[SPARK-2796] [mllib] DecisionTree bug fix: ordered categorical features

Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.

Added new test to DecisionTreeSuite to catch this: "regression stump with categorical variables of arity 2"

Bug fix: Modified upper bound discussed above.

Also: Small improvements to coding style in DecisionTree.

CC mengxr manishamde

Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>

Closes #1720 from jkbradley/decisiontree-bugfix2 and squashes the following commits:

225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
[SPARK-2010] [PySpark] [SQL] support nested structure in SchemaRDD

Convert Row in JavaSchemaRDD into Array[Any] and unpickle them as tuple in Python, then convert them into namedtuple, so use can access fields just like attributes.

This will let nested structure can be accessed as object, also it will reduce the size of serialized data and better performance.

root
 |-- field1: integer (nullable = true)
 |-- field2: string (nullable = true)
 |-- field3: struct (nullable = true)
 |    |-- field4: integer (nullable = true)
 |    |-- field5: array (nullable = true)
 |    |    |-- element: integer (containsNull = false)
 |-- field6: array (nullable = true)
 |    |-- element: struct (containsNull = false)
 |    |    |-- field7: string (nullable = true)

Then we can access them by row.field3.field5[0]  or row.field6[5].field7

It also will infer the schema in Python, convert Row/dict/namedtuple/objects into tuple before serialization, then call applySchema in JVM. During inferSchema(), the top level of dict in row will be StructType, but any nested dictionary will be MapType.

You can use pyspark.sql.Row to convert unnamed structure into Row object, make the RDD can be inferable. Such as:

ctx.inferSchema(rdd.map(lambda x: Row(a=x[0], b=x[1]))

Or you could use Row to create a class just like namedtuple, for example:

Person = Row("name", "age")
ctx.inferSchema(rdd.map(lambda x: Person(*x)))

Also, you can call applySchema to apply an schema to a RDD of tuple/list and turn it into a SchemaRDD. The `schema` should be StructType, see the API docs for details.

schema = StructType([StructField("name, StringType, True),
                                    StructType("age", IntegerType, True)])
ctx.applySchema(rdd, schema)

PS: In order to use namedtuple to inferSchema, you should make namedtuple picklable.

Author: Davies Liu <davies.liu@gmail.com>

Closes #1598 from davies/nested and squashes the following commits:

f1d15b6 [Davies Liu] verify schema with the first few rows
8852aaf [Davies Liu] check type of schema
abe9e6e [Davies Liu] address comments
61b2292 [Davies Liu] add @deprecated to pythonToJavaMap
1e5b801 [Davies Liu] improve cache of classes
51aa135 [Davies Liu] use Row to infer schema
e9c0d5c [Davies Liu] remove string typed schema
353a3f2 [Davies Liu] fix code style
63de8f8 [Davies Liu] fix typo
c79ca67 [Davies Liu] fix serialization of nested data
6b258b5 [Davies Liu] fix pep8
9d8447c [Davies Liu] apply schema provided by string of names
f5df97f [Davies Liu] refactor, address comments
9d9af55 [Davies Liu] use arrry to applySchema and infer schema in Python
84679b3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into nested
0eaaf56 [Davies Liu] fix doc tests
b3559b4 [Davies Liu] use generated Row instead of namedtuple
c4ddc30 [Davies Liu] fix conflict between name of fields and variables
7f6f251 [Davies Liu] address all comments
d69d397 [Davies Liu] refactor
2cc2d45 [Davies Liu] refactor
182fb46 [Davies Liu] refactor
bc6e9e1 [Davies Liu] switch to new Schema API
547bf3e [Davies Liu] Merge branch 'master' into nested
a435b5a [Davies Liu] add docs and code refactor
2c8debc [Davies Liu] Merge branch 'master' into nested
644665a [Davies Liu] use tuple and namedtuple for schemardd
[SPARK-2212][SQL] Hash Outer Join (follow-up bug fix).

We need to carefully set the ouputPartitioning of the HashOuterJoin Operator. Otherwise, we may not correctly handle nulls.

Author: Yin Huai <huai@cse.ohio-state.edu>

Closes #1721 from yhuai/SPARK-2212-BugFix and squashes the following commits:

ed5eef7 [Yin Huai] Correctly choosing outputPartitioning for the HashOuterJoin operator.
[SPARK-2116] Load spark-defaults.conf from SPARK_CONF_DIR if set

If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.

Author: Albert Chu <chu11@llnl.gov>

Closes #1059 from chu11/SPARK-2116 and squashes the following commits:

9f3ac94 [Albert Chu] SPARK-2116: If SPARK_CONF_DIR environment variable is set, search it for spark-defaults.conf.
[SPARK-2800]: Exclude scalastyle-output.xml Apache RAT checks

Author: GuoQiang Li <witgo@qq.com>

Closes #1729 from witgo/SPARK-2800 and squashes the following commits:

13ca966 [GuoQiang Li] Add scalastyle-output.xml  to .rat-excludes file
[SPARK-2764] Simplify daemon.py process structure

Curently, daemon.py forks a pool of numProcessors subprocesses, and those processes fork themselves again to create the actual Python worker processes that handle data.

I think that this extra layer of indirection is unnecessary and adds a lot of complexity.  This commit attempts to remove this middle layer of subprocesses by launching the workers directly from daemon.py.

See https://github.com/mesos/spark/pull/563 for the original PR that added daemon.py, where I raise some issues with the current design.

Author: Josh Rosen <joshrosen@apache.org>

Closes #1680 from JoshRosen/pyspark-daemon and squashes the following commits:

5abbcb9 [Josh Rosen] Replace magic number: 4 -> EINTR
5495dff [Josh Rosen] Throw IllegalStateException if worker launch fails.
b79254d [Josh Rosen] Detect failed fork() calls; improve error logging.
282c2c4 [Josh Rosen] Remove daemon.py exit logging, since it caused problems:
8554536 [Josh Rosen] Fix daemon’s shutdown(); log shutdown reason.
4e0fab8 [Josh Rosen] Remove shared-memory exit_flag; don't die on worker death.
e9892b4 [Josh Rosen] [WIP] [SPARK-2764] Simplify daemon.py process structure.
Streaming mllib [SPARK-2438][MLLIB]

This PR implements a streaming linear regression analysis, in which a linear regression model is trained online as new data arrive. The design is based on discussions with tdas and mengxr, in which we determined how to add this functionality in a general way, with minimal changes to existing libraries.

__Summary of additions:__

_StreamingLinearAlgorithm_
- An abstract class for fitting generalized linear models online to streaming data, including training on (and updating) a model, and making predictions.

_StreamingLinearRegressionWithSGD_
- Class and companion object for running streaming linear regression

_StreamingLinearRegressionTestSuite_
- Unit tests

_StreamingLinearRegression_
- Example use case: fitting a model online to data from one stream, and making predictions on other data

__Notes__
- If this looks good, I can use the StreamingLinearAlgorithm class to easily implement other analyses that follow the same logic (Ridge, Lasso, Logistic, SVM).

Author: Jeremy Freeman <the.freeman.lab@gmail.com>
Author: freeman <the.freeman.lab@gmail.com>

Closes #1361 from freeman-lab/streaming-mllib and squashes the following commits:

775ea29 [Jeremy Freeman] Throw error if user doesn't initialize weights
4086fee [Jeremy Freeman] Fixed current weight formatting
8b95b27 [Jeremy Freeman] Restored broadcasting
29f27ec [Jeremy Freeman] Formatting
8711c41 [Jeremy Freeman] Used return to avoid indentation
777b596 [Jeremy Freeman] Restored treeAggregate
74cf440 [Jeremy Freeman] Removed static methods
d28cf9a [Jeremy Freeman] Added usage notes
c3326e7 [Jeremy Freeman] Improved documentation
9541a41 [Jeremy Freeman] Merge remote-tracking branch 'upstream/master' into streaming-mllib
66eba5e [Jeremy Freeman] Fixed line lengths
2fe0720 [Jeremy Freeman] Minor cleanup
7d51378 [Jeremy Freeman] Moved streaming loader to MLUtils
b9b69f6 [Jeremy Freeman] Added setter methods
c3f8b5a [Jeremy Freeman] Modified logging
00aafdc [Jeremy Freeman] Add modifiers
14b801e [Jeremy Freeman] Name changes
c7d38a3 [Jeremy Freeman] Move check for empty data to GradientDescent
4b0a5d3 [Jeremy Freeman] Cleaned up tests
74188d6 [Jeremy Freeman] Eliminate dependency on commons
50dd237 [Jeremy Freeman] Removed experimental tag
6bfe1e6 [Jeremy Freeman] Fixed imports
a2a63ad [freeman] Makes convergence test more robust
86220bc [freeman] Streaming linear regression unit tests
fb4683a [freeman] Minor changes for scalastyle consistency
fd31e03 [freeman] Changed logging behavior
453974e [freeman] Fixed indentation
c4b1143 [freeman] Streaming linear regression
604f4d7 [freeman] Expanded private class to include mllib
d99aa85 [freeman] Helper methods for streaming MLlib apps
0898add [freeman] Added dependency on streaming
[SPARK-2550][MLLIB][APACHE SPARK] Support regularization and intercept in pyspark's linear methods.

Related to issue: [SPARK-2550](https://issues.apache.org/jira/browse/SPARK-2550?jql=project%20%3D%20SPARK%20AND%20resolution%20%3D%20Unresolved%20AND%20priority%20%3D%20Major%20ORDER%20BY%20key%20DESC).

Author: Michael Giannakopoulos <miccagiann@gmail.com>

Closes #1624 from miccagiann/new-branch and squashes the following commits:

c02e5f5 [Michael Giannakopoulos] Merge cleanly with upstream/master.
8dcb888 [Michael Giannakopoulos] Putting the if/else if statements in brackets.
fed8eaa [Michael Giannakopoulos] Adding a space in the message related to the IllegalArgumentException.
44e6ff0 [Michael Giannakopoulos] Adding a blank line before python class LinearRegressionWithSGD.
8eba9c5 [Michael Giannakopoulos] Change function signatures. Exception is thrown from the scala component and not from the python one.
638be47 [Michael Giannakopoulos] Modified code to comply with code standards.
ec50ee9 [Michael Giannakopoulos] Shorten the if-elif-else statement in regression.py file
b962744 [Michael Giannakopoulos] Replaced the enum classes, with strings-keywords for defining the values of 'regType' parameter.
78853ec [Michael Giannakopoulos] Providing intercept and regualizer functionallity for linear methods in only one function.
3ac8874 [Michael Giannakopoulos] Added support for regularizer and intercection parameters for linear regression method.
[SPARK-1580][MLLIB] Estimate ALS communication and computation costs.

Continue the work from #493.

Closes #493 and Closes #593

Author: Tor Myklebust <tmyklebu@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #1731 from mengxr/tmyklebu-alscost and squashes the following commits:

9b56a8b [Xiangrui Meng] updated API and added a simple test
68a3229 [Xiangrui Meng] merge master
217bd1d [Tor Myklebust] Documentation and choleskies -> subproblems.
8cbb718 [Tor Myklebust] Braces get spaces.
0455cd4 [Tor Myklebust] Parens for collectAsMap.
2b2febe [Tor Myklebust] Use `makeLinkRDDs` when estimating costs.
2ab7a5d [Tor Myklebust] Reindent estimateCost's declaration and make it return Seqs.
8b21e6d [Tor Myklebust] Fix overlong lines.
8cbebf1 [Tor Myklebust] Rename and clean up the return format of cost estimator.
6615ed5 [Tor Myklebust] It's more useful to give per-partition estimates.  Do that.
5530678 [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark into alscost
6c31324 [Tor Myklebust] Make it actually build...
a1184d1 [Tor Myklebust] Mark ALS.evaluatePartitioner DeveloperApi.
657a71b [Tor Myklebust] Simple-minded estimates of computation and communication costs in ALS.
dcf583a [Tor Myklebust] Remove the partitioner member variable; instead, thread that needle everywhere it needs to go.
23d6f91 [Tor Myklebust] Stop making the partitioner configurable.
495784f [Tor Myklebust] Merge branch 'master' of https://github.com/apache/spark
674933a [Tor Myklebust] Fix style.
40edc23 [Tor Myklebust] Fix missing space.
f841345 [Tor Myklebust] Fix daft bug creating 'pairs', also for -> foreach.
5ec9e6c [Tor Myklebust] Clean a couple of things up using 'map'.
36a0f43 [Tor Myklebust] Make the partitioner private.
d872b09 [Tor Myklebust] Add negative id ALS test.
df27697 [Tor Myklebust] Support custom partitioners.  Currently we use the same partitioner for users and products.
c90b6d8 [Tor Myklebust] Scramble user and product ids before bucketing.
c774d7d [Tor Myklebust] Make the partitioner a member variable and use it instead of modding directly.
[SPARK-2801][MLlib]: DistributionGenerator renamed to RandomDataGenerator. RandomRDD is now of generic type

The RandomRDDGenerators used to only output RDD[Double].
Now RandomRDDGenerators.randomRDD can be used to generate a random RDD[T] via a class that extends RandomDataGenerator, by supplying a type T and overriding the nextValue() function as they wish.

Author: Burak <brkyvz@gmail.com>

Closes #1732 from brkyvz/SPARK-2801 and squashes the following commits:

c94a694 [Burak] [SPARK-2801][MLlib] Missing ClassTags added
22d96fe [Burak] [SPARK-2801][MLlib]: DistributionGenerator renamed to RandomDataGenerator, generic types added for RandomRDD instead of Double
StatCounter on NumPy arrays [PYSPARK][SPARK-2012]

These changes allow StatCounters to work properly on NumPy arrays, to fix the issue reported here  (https://issues.apache.org/jira/browse/SPARK-2012).

If NumPy is installed, the NumPy functions ``maximum``, ``minimum``, and ``sqrt``, which work on arrays, are used to merge statistics. If not, we fall back on scalar operators, so it will work on arrays with NumPy, but will also work without NumPy.

New unit tests added, along with a check for NumPy in the tests.

Author: Jeremy Freeman <the.freeman.lab@gmail.com>

Closes #1725 from freeman-lab/numpy-max-statcounter and squashes the following commits:

fe973b1 [Jeremy Freeman] Avoid duplicate array import in tests
7f0e397 [Jeremy Freeman] Refactored check for numpy
8e764dd [Jeremy Freeman] Explicit numpy imports
875414c [Jeremy Freeman] Fixed indents
1c8a832 [Jeremy Freeman] Unit tests for StatCounter with NumPy arrays
176a127 [Jeremy Freeman] Use numpy arrays in StatCounter
[SPARK-1470][SPARK-1842] Use the scala-logging wrapper instead of the directly sfl4j api

Author: GuoQiang Li <witgo@qq.com>

Closes #1369 from witgo/SPARK-1470_new and squashes the following commits:

66a1641 [GuoQiang Li] IncompatibleResultTypeProblem
73a89ba [GuoQiang Li] Use the scala-logging wrapper instead of the directly sfl4j api.
Revert "[SPARK-1470][SPARK-1842] Use the scala-logging wrapper instead of the directly sfl4j api"

This reverts commit adc8303294e26efb4ed15e5f5ba1062f7988625d.
[SPARK-2316] Avoid O(blocks) operations in listeners

The existing code in `StorageUtils` is not the most efficient. Every time we want to update an `RDDInfo` we end up iterating through all blocks on all block managers just to discard most of them. The symptoms manifest themselves in the bountiful UI bugs observed in the wild. Many of these bugs are caused by the slow consumption of events in `LiveListenerBus`, which frequently leads to the event queue overflowing and `SparkListenerEvent`s being dropped on the floor. The changes made in this PR avoid this by first filtering out only the blocks relevant to us before computing storage information from them.

It's worth a mention that this corner of the Spark code is also not very well-tested at all. The bulk of the changes in this PR (more than 60%) is actually test cases for the various logic in `StorageUtils.scala` as well as `StorageTab.scala`. These will eventually be extended to cover the various listeners that constitute the `SparkUI`.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1679 from andrewor14/fix-drop-events and squashes the following commits:

f80c1fa [Andrew Or] Rewrite fold and reduceOption as sum
e132d69 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
14fa1c3 [Andrew Or] Simplify some code + update a few comments
a91be46 [Andrew Or] Make ExecutorsPage blazingly fast
bf6f09b [Andrew Or] Minor changes
8981de1 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
af19bc0 [Andrew Or] *UsedByRDD -> *UsedByRdd (minor)
6970bc8 [Andrew Or] Add extensive tests for StorageListener and the new code in StorageUtils
e080b9e [Andrew Or] Reduce run time of StorageUtils.updateRddInfo to near constant
2c3ef6a [Andrew Or] Actually filter out only the relevant RDDs
6fef86a [Andrew Or] Add extensive tests for new code in StorageStatus
b66b6b0 [Andrew Or] Use more efficient underlying data structures for blocks
6a7b7c0 [Andrew Or] Avoid chained operations on TraversableLike
a9ec384 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
b12fcd7 [Andrew Or] Fix tests + simplify sc.getRDDStorageInfo
da8e322 [Andrew Or] Merge branch 'master' of github.com:apache/spark into fix-drop-events
8e91921 [Andrew Or] Iterate through a filtered set of blocks when updating RDDInfo
7b2c4aa [Andrew Or] Rewrite blockLocationsFromStorageStatus + clean up method signatures
41fa50d [Andrew Or] Add a legacy constructor for StorageStatus
53af15d [Andrew Or] Refactor StorageStatus + add a bunch of tests
[SPARK-2454] Do not ship spark home to Workers

When standalone Workers launch executors, they inherit the Spark home set by the driver. This means if the worker machines do not share the same directory structure as the driver node, the Workers will attempt to run scripts (e.g. bin/compute-classpath.sh) that do not exist locally and fail. This is a common scenario if the driver is launched from outside of the cluster.

The solution is to simply not pass the driver's Spark home to the Workers. This PR further makes an attempt to avoid overloading the usages of `spark.home`, which is now only used for setting executor Spark home on Mesos and in python.

This is based on top of #1392 and originally reported by YanTangZhai. Tested on standalone cluster.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1734 from andrewor14/spark-home-reprise and squashes the following commits:

f71f391 [Andrew Or] Revert changes in python
1c2532c [Andrew Or] Merge branch 'master' of github.com:apache/spark into spark-home-reprise
188fc5d [Andrew Or] Avoid using spark.home where possible
09272b7 [Andrew Or] Always use Worker's working directory as spark home
[SPARK-1812]  sql/catalyst - Provide explicit type information

For Scala 2.11 compatibility.

Without the explicit type specification, withNullability
return type is inferred to be Attribute, and thus calling
at() on the returned object fails in these tests:

[ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:370: value at is not a
[ERROR]     val c4_notNull = 'a.boolean.notNull.at(3)
[ERROR]                                         ^
[ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:371: value at is not a
[ERROR]     val c5_notNull = 'a.boolean.notNull.at(4)
[ERROR]                                         ^
[ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:372: value at is not a
[ERROR]     val c6_notNull = 'a.boolean.notNull.at(5)
[ERROR]                                         ^
[ERROR] /Users/avati/work/spark/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala:558: value at is not a
[ERROR]     val s_notNull = 'a.string.notNull.at(0)

Signed-off-by: Anand Avati <avatiredhat.com>

Author: Anand Avati <avati@redhat.com>

Closes #1709 from avati/SPARK-1812-notnull and squashes the following commits:

0470eb3 [Anand Avati] SPARK-1812: sql/catalyst - Provide explicit type information
HOTFIX: Fixing test error in maven for flume-sink.

We needed to add an explicit dependency on scalatest since this
module will not get it from spark core like others do.
HOTFIX: Fix concurrency issue in FlumePollingStreamSuite.

This has been failing on master. One possible cause is that the port
gets contended if multiple test runs happen concurrently and they
hit this test at the same time. Since this test takes a long time
(60 seconds) that's very plausible. This patch randomizes the port
used in this test to avoid contention.
MAINTENANCE: Automated closing of pull requests.

This commit exists to close the following pull requests on Github:

Closes #706 (close requested by 'pwendell')
Closes #453 (close requested by 'pwendell')
Closes #557 (close requested by 'tdas')
Closes #495 (close requested by 'tdas')
Closes #1232 (close requested by 'pwendell')
Closes #82 (close requested by 'pwendell')
Closes #600 (close requested by 'pwendell')
Closes #473 (close requested by 'pwendell')
Closes #351 (close requested by 'pwendell')
[HOTFIX] Do not throw NPE if spark.test.home is not set

`spark.test.home` was introduced in #1734. This is fine for SBT but is failing maven tests. Either way it shouldn't throw an NPE.

Author: Andrew Or <andrewor14@gmail.com>

Closes #1739 from andrewor14/fix-spark-test-home and squashes the following commits:

ce2624c [Andrew Or] Do not throw NPE if spark.test.home is not set