[SPARK-4486][MLLIB] Improve GradientBoosting APIs and doc

There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent.

1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly.
1. GradientBoosting -> GradientBoostedTrees
1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy
1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.)
1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train`
1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method.
1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting.
1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError
1. doc updates

manishamde jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #3374 from mengxr/SPARK-4486 and squashes the following commits:

7097251 [Xiangrui Meng] address joseph's comments
98dea09 [Xiangrui Meng] address manish's comments
4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy
ea4c467 [Xiangrui Meng] fix unit tests
751da4e [Xiangrui Meng] rename class method train -> run
19030a5 [Xiangrui Meng] update boosting public APIs
[SPARK-3938][SQL] Names in-memory columnar RDD with corresponding table name

This PR enables the Web UI storage tab to show the in-memory table name instead of the mysterious query plan string as the name of the in-memory columnar RDD.

Note that after #2501, a single columnar RDD can be shared by multiple in-memory tables, as long as their query results are the same. In this case, only the first cached table name is shown. For example:

```sql
CACHE TABLE first AS SELECT * FROM src;
CACHE TABLE second AS SELECT * FROM src;
```

The Web UI only shows "In-memory table first".

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Author: Cheng Lian <lian@databricks.com>

Closes #3383 from liancheng/columnar-rdd-name and squashes the following commits:

071907f [Cheng Lian] Fixes tests
12ddfa6 [Cheng Lian] Names in-memory columnar RDD with corresponding table name
[SPARK-4228][SQL] SchemaRDD to JSON

Here's a simple fix for SchemaRDD to JSON.

Author: Dan McClary <dan.mcclary@gmail.com>

Closes #3213 from dwmclary/SPARK-4228 and squashes the following commits:

d714e1d [Dan McClary] fixed PEP 8 error
cac2879 [Dan McClary] move pyspark comment and doctest to correct location
f9471d3 [Dan McClary] added pyspark doc and doctest
6598cee [Dan McClary] adding complex type queries
1a5fd30 [Dan McClary] removing SPARK-4228 from SQLQuerySuite
4a651f0 [Dan McClary] cleaned PEP and Scala style failures.  Moved tests to JsonSuite
47ceff6 [Dan McClary] cleaned up scala style issues
2ee1e70 [Dan McClary] moved rowToJSON to JsonRDD
4387dd5 [Dan McClary] Added UserDefinedType, cleaned up case formatting
8f7bfb6 [Dan McClary] Map type added to SchemaRDD.toJSON
1b11980 [Dan McClary] Map and UserDefinedTypes partially done
11d2016 [Dan McClary] formatting and unicode deserialization default fixed
6af72d1 [Dan McClary] deleted extaneous comment
4d11c0c [Dan McClary] JsonFactory rewrite of toJSON for SchemaRDD
149dafd [Dan McClary] wrapped scala toJSON in sql.py
5e5eb1b [Dan McClary] switched to Jackson for JSON processing
6c94a54 [Dan McClary] added toJSON to pyspark SchemaRDD
aaeba58 [Dan McClary] added toJSON to pyspark SchemaRDD
1d171aa [Dan McClary] upated missing brace on if statement
319e3ba [Dan McClary] updated to upstream master with merged SPARK-4228
424f130 [Dan McClary] tests pass, ready for pull and PR
626a5b1 [Dan McClary] added toJSON to SchemaRDD
f7d166a [Dan McClary] added toJSON method
5d34e37 [Dan McClary] merge resolved
d6d19e9 [Dan McClary] pr example
[SPARK-4439] [MLlib] add python api for random forest

```
    class RandomForestModel
     |  A model trained by RandomForest
     |
     |  numTrees(self)
     |      Get number of trees in forest.
     |
     |  predict(self, x)
     |      Predict values for a single data point or an RDD of points using the model trained.
     |
     |  toDebugString(self)
     |      Full model
     |
     |  totalNumNodes(self)
     |      Get total number of nodes, summed over all trees in the forest.
     |

    class RandomForest
     |  trainClassifier(cls, data, numClassesForClassification, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None):
     |      Method to train a decision tree model for binary or multiclass classification.
     |
     |      :param data: Training dataset: RDD of LabeledPoint.
     |                   Labels should take values {0, 1, ..., numClasses-1}.
     |      :param numClassesForClassification: number of classes for classification.
     |      :param categoricalFeaturesInfo: Map storing arity of categorical features.
     |                                  E.g., an entry (n -> k) indicates that feature n is categorical
     |                                  with k categories indexed from 0: {0, 1, ..., k-1}.
     |      :param numTrees: Number of trees in the random forest.
     |      :param featureSubsetStrategy: Number of features to consider for splits at each node.
     |                                Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
     |                                If "auto" is set, this parameter is set based on numTrees:
     |                                  if numTrees == 1, set to "all";
     |                                  if numTrees > 1 (forest) set to "sqrt".
     |      :param impurity: Criterion used for information gain calculation.
     |                   Supported values: "gini" (recommended) or "entropy".
     |      :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
     |                       1 internal node + 2 leaf nodes. (default: 4)
     |      :param maxBins: maximum number of bins used for splitting features (default: 100)
     |      :param seed:  Random seed for bootstrapping and choosing feature subsets.
     |      :return: RandomForestModel that can be used for prediction
     |
     |   trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32, seed=None):
     |      Method to train a decision tree model for regression.
     |
     |      :param data: Training dataset: RDD of LabeledPoint.
     |                   Labels are real numbers.
     |      :param categoricalFeaturesInfo: Map storing arity of categorical features.
     |                                   E.g., an entry (n -> k) indicates that feature n is categorical
     |                                   with k categories indexed from 0: {0, 1, ..., k-1}.
     |      :param numTrees: Number of trees in the random forest.
     |      :param featureSubsetStrategy: Number of features to consider for splits at each node.
     |                                 Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
     |                                 If "auto" is set, this parameter is set based on numTrees:
     |                                 if numTrees == 1, set to "all";
     |                                 if numTrees > 1 (forest) set to "onethird".
     |      :param impurity: Criterion used for information gain calculation.
     |                       Supported values: "variance".
     |      :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
     |                       1 internal node + 2 leaf nodes.(default: 4)
     |      :param maxBins: maximum number of bins used for splitting features (default: 100)
     |      :param seed:  Random seed for bootstrapping and choosing feature subsets.
     |      :return: RandomForestModel that can be used for prediction
     |
```

Author: Davies Liu <davies@databricks.com>

Closes #3320 from davies/forest and squashes the following commits:

8003dfc [Davies Liu] reorder
53cf510 [Davies Liu] fix docs
4ca593d [Davies Liu] fix docs
e0df852 [Davies Liu] fix docs
0431746 [Davies Liu] rebased
2b6f239 [Davies Liu] Merge branch 'master' of github.com:apache/spark into forest
885abee [Davies Liu] address comments
dae7fc0 [Davies Liu] address comments
89a000f [Davies Liu] fix docs
565d476 [Davies Liu] add python api for random forest
[SPARK-4513][SQL] Support relational operator '<=>' in Spark SQL

The relational operator '<=>' is not working in Spark SQL. Same works in Spark HiveQL

Author: ravipesala <ravindra.pesala@huawei.com>

Closes #3387 from ravipesala/<=> and squashes the following commits:

7198e90 [ravipesala] Supporting relational operator '<=>' in Spark SQL
[SPARK-4318][SQL] Fix empty sum distinct.

Executing sum distinct for empty table throws `java.lang.UnsupportedOperationException: empty.reduceLeft`.

Author: Takuya UESHIN <ueshin@happy-camper.st>

Closes #3184 from ueshin/issues/SPARK-4318 and squashes the following commits:

8168c42 [Takuya UESHIN] Merge branch 'master' into issues/SPARK-4318
66fdb0a [Takuya UESHIN] Re-refine aggregate functions.
6186eb4 [Takuya UESHIN] Fix Sum of GeneratedAggregate.
d2975f6 [Takuya UESHIN] Refine Sum and Average of GeneratedAggregate.
1bba675 [Takuya UESHIN] Refine Sum, SumDistinct and Average functions.
917e533 [Takuya UESHIN] Use aggregate instead of groupBy().
1a5f874 [Takuya UESHIN] Add tests to be executed as non-partial aggregation.
a5a57d2 [Takuya UESHIN] Fix empty Average.
22799dc [Takuya UESHIN] Fix empty Sum and SumDistinct.
65b7dd2 [Takuya UESHIN] Fix empty sum distinct.
[SPARK-2918] [SQL] Support the CTAS in EXPLAIN command

Hive supports the `explain` the CTAS, which was supported by Spark SQL previously, however, seems it was reverted after the code refactoring in HiveQL.

Author: Cheng Hao <hao.cheng@intel.com>

Closes #3357 from chenghao-intel/explain and squashes the following commits:

7aace63 [Cheng Hao] Support the CTAS in EXPLAIN command
[SQL] fix function description mistake

Sample code in the description of SchemaRDD.where is not correct

Author: Jacky Li <jacky.likun@gmail.com>

Closes #3344 from jackylk/patch-6 and squashes the following commits:

62cd126 [Jacky Li] [SQL] fix function description mistake
[SPARK-4477] [PySpark] remove numpy from RDDSampler

In RDDSampler, it try use numpy to gain better performance for possion(), but the number of call of random() is only (1+faction) * N in the pure python implementation of possion(), so there is no much performance gain from numpy.

numpy is not a dependent of pyspark, so it maybe introduce some problem, such as there is no numpy installed in slaves, but only installed master, as reported in SPARK-927.

It also complicate the code a lot, so we may should remove numpy from RDDSampler.

I also did some benchmark to verify that:
```
>>> from pyspark.mllib.random import RandomRDDs
>>> rdd = RandomRDDs.uniformRDD(sc, 1 << 20, 1).cache()
>>> rdd.count()  # cache it
>>> rdd.sample(True, 0.9).count()    # measure this line
```
the results:

|withReplacement      |  random  | numpy.random |
 ------- | ------------ |  -------
|True | 1.5 s|  1.4 s|
|False|  0.6 s | 0.8 s|

closes #2313

Note: this patch including some commits that not mirrored to github, it will be OK after it catches up.

Author: Davies Liu <davies@databricks.com>
Author: Xiangrui Meng <meng@databricks.com>

Closes #3351 from davies/numpy and squashes the following commits:

5c438d7 [Davies Liu] fix comment
c5b9252 [Davies Liu] Merge pull request #1 from mengxr/SPARK-4477
98eb31b [Xiangrui Meng] make poisson sampling slightly faster
ee17d78 [Davies Liu] remove = for float
13f7b05 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into numpy
f583023 [Davies Liu] fix tests
51649f5 [Davies Liu] remove numpy in RDDSampler
78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain
f5fdf63 [Davies Liu] fix bug with int in weights
4dfa2cd [Davies Liu] refactor
f866bcf [Davies Liu] remove unneeded change
c7a2007 [Davies Liu] switch to python implementation
95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit
0d9b256 [Davies Liu] refactor
1715ee3 [Davies Liu] address comments
41fce54 [Davies Liu] randomSplit()
[SPARK-4244] [SQL] Support Hive Generic UDFs with constant object inspector parameters

Query `SELECT named_struct(lower("AA"), "12", lower("Bb"), "13") FROM src LIMIT 1` will throw exception, some of the Hive Generic UDF/UDAF requires the input object inspector is `ConstantObjectInspector`, however, we won't get that before the expression optimization executed. (Constant Folding).

This PR is a work around to fix this. (As ideally, the `output` of LogicalPlan should be identical before and after Optimization).

Author: Cheng Hao <hao.cheng@intel.com>

Closes #3109 from chenghao-intel/optimized and squashes the following commits:

487ff79 [Cheng Hao] rebase to the latest master & update the unittest
[SPARK-4413][SQL] Parquet support through datasource API

Goals:
 - Support for accessing parquet using SQL but not requiring Hive (thus allowing support of parquet tables with decimal columns)
 - Support for folder based partitioning with automatic discovery of available partitions
 - Caching of file metadata

See scaladoc of `ParquetRelation2` for more details.

Author: Michael Armbrust <michael@databricks.com>

Closes #3269 from marmbrus/newParquet and squashes the following commits:

1dd75f1 [Michael Armbrust] Pass all paths for FileInputFormat at once.
645768b [Michael Armbrust] Review comments.
abd8e2f [Michael Armbrust] Alternative implementation of parquet based on the datasources API.
938019e [Michael Armbrust] Add an experimental interface to data sources that exposes catalyst expressions.
e9d2641 [Michael Armbrust] logging / formatting improvements.
add Sphinx as a dependency of building docs

Author: Davies Liu <davies@databricks.com>

Closes #3388 from davies/doc_readme and squashes the following commits:

daa1482 [Davies Liu] add Sphinx dependency
[SPARK-4522][SQL] Parse schema with missing metadata.

This is just a quick fix for 1.2.  SPARK-4523 describes a more complete solution.

Author: Michael Armbrust <michael@databricks.com>

Closes #3392 from marmbrus/parquetMetadata and squashes the following commits:

bcc6626 [Michael Armbrust] Parse schema with missing metadata.
[Doc][GraphX] Remove Motivation section and did some minor update.
[Doc][GraphX] Remove unused png files.
[SPARK-4472][Shell] Print "Spark context available as sc." only when SparkContext is created...

... successfully

It's weird that printing "Spark context available as sc" when creating SparkContext unsuccessfully.

Author: zsxwing <zsxwing@gmail.com>

Closes #3341 from zsxwing/SPARK-4472 and squashes the following commits:

4850093 [zsxwing] Print "Spark context available as sc." only when SparkContext is created successfully
[SPARK-4397][Core] Reorganize 'implicit's to improve the API convenience

This PR moved `implicit`s to `package object` and `companion object` to enable the Scala compiler search them automatically without explicit importing.

It should not break any API. A test project for backforward compatibility is [here](https://github.com/zsxwing/SPARK-4397-Backforward-Compatibility). It proves the codes compiled with Spark 1.1.0 can run with this PR.

To summarize, the changes are:

* Deprecated the old implicit conversion functions: this preserves binary compatibility for code compiled against earlier versions of Spark.
* Removed "implicit" from them so they are just normal functions: this made sure the compiler doesn't get confused and warn about multiple implicits in scope.
* Created new implicit functions in package rdd object, which is part of the scope that scalac will search when looking for implicit conversions on various RDD objects.

The disadvantage is there are duplicated codes in SparkContext for backforward compatibility.

Author: zsxwing <zsxwing@gmail.com>

Closes #3262 from zsxwing/SPARK-4397 and squashes the following commits:

fc30314 [zsxwing] Update the comments
9c27aff [zsxwing] Move implicit functions to object RDD and forward old functions to new implicit ones directly
2b5f5a4 [zsxwing] Comments for the deprecated functions
52353de [zsxwing] Remove private[spark] from object WritableConverter
34641d4 [zsxwing] Move ImplicitSuite to org.apache.sparktest
7266218 [zsxwing] Add comments to warn the duplicate codes in SparkContext
185c12f [zsxwing] Remove simpleWritableConverter from SparkContext
3bdcae2 [zsxwing] Move WritableConverter implicits to object WritableConverter
9b73188 [zsxwing] Fix the code style issue
3ac4f07 [zsxwing] Add license header
1eda9e4 [zsxwing] Reorganize 'implicit's to improve the API convenience
SPARK-4532: Fix bug in detection of Hive in Spark 1.2

Because the Hive profile is no longer defined in the root pom,
we need to check specifically in the sql/hive pom when we
perform the check in make-distribtion.sh.

Author: Patrick Wendell <pwendell@gmail.com>

Closes #3398 from pwendell/make-distribution and squashes the following commits:

8a58279 [Patrick Wendell] Fix bug in detection of Hive in Spark 1.2
[SPARK-4531] [MLlib] cache serialized java object

The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step.

This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster.

Author: Davies Liu <davies@databricks.com>

Closes #3397 from davies/cache and squashes the following commits:

7f6e6ce [Davies Liu] Update -> Updater
4b52edd [Davies Liu] using named argument
63b984e [Davies Liu] fix
7da0332 [Davies Liu] add unpersist()
dff33e1 [Davies Liu] address comments
c2bdfc2 [Davies Liu] refactor
d572f00 [Davies Liu] Merge branch 'master' into cache
f1063e1 [Davies Liu] cache serialized java object
[SPARK-4431][MLlib] Implement efficient foreachActive for dense and sparse vector

Previously, we were using Breeze's activeIterator to access the non-zero elements
in dense/sparse vector. Due to the overhead, we switched back to native `while loop`
in #SPARK-4129.

However, #SPARK-4129 requires de-reference the dv.values/sv.values in
each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer,
we're using Breeze's dense vector to store the partial stats, and this is very expensive compared
with using primitive scala array.

In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse
vector operation which makes codebase easier to maintain. Breeze dense vector is replaced
by primitive array to reduce the overhead further.

Benchmarking with mnist8m dataset on single JVM
with first 200 samples loaded in memory, and repeating 5000 times.

Before change:
Sparse Vector - 30.02
Dense Vector - 38.27

With this PR:
Sparse Vector - 6.29
Dense Vector - 11.72

Author: DB Tsai <dbtsai@alpinenow.com>

Closes #3288 from dbtsai/activeIterator and squashes the following commits:

844b0e6 [DB Tsai] formating
03dd693 [DB Tsai] futher performance tunning.
1907ae1 [DB Tsai] address feedback
98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation.
c0cbd5a [DB Tsai] fix a bug
6441f92 [DB Tsai] Finished SPARK-4431