This is #9263 from gliptak (improving grouping/display of test case results) with a small fix of bisecting k-means unit test.

Author: Gábor Lipták <gliptak@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #10850 from mengxr/SPARK-11295.

This reverts commit c6f971b4.

This PR aims to allow the prediction column of `BinaryClassificationEvaluator` to be of double type.

Author: BenFradet <benjamin.fradet@gmail.com>

Closes #10472 from BenFradet/SPARK-9716.

SPARK-11295 Add packages to JUnit output for Python tests

This improves grouping/display of test case results.

Author: Gábor Lipták <gliptak@gmail.com>

Closes #9263 from gliptak/SPARK-11295.

From the coverage issues for 1.6 : Add Python API for mllib.clustering.BisectingKMeans.

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

Closes #10150 from holdenk/SPARK-11937-python-api-coverage-SPARK-11944-python-mllib.clustering.BisectingKMeans.

Fix order of arguments that Pyspark RDD.fold passes to its op -  should be (acc, obj) like other implementations.

Obviously, this is a potentially breaking change, so can only happen for 2.x

CC davies

Author: Sean Owen <sowen@cloudera.com>

Closes #10771 from srowen/SPARK-7683.

Add PySpark missing methods and params for ml.feature:
* ```RegexTokenizer``` should support setting ```toLowercase```.
* ```MinMaxScalerModel``` should support output ```originalMin``` and ```originalMax```.
* ```PCAModel``` should support output ```pc```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9908 from yanboliang/spark-11925.

In this PR the new CatalystQl parser stack reaches grammar parity with the old Parser-Combinator based SQL Parser. This PR also replaces all uses of the old Parser, and removes it from the code base.

Although the existing Hive and SQL parser dialects were mostly the same, some kinks had to be worked out:
- The SQL Parser allowed syntax like ```APPROXIMATE(0.01) COUNT(DISTINCT a)```. In order to make this work we needed to hardcode approximate operators in the parser, or we would have to create an approximate expression. ```APPROXIMATE_COUNT_DISTINCT(a, 0.01)``` would also do the job and is much easier to maintain. So, this PR **removes** this keyword.
- The old SQL Parser supports ```LIMIT``` clauses in nested queries. This is **not supported** anymore. See https://github.com/apache/spark/pull/10689 for the rationale for this.
- Hive has a charset name char set literal combination it supports, for instance the following expression ```_ISO-8859-1 0x4341464562616265``` would yield this string: ```CAFEbabe```. Hive will only allow charset names to start with an underscore. This is quite annoying in spark because as soon as you use a tuple names will start with an underscore. In this PR we **remove** this feature from the parser. It would be quite easy to implement such a feature as an Expression later on.
- Hive and the SQL Parser treat decimal literals differently. Hive will turn any decimal into a ```Double``` whereas the SQL Parser would convert a non-scientific decimal into a ```BigDecimal```, and would turn a scientific decimal into a Double. We follow Hive's behavior here. The new parser supports a big decimal literal, for instance: ```81923801.42BD```, which can be used when a big decimal is needed.

cc rxin viirya marmbrus yhuai cloud-fan

Author: Herman van Hovell <hvanhovell@questtec.nl>

Closes #10745 from hvanhovell/SPARK-12575-2.

This PR makes bucketing and exchange share one common hash algorithm, so that we can guarantee the data distribution is same between shuffle and bucketed data source, which enables us to only shuffle one side when join a bucketed table and a normal one.

This PR also fixes the tests that are broken by the new hash behaviour in shuffle.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10703 from cloud-fan/use-hash-expr-in-shuffle.

This pull request rewrites CaseWhen expression to break the single, monolithic "branches" field into a sequence of tuples (Seq[(condition, value)]) and an explicit optional elseValue field.

Prior to this pull request, each even position in "branches" represents the condition for each branch, and each odd position represents the value for each branch. The use of them have been pretty confusing with a lot sliding windows or grouped(2) calls.

Author: Reynold Xin <rxin@databricks.com>

Closes #10734 from rxin/simplify-case.

https://issues.apache.org/jira/browse/SPARK-12642

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10694 from cloud-fan/hash-expr.

This replaces the `execfile` used for running custom python shell scripts
with explicit open, compile and exec (as recommended by 2to3). The reason
for this change is to make the pythonstartup option compatible with python3.

Author: Erik Selin <erik.selin@gmail.com>

Closes #10255 from tyro89/pythonstartup-python3.

- [x] Upgrade Py4J to 0.9.1
- [x] SPARK-12657: Revert SPARK-12617
- [x] SPARK-12658: Revert SPARK-12511
  - Still keep the change that only reading checkpoint once. This is a manual change and worth to take a look carefully. https://github.com/zsxwing/spark/commit/bfd4b5c040eb29394c3132af3c670b1a7272457c
- [x] Verify no leak any more after reverting our workarounds

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10692 from zsxwing/py4j-0.9.1.

[SPARK-12603][MLLIB] PySpark MLlib GaussianMixtureModel should support single instance predict/predictSoft

PySpark MLlib ```GaussianMixtureModel``` should support single instance ```predict/predictSoft``` just like Scala do.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10552 from yanboliang/spark-12603.

Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.

Author: Sean Owen <sowen@cloudera.com>

Closes #10570 from srowen/SPARK-12618.

If initial model passed to GMM is not empty it causes net.razorvine.pickle.PickleException. It can be fixed by converting initialModel.weights to list.

Author: zero323 <matthew.szymkiewicz@gmail.com>

Closes #10644 from zero323/SPARK-12006.

This reverts commit fcd013cf.

Author: Yin Huai <yhuai@databricks.com>

Closes #10632 from yhuai/pythonStyle.

Move Py4jCallbackConnectionCleaner to Streaming because the callback server starts only in StreamingContext.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10621 from zsxwing/SPARK-12617-2.

If initial model passed to GMM is not empty it causes `net.razorvine.pickle.PickleException`. It can be fixed by converting `initialModel.weights` to `list`.

Author: zero323 <matthew.szymkiewicz@gmail.com>

Closes #9986 from zero323/SPARK-12006.

[SPARK-11815][ML][PYSPARK] PySpark DecisionTreeClassifier & DecisionTreeRegressor should support setSeed

PySpark ```DecisionTreeClassifier``` & ```DecisionTreeRegressor``` should support ```setSeed``` like what we do at Scala side.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9807 from yanboliang/spark-11815.

Add ```computeCost``` to ```KMeansModel``` as evaluator for PySpark spark.ml.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9931 from yanboliang/SPARK-11945.

PySpark SparseVector should have "Found duplicate indices" error message

Author: Joshi <rekhajoshm@gmail.com>
Author: Rekha Joshi <rekhajoshm@gmail.com>

Closes #9525 from rekhajoshm/SPARK-11531.

From JIRA:
Currently, PySpark wrappers for spark.ml Scala classes are brittle when accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an integer); it must be set to "2.0" (a float). Fixing this is not trivial since there does not appear to be a natural place to insert the conversion before Python wrappers call Java's Params setter method.

A possible fix will be to include a method "_checkType" to PySpark's Param class which checks the type, prints an error if needed, and converts types when relevant (e.g., int to float, or scipy matrix to array). The Java wrapper method which copies params to Scala can call this method when available.

This fix instead checks the types at set time since I think failing sooner is better, but I can switch it around to check at copy time if that would be better. So far this only converts int to float and other conversions (like scipymatrix to array) are left for the future.

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

Closes #9581 from holdenk/SPARK-7675-PySpark-sparkml-Params-type-conversion.

Add `columnSimilarities` to IndexedRowMatrix for PySpark spark.mllib.linalg.

Author: Kai Jiang <jiangkai@gmail.com>

Closes #10158 from vectorijk/spark-12041.

There is an issue that Py4J's PythonProxyHandler.finalize blocks forever. (https://github.com/bartdag/py4j/pull/184)

Py4j will create a PythonProxyHandler in Java for "transformer_serializer" when calling "registerSerializer". If we call "registerSerializer" twice, the second PythonProxyHandler will override the first one, then the first one will be GCed and trigger "PythonProxyHandler.finalize". To avoid that, we should not call"registerSerializer" more than once, so that "PythonProxyHandler" in Java side won't be GCed.

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10514 from zsxwing/SPARK-12511.

This patch added Py4jCallbackConnectionCleaner to clean the leak sockets of Py4J every 30 seconds. This is a workaround before Py4J fixes the leak issue https://github.com/bartdag/py4j/issues/187

Author: Shixiong Zhu <shixiong@databricks.com>

Closes #10579 from zsxwing/SPARK-12617.

address comments in #10435

This makes the API easier to use if user programmatically generate the call to hash, and they will get analysis exception if the arguments of hash is empty.

Author: Wenchen Fan <wenchen@databricks.com>

Closes #10588 from cloud-fan/hash.

Author: Reynold Xin <rxin@databricks.com>

Closes #10559 from rxin/remove-deprecated-sql.

Previously (when the PR was first created) not specifying b= explicitly was fine (and treated as default null) - instead be explicit about b being None in the test.

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

Closes #10564 from holdenk/SPARK-12611-fix-test-infer-schema-local.

We can provides the option to choose JSON parser can be enabled to accept quoting of all character or not.

Author: Cazen <Cazen@korea.com>
Author: Cazen Lee <cazen.lee@samsung.com>
Author: Cazen Lee <Cazen@korea.com>
Author: cazen.lee <cazen.lee@samsung.com>

Closes #10497 from Cazen/master.

Current schema inference for local python collections halts as soon as there are no NullTypes. This is different than when we specify a sampling ratio of 1.0 on a distributed collection. This could result in incomplete schema information.

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

Closes #10275 from holdenk/SPARK-12300-fix-schmea-inferance-on-local-collections.

The semantics of Python countByValue is different from Scala API, it is more like countDistinctValue, so here change to make it consistent with Scala/Java API.

Author: jerryshao <sshao@hortonworks.com>

Closes #10350 from jerryshao/SPARK-12353.

After reading the JIRA https://issues.apache.org/jira/browse/SPARK-12520, I double checked the code.

For example, users can do the Equi-Join like
  ```df.join(df2, 'name', 'outer').select('name', 'height').collect()```
- There exists a bug in 1.5 and 1.4. The code just ignores the third parameter (join type) users pass. However, the join type we called is `Inner`, even if the user-specified type is the other type (e.g., `Outer`).
- After a PR: https://github.com/apache/spark/pull/8600, the 1.6 does not have such an issue, but the description has not been updated.

Plan to submit another PR to fix 1.5 and issue an error message if users specify a non-inner join type when using Equi-Join.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #10477 from gatorsmile/pyOuterJoin.

Some methods are missing, such as ways to access the std, mean, etc. This PR is for feature parity for pyspark.mllib.feature.StandardScaler & StandardScalerModel.

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

Closes #10298 from holdenk/SPARK-12296-feature-parity-pyspark-mllib-StandardScalerModel.

Author: pshearer <pshearer@massmutual.com>

Closes #10414 from pshearer/patch-1.

No jira is created since this is a trivial change.

davies  Please help review it

Author: Jeff Zhang <zjffdu@apache.org>

Closes #10143 from zjffdu/pyspark_typo.

Added catch for casting Long to Int exception when PySpark ALS Ratings are serialized.  It is easy to accidentally use Long IDs for user/product and before, it would fail with a somewhat cryptic "ClassCastException: java.lang.Long cannot be cast to java.lang.Integer."  Now if this is done, a more descriptive error is shown, e.g. "PickleException: Ratings id 1205640308657491975 exceeds max integer value of 2147483647."

Author: Bryan Cutler <bjcutler@us.ibm.com>

Closes #9361 from BryanCutler/als-pyspark-long-id-error-SPARK-10158.

Fix mistake doc of join type for ```dataframe.join```.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10378 from yanboliang/leftsemi.

The current default storage level of Python persist API is MEMORY_ONLY_SER. This is different from the default level MEMORY_ONLY in the official document and RDD APIs.

davies Is this inconsistency intentional? Thanks!

Updates: Since the data is always serialized on the Python side, the storage levels of JAVA-specific deserialization are not removed, such as MEMORY_ONLY.

Updates: Based on the reviewers' feedback. In Python, stored objects will always be serialized with the [Pickle](https://docs.python.org/2/library/pickle.html) library, so it does not matter whether you choose a serialized level. The available storage levels in Python include `MEMORY_ONLY`, `MEMORY_ONLY_2`, `MEMORY_AND_DISK`, `MEMORY_AND_DISK_2`, `DISK_ONLY`, `DISK_ONLY_2` and `OFF_HEAP`.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #10092 from gatorsmile/persistStorageLevel.

Since we rename the column name from ```text``` to ```value``` for DataFrame load by ```SQLContext.read.text```, we need to update doc.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10349 from yanboliang/text-value.