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spark.mllib and mllib in the documentation.
Replaces a number of occurences of `MLlib` in the documentation that were meant to refer to the `spark.mllib` package instead. It should clarify for new users the difference between `spark.mllib` (the package) and MLlib (the umbrella project for ML in spark).
It also removes some files that I forgot to delete with #10207
Author: Timothy Hunter <timhunter@databricks.com>
Closes #10234 from thunterdb/12212.
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spark.mllib
In the Markdown docs for the spark.mllib Programming Guide, we have code examples with codetabs for each language. We should link to each language's API docs within the corresponding codetab, but we are inconsistent about this. For an example of what we want to do, see the "ChiSqSelector" section in https://github.com/apache/spark/blob/64743870f23bffb8d96dcc8a0181c1452782a151/docs/mllib-feature-extraction.md
This JIRA is just for spark.mllib, not spark.ml.
Please let me know if more work is needed, thanks a lot.
Author: Xin Ren <iamshrek@126.com>
Closes #8977 from keypointt/SPARK-10669.
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0.0 (original: 1.0)
Small typo in the example for `LabelledPoint` in the MLLib docs.
Author: Sean Paradiso <seanparadiso@gmail.com>
Closes #8680 from sparadiso/docs_mllib_smalltypo.
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jira: https://issues.apache.org/jira/browse/SPARK-9901
The jira covers only the document update. I can further provide example code for QR (like the ones for SVD and PCA) in a separate PR.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #8462 from hhbyyh/qrDoc.
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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.
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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.
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Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes #6958 from dusenberrymw/Improve_MLlib_Local_Matrix_Documentation and squashes the following commits:
ceae407 [Mike Dusenberry] Updated MLlib Data Types Local Matrix section to include information on sparse matrices, added sparse matrix examples to the Scala and Java examples, and added Python examples for both dense and sparse matrices.
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Types" documentation should be reordered.
The documentation for BlockMatrix should come after RowMatrix, IndexedRowMatrix, and CoordinateMatrix, as BlockMatrix references the later three types, and RowMatrix is considered the "basic" distributed matrix. This will improve comprehensibility of the "Distributed matrix" section, especially for the new reader.
Author: Mike Dusenberry <dusenberrymw@gmail.com>
Closes #6270 from dusenberrymw/Reorder_MLlib_Data_Types_Distributed_matrix_docs and squashes the following commits:
6313bab [Mike Dusenberry] The documentation for BlockMatrix should come after RowMatrix, IndexedRowMatrix, and CoordinateMatrix, as BlockMatrix references the later three types, and RowMatrix is considered the "basic" distributed matrix. This will improve comprehensibility of the "Distributed matrix" section, especially for the new reader.
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There is a mistake in the URL of Matrices in the MLlib Data Types documentation (Local matrix scala section), the URL points to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices which is a mistake, since Matrices is an object that implements factory methods for Matrix that does not have a companion class. The correct link should point to https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Matrices$
There is another mistake, in the Local Vector section in Scala, Java and Python
In the Scala section the URL of Vectors points to the trait Vector (https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vector) and not to the factory methods implemented in Vectors.
The correct link should be: https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.mllib.linalg.Vectors$
In the Java section the URL of Vectors points to the Interface Vector (https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vector.html) and not to the Class Vectors
The correct link should be:
https://spark.apache.org/docs/latest/api/java/org/apache/spark/mllib/linalg/Vectors.html
In the Python section the URL of Vectors points to the class Vector (https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vector) and not the Class Vectors
The correct link should be:
https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.linalg.Vectors
Author: FavioVazquez <favio.vazquezp@gmail.com>
Closes #6196 from FavioVazquez/fix-typo-matrices-mllib-datatypes and squashes the following commits:
3e9efd5 [FavioVazquez] - Fixed wrong URLs in the MLlib Data Types Documentation
9af7074 [FavioVazquez] Merge remote-tracking branch 'upstream/master'
edab1ef [FavioVazquez] Merge remote-tracking branch 'upstream/master'
b2e2f8c [FavioVazquez] Merge remote-tracking branch 'upstream/master'
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Author: Kamil Smuga <smugakamil@gmail.com>
Author: stderr <smugakamil@gmail.com>
Closes #5120 from kamilsmuga/master and squashes the following commits:
fee3281 [Kamil Smuga] more python api links fixed for docs
13240cb [Kamil Smuga] resolved merge conflicts with upstream/master
6649b3b [Kamil Smuga] fix broken docs links to Python API
92f03d7 [stderr] Fix links to pyspark api
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* Removed SVD code from examples.
* Corrected Java API doc link.
* Updated variable names: `AtransposeA` -> `ata`.
* Minor changes.
brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes #4737 from mengxr/update-block-matrix-user-guide and squashes the following commits:
70f53ac [Xiangrui Meng] update block matrix user guide
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Docs for BlockMatrix. mengxr
Author: Burak Yavuz <brkyvz@gmail.com>
Closes #4664 from brkyvz/SPARK-5507PR and squashes the following commits:
4db30b0 [Burak Yavuz] [SPARK-5507] Added documentation for BlockMatrix
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1. renamed mllib-basics to mllib-data-types
1. renamed mllib-stats to mllib-statistics
1. moved random data generation to the bottom of mllib-stats
1. updated toc accordingly
atalwalkar
Author: Xiangrui Meng <meng@databricks.com>
Closes #2151 from mengxr/mllib-doc-1.1 and squashes the following commits:
0bd79f3 [Xiangrui Meng] add mllib-data-types
b64a5d7 [Xiangrui Meng] update the content list of basis statistics in mllib-guide
f625cc2 [Xiangrui Meng] move mllib-basics to mllib-data-types
4d69250 [Xiangrui Meng] move random data generation to the bottom of statistics
e64f3ce [Xiangrui Meng] move mllib-stats.md to mllib-statistics.md
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