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* [SPARK-14952][CORE][ML] Remove methods that were deprecated in 1.6.0Herman van Hovell2016-04-302-19/+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-13289][MLLIB] Fix infinite distances between word vectors in ↵Junyang2016-04-301-7/+8
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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.
* [SPARK-14412][.2][ML] rename *RDDStorageLevel to *StorageLevel in ml.ALSXiangrui Meng2016-04-302-40/+40
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As discussed in #12660, this PR renames * intermediateRDDStorageLevel -> intermediateStorageLevel * finalRDDStorageLevel -> finalStorageLevel The argument name in `ALS.train` will be addressed in SPARK-15027. ## How was this patch tested? Existing unit tests. Author: Xiangrui Meng <meng@databricks.com> Closes #12803 from mengxr/SPARK-14412.
* [SPARK-14412][ML][PYSPARK] Add StorageLevel params to ALSNick Pentreath2016-04-292-5/+80
| | | | | | | | | | | | | | `mllib` `ALS` supports `setIntermediateRDDStorageLevel` and `setFinalRDDStorageLevel`. This PR adds these as Params in `ml` `ALS`. They are put in group **expertParam** since few users will need them. ## How was this patch tested? New test cases in `ALSSuite` and `tests.py`. cc yanboliang jkbradley sethah rishabhbhardwaj Author: Nick Pentreath <nickp@za.ibm.com> Closes #12660 from MLnick/SPARK-14412-als-storage-params.
* [SPARK-13786][ML][PYTHON] Removed save/load for python tuningJoseph K. Bradley2016-04-292-262/+21
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Per discussion on [https://github.com/apache/spark/pull/12604], this removes ML persistence for Python tuning (TrainValidationSplit, CrossValidator, and their Models) since they do not handle nesting easily. This support should be re-designed and added in the next release. ## How was this patch tested? Removed unit test elements saving and loading the tuning algorithms, but kept tests to save and load their bestModel fields. Author: Joseph K. Bradley <joseph@databricks.com> Closes #12782 from jkbradley/remove-python-tuning-saveload.
* [SPARK-15012][SQL] Simplify configuration API furtherAndrew Or2016-04-293-33/+4
| | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? 1. Remove all the `spark.setConf` etc. Just expose `spark.conf` 2. Make `spark.conf` take in things set in the core `SparkConf` as well, otherwise users may get confused This was done for both the Python and Scala APIs. ## How was this patch tested? `SQLConfSuite`, python tests. This one fixes the failed tests in #12787 Closes #12787 Author: Andrew Or <andrew@databricks.com> Author: Yin Huai <yhuai@databricks.com> Closes #12798 from yhuai/conf-api.
* [SPARK-14988][PYTHON] SparkSession API follow-upsAndrew Or2016-04-295-209/+228
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Addresses comments in #12765. ## How was this patch tested? Python tests. Author: Andrew Or <andrew@databricks.com> Closes #12784 from andrewor14/python-followup.
* [SPARK-11940][PYSPARK][ML] Python API for ml.clustering.LDA PR2Jeff Zhang2016-04-292-2/+543
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? pyspark.ml API for LDA * LDA, LDAModel, LocalLDAModel, DistributedLDAModel * includes persistence This replaces [https://github.com/apache/spark/pull/10242] ## How was this patch tested? * doc test for LDA, including Param setters * unit test for persistence Author: Joseph K. Bradley <joseph@databricks.com> Author: Jeff Zhang <zjffdu@apache.org> Closes #12723 from jkbradley/zjffdu-SPARK-11940.
* [SPARK-14988][PYTHON] SparkSession catalog and conf APIAndrew Or2016-04-294-85/+605
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API. ## How was this patch tested? Python tests. Author: Andrew Or <andrew@databricks.com> Closes #12765 from andrewor14/python-spark-session-more.
* [SPARK-14829][MLLIB] Deprecate GLM APIs using SGDZheng RuiFeng2016-04-282-0/+25
| | | | | | | | | | | | ## What changes were proposed in this pull request? According to the [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829), deprecate API of LogisticRegression and LinearRegression using SGD ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #12596 from zhengruifeng/deprecate_sgd.
* [SPARK-14555] Second cut of Python API for Structured StreamingBurak Yavuz2016-04-285-46/+217
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR adds Python APIs for: - `ContinuousQueryManager` - `ContinuousQueryException` The `ContinuousQueryException` is a very basic wrapper, it doesn't provide the functionality that the Scala side provides, but it follows the same pattern for `AnalysisException`. For `ContinuousQueryManager`, all APIs are provided except for registering listeners. This PR also attempts to fix test flakiness by stopping all active streams just before tests. ## How was this patch tested? Python Doc tests and unit tests Author: Burak Yavuz <brkyvz@gmail.com> Closes #12673 from brkyvz/pyspark-cqm.
* [SPARK-12810][PYSPARK] PySpark CrossValidatorModel should support avgMetricsKai Jiang2016-04-282-8/+37
| | | | | | | | | | | | ## What changes were proposed in this pull request? support avgMetrics in CrossValidatorModel with Python ## How was this patch tested? Doctest and `test_save_load` in `pyspark/ml/test.py` [JIRA](https://issues.apache.org/jira/browse/SPARK-12810) Author: Kai Jiang <jiangkai@gmail.com> Closes #12464 from vectorijk/spark-12810.
* [SPARK-14945][PYTHON] SparkSession Python APIAndrew Or2016-04-286-240/+585
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? ``` Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /__ / .__/\_,_/_/ /_/\_\ version 2.0.0-SNAPSHOT /_/ Using Python version 2.7.5 (default, Mar 9 2014 22:15:05) SparkSession available as 'spark'. >>> spark <pyspark.sql.session.SparkSession object at 0x101f3bfd0> >>> spark.sql("SHOW TABLES").show() ... +---------+-----------+ |tableName|isTemporary| +---------+-----------+ | src| false| +---------+-----------+ >>> spark.range(1, 10, 2).show() +---+ | id| +---+ | 1| | 3| | 5| | 7| | 9| +---+ ``` **Note**: This API is NOT complete in its current state. In particular, for now I left out the `conf` and `catalog` APIs, which were added later in Scala. These will be added later before 2.0. ## How was this patch tested? Python tests. Author: Andrew Or <andrew@databricks.com> Closes #12746 from andrewor14/python-spark-session.
* [SPARK-14899][ML][PYSPARK] Remove spark.ml HashingTF hashingAlg optionYanbo Liang2016-04-272-36/+14
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Since [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574) breaks behavior of ```HashingTF```, we should try to enforce good practice by removing the "native" hashAlgorithm option in spark.ml and pyspark.ml. We can leave spark.mllib and pyspark.mllib alone. ## How was this patch tested? Unit tests. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12702 from yanboliang/spark-14899.
* [SPARK-9656][MLLIB][PYTHON] Add missing methods to PySpark's Distributed ↵Mike Dusenberry2016-04-272-3/+299
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Linear Algebra Classes This PR adds the remaining group of methods to PySpark's distributed linear algebra classes as follows: * `RowMatrix` <sup>**[1]**</sup> 1. `computeGramianMatrix` 2. `computeCovariance` 3. `computeColumnSummaryStatistics` 4. `columnSimilarities` 5. `tallSkinnyQR` <sup>**[2]**</sup> * `IndexedRowMatrix` <sup>**[3]**</sup> 1. `computeGramianMatrix` * `CoordinateMatrix` 1. `transpose` * `BlockMatrix` 1. `validate` 2. `cache` 3. `persist` 4. `transpose` **[1]**: Note: `multiply`, `computeSVD`, and `computePrincipalComponents` are already part of PR #7963 for SPARK-6227. **[2]**: Implementing `tallSkinnyQR` uncovered a bug with our PySpark `RowMatrix` constructor. As discussed on the dev list [here](http://apache-spark-developers-list.1001551.n3.nabble.com/K-Means-And-Class-Tags-td10038.html), there appears to be an issue with type erasure with RDDs coming from Java, and by extension from PySpark. Although we are attempting to construct a `RowMatrix` from an `RDD[Vector]` in [PythonMLlibAPI](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala#L1115), the `Vector` type is erased, resulting in an `RDD[Object]`. Thus, when calling Scala's `tallSkinnyQR` from PySpark, we get a Java `ClassCastException` in which an `Object` cannot be cast to a Spark `Vector`. As noted in the aforementioned dev list thread, this issue was also encountered with `DecisionTrees`, and the fix involved an explicit `retag` of the RDD with a `Vector` type. Thus, this PR currently contains that fix applied to the `createRowMatrix` helper function in `PythonMLlibAPI`. `IndexedRowMatrix` and `CoordinateMatrix` do not appear to have this issue likely due to their related helper functions in `PythonMLlibAPI` creating the RDDs explicitly from DataFrames with pattern matching, thus preserving the types. However, this fix may be out of scope for this single PR, and it may be better suited in a separate JIRA/PR. Therefore, I have marked this PR as WIP and am open to discussion. **[3]**: Note: `multiply` and `computeSVD` are already part of PR #7963 for SPARK-6227. Author: Mike Dusenberry <mwdusenb@us.ibm.com> Closes #9441 from dusenberrymw/SPARK-9656_Add_Missing_Methods_to_PySpark_Distributed_Linear_Algebra.
* [SPARK-14732][ML] spark.ml GaussianMixture should use MultivariateGaussian ↵Joseph K. Bradley2016-04-261-7/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | in mllib-local ## What changes were proposed in this pull request? Before, spark.ml GaussianMixtureModel used the spark.mllib MultivariateGaussian in its public API. This was added after 1.6, so we can modify this API without breaking APIs. This PR copies MultivariateGaussian to mllib-local in spark.ml, with a few changes: * Renamed fields to match numpy, scipy: mu => mean, sigma => cov This PR then uses the spark.ml MultivariateGaussian in the spark.ml GaussianMixtureModel, which involves: * Modifying the constructor * Adding a computeProbabilities method Also: * Added EPSILON to mllib-local for use in MultivariateGaussian ## How was this patch tested? Existing unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #12593 from jkbradley/sparkml-gmm-fix.
* [SPARK-14903][SPARK-14071][ML][PYTHON] Revert : MLWritable.write propertyJoseph K. Bradley2016-04-262-8/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? SPARK-14071 changed MLWritable.write to be a property. This reverts that change since there was not a good way to make MLReadable.read appear to be a property. ## How was this patch tested? existing unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #12671 from jkbradley/revert-MLWritable-write-py.
* [SPARK-11559][MLLIB] Make `runs` no effect in mllib.KMeansYanbo Liang2016-04-262-9/+5
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? We deprecated ```runs``` of mllib.KMeans in Spark 1.6 (SPARK-11358). In 2.0, we will make it no effect (with warning messages). We did not remove ```setRuns/getRuns``` for better binary compatibility. This PR change `runs` which are appeared at the public API. Usage inside of ```KMeans.runAlgorithm()``` will be resolved at #10806. ## How was this patch tested? Existing unit tests. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12608 from yanboliang/spark-11559.
* [SPARK-14721][SQL] Remove HiveContext (part 2)Andrew Or2016-04-251-1/+2
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class. Note: A couple of things will break after this patch. These will be fixed separately. - the python HiveContext - all the documentation / comments referencing HiveContext - there will be no more HiveContext in the REPL (fixed by #12589) ## How was this patch tested? No change in functionality. Author: Andrew Or <andrew@databricks.com> Closes #12585 from andrewor14/delete-hive-context.
* [SPARK-10574][ML][MLLIB] HashingTF supports MurmurHash3Yanbo Liang2016-04-252-9/+33
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? As the discussion at [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574), ```HashingTF``` should support MurmurHash3 and make it as the default hash algorithm. We should also expose set/get API for ```hashAlgorithm```, then users can choose the hash method. Note: The problem that ```mllib.feature.HashingTF``` behaves differently between Scala/Java and Python will be resolved in the followup work. ## How was this patch tested? unit tests. cc jkbradley MLnick Author: Yanbo Liang <ybliang8@gmail.com> Author: Joseph K. Bradley <joseph@databricks.com> Closes #12498 from yanboliang/spark-10574.
* [MINOR][ML][PYTHON][DOC] Remove use of JavaMLWriter/Reader in public Python ↵Joseph K. Bradley2016-04-251-4/+4
| | | | | | | | | | | | | | | | API docs ## What changes were proposed in this pull request? Removed instances of JavaMLWriter, JavaMLReader appearing in public Python API docs ## How was this patch tested? n/a Author: Joseph K. Bradley <joseph@databricks.com> Closes #12542 from jkbradley/javamlwriter-doc.
* [SPARK-14433][PYSPARK][ML] PySpark ml GaussianMixturewm624@hotmail.com2016-04-251-1/+145
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add Python API in ML for GaussianMixture ## How was this patch tested? (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests) Add doctest and test cases are the same as mllib Python tests ./dev/lint-python PEP8 checks passed. rm -rf _build/* pydoc checks passed. ./python/run-tests --python-executables=python2.7 --modules=pyspark-ml Running PySpark tests. Output is in /Users/mwang/spark_ws_0904/python/unit-tests.log Will test against the following Python executables: ['python2.7'] Will test the following Python modules: ['pyspark-ml'] Finished test(python2.7): pyspark.ml.evaluation (18s) Finished test(python2.7): pyspark.ml.clustering (40s) Finished test(python2.7): pyspark.ml.classification (49s) Finished test(python2.7): pyspark.ml.recommendation (44s) Finished test(python2.7): pyspark.ml.feature (64s) Finished test(python2.7): pyspark.ml.regression (45s) Finished test(python2.7): pyspark.ml.tuning (30s) Finished test(python2.7): pyspark.ml.tests (56s) Tests passed in 106 seconds Author: wm624@hotmail.com <wm624@hotmail.com> Closes #12402 from wangmiao1981/gmm.
* [SPARK-14768][ML][PYSPARK] removed expectedType from Param __init__()Jason Lee2016-04-251-8/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? Removed expectedType arg from PySpark Param __init__, as suggested by the JIRA. ## How was this patch tested? Manually looked through all places that use Param. Compiled and ran all ML PySpark test cases before and after the fix. Author: Jason Lee <cjlee@us.ibm.com> Closes #12581 from jasoncl/SPARK-14768.
* Support single argument version of sqlContext.getConfmathieu longtin2016-04-231-3/+17
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In Python, sqlContext.getConf didn't allow getting the system default (getConf with one parameter). Now the following are supported: ``` sqlContext.getConf(confName) # System default if not locally set, this is new sqlContext.getConf(confName, myDefault) # myDefault if not locally set, old behavior ``` I also added doctests to this function. The original behavior does not change. ## How was this patch tested? Manually, but doctests were added. Author: mathieu longtin <mathieu.longtin@nuance.com> Closes #12488 from mathieulongtin/pyfixgetconf3.
* [SPARK-13266] [SQL] None read/writer options were not transalated to "null"Liang-Chi Hsieh2016-04-222-3/+9
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem. This is based on #11305 from mathieulongtin. ## How was this patch tested? Added test to readwriter.py. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: mathieu longtin <mathieu.longtin@nuance.com> Closes #12494 from viirya/py-df-none-option.
* [SPARK-14739][PYSPARK] Fix Vectors parser bugsArash Parsa2016-04-212-8/+14
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The PySpark deserialization has a bug that shows while deserializing all zero sparse vectors. This fix filters out empty string tokens before casting, hence properly stringified SparseVectors successfully get parsed. ## How was this patch tested? Standard unit-tests similar to other methods. Author: Arash Parsa <arash@ip-192-168-50-106.ec2.internal> Author: Arash Parsa <arashpa@gmail.com> Author: Vishnu Prasad <vishnu667@gmail.com> Author: Vishnu Prasad S <vishnu667@gmail.com> Closes #12516 from arashpa/SPARK-14739.
* [SPARK-13842] [PYSPARK] pyspark.sql.types.StructType accessor enhancementsSheamus K. Parkes2016-04-202-9/+58
| | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Expand the possible ways to interact with the contents of a `pyspark.sql.types.StructType` instance. - Iterating a `StructType` will iterate its fields - `[field.name for field in my_structtype]` - Indexing with a string will return a field by name - `my_structtype['my_field_name']` - Indexing with an integer will return a field by position - `my_structtype[0]` - Indexing with a slice will return a new `StructType` with just the chosen fields: - `my_structtype[1:3]` - The length is the number of fields (should also provide "truthiness" for free) - `len(my_structtype) == 2` ## How was this patch tested? Extended the unit test coverage in the accompanying `tests.py`. Author: Sheamus K. Parkes <shea.parkes@milliman.com> Closes #12251 from skparkes/pyspark-structtype-enhance.
* [MINOR][ML][PYSPARK] Fix omissive params which should use TypeConverterYanbo Liang2016-04-202-4/+5
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? #11663 adds type conversion functionality for parameters in Pyspark. This PR find out the omissive ```Param``` that did not pass corresponding ```TypeConverter``` argument and fix them. After this PR, all params in pyspark/ml/ used ```TypeConverter```. ## How was this patch tested? Existing tests. cc jkbradley sethah Author: Yanbo Liang <ybliang8@gmail.com> Closes #12529 from yanboliang/typeConverter.
* [MINOR][ML][PYSPARK] Fix omissive param setters which should use _set methodYanbo Liang2016-04-201-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? #11939 make Python param setters use the `_set` method. This PR fix omissive ones. ## How was this patch tested? Existing tests. cc jkbradley sethah Author: Yanbo Liang <ybliang8@gmail.com> Closes #12531 from yanboliang/setters-omissive.
* [SPARK-14555] First cut of Python API for Structured StreamingBurak Yavuz2016-04-2016-29/+378
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes: - ContinuousQuery - Trigger - ProcessingTime in pyspark under `pyspark.sql.streaming`. In addition, it contains the new methods added under: - `DataFrameWriter` a) `startStream` b) `trigger` c) `queryName` - `DataFrameReader` a) `stream` - `DataFrame` a) `isStreaming` This PR doesn't contain all methods exposed for `ContinuousQuery`, for example: - `exception` - `sourceStatuses` - `sinkStatus` They may be added in a follow up. This PR also contains some very minor doc fixes in the Scala side. ## How was this patch tested? Python doc tests TODO: - [ ] verify Python docs look good Author: Burak Yavuz <brkyvz@gmail.com> Author: Burak Yavuz <burak@databricks.com> Closes #12320 from brkyvz/stream-python.
* [SPARK-14639] [PYTHON] [R] Add `bround` function in Python/R.Dongjoon Hyun2016-04-191-3/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This issue aims to expose Scala `bround` function in Python/R API. `bround` function is implemented in SPARK-14614 by extending current `round` function. We used the following semantics from Hive. ```java public static double bround(double input, int scale) { if (Double.isNaN(input) || Double.isInfinite(input)) { return input; } return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue(); } ``` After this PR, `pyspark` and `sparkR` also support `bround` function. **PySpark** ```python >>> from pyspark.sql.functions import bround >>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect() [Row(r=2.0)] ``` **SparkR** ```r > df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5))) > head(collect(select(df, bround(df$x, 0)))) bround(x, 0) 1 2 2 4 ``` ## How was this patch tested? Pass the Jenkins tests (including new testcases). Author: Dongjoon Hyun <dongjoon@apache.org> Closes #12509 from dongjoon-hyun/SPARK-14639.
* [SPARK-14717] [PYTHON] Scala, Python APIs for Dataset.unpersist differ in ↵felixcheung2016-04-192-2/+4
| | | | | | | | | | | | | | | | | | default blocking value ## What changes were proposed in this pull request? Change unpersist blocking parameter default value to match Scala ## How was this patch tested? unit tests, manual tests jkbradley davies Author: felixcheung <felixcheung_m@hotmail.com> Closes #12507 from felixcheung/pyunpersist.
* [SPARK-14714][ML][PYTHON] Fixed issues with non-kwarg typeConverter arg for ↵Joseph K. Bradley2016-04-183-13/+19
| | | | | | | | | | | | | | | | | | | | | | | | | Param constructor ## What changes were proposed in this pull request? PySpark Param constructors need to pass the TypeConverter argument by name, partly to make sure it is not mistaken for the expectedType arg and partly because we will remove the expectedType arg in 2.1. In several places, this is not being done correctly. This PR changes all usages in pyspark/ml/ to keyword args. ## How was this patch tested? Existing unit tests. I will not test type conversion for every Param unless we really think it necessary. Also, if you start the PySpark shell and import classes (e.g., pyspark.ml.feature.StandardScaler), then you no longer get this warning: ``` /Users/josephkb/spark/python/pyspark/ml/param/__init__.py:58: UserWarning: expectedType is deprecated and will be removed in 2.1. Use typeConverter instead, as a keyword argument. "Use typeConverter instead, as a keyword argument.") ``` That warning came from the typeConverter argument being passes as the expectedType arg by mistake. Author: Joseph K. Bradley <joseph@databricks.com> Closes #12480 from jkbradley/typeconverter-fix.
* [SPARK-14440][PYSPARK] Remove pipeline specific reader and writerXusen Yin2016-04-181-46/+7
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14440 Remove * PipelineMLWriter * PipelineMLReader * PipelineModelMLWriter * PipelineModelMLReader and modify comments. ## How was this patch tested? test with unit test. Author: Xusen Yin <yinxusen@gmail.com> Closes #12216 from yinxusen/SPARK-14440.
* [SPARK-14564][ML][MLLIB][PYSPARK] Python Word2Vec missing setWindowSize methodJason Lee2016-04-184-7/+41
| | | | | | | | | | | | ## What changes were proposed in this pull request? Added windowSize getter/setter to ML/MLlib ## How was this patch tested? Added test cases in tests.py under both ML and MLlib Author: Jason Lee <cjlee@us.ibm.com> Closes #12428 from jasoncl/SPARK-14564.
* [SPARK-14306][ML][PYSPARK] PySpark ml.classification OneVsRest support ↵Xusen Yin2016-04-182-23/+144
| | | | | | | | | | | | | | | | | | export/import ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-14306 Add PySpark OneVsRest save/load supports. ## How was this patch tested? Test with Python unit test. Author: Xusen Yin <yinxusen@gmail.com> Closes #12439 from yinxusen/SPARK-14306-0415.
* [SPARK-14605][ML][PYTHON] Changed Python to use unicode UIDs for spark.ml ↵Joseph K. Bradley2016-04-164-4/+8
| | | | | | | | | | | | | | | | | | Identifiable ## What changes were proposed in this pull request? Python spark.ml Identifiable classes use UIDs of type str, but they should use unicode (in Python 2.x) to match Java. This could be a problem if someone created a class in Java with odd unicode characters, saved it, and loaded it in Python. This PR: Use unicode everywhere in Python. ## How was this patch tested? Updated persistence unit test to check uid type Author: Joseph K. Bradley <joseph@databricks.com> Closes #12368 from jkbradley/python-uid-unicode.
* [SPARK-7861][ML] PySpark OneVsRestXusen Yin2016-04-152-7/+249
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? https://issues.apache.org/jira/browse/SPARK-7861 Add PySpark OneVsRest. I implement it with Python since it's a meta-pipeline. ## How was this patch tested? Test with doctest. Author: Xusen Yin <yinxusen@gmail.com> Closes #12124 from yinxusen/SPARK-14306-7861.
* [SPARK-14104][PYSPARK][ML] All Python param setters should use the `_set` methodsethah2016-04-1512-91/+110
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Param setters in python previously accessed the _paramMap directly to update values. The `_set` method now implements type checking, so it should be used to update all parameters. This PR eliminates all direct accesses to `_paramMap` besides the one in the `_set` method to ensure type checking happens. Additional changes: * [SPARK-13068](https://github.com/apache/spark/pull/11663) missed adding type converters in evaluation.py so those are done here * An incorrect `toBoolean` type converter was used for StringIndexer `handleInvalid` param in previous PR. This is fixed here. ## How was this patch tested? Existing unit tests verify that parameters are still set properly. No new functionality is actually added in this PR. Author: sethah <seth.hendrickson16@gmail.com> Closes #11939 from sethah/SPARK-14104.
* [SPARK-14665][ML][PYTHON] Fixed bug with StopWordsRemover default stopwordsJoseph K. Bradley2016-04-152-1/+4
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The default stopwords were a Java object. They are no longer. ## How was this patch tested? Unit test which failed before the fix Author: Joseph K. Bradley <joseph@databricks.com> Closes #12422 from jkbradley/pyspark-stopwords.
* [SPARK-14374][ML][PYSPARK] PySpark ml GBTClassifier, Regressor support ↵Yanbo Liang2016-04-142-4/+30
| | | | | | | | | | | | | | | | export/import ## What changes were proposed in this pull request? PySpark ml GBTClassifier, Regressor support export/import. ## How was this patch tested? Doc test. cc jkbradley Author: Yanbo Liang <ybliang8@gmail.com> Closes #12383 from yanboliang/spark-14374.
* [SPARK-14238][ML][MLLIB][PYSPARK] Add binary toggle Param to PySpark ↵Yong Tang2016-04-144-3/+69
| | | | | | | | | | | | | | | | | | HashingTF in ML & MLlib ## What changes were proposed in this pull request? This fix tries to add binary toggle Param to PySpark HashingTF in ML & MLlib. If this toggle is set, then all non-zero counts will be set to 1. Note: This fix (SPARK-14238) is extended from SPARK-13963 where Scala implementation was done. ## How was this patch tested? This fix adds two tests to cover the code changes. One for HashingTF in PySpark's ML and one for HashingTF in PySpark's MLLib. Author: Yong Tang <yong.tang.github@outlook.com> Closes #12079 from yongtang/SPARK-14238.
* [SPARK-13967][PYSPARK][ML] Added binary Param to Python CountVectorizerBryan Cutler2016-04-142-5/+45
| | | | | | | | | | Added binary toggle param to CountVectorizer feature transformer in PySpark. Created a unit test for using CountVectorizer with the binary toggle on. Author: Bryan Cutler <cutlerb@gmail.com> Closes #12308 from BryanCutler/binary-param-python-CountVectorizer-SPARK-13967.
* [SPARK-14573][PYSPARK][BUILD] Fix PyDoc Makefile & highlighting issuesHolden Karau2016-04-144-7/+7
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The PyDoc Makefile used "=" rather than "?=" for setting env variables so it overwrote the user values. This ignored the environment variables we set for linting allowing warnings through. This PR also fixes the warnings that had been introduced. ## How was this patch tested? manual local export & make Author: Holden Karau <holden@us.ibm.com> Closes #12336 from holdenk/SPARK-14573-fix-pydoc-makefile.
* [SPARK-14472][PYSPARK][ML] Cleanup ML JavaWrapper and related class hierarchyBryan Cutler2016-04-138-70/+62
| | | | | | | | | | Currently, JavaWrapper is only a wrapper class for pipeline classes that have Params and JavaCallable is a separate mixin that provides methods to make Java calls. This change simplifies the class structure and to define the Java wrapper in a plain base class along with methods to make Java calls. Also, renames Java wrapper classes to better reflect their purpose. Ran existing Python ml tests and generated documentation to test this change. Author: Bryan Cutler <cutlerb@gmail.com> Closes #12304 from BryanCutler/pyspark-cleanup-JavaWrapper-SPARK-14472.
* [SPARK-13992][CORE][PYSPARK][FOLLOWUP] Update OFF_HEAP semantics for Java ↵Liwei Lin2016-04-121-1/+1
| | | | | | | | | | | | | | | | | api and Python api ## What changes were proposed in this pull request? - updated `OFF_HEAP` semantics for `StorageLevels.java` - updated `OFF_HEAP` semantics for `storagelevel.py` ## How was this patch tested? no need to test Author: Liwei Lin <lwlin7@gmail.com> Closes #12126 from lw-lin/storagelevel.py.
* [SPARK-13597][PYSPARK][ML] Python API for GeneralizedLinearRegressionKai Jiang2016-04-121-0/+145
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Python API for GeneralizedLinearRegression JIRA: https://issues.apache.org/jira/browse/SPARK-13597 ## How was this patch tested? The patch is tested with Python doctest. Author: Kai Jiang <jiangkai@gmail.com> Closes #11468 from vectorijk/spark-13597.
* [SPARK-13687][PYTHON] Cleanup PySpark parallelize temporary filesHolden Karau2016-04-102-9/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Eagerly cleanup PySpark's temporary parallelize cleanup files rather than waiting for shut down. ## How was this patch tested? Unit tests Author: Holden Karau <holden@us.ibm.com> Closes #12233 from holdenk/SPARK-13687-cleanup-pyspark-temporary-files.
* [SPARK-14498][ML][PYTHON][SQL] Many cleanups to ML and ML-related docsJoseph K. Bradley2016-04-084-2/+13
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Cleanups to documentation. No changes to code. * GBT docs: Move Scala doc for private object GradientBoostedTrees to public docs for GBTClassifier,Regressor * GLM regParam: needs doc saying it is for L2 only * TrainValidationSplitModel: add .. versionadded:: 2.0.0 * Rename “_transformer_params_from_java” to “_transfer_params_from_java” * LogReg Summary classes: “probability” col should not say “calibrated” * LR summaries: coefficientStandardErrors —> document that intercept stderr comes last. Same for t,p-values * approxCountDistinct: Document meaning of “rsd" argument. * LDA: note which params are for online LDA only ## How was this patch tested? Doc build Author: Joseph K. Bradley <joseph@databricks.com> Closes #12266 from jkbradley/ml-doc-cleanups.
* [SPARK-12569][PYSPARK][ML] DecisionTreeRegressor: provide variance of ↵wm624@hotmail.com2016-04-083-7/+35
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | prediction: Python API ## What changes were proposed in this pull request? A new column VarianceCol has been added to DecisionTreeRegressor in ML scala code. This patch adds the corresponding Python API, HasVarianceCol, to class DecisionTreeRegressor. ## How was this patch tested? ./dev/lint-python PEP8 checks passed. rm -rf _build/* pydoc checks passed. ./python/run-tests --python-executables=python2.7 --modules=pyspark-ml Running PySpark tests. Output is in /Users/mwang/spark_ws_0904/python/unit-tests.log Will test against the following Python executables: ['python2.7'] Will test the following Python modules: ['pyspark-ml'] Finished test(python2.7): pyspark.ml.evaluation (12s) Finished test(python2.7): pyspark.ml.clustering (18s) Finished test(python2.7): pyspark.ml.classification (30s) Finished test(python2.7): pyspark.ml.recommendation (28s) Finished test(python2.7): pyspark.ml.feature (43s) Finished test(python2.7): pyspark.ml.regression (31s) Finished test(python2.7): pyspark.ml.tuning (19s) Finished test(python2.7): pyspark.ml.tests (34s) (If this patch involves UI changes, please attach a screenshot; otherwise, remove this) Author: wm624@hotmail.com <wm624@hotmail.com> Closes #12116 from wangmiao1981/fix_api.
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