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* [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.
* [SPARK-10779] [PYSPARK] [MLLIB] Set initialModel for KMeans model in PySpark ↵Evan Chen2015-10-071-2/+15
| | | | | | | | | | (spark.mllib) Provide initialModel param for pyspark.mllib.clustering.KMeans Author: Evan Chen <chene@us.ibm.com> Closes #8967 from evanyc15/SPARK-10779-pyspark-mllib.
* [DOC] [PYSPARK] [MLLIB] Added newlines to docstrings to fix parameter formattingnoelsmith2015-09-212-1/+2
| | | | | | | | | | | | | | Added newlines before `:param ...:` and `:return:` markup. Without these, parameter lists aren't formatted correctly in the API docs. I.e: ![screen shot 2015-09-21 at 21 49 26](https://cloud.githubusercontent.com/assets/11915197/10004686/de3c41d4-60aa-11e5-9c50-a46dcb51243f.png) .. looks like this once newline is added: ![screen shot 2015-09-21 at 21 50 14](https://cloud.githubusercontent.com/assets/11915197/10004706/f86bfb08-60aa-11e5-8524-ae4436713502.png) Author: noelsmith <mail@noelsmith.com> Closes #8851 from noel-smith/docstring-missing-newline-fix.
* [SPARK-10631] [DOCUMENTATION, MLLIB, PYSPARK] Added documentation for few APIsvinodkc2015-09-201-5/+17
| | | | | | | | There are some missing API docs in pyspark.mllib.linalg.Vector (including DenseVector and SparseVector). We should add them based on their Scala counterparts. Author: vinodkc <vinod.kc.in@gmail.com> Closes #8834 from vinodkc/fix_SPARK-10631.
* [SPARK-10615] [PYSPARK] change assertEquals to assertEqualYanbo Liang2015-09-181-81/+81
| | | | | | | | As ```assertEquals``` is deprecated, so we need to change ```assertEquals``` to ```assertEqual``` for existing python unit tests. Author: Yanbo Liang <ybliang8@gmail.com> Closes #8814 from yanboliang/spark-10615.
* [SPARK-10274] [MLLIB] Add @since annotation to pyspark.mllib.fpmYu ISHIKAWA2015-09-171-1/+9
| | | | | | Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8665 from yu-iskw/SPARK-10274.
* [SPARK-10279] [MLLIB] [PYSPARK] [DOCS] Add @since annotation to ↵Yu ISHIKAWA2015-09-171-2/+26
| | | | | | | | pyspark.mllib.util Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8689 from yu-iskw/SPARK-10279.
* [SPARK-10278] [MLLIB] [PYSPARK] Add @since annotation to pyspark.mllib.treeYu ISHIKAWA2015-09-171-1/+35
| | | | | | Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8685 from yu-iskw/SPARK-10278.
* [SPARK-10276] [MLLIB] [PYSPARK] Add @since annotation to ↵Yu ISHIKAWA2015-09-161-1/+35
| | | | | | | | pyspark.mllib.recommendation Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8677 from yu-iskw/SPARK-10276.
* [SPARK-10516] [ MLLIB] Added values property in DenseVectorVinod K C2015-09-151-0/+4
| | | | | | Author: Vinod K C <vinod.kc@huawei.com> Closes #8682 from vinodkc/fix_SPARK-10516.
* [PYSPARK] [MLLIB] [DOCS] Replaced addversion with versionadded in mllib.randomnoelsmith2015-09-151-1/+1
| | | | | | | | Missed this when reviewing `pyspark.mllib.random` for SPARK-10275. Author: noelsmith <mail@noelsmith.com> Closes #8773 from noel-smith/mllib-random-versionadded-fix.
* [SPARK-10275] [MLLIB] Add @since annotation to pyspark.mllib.randomYu ISHIKAWA2015-09-141-0/+15
| | | | | | Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #8666 from yu-iskw/SPARK-10275.
* [SPARK-10273] Add @since annotation to pyspark.mllib.featurenoelsmith2015-09-141-1/+57
| | | | | | | | | | 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 #8633 from noel-smith/SPARK-10273-since-mllib-feature.
* [SPARK-9793] [MLLIB] [PYSPARK] PySpark DenseVector, SparseVector implement ↵Yanbo Liang2015-09-142-15/+107
| | | | | | | | | | | __eq__ and __hash__ correctly PySpark DenseVector, SparseVector ```__eq__``` method should use semantics equality, and DenseVector can compared with SparseVector. Implement PySpark DenseVector, SparseVector ```__hash__``` method based on the first 16 entries. That will make PySpark Vector objects can be used in collections. Author: Yanbo Liang <ybliang8@gmail.com> Closes #8166 from yanboliang/spark-9793.
* [SPARK-10194] [MLLIB] [PYSPARK] SGD algorithms need convergenceTol parameter ↵Yanbo Liang2015-09-142-16/+33
| | | | | | | | | | in Python [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382) added a ```convergenceTol``` parameter for GradientDescent-based methods in Scala. We need that parameter in Python; otherwise, Python users will not be able to adjust that behavior (or even reproduce behavior from previous releases since the default changed). Author: Yanbo Liang <ybliang8@gmail.com> Closes #8457 from yanboliang/spark-10194.
* [SPARK-9805] [MLLIB] [PYTHON] [STREAMING] Added _eventually for ml streaming ↵Joseph K. Bradley2015-08-151-48/+129
| | | | | | | | | | | | pyspark tests Recently, PySpark ML streaming tests have been flaky, most likely because of the batches not being processed in time. Proposal: Replace the use of _ssc_wait (which waits for a fixed amount of time) with a method which waits for a fixed amount of time but can terminate early based on a termination condition method. With this, we can extend the waiting period (to make tests less flaky) but also stop early when possible (making tests faster on average, which I verified locally). CC: mengxr tdas freeman-lab Author: Joseph K. Bradley <joseph@databricks.com> Closes #8087 from jkbradley/streaming-ml-tests.
* [MINOR] [DOC] fix mllib pydoc warningsXiangrui Meng2015-08-132-4/+11
| | | | | | | | Switch to correct Sphinx syntax. MechCoder Author: Xiangrui Meng <meng@databricks.com> Closes #8169 from mengxr/mllib-pydoc-fix.
* [SPARK-6486] [MLLIB] [PYTHON] Add BlockMatrix to PySpark.Mike Dusenberry2015-08-051-6/+322
| | | | | | | | | | | | | | | | mengxr This adds the `BlockMatrix` to PySpark. I have the conversions to `IndexedRowMatrix` and `CoordinateMatrix` ready as well, so once PR #7554 is completed (which relies on PR #7746), this PR can be finished. Author: Mike Dusenberry <mwdusenb@us.ibm.com> Closes #7761 from dusenberrymw/SPARK-6486_Add_BlockMatrix_to_PySpark and squashes the following commits: 27195c2 [Mike Dusenberry] Adding one more check to _convert_to_matrix_block_tuple, and a few minor documentation changes. ae50883 [Mike Dusenberry] Minor update: BlockMatrix should inherit from DistributedMatrix. b8acc1c [Mike Dusenberry] Moving BlockMatrix to pyspark.mllib.linalg.distributed, updating the logic to match that of the other distributed matrices, adding conversions, and adding documentation. c014002 [Mike Dusenberry] Using properties for better documentation. 3bda6ab [Mike Dusenberry] Adding documentation. 8fb3095 [Mike Dusenberry] Small cleanup. e17af2e [Mike Dusenberry] Adding BlockMatrix to PySpark.
* [SPARK-6485] [MLLIB] [PYTHON] Add ↵Mike Dusenberry2015-08-042-0/+539
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | CoordinateMatrix/RowMatrix/IndexedRowMatrix to PySpark. This PR adds the RowMatrix, IndexedRowMatrix, and CoordinateMatrix distributed matrices to PySpark. Each distributed matrix class acts as a wrapper around the Scala/Java counterpart by maintaining a reference to the Java object. New distributed matrices can be created using factory methods added to DistributedMatrices, which creates the Java distributed matrix and then wraps it with the corresponding PySpark class. This design allows for simple conversion between the various distributed matrices, and lets us re-use the Scala code. Serialization between Python and Java is implemented using DataFrames as needed for IndexedRowMatrix and CoordinateMatrix for simplicity. Associated documentation and unit-tests have also been added. To facilitate code review, this PR implements access to the rows/entries as RDDs, the number of rows & columns, and conversions between the various distributed matrices (not including BlockMatrix), and does not implement the other linear algebra functions of the matrices, although this will be very simple to add now. Author: Mike Dusenberry <mwdusenb@us.ibm.com> Closes #7554 from dusenberrymw/SPARK-6485_Add_CoordinateMatrix_RowMatrix_IndexedMatrix_to_PySpark and squashes the following commits: bb039cb [Mike Dusenberry] Minor documentation update. b887c18 [Mike Dusenberry] Updating the matrix conversion logic again to make it even cleaner. Now, we allow the 'rows' parameter in the constructors to be either an RDD or the Java matrix object. If 'rows' is an RDD, we create a Java matrix object, wrap it, and then store that. If 'rows' is a Java matrix object of the correct type, we just wrap and store that directly. This is only for internal usage, and publicly, we still require 'rows' to be an RDD. We no longer store the 'rows' RDD, and instead just compute it from the Java object when needed. The point of this is that when we do matrix conversions, we do the conversion on the Scala/Java side, which returns a Java object, so we should use that directly, but exposing 'java_matrix' parameter in the public API is not ideal. This non-public feature of allowing 'rows' to be a Java matrix object is documented in the '__init__' constructor docstrings, which are not part of the generated public API, and doctests are also included. 7f0dcb6 [Mike Dusenberry] Updating module docstring. cfc1be5 [Mike Dusenberry] Use 'new SQLContext(matrix.rows.sparkContext)' rather than 'SQLContext.getOrCreate', as the later doesn't guarantee that the SparkContext will be the same as for the matrix.rows data. 687e345 [Mike Dusenberry] Improving conversion performance. This adds an optional 'java_matrix' parameter to the constructors, and pulls the conversion logic out into a '_create_from_java' function. Now, if the constructors are given a valid Java distributed matrix object as 'java_matrix', they will store those internally, rather than create a new one on the Scala/Java side. 3e50b6e [Mike Dusenberry] Moving the distributed matrices to pyspark.mllib.linalg.distributed. 308f197 [Mike Dusenberry] Using properties for better documentation. 1633f86 [Mike Dusenberry] Minor documentation cleanup. f0c13a7 [Mike Dusenberry] CoordinateMatrix should inherit from DistributedMatrix. ffdd724 [Mike Dusenberry] Updating doctests to make documentation cleaner. 3fd4016 [Mike Dusenberry] Updating docstrings. 27cd5f6 [Mike Dusenberry] Simplifying input conversions in the constructors for each distributed matrix. a409cf5 [Mike Dusenberry] Updating doctests to be less verbose by using lists instead of DenseVectors explicitly. d19b0ba [Mike Dusenberry] Updating code and documentation to note that a vector-like object (numpy array, list, etc.) can be used in place of explicit Vector object, and adding conversions when necessary to RowMatrix construction. 4bd756d [Mike Dusenberry] Adding param documentation to IndexedRow and MatrixEntry. c6bded5 [Mike Dusenberry] Move conversion logic from tuples to IndexedRow or MatrixEntry types from within the IndexedRowMatrix and CoordinateMatrix constructors to separate _convert_to_indexed_row and _convert_to_matrix_entry functions. 329638b [Mike Dusenberry] Moving the Experimental tag to the top of each docstring. 0be6826 [Mike Dusenberry] Simplifying doctests by removing duplicated rows/entries RDDs within the various tests. c0900df [Mike Dusenberry] Adding the colons that were accidentally not inserted. 4ad6819 [Mike Dusenberry] Documenting the and parameters. 3b854b9 [Mike Dusenberry] Minor updates to documentation. 10046e8 [Mike Dusenberry] Updating documentation to use class constructors instead of the removed DistributedMatrices factory methods. 119018d [Mike Dusenberry] Adding static methods to each of the distributed matrix classes to consolidate conversion logic. 4d7af86 [Mike Dusenberry] Adding type checks to the constructors. Although it is slightly verbose, it is better for the user to have a good error message than a cryptic stacktrace. 93b6a3d [Mike Dusenberry] Pulling the DistributedMatrices Python class out of this pull request. f6f3c68 [Mike Dusenberry] Pulling the DistributedMatrices Scala class out of this pull request. 6a3ecb7 [Mike Dusenberry] Updating pattern matching. 08f287b [Mike Dusenberry] Slight reformatting of the documentation. a245dc0 [Mike Dusenberry] Updating Python doctests for compatability between Python 2 & 3. Since Python 3 removed the idea of a separate 'long' type, all values that would have been outputted as a 'long' (ex: '4L') will now be treated as an 'int' and outputed as one (ex: '4'). The doctests now explicitly convert to ints so that both Python 2 and 3 will have the same output. This is fine since the values are all small, and thus can be easily represented as ints. 4d3a37e [Mike Dusenberry] Reformatting a few long Python doctest lines. 7e3ca16 [Mike Dusenberry] Fixing long lines. f721ead [Mike Dusenberry] Updating documentation for each of the distributed matrices. ab0e8b6 [Mike Dusenberry] Updating unit test to be more useful. dda2f89 [Mike Dusenberry] Added wrappers for the conversions between the various distributed matrices. Added logic to be able to access the rows/entries of the distributed matrices, which requires serialization through DataFrames for IndexedRowMatrix and CoordinateMatrix types. Added unit tests. 0cd7166 [Mike Dusenberry] Implemented the CoordinateMatrix API in PySpark, following the idea of the IndexedRowMatrix API, including using DataFrames for serialization. 3c369cb [Mike Dusenberry] Updating the architecture a bit to make conversions between the various distributed matrix types easier. The different distributed matrix classes are now only wrappers around the Java objects, and take the Java object as an argument during construction. This way, we can call for example on an , which returns a reference to a Java RowMatrix object, and then construct a PySpark RowMatrix object wrapped around the Java object. This is analogous to the behavior of PySpark RDDs and DataFrames. We now delegate creation of the various distributed matrices from scratch in PySpark to the factory methods on . 4bdd09b [Mike Dusenberry] Implemented the IndexedRowMatrix API in PySpark, following the idea of the RowMatrix API. Note that for the IndexedRowMatrix, we use DataFrames to serialize the data between Python and Scala/Java, so we accept PySpark RDDs, then convert to a DataFrame, then convert back to RDDs on the Scala/Java side before constructing the IndexedRowMatrix. 23bf1ec [Mike Dusenberry] Updating documentation to add PySpark RowMatrix. Inserting newline above doctest so that it renders properly in API docs. b194623 [Mike Dusenberry] Updating design to have a PySpark RowMatrix simply create and keep a reference to a wrapper over a Java RowMatrix. Updating DistributedMatrices factory methods to accept numRows and numCols with default values. Updating PySpark DistributedMatrices factory method to simply create a PySpark RowMatrix. Adding additional doctests for numRows and numCols parameters. bc2d220 [Mike Dusenberry] Adding unit tests for RowMatrix methods. d7e316f [Mike Dusenberry] Implemented the RowMatrix API in PySpark by doing the following: Added a DistributedMatrices class to contain factory methods for creating the various distributed matrices. Added a factory method for creating a RowMatrix from an RDD of Vectors. Added a createRowMatrix function to the PythonMLlibAPI to interface with the factory method. Added DistributedMatrix, DistributedMatrices, and RowMatrix classes to the pyspark.mllib.linalg api.
* [SPARK-9408] [PYSPARK] [MLLIB] Refactor linalg.py to /linalgXiangrui Meng2015-07-301-0/+0
| | | | | | | | | | | | | | | This is based on MechCoder 's PR https://github.com/apache/spark/pull/7731. Hopefully it could pass tests. MechCoder I tried to make minimal changes. If this passes Jenkins, we can merge this one first and then try to move `__init__.py` to `local.py` in a separate PR. Closes #7731 Author: Xiangrui Meng <meng@databricks.com> Closes #7746 from mengxr/SPARK-9408 and squashes the following commits: 0e05a3b [Xiangrui Meng] merge master 1135551 [Xiangrui Meng] add a comment for str(...) c48cae0 [Xiangrui Meng] update tests 173a805 [Xiangrui Meng] move linalg.py to linalg/__init__.py
* [SPARK-7105] [PYSPARK] [MLLIB] Support model save/load in GMMMechCoder2015-07-282-22/+59
| | | | | | | | | | | | This PR introduces save / load for GMM's in python API. Also I refactored `GaussianMixtureModel` and inherited it from `JavaModelWrapper` with model being `GaussianMixtureModelWrapper`, a wrapper which provides convenience methods to `GaussianMixtureModel` (due to serialization and deserialization issues) and I moved the creation of gaussians to the scala backend. Author: MechCoder <manojkumarsivaraj334@gmail.com> Closes #7617 from MechCoder/python_gmm_save_load and squashes the following commits: 9c305aa [MechCoder] [SPARK-7105] [PySpark] [MLlib] Support model save/load in GMM
* [SPARK-9122] [MLLIB] [PySpark] spark.mllib regression support batch predictYanbo Liang2015-07-231-2/+10
| | | | | | | | | | spark.mllib support batch predict for LinearRegressionModel, RidgeRegressionModel and LassoModel. Author: Yanbo Liang <ybliang8@gmail.com> Closes #7614 from yanboliang/spark-9122 and squashes the following commits: 4e610c0 [Yanbo Liang] spark.mllib regression support batch predict
* [SPARK-9223] [PYSPARK] [MLLIB] Support model save/load in LDAMechCoder2015-07-221-1/+42
| | | | | | | | | | Since save / load has been merged in LDA, it takes no time to write the wrappers in Python as well. Author: MechCoder <manojkumarsivaraj334@gmail.com> Closes #7587 from MechCoder/python_lda_save_load and squashes the following commits: c8e4ea7 [MechCoder] [SPARK-9223] [PySpark] Support model save/load in LDA
* [SPARK-9198] [MLLIB] [PYTHON] Fixed typo in pyspark sparsevector doc testsJoseph K. Bradley2015-07-201-4/+4
| | | | | | | | | | | | | | | | | Several places in the PySpark SparseVector docs have one defined as: ``` SparseVector(4, [2, 4], [1.0, 2.0]) ``` The index 4 goes out of bounds (but this is not checked). CC: mengxr Author: Joseph K. Bradley <joseph@databricks.com> Closes #7541 from jkbradley/sparsevec-doc-typo-fix and squashes the following commits: c806a65 [Joseph K. Bradley] fixed doc test e2dcb23 [Joseph K. Bradley] Fixed typo in pyspark sparsevector doc tests
* [SPARK-8996] [MLLIB] [PYSPARK] Python API for Kolmogorov-Smirnov TestMechCoder2015-07-203-13/+110
| | | | | | | | | | | | | | | Python API for the KS-test Statistics.kolmogorovSmirnovTest(data, distName, *params) I'm not quite sure how to support the callable function since it is not serializable. Author: MechCoder <manojkumarsivaraj334@gmail.com> Closes #7430 from MechCoder/spark-8996 and squashes the following commits: 2dd009d [MechCoder] minor 021d233 [MechCoder] Remove one wrapper and other minor stuff 49d07ab [MechCoder] [SPARK-8996] [MLlib] Python API for Kolmogorov-Smirnov Test
* [SPARK-9138] [MLLIB] fix Vectors.denseDavies Liu2015-07-171-3/+8
| | | | | | | | | | | | Vectors.dense() should accept numbers directly, like the one in Scala. We already use it in doctests, it worked by luck. cc mengxr jkbradley Author: Davies Liu <davies@databricks.com> Closes #7476 from davies/fix_vectors_dense and squashes the following commits: e0fd292 [Davies Liu] fix Vectors.dense
* [SPARK-9005] [MLLIB] Fix RegressionMetrics computation of explainedVarianceFeynman Liang2015-07-151-1/+1
| | | | | | | | | | | | | | | | Fixes implementation of `explainedVariance` and `r2` to be consistent with their definitions as described in [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005). Author: Feynman Liang <fliang@databricks.com> Closes #7361 from feynmanliang/SPARK-9005-RegressionMetrics-bugs and squashes the following commits: f1112fc [Feynman Liang] Add explainedVariance formula 1a3d098 [Feynman Liang] SROwen code review comments 08a0e1b [Feynman Liang] Fix pyspark tests db8605a [Feynman Liang] Style fix bde9761 [Feynman Liang] Fix RegressionMetrics tests, relax assumption predictor is unbiased c235de0 [Feynman Liang] Fix RegressionMetrics tests 4c4e56f [Feynman Liang] Fix RegressionMetrics computation of explainedVariance and r2
* [SPARK-6259] [MLLIB] Python API for LDAYu ISHIKAWA2015-07-141-1/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | I implemented the Python API for LDA. But I didn't implemented a method for `LDAModel.describeTopics()`, beause it's a little hard to implement it now. And adding document about that and an example code would fit for another issue. TODO: LDAModel.describeTopics() in Python must be also implemented. But it would be nice to fit for another issue. Implementing it is a little hard, since the return value of `describeTopics` in Scala consists of Tuple classes. Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com> Closes #6791 from yu-iskw/SPARK-6259 and squashes the following commits: 6855f59 [Yu ISHIKAWA] LDA inherits object 28bd165 [Yu ISHIKAWA] Change the place of testing code d7a332a [Yu ISHIKAWA] Remove the doc comment about the optimizer's default value 083e226 [Yu ISHIKAWA] Add the comment about the supported values and the default value of `optimizer` 9f8bed8 [Yu ISHIKAWA] Simplify casting faa9764 [Yu ISHIKAWA] Add some comments for the LDA paramters 98f645a [Yu ISHIKAWA] Remove the interface for `describeTopics`. Because it is not implemented. 57ac03d [Yu ISHIKAWA] Remove the unnecessary import in Python unit testing 73412c3 [Yu ISHIKAWA] Fix the typo 2278829 [Yu ISHIKAWA] Fix the indentation 39514ec [Yu ISHIKAWA] Modify how to cast the input data 8117e18 [Yu ISHIKAWA] Fix the validation problems by `lint-scala` 77fd1b7 [Yu ISHIKAWA] Not use LabeledPoint 68f0653 [Yu ISHIKAWA] Support some parameters for `ALS.train()` in Python 25ef2ac [Yu ISHIKAWA] Resolve conflicts with rebasing
* [SPARK-6266] [MLLIB] PySpark SparseVector missing doc for size, indices, valueslewuathe2015-07-091-2/+7
| | | | | | | | | | | | Write missing pydocs in `SparseVector` attributes. Author: lewuathe <lewuathe@me.com> Closes #7290 from Lewuathe/SPARK-6266 and squashes the following commits: 51d9895 [lewuathe] Update docs 0480d35 [lewuathe] Merge branch 'master' into SPARK-6266 ba42cf3 [lewuathe] [SPARK-6266] PySpark SparseVector missing doc for size, indices, values
* [SPARK-8068] [MLLIB] Add confusionMatrix method at class MulticlassMetrics ↵Yanbo Liang2015-07-081-0/+11
| | | | | | | | | | | | in pyspark/mllib Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib Author: Yanbo Liang <ybliang8@gmail.com> Closes #7286 from yanboliang/spark-8068 and squashes the following commits: 6109fe1 [Yanbo Liang] Add confusionMatrix method at class MulticlassMetrics in pyspark/mllib
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