[SPARK-17817] [PYSPARK] [FOLLOWUP] PySpark RDD Repartitioning Results in Highly Skewed Partition Sizes

## What changes were proposed in this pull request?

This change is a followup for #15389 which calls `_to_java_object_rdd()` to solve this issue. Due to the concern of the possible expensive cost of the call, we can choose to decrease the batch size to solve this issue too.

Simple benchmark:

    import time
    num_partitions = 20000
    a = sc.parallelize(range(int(1e6)), 2)
    start = time.time()
    l = a.repartition(num_partitions).glom().map(len).collect()
    end = time.time()
    print(end - start)

Before: 419.447577953
_to_java_object_rdd(): 421.916361094
decreasing the batch size: 423.712255955

## How was this patch tested?

Jenkins tests.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #15445 from viirya/repartition-batch-size.

## What changes were proposed in this pull request?

Add a crossJoin function to the DataFrame API similar to that in Scala. Joins with no condition (cartesian products) must be specified with the crossJoin API

## How was this patch tested?
Added python tests to ensure that an AnalysisException if a cartesian product is specified without crossJoin(), and that cartesian products can execute if specified via crossJoin()

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)

Please review https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark before opening a pull request.

Author: Srinath Shankar <srinath@databricks.com>

Closes #15493 from srinathshankar/crosspython.

Currently pyspark can only call the builtin java UDF, but can not call custom java UDF. It would be better to allow that. 2 benefits:
* Leverage the power of rich third party java library
* Improve the performance. Because if we use python UDF, python daemons will be started on worker which will affect the performance.

Author: Jeff Zhang <zjffdu@apache.org>

Closes #9766 from zjffdu/SPARK-11775.

[SPARK-11905](https://issues.apache.org/jira/browse/SPARK-11905

) added support for `persist`/`cache` for `Dataset`. However, there is no user-facing API to check if a `Dataset` is cached and if so what the storage level is. This PR adds `getStorageLevel` to `Dataset`, analogous to `RDD.getStorageLevel`.

Updated `DatasetCacheSuite`.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #13780 from MLnick/ds-storagelevel.

Signed-off-by: Michael Armbrust <michael@databricks.com>

[SPARK-17870][MLLIB][ML] Change statistic to pValue for SelectKBest and SelectPercentile because of DoF difference

## What changes were proposed in this pull request?

For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features.

So we change statistic to pValue for SelectKBest and SelectPercentile

## How was this patch tested?
change existing test

Author: Peng <peng.meng@intel.com>

Closes #15444 from mpjlu/chisqure-bug.

## What changes were proposed in this pull request?
Since ```ml.evaluation``` has supported save/load at Scala side, supporting it at Python side is very straightforward and easy.

## How was this patch tested?
Add python doctest.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #13194 from yanboliang/spark-15402.

## What changes were proposed in this pull request?
Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```.

## How was this patch tested?
Unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15430 from yanboliang/spark-15957-python.

## What changes were proposed in this pull request?

Metrics are needed for monitoring structured streaming apps. Here is the design doc for implementing the necessary metrics.
https://docs.google.com/document/d/1NIdcGuR1B3WIe8t7VxLrt58TJB4DtipWEbj5I_mzJys/edit?usp=sharing

Specifically, this PR adds the following public APIs changes.

### New APIs
- `StreamingQuery.status` returns a `StreamingQueryStatus` object (renamed from `StreamingQueryInfo`, see later)

- `StreamingQueryStatus` has the following important fields
  - inputRate - Current rate (rows/sec) at which data is being generated by all the sources
  - processingRate - Current rate (rows/sec) at which the query is processing data from
                                  all the sources
  - ~~outputRate~~ - *Does not work with wholestage codegen*
  - latency - Current average latency between the data being available in source and the sink writing the corresponding output
  - sourceStatuses: Array[SourceStatus] - Current statuses of the sources
  - sinkStatus: SinkStatus - Current status of the sink
  - triggerStatus - Low-level detailed status of the last completed/currently active trigger
    - latencies - getOffset, getBatch, full trigger, wal writes
    - timestamps - trigger start, finish, after getOffset, after getBatch
    - numRows - input, output, state total/updated rows for aggregations

- `SourceStatus` has the following important fields
  - inputRate - Current rate (rows/sec) at which data is being generated by the source
  - processingRate - Current rate (rows/sec) at which the query is processing data from the source
  - triggerStatus - Low-level detailed status of the last completed/currently active trigger

- Python API for `StreamingQuery.status()`

### Breaking changes to existing APIs
**Existing direct public facing APIs**
- Deprecated direct public-facing APIs `StreamingQuery.sourceStatuses` and `StreamingQuery.sinkStatus` in favour of `StreamingQuery.status.sourceStatuses/sinkStatus`.
  - Branch 2.0 should have it deprecated, master should have it removed.

**Existing advanced listener APIs**
- `StreamingQueryInfo` renamed to `StreamingQueryStatus` for consistency with `SourceStatus`, `SinkStatus`
   - Earlier StreamingQueryInfo was used only in the advanced listener API, but now it is used in direct public-facing API (StreamingQuery.status)

- Field `queryInfo` in listener events `QueryStarted`, `QueryProgress`, `QueryTerminated` changed have name `queryStatus` and return type `StreamingQueryStatus`.

- Field `offsetDesc` in `SourceStatus` was Option[String], converted it to `String`.

- For `SourceStatus` and `SinkStatus` made constructor private instead of private[sql] to make them more java-safe. Instead added `private[sql] object SourceStatus/SinkStatus.apply()` which are harder to accidentally use in Java.

## How was this patch tested?

Old and new unit tests.
- Rate calculation and other internal logic of StreamMetrics tested by StreamMetricsSuite.
- New info in statuses returned through StreamingQueryListener is tested in StreamingQueryListenerSuite.
- New and old info returned through StreamingQuery.status is tested in StreamingQuerySuite.
- Source-specific tests for making sure input rows are counted are is source-specific test suites.
- Additional tests to test minor additions in LocalTableScanExec, StateStore, etc.

Metrics also manually tested using Ganglia sink

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #15307 from tdas/SPARK-17731.

## What changes were proposed in this pull request?

update python api for NaiveBayes: add weight col parameter.

## How was this patch tested?

doctests added.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #15406 from WeichenXu123/nb_python_update.

## What changes were proposed in this pull request?
This patch improves the window function frame boundary API to make it more obvious to read and to use. The two high level changes are:

1. Create Window.currentRow, Window.unboundedPreceding, Window.unboundedFollowing to indicate the special values in frame boundaries. These methods map to the special integral values so we are not breaking backward compatibility here. This change makes the frame boundaries more self-evident (instead of Long.MinValue, it becomes Window.unboundedPreceding).

2. In Python, for any value less than or equal to JVM's Long.MinValue, treat it as Window.unboundedPreceding. For any value larger than or equal to JVM's Long.MaxValue, treat it as Window.unboundedFollowing. Before this change, if the user specifies any value that is less than Long.MinValue but not -sys.maxsize (e.g. -sys.maxsize + 1), the number we pass over to the JVM would overflow, resulting in a frame that does not make sense.

Code example required to specify a frame before this patch:
```
Window.rowsBetween(-Long.MinValue, 0)
```

While the above code should still work, the new way is more obvious to read:
```
Window.rowsBetween(Window.unboundedPreceding, Window.currentRow)
```

## How was this patch tested?
- Updated DataFrameWindowSuite (for Scala/Java)
- Updated test_window_functions_cumulative_sum (for Python)
- Renamed DataFrameWindowSuite DataFrameWindowFunctionsSuite to better reflect its purpose

Author: Reynold Xin <rxin@databricks.com>

Closes #15438 from rxin/SPARK-17845.

[SPARK-14761][SQL] Reject invalid join methods when join columns are not specified in PySpark DataFrame join.

## What changes were proposed in this pull request?

In PySpark, the invalid join type will not throw error for the following join:
```df1.join(df2, how='not-a-valid-join-type')```

The signature of the join is:
```def join(self, other, on=None, how=None):```
The existing code completely ignores the `how` parameter when `on` is `None`. This patch will process the arguments passed to join and pass in to JVM Spark SQL Analyzer, which will validate the join type passed.

## How was this patch tested?
Used manual and existing test suites.

Author: Bijay Pathak <bkpathak@mtu.edu>

Closes #15409 from bkpathak/SPARK-14761.

## What changes were proposed in this pull request?

SQLConf is session-scoped and mutable. However, we do have the requirement for a static SQL conf, which is global and immutable, e.g. the `schemaStringThreshold` in `HiveExternalCatalog`, the flag to enable/disable hive support, the global temp view database in https://github.com/apache/spark/pull/14897.

Actually we've already implemented static SQL conf implicitly via `SparkConf`, this PR just make it explicit and expose it to users, so that they can see the config value via SQL command or `SparkSession.conf`, and forbid users to set/unset static SQL conf.

## How was this patch tested?

new tests in SQLConfSuite

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15295 from cloud-fan/global-conf.

## What changes were proposed in this pull request?

The root cause that we would ignore SparkConf when launching JVM is that SparkConf require JVM to be created first.  https://github.com/apache/spark/blob/master/python/pyspark/conf.py#L106
In this PR, I would defer the launching of JVM until SparkContext is created so that we can pass SparkConf to JVM correctly.

## How was this patch tested?

Use the example code in the description of SPARK-17387,
```
$ SPARK_HOME=$PWD PYTHONPATH=python:python/lib/py4j-0.10.3-src.zip python
Python 2.7.12 (default, Jul  1 2016, 15:12:24)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> from pyspark import SparkContext
>>> from pyspark import SparkConf
>>> conf = SparkConf().set("spark.driver.memory", "4g")
>>> sc = SparkContext(conf=conf)
```
And verify the spark.driver.memory is correctly picked up.

```
...op/ -Xmx4g org.apache.spark.deploy.SparkSubmit --conf spark.driver.memory=4g pyspark-shell
```

Author: Jeff Zhang <zjffdu@apache.org>

Closes #14959 from zjffdu/SPARK-17387.

## What changes were proposed in this pull request?

Quoted from JIRA description:

Calling repartition on a PySpark RDD to increase the number of partitions results in highly skewed partition sizes, with most having 0 rows. The repartition method should evenly spread out the rows across the partitions, and this behavior is correctly seen on the Scala side.

Please reference the following code for a reproducible example of this issue:

    num_partitions = 20000
    a = sc.parallelize(range(int(1e6)), 2)  # start with 2 even partitions
    l = a.repartition(num_partitions).glom().map(len).collect()  # get length of each partition
    min(l), max(l), sum(l)/len(l), len(l)  # skewed!

In Scala's `repartition` code, we will distribute elements evenly across output partitions. However, the RDD from Python is serialized as a single binary data, so the distribution fails. We need to convert the RDD in Python to java object before repartitioning.

## How was this patch tested?

Jenkins tests.

Author: Liang-Chi Hsieh <viirya@gmail.com>

Closes #15389 from viirya/pyspark-rdd-repartition.

## What changes were proposed in this pull request?

address post hoc review comments for https://github.com/apache/spark/pull/14897

## How was this patch tested?

N/A

Author: Wenchen Fan <wenchen@databricks.com>

Closes #15424 from cloud-fan/global-temp-view.

## What changes were proposed in this pull request?
Upgraded to a newer version of Pyrolite which supports serialization of a BinaryType StructField for PySpark.SQL

## How was this patch tested?
Added a unit test which fails with a raised ValueError when using the previous version of Pyrolite 4.9 and Python3

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #15386 from BryanCutler/pyrolite-upgrade-SPARK-17808.

## What changes were proposed in this pull request?
When I was creating the example code for SPARK-10496, I realized it was pretty convoluted to define the frame boundaries for window functions when there is no partition column or ordering column. The reason is that we don't provide a way to create a WindowSpec directly with the frame boundaries. We can trivially improve this by adding rowsBetween and rangeBetween to Window object.

As an example, to compute cumulative sum using the natural ordering, before this pr:
```
df.select('key, sum("value").over(Window.partitionBy(lit(1)).rowsBetween(Long.MinValue, 0)))
```

After this pr:
```
df.select('key, sum("value").over(Window.rowsBetween(Long.MinValue, 0)))
```

Note that you could argue there is no point specifying a window frame without partitionBy/orderBy -- but it is strange that only rowsBetween and rangeBetween are not the only two APIs not available.

This also fixes https://issues.apache.org/jira/browse/SPARK-17656 (removing _root_.scala).

## How was this patch tested?
Added test cases to compute cumulative sum in DataFrameWindowSuite for Scala/Java and tests.py for Python.

Author: Reynold Xin <rxin@databricks.com>

Closes #15412 from rxin/SPARK-17844.

## What changes were proposed in this pull request?

Global temporary view is a cross-session temporary view, which means it's shared among all sessions. Its lifetime is the lifetime of the Spark application, i.e. it will be automatically dropped when the application terminates. It's tied to a system preserved database `global_temp`(configurable via SparkConf), and we must use the qualified name to refer a global temp view, e.g. SELECT * FROM global_temp.view1.

changes for `SessionCatalog`:

1. add a new field `gloabalTempViews: GlobalTempViewManager`, to access the shared global temp views, and the global temp db name.
2. `createDatabase` will fail if users wanna create `global_temp`, which is system preserved.
3. `setCurrentDatabase` will fail if users wanna set `global_temp`, which is system preserved.
4. add `createGlobalTempView`, which is used in `CreateViewCommand` to create global temp views.
5. add `dropGlobalTempView`, which is used in `CatalogImpl` to drop global temp view.
6. add `alterTempViewDefinition`, which is used in `AlterViewAsCommand` to update the view definition for local/global temp views.
7. `renameTable`/`dropTable`/`isTemporaryTable`/`lookupRelation`/`getTempViewOrPermanentTableMetadata`/`refreshTable` will handle global temp views.

changes for SQL commands:

1. `CreateViewCommand`/`AlterViewAsCommand` is updated to support global temp views
2. `ShowTablesCommand` outputs a new column `database`, which is used to distinguish global and local temp views.
3. other commands can also handle global temp views if they call `SessionCatalog` APIs which accepts global temp views, e.g. `DropTableCommand`, `AlterTableRenameCommand`, `ShowColumnsCommand`, etc.

changes for other public API

1. add a new method `dropGlobalTempView` in `Catalog`
2. `Catalog.findTable` can find global temp view
3. add a new method `createGlobalTempView` in `Dataset`

## How was this patch tested?

new tests in `SQLViewSuite`

Author: Wenchen Fan <wenchen@databricks.com>

Closes #14897 from cloud-fan/global-temp-view.

[SPARK-16960][SQL] Deprecate approxCountDistinct, toDegrees and toRadians according to FunctionRegistry

## What changes were proposed in this pull request?

It seems `approxCountDistinct`, `toDegrees` and `toRadians` are also missed while matching the names to the ones in `FunctionRegistry`. (please see [approx_count_distinct](https://github.com/apache/spark/blob/5c2ae79bfcf448d8dc9217efafa1409997c739de/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala#L244), [degrees](https://github.com/apache/spark/blob/5c2ae79bfcf448d8dc9217efafa1409997c739de/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala#L203) and [radians](https://github.com/apache/spark/blob/5c2ae79bfcf448d8dc9217efafa1409997c739de/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/FunctionRegistry.scala#L222) in `FunctionRegistry`).

I took a scan between `functions.scala` and `FunctionRegistry` and it seems these are all left. For `countDistinct` and `sumDistinct`, they are not registered in `FunctionRegistry`.

This PR deprecates `approxCountDistinct`, `toDegrees` and `toRadians` and introduces `approx_count_distinct`, `degrees` and `radians`.

## How was this patch tested?

Existing tests should cover this.

Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>

Closes #14538 from HyukjinKwon/SPARK-16588-followup.

## What changes were proposed in this pull request?
If given a list of paths, `pyspark.sql.readwriter.text` will attempt to use an undefined variable `paths`.  This change checks if the param `paths` is a basestring and then converts it to a list, so that the same variable `paths` can be used for both cases

## How was this patch tested?
Added unit test for reading list of files

Author: Bryan Cutler <cutlerb@gmail.com>

Closes #15379 from BryanCutler/sql-readtext-paths-SPARK-17805.

## What changes were proposed in this pull request?
1,parity check and add missing test suites for ml's NB
2,remove some unused imports

## How was this patch tested?
 manual tests in spark-shell

Author: Zheng RuiFeng <ruifengz@foxmail.com>

Closes #15312 from zhengruifeng/nb_test_parity.

## What changes were proposed in this pull request?

Replaces` ValueError` with `IndexError` when index passed to `ml` / `mllib` `SparseVector.__getitem__` is out of range. This ensures correct iteration behavior.

Replaces `ValueError` with `IndexError` for `DenseMatrix` and `SparkMatrix` in `ml` / `mllib`.

## How was this patch tested?

PySpark `ml` / `mllib` unit tests. Additional unit tests to prove that the problem has been resolved.

Author: zero323 <zero323@users.noreply.github.com>

Closes #15144 from zero323/SPARK-17587.

## What changes were proposed in this pull request?

This PR removes a patch on ListConverter from https://github.com/apache/spark/pull/5570, as it is no longer necessary. The underlying issue in Py4J https://github.com/bartdag/py4j/issues/160 was patched in https://github.com/bartdag/py4j/commit/224b94b6665e56a93a064073886e1d803a4969d2 and is present in 0.10.3, the version currently in use in Spark.

## How was this patch tested?

The original test added in https://github.com/apache/spark/pull/5570 remains.

Author: Jason White <jason.white@shopify.com>

Closes #15254 from JasonMWhite/remove_listconverter_patch.

## What changes were proposed in this pull request?

Partial revert of #15277 to instead sort and store input to model rather than require sorted input

## How was this patch tested?

Existing tests.

Author: Sean Owen <sowen@cloudera.com>

Closes #15299 from srowen/SPARK-17704.2.

Spark SQL has great support for reading text files that contain JSON data.  However, in many cases the JSON data is just one column amongst others.  This is particularly true when reading from sources such as Kafka.  This PR adds a new functions `from_json` that converts a string column into a nested `StructType` with a user specified schema.

Example usage:
```scala
val df = Seq("""{"a": 1}""").toDS()
val schema = new StructType().add("a", IntegerType)

df.select(from_json($"value", schema) as 'json) // => [json: <a: int>]
```

This PR adds support for java, scala and python.  I leveraged our existing JSON parsing support by moving it into catalyst (so that we could define expressions using it).  I left SQL out for now, because I'm not sure how users would specify a schema.

Author: Michael Armbrust <michael@databricks.com>

Closes #15274 from marmbrus/jsonParser.

## What changes were proposed in this pull request?

This PR proposes to fix wrongly indented examples in PySpark documentation

```
-        >>> json_sdf = spark.readStream.format("json")\
-                                       .schema(sdf_schema)\
-                                       .load(tempfile.mkdtemp())
+        >>> json_sdf = spark.readStream.format("json") \\
+        ...     .schema(sdf_schema) \\
+        ...     .load(tempfile.mkdtemp())
```

```
-        people.filter(people.age > 30).join(department, people.deptId == department.id)\
+        people.filter(people.age > 30).join(department, people.deptId == department.id) \\
```

```
-        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])), \
-                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, 1.23), (2, 4.56)])),
+        ...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
```

```
-        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])), \
-                        LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
+        >>> examples = [LabeledPoint(1.1, Vectors.sparse(3, [(0, -1.23), (2, 4.56e-7)])),
+        ...             LabeledPoint(0.0, Vectors.dense([1.01, 2.02, 3.03]))]
```

```
-        ...      for x in iterator:
-        ...           print(x)
+        ...     for x in iterator:
+        ...          print(x)
```

## How was this patch tested?

Manually tested.

**Before**

![2016-09-26 8 36 02](https://cloud.githubusercontent.com/assets/6477701/18834471/05c7a478-8431-11e6-94bb-09aa37b12ddb.png)

![2016-09-26 9 22 16](https://cloud.githubusercontent.com/assets/6477701/18834472/06c8735c-8431-11e6-8775-78631eab0411.png)

<img width="601" alt="2016-09-27 2 29 27" src="https://cloud.githubusercontent.com/assets/6477701/18861294/29c0d5b4-84bf-11e6-99c5-3c9d913c125d.png">

<img width="1056" alt="2016-09-27 2 29 58" src="https://cloud.githubusercontent.com/assets/6477701/18861298/31694cd8-84bf-11e6-9e61-9888cb8c2089.png">

<img width="1079" alt="2016-09-27 2 30 05" src="https://cloud.githubusercontent.com/assets/6477701/18861301/359722da-84bf-11e6-97f9-5f5365582d14.png">

**After**

![2016-09-26 9 29 47](https://cloud.githubusercontent.com/assets/6477701/18834467/0367f9da-8431-11e6-86d9-a490d3297339.png)

![2016-09-26 9 30 24](https://cloud.githubusercontent.com/assets/6477701/18834463/f870fae0-8430-11e6-9482-01fc47898492.png)

<img width="515" alt="2016-09-27 2 28 19" src="https://cloud.githubusercontent.com/assets/6477701/18861305/3ff88b88-84bf-11e6-902c-9f725e8a8b10.png">

<img width="652" alt="2016-09-27 3 50 59" src="https://cloud.githubusercontent.com/assets/6477701/18863053/592fbc74-84ca-11e6-8dbf-99cf57947de8.png">

<img width="709" alt="2016-09-27 3 51 03" src="https://cloud.githubusercontent.com/assets/6477701/18863060/601607be-84ca-11e6-80aa-a401df41c321.png">

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #15242 from HyukjinKwon/minor-example-pyspark.

## What changes were proposed in this pull request?

Add Python API for multinomial logistic regression.

- add `family` param in python api.
- expose `coefficientMatrix` and `interceptVector` for `LogisticRegressionModel`
- add python-side testcase for multinomial logistic regression
- update python doc.

## How was this patch tested?

existing and added doc tests.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #14852 from WeichenXu123/add_MLOR_python.

## What changes were proposed in this pull request?
#14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed:
* We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```.
* Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also.
* If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620)
* We should use lower case of the selector type names to follow MLlib convention.
* Add ML Python API.

## How was this patch tested?
Unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15214 from yanboliang/spark-17017.

## What changes were proposed in this pull request?

Match ProbabilisticClassifer.thresholds requirements to R randomForest cutoff, requiring all > 0

## How was this patch tested?

Jenkins tests plus new test cases

Author: Sean Owen <sowen@cloudera.com>

Closes #15149 from srowen/SPARK-17057.

## What changes were proposed in this pull request?

Move the internals of the PySpark accumulator API from the old deprecated API on top of the new accumulator API.

## How was this patch tested?

The existing PySpark accumulator tests (both unit tests and doc tests at the start of accumulator.py).

Author: Holden Karau <holden@us.ibm.com>

Closes #14467 from holdenk/SPARK-16861-refactor-pyspark-accumulator-api.

## What changes were proposed in this pull request?

Add treeAggregateDepth parameter for AFTSurvivalRegression to keep consistent with LiR/LoR.

## How was this patch tested?

Existing tests.

Author: WeichenXu <WeichenXu123@outlook.com>

Closes #14851 from WeichenXu123/add_treeAggregate_param_for_survival_regression.

[SPARK-17583][SQL] Remove uesless rowSeparator variable and set auto-expanding buffer as default for maxCharsPerColumn option in CSV

## What changes were proposed in this pull request?

This PR includes the changes below:

1. Upgrade Univocity library from 2.1.1 to 2.2.1

  This includes some performance improvement and also enabling auto-extending buffer in `maxCharsPerColumn` option in CSV. Please refer the [release notes](https://github.com/uniVocity/univocity-parsers/releases).

2. Remove useless `rowSeparator` variable existing in `CSVOptions`

  We have this unused variable in [CSVOptions.scala#L127](https://github.com/apache/spark/blob/29952ed096fd2a0a19079933ff691671d6f00835/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVOptions.scala#L127) but it seems possibly causing confusion that it actually does not care of `\r\n`. For example, we have an issue open about this, [SPARK-17227](https://issues.apache.org/jira/browse/SPARK-17227), describing this variable.

  This variable is virtually not being used because we rely on `LineRecordReader` in Hadoop which deals with only both `\n` and `\r\n`.

3. Set the default value of `maxCharsPerColumn` to auto-expending.

  We are setting 1000000 for the length of each column. It'd be more sensible we allow auto-expending rather than fixed length by default.

  To make sure, using `-1` is being described in the release note, [2.2.0](https://github.com/uniVocity/univocity-parsers/releases/tag/v2.2.0).

## How was this patch tested?

N/A

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #15138 from HyukjinKwon/SPARK-17583.

## What changes were proposed in this pull request?
This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value.
Sometimes, null value might also be useful to users, so in these cases, Bucketizer should
reserve one extra bucket for NaN values, instead of throwing an illegal exception.
Before:
```
Bucketizer.transform on NaN value threw an illegal exception.
```
After:
```
NaN values will be grouped in an extra bucket.
```
## How was this patch tested?
New test cases added in `BucketizerSuite`.
Signed-off-by: VinceShieh <vincent.xieintel.com>

Author: VinceShieh <vincent.xie@intel.com>

Closes #14858 from VinceShieh/spark-17219.

## What changes were proposed in this pull request?

Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection

## How was this patch tested?

Add Scala ut

Author: Peng, Meng <peng.meng@intel.com>

Closes #14597 from mpjlu/fprChiSquare.

## What changes were proposed in this pull request?
Users would like to add a directory as dependency in some cases, they can use ```SparkContext.addFile``` with argument ```recursive=true``` to recursively add all files under the directory by using Scala. But Python users can only add file not directory, we should also make it supported.

## How was this patch tested?
Unit test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #15140 from yanboliang/spark-17585.

## What changes were proposed in this pull request?

The Scala version of `SparkContext` has a handy field called `uiWebUrl` that tells you which URL the SparkUI spawned by that instance lives at. This is often very useful because the value for `spark.ui.port` in the config is only a suggestion; if that port number is taken by another Spark instance on the same machine, Spark will just keep incrementing the port until it finds a free one. So, on a machine with a lot of running PySpark instances, you often have to start trying all of them one-by-one until you find your application name.

Scala users have a way around this with `uiWebUrl` but Java and Python users do not. This pull request fixes this in the most straightforward way possible, simply propagating this field through the `JavaSparkContext` and into pyspark through the Java gateway.

Please let me know if any additional documentation/testing is needed.

## How was this patch tested?

Existing tests were run to make sure there were no regressions, and a binary distribution was created and tested manually for the correct value of `sc.uiWebPort` in a variety of circumstances.

Author: Adrian Petrescu <apetresc@gmail.com>

Closes #15000 from apetresc/pyspark-uiweburl.

## What changes were proposed in this pull request?

In optimizer, we try to evaluate the condition to see whether it's nullable or not, but some expressions are not evaluable, we should check that before evaluate it.

## How was this patch tested?

Added regression tests.

Author: Davies Liu <davies@databricks.com>

Closes #15103 from davies/udf_join.

## Problem

CSV in Spark 2.0.0:
-  does not read null values back correctly for certain data types such as `Boolean`, `TimestampType`, `DateType` -- this is a regression comparing to 1.6;
- does not read empty values (specified by `options.nullValue`) as `null`s for `StringType` -- this is compatible with 1.6 but leads to problems like SPARK-16903.

## What changes were proposed in this pull request?

This patch makes changes to read all empty values back as `null`s.

## How was this patch tested?

New test cases.

Author: Liwei Lin <lwlin7@gmail.com>

Closes #14118 from lw-lin/csv-cast-null.

[SPARK-17548][MLLIB] Word2VecModel.findSynonyms no longer spuriously rejects the best match when invoked with a vector

## What changes were proposed in this pull request?

This pull request changes the behavior of `Word2VecModel.findSynonyms` so that it will not spuriously reject the best match when invoked with a vector that does not correspond to a word in the model's vocabulary.  Instead of blindly discarding the best match, the changed implementation discards a match that corresponds to the query word (in cases where `findSynonyms` is invoked with a word) or that has an identical angle to the query vector.

## How was this patch tested?

I added a test to `Word2VecSuite` to ensure that the word with the most similar vector from a supplied vector would not be spuriously rejected.

Author: William Benton <willb@redhat.com>

Closes #15105 from willb/fix/findSynonyms.

## What changes were proposed in this pull request?

For large objects, pickle does not raise useful error messages. However, we can wrap them to be slightly more user friendly:

Example 1:
```
def run():
  import numpy.random as nr
  b = nr.bytes(8 * 1000000000)
  sc.parallelize(range(1000), 1000).map(lambda x: len(b)).count()

run()
```

Before:
```
error: 'i' format requires -2147483648 <= number <= 2147483647
```

After:
```
pickle.PicklingError: Object too large to serialize: 'i' format requires -2147483648 <= number <= 2147483647
```

Example 2:
```
def run():
  import numpy.random as nr
  b = sc.broadcast(nr.bytes(8 * 1000000000))
  sc.parallelize(range(1000), 1000).map(lambda x: len(b.value)).count()

run()
```

Before:
```
SystemError: error return without exception set
```

After:
```
cPickle.PicklingError: Could not serialize broadcast: SystemError: error return without exception set
```

## How was this patch tested?

Manually tried out these cases

cc davies

Author: Eric Liang <ekl@databricks.com>

Closes #15026 from ericl/spark-17472.