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* [SPARK-18901][ML] Require in LR LogisticAggregator is redundantwm624@hotmail.com2017-04-241-5/+0
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In MultivariateOnlineSummarizer, `add` and `merge` have check for weights and feature sizes. The checks in LR are redundant, which are removed from this PR. ## How was this patch tested? Existing tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #17478 from wangmiao1981/logit.
* [SPARK-20423][ML] fix MLOR coeffs centering when reg == 0WeichenXu2017-04-212-3/+14
| | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? When reg == 0, MLOR has multiple solutions and we need to centralize the coeffs to get identical result. BUT current implementation centralize the `coefficientMatrix` by the global coeffs means. In fact the `coefficientMatrix` should be centralized on each feature index itself. Because, according to the MLOR probability distribution function, it can be proven easily that: suppose `{ w0, w1, .. w(K-1) }` make up the `coefficientMatrix`, then `{ w0 + c, w1 + c, ... w(K - 1) + c}` will also be the equivalent solution. `c` is an arbitrary vector of `numFeatures` dimension. reference https://core.ac.uk/download/pdf/6287975.pdf So that we need to centralize the `coefficientMatrix` on each feature dimension separately. **We can also confirm this through R library `glmnet`, that MLOR in `glmnet` always generate coefficients result that the sum of each dimension is all `zero`, when reg == 0.** ## How was this patch tested? Tests added. Author: WeichenXu <WeichenXu123@outlook.com> Closes #17706 from WeichenXu123/mlor_center.
* [SPARK-20265][MLLIB] Improve Prefix'span pre-processing efficiencySyrux2017-04-132-35/+115
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Improve PrefixSpan pre-processing efficency by preventing sequences of zero in the cleaned database. The efficiency gain is reflected in the following graph : https://postimg.org/image/9x6ireuvn/ ## How was this patch tested? Using MLlib's PrefixSpan existing tests and tests of my own on the 8 datasets shown in the graph. All result obtained were stricly the same as the original implementation (without this change). dev/run-tests was also runned, no error were found. Author : Cyril de Vogelaere <cyril.devogelaeregmail.com> Author: Syrux <pokcyril@hotmail.com> Closes #17575 from Syrux/SPARK-20265.
* [SPARK-18692][BUILD][DOCS] Test Java 8 unidoc build on Jenkinshyukjinkwon2017-04-1210-24/+36
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable. There are several problems with it: - It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?". - > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up. (see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627)) To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above. There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013 Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings. ## How was this patch tested? Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`. This was tested via manually adding `time.time()` as below: ```diff profiles_and_goals = build_profiles + sbt_goals print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ", " ".join(profiles_and_goals)) + import time + st = time.time() exec_sbt(profiles_and_goals) + print("Elapsed :[%s]" % str(time.time() - st)) ``` produces ``` ... ======================================================================== Building Unidoc API Documentation ======================================================================== ... [info] Main Java API documentation successful. ... Elapsed :[94.8746569157] ... Author: hyukjinkwon <gurwls223@gmail.com> Closes #17477 from HyukjinKwon/SPARK-18692.
* [SPARK-20097][ML] Fix visibility discrepancy with numInstances and ↵Benjamin Fradet2017-04-112-2/+4
| | | | | | | | | | | | | | | | | degreesOfFreedom in LR and GLR ## What changes were proposed in this pull request? - made `numInstances` public in GLR - made `degreesOfFreedom` public in LR ## How was this patch tested? reran the concerned test suites Author: Benjamin Fradet <benjamin.fradet@gmail.com> Closes #17431 from BenFradet/SPARK-20097.
* [SPARK-20156][CORE][SQL][STREAMING][MLLIB] Java String toLowerCase "Turkish ↵Sean Owen2017-04-108-42/+69
| | | | | | | | | | | | | | | | | | locale bug" causes Spark problems ## What changes were proposed in this pull request? Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem"). The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods. ## How was this patch tested? Existing tests. Author: Sean Owen <sowen@cloudera.com> Closes #17527 from srowen/SPARK-20156.
* [SPARK-20260][MLLIB] String interpolation required for error messageVijay Ramesh2017-04-094-5/+5
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This error message doesn't get properly formatted because of a missing `s`. Currently the error looks like: ``` Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line" ``` (note the literal `$current` instead of the interpolated value) Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Vijay Ramesh <vramesh@demandbase.com> Closes #17572 from vijaykramesh/master.
* [SPARK-20076][ML][PYSPARK] Add Python interface for ml.stats.CorrelationLiang-Chi Hsieh2017-04-071-4/+4
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The Dataframes-based support for the correlation statistics is added in #17108. This patch adds the Python interface for it. ## How was this patch tested? Python unit test. Please review http://spark.apache.org/contributing.html before opening a pull request. Author: Liang-Chi Hsieh <viirya@gmail.com> Closes #17494 from viirya/correlation-python-api.
* [SPARK-19953][ML] Random Forest Models use parent UID when being fitBryan Cutler2017-04-0641-100/+98
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? The ML `RandomForestClassificationModel` and `RandomForestRegressionModel` were not using the estimator parent UID when being fit. This change fixes that so the models can be properly be identified with their parents. ## How was this patch tested?Existing tests. Added check to verify that model uid matches that of the parent, then renamed `checkCopy` to `checkCopyAndUids` and verified that it was called by one test for each ML algorithm. Author: Bryan Cutler <cutlerb@gmail.com> Closes #17296 from BryanCutler/rfmodels-use-parent-uid-SPARK-19953.
* [SPARK-20003][ML] FPGrowthModel setMinConfidence should affect rules ↵Yuhao Yang2017-04-042-21/+56
| | | | | | | | | | | | | | | | | | | | generation and transform ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-20003 I was doing some test and found the issue. ml.fpm.FPGrowthModel `setMinConfidence` should always affect rules generation and transform. Currently associationRules in FPGrowthModel is a lazy val and `setMinConfidence` in FPGrowthModel has no impact once associationRules got computed . I try to cache the associationRules to avoid re-computation if `minConfidence` is not changed, but this makes FPGrowthModel somehow stateful. Let me know if there's any concern. ## How was this patch tested? new unit test and I strength the unit test for model save/load to ensure the cache mechanism. Author: Yuhao Yang <yuhao.yang@intel.com> Closes #17336 from hhbyyh/fpmodelminconf.
* [SPARK-20183][ML] Added outlierRatio arg to ↵Seth Hendrickson2017-04-045-6/+8
| | | | | | | | | | | | | | | | | | MLTestingUtils.testOutliersWithSmallWeights ## What changes were proposed in this pull request? This is a small piece from https://github.com/apache/spark/pull/16722 which ultimately will add sample weights to decision trees. This is to allow more flexibility in testing outliers since linear models and trees behave differently. Note: The primary author when this is committed should be sethah since this is taken from his code. ## How was this patch tested? Existing tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #17501 from jkbradley/SPARK-20183.
* [SPARK-19825][R][ML] spark.ml R API for FPGrowthzero3232017-04-032-0/+88
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Adds SparkR API for FPGrowth: [SPARK-19825](https://issues.apache.org/jira/browse/SPARK-19825): - `spark.fpGrowth` -model training. - `freqItemsets` and `associationRules` methods with new corresponding generics. - Scala helper: `org.apache.spark.ml.r. FPGrowthWrapper` - unit tests. ## How was this patch tested? Feature specific unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #17170 from zero323/SPARK-19825.
* [SPARK-19969][ML] Imputer doc and exampleYuhao Yang2017-04-031-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add docs and examples for spark.ml.feature.Imputer. Currently scala and Java examples are included. Python example will be added after https://github.com/apache/spark/pull/17316 ## How was this patch tested? local doc generation and example execution Author: Yuhao Yang <yuhao.yang@intel.com> Closes #17324 from hhbyyh/imputerdoc.
* [SPARK-19985][ML] Fixed copy method for some ML ModelsBryan Cutler2017-04-038-8/+30
| | | | | | | | | | | | ## What changes were proposed in this pull request? Some ML Models were using `defaultCopy` which expects a default constructor, and others were not setting the parent estimator. This change fixes these by creating a new instance of the model and explicitly setting values and parent. ## How was this patch tested? Added `MLTestingUtils.checkCopy` to the offending models to tests to verify the copy is made and parent is set. Author: Bryan Cutler <cutlerb@gmail.com> Closes #17326 from BryanCutler/ml-model-copy-error-SPARK-19985.
* [DOCS] Docs-only improvementsJacek Laskowski2017-03-301-1/+1
| | | | | | | | | | | | | | | | …adoc ## What changes were proposed in this pull request? Use recommended values for row boundaries in Window's scaladoc, i.e. `Window.unboundedPreceding`, `Window.unboundedFollowing`, and `Window.currentRow` (that were introduced in 2.1.0). ## How was this patch tested? Local build Author: Jacek Laskowski <jacek@japila.pl> Closes #17417 from jaceklaskowski/window-expression-scaladoc.
* [SPARK-20040][ML][PYTHON] pyspark wrapper for ChiSquareTestBago Amirbekian2017-03-281-3/+3
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? A pyspark wrapper for spark.ml.stat.ChiSquareTest ## How was this patch tested? unit tests doctests Author: Bago Amirbekian <bago@databricks.com> Closes #17421 from MrBago/chiSquareTestWrapper.
* [SPARK-20043][ML] DecisionTreeModel: ImpurityCalculator builder fails for ↵颜发才（Yan Facai）2017-03-282-1/+15
| | | | | | | | | | | | | | uppercase impurity type Gini Fix bug: DecisionTreeModel can't recongnize Impurity "Gini" when loading TODO: + [x] add unit test + [x] fix the bug Author: 颜发才（Yan Facai） <facai.yan@gmail.com> Closes #17407 from facaiy/BUG/decision_tree_loader_failer_with_Gini_impurity.
* [SPARK-17137][ML][WIP] Compress logistic regression coefficientssethah2017-03-252-37/+49
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Use the new `compressed` method on matrices to store the logistic regression coefficients as sparse or dense - whichever is requires less memory. Marked as WIP so we can add some performance test results. Basically, we should see if prediction is slower because of using a sparse matrix over a dense one. This can happen since sparse matrices do not use native BLAS operations when computing the margins. ## How was this patch tested? Unit tests added. Author: sethah <seth.hendrickson16@gmail.com> Closes #17426 from sethah/SPARK-17137.
* [SPARK-15040][ML][PYSPARK] Add Imputer to PySparkNick Pentreath2017-03-241-5/+5
| | | | | | | | | | | | Add Python wrapper for `Imputer` feature transformer. ## How was this patch tested? New doc tests and tweak to PySpark ML `tests.py` Author: Nick Pentreath <nickp@za.ibm.com> Closes #17316 from MLnick/SPARK-15040-pyspark-imputer.
* [SPARK-19636][ML] Feature parity for correlation statistics in MLlibTimothy Hunter2017-03-232-0/+163
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds the Dataframes-based support for the correlation statistics found in the `org.apache.spark.mllib.stat.correlation.Statistics`, following the design doc discussed in the JIRA ticket. The current implementation is a simple wrapper around the `spark.mllib` implementation. Future optimizations can be implemented at a later stage. ## How was this patch tested? ``` build/sbt "testOnly org.apache.spark.ml.stat.StatisticsSuite" ``` Author: Timothy Hunter <timhunter@databricks.com> Closes #17108 from thunterdb/19636.
* [MINOR][BUILD] Fix javadoc8 breakhyukjinkwon2017-03-231-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several javadoc8 breaks have been introduced. This PR proposes fix those instances so that we can build Scala/Java API docs. ``` [error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:6: error: reference not found [error] * <code>flatMapGroupsWithState</code> operations on {link KeyValueGroupedDataset}. [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:10: error: reference not found [error] * Both, <code>mapGroupsWithState</code> and <code>flatMapGroupsWithState</code> in {link KeyValueGroupedDataset} [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:51: error: reference not found [error] * {link GroupStateTimeout.ProcessingTimeTimeout}) or event time (i.e. [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:52: error: reference not found [error] * {link GroupStateTimeout.EventTimeTimeout}). [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/GroupState.java:158: error: reference not found [error] * Spark SQL types (see {link Encoder} for more details). [error] ^ [error] .../spark/mllib/target/java/org/apache/spark/ml/fpm/FPGrowthParams.java:26: error: bad use of '>' [error] * Number of partitions (>=1) used by parallel FP-growth. By default the param is not set, and [error] ^ [error] .../spark/sql/core/src/main/java/org/apache/spark/api/java/function/FlatMapGroupsWithStateFunction.java:30: error: reference not found [error] * {link org.apache.spark.sql.KeyValueGroupedDataset#flatMapGroupsWithState( [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:211: error: reference not found [error] * See {link GroupState} for more details. [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:232: error: reference not found [error] * See {link GroupState} for more details. [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:254: error: reference not found [error] * See {link GroupState} for more details. [error] ^ [error] .../spark/sql/core/target/java/org/apache/spark/sql/KeyValueGroupedDataset.java:277: error: reference not found [error] * See {link GroupState} for more details. [error] ^ [error] .../spark/core/target/java/org/apache/spark/TaskContextImpl.java:10: error: reference not found [error] * {link TaskMetrics} &amp; {link MetricsSystem} objects are not thread safe. [error] ^ [error] .../spark/core/target/java/org/apache/spark/TaskContextImpl.java:10: error: reference not found [error] * {link TaskMetrics} &amp; {link MetricsSystem} objects are not thread safe. [error] ^ [info] 13 errors ``` ``` jekyll 3.3.1 | Error: Unidoc generation failed ``` ## How was this patch tested? Manually via `jekyll build` Author: hyukjinkwon <gurwls223@gmail.com> Closes #17389 from HyukjinKwon/minor-javadoc8-fix.
* [SPARK-20039][ML] rename ChiSquare to ChiSquareTestJoseph K. Bradley2017-03-212-6/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? I realized that since ChiSquare is in the package stat, it's pretty unclear if it's the hypothesis test, distribution, or what. This PR renames it to ChiSquareTest to clarify this. ## How was this patch tested? Existing unit tests Author: Joseph K. Bradley <joseph@databricks.com> Closes #17368 from jkbradley/SPARK-20039.
* [SPARK-20041][DOC] Update docs for NaN handling in approxQuantileZheng RuiFeng2017-03-211-2/+2
| | | | | | | | | | | | ## What changes were proposed in this pull request? Update docs for NaN handling in approxQuantile. ## How was this patch tested? existing tests. Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #17369 from zhengruifeng/doc_quantiles_nan.
* [SPARK-20011][ML][DOCS] Clarify documentation for ALS 'rank' parameterchristopher snow2017-03-211-8/+8
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? API documentation and collaborative filtering documentation page changes to clarify inconsistent description of ALS rank parameter. - [DOCS] was previously: "rank is the number of latent factors in the model." - [API] was previously: "rank - number of features to use" This change describes rank in both places consistently as: - "Number of features to use (also referred to as the number of latent factors)" Author: Chris Snow <chris.snowuk.ibm.com> Author: christopher snow <chsnow123@gmail.com> Closes #17345 from snowch/SPARK-20011.
* [SPARK-19899][ML] Replace featuresCol with itemsCol in ml.fpm.FPGrowthzero3232017-03-202-18/+31
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Replaces `featuresCol` `Param` with `itemsCol`. See [SPARK-19899](https://issues.apache.org/jira/browse/SPARK-19899). ## How was this patch tested? Manual tests. Existing unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #17321 from zero323/SPARK-19899.
* [SPARK-19635][ML] DataFrame-based API for chi square testJoseph K. Bradley2017-03-164-6/+192
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Wrapper taking and return a DataFrame ## How was this patch tested? Copied unit tests from RDD-based API Author: Joseph K. Bradley <joseph@databricks.com> Closes #17110 from jkbradley/df-hypotests.
* [SPARK-13568][ML] Create feature transformer to impute missing valuesYuhao Yang2017-03-162-0/+444
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-13568 It is quite common to encounter missing values in data sets. It would be useful to implement a Transformer that can impute missing data points, similar to e.g. Imputer in scikit-learn. Initially, options for imputation could include mean, median and most frequent, but we could add various other approaches, where possible existing DataFrame code can be used (e.g. for approximate quantiles etc). Currently this PR supports imputation for Double and Vector (null and NaN in Vector). ## How was this patch tested? new unit tests and manual test Author: Yuhao Yang <hhbyyh@gmail.com> Author: Yuhao Yang <yuhao.yang@intel.com> Author: Yuhao <yuhao.yang@intel.com> Closes #11601 from hhbyyh/imputer.
* [SPARK-11569][ML] Fix StringIndexer to handle null value properlyMenglong TAN2017-03-142-22/+77
| | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is to enhance StringIndexer with NULL values handling. Before the PR, StringIndexer will throw an exception when encounters NULL values. With this PR: - handleInvalid=error: Throw an exception as before - handleInvalid=skip: Skip null values as well as unseen labels - handleInvalid=keep: Give null values an additional index as well as unseen labels BTW, I noticed someone was trying to solve the same problem ( #9920 ) but seems getting no progress or response for a long time. Would you mind to give me a chance to solve it ? I'm eager to help. :-) ## How was this patch tested? new unit tests Author: Menglong TAN <tanmenglong@renrenche.com> Author: Menglong TAN <tanmenglong@gmail.com> Closes #17233 from crackcell/11569_StringIndexer_NULL.
* [SPARK-19940][ML][MINOR] FPGrowthModel.transform should skip duplicated itemszero3232017-03-142-2/+16
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This commit moved `distinct` in its intended place to avoid duplicated predictions and adds unit test covering the issue. ## How was this patch tested? Unit tests. Author: zero323 <zero323@users.noreply.github.com> Closes #17283 from zero323/SPARK-19940.
* [SPARK-19922][ML] small speedups to findSynonymsAsher Krim2017-03-141-15/+19
| | | | | | | | | | | | | | | | | | | | | | | | | Currently generating synonyms using a large model (I've tested with 3m words) is very slow. These efficiencies have sped things up for us by ~17% I wasn't sure if such small changes were worthy of a jira, but the guidelines seemed to suggest that that is the preferred approach ## What changes were proposed in this pull request? Address a few small issues in the findSynonyms logic: 1) remove usage of ``Array.fill`` to zero out the ``cosineVec`` array. The default float value in Scala and Java is 0.0f, so explicitly setting the values to zero is not needed 2) use Floats throughout. The conversion to Doubles before doing the ``priorityQueue`` is totally superfluous, since all the similarity computations are done using Floats anyway. Creating a second large array just serves to put extra strain on the GC 3) convert the slow ``for(i <- cosVec.indices)`` to an ugly, but faster, ``while`` loop These efficiencies are really only apparent when working with a large model ## How was this patch tested? Existing unit tests + some in-house tests to time the difference cc jkbradley MLNick srowen Author: Asher Krim <krim.asher@gmail.com> Author: Asher Krim <krim.asher@gmail> Closes #17263 from Krimit/fasterFindSynonyms.
* [SPARK-19391][SPARKR][ML] Tweedie GLM API for SparkRactuaryzhang2017-03-141-4/+15
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Port Tweedie GLM #16344 to SparkR felixcheung yanboliang ## How was this patch tested? new test in SparkR Author: actuaryzhang <actuaryzhang10@gmail.com> Closes #16729 from actuaryzhang/sparkRTweedie.
* [MINOR][ML] Improve MLWriter overwrite error messageJoseph K. Bradley2017-03-131-2/+3
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Give proper syntax for Java and Python in addition to Scala. ## How was this patch tested? Manually. Author: Joseph K. Bradley <joseph@databricks.com> Closes #17215 from jkbradley/write-err-msg.
* [SPARK-19282][ML][SPARKR] RandomForest Wrapper and GBT Wrapper return param ↵Xin Ren2017-03-124-0/+4
| | | | | | | | | | | | | | | | | | | | | | "maxDepth" to R models ## What changes were proposed in this pull request? RandomForest R Wrapper and GBT R Wrapper return param `maxDepth` to R models. Below 4 R wrappers are changed: * `RandomForestClassificationWrapper` * `RandomForestRegressionWrapper` * `GBTClassificationWrapper` * `GBTRegressionWrapper` ## How was this patch tested? Test manually on my local machine. Author: Xin Ren <iamshrek@126.com> Closes #17207 from keypointt/SPARK-19282.
* [SPARK-16440][MLLIB] Ensure broadcasted variables are destroyed even in case ↵Anthony Truchet2017-03-081-3/+15
| | | | | | | | | | | | | | | of exception ## What changes were proposed in this pull request? Ensure broadcasted variable are destroyed even in case of exception ## How was this patch tested? Word2VecSuite was run locally Author: Anthony Truchet <a.truchet@criteo.com> Closes #14299 from AnthonyTruchet/SPARK-16440.
* [SPARK-19806][ML][PYSPARK] PySpark GeneralizedLinearRegression supports ↵Yanbo Liang2017-03-081-4/+4
| | | | | | | | | | | | | | tweedie distribution. ## What changes were proposed in this pull request? PySpark ```GeneralizedLinearRegression``` supports tweedie distribution. ## How was this patch tested? Add unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17146 from yanboliang/spark-19806.
* [ML][MINOR] Separate estimator and model params for read/write test.Yanbo Liang2017-03-0823-54/+59
| | | | | | | | | | | | ## What changes were proposed in this pull request? Since we allow ```Estimator``` and ```Model``` not always share same params (see ```ALSParams``` and ```ALSModelParams```), we should pass in test params for estimator and model separately in function ```testEstimatorAndModelReadWrite```. ## How was this patch tested? Existing tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #17151 from yanboliang/test-rw.
* [SPARK-17629][ML] methods to return synonyms directlyAsher Krim2017-03-072-12/+45
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? provide methods to return synonyms directly, without wrapping them in a dataframe In performance sensitive applications (such as user facing apis) the roundtrip to and from dataframes is costly and unnecessary The methods are named ``findSynonymsArray`` to make the return type clear, which also implies a local datastructure ## How was this patch tested? updated word2vec tests Author: Asher Krim <akrim@hubspot.com> Closes #16811 from Krimit/w2vFindSynonymsLocal.
* [SPARK-17498][ML] StringIndexer enhancement for handling unseen labelsVinceShieh2017-03-072-28/+71
| | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR is an enhancement to ML StringIndexer. Before this PR, String Indexer only supports "skip"/"error" options to deal with unseen records. But those unseen records might still be useful and user would like to keep the unseen labels in certain use cases, This PR enables StringIndexer to support keeping unseen labels as indices [numLabels]. '''Before StringIndexer().setHandleInvalid("skip") StringIndexer().setHandleInvalid("error") '''After support the third option "keep" StringIndexer().setHandleInvalid("keep") ## How was this patch tested? Test added in StringIndexerSuite Signed-off-by: VinceShieh <vincent.xieintel.com> (Please fill in changes proposed in this fix) Author: VinceShieh <vincent.xie@intel.com> Closes #16883 from VinceShieh/spark-17498.
* [SPARK-19382][ML] Test sparse vectors in LinearSVCSuitewm624@hotmail.com2017-03-061-2/+22
| | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add unit tests for testing SparseVector. We can't add mixed DenseVector and SparseVector test case, as discussed in JIRA 19382. def merge(other: MultivariateOnlineSummarizer): this.type = { if (this.totalWeightSum != 0.0 && other.totalWeightSum != 0.0) { require(n == other.n, s"Dimensions mismatch when merging with another summarizer. " + s"Expecting $n but got $ {other.n} .") ## How was this patch tested? Unit tests Author: wm624@hotmail.com <wm624@hotmail.com> Author: Miao Wang <wangmiao1981@users.noreply.github.com> Closes #16784 from wangmiao1981/bk.
* [SPARK-19535][ML] RecommendForAllUsers RecommendForAllItems for ALS on DataframeSue Ann Hong2017-03-054-9/+297
| | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This is a simple implementation of RecommendForAllUsers & RecommendForAllItems for the Dataframe version of ALS. It uses Dataframe operations (not a wrapper on the RDD implementation). Haven't benchmarked against a wrapper, but unit test examples do work. ## How was this patch tested? Unit tests ``` $ build/sbt > mllib/testOnly *ALSSuite -- -z "recommendFor" > mllib/testOnly ``` Author: Your Name <you@example.com> Author: sueann <sueann@databricks.com> Closes #17090 from sueann/SPARK-19535.
* [SPARK-19745][ML] SVCAggregator captures coefficients in its closuresethah2017-03-026-24/+34
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? JIRA: [SPARK-19745](https://issues.apache.org/jira/browse/SPARK-19745) Reorganize SVCAggregator to avoid serializing coefficients. This patch also makes the gradient array a `lazy val` which will avoid materializing a large array on the driver before shipping the class to the executors. This improvement stems from https://github.com/apache/spark/pull/16037. Actually, probably all ML aggregators can benefit from this. We can either: a.) separate the gradient improvement into another patch b.) keep what's here _plus_ add the lazy evaluation to all other aggregators in this patch or c.) keep it as is. ## How was this patch tested? This is an interesting question! I don't know of a reasonable way to test this right now. Ideally, we could perform an optimization and look at the shuffle write data for each task, and we could compare the size to what it we know it should be: `numCoefficients * 8 bytes`. Not sure if there is a good way to do that right now? We could discuss this here or in another JIRA, but I suspect it would be a significant undertaking. Author: sethah <seth.hendrickson16@gmail.com> Closes #17076 from sethah/svc_agg.
* [SPARK-19704][ML] AFTSurvivalRegression should support numeric censorColZheng RuiFeng2017-03-023-5/+37
| | | | | | | | | | | ## What changes were proposed in this pull request? make `AFTSurvivalRegression` support numeric censorCol ## How was this patch tested? existing tests and added tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #17034 from zhengruifeng/aft_numeric_censor.
* [SPARK-19733][ML] Removed unnecessary castings and refactored checked casts ↵Vasilis Vryniotis2017-03-022-20/+95
| | | | | | | | | | | | | | | | in ALS. ## What changes were proposed in this pull request? The original ALS was performing unnecessary casting to the user and item ids because the protected checkedCast() method required a double. I removed the castings and refactored the method to receive Any and efficiently handle all permitted numeric values. ## How was this patch tested? I tested it by running the unit-tests and by manually validating the result of checkedCast for various legal and illegal values. Author: Vasilis Vryniotis <bbriniotis@datumbox.com> Closes #17059 from datumbox/als_casting_fix.
* [SPARK-19787][ML] Changing the default parameter of regParam.Vasilis Vryniotis2017-03-011-1/+1
| | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? In the ALS method the default values of regParam do not match within the same file (lines [224](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L224) and [714](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala#L714)). In one place we set it to 1.0 and in the other to 0.1. I changed the one of train() method to 0.1 and now it matches the default value which is visible to Spark users. The method is marked with DeveloperApi so it should not affect the users. Whenever we use the particular method we provide all parameters, so the default does not matter. Only exception is the unit-tests on ALSSuite but the change does not break them. Note: This PR should get the award of the laziest commit in Spark history. Originally I wanted to correct this on another PR but MLnick [suggested](https://github.com/apache/spark/pull/17059#issuecomment-283333572) to create a separate PR & ticket. If you think this change is too insignificant/minor, you are probably right, so feel free to reject and close this. :) ## How was this patch tested? Unit-tests Author: Vasilis Vryniotis <vvryniotis@hotels.com> Closes #17121 from datumbox/als_regparam.
* [SPARK-14503][ML] spark.ml API for FPGrowthYuhao2017-02-282-0/+469
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? jira: https://issues.apache.org/jira/browse/SPARK-14503 Function parity: Add FPGrowth and AssociationRules to ML. design doc: https://docs.google.com/document/d/1bVhABn5DiEj8bw0upqGMJT2L4nvO_0_cXdwu4uMT6uU/pub Currently I make FPGrowthModel a transformer. For each association rule, it will just examine the input items against antecedents and summarize the consequents. Update: Thinking again, FPGrowth is only the algorithm to find the frequent itemsets, and can be replaced by other algorithms. The frequent itemsets are used by AssociationRules to generate the association rules. Then we can use the association rules to predict with other records. ![drawing1](https://cloud.githubusercontent.com/assets/7981698/22489294/76b9302c-e7cb-11e6-8d2d-3fc53f407b2f.png) **For reviewers**, Let's first decide if the current `transform` function meets your expectation. Current options: 1. Current implementation: Use Estimator and Transformer pattern in ML, the `transform` function will examine the input items against all the association rules and summarize the consequents. Users can also access frequent items and association rules via other model members. 2. Keep the Estimator and Transformer pattern. But AssociationRulesModel and FPGrowthModel will have empty `transform` function, meaning DataFrame has no change after transform. But users can access frequent items and association rules via other model members. 3. (mentioned by zhengruifeng) Keep the Estimator and Transformer pattern. But `FPGrowthModel` and `AssociationRulesModel` will just return frequent itemsets and association rules DataFrame in the `transform` function. Meaning the resulting DataFrame after `transform` will not be related to the input DataFrame. 4. Discard the Estimator and Transformer pattern. Both FPGrowth and FPGrowthModel will directly extend from PipelineStage, thus we don't need to have a `transform` function. I'd like to hear more concrete suggestions. I would prefer option 1 or 2. update 2: As discussed in the jira, we will not expose AssociationRules as a public API for now. ## How was this patch tested? new unit test suites Author: Yuhao <yuhao.yang@intel.com> Author: Yuhao Yang <yuhao.yang@intel.com> Author: Yuhao Yang <hhbyyh@gmail.com> Closes #15415 from hhbyyh/mlfpm.
* [SPARK-14489][ML][PYSPARK] ALS unknown user/item prediction strategyNick Pentreath2017-02-282-4/+91
| | | | | | | | | | | | | | | This PR adds a param to `ALS`/`ALSModel` to set the strategy used when encountering unknown users or items at prediction time in `transform`. This can occur in 2 scenarios: (a) production scoring, and (b) cross-validation & evaluation. The current behavior returns `NaN` if a user/item is unknown. In scenario (b), this can easily occur when using `CrossValidator` or `TrainValidationSplit` since some users/items may only occur in the test set and not in the training set. In this case, the evaluator returns `NaN` for all metrics, making model selection impossible. The new param, `coldStartStrategy`, defaults to `nan` (the current behavior). The other option supported initially is `drop`, which drops all rows with `NaN` predictions. This flag allows users to use `ALS` in cross-validation settings. It is made an `expertParam`. The param is made a string so that the set of strategies can be extended in future (some options are discussed in [SPARK-14489](https://issues.apache.org/jira/browse/SPARK-14489)). ## How was this patch tested? New unit tests, and manual "before and after" tests for Scala & Python using MovieLens `ml-latest-small` as example data. Here, using `CrossValidator` or `TrainValidationSplit` with the default param setting results in metrics that are all `NaN`, while setting `coldStartStrategy` to `drop` results in valid metrics. Author: Nick Pentreath <nickp@za.ibm.com> Closes #12896 from MLnick/SPARK-14489-als-nan.
* [SPARK-19746][ML] Faster indexing for logistic aggregatorsethah2017-02-282-3/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? JIRA: [SPARK-19746](https://issues.apache.org/jira/browse/SPARK-19746) The following code is inefficient: ````scala val localCoefficients: Vector = bcCoefficients.value features.foreachActive { (index, value) => val stdValue = value / localFeaturesStd(index) var j = 0 while (j < numClasses) { margins(j) += localCoefficients(index * numClasses + j) * stdValue j += 1 } } ```` `localCoefficients(index * numClasses + j)` calls `Vector.apply` which creates a new Breeze vector and indexes that. Even if it is not that slow to create the object, we will generate a lot of extra garbage that may result in longer GC pauses. This is a hot inner loop, so we should optimize wherever possible. ## How was this patch tested? I don't think there's a great way to test this patch. It's purely performance related, so unit tests should guarantee that we haven't made any unwanted changes. Empirically I observed between 10-40% speedups just running short local tests. I suspect the big differences will be seen when large data/coefficient sizes have to pause for GC more often. I welcome other ideas for testing. Author: sethah <seth.hendrickson16@gmail.com> Closes #17078 from sethah/logistic_agg_indexing.
* [MINOR][BUILD] Fix lint-java breaks in Javahyukjinkwon2017-02-271-1/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR proposes to fix the lint-breaks as below: ``` [ERROR] src/test/java/org/apache/spark/network/TransportResponseHandlerSuite.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.network.buffer.ManagedBuffer. [ERROR] src/main/java/org/apache/spark/unsafe/types/UTF8String.java:[156,10] (modifier) ModifierOrder: 'Nonnull' annotation modifier does not precede non-annotation modifiers. [ERROR] src/main/java/org/apache/spark/SparkFirehoseListener.java:[122] (sizes) LineLength: Line is longer than 100 characters (found 105). [ERROR] src/main/java/org/apache/spark/util/collection/unsafe/sort/UnsafeExternalSorter.java:[164,78] (coding) OneStatementPerLine: Only one statement per line allowed. [ERROR] src/test/java/test/org/apache/spark/JavaAPISuite.java:[1157] (sizes) LineLength: Line is longer than 100 characters (found 121). [ERROR] src/test/java/org/apache/spark/streaming/JavaMapWithStateSuite.java:[149] (sizes) LineLength: Line is longer than 100 characters (found 113). [ERROR] src/test/java/test/org/apache/spark/streaming/Java8APISuite.java:[146] (sizes) LineLength: Line is longer than 100 characters (found 122). [ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[32,8] (imports) UnusedImports: Unused import - org.apache.spark.streaming.Time. [ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[611] (sizes) LineLength: Line is longer than 100 characters (found 101). [ERROR] src/test/java/test/org/apache/spark/streaming/JavaAPISuite.java:[1317] (sizes) LineLength: Line is longer than 100 characters (found 102). [ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetAggregatorSuite.java:[91] (sizes) LineLength: Line is longer than 100 characters (found 102). [ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[113] (sizes) LineLength: Line is longer than 100 characters (found 101). [ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[164] (sizes) LineLength: Line is longer than 100 characters (found 110). [ERROR] src/test/java/test/org/apache/spark/sql/JavaDatasetSuite.java:[212] (sizes) LineLength: Line is longer than 100 characters (found 114). [ERROR] src/test/java/org/apache/spark/mllib/tree/JavaDecisionTreeSuite.java:[36] (sizes) LineLength: Line is longer than 100 characters (found 101). [ERROR] src/main/java/org/apache/spark/examples/streaming/JavaKinesisWordCountASL.java:[26,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils. [ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[20,8] (imports) UnusedImports: Unused import - com.amazonaws.regions.RegionUtils. [ERROR] src/test/java/org/apache/spark/streaming/kinesis/JavaKinesisStreamSuite.java:[94] (sizes) LineLength: Line is longer than 100 characters (found 103). [ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[30,8] (imports) UnusedImports: Unused import - org.apache.spark.sql.api.java.UDF1. [ERROR] src/main/java/org/apache/spark/examples/ml/JavaTokenizerExample.java:[72] (sizes) LineLength: Line is longer than 100 characters (found 104). [ERROR] src/main/java/org/apache/spark/examples/mllib/JavaRankingMetricsExample.java:[121] (sizes) LineLength: Line is longer than 100 characters (found 101). [ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[28,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaRDD. [ERROR] src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java:[29,8] (imports) UnusedImports: Unused import - org.apache.spark.api.java.JavaSparkContext. ``` ## How was this patch tested? Manually via ```bash ./dev/lint-java ``` Author: hyukjinkwon <gurwls223@gmail.com> Closes #17072 from HyukjinKwon/java-lint.
* [MINOR][ML][DOC] Document default value for ↵Joseph K. Bradley2017-02-251-0/+2
| | | | | | | | | | | | GeneralizedLinearRegression.linkPower Add Scaladoc for GeneralizedLinearRegression.linkPower default value Follow-up to https://github.com/apache/spark/pull/16344 Author: Joseph K. Bradley <joseph@databricks.com> Closes #17069 from jkbradley/tweedie-comment.
* [SPARK-19616][SPARKR] weightCol and aggregationDepth should be improved for ↵wm624@hotmail.com2017-02-224-5/+15
| | | | | | | | | | | | | | | | | some SparkR APIs ## What changes were proposed in this pull request? This is a follow-up PR of #16800 When doing SPARK-19456, we found that "" should be consider a NULL column name and should not be set. aggregationDepth should be exposed as an expert parameter. ## How was this patch tested? Existing tests. Author: wm624@hotmail.com <wm624@hotmail.com> Closes #16945 from wangmiao1981/svc.
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