SPARK-1501: Ensure assertions in Graph.apply are asserted.

The Graph.apply test in GraphSuite had some assertions in a closure in
a graph transformation. As a consequence, these assertions never
actually executed.  Furthermore, these closures had a reference to
(non-serializable) test harness classes because they called assert(),
which could be a problem if we proactively check closure serializability
in the future.

This commit simply changes the Graph.apply test to collect the graph
triplets so it can assert about each triplet from a map method.

Author: William Benton <willb@redhat.com>

Closes #415 from willb/graphsuite-nop-fix and squashes the following commits:

0b63658 [William Benton] Ensure assertions in Graph.apply are asserted.
[SPARK-1157][MLlib] L-BFGS Optimizer based on Breeze's implementation.

This PR uses Breeze's L-BFGS implement, and Breeze dependency has already been introduced by Xiangrui's sparse input format work in SPARK-1212. Nice work, @mengxr !

When use with regularized updater, we need compute the regVal and regGradient (the gradient of regularized part in the cost function), and in the currently updater design, we can compute those two values by the following way.

Let's review how updater works when returning newWeights given the input parameters.

w' = w - thisIterStepSize * (gradient + regGradient(w))  Note that regGradient is function of w!
If we set gradient = 0, thisIterStepSize = 1, then
regGradient(w) = w - w'

As a result, for regVal, it can be computed by

    val regVal = updater.compute(
      weights,
      new DoubleMatrix(initialWeights.length, 1), 0, 1, regParam)._2
and for regGradient, it can be obtained by

      val regGradient = weights.sub(
        updater.compute(weights, new DoubleMatrix(initialWeights.length, 1), 1, 1, regParam)._1)

The PR includes the tests which compare the result with SGD with/without regularization.

We did a comparison between LBFGS and SGD, and often we saw 10x less
steps in LBFGS while the cost of per step is the same (just computing
the gradient).

The following is the paper by Prof. Ng at Stanford comparing different
optimizers including LBFGS and SGD. They use them in the context of
deep learning, but worth as reference.
http://cs.stanford.edu/~jngiam/papers/LeNgiamCoatesLahiriProchnowNg2011.pdf

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #353 from dbtsai/dbtsai-LBFGS and squashes the following commits:

984b18e [DB Tsai] L-BFGS Optimizer based on Breeze's implementation. Also fixed indentation issue in GradientDescent optimizer.
Decision Tree documentation for MLlib programming guide

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
SPARK-1455: Better isolation for unit tests.

This is a simple first step towards avoiding running the Hive tests
whenever possible.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #420 from pwendell/test-isolation and squashes the following commits:

350c8af [Patrick Wendell] SPARK-1455: Better isolation for unit tests.
[FIX] update sbt-idea to version 1.6.0

I saw `No "scala-library*.jar" in Scala compiler library` error in IDEA. It seems upgrading `sbt-idea` to 1.6.0 fixed the problem.

Author: Xiangrui Meng <meng@databricks.com>

Closes #419 from mengxr/idea-plugin and squashes the following commits:

fb3c35f [Xiangrui Meng] update sbt-idea to version 1.6.0
[WIP] SPARK-1430: Support sparse data in Python MLlib

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
[SQL] SPARK-1424 Generalize insertIntoTable functions on SchemaRDDs

This makes it possible to create tables and insert into them using the DSL and SQL for the scala and java apis.

Author: Michael Armbrust <michael@databricks.com>

Closes #354 from marmbrus/insertIntoTable and squashes the following commits:

6c6f227 [Michael Armbrust] Create random temporary files in python parquet unit tests.
f5e6d5c [Michael Armbrust] Merge remote-tracking branch 'origin/master' into insertIntoTable
765c506 [Michael Armbrust] Add to JavaAPI.
77b512c [Michael Armbrust] typos.
5c3ef95 [Michael Armbrust] use names for boolean args.
882afdf [Michael Armbrust] Change createTableAs to saveAsTable.  Clean up api annotations.
d07d94b [Michael Armbrust] Add tests, support for creating parquet files and hive tables.
fa3fe81 [Michael Armbrust] Make insertInto available on JavaSchemaRDD as well.  Add createTableAs function.
[SPARK-959] Updated SBT from 0.13.1 to 0.13.2

JIRA issue: [SPARK-959](https://spark-project.atlassian.net/browse/SPARK-959)

SBT 0.13.2 has been officially released. This version updated Ivy 2.0 to Ivy 2.3, which fixes [IVY-899](https://issues.apache.org/jira/browse/IVY-899). This PR also removed previous workaround.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #426 from liancheng/updateSbt and squashes the following commits:

95e3dc8 [Cheng Lian] Updated SBT from 0.13.1 to 0.13.2 to fix SPARK-959
Make "spark logo" link refer to "/".

This is not an issue with the driver UI, but when you fire
up the history server, there's currently no way to go back to
the app listing page without editing the browser's location
field (since the logo's link points to the root of the
application's own UI - i.e. the "stages" tab).

The change just points the logo link to "/", which is the app
listing for the history server, and the stages tab for the
driver's UI.

Tested with both history server and live driver.

Author: Marcelo Vanzin <vanzin@cloudera.com>

Closes #408 from vanzin/web-ui-root and squashes the following commits:

1b60cb6 [Marcelo Vanzin] Make "spark logo" link refer to "/".
Loads test tables when running "sbt hive/console" without HIVE_DEV_HOME

When running Hive tests, the working directory is `$SPARK_HOME/sql/hive`, while when running `sbt hive/console`, it becomes `$SPARK_HOME`, and test tables are not loaded if `HIVE_DEV_HOME` is not defined.

Author: Cheng Lian <lian.cs.zju@gmail.com>

Closes #417 from liancheng/loadTestTables and squashes the following commits:

7cea8d6 [Cheng Lian] Loads test tables when running "sbt hive/console" without HIVE_DEV_HOME
update spark.default.parallelism

actually, the value 8 is only valid in mesos fine-grained mode :
<code>
  override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8)
</code>

while in coarse-grained model including mesos coares-grained, the value of the property depending on core numbers!
<code>
override def defaultParallelism(): Int = {
   conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2))
  }
</code>

Author: Chen Chao <crazyjvm@gmail.com>

Closes #389 from CrazyJvm/patch-2 and squashes the following commits:

84a7fe4 [Chen Chao] miss </li> at the end of every single line
04a9796 [Chen Chao] change format
ee0fae0 [Chen Chao] update spark.default.parallelism
SPARK-1310: Start adding k-fold cross validation to MLLib [adds kFold to MLUtils & fixes bug in BernoulliSampler]

Author: Holden Karau <holden@pigscanfly.ca>

Closes #18 from holdenk/addkfoldcrossvalidation and squashes the following commits:

208db9b [Holden Karau] Fix a bad space
e84f2fc [Holden Karau] Fix the test, we should be looking at the second element instead
6ddbf05 [Holden Karau] swap training and validation order
7157ae9 [Holden Karau] CR feedback
90896c7 [Holden Karau] New line
150889c [Holden Karau] Fix up error messages in the MLUtilsSuite
2cb90b3 [Holden Karau] Fix the names in kFold
c702a96 [Holden Karau] Fix imports in MLUtils
e187e35 [Holden Karau] Move { up to same line as whenExecuting(random) in RandomSamplerSuite.scala
c5b723f [Holden Karau] clean up
7ebe4d5 [Holden Karau] CR feedback, remove unecessary learners (came back during merge mistake) and insert an empty line
bb5fa56 [Holden Karau] extra line sadness
163c5b1 [Holden Karau] code review feedback 1.to -> 1 to and folds -> numFolds
5a33f1d [Holden Karau] Code review follow up.
e8741a7 [Holden Karau] CR feedback
b78804e [Holden Karau] Remove cross validation [TODO in another pull request]
91eae64 [Holden Karau] Consolidate things in mlutils
264502a [Holden Karau] Add a test for the bug that was found with BernoulliSampler not copying the complement param
dd0b737 [Holden Karau] Wrap long lines (oops)
c0b7fa4 [Holden Karau] Switch FoldedRDD to use BernoulliSampler and PartitionwiseSampledRDD
08f8e4d [Holden Karau] Fix BernoulliSampler to respect complement
a751ec6 [Holden Karau] Add k-fold cross validation to MLLib
SPARK-1497. Fix scalastyle warnings in YARN, Hive code

(I wasn't sure how to automatically set `SPARK_YARN=true` and `SPARK_HIVE=true` when running scalastyle, but these are the errors that turn up.)

Author: Sean Owen <sowen@cloudera.com>

Closes #413 from srowen/SPARK-1497 and squashes the following commits:

f0c9318 [Sean Owen] Fix more scalastyle warnings in yarn
80bf4c3 [Sean Owen] Add YARN alpha / YARN profile to scalastyle check
026319c [Sean Owen] Fix scalastyle warnings in YARN, Hive code
Minor addition to SPARK-1497
SPARK-1469: Scheduler mode should accept lower-case definitions and have...

... nicer error messages

There are  two improvements to Scheduler Mode:
1. Made the built in ones case insensitive (fair/FAIR, fifo/FIFO).
2. If an invalid mode is given we should print a better error message.

Author: Sandeep <sandeep@techaddict.me>

Closes #388 from techaddict/1469 and squashes the following commits:

a31bbd5 [Sandeep] SPARK-1469: Scheduler mode should accept lower-case definitions and have nicer error messages There are  two improvements to Scheduler Mode: 1. Made the built in ones case insensitive (fair/FAIR, fifo/FIFO). 2. If an invalid mode is given we should print a better error message.
SPARK-1465: Spark compilation is broken with the latest hadoop-2.4.0 release

YARN-1824 changes the APIs (addToEnvironment, setEnvFromInputString) in Apps, which causes the spark build to break if built against a version 2.4.0. To fix this, create the spark own function to do that functionality which will not break compiling against 2.3 and other 2.x versions.

Author: xuan <xuan@MacBook-Pro.local>
Author: xuan <xuan@macbook-pro.home>

Closes #396 from xgong/master and squashes the following commits:

42b5984 [xuan] Remove two extra imports
bc0926f [xuan] Remove usage of org.apache.hadoop.util.Shell
be89fa7 [xuan] fix Spark compilation is broken with the latest hadoop-2.4.0 release