The move to `ml.linalg` created `asML`/`fromML` utility methods in Scala/Java for converting between representations. These are missing in Python, this PR adds them.
## How was this patch tested?
New doctests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13997 from MLnick/SPARK-16328-python-linalg-convert.
## What changes were proposed in this pull request?
Spark silently drops exceptions during file listing. This is a very bad behavior because it can mask legitimate errors and the resulting plan will silently have 0 rows. This patch changes it to not silently drop the errors.
## How was this patch tested?
Manually verified.
Author: Reynold Xin <rxin@databricks.com>
Closes#13987 from rxin/SPARK-16313.
## What changes were proposed in this pull request?
This PR implements `posexplode` table generating function. Currently, master branch raises the following exception for `map` argument. It's different from Hive.
**Before**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
org.apache.spark.sql.AnalysisException: No handler for Hive UDF ... posexplode() takes an array as a parameter; line 1 pos 7
```
**After**
```scala
scala> sql("select posexplode(map('a', 1, 'b', 2))").show
+---+---+-----+
|pos|key|value|
+---+---+-----+
| 0| a| 1|
| 1| b| 2|
+---+---+-----+
```
For `array` argument, `after` is the same with `before`.
```
scala> sql("select posexplode(array(1, 2, 3))").show
+---+---+
|pos|col|
+---+---+
| 0| 1|
| 1| 2|
| 2| 3|
+---+---+
```
## How was this patch tested?
Pass the Jenkins tests with newly added testcases.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13971 from dongjoon-hyun/SPARK-16289.
## What changes were proposed in this pull request?
Add Catalog.refreshTable API into python interface for Spark-SQL.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13558 from WeichenXu123/update_python_sql_interface_refreshTable.
## What changes were proposed in this pull request?
This PR corrects ORC compression option for PySpark as well. I think this was missed mistakenly in https://github.com/apache/spark/pull/13948.
## How was this patch tested?
N/A
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#13963 from HyukjinKwon/minor-orc-compress.
#### What changes were proposed in this pull request?
In Python API, we have the same issue. Thanks for identifying this issue, zsxwing ! Below is an example:
```Python
spark.read.format('json').load('python/test_support/sql/people.json')
```
#### How was this patch tested?
Existing test cases cover the changes by this PR
Author: gatorsmile <gatorsmile@gmail.com>
Closes#13965 from gatorsmile/optionPaths.
## What changes were proposed in this pull request?
- Moved DataStreamReader/Writer from pyspark.sql to pyspark.sql.streaming to make them consistent with scala packaging
- Exposed the necessary classes in sql.streaming package so that they appear in the docs
- Added pyspark.sql.streaming module to the docs
## How was this patch tested?
- updated unit tests.
- generated docs for testing visibility of pyspark.sql.streaming classes.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13955 from tdas/SPARK-16266.
## What changes were proposed in this pull request?
Fixed the following error:
```
>>> sqlContext.readStream
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...", line 442, in readStream
return DataStreamReader(self._wrapped)
NameError: global name 'DataStreamReader' is not defined
```
## How was this patch tested?
The added test.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13958 from zsxwing/fix-import.
## What changes were proposed in this pull request?
Fixes a couple old references to `DataFrameWriter.startStream` to `DataStreamWriter.start
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#13952 from brkyvz/minor-doc-fix.
## What changes were proposed in this pull request?
Scala UDT will bypass all the null and will not pass them into serialize() and deserialize() of UDT, this PR update the Python UDT to do this as well.
## How was this patch tested?
Added tests.
Author: Davies Liu <davies@databricks.com>
Closes#13878 from davies/udt_null.
## What changes were proposed in this pull request?
There are some duplicated code for options in DataFrame reader/writer API, this PR clean them up, it also fix a bug for `escapeQuotes` of csv().
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#13948 from davies/csv_options.
## What changes were proposed in this pull request?
When we create a SparkSession at the Python side, it is possible that a SparkContext has been created. For this case, we need to set configs of the SparkSession builder to the Scala SparkContext's SparkConf (we need to do so because conf changes on a active Python SparkContext will not be propagated to the JVM side). Otherwise, we may create a wrong SparkSession (e.g. Hive support is not enabled even if enableHiveSupport is called).
## How was this patch tested?
New tests and manual tests.
Author: Yin Huai <yhuai@databricks.com>
Closes#13931 from yhuai/SPARK-16224.
## What changes were proposed in this pull request?
This PR implements python wrappers for #13888 to convert old/new matrix columns in a DataFrame.
## How was this patch tested?
Doctest in python.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13935 from yanboliang/spark-16242.
## What changes were proposed in this pull request?
Allowing truncate to a specific number of character is convenient at times, especially while operating from the REPL. Sometimes those last few characters make all the difference, and showing everything brings in whole lot of noise.
## How was this patch tested?
Existing tests. + 1 new test in DataFrameSuite.
For SparkR and pyspark, existing tests and manual testing.
Author: Prashant Sharma <prashsh1@in.ibm.com>
Author: Prashant Sharma <prashant@apache.org>
Closes#13839 from ScrapCodes/add_truncateTo_DF.show.
## What changes were proposed in this pull request?
- Fix tests regarding show functions functionality
- Revert `catalog.ListFunctions` and `SHOW FUNCTIONS` to return to `Spark 1.X` functionality.
Cherry picked changes from this PR: https://github.com/apache/spark/pull/13413/files
## How was this patch tested?
Unit tests.
Author: Bill Chambers <bill@databricks.com>
Author: Bill Chambers <wchambers@ischool.berkeley.edu>
Closes#13916 from anabranch/master.
## What changes were proposed in this pull request?
This PR fix the bug when Python UDF is used in explode (generator), GenerateExec requires that all the attributes in expressions should be resolvable from children when creating, we should replace the children first, then replace it's expressions.
```
>>> df.select(explode(f(*df))).show()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/vlad/dev/spark/python/pyspark/sql/dataframe.py", line 286, in show
print(self._jdf.showString(n, truncate))
File "/home/vlad/dev/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
File "/home/vlad/dev/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/home/vlad/dev/spark/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 o52.showString.
: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: makeCopy, tree:
Generate explode(<lambda>(_1#0L)), false, false, [col#15L]
+- Scan ExistingRDD[_1#0L]
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.trees.TreeNode.makeCopy(TreeNode.scala:387)
at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:69)
at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:45)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:177)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:144)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.org$apache$spark$sql$execution$python$ExtractPythonUDFs$$extract(ExtractPythonUDFs.scala:153)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:114)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$$anonfun$apply$2.applyOrElse(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:301)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:300)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:298)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:298)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:113)
at org.apache.spark.sql.execution.python.ExtractPythonUDFs$.apply(ExtractPythonUDFs.scala:93)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
at org.apache.spark.sql.execution.QueryExecution$$anonfun$prepareForExecution$1.apply(QueryExecution.scala:95)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.execution.QueryExecution.prepareForExecution(QueryExecution.scala:95)
at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:85)
at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:85)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2557)
at org.apache.spark.sql.Dataset.head(Dataset.scala:1923)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2138)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:239)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
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)
Caused by: java.lang.reflect.InvocationTargetException
at sun.reflect.NativeConstructorAccessorImpl.newInstance0(Native Method)
at sun.reflect.NativeConstructorAccessorImpl.newInstance(NativeConstructorAccessorImpl.java:62)
at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:45)
at java.lang.reflect.Constructor.newInstance(Constructor.java:423)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1$$anonfun$apply$13.apply(TreeNode.scala:413)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:412)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$makeCopy$1.apply(TreeNode.scala:387)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
... 42 more
Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Binding attribute, tree: pythonUDF0#20
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:50)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1.applyOrElse(BoundAttribute.scala:87)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:279)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:278)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:321)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:179)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:319)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:284)
at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:268)
at org.apache.spark.sql.catalyst.expressions.BindReferences$.bindReference(BoundAttribute.scala:87)
at org.apache.spark.sql.execution.GenerateExec.<init>(GenerateExec.scala:63)
... 52 more
Caused by: java.lang.RuntimeException: Couldn't find pythonUDF0#20 in [_1#0L]
at scala.sys.package$.error(package.scala:27)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:94)
at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:88)
at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:49)
... 67 more
```
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13883 from davies/udf_in_generate.
## What changes were proposed in this pull request?
In the case that we don't know which module a object came from, will call pickle.whichmodule() to go throught all the loaded modules to find the object, which could fail because some modules, for example, six, see https://bitbucket.org/gutworth/six/issues/63/importing-six-breaks-pickling
We should ignore the exception here, use `__main__` as the module name (it means we can't find the module).
## How was this patch tested?
Manual tested. Can't have a unit test for this.
Author: Davies Liu <davies@databricks.com>
Closes#13788 from davies/whichmodule.
## What changes were proposed in this pull request?
Since SPARK-13220(Deprecate "yarn-client" and "yarn-cluster"), YarnClusterSuite doesn't test "yarn cluster" mode correctly.
This pull request fixes it.
## How was this patch tested?
Unit test
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: peng.zhang <peng.zhang@xiaomi.com>
Closes#13836 from renozhang/SPARK-16125-test-yarn-cluster-mode.
[SPARK-14615](https://issues.apache.org/jira/browse/SPARK-14615) and #12627 changed `spark.ml` pipelines to use the new `ml.linalg` classes for `Vector`/`Matrix`. Some `Since` annotations for public methods/vals have not been updated accordingly to be `2.0.0`. This PR updates them.
## How was this patch tested?
Existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13840 from MLnick/SPARK-16127-ml-linalg-since.
## What changes were proposed in this pull request?
Mark ml.classification algorithms as experimental to match Scala algorithms, update PyDoc for for thresholds on `LogisticRegression` to have same level of info as Scala, and enable mathjax for PyDoc.
## How was this patch tested?
Built docs locally & PySpark SQL tests
Author: Holden Karau <holden@us.ibm.com>
Closes#12938 from holdenk/SPARK-15162-SPARK-15164-update-some-pydocs.
## What changes were proposed in this pull request?
Several places set the seed Param default value to None which will translate to a zero value on the Scala side. This is unnecessary because a default fixed value already exists and if a test depends on a zero valued seed, then it should explicitly set it to zero instead of relying on this translation. These cases can be safely removed except for the ALS doc test, which has been changed to set the seed value to zero.
## How was this patch tested?
Ran PySpark tests locally
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13672 from BryanCutler/pyspark-cleanup-setDefault-seed-SPARK-15741.
## What changes were proposed in this pull request?
This PR allows us to create a Row without any fields.
## How was this patch tested?
Added a test for empty row and udf without arguments.
Author: Davies Liu <davies@databricks.com>
Closes#13812 from davies/no_argus.
## What changes were proposed in this pull request?
This is a follow-up to https://github.com/apache/spark/pull/13795 to properly set CSV options in Python API. As part of this, I also make the Python option setting for both CSV and JSON more robust against positional errors.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#13800 from rxin/SPARK-13792-2.
This PR adds missing `Since` annotations to `ml.feature` package.
Closes#8505.
## How was this patch tested?
Existing tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13641 from MLnick/add-since-annotations.
## What changes were proposed in this pull request?
This pull request adds a new option (maxMalformedLogPerPartition) in CSV reader to limit the maximum of logging message Spark generates per partition for malformed records.
The error log looks something like
```
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: Dropping malformed line: adsf,1,4
16/06/20 18:50:14 WARN CSVRelation: More than 10 malformed records have been found on this partition. Malformed records from now on will not be logged.
```
Closes#12173
## How was this patch tested?
Manually tested.
Author: Reynold Xin <rxin@databricks.com>
Closes#13795 from rxin/SPARK-13792.
Fix the bug for Python UDF that does not have any arguments.
Added regression tests.
Author: Davies Liu <davies.liu@gmail.com>
Closes#13793 from davies/fix_no_arguments.
(cherry picked from commit abe36c53d1)
Signed-off-by: Davies Liu <davies.liu@gmail.com>
## What changes were proposed in this pull request?
Fixed missing import for DecisionTreeRegressionModel used in GBTClassificationModel trees method.
## How was this patch tested?
Local tests
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13787 from BryanCutler/pyspark-GBTClassificationModel-import-SPARK-16079.
*This contribution is my original work and that I license the work to the project under the project's open source license.*
## What changes were proposed in this pull request?
Documentation updates to PySpark's GroupedData
## How was this patch tested?
Manual Tests
Author: Josh Howes <josh.howes@gmail.com>
Author: Josh Howes <josh.howes@maxpoint.com>
Closes#13724 from josh-howes/bugfix/SPARK-15973.
## What changes were proposed in this pull request?
Support with statement syntax for SparkSession in pyspark
## How was this patch tested?
Manually verify it. Although I can add unit test for it, it would affect other unit test because the SparkContext is stopped after the with statement.
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13541 from zjffdu/SPARK-15803.
## What changes were proposed in this pull request?
The check on the end parenthesis of the expression to parse was using the wrong variable. I corrected that.
## How was this patch tested?
Manual test
Author: andreapasqua <andrea@radius.com>
Closes#13750 from andreapasqua/sparse-vector-parser-assertion-fix.
## What changes were proposed in this pull request?
This PR implements python wrappers for #13662 to convert old/new vector columns in a DataFrame.
## How was this patch tested?
doctest in Python
cc: yanboliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#13731 from mengxr/SPARK-15946.
## What changes were proposed in this pull request?
- Fixed bug in Python API of DataStreamReader. Because a single path was being converted to a array before calling Java DataStreamReader method (which takes a string only), it gave the following error.
```
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 947, in pyspark.sql.readwriter.DataStreamReader.json
Failed example:
json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema)
Exception raised:
Traceback (most recent call last):
File "/System/Library/Frameworks/Python.framework/Versions/2.6/lib/python2.6/doctest.py", line 1253, in __run
compileflags, 1) in test.globs
File "<doctest pyspark.sql.readwriter.DataStreamReader.json[0]>", line 1, in <module>
json_sdf = spark.readStream.json(os.path.join(tempfile.mkdtemp(), 'data'), schema = sdf_schema)
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/readwriter.py", line 963, in json
return self._df(self._jreader.json(path))
File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/java_gateway.py", line 933, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/Users/tdas/Projects/Spark/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/Users/tdas/Projects/Spark/spark/python/lib/py4j-0.10.1-src.zip/py4j/protocol.py", line 316, in get_return_value
format(target_id, ".", name, value))
Py4JError: An error occurred while calling o121.json. Trace:
py4j.Py4JException: Method json([class java.util.ArrayList]) does not exist
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:318)
at py4j.reflection.ReflectionEngine.getMethod(ReflectionEngine.java:326)
at py4j.Gateway.invoke(Gateway.java:272)
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:744)
```
- Reduced code duplication between DataStreamReader and DataFrameWriter
- Added missing Python doctests
## How was this patch tested?
New tests
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13703 from tdas/SPARK-15981.
## What changes were proposed in this pull request?
After we move the ExtractPythonUDF rule into physical plan, Python UDF can't work on top of aggregate anymore, because they can't be evaluated before aggregate, should be evaluated after aggregate. This PR add another rule to extract these kind of Python UDF from logical aggregate, create a Project on top of Aggregate.
## How was this patch tested?
Added regression tests. The plan of added test query looks like this:
```
== Parsed Logical Plan ==
'Project [<lambda>('k, 's) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L]
+- LogicalRDD [key#5L, value#6]
== Analyzed Logical Plan ==
t: int
Project [<lambda>(k#17, s#22L) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS k#17, sum(cast(<lambda>(value#6) as bigint)) AS s#22L]
+- LogicalRDD [key#5L, value#6]
== Optimized Logical Plan ==
Project [<lambda>(agg#29, agg#30L) AS t#26]
+- Aggregate [<lambda>(key#5L)], [<lambda>(key#5L) AS agg#29, sum(cast(<lambda>(value#6) as bigint)) AS agg#30L]
+- LogicalRDD [key#5L, value#6]
== Physical Plan ==
*Project [pythonUDF0#37 AS t#26]
+- BatchEvalPython [<lambda>(agg#29, agg#30L)], [agg#29, agg#30L, pythonUDF0#37]
+- *HashAggregate(key=[<lambda>(key#5L)#31], functions=[sum(cast(<lambda>(value#6) as bigint))], output=[agg#29,agg#30L])
+- Exchange hashpartitioning(<lambda>(key#5L)#31, 200)
+- *HashAggregate(key=[pythonUDF0#34 AS <lambda>(key#5L)#31], functions=[partial_sum(cast(pythonUDF1#35 as bigint))], output=[<lambda>(key#5L)#31,sum#33L])
+- BatchEvalPython [<lambda>(key#5L), <lambda>(value#6)], [key#5L, value#6, pythonUDF0#34, pythonUDF1#35]
+- Scan ExistingRDD[key#5L,value#6]
```
Author: Davies Liu <davies@databricks.com>
Closes#13682 from davies/fix_py_udf.
Renamed for simplicity, so that its obvious that its related to streaming.
Existing unit tests.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13673 from tdas/SPARK-15953.
## What changes were proposed in this pull request?
A follow up PR for #13655 to fix a wrong format tag.
## How was this patch tested?
Jenkins unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13665 from zsxwing/fix.
## What changes were proposed in this pull request?
Currently, the DataFrameReader/Writer has method that are needed for streaming and non-streaming DFs. This is quite awkward because each method in them through runtime exception for one case or the other. So rather having half the methods throw runtime exceptions, its just better to have a different reader/writer API for streams.
- [x] Python API!!
## How was this patch tested?
Existing unit tests + two sets of unit tests for DataFrameReader/Writer and DataStreamReader/Writer.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13653 from tdas/SPARK-15933.
## What changes were proposed in this pull request?
This PR just enables tests for sql/streaming.py and also fixes the failures.
## How was this patch tested?
Existing unit tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13655 from zsxwing/python-streaming-test.
## What changes were proposed in this pull request?
SparkSession.catalog.listFunctions currently returns all functions, including the list of built-in functions. This makes the method not as useful because anytime it is run the result set contains over 100 built-in functions.
## How was this patch tested?
CatalogSuite
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13413 from techaddict/SPARK-15663.
## What changes were proposed in this pull request?
Now we have PySpark picklers for new and old vector/matrix, individually. However, they are all implemented under `PythonMLlibAPI`. To separate spark.mllib from spark.ml, we should implement the picklers of new vector/matrix under `spark.ml.python` instead.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13219 from viirya/pyspark-pickler-ml.
## What changes were proposed in this pull request?
This pr is to add doc for turning off quotations because this behavior is different from `com.databricks.spark.csv`.
## How was this patch tested?
Check behavior to put an empty string in csv options.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13616 from maropu/SPARK-15585-2.
## What changes were proposed in this pull request?
Adding __str__ to RFormula and model that will show the set formula param and resolved formula. This is currently present in the Scala API, found missing in PySpark during Spark 2.0 coverage review.
## How was this patch tested?
run pyspark-ml tests locally
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13481 from BryanCutler/pyspark-ml-rformula_str-SPARK-15738.
## What changes were proposed in this pull request?
Word2vec python add maxsentence parameter.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13578 from WeichenXu123/word2vec_python_add_maxsentence.
## What changes were proposed in this pull request?
`accuracy` should be decorated with `property` to keep step with other methods in `pyspark.MulticlassMetrics`, like `weightedPrecision`, `weightedRecall`, etc
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13560 from zhengruifeng/add_accuracy_property.
## What changes were proposed in this pull request?
add method idf to IDF in pyspark
## How was this patch tested?
add unit test
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13540 from zjffdu/SPARK-15788.
## What changes were proposed in this pull request?
1, add accuracy for MulticlassMetrics
2, deprecate overall precision,recall,f1 and recommend accuracy usage
## How was this patch tested?
manual tests in pyspark shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13511 from zhengruifeng/deprecate_py_precisonrecall.
## What changes were proposed in this pull request?
Since [SPARK-15617](https://issues.apache.org/jira/browse/SPARK-15617) deprecated ```precision``` in ```MulticlassClassificationEvaluator```, many ML examples broken.
```python
pyspark.sql.utils.IllegalArgumentException: u'MulticlassClassificationEvaluator_4c3bb1d73d8cc0cedae6 parameter metricName given invalid value precision.'
```
We should use ```accuracy``` to replace ```precision``` in these examples.
## How was this patch tested?
Offline tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13519 from yanboliang/spark-15771.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
## What changes were proposed in this pull request?
This pr fixes the behaviour of `format("csv").option("quote", null)` along with one of spark-csv.
Also, it explicitly sets default values for CSV options in python.
## How was this patch tested?
Added tests in CSVSuite.
Author: Takeshi YAMAMURO <linguin.m.s@gmail.com>
Closes#13372 from maropu/SPARK-15585.
## What changes were proposed in this pull request?
1, del precision,recall in `ml.MulticlassClassificationEvaluator`
2, update user guide for `mlllib.weightedFMeasure`
## How was this patch tested?
local build
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#13390 from zhengruifeng/clarify_f1.
## What changes were proposed in this pull request?
MultilayerPerceptronClassifier is missing step size, solver, and weights. Add these params. Also clarify the scaladoc a bit while we are updating these params.
Eventually we should follow up and unify the HasSolver params (filed https://issues.apache.org/jira/browse/SPARK-15169 )
## How was this patch tested?
Doc tests
Author: Holden Karau <holden@us.ibm.com>
Closes#12943 from holdenk/SPARK-15168-add-missing-params-to-MultilayerPerceptronClassifier.
## What changes were proposed in this pull request?
Add `toDebugString` and `totalNumNodes` to `TreeEnsembleModels` and add `toDebugString` to `DecisionTreeModel`
## How was this patch tested?
Extended doc tests.
Author: Holden Karau <holden@us.ibm.com>
Closes#12919 from holdenk/SPARK-15139-pyspark-treeEnsemble-missing-methods.
## What changes were proposed in this pull request?
ML 2.0 QA: Scala APIs audit for ml.feature. Mainly include:
* Remove seed for ```QuantileDiscretizer```, since we use ```approxQuantile``` to produce bins and ```seed``` is useless.
* Scala API docs update.
* Sync Scala and Python API docs for these changes.
## How was this patch tested?
Exist tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13410 from yanboliang/spark-15587.
## What changes were proposed in this pull request?
This patch moves all user-facing structured streaming classes into sql.streaming. As part of this, I also added some since version annotation to methods and classes that don't have them.
## How was this patch tested?
Updated tests to reflect the moves.
Author: Reynold Xin <rxin@databricks.com>
Closes#13429 from rxin/SPARK-15686.
## What changes were proposed in this pull request?
Currently structured streaming only supports append output mode. This PR adds the following.
- Added support for Complete output mode in the internal state store, analyzer and planner.
- Added public API in Scala and Python for users to specify output mode
- Added checks for unsupported combinations of output mode and DF operations
- Plans with no aggregation should support only Append mode
- Plans with aggregation should support only Update and Complete modes
- Default output mode is Append mode (**Question: should we change this to automatically set to Complete mode when there is aggregation?**)
- Added support for Complete output mode in Memory Sink. So Memory Sink internally supports append and complete, update. But from public API only Complete and Append output modes are supported.
## How was this patch tested?
Unit tests in various test suites
- StreamingAggregationSuite: tests for complete mode
- MemorySinkSuite: tests for checking behavior in Append and Complete modes.
- UnsupportedOperationSuite: tests for checking unsupported combinations of DF ops and output modes
- DataFrameReaderWriterSuite: tests for checking that output mode cannot be called on static DFs
- Python doc test and existing unit tests modified to call write.outputMode.
Author: Tathagata Das <tathagata.das1565@gmail.com>
Closes#13286 from tdas/complete-mode.
## What changes were proposed in this pull request?
Since we done Scala API audit for ml.clustering at #13148, we should also fix and update the corresponding Python API docs to keep them in sync.
## How was this patch tested?
Docs change, no tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13291 from yanboliang/spark-15361-followup.
## What changes were proposed in this pull request?
This reverts commit c24b6b679c. Sent a PR to run Jenkins tests due to the revert conflicts of `dev/deps/spark-deps-hadoop*`.
## How was this patch tested?
Jenkins unit tests, integration tests, manual tests)
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#13417 from zsxwing/revert-SPARK-11753.
## What changes were proposed in this pull request?
1. Add `_transfer_param_map_to/from_java` for OneVsRest;
2. Add `_compare_params` in ml/tests.py to help compare params.
3. Add `test_onevsrest` as the integration test for OneVsRest.
## How was this patch tested?
Python unit test.
Author: yinxusen <yinxusen@gmail.com>
Closes#12875 from yinxusen/SPARK-15008.
## What changes were proposed in this pull request?
`a` -> `an`
I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.
## How was this patch tested?
local build
`lint-java` checking
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13317 from zhengruifeng/a_an.
## What changes were proposed in this pull request?
Also sets confs in the underlying sc when using SparkSession.builder.getOrCreate(). This is a bug-fix from a post-merge comment in https://github.com/apache/spark/pull/13289
## How was this patch tested?
Python doc-tests.
Author: Eric Liang <ekl@databricks.com>
Closes#13309 from ericl/spark-15520-1.
Remove "Default: MEMORY_AND_DISK" from `Param` doc field in ALS storage level params. This fixes up the output of `explainParam(s)` so that default values are not displayed twice.
We can revisit in the case that [SPARK-15130](https://issues.apache.org/jira/browse/SPARK-15130) moves ahead with adding defaults in some way to PySpark param doc fields.
Tests N/A.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13277 from MLnick/SPARK-15500-als-remove-default-storage-param.
## What changes were proposed in this pull request?
This fixes the python SparkSession builder to allow setting confs correctly. This was a leftover TODO from https://github.com/apache/spark/pull/13200.
## How was this patch tested?
Python doc tests.
cc andrewor14
Author: Eric Liang <ekl@databricks.com>
Closes#13289 from ericl/spark-15520.
## What changes were proposed in this pull request?
PySpark: Add links to the predictors from the models in regression.py, improve linear and isotonic pydoc in minor ways.
User guide / R: Switch the installed package list to be enough to build the R docs on a "fresh" install on ubuntu and add sudo to match the rest of the commands.
User Guide: Add a note about using gem2.0 for systems with both 1.9 and 2.0 (e.g. some ubuntu but maybe more).
## How was this patch tested?
built pydocs locally, tested new user build instructions
Author: Holden Karau <holden@us.ibm.com>
Closes#13199 from holdenk/SPARK-15412-improve-linear-isotonic-regression-pydoc.
## What changes were proposed in this pull request?
Currently PySpark core test uses the `SerDe` from `PythonMLLibAPI` which includes many MLlib things. It should use `SerDeUtil` instead.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13214 from viirya/pycore-use-serdeutil.
## What changes were proposed in this pull request?
Jackson suppprts `allowNonNumericNumbers` option to parse non-standard non-numeric numbers such as "NaN", "Infinity", "INF". Currently used Jackson version (2.5.3) doesn't support it all. This patch upgrades the library and make the two ignored tests in `JsonParsingOptionsSuite` passed.
## How was this patch tested?
`JsonParsingOptionsSuite`.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: Liang-Chi Hsieh <viirya@appier.com>
Closes#9759 from viirya/fix-json-nonnumric.
This PR adds the `relativeError` param to PySpark's `QuantileDiscretizer` to match Scala.
Also cleaned up a duplication of `numBuckets` where the param is both a class and instance attribute (I removed the instance attr to match the style of params throughout `ml`).
Finally, cleaned up the docs for `QuantileDiscretizer` to reflect that it now uses `approxQuantile`.
## How was this patch tested?
A little doctest and built API docs locally to check HTML doc generation.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13228 from MLnick/SPARK-15442-py-relerror-param.
## What changes were proposed in this pull request?
in hive, `locate("aa", "aaa", 0)` would yield 0, `locate("aa", "aaa", 1)` would yield 1 and `locate("aa", "aaa", 2)` would yield 2, while in Spark, `locate("aa", "aaa", 0)` would yield 1, `locate("aa", "aaa", 1)` would yield 2 and `locate("aa", "aaa", 2)` would yield 0. This results from the different understanding of the third parameter in udf `locate`. It means the starting index and starts from 1, so when we use 0, the return would always be 0.
## How was this patch tested?
tested with modified `StringExpressionsSuite` and `StringFunctionsSuite`
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#13186 from adrian-wang/locate.
## What changes were proposed in this pull request?
Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13242 from WeichenXu123/python_doctest_update_sparksession.
## What changes were proposed in this pull request?
Spark assumes that UDF functions are deterministic. This PR adds explicit notes about that.
## How was this patch tested?
It's only about docs.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13087 from dongjoon-hyun/SPARK-15282.
## What changes were proposed in this pull request?
When PySpark shell cannot find HiveConf, it will fallback to create a SparkSession from a SparkContext. This fixes a bug caused by using a variable to SparkContext before it was initialized.
## How was this patch tested?
Manually starting PySpark shell and using the SparkContext
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13237 from BryanCutler/pyspark-shell-session-context-SPARK-15456.
## What changes were proposed in this pull request?
Default value mismatch of param linkPredictionCol for GeneralizedLinearRegression between PySpark and Scala. That is because default value conflict between #13106 and #13129. This causes ml.tests failed.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13220 from viirya/hotfix-regresstion.
## What changes were proposed in this pull request?
There is no way to use the Hive catalog in `pyspark-shell`. This is because we used to create a `SparkContext` before calling `SparkSession.enableHiveSupport().getOrCreate()`, which just gets the existing `SparkContext` instead of creating a new one. As a result, `spark.sql.catalogImplementation` was never propagated.
## How was this patch tested?
Manual.
Author: Andrew Or <andrew@databricks.com>
Closes#13203 from andrewor14/fix-pyspark-shell.
## 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.
## What changes were proposed in this pull request?
This PR fixes the bug that generates infinite distances between word vectors. For example,
Before this PR, we have
```
val synonyms = model.findSynonyms("who", 40)
```
will give the following results:
```
to Infinity
and Infinity
that Infinity
with Infinity
```
With this PR, the distance between words is a value between 0 and 1, as follows:
```
scala> model.findSynonyms("who", 10)
res0: Array[(String, Double)] = Array((Harvard-educated,0.5253688097000122), (ex-SAS,0.5213794708251953), (McMutrie,0.5187736749649048), (fellow,0.5166833400726318), (businessman,0.5145374536514282), (American-born,0.5127736330032349), (British-born,0.5062344074249268), (gray-bearded,0.5047978162765503), (American-educated,0.5035858750343323), (mentored,0.49849334359169006))
scala> model.findSynonyms("king", 10)
res1: Array[(String, Double)] = Array((queen,0.6787897944450378), (prince,0.6786158084869385), (monarch,0.659771203994751), (emperor,0.6490438580513), (goddess,0.643266499042511), (dynasty,0.635733425617218), (sultan,0.6166239380836487), (pharaoh,0.6150713562965393), (birthplace,0.6143025159835815), (empress,0.6109727025032043))
scala> model.findSynonyms("queen", 10)
res2: Array[(String, Double)] = Array((princess,0.7670737504959106), (godmother,0.6982434988021851), (raven-haired,0.6877717971801758), (swan,0.684934139251709), (hunky,0.6816608309745789), (Titania,0.6808111071586609), (heroine,0.6794036030769348), (king,0.6787897944450378), (diva,0.67848801612854), (lip-synching,0.6731793284416199))
```
### There are two places changed in this PR:
- Normalize the word vector to avoid overflow when calculating inner product between word vectors. This also simplifies the distance calculation, since the word vectors only need to be normalized once.
- Scale the learning rate by number of iteration, to be consistent with Google Word2Vec implementation
## How was this patch tested?
Use word2vec to train text corpus, and run model.findSynonyms() to get the distances between word vectors.
Author: Junyang <fly.shenjy@gmail.com>
Author: flyskyfly <fly.shenjy@gmail.com>
Closes#11812 from flyjy/TVec.
## What changes were proposed in this pull request?
As discussed in #12660, this PR renames
* intermediateRDDStorageLevel -> intermediateStorageLevel
* finalRDDStorageLevel -> finalStorageLevel
The argument name in `ALS.train` will be addressed in SPARK-15027.
## How was this patch tested?
Existing unit tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes#12803 from mengxr/SPARK-14412.
`mllib` `ALS` supports `setIntermediateRDDStorageLevel` and `setFinalRDDStorageLevel`. This PR adds these as Params in `ml` `ALS`. They are put in group **expertParam** since few users will need them.
## How was this patch tested?
New test cases in `ALSSuite` and `tests.py`.
cc yanboliang jkbradley sethah rishabhbhardwaj
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#12660 from MLnick/SPARK-14412-als-storage-params.
## What changes were proposed in this pull request?
Per discussion on [https://github.com/apache/spark/pull/12604], this removes ML persistence for Python tuning (TrainValidationSplit, CrossValidator, and their Models) since they do not handle nesting easily. This support should be re-designed and added in the next release.
## How was this patch tested?
Removed unit test elements saving and loading the tuning algorithms, but kept tests to save and load their bestModel fields.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12782 from jkbradley/remove-python-tuning-saveload.
## What changes were proposed in this pull request?
1. Remove all the `spark.setConf` etc. Just expose `spark.conf`
2. Make `spark.conf` take in things set in the core `SparkConf` as well, otherwise users may get confused
This was done for both the Python and Scala APIs.
## How was this patch tested?
`SQLConfSuite`, python tests.
This one fixes the failed tests in #12787Closes#12787
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#12798 from yhuai/conf-api.
## What changes were proposed in this pull request?
Addresses comments in #12765.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12784 from andrewor14/python-followup.
## What changes were proposed in this pull request?
pyspark.ml API for LDA
* LDA, LDAModel, LocalLDAModel, DistributedLDAModel
* includes persistence
This replaces [https://github.com/apache/spark/pull/10242]
## How was this patch tested?
* doc test for LDA, including Param setters
* unit test for persistence
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Jeff Zhang <zjffdu@apache.org>
Closes#12723 from jkbradley/zjffdu-SPARK-11940.
## What changes were proposed in this pull request?
The `catalog` and `conf` APIs were exposed in `SparkSession` in #12713 and #12669. This patch adds those to the python API.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12765 from andrewor14/python-spark-session-more.
## What changes were proposed in this pull request?
According to the [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829), deprecate API of LogisticRegression and LinearRegression using SGD
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12596 from zhengruifeng/deprecate_sgd.
## What changes were proposed in this pull request?
This PR adds Python APIs for:
- `ContinuousQueryManager`
- `ContinuousQueryException`
The `ContinuousQueryException` is a very basic wrapper, it doesn't provide the functionality that the Scala side provides, but it follows the same pattern for `AnalysisException`.
For `ContinuousQueryManager`, all APIs are provided except for registering listeners.
This PR also attempts to fix test flakiness by stopping all active streams just before tests.
## How was this patch tested?
Python Doc tests and unit tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12673 from brkyvz/pyspark-cqm.
## What changes were proposed in this pull request?
support avgMetrics in CrossValidatorModel with Python
## How was this patch tested?
Doctest and `test_save_load` in `pyspark/ml/test.py`
[JIRA](https://issues.apache.org/jira/browse/SPARK-12810)
Author: Kai Jiang <jiangkai@gmail.com>
Closes#12464 from vectorijk/spark-12810.
## What changes were proposed in this pull request?
```
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 2.0.0-SNAPSHOT
/_/
Using Python version 2.7.5 (default, Mar 9 2014 22:15:05)
SparkSession available as 'spark'.
>>> spark
<pyspark.sql.session.SparkSession object at 0x101f3bfd0>
>>> spark.sql("SHOW TABLES").show()
...
+---------+-----------+
|tableName|isTemporary|
+---------+-----------+
| src| false|
+---------+-----------+
>>> spark.range(1, 10, 2).show()
+---+
| id|
+---+
| 1|
| 3|
| 5|
| 7|
| 9|
+---+
```
**Note**: This API is NOT complete in its current state. In particular, for now I left out the `conf` and `catalog` APIs, which were added later in Scala. These will be added later before 2.0.
## How was this patch tested?
Python tests.
Author: Andrew Or <andrew@databricks.com>
Closes#12746 from andrewor14/python-spark-session.
## What changes were proposed in this pull request?
Since [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574) breaks behavior of ```HashingTF```, we should try to enforce good practice by removing the "native" hashAlgorithm option in spark.ml and pyspark.ml. We can leave spark.mllib and pyspark.mllib alone.
## How was this patch tested?
Unit tests.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12702 from yanboliang/spark-14899.
This PR adds the remaining group of methods to PySpark's distributed linear algebra classes as follows:
* `RowMatrix` <sup>**[1]**</sup>
1. `computeGramianMatrix`
2. `computeCovariance`
3. `computeColumnSummaryStatistics`
4. `columnSimilarities`
5. `tallSkinnyQR` <sup>**[2]**</sup>
* `IndexedRowMatrix` <sup>**[3]**</sup>
1. `computeGramianMatrix`
* `CoordinateMatrix`
1. `transpose`
* `BlockMatrix`
1. `validate`
2. `cache`
3. `persist`
4. `transpose`
**[1]**: Note: `multiply`, `computeSVD`, and `computePrincipalComponents` are already part of PR #7963 for SPARK-6227.
**[2]**: Implementing `tallSkinnyQR` uncovered a bug with our PySpark `RowMatrix` constructor. As discussed on the dev list [here](http://apache-spark-developers-list.1001551.n3.nabble.com/K-Means-And-Class-Tags-td10038.html), there appears to be an issue with type erasure with RDDs coming from Java, and by extension from PySpark. Although we are attempting to construct a `RowMatrix` from an `RDD[Vector]` in [PythonMLlibAPI](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala#L1115), the `Vector` type is erased, resulting in an `RDD[Object]`. Thus, when calling Scala's `tallSkinnyQR` from PySpark, we get a Java `ClassCastException` in which an `Object` cannot be cast to a Spark `Vector`. As noted in the aforementioned dev list thread, this issue was also encountered with `DecisionTrees`, and the fix involved an explicit `retag` of the RDD with a `Vector` type. Thus, this PR currently contains that fix applied to the `createRowMatrix` helper function in `PythonMLlibAPI`. `IndexedRowMatrix` and `CoordinateMatrix` do not appear to have this issue likely due to their related helper functions in `PythonMLlibAPI` creating the RDDs explicitly from DataFrames with pattern matching, thus preserving the types. However, this fix may be out of scope for this single PR, and it may be better suited in a separate JIRA/PR. Therefore, I have marked this PR as WIP and am open to discussion.
**[3]**: Note: `multiply` and `computeSVD` are already part of PR #7963 for SPARK-6227.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#9441 from dusenberrymw/SPARK-9656_Add_Missing_Methods_to_PySpark_Distributed_Linear_Algebra.
## What changes were proposed in this pull request?
Before, spark.ml GaussianMixtureModel used the spark.mllib MultivariateGaussian in its public API. This was added after 1.6, so we can modify this API without breaking APIs.
This PR copies MultivariateGaussian to mllib-local in spark.ml, with a few changes:
* Renamed fields to match numpy, scipy: mu => mean, sigma => cov
This PR then uses the spark.ml MultivariateGaussian in the spark.ml GaussianMixtureModel, which involves:
* Modifying the constructor
* Adding a computeProbabilities method
Also:
* Added EPSILON to mllib-local for use in MultivariateGaussian
## How was this patch tested?
Existing unit tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12593 from jkbradley/sparkml-gmm-fix.
## What changes were proposed in this pull request?
SPARK-14071 changed MLWritable.write to be a property. This reverts that change since there was not a good way to make MLReadable.read appear to be a property.
## How was this patch tested?
existing unit tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12671 from jkbradley/revert-MLWritable-write-py.
## What changes were proposed in this pull request?
We deprecated ```runs``` of mllib.KMeans in Spark 1.6 (SPARK-11358). In 2.0, we will make it no effect (with warning messages). We did not remove ```setRuns/getRuns``` for better binary compatibility.
This PR change `runs` which are appeared at the public API. Usage inside of ```KMeans.runAlgorithm()``` will be resolved at #10806.
## How was this patch tested?
Existing unit tests.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12608 from yanboliang/spark-11559.
## What changes were proposed in this pull request?
This removes the class `HiveContext` itself along with all code usages associated with it. The bulk of the work was already done in #12485. This is mainly just code cleanup and actually removing the class.
Note: A couple of things will break after this patch. These will be fixed separately.
- the python HiveContext
- all the documentation / comments referencing HiveContext
- there will be no more HiveContext in the REPL (fixed by #12589)
## How was this patch tested?
No change in functionality.
Author: Andrew Or <andrew@databricks.com>
Closes#12585 from andrewor14/delete-hive-context.
## What changes were proposed in this pull request?
As the discussion at [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574), ```HashingTF``` should support MurmurHash3 and make it as the default hash algorithm. We should also expose set/get API for ```hashAlgorithm```, then users can choose the hash method.
Note: The problem that ```mllib.feature.HashingTF``` behaves differently between Scala/Java and Python will be resolved in the followup work.
## How was this patch tested?
unit tests.
cc jkbradley MLnick
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12498 from yanboliang/spark-10574.
## What changes were proposed in this pull request?
Removed instances of JavaMLWriter, JavaMLReader appearing in public Python API docs
## How was this patch tested?
n/a
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12542 from jkbradley/javamlwriter-doc.
## 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?
Removed expectedType arg from PySpark Param __init__, as suggested by the JIRA.
## How was this patch tested?
Manually looked through all places that use Param. Compiled and ran all ML PySpark test cases before and after the fix.
Author: Jason Lee <cjlee@us.ibm.com>
Closes#12581 from jasoncl/SPARK-14768.
## What changes were proposed in this pull request?
In Python, sqlContext.getConf didn't allow getting the system default (getConf with one parameter).
Now the following are supported:
```
sqlContext.getConf(confName) # System default if not locally set, this is new
sqlContext.getConf(confName, myDefault) # myDefault if not locally set, old behavior
```
I also added doctests to this function. The original behavior does not change.
## How was this patch tested?
Manually, but doctests were added.
Author: mathieu longtin <mathieu.longtin@nuance.com>
Closes#12488 from mathieulongtin/pyfixgetconf3.
## What changes were proposed in this pull request?
In Python, the `option` and `options` method of `DataFrameReader` and `DataFrameWriter` were sending the string "None" instead of `null` when passed `None`, therefore making it impossible to send an actual `null`. This fixes that problem.
This is based on #11305 from mathieulongtin.
## How was this patch tested?
Added test to readwriter.py.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Author: mathieu longtin <mathieu.longtin@nuance.com>
Closes#12494 from viirya/py-df-none-option.
