## What changes were proposed in this pull request?
Currently SparkSession.Builder use SQLContext.getOrCreate. It should probably the the other way around, i.e. all the core logic goes in SparkSession, and SQLContext just calls that. This patch does that.
This patch also makes sure config options specified in the builder are propagated to the existing (and of course the new) SparkSession.
## How was this patch tested?
Updated tests to reflect the change, and also introduced a new SparkSessionBuilderSuite that should cover all the branches.
Author: Reynold Xin <rxin@databricks.com>
Closes#13200 from rxin/SPARK-15075.
## What changes were proposed in this pull request?
```ml.evaluation``` Scala and Python API sync.
## How was this patch tested?
Only API docs change, no new tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13195 from yanboliang/evaluation-doc.
## What changes were proposed in this pull request?
We use autoBroadcastJoinThreshold + 1L as the default value of size estimation, that is not good in 2.0, because we will calculate the size based on size of schema, then the estimation could be less than autoBroadcastJoinThreshold if you have an SELECT on top of an DataFrame created from RDD.
This PR change the default value to Long.MaxValue.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13183 from davies/fix_default_size.
## What changes were proposed in this pull request?
Add linkPredictionCol to GeneralizedLinearRegression and fix the PyDoc to generate the bullet list
## How was this patch tested?
doctests & built docs locally
Author: Holden Karau <holden@us.ibm.com>
Closes#13106 from holdenk/SPARK-15316-add-linkPredictionCol-toGeneralizedLinearRegression.
#### What changes were proposed in this pull request?
This follow-up PR is to address the remaining comments in https://github.com/apache/spark/pull/12385
The major change in this PR is to issue better error messages in PySpark by using the mechanism that was proposed by davies in https://github.com/apache/spark/pull/7135
For example, in PySpark, if we input the following statement:
```python
>>> l = [('Alice', 1)]
>>> df = sqlContext.createDataFrame(l)
>>> df.createTempView("people")
>>> df.createTempView("people")
```
Before this PR, the exception we will get is like
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView
self._jdf.createTempView(name)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 312, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o35.createTempView.
: org.apache.spark.sql.catalyst.analysis.TempTableAlreadyExistsException: Temporary table 'people' already exists;
at org.apache.spark.sql.catalyst.catalog.SessionCatalog.createTempView(SessionCatalog.scala:324)
at org.apache.spark.sql.SparkSession.createTempView(SparkSession.scala:523)
at org.apache.spark.sql.Dataset.createTempView(Dataset.scala:2328)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:237)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:128)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:211)
at java.lang.Thread.run(Thread.java:745)
```
After this PR, the exception we will get become cleaner:
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/dataframe.py", line 152, in createTempView
self._jdf.createTempView(name)
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/Users/xiaoli/IdeaProjects/sparkDelivery/python/pyspark/sql/utils.py", line 75, in deco
raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u"Temporary table 'people' already exists;"
```
#### How was this patch tested?
Fixed an existing PySpark test case
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13126 from gatorsmile/followup-14684.
## What changes were proposed in this pull request?
I reviewed Scala and Python APIs for ml.feature and corrected discrepancies.
## How was this patch tested?
Built docs locally, ran style checks
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13159 from BryanCutler/ml.feature-api-sync.
## What changes were proposed in this pull request?
This patch is a follow-up to https://github.com/apache/spark/pull/13104 and adds documentation to clarify the semantics of read.text with respect to partitioning.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13184 from rxin/SPARK-14463.
This PR adds schema validation to `ml`'s ALS and ALSModel. Currently, no schema validation was performed as `transformSchema` was never called in `ALS.fit` or `ALSModel.transform`. Furthermore, due to no schema validation, if users passed in Long (or Float etc) ids, they would be silently cast to Int with no warning or error thrown.
With this PR, ALS now supports all numeric types for `user`, `item`, and `rating` columns. The rating column is cast to `Float` and the user and item cols are cast to `Int` (as is the case currently) - however for user/item, the cast throws an error if the value is outside integer range. Behavior for rating col is unchanged (as it is not an issue).
## How was this patch tested?
New test cases in `ALSSuite`.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#12762 from MLnick/SPARK-14891-als-validate-schema.
## What changes were proposed in this pull request?
The PySpark SQL `test_column_name_with_non_ascii` wants to test non-ascii column name. But it doesn't actually test it. We need to construct an unicode explicitly using `unicode` under Python 2.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13134 from viirya/correct-non-ascii-colname-pytest.
## What changes were proposed in this pull request?
This pull request includes supporting validationMetrics for TrainValidationSplitModel with Python and test for it.
## How was this patch tested?
test in `python/pyspark/ml/tests.py`
Author: Takuya Kuwahara <taakuu19@gmail.com>
Closes#12767 from taku-k/spark-14978.
## What changes were proposed in this pull request?
Update the unit test code, examples, and documents to remove calls to deprecated method `dataset.registerTempTable`.
## How was this patch tested?
This PR only changes the unit test code, examples, and comments. It should be safe.
This is a follow up of PR https://github.com/apache/spark/pull/12945 which was merged.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#13098 from clockfly/spark-15171-remove-deprecation.
## What changes were proposed in this pull request?
**createDataFrame** returns inconsistent types for column names.
```python
>>> from pyspark.sql.types import StructType, StructField, StringType
>>> schema = StructType([StructField(u"col", StringType())])
>>> df1 = spark.createDataFrame([("a",)], schema)
>>> df1.columns # "col" is str
['col']
>>> df2 = spark.createDataFrame([("a",)], [u"col"])
>>> df2.columns # "col" is unicode
[u'col']
```
The reason is only **StructField** has the following code.
```
if not isinstance(name, str):
name = name.encode('utf-8')
```
This PR adds the same logic into **createDataFrame** for consistency.
```
if isinstance(schema, list):
schema = [x.encode('utf-8') if not isinstance(x, str) else x for x in schema]
```
## How was this patch tested?
Pass the Jenkins test (with new python doctest)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13097 from dongjoon-hyun/SPARK-15244.
## What changes were proposed in this pull request?
Once SPARK-14487 and SPARK-14549 are merged, we will migrate to use the new vector and matrix type in the new ml pipeline based apis.
## How was this patch tested?
Unit tests
Author: DB Tsai <dbt@netflix.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#12627 from dbtsai/SPARK-14615-NewML.
## What changes were proposed in this pull request?
Copy the linalg (Vector/Matrix and VectorUDT/MatrixUDT) in PySpark to new ML package.
## How was this patch tested?
Existing tests.
Author: Xiangrui Meng <meng@databricks.com>
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#13099 from viirya/move-pyspark-vector-matrix-udt4.
## What changes were proposed in this pull request?
This upgrades to Py4J 0.10.1 which reduces syscal overhead in Java gateway ( see https://github.com/bartdag/py4j/issues/201 ). Related https://issues.apache.org/jira/browse/SPARK-6728 .
## How was this patch tested?
Existing doctests & unit tests pass
Author: Holden Karau <holden@us.ibm.com>
Closes#13064 from holdenk/SPARK-15061-upgrade-to-py4j-0.10.1.
## What changes were proposed in this pull request?
This patch adds a python API for generalized linear regression summaries (training and test). This helps provide feature parity for Python GLMs.
## How was this patch tested?
Added a unit test to `pyspark.ml.tests`
Author: sethah <seth.hendrickson16@gmail.com>
Closes#12961 from sethah/GLR_summary.
## What changes were proposed in this pull request?
1, add arg-checkings for `tol` and `stepSize` to keep in line with `SharedParamsCodeGen.scala`
2, fix one typo
## How was this patch tested?
local build
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12996 from zhengruifeng/py_args_checking.
## What changes were proposed in this pull request?
Add missing thresholds param to NiaveBayes
## How was this patch tested?
doctests
Author: Holden Karau <holden@us.ibm.com>
Closes#12963 from holdenk/SPARK-15188-add-missing-naive-bayes-param.
## What changes were proposed in this pull request?
Deprecates registerTempTable and add dataset.createTempView, dataset.createOrReplaceTempView.
## How was this patch tested?
Unit tests.
Author: Sean Zhong <seanzhong@databricks.com>
Closes#12945 from clockfly/spark-15171.
## What changes were proposed in this pull request?
Add impurity param to GBTRegressor and mark the of the models & regressors in regression.py as experimental to match Scaladoc.
## How was this patch tested?
Added default value to init, tested with unit/doc tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#13071 from holdenk/SPARK-15281-GBTRegressor-impurity.
## What changes were proposed in this pull request?
Seems db573fc743 did not remove withHiveSupport from readwrite.py
Author: Yin Huai <yhuai@databricks.com>
Closes#13069 from yhuai/fixPython.
## What changes were proposed in this pull request?
This is a followup of https://github.com/apache/spark/pull/12851
Remove `SparkSession.withHiveSupport` in PySpark and instead use `SparkSession.builder. enableHiveSupport`
## How was this patch tested?
Existing tests.
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13063 from techaddict/SPARK-15072-followup.
## What changes were proposed in this pull request?
When a CSV begins with:
- `,,`
OR
- `"","",`
meaning that the first column names are either empty or blank strings and `header` is specified to be `true`, then the column name is replaced with `C` + the index number of that given column. For example, if you were to read in the CSV:
```
"","second column"
"hello", "there"
```
Then column names would become `"C0", "second column"`.
This behavior aligns with what currently happens when `header` is specified to be `false` in recent versions of Spark.
### Current Behavior in Spark <=1.6
In Spark <=1.6, a CSV with a blank column name becomes a blank string, `""`, meaning that this column cannot be accessed. However the CSV reads in without issue.
### Current Behavior in Spark 2.0
Spark throws a NullPointerError and will not read in the file.
#### Reproduction in 2.0
https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/346304/2828750690305044/484361/latest.html
## How was this patch tested?
A new test was added to `CSVSuite` to account for this issue. We then have asserts that test for being able to select both the empty column names as well as the regular column names.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13041 from anabranch/master.
This PR:
* Corrects the documentation for the `properties` parameter, which is supposed to be a dictionary and not a list.
* Generally clarifies the Python docstring for DataFrameReader.jdbc() by pulling from the [Scala docstrings](b281377647/sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala (L201-L251)) and rephrasing things.
* Corrects minor Sphinx typos.
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#13034 from nchammas/SPARK-15256.
## What changes were proposed in this pull request?
Earlier we removed experimental tag for Scala/Java DataFrames, but haven't done so for Python. This patch removes the experimental flag for Python and declares them stable.
## How was this patch tested?
N/A.
Author: Reynold Xin <rxin@databricks.com>
Closes#13062 from rxin/SPARK-15278.
## What changes were proposed in this pull request?
Before:
Creating a hiveContext was failing
```python
from pyspark.sql import HiveContext
hc = HiveContext(sc)
```
with
```
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "spark-2.0/python/pyspark/sql/context.py", line 458, in __init__
sparkSession = SparkSession.withHiveSupport(sparkContext)
File "spark-2.0/python/pyspark/sql/session.py", line 192, in withHiveSupport
jsparkSession = sparkContext._jvm.SparkSession.withHiveSupport(sparkContext._jsc.sc())
File "spark-2.0/python/lib/py4j-0.9.2-src.zip/py4j/java_gateway.py", line 1048, in __getattr__
py4j.protocol.Py4JError: org.apache.spark.sql.SparkSession.withHiveSupport does not exist in the JVM
```
Now:
```python
>>> from pyspark.sql import HiveContext
>>> hc = HiveContext(sc)
>>> hc.range(0, 100)
DataFrame[id: bigint]
>>> hc.range(0, 100).count()
100
```
## How was this patch tested?
Existing Tests, tested manually in python shell
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13056 from techaddict/SPARK-15270.
## What changes were proposed in this pull request?
Renaming the streaming-kafka artifact to include kafka version, in anticipation of needing a different artifact for later kafka versions
## How was this patch tested?
Unit tests
Author: cody koeninger <cody@koeninger.org>
Closes#12946 from koeninger/SPARK-15085.
## What changes were proposed in this pull request?
Use SparkSession instead of SQLContext in Python TestSuites
## How was this patch tested?
Existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13044 from techaddict/SPARK-15037-python.
## What changes were proposed in this pull request?
Fix doctest issue, short param description, and tag items as Experimental
## How was this patch tested?
build docs locally & doctests
Author: Holden Karau <holden@us.ibm.com>
Closes#12964 from holdenk/SPARK-15189-ml.Evaluation-PyDoc-issues.
## What changes were proposed in this pull request?
This PR removes the old `json(path: String)` API which is covered by the new `json(paths: String*)`.
## How was this patch tested?
Jenkins tests (existing tests should cover this)
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#13040 from HyukjinKwon/SPARK-15250.
## What changes were proposed in this pull request?
This patch removes experimental tag from DataFrameReader and DataFrameWriter, and explicitly tags a few methods added for structured streaming as experimental.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13038 from rxin/SPARK-15261.
https://issues.apache.org/jira/browse/SPARK-14936
## What changes were proposed in this pull request?
FlumePollingStreamSuite contains two tests which run for a minute each. This seems excessively slow and we should speed it up if possible.
In this PR, instead of creating `StreamingContext` directly from `conf`, here an underlying `SparkContext` is created before all and it is used to create each`StreamingContext`.
Running time is reduced by avoiding multiple `SparkContext` creations and destroys.
## How was this patch tested?
Tested on my local machine running `testOnly *.FlumePollingStreamSuite`
Author: Xin Ren <iamshrek@126.com>
Closes#12845 from keypointt/SPARK-14936.
## What changes were proposed in this pull request?
Tag classes in ml.tuning as experimental, add docs for kfolds avg metric, and copy TrainValidationSplit scaladoc for more detailed explanation.
## How was this patch tested?
built docs locally
Author: Holden Karau <holden@us.ibm.com>
Closes#12967 from holdenk/SPARK-15195-pydoc-ml-tuning.
Since we cannot really trust if the underlying external catalog can throw exceptions when there is an invalid metadata operation, let's do it in SessionCatalog.
- [X] The first step is to unify the error messages issued in Hive-specific Session Catalog and general Session Catalog.
- [X] The second step is to verify the inputs of metadata operations for partitioning-related operations. This is moved to a separate PR: https://github.com/apache/spark/pull/12801
- [X] The third step is to add database existence verification in `SessionCatalog`
- [X] The fourth step is to add table existence verification in `SessionCatalog`
- [X] The fifth step is to add function existence verification in `SessionCatalog`
Add test cases and verify the error messages we issued
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#12385 from gatorsmile/verifySessionAPIs.
## What changes were proposed in this pull request?
PyDoc links in ml are in non-standard format. Switch to standard sphinx link format for better formatted documentation. Also add a note about default value in one place. Copy some extended docs from scala for GBT
## How was this patch tested?
Built docs locally.
Author: Holden Karau <holden@us.ibm.com>
Closes#12918 from holdenk/SPARK-15137-linkify-pyspark-ml-classification.
## What changes were proposed in this pull request?
This PR continues the work from #11871 with the following changes:
* load English stopwords as default
* covert stopwords to list in Python
* update some tests and doc
## How was this patch tested?
Unit tests.
Closes#11871
cc: burakkose srowen
Author: Burak Köse <burakks41@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Burak KOSE <burakks41@gmail.com>
Closes#12843 from mengxr/SPARK-14050.
## What changes were proposed in this pull request?
Copy the package documentation from Scala/Java to Python for ML package and remove beta tags. Not super sure if we want to keep the BETA tag but since we are making it the default it seems like probably the time to remove it (happy to put it back in if we want to keep it BETA).
## How was this patch tested?
Python documentation built locally as HTML and text and verified output.
Author: Holden Karau <holden@us.ibm.com>
Closes#12883 from holdenk/SPARK-15106-add-pyspark-package-doc-for-ml.
## What changes were proposed in this pull request?
See title.
## How was this patch tested?
PySpark tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12917 from andrewor14/deprecate-hive-context-python.
## What changes were proposed in this pull request?
This PR aims to update Scala/Python/Java examples by replacing `SQLContext` with newly added `SparkSession`.
- Use **SparkSession Builder Pattern** in 154(Scala 55, Java 52, Python 47) files.
- Add `getConf` in Python SparkContext class: `python/pyspark/context.py`
- Replace **SQLContext Singleton Pattern** with **SparkSession Singleton Pattern**:
- `SqlNetworkWordCount.scala`
- `JavaSqlNetworkWordCount.java`
- `sql_network_wordcount.py`
Now, `SQLContexts` are used only in R examples and the following two Python examples. The python examples are untouched in this PR since it already fails some unknown issue.
- `simple_params_example.py`
- `aft_survival_regression.py`
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12809 from dongjoon-hyun/SPARK-15031.
## What changes were proposed in this pull request?
Currently we return RuntimeConfig itself to facilitate chaining. However, it makes the output in interactive environments (e.g. notebooks, scala repl) weird because it'd show the response of calling set as a RuntimeConfig itself.
## How was this patch tested?
Updated unit tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#12902 from rxin/SPARK-15126.
## What changes were proposed in this pull request?
This is a python port of corresponding Scala builder pattern code. `sql.py` is modified as a target example case.
## How was this patch tested?
Manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12860 from dongjoon-hyun/SPARK-15084.
- that reduceFunc and invReduceFunc should be associative
- that the intermediate result in iterated applications of inverseReduceFunc
is its first argument
Author: François Garillot <francois@garillot.net>
Closes#8103 from huitseeker/issue/invReduceFuncDoc.
# What changes were proposed in this pull request?
Support partitioning in the file stream sink. This is implemented using a new, but simpler code path for writing parquet files - both unpartitioned and partitioned. This new code path does not use Output Committers, as we will eventually write the file names to the metadata log for "committing" them.
This patch duplicates < 100 LOC from the WriterContainer. But its far simpler that WriterContainer as it does not involve output committing. In addition, it introduces the new APIs in FileFormat and OutputWriterFactory in an attempt to simplify the APIs (not have Job in the `FileFormat` API, not have bucket and other stuff in the `OutputWriterFactory.newInstance()` ).
# Tests
- New unit tests to test the FileStreamSinkWriter for partitioned and unpartitioned files
- New unit test to partially test the FileStreamSink for partitioned files (does not test recovery of partition column data, as that requires change in the StreamFileCatalog, future PR).
- Updated FileStressSuite to test number of records read from partitioned output files.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#12409 from tdas/streaming-partitioned-parquet.
## What changes were proposed in this pull request?
PySpark ML Params setter code clean up.
For examples,
```setInputCol``` can be simplified from
```
self._set(inputCol=value)
return self
```
to:
```
return self._set(inputCol=value)
```
This is a pretty big sweeps, and we cleaned wherever possible.
## How was this patch tested?
Exist unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12749 from yanboliang/spark-14971.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-15050
This PR adds function parameters for Python API for reading and writing `csv()`.
## How was this patch tested?
This was tested by `./dev/run_tests`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#12834 from HyukjinKwon/SPARK-15050.
## What changes were proposed in this pull request?
This PR adds the explanation and documentation for CSV options for reading and writing.
## How was this patch tested?
Style tests with `./dev/run_tests` for documentation style.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#12817 from HyukjinKwon/SPARK-13425.
## What changes were proposed in this pull request?
This PR is an update for [https://github.com/apache/spark/pull/12738] which:
* Adds a generic unit test for JavaParams wrappers in pyspark.ml for checking default Param values vs. the defaults in the Scala side
* Various fixes for bugs found
* This includes changing classes taking weightCol to treat unset and empty String Param values the same way.
Defaults changed:
* Scala
* LogisticRegression: weightCol defaults to not set (instead of empty string)
* StringIndexer: labels default to not set (instead of empty array)
* GeneralizedLinearRegression:
* maxIter always defaults to 25 (simpler than defaulting to 25 for a particular solver)
* weightCol defaults to not set (instead of empty string)
* LinearRegression: weightCol defaults to not set (instead of empty string)
* Python
* MultilayerPerceptron: layers default to not set (instead of [1,1])
* ChiSqSelector: numTopFeatures defaults to 50 (instead of not set)
## How was this patch tested?
Generic unit test. Manually tested that unit test by changing defaults and verifying that broke the test.
Author: Joseph K. Bradley <joseph@databricks.com>
Author: yinxusen <yinxusen@gmail.com>
Closes#12816 from jkbradley/yinxusen-SPARK-14931.
#### What changes were proposed in this pull request?
This PR removes three methods the were deprecated in 1.6.0:
- `PortableDataStream.close()`
- `LinearRegression.weights`
- `LogisticRegression.weights`
The rationale for doing this is that the impact is small and that Spark 2.0 is a major release.
#### How was this patch tested?
Compilation succeded.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12732 from hvanhovell/SPARK-14952.
