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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
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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
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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
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