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* [SPARK-17744][ML] Parity check between the ml and mllib test suites for NBZheng RuiFeng2016-10-041-1/+0
| | | | | | | | | | | | | ## What changes were proposed in this pull request? 1,parity check and add missing test suites for ml's NB 2,remove some unused imports ## How was this patch tested? manual tests in spark-shell Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #15312 from zhengruifeng/nb_test_parity.
* [SPARK-17138][ML][MLIB] Add Python API for multinomial logistic regressionWeichenXu2016-09-271-20/+70
| | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add Python API for multinomial logistic regression. - add `family` param in python api. - expose `coefficientMatrix` and `interceptVector` for `LogisticRegressionModel` - add python-side testcase for multinomial logistic regression - update python doc. ## How was this patch tested? existing and added doc tests. Author: WeichenXu <WeichenXu123@outlook.com> Closes #14852 from WeichenXu123/add_MLOR_python.
* [MINOR][ML] Correct weights doc of MultilayerPerceptronClassificationModel.Yanbo Liang2016-09-061-1/+1
| | | | | | | | | | | | ## What changes were proposed in this pull request? ```weights``` of ```MultilayerPerceptronClassificationModel``` should be the output weights of layers rather than initial weights, this PR correct it. ## How was this patch tested? Doc change. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14967 from yanboliang/mlp-weights.
* [SPARK-17197][ML][PYSPARK] PySpark LiR/LoR supports tree aggregation level ↵Yanbo Liang2016-08-251-5/+9
| | | | | | | | | | | | | | | configurable. ## What changes were proposed in this pull request? [SPARK-17090](https://issues.apache.org/jira/browse/SPARK-17090) makes tree aggregation level in LiR/LoR configurable, this PR makes PySpark support this function. ## How was this patch tested? Since ```aggregationDepth``` is an expert param, I'm not prefer to test it in doctest which is also used for example. Here is the offline test result: ![image](https://cloud.githubusercontent.com/assets/1962026/17879457/f83d7760-68a6-11e6-9936-d0a884d5d6ec.png) Author: Yanbo Liang <ybliang8@gmail.com> Closes #14766 from yanboliang/spark-17197.
* [SPARK-15113][PYSPARK][ML] Add missing num features num classesHolden Karau2016-08-221-6/+31
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add missing `numFeatures` and `numClasses` to the wrapped Java models in PySpark ML pipelines. Also tag `DecisionTreeClassificationModel` as Expiremental to match Scala doc. ## How was this patch tested? Extended doctests Author: Holden Karau <holden@us.ibm.com> Closes #12889 from holdenk/SPARK-15113-add-missing-numFeatures-numClasses.
* [MINOR][ML] Rename TreeEnsembleModels to TreeEnsembleModel for PySparkYanbo Liang2016-08-111-3/+3
| | | | | | | | | | | | ## What changes were proposed in this pull request? Fix the typo of ```TreeEnsembleModels``` for PySpark, it should ```TreeEnsembleModel``` which will be consistent with Scala. What's more, it represents a tree ensemble model, so ```TreeEnsembleModel``` should be more reasonable. This should not be used public, so it will not involve breaking change. ## How was this patch tested? No new tests, should pass existing ones. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14454 from yanboliang/TreeEnsembleModel.
* [SPARK-16653][ML][OPTIMIZER] update ANN convergence tolerance param default ↵WeichenXu2016-07-251-4/+4
| | | | | | | | | | | | | | | | | | | to 1e-6 ## What changes were proposed in this pull request? replace ANN convergence tolerance param default from 1e-4 to 1e-6 so that it will be the same with other algorithms in MLLib which use LBFGS as optimizer. ## How was this patch tested? Existing Test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #14286 from WeichenXu123/update_ann_tol.
* [SPARK-16494][ML] Upgrade breeze version to 0.12Yanbo Liang2016-07-191-1/+1
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? breeze 0.12 has been released for more than half a year, and it brings lots of new features, performance improvement and bug fixes. One of the biggest features is ```LBFGS-B``` which is an implementation of ```LBFGS``` with box constraints and much faster for some special case. We would like to implement Huber loss function for ```LinearRegression``` ([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)) and it requires ```LBFGS-B``` as the optimization solver. So we should bump up the dependent breeze version to 0.12. For more features, improvements and bug fixes of breeze 0.12, you can refer the following link: https://groups.google.com/forum/#!topic/scala-breeze/nEeRi_DcY5c ## How was this patch tested? No new tests, should pass the existing ones. Author: Yanbo Liang <ybliang8@gmail.com> Closes #14150 from yanboliang/spark-16494.
* [SPARK-14812][ML][MLLIB][PYTHON] Experimental, DeveloperApi annotation audit ↵Joseph K. Bradley2016-07-131-20/+4
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | for ML ## What changes were proposed in this pull request? General decisions to follow, except where noted: * spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone. * spark.ml, pyspark.ml ** Annotate Estimator-Model pairs of classes and companion objects the same way. ** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation. ** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation. * DeveloperApi annotations are left alone, except where noted. * No changes to which types are sealed. Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new: * Model Summary classes * MLWriter, MLReader, MLWritable, MLReadable * Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency. * RFormula: Its behavior may need to change slightly to match R in edge cases. * AFTSurvivalRegression * MultilayerPerceptronClassifier DeveloperApi changes: * ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi ## How was this patch tested? N/A Note to reviewers: * spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental. * Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify. Author: Joseph K. Bradley <joseph@databricks.com> Closes #14147 from jkbradley/experimental-audit.
* [SPARK-16127][ML][PYPSARK] Audit @Since annotations related to ml.linalgNick Pentreath2016-06-221-4/+4
| | | | | | | | | | | | [SPARK-14615](https://issues.apache.org/jira/browse/SPARK-14615) and #12627 changed `spark.ml` pipelines to use the new `ml.linalg` classes for `Vector`/`Matrix`. Some `Since` annotations for public methods/vals have not been updated accordingly to be `2.0.0`. This PR updates them. ## How was this patch tested? Existing unit tests. Author: Nick Pentreath <nickp@za.ibm.com> Closes #13840 from MLnick/SPARK-16127-ml-linalg-since.
* [SPARK-15162][SPARK-15164][PYSPARK][DOCS][ML] update some pydocsHolden Karau2016-06-221-2/+36
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Mark ml.classification algorithms as experimental to match Scala algorithms, update PyDoc for for thresholds on `LogisticRegression` to have same level of info as Scala, and enable mathjax for PyDoc. ## How was this patch tested? Built docs locally & PySpark SQL tests Author: Holden Karau <holden@us.ibm.com> Closes #12938 from holdenk/SPARK-15162-SPARK-15164-update-some-pydocs.
* [SPARK-15741][PYSPARK][ML] Pyspark cleanup of set default seed to NoneBryan Cutler2016-06-211-2/+2
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Several places set the seed Param default value to None which will translate to a zero value on the Scala side. This is unnecessary because a default fixed value already exists and if a test depends on a zero valued seed, then it should explicitly set it to zero instead of relying on this translation. These cases can be safely removed except for the ALS doc test, which has been changed to set the seed value to zero. ## How was this patch tested? Ran PySpark tests locally Author: Bryan Cutler <cutlerb@gmail.com> Closes #13672 from BryanCutler/pyspark-cleanup-setDefault-seed-SPARK-15741.
* [SPARK-16079][PYSPARK][ML] Added missing import for ↵Bryan Cutler2016-06-201-2/+4
| | | | | | | | | | | | | | | | DecisionTreeRegressionModel used in GBTClassificationModel ## What changes were proposed in this pull request? Fixed missing import for DecisionTreeRegressionModel used in GBTClassificationModel trees method. ## How was this patch tested? Local tests Author: Bryan Cutler <cutlerb@gmail.com> Closes #13787 from BryanCutler/pyspark-GBTClassificationModel-import-SPARK-16079.
* [SPARK-15364][ML][PYSPARK] Implement PySpark picklers for ml.Vector and ↵Liang-Chi Hsieh2016-06-131-1/+1
| | | | | | | | | | | | | | | ml.Matrix under spark.ml.python ## What changes were proposed in this pull request? Now we have PySpark picklers for new and old vector/matrix, individually. However, they are all implemented under `PythonMLlibAPI`. To separate spark.mllib from spark.ml, we should implement the picklers of new vector/matrix under `spark.ml.python` instead. ## How was this patch tested? Existing tests. Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Closes #13219 from viirya/pyspark-pickler-ml.
* [MINOR] Fix Typos 'an -> a'Zheng RuiFeng2016-06-061-2/+2
| | | | | | | | | | | | | | | ## What changes were proposed in this pull request? `an -> a` Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one. ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #13515 from zhengruifeng/an_a.
* [SPARK-15168][PYSPARK][ML] Add missing params to MultilayerPerceptronClassifierHolden Karau2016-06-031-9/+66
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? MultilayerPerceptronClassifier is missing step size, solver, and weights. Add these params. Also clarify the scaladoc a bit while we are updating these params. Eventually we should follow up and unify the HasSolver params (filed https://issues.apache.org/jira/browse/SPARK-15169 ) ## How was this patch tested? Doc tests Author: Holden Karau <holden@us.ibm.com> Closes #12943 from holdenk/SPARK-15168-add-missing-params-to-MultilayerPerceptronClassifier.
* [SPARK-15092][SPARK-15139][PYSPARK][ML] Pyspark TreeEnsemble missing methodsHolden Karau2016-06-021-0/+20
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add `toDebugString` and `totalNumNodes` to `TreeEnsembleModels` and add `toDebugString` to `DecisionTreeModel` ## How was this patch tested? Extended doc tests. Author: Holden Karau <holden@us.ibm.com> Closes #12919 from holdenk/SPARK-15139-pyspark-treeEnsemble-missing-methods.
* [SPARK-15464][ML][MLLIB][SQL][TESTS] Replace SQLContext and SparkContext ↵WeichenXu2016-05-231-18/+20
| | | | | | | | | | | | | | | | with SparkSession using builder pattern in python test code ## What changes were proposed in this pull request? Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code. ## How was this patch tested? Existing test. Author: WeichenXu <WeichenXu123@outlook.com> Closes #13242 from WeichenXu123/python_doctest_update_sparksession.
* [SPARK-14615][ML] Use the new ML Vector and Matrix in the ML pipeline based ↵DB Tsai2016-05-171-7/+7
| | | | | | | | | | | | | | | | | | algorithms ## What changes were proposed in this pull request? Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis. ## How was this patch tested? Unit tests Author: DB Tsai <dbt@netflix.com> Author: Liang-Chi Hsieh <simonh@tw.ibm.com> Author: Xiangrui Meng <meng@databricks.com> Closes #12627 from dbtsai/SPARK-14615-NewML.
* [SPARK-15188] Add missing thresholds param to NaiveBayes in PySparkHolden Karau2016-05-131-5/+10
| | | | | | | | | | | | | ## What changes were proposed in this pull request? Add missing thresholds param to NiaveBayes ## How was this patch tested? doctests Author: Holden Karau <holden@us.ibm.com> Closes #12963 from holdenk/SPARK-15188-add-missing-naive-bayes-param.
* [SPARK-15136][PYSPARK][DOC] Fix links to sphinx style and add a default ↵Holden Karau2016-05-091-8/+20
| | | | | | | | | | | | | | | | param doc note ## What changes were proposed in this pull request? PyDoc links in ml are in non-standard format. Switch to standard sphinx link format for better formatted documentation. Also add a note about default value in one place. Copy some extended docs from scala for GBT ## How was this patch tested? Built docs locally. Author: Holden Karau <holden@us.ibm.com> Closes #12918 from holdenk/SPARK-15137-linkify-pyspark-ml-classification.
* [SPARK-14971][ML][PYSPARK] PySpark ML Params setter code clean upYanbo Liang2016-05-031-14/+7
| | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? PySpark ML Params setter code clean up. For examples, ```setInputCol``` can be simplified from ``` self._set(inputCol=value) return self ``` to: ``` return self._set(inputCol=value) ``` This is a pretty big sweeps, and we cleaned wherever possible. ## How was this patch tested? Exist unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #12749 from yanboliang/spark-14971.
* [SPARK-14931][ML][PYTHON] Mismatched default values between pipelines in ↵Xusen Yin2016-05-011-8/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Spark and PySpark - update ## What changes were proposed in this pull request? This PR is an update for [https://github.com/apache/spark/pull/12738] which: * Adds a generic unit test for JavaParams wrappers in pyspark.ml for checking default Param values vs. the defaults in the Scala side * Various fixes for bugs found * This includes changing classes taking weightCol to treat unset and empty String Param values the same way. Defaults changed: * Scala * LogisticRegression: weightCol defaults to not set (instead of empty string) * StringIndexer: labels default to not set (instead of empty array) * GeneralizedLinearRegression: * maxIter always defaults to 25 (simpler than defaulting to 25 for a particular solver) * weightCol defaults to not set (instead of empty string) * LinearRegression: weightCol defaults to not set (instead of empty string) * Python * MultilayerPerceptron: layers default to not set (instead of [1,1]) * ChiSqSelector: numTopFeatures defaults to 50 (instead of not set) ## How was this patch tested? Generic unit test. Manually tested that unit test by changing defaults and verifying that broke the test. Author: Joseph K. Bradley <joseph@databricks.com> Author: yinxusen <yinxusen@gmail.com> Closes #12816 from jkbradley/yinxusen-SPARK-14931.
* [SPARK-14952][CORE][ML] Remove methods that were deprecated in 1.6.0Herman van Hovell2016-04-301-10/+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-14555] First cut of Python API for Structured StreamingBurak Yavuz2016-04-201-0/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## 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-14306][ML][PYSPARK] PySpark ml.classification OneVsRest support ↵Xusen Yin2016-04-181-22/+120
| | | | | | | | | | | | | | | | | | 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-7861][ML] PySpark OneVsRestXusen Yin2016-04-151-6/+218
| | | | | | | | | | | | | | | | ## 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` methodsethah2016-04-151-12/+10
| | | | | | | | | | | | | | | | | | ## 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-14374][ML][PYSPARK] PySpark ml GBTClassifier, Regressor support ↵Yanbo Liang2016-04-141-2/+15
| | | | | | | | | | | | | | | | 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-14472][PYSPARK][ML] Cleanup ML JavaWrapper and related class hierarchyBryan Cutler2016-04-131-2/+2
| | | | | | | | | | 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-14498][ML][PYTHON][SQL] Many cleanups to ML and ML-related docsJoseph K. Bradley2016-04-081-1/+1
| | | | | | | | | | | | | | | | | | | | | | ## 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-14373][PYSPARK] PySpark RandomForestClassifier, Regressor support ↵Kai Jiang2016-04-081-2/+13
| | | | | | | | | | | | | | export/import ## What changes were proposed in this pull request? supporting `RandomForest{Classifier, Regressor}` save/load for Python API. [JIRA](https://issues.apache.org/jira/browse/SPARK-14373) ## How was this patch tested? doctest Author: Kai Jiang <jiangkai@gmail.com> Closes #12238 from vectorijk/spark-14373.
* [SPARK-13430][PYSPARK][ML] Python API for training summaries of linear and ↵Bryan Cutler2016-04-061-1/+217
| | | | | | | | | | | | | | | logistic regression ## What changes were proposed in this pull request? Adding Python API for training summaries of LogisticRegression and LinearRegression in PySpark ML. ## How was this patch tested? Added unit tests to exercise the api calls for the summary classes. Also, manually verified values are expected and match those from Scala directly. Author: Bryan Cutler <cutlerb@gmail.com> Closes #11621 from BryanCutler/pyspark-ml-summary-SPARK-13430.
* [SPARK-11262][ML] Unit test for gradient, loss layers, memory management for ↵Alexander Ulanov2016-03-311-1/+1
| | | | | | | | | | | | | | | | | | | | | multilayer perceptron 1.Implement LossFunction trait and implement squared error and cross entropy loss with it 2.Implement unit test for gradient and loss 3.Implement InPlace trait and in-place layer evaluation 4.Refactor interface for ActivationFunction 5.Update of Layer and LayerModel interfaces 6.Fix random weights assignment 7.Implement memory allocation by MLP model instead of individual layers These features decreased the memory usage and increased flexibility of internal API. Author: Alexander Ulanov <nashb@yandex.ru> Author: avulanov <avulanov@gmail.com> Closes #9229 from avulanov/mlp-refactoring.
* [SPARK-14264][PYSPARK][ML] Add feature importance for GBTs in pysparksethah2016-03-311-10/+23
| | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Feature importances are exposed in the python API for GBTs. Other changes: * Update the random forest feature importance documentation to not repeat decision tree docstring and instead place a reference to it. ## How was this patch tested? Python doc tests were updated to validate GBT feature importance. Author: sethah <seth.hendrickson16@gmail.com> Closes #12056 from sethah/Pyspark_GBT_feature_importance.
* [SPARK-14152][ML][PYSPARK] MultilayerPerceptronClassifier supports save/load ↵Yanbo Liang2016-03-301-2/+14
| | | | | | | | | | | | | | | | for Python API ## What changes were proposed in this pull request? ```MultilayerPerceptronClassifier``` supports save/load for Python API. ## How was this patch tested? doctest. cc mengxr jkbradley yinxusen Author: Yanbo Liang <ybliang8@gmail.com> Closes #11952 from yanboliang/spark-14152.
* [SPARK-13949][ML][PYTHON] PySpark ml DecisionTreeClassifier, Regressor ↵GayathriMurali2016-03-241-2/+14
| | | | | | | | | | | | | | | | support export/import ## What changes were proposed in this pull request? Added MLReadable and MLWritable to Decision Tree Classifier and Regressor. Added doctests. ## How was this patch tested? Python Unit tests. Tests added to check persistence in DecisionTreeClassifier and DecisionTreeRegressor. Author: GayathriMurali <gayathri.m.softie@gmail.com> Closes #11892 from GayathriMurali/SPARK-13949.
* [SPARK-14107][PYSPARK][ML] Add seed as named argument to GBTs in pysparksethah2016-03-241-6/+6
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? GBTs in pyspark previously had seed parameters, but they could not be passed as keyword arguments through the class constructor. This patch adds seed as a keyword argument and also sets default value. ## How was this patch tested? Doc tests were updated to pass a random seed through the GBTClassifier and GBTRegressor constructors. Author: sethah <seth.hendrickson16@gmail.com> Closes #11944 from sethah/SPARK-14107.
* [SPARK-13068][PYSPARK][ML] Type conversion for Pyspark paramssethah2016-03-231-7/+13
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This patch adds type conversion functionality for parameters in Pyspark. A `typeConverter` field is added to the constructor of `Param` class. This argument is a function which converts values passed to this param to the appropriate type if possible. This is beneficial so that the params can fail at set time if they are given inappropriate values, but even more so because coherent error messages are now provided when Py4J cannot cast the python type to the appropriate Java type. This patch also adds a `TypeConverters` class with factory methods for common type conversions. Most of the changes involve adding these factory type converters to existing params. The previous solution to this issue, `expectedType`, is deprecated and can be removed in 2.1.0 as discussed on the Jira. ## How was this patch tested? Unit tests were added in python/pyspark/ml/tests.py to test parameter type conversion. These tests check that values that should be convertible are converted correctly, and that the appropriate errors are thrown when invalid values are provided. Author: sethah <seth.hendrickson16@gmail.com> Closes #11663 from sethah/SPARK-13068-tc.
* [SPARK-13951][ML][PYTHON] Nested Pipeline persistenceJoseph K. Bradley2016-03-221-4/+4
| | | | | | | | | | | | | | | Adds support for saving and loading nested ML Pipelines from Python. Pipeline and PipelineModel do not extend JavaWrapper, but they are able to utilize the JavaMLWriter, JavaMLReader implementations. Also: * Separates out interfaces from Java wrapper implementations for MLWritable, MLReadable, MLWriter, MLReader. * Moves methods _stages_java2py, _stages_py2java into Pipeline, PipelineModel as _transfer_stage_from_java, _transfer_stage_to_java Added new unit test for nested Pipelines. Abstracted validity check into a helper method for the 2 unit tests. Author: Joseph K. Bradley <joseph@databricks.com> Closes #11866 from jkbradley/nested-pipeline-io. Closes #11835
* [SPARK-13034] PySpark ml.classification support export/importGayathriMurali2016-03-161-9/+43
| | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? Add export/import for all estimators and transformers(which have Scala implementation) under pyspark/ml/classification.py. ## How was this patch tested? ./python/run-tests ./dev/lint-python Unit tests added to check persistence in Logistic Regression Author: GayathriMurali <gayathri.m.softie@gmail.com> Closes #11707 from GayathriMurali/SPARK-13034.
* [SPARK-13787][ML][PYSPARK] Pyspark feature importances for decision tree and ↵sethah2016-03-111-0/+44
| | | | | | | | | | | | | | | | random forest ## What changes were proposed in this pull request? This patch adds a `featureImportance` property to the Pyspark API for `DecisionTreeRegressionModel`, `DecisionTreeClassificationModel`, `RandomForestRegressionModel` and `RandomForestClassificationModel`. ## How was this patch tested? Python doc tests for the affected classes were updated to check feature importances. Author: sethah <seth.hendrickson16@gmail.com> Closes #11622 from sethah/SPARK-13787.
* [SPARK-13676] Fix mismatched default values for regParam in LogisticRegressionDongjoon Hyun2016-03-041-5/+5
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? The default value of regularization parameter for `LogisticRegression` algorithm is different in Scala and Python. We should provide the same value. **Scala** ``` scala> new org.apache.spark.ml.classification.LogisticRegression().getRegParam res0: Double = 0.0 ``` **Python** ``` >>> from pyspark.ml.classification import LogisticRegression >>> LogisticRegression().getRegParam() 0.1 ``` ## How was this patch tested? manual. Check the following in `pyspark`. ``` >>> from pyspark.ml.classification import LogisticRegression >>> LogisticRegression().getRegParam() 0.0 ``` Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11519 from dongjoon-hyun/SPARK-13676.
* [SPARK-13008][ML][PYTHON] Put one alg per line in pyspark.ml all listsJoseph K. Bradley2016-03-011-5/+6
| | | | | | | | | | | | This is to fix a long-time annoyance: Whenever we add a new algorithm to pyspark.ml, we have to add it to the ```__all__``` list at the top. Since we keep it alphabetized, it often creates a lot more changes than needed. It is also easy to add the Estimator and forget the Model. I'm going to switch it to have one algorithm per line. This also alphabetizes a few out-of-place classes in pyspark.ml.feature. No changes have been made to the moved classes. CC: thunterdb Author: Joseph K. Bradley <joseph@databricks.com> Closes #10927 from jkbradley/ml-python-all-list.
* [SPARK-10509][PYSPARK] Reduce excessive param boiler plate codeHolden Karau2016-01-261-32/+0
| | | | | | | | The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh). Author: Holden Karau <holden@us.ibm.com> Closes #10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.
* [SPARK-11815][ML][PYSPARK] PySpark DecisionTreeClassifier & ↵Yanbo Liang2016-01-061-5/+8
| | | | | | | | | | DecisionTreeRegressor should support setSeed PySpark ```DecisionTreeClassifier``` & ```DecisionTreeRegressor``` should support ```setSeed``` like what we do at Scala side. Author: Yanbo Liang <ybliang8@gmail.com> Closes #9807 from yanboliang/spark-11815.
* [MINOR][ML] Use coefficients replace weightsYanbo Liang2015-12-031-1/+1
| | | | | | | | | Use ```coefficients``` replace ```weights```, I wish they are the last two. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #10065 from yanboliang/coefficients.
* [SPARK-11820][ML][PYSPARK] PySpark LiR & LoR should support weightColYanbo Liang2015-11-181-8/+9
| | | | | | | | [SPARK-7685](https://issues.apache.org/jira/browse/SPARK-7685) and [SPARK-9642](https://issues.apache.org/jira/browse/SPARK-9642) have already supported setting weight column for ```LogisticRegression``` and ```LinearRegression```. It's a very important feature, PySpark should also support. mengxr Author: Yanbo Liang <ybliang8@gmail.com> Closes #9811 from yanboliang/spark-11820.
* [SPARK-10280][MLLIB][PYSPARK][DOCS] Add @since annotation to ↵Yu ISHIKAWA2015-11-091-0/+56
| | | | | | | | pyspark.ml.classification Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8690 from yu-iskw/SPARK-10280.
* [SPARK-10592] [ML] [PySpark] Deprecate weights and use coefficients instead ↵vectorijk2015-11-021-0/+13
| | | | | | | | | | in ML models Deprecated in `LogisticRegression` and `LinearRegression` Author: vectorijk <jiangkai@gmail.com> Closes #9311 from vectorijk/spark-10592.
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