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* [SPARK-13672][ML] Add python examples of BisectingKMeans in ML and MLLIBZheng RuiFeng2016-03-111-0/+1
| | | | | | | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-13672 ## What changes were proposed in this pull request? add two python examples of BisectingKMeans for ml and mllib ## How was this patch tested? manual tests Author: Zheng RuiFeng <ruifengz@foxmail.com> Closes #11515 from zhengruifeng/mllib_bkm_pe.
* [SPARK-13244][SQL] Migrates DataFrame to DatasetCheng Lian2016-03-101-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`. Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`). There are several noticeable API changes related to those returning arrays: 1. `collect`/`take` - Old APIs in class `DataFrame`: ```scala def collect(): Array[Row] def take(n: Int): Array[Row] ``` - New APIs in class `Dataset[T]`: ```scala def collect(): Array[T] def take(n: Int): Array[T] def collectRows(): Array[Row] def takeRows(n: Int): Array[Row] ``` Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side. Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here). 1. `randomSplit` - Old APIs in class `DataFrame`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame] def randomSplit(weights: Array[Double]): Array[DataFrame] ``` - New APIs in class `Dataset[T]`: ```scala def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]] def randomSplit(weights: Array[Double]): Array[Dataset[T]] ``` Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one. 1. `groupBy` Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`. Other noticeable changes: 1. Dataset always do eager analysis now We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`. ## How was this patch tested? Existing tests do the work. ## TODO - [ ] Fix all tests - [ ] Re-enable MiMA check - [ ] Update ScalaDoc (`since`, `group`, and example code) Author: Cheng Lian <lian@databricks.com> Author: Yin Huai <yhuai@databricks.com> Author: Wenchen Fan <wenchen@databricks.com> Author: Cheng Lian <liancheng@users.noreply.github.com> Closes #11443 from liancheng/ds-to-df.
* [MINOR] Fix typos in comments and testcase name of codeDongjoon Hyun2016-03-031-1/+1
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? This PR fixes typos in comments and testcase name of code. ## How was this patch tested? manual. Author: Dongjoon Hyun <dongjoon@apache.org> Closes #11481 from dongjoon-hyun/minor_fix_typos_in_code.
* [SPARK-13594][SQL] remove typed operations(e.g. map, flatMap) from python ↵Wenchen Fan2016-03-022-5/+5
| | | | | | | | | | | | | | | | DataFrame ## What changes were proposed in this pull request? Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future. ## How was this patch tested? existing tests Author: Wenchen Fan <wenchen@databricks.com> Closes #11445 from cloud-fan/python-clean.
* [SPARK-12633][PYSPARK] [DOC] PySpark regression parameter desc to consistent ↵vijaykiran2016-02-292-164/+166
| | | | | | | | | | | | | format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the regression module. Also, updated 2 params in classification to read as `Supported values:` to be consistent. closes #10600 Author: vijaykiran <mail@vijaykiran.com> Author: Bryan Cutler <cutlerb@gmail.com> Closes #11404 from BryanCutler/param-desc-consistent-regression-SPARK-12633.
* [SPARK-13545][MLLIB][PYSPARK] Make MLlib LogisticRegressionWithLBFGS's ↵Yanbo Liang2016-02-291-3/+5
| | | | | | | | | | | | | | | | | default parameters consistent in Scala and Python ## What changes were proposed in this pull request? * The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.) * BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route. * Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly. cc mengxr dbtsai ## How was this patch tested? No new tests, it should pass all current tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #11424 from yanboliang/spark-13545.
* [SPARK-12634][PYSPARK][DOC] PySpark tree parameter desc to consistent formatBryan Cutler2016-02-261-142/+197
| | | | | | | | | | | Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module. closes #10601 Author: Bryan Cutler <cutlerb@gmail.com> Author: vijaykiran <mail@vijaykiran.com> Closes #11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
* [SPARK-7106][MLLIB][PYSPARK] Support model save/load in Python's FPGrowthKai Jiang2016-02-241-4/+31
| | | | | | | | | | | | | | ## What changes were proposed in this pull request? Python API supports mode save/load in FPGrowth JIRA: [https://issues.apache.org/jira/browse/SPARK-7106](https://issues.apache.org/jira/browse/SPARK-7106) ## How was the this patch tested? The patch is tested with Python doctest. Author: Kai Jiang <jiangkai@gmail.com> Closes #11321 from vectorijk/spark-7106.
* [SPARK-13429][MLLIB] Unify Logistic Regression convergence tolerance of ML & ↵Yanbo Liang2016-02-221-2/+2
| | | | | | | | | | | | | | MLlib ## What changes were proposed in this pull request? In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```. cc dbtsai ## How was the this patch tested? unit tests Author: Yanbo Liang <ybliang8@gmail.com> Closes #11299 from yanboliang/spark-13429.
* [SPARK-12632][PYSPARK][DOC] PySpark fpm and als parameter desc to consistent ↵Bryan Cutler2016-02-222-34/+102
| | | | | | | | | | | | | | format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules. Closes #10602 Closes #10897 Author: Bryan Cutler <cutlerb@gmail.com> Author: somideshmukh <somilde@us.ibm.com> Closes #11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
* Correct SparseVector.parse documentationMiles Yucht2016-02-161-1/+1
| | | | | | | | There's a small typo in the SparseVector.parse docstring (which says that it returns a DenseVector rather than a SparseVector), which seems to be incorrect. Author: Miles Yucht <miles@databricks.com> Closes #11213 from mgyucht/fix-sparsevector-docs.
* [SPARK-12363][MLLIB] Remove setRun and fix PowerIterationClustering failed testLiang-Chi Hsieh2016-02-131-6/+19
| | | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12363 This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work. Author: Liang-Chi Hsieh <viirya@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #10539 from viirya/fix-poweriter.
* [SPARK-12630][PYSPARK] [DOC] PySpark classification parameter desc to ↵vijaykiran2016-02-121-118/+143
| | | | | | | | | | | consistent format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the classification module. Author: vijaykiran <mail@vijaykiran.com> Author: Bryan Cutler <cutlerb@gmail.com> Closes #11183 from BryanCutler/pyspark-consistent-param-classification-SPARK-12630.
* [SPARK-12986][DOC] Fix pydoc warnings in mllib/regression.pyNam Pham2016-02-081-13/+21
| | | | | | | | I have fixed the warnings by running "make html" under "python/docs/". They are caused by not having blank lines around indented paragraphs. Author: Nam Pham <phamducnam@gmail.com> Closes #11025 from nampham2/SPARK-12986.
* [SPARK-12631][PYSPARK][DOC] PySpark clustering parameter desc to consistent ↵Bryan Cutler2016-02-021-74/+191
| | | | | | | | | | format Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the clustering module. Author: Bryan Cutler <cutlerb@gmail.com> Closes #10610 from BryanCutler/param-desc-consistent-cluster-SPARK-12631.
* [SPARK-10086][MLLIB][STREAMING][PYSPARK] ignore StreamingKMeans test in ↵Xiangrui Meng2016-01-251-0/+1
| | | | | | | | | | | | | | PySpark for now I saw several failures from recent PR builds, e.g., https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/50015/consoleFull. This PR marks the test as ignored and we will fix the flakyness in SPARK-10086. gliptak Do you know why the test failure didn't show up in the Jenkins "Test Result"? cc: jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #10909 from mengxr/SPARK-10086.
* [SPARK-11295][PYSPARK] Add packages to JUnit output for Python testsGábor Lipták2016-01-201-11/+15
| | | | | | | | | This is #9263 from gliptak (improving grouping/display of test case results) with a small fix of bisecting k-means unit test. Author: Gábor Lipták <gliptak@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #10850 from mengxr/SPARK-11295.
* Revert "[SPARK-11295] Add packages to JUnit output for Python tests"Xiangrui Meng2016-01-191-14/+10
| | | | This reverts commit c6f971b4aeca7265ab374fa46c5c452461d9b6a7.
* [SPARK-11295] Add packages to JUnit output for Python testsGábor Lipták2016-01-191-10/+14
| | | | | | | | | | SPARK-11295 Add packages to JUnit output for Python tests This improves grouping/display of test case results. Author: Gábor Lipták <gliptak@gmail.com> Closes #9263 from gliptak/SPARK-11295.
* [SPARK-11944][PYSPARK][MLLIB] python mllib.clustering.bisecting k meansHolden Karau2016-01-192-5/+142
| | | | | | | | From the coverage issues for 1.6 : Add Python API for mllib.clustering.BisectingKMeans. Author: Holden Karau <holden@us.ibm.com> Closes #10150 from holdenk/SPARK-11937-python-api-coverage-SPARK-11944-python-mllib.clustering.BisectingKMeans.
* [SPARK-12603][MLLIB] PySpark MLlib GaussianMixtureModel should support ↵Yanbo Liang2016-01-111-13/+22
| | | | | | | | | | single instance predict/predictSoft PySpark MLlib ```GaussianMixtureModel``` should support single instance ```predict/predictSoft``` just like Scala do. Author: Yanbo Liang <ybliang8@gmail.com> Closes #10552 from yanboliang/spark-12603.
* [SPARK-12618][CORE][STREAMING][SQL] Clean up build warnings: 2.0.0 editionSean Owen2016-01-081-1/+1
| | | | | | | | Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs. Author: Sean Owen <sowen@cloudera.com> Closes #10570 from srowen/SPARK-12618.
* [SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not Nonezero3232016-01-072-1/+13
| | | | | | | | If initial model passed to GMM is not empty it causes net.razorvine.pickle.PickleException. It can be fixed by converting initialModel.weights to list. Author: zero323 <matthew.szymkiewicz@gmail.com> Closes #10644 from zero323/SPARK-12006.
* Revert "[SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not None"Yin Huai2016-01-062-13/+1
| | | | | | | | This reverts commit fcd013cf70e7890aa25a8fe3cb6c8b36bf0e1f04. Author: Yin Huai <yhuai@databricks.com> Closes #10632 from yhuai/pythonStyle.
* [SPARK-12006][ML][PYTHON] Fix GMM failure if initialModel is not Nonezero3232016-01-062-1/+13
| | | | | | | | If initial model passed to GMM is not empty it causes `net.razorvine.pickle.PickleException`. It can be fixed by converting `initialModel.weights` to `list`. Author: zero323 <matthew.szymkiewicz@gmail.com> Closes #9986 from zero323/SPARK-12006.
* [SPARK-11531][ML] SparseVector error MsgJoshi2016-01-061-1/+3
| | | | | | | | | PySpark SparseVector should have "Found duplicate indices" error message Author: Joshi <rekhajoshm@gmail.com> Author: Rekha Joshi <rekhajoshm@gmail.com> Closes #9525 from rekhajoshm/SPARK-11531.
* [SPARK-12041][ML][PYSPARK] Add columnSimilarities to IndexedRowMatrixKai Jiang2016-01-051-0/+14
| | | | | | | | Add `columnSimilarities` to IndexedRowMatrix for PySpark spark.mllib.linalg. Author: Kai Jiang <jiangkai@gmail.com> Closes #10158 from vectorijk/spark-12041.
* [SPARK-12296][PYSPARK][MLLIB] Feature parity for pyspark mllib standard ↵Holden Karau2015-12-221-0/+40
| | | | | | | | | | scaler model Some methods are missing, such as ways to access the std, mean, etc. This PR is for feature parity for pyspark.mllib.feature.StandardScaler & StandardScalerModel. Author: Holden Karau <holden@us.ibm.com> Closes #10298 from holdenk/SPARK-12296-feature-parity-pyspark-mllib-StandardScalerModel.
* [SPARK-10158][PYSPARK][MLLIB] ALS better error message when using Long IDsBryan Cutler2015-12-201-0/+17
| | | | | | | | Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized. It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer." Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647." Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.
* [SPARK-12380] [PYSPARK] use SQLContext.getOrCreate in mllibDavies Liu2015-12-163-11/+9
| | | | | | | | MLlib should use SQLContext.getOrCreate() instead of creating new SQLContext. Author: Davies Liu <davies@databricks.com> Closes #10338 from davies/create_context.
* [SPARK-12016] [MLLIB] [PYSPARK] Wrap Word2VecModel when loading it in pysparkLiang-Chi Hsieh2015-12-141-1/+5
| | | | | | | | | | JIRA: https://issues.apache.org/jira/browse/SPARK-12016 We should not directly use Word2VecModel in pyspark. We need to wrap it in a Word2VecModelWrapper when loading it in pyspark. Author: Liang-Chi Hsieh <viirya@appier.com> Closes #10100 from viirya/fix-load-py-wordvecmodel.
* [SPARK-10560][PYSPARK][MLLIB][DOCS] Make StreamingLogisticRegressionWithSGD ↵Bryan Cutler2015-11-232-23/+46
| | | | | | | | | | | | | | Python API equal to Scala one This is to bring the API documentation of StreamingLogisticReressionWithSGD and StreamingLinearRegressionWithSGC in line with the Scala versions. -Fixed the algorithm descriptions -Added default values to parameter descriptions -Changed StreamingLogisticRegressionWithSGD regParam to default to 0, as in the Scala version Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9141 from BryanCutler/StreamingLogisticRegressionWithSGD-python-api-sync.
* [SPARK-11566] [MLLIB] [PYTHON] Refactoring GaussianMixtureModel.gaussians in ↵Yu ISHIKAWA2015-11-101-1/+1
| | | | | | | | | | Python cc jkbradley Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9534 from yu-iskw/SPARK-11566.
* [SPARK-11610][MLLIB][PYTHON][DOCS] Make the docs of LDAModel.describeTopics ↵Yu ISHIKAWA2015-11-091-0/+6
| | | | | | | | | | in Python more specific cc jkbradley Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9577 from yu-iskw/SPARK-11610.
* [SPARK-8467] [MLLIB] [PYSPARK] Add LDAModel.describeTopics() in PythonYu ISHIKAWA2015-11-061-15/+18
| | | | | | | | | | | | | Could jkbradley and davies review it? - Create a wrapper class: `LDAModelWrapper` for `LDAModel`. Because we can't deal with the return value of`describeTopics` in Scala from pyspark directly. `Array[(Array[Int], Array[Double])]` is too complicated to convert it. - Add `loadLDAModel` in `PythonMLlibAPI`. Since `LDAModel` in Scala is an abstract class and we need to call `load` of `DistributedLDAModel`. [[SPARK-8467] Add LDAModel.describeTopics() in Python - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-8467) Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8643 from yu-iskw/SPARK-8467-2.
* [SPARK-10116][CORE] XORShiftRandom.hashSeed is random in high bitsImran Rashid2015-11-061-2/+2
| | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-10116 This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`. mengxr mkolod Author: Imran Rashid <irashid@cloudera.com> Closes #8314 from squito/SPARK-10116.
* [SPARK-10028][MLLIB][PYTHON] Add Python API for PrefixSpanYu ISHIKAWA2015-11-041-1/+68
| | | | | | Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #9469 from yu-iskw/SPARK-10028.
* [SPARK-11358][MLLIB] deprecate runs in k-meansXiangrui Meng2015-11-021-0/+4
| | | | | | | | | | This PR deprecates `runs` in k-means. `runs` introduces extra complexity and overhead in MLlib's k-means implementation. I haven't seen much usage with `runs` not equal to `1`. We don't have a unit test for it either. We can deprecate this method in 1.6, and void it in 1.7. It helps us simplify the implementation. cc: srowen Author: Xiangrui Meng <meng@databricks.com> Closes #9322 from mengxr/SPARK-11358.
* [SPARK-11302][MLLIB] 2) Multivariate Gaussian Model with Covariance matrix ↵Sean Owen2015-10-271-2/+2
| | | | | | | | | | | | returns incorrect answer in some cases Fix computation of root-sigma-inverse in multivariate Gaussian; add a test and fix related Python mixture model test. Supersedes https://github.com/apache/spark/pull/9293 Author: Sean Owen <sowen@cloudera.com> Closes #9309 from srowen/SPARK-11302.2.
* [SPARK-6488][MLLIB][PYTHON] Support addition/multiplication in PySpark's ↵Mike Dusenberry2015-10-271-0/+68
| | | | | | | | | | BlockMatrix This PR adds addition and multiplication to PySpark's `BlockMatrix` class via `add` and `multiply` functions. Author: Mike Dusenberry <mwdusenb@us.ibm.com> Closes #9139 from dusenberrymw/SPARK-6488_Add_Addition_and_Multiplication_to_PySpark_BlockMatrix.
* [SPARK-10271][PYSPARK][MLLIB] Added @since tags to pyspark.mllib.clusteringnoelsmith2015-10-261-1/+68
| | | | | | | | | | Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings). Added since to methods + "versionadded::" to classes (derived from the git file history in pyspark). Author: noelsmith <mail@noelsmith.com> Closes #8627 from noel-smith/SPARK-10271-since-mllib-clustering.
* [SPARK-10277] [MLLIB] [PYSPARK] Add @since annotation to ↵Yu ISHIKAWA2015-10-231-1/+101
| | | | | | | | pyspark.mllib.regression Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8684 from yu-iskw/SPARK-10277.
* [SPARK-7021] Add JUnit output for Python unit testsGábor Lipták2015-10-221-1/+8
| | | | | | | | WIP Author: Gábor Lipták <gliptak@gmail.com> Closes #8323 from gliptak/SPARK-7021.
* [SPARK-10269][PYSPARK][MLLIB] Add @since annotation to ↵noelsmith2015-10-201-4/+66
| | | | | | | | | | | | | | pyspark.mllib.classification Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings). Added since to methods + "versionadded::" to classes derived from the file history. Note - some methods are inherited from the regression module (i.e. LinearModel.intercept) so these won't have version numbers in the API docs until that model is updated. Author: noelsmith <mail@noelsmith.com> Closes #8626 from noel-smith/SPARK-10269-since-mlib-classification.
* [SPARK-10272][PYSPARK][MLLIB] Added @since tags to pyspark.mllib.evaluationnoelsmith2015-10-201-0/+41
| | | | | | | | | | | | Duplicated the since decorator from pyspark.sql into pyspark (also tweaked to handle functions without docstrings). Added since to public methods + "versionadded::" to classes (derived from the git file history in pyspark). Note - I added also the tags to MultilabelMetrics even though it isn't declared as public in the __all__ statement... if that's incorrect - I'll remove. Author: noelsmith <mail@noelsmith.com> Closes #8628 from noel-smith/SPARK-10272-since-mllib-evalutation.
* [SPARK-11084] [ML] [PYTHON] Check if index can contain non-zero value before ↵zero3232015-10-162-2/+12
| | | | | | | | | | binary search At this moment `SparseVector.__getitem__` executes `np.searchsorted` first and checks if result is in an expected range after that. It is possible to check if index can contain non-zero value before executing `np.searchsorted`. Author: zero323 <matthew.szymkiewicz@gmail.com> Closes #9098 from zero323/sparse_vector_getitem_improved.
* [SPARK-11050] [MLLIB] PySpark SparseVector can return wrong index in e…Bhargav Mangipudi2015-10-161-2/+3
| | | | | | | | | | | | …rror message For negative indices in the SparseVector, we update the index value. If we have an incorrect index at this point, the error message has the incorrect *updated* index instead of the original one. This change contains the fix for the same. Author: Bhargav Mangipudi <bhargav.mangipudi@gmail.com> Closes #9069 from bhargav/spark-10759.
* [SPARK-10535] Sync up API for matrix factorization model between Scala and ↵Vladimir Vladimirov2015-10-091-4/+28
| | | | | | | | | | PySpark Support for recommendUsersForProducts and recommendProductsForUsers in matrix factorization model for PySpark Author: Vladimir Vladimirov <vladimir.vladimirov@magnetic.com> Closes #8700 from smartkiwi/SPARK-10535_.
* [SPARK-10959] [PYSPARK] StreamingLogisticRegressionWithSGD does not train ↵Bryan Cutler2015-10-082-2/+3
| | | | | | | | | | with given regParam and convergenceTol parameters These params were being passed into the StreamingLogisticRegressionWithSGD constructor, but not transferred to the call for model training. Same with StreamingLinearRegressionWithSGD. I added the params as named arguments to the call and also fixed the intercept parameter, which was being passed as regularization value. Author: Bryan Cutler <bjcutler@us.ibm.com> Closes #9002 from BryanCutler/StreamingSGD-convergenceTol-bug-10959.
* [SPARK-10973] [ML] [PYTHON] __gettitem__ method throws IndexError exception ↵zero3232015-10-082-5/+10
| | | | | | | | | | | | | | | | | | | | | | | when we… __gettitem__ method throws IndexError exception when we try to access index after the last non-zero entry from pyspark.mllib.linalg import Vectors sv = Vectors.sparse(5, {1: 3}) sv[0] ## 0.0 sv[1] ## 3.0 sv[2] ## Traceback (most recent call last): ## File "<stdin>", line 1, in <module> ## File "/python/pyspark/mllib/linalg/__init__.py", line 734, in __getitem__ ## row_ind = inds[insert_index] ## IndexError: index out of bounds Author: zero323 <matthew.szymkiewicz@gmail.com> Closes #9009 from zero323/sparse_vector_index_error.
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