Some improvements to MLlib guide:
1. [SPARK-1872] Update API links for unidoc.
2. [SPARK-1783] Added `page.displayTitle` to the global layout. If it is defined, use it instead of `page.title` for title display.
3. Add more Java/Python examples.
Author: Xiangrui Meng <meng@databricks.com>
Closes#816 from mengxr/mllib-doc and squashes the following commits:
ec2e407 [Xiangrui Meng] format scala example for ALS
cd9f40b [Xiangrui Meng] add a paragraph to summarize distributed matrix types
4617f04 [Xiangrui Meng] add python example to loadLibSVMFile and fix Java example
d6509c2 [Xiangrui Meng] [SPARK-1783] update mllib titles
561fdc0 [Xiangrui Meng] add a displayTitle option to global layout
195d06f [Xiangrui Meng] add Java example for summary stats and minor fix
9f1ff89 [Xiangrui Meng] update java api links in mllib-basics
7dad18e [Xiangrui Meng] update java api links in NB
3a0f4a6 [Xiangrui Meng] api/pyspark -> api/python
35bdeb9 [Xiangrui Meng] api/mllib -> api/scala
e4afaa8 [Xiangrui Meng] explicity state what might change
While play-testing the Scala and Java code examples in the MLlib docs, I noticed a number of small compile errors, and some typos. This led to finding and fixing a few similar items in other docs.
Then in the course of building the site docs to check the result, I found a few small suggestions for the build instructions. I also found a few more formatting and markdown issues uncovered when I accidentally used maruku instead of kramdown.
Author: Sean Owen <sowen@cloudera.com>
Closes#653 from srowen/SPARK-1727 and squashes the following commits:
6e7c38a [Sean Owen] Final doc updates - one more compile error, and use of mean instead of sum and count
8f5e847 [Sean Owen] Fix markdown syntax issues that maruku flags, even though we use kramdown (but only those that do not affect kramdown's output)
99966a9 [Sean Owen] Update issue tracker URL in docs
23c9ac3 [Sean Owen] Add Scala Naive Bayes example, to use existing example data file (whose format needed a tweak)
8c81982 [Sean Owen] Fix small compile errors and typos across MLlib docs
I used the sbt-unidoc plugin (https://github.com/sbt/sbt-unidoc) to create a unified Scaladoc of our public packages, and generate Javadocs as well. One limitation is that I haven't found an easy way to exclude packages in the Javadoc; there is a SBT task that identifies Java sources to run javadoc on, but it's been very difficult to modify it from outside to change what is set in the unidoc package. Some SBT-savvy people should help with this. The Javadoc site also lacks package-level descriptions and things like that, so we may want to look into that. We may decide not to post these right now if it's too limited compared to the Scala one.
Example of the built doc site: http://people.csail.mit.edu/matei/spark-unified-docs/
Author: Matei Zaharia <matei@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Patrick Wendell <pwendell@gmail.com>
Closes#457 from mateiz/better-docs and squashes the following commits:
a63d4a3 [Matei Zaharia] Skip Java/Scala API docs for Python package
5ea1f43 [Matei Zaharia] Fix links to Java classes in Java guide, fix some JS for scrolling to anchors on page load
f05abc0 [Matei Zaharia] Don't include java.lang package names
995e992 [Matei Zaharia] Skip internal packages and class names with $ in JavaDoc
a14a93c [Matei Zaharia] typo
76ce64d [Matei Zaharia] Add groups to Javadoc index page, and a first package-info.java
ed6f994 [Matei Zaharia] Generate JavaDoc as well, add titles, update doc site to use unified docs
acb993d [Matei Zaharia] Add Unidoc plugin for the projects we want Unidoced
This PR adds a SparseVector class in PySpark and updates all the regression, classification and clustering algorithms and models to support sparse data, similar to MLlib. I chose to add this class because SciPy is quite difficult to install in many environments (more so than NumPy), but I plan to add support for SciPy sparse vectors later too, and make the methods work transparently on objects of either type.
On the Scala side, we keep Python sparse vectors sparse and pass them to MLlib. We always return dense vectors from our models.
Some to-do items left:
- [x] Support SciPy's scipy.sparse matrix objects when SciPy is available. We can easily add a function to convert these to our own SparseVector.
- [x] MLlib currently uses a vector with one extra column on the left to represent what we call LabeledPoint in Scala. Do we really want this? It may get annoying once you deal with sparse data since you must add/subtract 1 to each feature index when training. We can remove this API in 1.0 and use tuples for labeling.
- [x] Explain how to use these in the Python MLlib docs.
CC @mengxr, @joshrosen
Author: Matei Zaharia <matei@databricks.com>
Closes#341 from mateiz/py-ml-update and squashes the following commits:
d52e763 [Matei Zaharia] Remove no-longer-needed slice code and handle review comments
ea5a25a [Matei Zaharia] Fix remaining uses of copyto() after merge
b9f97a3 [Matei Zaharia] Fix test
1e1bd0f [Matei Zaharia] Add MLlib logistic regression example in Python
88bc01f [Matei Zaharia] Clean up inheritance of LinearModel in Python, and expose its parametrs
37ab747 [Matei Zaharia] Fix some examples and docs due to changes in MLlib API
da0f27e [Matei Zaharia] Added a MLlib K-means example and updated docs to discuss sparse data
c48e85a [Matei Zaharia] Added some tests for passing lists as input, and added mllib/tests.py to run-tests script.
a07ba10 [Matei Zaharia] Fix some typos and calculation of initial weights
74eefe7 [Matei Zaharia] Added LabeledPoint class in Python
889dde8 [Matei Zaharia] Support scipy.sparse matrices in all our algorithms and models
ab244d1 [Matei Zaharia] Allow SparseVectors to be initialized using a dict
a5d6426 [Matei Zaharia] Add linalg.py to run-tests script
0e7a3d8 [Matei Zaharia] Keep vectors sparse in Java when reading LabeledPoints
eaee759 [Matei Zaharia] Update regression, classification and clustering models for sparse data
2abbb44 [Matei Zaharia] Further work to get linear models working with sparse data
154f45d [Matei Zaharia] Update docs, name some magic values
881fef7 [Matei Zaharia] Added a sparse vector in Python and made Java-Python format more compact
Added documentation for user to use the decision tree algorithms for classification and regression in Spark 1.0 release.
Apart from a general review, I need specific input on the following:
* I had to move a lot of the existing documentation under the *linear methods* umbrella to accommodate decision trees. I wonder if there is a better way to organize the programming guide given we are so close to the release.
* I have not looked closely at pyspark but I am wondering new mllib algorithms are automatically plugged in or do we need to some extra work to call mllib functions from pyspark. I will add to the pyspark examples based upon the advice I get.
cc: @mengxr, @hirakendu, @etrain, @atalwalkar
Author: Manish Amde <manish9ue@gmail.com>
Closes#402 from manishamde/tree_doc and squashes the following commits:
022485a [Manish Amde] more documentation
865826e [Manish Amde] minor: grammar
dbb0e5e [Manish Amde] minor improvements to text
b9ef6c4 [Manish Amde] basic decision tree code examples
6e297d7 [Manish Amde] added subsections
f427e84 [Manish Amde] renaming sections
9c0c4be [Manish Amde] split candidate
6925275 [Manish Amde] impurity and information gain
94fd2f9 [Manish Amde] more reorg
b93125c [Manish Amde] more subsection reorg
3ecb2ad [Manish Amde] minor text addition
1537dd3 [Manish Amde] added placeholders and some doc
d06511d [Manish Amde] basic skeleton
Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array.
Replace it with a fallback
Author: Sandeep <sandeep@techaddict.me>
Closes#391 from techaddict/1426 and squashes the following commits:
d365962 [Sandeep] SPARK-1426: Make MLlib work with NumPy versions older than 1.7 Currently it requires NumPy 1.7 due to using the copyto method (http://docs.scipy.org/doc/numpy/reference/generated/numpy.copyto.html) for extracting data out of an array. Replace it with a fallback
The reason it wasn't working was passing a bytearray to stream.write(), which is not supported in Python 2.6 but is in 2.7. (This array came from NumPy when we converted data to send it over to Java). Now we just convert those bytearrays to strings of bytes, which preserves nonprintable characters as well.
Author: Matei Zaharia <matei@databricks.com>
Closes#335 from mateiz/mllib-python-2.6 and squashes the following commits:
f26c59f [Matei Zaharia] Update docs to no longer say we need Python 2.7
a84d6af [Matei Zaharia] SPARK-1421. Make MLlib work on Python 2.6
# Principal Component Analysis
Computes the top k principal component coefficients for the m-by-n data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is n-by-k. Each column of the coefficients return matrix contains coefficients for one principal component, and the columns are in descending order of component variance. This function centers the data and uses the singular value decomposition (SVD) algorithm.
## Testing
Tests included:
* All principal components
* Only top k principal components
* Dense SVD tests
* Dense/sparse matrix tests
The results are tested against MATLAB's pca: http://www.mathworks.com/help/stats/pca.html
## Documentation
Added to mllib-guide.md
## Example Usage
Added to examples directory under SparkPCA.scala
Author: Reza Zadeh <rizlar@gmail.com>
Closes#88 from rezazadeh/sparkpca and squashes the following commits:
e298700 [Reza Zadeh] reformat using IDE
3f23271 [Reza Zadeh] documentation and cleanup
b025ab2 [Reza Zadeh] documentation
e2667d4 [Reza Zadeh] assertMatrixApproximatelyEquals
3787bb4 [Reza Zadeh] stylin
c6ecc1f [Reza Zadeh] docs
aa2bbcb [Reza Zadeh] rename sparseToTallSkinnyDense
56975b0 [Reza Zadeh] docs
2df9bde [Reza Zadeh] docs update
8fb0015 [Reza Zadeh] rcond documentation
dbf7797 [Reza Zadeh] correct argument number
a9f1f62 [Reza Zadeh] documentation
4ce6caa [Reza Zadeh] style changes
9a56a02 [Reza Zadeh] use rcond relative to larget svalue
120f796 [Reza Zadeh] housekeeping
156ff78 [Reza Zadeh] string comprehension
2e1cf43 [Reza Zadeh] rename rcond
ea223a6 [Reza Zadeh] many style changes
f4002d7 [Reza Zadeh] more docs
bd53c7a [Reza Zadeh] proper accumulator
a8b5ecf [Reza Zadeh] Don't use for loops
0dc7980 [Reza Zadeh] filter zeros in sparse
6115610 [Reza Zadeh] More documentation
36d51e8 [Reza Zadeh] use JBLAS for UVS^-1 computation
bc4599f [Reza Zadeh] configurable rcond
86f7515 [Reza Zadeh] compute per parition, use while
09726b3 [Reza Zadeh] more style changes
4195e69 [Reza Zadeh] private, accumulator
17002be [Reza Zadeh] style changes
4ba7471 [Reza Zadeh] style change
f4982e6 [Reza Zadeh] Use dense matrix in example
2828d28 [Reza Zadeh] optimizations: normalize once, use inplace ops
72c9fa1 [Reza Zadeh] rename DenseMatrix to TallSkinnyDenseMatrix, lean
f807be9 [Reza Zadeh] fix typo
2d7ccde [Reza Zadeh] Array interface for dense svd and pca
cd290fa [Reza Zadeh] provide RDD[Array[Double]] support
398d123 [Reza Zadeh] style change
55abbfa [Reza Zadeh] docs fix
ef29644 [Reza Zadeh] bad chnage undo
472566e [Reza Zadeh] all files from old pr
555168f [Reza Zadeh] initial files
tex formulas in the documentation
using mathjax.
and spliting the MLlib documentation by techniques
see jira
https://spark-project.atlassian.net/browse/MLLIB-19
and
https://github.com/shivaram/spark/compare/mathjax
Author: Martin Jaggi <m.jaggi@gmail.com>
== Merge branch commits ==
commit 0364bfabbfc347f917216057a20c39b631842481
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Fri Feb 7 03:19:38 2014 +0100
minor polishing, as suggested by @pwendell
commit dcd2142c164b2f602bf472bb152ad55bae82d31a
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 18:04:26 2014 +0100
enabling inline latex formulas with $.$
same mathjax configuration as used in math.stackexchange.com
sample usage in the linear algebra (SVD) documentation
commit bbafafd2b497a5acaa03a140bb9de1fbb7d67ffa
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 17:31:29 2014 +0100
split MLlib documentation by techniques
and linked from the main mllib-guide.md site
commit d1c5212b93c67436543c2d8ddbbf610fdf0a26eb
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 16:59:43 2014 +0100
enable mathjax formula in the .md documentation files
code by @shivaram
commit d73948db0d9bc36296054e79fec5b1a657b4eab4
Author: Martin Jaggi <m.jaggi@gmail.com>
Date: Thu Feb 6 16:57:23 2014 +0100
minor update on how to compile the documentation
Updated Spark Streaming Programming Guide
Here is the updated version of the Spark Streaming Programming Guide. This is still a work in progress, but the major changes are in place. So feedback is most welcome.
In general, I have tried to make the guide to easier to understand even if the reader does not know much about Spark. The updated website is hosted here -
http://www.eecs.berkeley.edu/~tdas/spark_docs/streaming-programming-guide.html
The major changes are:
- Overview illustrates the usecases of Spark Streaming - various input sources and various output sources
- An example right after overview to quickly give an idea of what Spark Streaming program looks like
- Made Java API and examples a first class citizen like Scala by using tabs to show both Scala and Java examples (similar to AMPCamp tutorial's code tabs)
- Highlighted the DStream operations updateStateByKey and transform because of their powerful nature
- Updated driver node failure recovery text to highlight automatic recovery in Spark standalone mode
- Added information about linking and using the external input sources like Kafka and Flume
- In general, reorganized the sections to better show the Basic section and the more advanced sections like Tuning and Recovery.
Todos:
- Links to the docs of external Kafka, Flume, etc
- Illustrate window operation with figure as well as example.
Author: Tathagata Das <tathagata.das1565@gmail.com>
== Merge branch commits ==
commit 18ff10556570b39d672beeb0a32075215cfcc944
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue Jan 28 21:49:30 2014 -0800
Fixed a lot of broken links.
commit 34a5a6008dac2e107624c7ff0db0824ee5bae45f
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Tue Jan 28 18:02:28 2014 -0800
Updated github url to use SPARK_GITHUB_URL variable.
commit f338a60ae8069e0a382d2cb170227e5757cc0b7a
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon Jan 27 22:42:42 2014 -0800
More updates based on Patrick and Harvey's comments.
commit 89a81ff25726bf6d26163e0dd938290a79582c0f
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Mon Jan 27 13:08:34 2014 -0800
Updated docs based on Patricks PR comments.
commit d5b6196b532b5746e019b959a79ea0cc013a8fc3
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sun Jan 26 20:15:58 2014 -0800
Added spark.streaming.unpersist config and info on StreamingListener interface.
commit e3dcb46ab83d7071f611d9b5008ba6bc16c9f951
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Sun Jan 26 18:41:12 2014 -0800
Fixed docs on StreamingContext.getOrCreate.
commit 6c29524639463f11eec721e4d17a9d7159f2944b
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Thu Jan 23 18:49:39 2014 -0800
Added example and figure for window operations, and links to Kafka and Flume API docs.
commit f06b964a51bb3b21cde2ff8bdea7d9785f6ce3a9
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed Jan 22 22:49:12 2014 -0800
Fixed missing endhighlight tag in the MLlib guide.
commit 036a7d46187ea3f2a0fb8349ef78f10d6c0b43a9
Merge: eab351d a1cd185
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed Jan 22 22:17:42 2014 -0800
Merge remote-tracking branch 'apache/master' into docs-update
commit eab351d05c0baef1d4b549e1581310087158d78d
Author: Tathagata Das <tathagata.das1565@gmail.com>
Date: Wed Jan 22 22:17:15 2014 -0800
Update Spark Streaming Programming Guide.
We've used camel case in other Spark methods so it felt reasonable to
keep using it here and make the code match Scala/Java as much as
possible. Note that parameter names matter in Python because it allows
passing optional parameters by name.
- Added a Python wrapper for Naive Bayes
- Updated the Scala Naive Bayes to match the style of our other
algorithms better and in particular make it easier to call from Java
(added builder pattern, removed default value in train method)
- Updated Python MLlib functions to not require a SparkContext; we can
get that from the RDD the user gives
- Added a toString method in LabeledPoint
- Made the Python MLlib tests run as part of run-tests as well (before
they could only be run individually through each file)
