <ybliang8@gmail.com> Closes #11299 from yanboliang/spark-13429.
* [SPARK-13295][ ML, MLLIB ] AFTSurvivalRegression.AFTAggregator improvements ↵Narine Kokhlikyan2016-02-221-15/+17
| | | | | | | | | | | | | | | | | | | - avoid creating new instances of arrays/vectors for each record As also mentioned/marked by TODO in AFTAggregator.AFTAggregator.add(data: AFTPoint) method a new array is being created for intercept value and it is being concatenated with another array which contains the betas, the resulted Array is being converted into a Dense vector which in its turn is being converted into breeze vector. This is expensive and not necessarily beautiful. I've tried to solve above mentioned problem by simple algebraic decompositions - keeping and treating intercept independently. Please let me know what do you think and if you have any questions. Thanks, Narine Author: Narine Kokhlikyan <narine.kokhlikyan@gmail.com> Closes #11179 from NarineK/survivaloptim.
* [SPARK-13334][ML] ML KMeansModel / BisectingKMeansModel / ↵Yanbo Liang2016-02-226-4/+8
| | | | | | | | | | | | QuantileDiscretizer should set parent ML ```KMeansModel / BisectingKMeansModel / QuantileDiscretizer``` should set parent. cc mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #11214 from yanboliang/spark-13334.
* [SPARK-12632][PYSPARK][DOC] PySpark fpm and als parameter desc to consistent ↵Bryan Cutler2016-02-224-66/+60
| | | | | | | | | | | | | | format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules. Closes #10602 Closes #10897 Author: Bryan Cutler <cutlerb@gmail.com> Author: somideshmukh <somilde@us.ibm.com> Closes #11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
* [MINOR][DOCS] Fix all typos in markdown files of `doc` and similar patterns ↵Dongjoon Hyun2016-02-221-1/+1
| | | | | | | | | | | | | | | | | in other comments ## What changes were proposed in this pull request? This PR tries to fix all typos in all markdown files under `docs` module, and fixes similar typos in other comments, too. ## How was the this patch tested? manual tests. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11300 from dongjoon-hyun/minor_fix_typos.
* [SPARK-12153][SPARK-7617][MLLIB] add support of arbitrary length sentence ↵Yong Gang Cao2016-02-222-38/+38
| | | | | | | | | | | | | | | | | | and other tuning for Word2Vec add support of arbitrary length sentence by using the nature representation of sentences in the input. add new similarity functions and add normalization option for distances in synonym finding add new accessor for internal structure(the vocabulary and wordindex) for convenience need instructions about how to set value for the Since annotation for newly added public functions. 1.5.3? jira link: https://issues.apache.org/jira/browse/SPARK-12153 Author: Yong Gang Cao <ygcao@amazon.com> Author: Yong-Gang Cao <ygcao@users.noreply.github.com> Closes #10152 from ygcao/improvementForSentenceBoundary.
* [SPARK-13379][MLLIB] Fix MLlib LogisticRegressionWithLBFGS set ↵Yanbo Liang2016-02-212-2/+350
| | | | | | | | | | | | | | | | regularization incorrectly ## What changes were proposed in this pull request? Fix MLlib LogisticRegressionWithLBFGS regularization map as: ```SquaredL2Updater``` -> ```elasticNetParam = 0.0``` ```L1Updater``` -> ```elasticNetParam = 1.0``` cc dbtsai ## How was the this patch tested? unit tests Author: Yanbo Liang <ybliang8@gmail.com> Closes #11258 from yanboliang/spark-13379.
* [MINOR][MLLIB] fix mllib compile warningsXiangrui Meng2016-02-172-0/+6
| | | | | | | | This PR fixes some warnings found by `build/sbt mllib/test:compile`. Author: Xiangrui Meng <meng@databricks.com> Closes #11227 from mengxr/fix-mllib-warnings-201602.
* [SPARK-12247][ML][DOC] Documentation for spark.ml's ALS and collaborative ↵BenFradet2016-02-161-1/+1
| | | | | | | | | | filtering in general This documents the implementation of ALS in `spark.ml` with example code in scala, java and python. Author: BenFradet <benjamin.fradet@gmail.com> Closes #10411 from BenFradet/SPARK-12247.
* [SPARK-13097][ML] Binarizer allowing Double AND Vector input typesseddonm12016-02-152-17/+81
| | | | | | | | | | | | | | This enhancement extends the existing SparkML Binarizer [SPARK-5891] to allow Vector in addition to the existing Double input column type. A use case for this enhancement is for when a user wants to Binarize many similar feature columns at once using the same threshold value (for example a binary threshold applied to many pixels in an image). This contribution is my original work and I license the work to the project under the project's open source license. viirya mengxr Author: seddonm1 <seddonm1@gmail.com> Closes #10976 from seddonm1/master.
* [SPARK-12363][MLLIB] Remove setRun and fix PowerIterationClustering failed testLiang-Chi Hsieh2016-02-132-47/+56
| | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12363 This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work. Author: Liang-Chi Hsieh <viirya@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #10539 from viirya/fix-poweriter.
* [SPARK-12746][ML] ArrayType(_, true) should also accept ArrayType(_, false)Earthson Lu2016-02-111-1/+2
| | | | | | | | https://issues.apache.org/jira/browse/SPARK-12746 Author: Earthson Lu <Earthson.Lu@gmail.com> Closes #10697 from Earthson/SPARK-12746.
* [SPARK-12765][ML][COUNTVECTORIZER] fix CountVectorizer.transform's lost ↵Liu Xiang2016-02-111-0/+1
| | | | | | | | | | transformSchema https://issues.apache.org/jira/browse/SPARK-12765 Author: Liu Xiang <lxmtlab@gmail.com> Closes #10720 from sloth2012/sloth.
* [SPARK-11515][ML] QuantileDiscretizer should take random seedYu ISHIKAWA2016-02-112-6/+11
| | | | | | | | cc jkbradley Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9535 from yu-iskw/SPARK-11515.
* [SPARK-13265][ML] Refactoring of basic ML import/export for other file ↵Yu ISHIKAWA2016-02-111-6/+7
| | | | | | | | | | system besides HDFS jkbradley I tried to improve the function to export a model. When I tried to export a model to S3 under Spark 1.6, we couldn't do that. So, it should offer S3 besides HDFS. Can you review it when you have time? Thanks! Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #11151 from yu-iskw/SPARK-13265.
* [SPARK-13264][DOC] Removed multi-byte characters in spark-env.sh.templateSasaki Toru2016-02-111-1/+1
| | | | | | | | In spark-env.sh.template, there are multi-byte characters, this PR will remove it. Author: Sasaki Toru <sasakitoa@nttdata.co.jp> Closes #11149 from sasakitoa/remove_multibyte_in_sparkenv.
* [SPARK-10524][ML] Use the soft prediction to order categories' binsLiang-Chi Hsieh2016-02-094-133/+194
| | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-10524 Currently we use the hard prediction (`ImpurityCalculator.predict`) to order categories' bins. But we should use the soft prediction. Author: Liang-Chi Hsieh <viirya@gmail.com> Author: Liang-Chi Hsieh <viirya@appier.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #8734 from viirya/dt-soft-centroids.
* [SPARK-13201][SPARK-13200] Deprecation warning cleanups: KMeans & ↵Holden Karau2016-02-093-5/+13
| | | | | | | | | | | | | | | | MFDataGenerator KMeans: Make a private non-deprecated version of setRuns API so that we can call it from the PythonAPI without deprecation warnings in our own build. Also use it internally when being called from train. Add a logWarning for non-1 values MFDataGenerator: Apparently we are calling round on an integer which now in Scala 2.11 results in a warning (it didn't make any sense before either). Figure out if this is a mistake we can just remove or if we got the types wrong somewhere. I put these two together since they are both deprecation fixes in MLlib and pretty small, but I can split them up if we would prefer it that way. Author: Holden Karau <holden@us.ibm.com> Closes #11112 from holdenk/SPARK-13201-non-deprecated-setRuns-SPARK-mathround-integer.
* [SPARK-13132][MLLIB] cache standardization param value in LogisticRegressionGary King2016-02-072-2/+5
| | | | | | | | | | | | cache the value of the standardization Param in LogisticRegression, rather than re-fetching it from the ParamMap for every index and every optimization step in the quasi-newton optimizer also, fix Param#toString to cache the stringified representation, rather than re-interpolating it on every call, so any other implementations that have similar repeated access patterns will see a benefit. this change improves training times for one of my test sets from ~7m30s to ~4m30s Author: Gary King <gary@idibon.com> Closes #11027 from idigary/spark-13132-optimize-logistic-regression.
* [SPARK-12732][ML] bug fix in linear regression trainImran Younus2016-02-022-25/+146
| | | | | | | | Fixed the bug in linear regression train for the case when the target variable is constant. The two cases for `fitIntercept=true` or `fitIntercept=false` should be treated differently. Author: Imran Younus <iyounus@us.ibm.com> Closes #10702 from iyounus/SPARK-12732_bug_fix_in_linear_regression_train.
* [SPARK-12711][ML] ML StopWordsRemover does not protect itself from column ↵Grzegorz Chilkiewicz2016-02-023-8/+19
| | | | | | | | | | | | name duplication Fixes problem and verifies fix by test suite. Also - adds optional parameter: nullable (Boolean) to: SchemaUtils.appendColumn and deduplicates SchemaUtils.appendColumn functions. Author: Grzegorz Chilkiewicz <grzegorz.chilkiewicz@codilime.com> Closes #10741 from grzegorz-chilkiewicz/master.
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