## What changes were proposed in this pull request?

Add Python API in ML for GaussianMixture

## How was this patch tested?

(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)

Add doctest and test cases are the same as mllib Python tests
./dev/lint-python
PEP8 checks passed.
rm -rf _build/*
pydoc checks passed.

./python/run-tests --python-executables=python2.7 --modules=pyspark-ml
Running PySpark tests. Output is in /Users/mwang/spark_ws_0904/python/unit-tests.log
Will test against the following Python executables: ['python2.7']
Will test the following Python modules: ['pyspark-ml']
Finished test(python2.7): pyspark.ml.evaluation (18s)
Finished test(python2.7): pyspark.ml.clustering (40s)
Finished test(python2.7): pyspark.ml.classification (49s)
Finished test(python2.7): pyspark.ml.recommendation (44s)
Finished test(python2.7): pyspark.ml.feature (64s)
Finished test(python2.7): pyspark.ml.regression (45s)
Finished test(python2.7): pyspark.ml.tuning (30s)
Finished test(python2.7): pyspark.ml.tests (56s)
Tests passed in 106 seconds

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes #12402 from wangmiao1981/gmm.

## What changes were proposed in this pull request?

This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes:
 - ContinuousQuery
 - Trigger
 - ProcessingTime
in pyspark under `pyspark.sql.streaming`.

In addition, it contains the new methods added under:
 -  `DataFrameWriter`
     a) `startStream`
     b) `trigger`
     c) `queryName`

 -  `DataFrameReader`
     a) `stream`

 - `DataFrame`
    a) `isStreaming`

This PR doesn't contain all methods exposed for `ContinuousQuery`, for example:
 - `exception`
 - `sourceStatuses`
 - `sinkStatus`

They may be added in a follow up.

This PR also contains some very minor doc fixes in the Scala side.

## How was this patch tested?

Python doc tests

TODO:
 - [ ] verify Python docs look good

Author: Burak Yavuz <brkyvz@gmail.com>
Author: Burak Yavuz <burak@databricks.com>

Closes #12320 from brkyvz/stream-python.

## What changes were proposed in this pull request?

PySpark Param constructors need to pass the TypeConverter argument by name, partly to make sure it is not mistaken for the expectedType arg and partly because we will remove the expectedType arg in 2.1. In several places, this is not being done correctly.

This PR changes all usages in pyspark/ml/ to keyword args.

## How was this patch tested?

Existing unit tests.  I will not test type conversion for every Param unless we really think it necessary.

Also, if you start the PySpark shell and import classes (e.g., pyspark.ml.feature.StandardScaler), then you no longer get this warning:
```
/Users/josephkb/spark/python/pyspark/ml/param/__init__.py:58: UserWarning: expectedType is deprecated and will be removed in 2.1. Use typeConverter instead, as a keyword argument.
  "Use typeConverter instead, as a keyword argument.")
```
That warning came from the typeConverter argument being passes as the expectedType arg by mistake.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12480 from jkbradley/typeconverter-fix.

## What changes were proposed in this pull request?

Param setters in python previously accessed the _paramMap directly to update values. The `_set` method now implements type checking, so it should be used to update all parameters. This PR eliminates all direct accesses to `_paramMap` besides the one in the `_set` method to ensure type checking happens.

Additional changes:
* [SPARK-13068](https://github.com/apache/spark/pull/11663) missed adding type converters in evaluation.py so those are done here
* An incorrect `toBoolean` type converter was used for StringIndexer `handleInvalid` param in previous PR. This is fixed here.

## How was this patch tested?

Existing unit tests verify that parameters are still set properly. No new functionality is actually added in this PR.

Author: sethah <seth.hendrickson16@gmail.com>

Closes #11939 from sethah/SPARK-14104.

## What changes were proposed in this pull request?
PySpark ml.clustering BisectingKMeans support export/import
## How was this patch tested?
doc test.

cc jkbradley

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #12112 from yanboliang/spark-14305.

## What changes were proposed in this pull request?

This patch adds type conversion functionality for parameters in Pyspark. A `typeConverter` field is added to the constructor of `Param` class. This argument is a function which converts values passed to this param to the appropriate type if possible. This is beneficial so that the params can fail at set time if they are given inappropriate values, but even more so because coherent error messages are now provided when Py4J cannot cast the python type to the appropriate Java type.

This patch also adds a `TypeConverters` class with factory methods for common type conversions. Most of the changes involve adding these factory type converters to existing params. The previous solution to this issue, `expectedType`, is deprecated and can be removed in 2.1.0 as discussed on the Jira.

## How was this patch tested?

Unit tests were added in python/pyspark/ml/tests.py to test parameter type conversion. These tests check that values that should be convertible are converted correctly, and that the appropriate errors are thrown when invalid values are provided.

Author: sethah <seth.hendrickson16@gmail.com>

Closes #11663 from sethah/SPARK-13068-tc.

Adds support for saving and loading nested ML Pipelines from Python.  Pipeline and PipelineModel do not extend JavaWrapper, but they are able to utilize the JavaMLWriter, JavaMLReader implementations.

Also:
* Separates out interfaces from Java wrapper implementations for MLWritable, MLReadable, MLWriter, MLReader.
* Moves methods _stages_java2py, _stages_py2java into Pipeline, PipelineModel as _transfer_stage_from_java, _transfer_stage_to_java

Added new unit test for nested Pipelines.  Abstracted validity check into a helper method for the 2 unit tests.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #11866 from jkbradley/nested-pipeline-io.
Closes #11835

This is to fix a long-time annoyance: Whenever we add a new algorithm to pyspark.ml, we have to add it to the ```__all__``` list at the top.  Since we keep it alphabetized, it often creates a lot more changes than needed.  It is also easy to add the Estimator and forget the Model.  I'm going to switch it to have one algorithm per line.

This also alphabetizes a few out-of-place classes in pyspark.ml.feature.  No changes have been made to the moved classes.

CC: thunterdb

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #10927 from jkbradley/ml-python-all-list.

Some of the new doctests in ml/clustering.py have a lot of setup code, move the setup code to the general test init to keep the doctest more example-style looking.
In part this is a follow up to https://github.com/apache/spark/pull/10999
Note that the same pattern is followed in regression & recommendation - might as well clean up all three at the same time.

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

Closes #11197 from holdenk/SPARK-13302-cleanup-doctests-in-ml-clustering.

Add Python API for spark.ml bisecting k-means.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10889 from yanboliang/spark-12974.

PySpark ml.clustering support export/import.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10999 from yanboliang/spark-13035.

Test cases should be removed from annotation of ```setXXX``` function, otherwise it will be parts of [Python API docs](https://spark.apache.org/docs/latest/api/python/pyspark.ml.html#pyspark.ml.clustering.KMeans.setInitMode).
cc mengxr jkbradley

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #10975 from yanboliang/clustering-cleanup.

The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh).

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

Closes #10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.

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

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #9931 from yanboliang/SPARK-11945.

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #8691 from yu-iskw/SPARK-10281.

This requires some discussion. I'm not sure whether `runs` is a useful parameter. It certainly complicates the implementation. We might want to optimize the k-means implementation with block matrix operations. In this case, having `runs` may not be worth the trade-off. Also it increases the communication cost in a single job, which might cause other issues.

This PR also renames `epsilon` to `tol` to have consistent naming among algorithms. The Python constructor is updated to include all parameters.

jkbradley yu-iskw

Author: Xiangrui Meng <meng@databricks.com>

Closes #8148 from mengxr/SPARK-9918 and squashes the following commits:

149b9e5 [Xiangrui Meng] fix constructor in Python and rename epsilon to tol
3cc15b3 [Xiangrui Meng] fix test and change initStep to initSteps in python
a0a0274 [Xiangrui Meng] remove runs from k-means in the pipeline API

Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #8059 from yanboliang/SPARK-9766.

I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks.

[SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879

Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>

Closes #6756 from yu-iskw/SPARK-7879 and squashes the following commits:

be752de [Yu ISHIKAWA] Add assertions
a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst
4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python
fb2417c [Yu ISHIKAWA] Use getInt, instead of get
f397be4 [Yu ISHIKAWA] Switch the comparisons.
ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter.
effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam
c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test
19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests
1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst
f8338bc [Yu ISHIKAWA] Add the placeholders in Python
4a03003 [Yu ISHIKAWA] Test for contains in Python
6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply`
288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names
5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception
97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy`
e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class
978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans
2ec80bc [Yu ISHIKAWA] Fit on 1 line
e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones
b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python
f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python
3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation
4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon
2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam`
19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF
4d2ad1e [Yu ISHIKAWA] Modify the indentations
0ae422f [Yu ISHIKAWA] Add a test for `setParams`
4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala
11ffdf1 [Yu ISHIKAWA] Use `===` and the variable
220a176 [Yu ISHIKAWA] Set a random seed in the unit testing
92c3efc [Yu ISHIKAWA] Make the points for a test be fewer
c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python
6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods
687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala
a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations
5bedc51 [Yu ISHIKAWA] Remve an extra new line
444c289 [Yu ISHIKAWA] Add the validation for `runs`
e41989c [Yu ISHIKAWA] Modify how to validate `initStep`
7ea133a [Yu ISHIKAWA] Change how to validate `initMode`
7991e15 [Yu ISHIKAWA] Add a validation for `k`
c2df35d [Yu ISHIKAWA] Make `predict` private
93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform`
d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs
e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private
8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans
6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps`
99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode`
79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs
6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault`
20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault`
11c2a12 [Yu ISHIKAWA] Limit the imports
badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel}
f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods
85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol`
aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x
c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline
598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python
63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala