[SPARK-3530][MLLIB] pipeline and parameters with examples

This PR adds package "org.apache.spark.ml" with pipeline and parameters, as discussed on the JIRA. This is a joint work of jkbradley etrain shivaram and many others who helped on the design, also with help from  marmbrus and liancheng on the Spark SQL side. The design doc can be found at:

https://docs.google.com/document/d/1rVwXRjWKfIb-7PI6b86ipytwbUH7irSNLF1_6dLmh8o/edit?usp=sharing

**org.apache.spark.ml**

This is a new package with new set of ML APIs that address practical machine learning pipelines. (Sorry for taking so long!) It will be an alpha component, so this is definitely not something set in stone. The new set of APIs, inspired by the MLI project from AMPLab and scikit-learn, takes leverage on Spark SQL's schema support and execution plan optimization. It introduces the following components that help build a practical pipeline:

1. Transformer, which transforms a dataset into another
2. Estimator, which fits models to data, where models are transformers
3. Evaluator, which evaluates model output and returns a scalar metric
4. Pipeline, a simple pipeline that consists of transformers and estimators

Parameters could be supplied at fit/transform or embedded with components.

1. Param: a strong-typed parameter key with self-contained doc
2. ParamMap: a param -> value map
3. Params: trait for components with parameters

For any component that implements `Params`, user can easily check the doc by calling `explainParams`:

~~~
> val lr = new LogisticRegression
> lr.explainParams
maxIter: max number of iterations (default: 100)
regParam: regularization constant (default: 0.1)
labelCol: label column name (default: label)
featuresCol: features column name (default: features)
~~~

or user can check individual param:

~~~
> lr.maxIter
maxIter: max number of iterations (default: 100)
~~~

**Please start with the example code in test suites and under `org.apache.spark.examples.ml`, where I put several examples:**

1. run a simple logistic regression job

~~~
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
    val model = lr.fit(dataset)
    model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
      .select('label, 'score, 'prediction).collect()
      .foreach(println)
~~~

2. run logistic regression with cross-validation and grid search using areaUnderROC (default) as the metric

~~~
    val lr = new LogisticRegression
    val lrParamMaps = new ParamGridBuilder()
      .addGrid(lr.regParam, Array(0.1, 100.0))
      .addGrid(lr.maxIter, Array(0, 5))
      .build()
    val eval = new BinaryClassificationEvaluator
    val cv = new CrossValidator()
      .setEstimator(lr)
      .setEstimatorParamMaps(lrParamMaps)
      .setEvaluator(eval)
      .setNumFolds(3)
    val bestModel = cv.fit(dataset)
~~~

3. run a pipeline that consists of a standard scaler and a logistic regression component

~~~
    val scaler = new StandardScaler()
      .setInputCol("features")
      .setOutputCol("scaledFeatures")
    val lr = new LogisticRegression()
      .setFeaturesCol(scaler.getOutputCol)
    val pipeline = new Pipeline()
      .setStages(Array(scaler, lr))
    val model = pipeline.fit(dataset)
    val predictions = model.transform(dataset)
      .select('label, 'score, 'prediction)
      .collect()
      .foreach(println)
~~~

4. a simple text classification pipeline, which recognizes "spark":

~~~
    val training = sparkContext.parallelize(Seq(
      LabeledDocument(0L, "a b c d e spark", 1.0),
      LabeledDocument(1L, "b d", 0.0),
      LabeledDocument(2L, "spark f g h", 1.0),
      LabeledDocument(3L, "hadoop mapreduce", 0.0)))
    val tokenizer = new Tokenizer()
      .setInputCol("text")
      .setOutputCol("words")
    val hashingTF = new HashingTF()
      .setInputCol(tokenizer.getOutputCol)
      .setOutputCol("features")
    val lr = new LogisticRegression()
      .setMaxIter(10)
    val pipeline = new Pipeline()
      .setStages(Array(tokenizer, hashingTF, lr))
    val model = pipeline.fit(training)
    val test = sparkContext.parallelize(Seq(
      Document(4L, "spark i j k"),
      Document(5L, "l m"),
      Document(6L, "mapreduce spark"),
      Document(7L, "apache hadoop")))
    model.transform(test)
      .select('id, 'text, 'prediction, 'score)
      .collect()
      .foreach(println)
~~~

Java examples are very similar. I put example code that creates a simple text classification pipeline in Scala and Java, where a simple tokenizer is defined as a transformer outside `org.apache.spark.ml`.

**What are missing now and will be added soon:**

1. ~~Runtime check of schemas. So before we touch the data, we will go through the schema and make sure column names and types match the input parameters.~~
2. ~~Java examples.~~
3. ~~Store training parameters in trained models.~~
4. (later) Serialization and Python API.

Author: Xiangrui Meng <meng@databricks.com>

Closes #3099 from mengxr/SPARK-3530 and squashes the following commits:

2cc93fd [Xiangrui Meng] hide APIs as much as I can
34319ba [Xiangrui Meng] use local instead local[2] for unit tests
2524251 [Xiangrui Meng] rename PipelineStage.transform to transformSchema
c9daab4 [Xiangrui Meng] remove mockito version
1397ab5 [Xiangrui Meng] use sqlContext from LocalSparkContext instead of TestSQLContext
6ffc389 [Xiangrui Meng] try to fix unit test
a59d8b7 [Xiangrui Meng] doc updates
977fd9d [Xiangrui Meng] add scala ml package object
6d97fe6 [Xiangrui Meng] add AlphaComponent annotation
731f0e4 [Xiangrui Meng] update package doc
0435076 [Xiangrui Meng] remove ;this from setters
fa21d9b [Xiangrui Meng] update extends indentation
f1091b3 [Xiangrui Meng] typo
228a9f4 [Xiangrui Meng] do not persist before calling binary classification metrics
f51cd27 [Xiangrui Meng] rename default to defaultValue
b3be094 [Xiangrui Meng] refactor schema transform in lr
8791e8e [Xiangrui Meng] rename copyValues to inheritValues and make it do the right thing
51f1c06 [Xiangrui Meng] remove leftover code in Transformer
494b632 [Xiangrui Meng] compure score once
ad678e9 [Xiangrui Meng] more doc for Transformer
4306ed4 [Xiangrui Meng] org imports in text pipeline
6e7c1c7 [Xiangrui Meng] update pipeline
4f9e34f [Xiangrui Meng] more doc for pipeline
aa5dbd4 [Xiangrui Meng] fix typo
11be383 [Xiangrui Meng] fix unit tests
3df7952 [Xiangrui Meng] clean up
986593e [Xiangrui Meng] re-org java test suites
2b11211 [Xiangrui Meng] remove external data deps
9fd4933 [Xiangrui Meng] add unit test for pipeline
2a0df46 [Xiangrui Meng] update tests
2d52e4d [Xiangrui Meng] add @AlphaComponent to package-info
27582a4 [Xiangrui Meng] doc changes
73a000b [Xiangrui Meng] add schema transformation layer
6736e87 [Xiangrui Meng] more doc / remove HasMetricName trait
80a8b5e [Xiangrui Meng] rename SimpleTransformer to UnaryTransformer
62ca2bb [Xiangrui Meng] check param parent in set/get
1622349 [Xiangrui Meng] add getModel to PipelineModel
a0e0054 [Xiangrui Meng] update StandardScaler to use SimpleTransformer
d0faa04 [Xiangrui Meng] remove implicit mapping from ParamMap
c7f6921 [Xiangrui Meng] move ParamGridBuilder test to ParamGridBuilderSuite
e246f29 [Xiangrui Meng] re-org:
7772430 [Xiangrui Meng] remove modelParams add a simple text classification pipeline
b95c408 [Xiangrui Meng] remove implicits add unit tests to params
bab3e5b [Xiangrui Meng] update params
fe0ee92 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
6e86d98 [Xiangrui Meng] some code clean-up
2d040b3 [Xiangrui Meng] implement setters inside each class, add Params.copyValues [ci skip]
fd751fc [Xiangrui Meng] add java-friendly versions of fit and tranform
3f810cd [Xiangrui Meng] use multi-model training api in cv
5b8f413 [Xiangrui Meng] rename model to modelParams
9d2d35d [Xiangrui Meng] test varargs and chain model params
f46e927 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
1ef26e0 [Xiangrui Meng] specialize methods/types for Java
df293ed [Xiangrui Meng] switch to setter/getter
376db0a [Xiangrui Meng] pipeline and parameters
[SPARK-3660][STREAMING] Initial RDD for updateStateByKey transformation

SPARK-3660 : Initial RDD for updateStateByKey transformation

I have added a sample StatefulNetworkWordCountWithInitial inspired by StatefulNetworkWordCount.

Please let me know if any changes are required.

Author: Soumitra Kumar <kumar.soumitra@gmail.com>

Closes #2665 from soumitrak/master and squashes the following commits:

ee8980b [Soumitra Kumar] Fixed copy/paste issue.
304f636 [Soumitra Kumar] Added simpler version of updateStateByKey API with initialRDD and test.
9781135 [Soumitra Kumar] Fixed test, and renamed variable.
3da51a2 [Soumitra Kumar] Adding updateStateByKey with initialRDD API to JavaPairDStream.
2f78f7e [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
d4fdd18 [Soumitra Kumar] Renamed variable and moved method.
d0ce2cd [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
31399a4 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
4efa58b [Soumitra Kumar] [SPARK-3660][STREAMING] Initial RDD for updateStateByKey transformation
8f40ca0 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
dde4271 [Soumitra Kumar] Merge remote-tracking branch 'upstream/master'
fdd7db3 [Soumitra Kumar] Adding support of initial value for state update. SPARK-3660 : Initial RDD for updateStateByKey transformation
[Test] Better exception message from SparkSubmitSuite

Before:
```
Exception in thread "main" java.lang.Exception: Could not load user defined classes inside of executors
	at org.apache.spark.deploy.JarCreationTest$.main(SparkSubmitSuite.scala:471)
	at org.apache.spark.deploy.JarCreationTest.main(SparkSubmitSuite.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```
After:
```
Exception in thread "main" java.lang.Exception: Could not load user class from jar:
java.lang.UnsupportedClassVersionError: SparkSubmitClassA : Unsupported major.minor version 51.0
	java.lang.ClassLoader.defineClass1(Native Method)
	java.lang.ClassLoader.defineClass(ClassLoader.java:643)
	...
	at org.apache.spark.deploy.JarCreationTest$.main(SparkSubmitSuite.scala:472)
	at org.apache.spark.deploy.JarCreationTest.main(SparkSubmitSuite.scala)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
```

Author: Andrew Or <andrew@databricks.com>

Closes #3212 from andrewor14/submit-suite-message and squashes the following commits:

7779248 [Andrew Or] Format exception
8fe6719 [Andrew Or] Better exception message from failed test
[SPARK-4281][Build] Package Yarn shuffle service into its own jar

This is another addendum to #3082, which added the Yarn shuffle service to run inside the NM. This PR makes the feature much more usable by packaging enough dependencies into the jar to run the service inside an NM. After these changes, the user can run `./make-distribution.sh` and find a `spark-network-yarn*.jar` in their `lib` directory. The equivalent change is done in SBT by making the `network-yarn` module an assembly project.

Author: Andrew Or <andrew@databricks.com>

Closes #3147 from andrewor14/yarn-shuffle-build and squashes the following commits:

bda58d0 [Andrew Or] Fix line too long
81e9705 [Andrew Or] Merge branch 'master' of github.com:apache/spark into yarn-shuffle-build
fb7f398 [Andrew Or] Rename jar to spark-{VERSION}-yarn-shuffle.jar
65db822 [Andrew Or] Actually mark slf4j as provided
abcefd1 [Andrew Or] Do the same for SBT
c653028 [Andrew Or] Package network-yarn and its dependencies
Internal cleanup for aggregateMessages

1. Add EdgeActiveness enum to represent activeness criteria more cleanly than using booleans.
2. Comments and whitespace.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #3231 from ankurdave/aggregateMessages-followup and squashes the following commits:

3d485c3 [Ankur Dave] Internal cleanup for aggregateMessages
[Release] Correct make-distribution.sh log path
[SPARK-3666] Extract interfaces for EdgeRDD and VertexRDD

This discourages users from calling the VertexRDD and EdgeRDD constructor and makes it easier for future changes to ensure backward compatibility.

Author: Ankur Dave <ankurdave@gmail.com>

Closes #2530 from ankurdave/SPARK-3666 and squashes the following commits:

d681f45 [Ankur Dave] Define getPartitions and compute in abstract class for MIMA
1472390 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into SPARK-3666
24201d4 [Ankur Dave] Merge remote-tracking branch 'apache-spark/master' into SPARK-3666
cbe15f2 [Ankur Dave] Remove specialized annotation from VertexRDD and EdgeRDD
931b587 [Ankur Dave] Use abstract class instead of trait for binary compatibility
9ba4ec4 [Ankur Dave] Mark (Vertex|Edge)RDDImpl constructors package-private
620e603 [Ankur Dave] Extract VertexRDD interface and move implementation to VertexRDDImpl
55b6398 [Ankur Dave] Extract EdgeRDD interface and move implementation to EdgeRDDImpl
[SPARK-4369] [MLLib] fix TreeModel.predict() with RDD

Fix  TreeModel.predict() with RDD, added tests for it.

(Also checked that other models don't have this issue)

Author: Davies Liu <davies@databricks.com>

Closes #3230 from davies/predict and squashes the following commits:

81172aa [Davies Liu] fix predict
[SPARK-2672] support compressed file in wholeTextFile

The wholeFile() can not read compressed files, it should be, just like textFile().

Author: Davies Liu <davies@databricks.com>

Closes #3005 from davies/whole and squashes the following commits:

a43fcfb [Davies Liu] remove semicolon
c83571a [Davies Liu] remove = if return type is Unit
83c844f [Davies Liu] Merge branch 'master' of github.com:apache/spark into whole
22e8b3e [Davies Liu] support compressed file in wholeTextFile
[Release] Bring audit scripts up-to-date

This involves a few main changes:
- Log all output message to the log file. Previously the log file
  was not useful because it did not indicate progress.
- Remove hive-site.xml in sbt_hive_app to avoid interference
- Add the appropriate repositories for new dependencies
[SPARK-4373][MLLIB] fix MLlib maven tests

We want to make sure there is at most one spark context inside the same jvm. JoshRosen

Author: Xiangrui Meng <meng@databricks.com>

Closes #3235 from mengxr/SPARK-4373 and squashes the following commits:

6574b69 [Xiangrui Meng] rename LocalSparkContext to MLlibTestSparkContext
913d48d [Xiangrui Meng] make sure there is at most one spark context inside the same jvm
[SPARK-4370] [Core] Limit number of Netty cores based on executor size

Author: Aaron Davidson <aaron@databricks.com>

Closes #3155 from aarondav/conf and squashes the following commits:

7045e77 [Aaron Davidson] Add mesos comment
4770f6e [Aaron Davidson] [SPARK-4370] [Core] Limit number of Netty cores based on executor size
[SPARK-4256] Make Binary Evaluation Metrics functions defined in cases where there ar...

...e 0 positive or 0 negative examples.

Author: Andrew Bullen <andrew.bullen@workday.com>

Closes #3118 from abull/master and squashes the following commits:

c2bf2b1 [Andrew Bullen] [SPARK-4256] Update Code formatting for BinaryClassificationMetricsSpec
36b0533 [Andrew Bullen] [SYMAN-4256] Extract BinaryClassificationMetricsSuite assertions into private method
4d2f79a [Andrew Bullen] [SPARK-4256] Refactor classification metrics tests - extract comparison functions in test
f411e70 [Andrew Bullen] [SPARK-4256] Define precision as 1.0 when there are no positive examples; update code formatting per pull request comments
d9a09ef [Andrew Bullen] Make Binary Evaluation Metrics functions defined in cases where there are 0 positive or 0 negative examples.
[SPARK-4348] [PySpark] [MLlib] rename random.py to rand.py

This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before.

```
>>> from pyspark.mllib.random import RandomRDDs
```

```
$ pydoc pyspark.mllib.random
Help on module random in pyspark.mllib:
NAME
    random - Python package for random data generation.

FILE
    /Users/davies/work/spark/python/pyspark/mllib/rand.py

CLASSES
    __builtin__.object
        pyspark.mllib.random.RandomRDDs

    class RandomRDDs(__builtin__.object)
     |  Generator methods for creating RDDs comprised of i.i.d samples from
     |  some distribution.
     |
     |  Static methods defined here:
     |
     |  normalRDD(sc, size, numPartitions=None, seed=None)
```

cc mengxr

reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/

Author: Davies Liu <davies@databricks.com>

Closes #3216 from davies/random and squashes the following commits:

7ac4e8b [Davies Liu] rename random.py to rand.py
[SPARK-4378][MLLIB] make ALS more Java-friendly

Add Java-friendly version of `run` and `predict`, and use bulk prediction in Java unit tests. The user guide update will come later (though we may not save many lines of code there). srowen

Author: Xiangrui Meng <meng@databricks.com>

Closes #3240 from mengxr/SPARK-4378 and squashes the following commits:

6581503 [Xiangrui Meng] check number of predictions
6c8bbd1 [Xiangrui Meng] make ALS more Java-friendly
[HOT FIX] make-distribution.sh fails if Yarn shuffle jar DNE

This is introduced in #3147 and is failing builds without the `-Pyarn` profile.

Author: Andrew Or <andrew@databricks.com>

Closes #3250 from andrewor14/fix-yarn-shuffle-build and squashes the following commits:

42b3d37 [Andrew Or] Do not fail fast if Yarn shuffle jar does not exist
[SPARK-4326] fix unidoc

There are two issues:

1. specifying guava 11.0.2 will cause hashInt not found in unidoc (any reason to force the version here?)
2. unidoc doesn't recognize static class defined in a base class

aarondav srowen vanzin

Author: Xiangrui Meng <meng@databricks.com>

Closes #3253 from mengxr/SPARK-4326 and squashes the following commits:

53967bf [Xiangrui Meng] fix unidoc
[SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala and Python

The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy.

In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default.

davies atalwalkar

Author: Xiangrui Meng <meng@databricks.com>

Closes #3232 from mengxr/SPARK-4372 and squashes the following commits:

9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments
d3ba096 [Xiangrui Meng] change 'none' back to None
1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
[SPARK-4310][WebUI] Sort 'Submitted' column in Stage page by time

Author: zsxwing <zsxwing@gmail.com>

Closes #3179 from zsxwing/SPARK-4310 and squashes the following commits:

b0d29f5 [zsxwing] Sort 'Submitted' column in Stage page by time
[SPARK-3722][Docs]minor improvement and fix in docs

https://issues.apache.org/jira/browse/SPARK-3722

Author: WangTao <barneystinson@aliyun.com>

Closes #2579 from WangTaoTheTonic/docsWork and squashes the following commits:

6f91cec [WangTao] use more wording express
29d22fa [WangTao] delete the specified version link
34cb4ea [WangTao] Update running-on-yarn.md
4ee1a26 [WangTao] minor improvement and fix in docs