## 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.
## What changes were proposed in this pull request?
The PySpark deserialization has a bug that shows while deserializing all zero sparse vectors. This fix filters out empty string tokens before casting, hence properly stringified SparseVectors successfully get parsed.
## How was this patch tested?
Standard unit-tests similar to other methods.
Author: Arash Parsa <arash@ip-192-168-50-106.ec2.internal>
Author: Arash Parsa <arashpa@gmail.com>
Author: Vishnu Prasad <vishnu667@gmail.com>
Author: Vishnu Prasad S <vishnu667@gmail.com>
Closes#12516 from arashpa/SPARK-14739.
## What changes were proposed in this pull request?
Expand the possible ways to interact with the contents of a `pyspark.sql.types.StructType` instance.
- Iterating a `StructType` will iterate its fields
- `[field.name for field in my_structtype]`
- Indexing with a string will return a field by name
- `my_structtype['my_field_name']`
- Indexing with an integer will return a field by position
- `my_structtype[0]`
- Indexing with a slice will return a new `StructType` with just the chosen fields:
- `my_structtype[1:3]`
- The length is the number of fields (should also provide "truthiness" for free)
- `len(my_structtype) == 2`
## How was this patch tested?
Extended the unit test coverage in the accompanying `tests.py`.
Author: Sheamus K. Parkes <shea.parkes@milliman.com>
Closes#12251 from skparkes/pyspark-structtype-enhance.
## What changes were proposed in this pull request?
#11663 adds type conversion functionality for parameters in Pyspark. This PR find out the omissive ```Param``` that did not pass corresponding ```TypeConverter``` argument and fix them. After this PR, all params in pyspark/ml/ used ```TypeConverter```.
## How was this patch tested?
Existing tests.
cc jkbradley sethah
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12529 from yanboliang/typeConverter.
## What changes were proposed in this pull request?
#11939 make Python param setters use the `_set` method. This PR fix omissive ones.
## How was this patch tested?
Existing tests.
cc jkbradley sethah
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12531 from yanboliang/setters-omissive.
## What changes were proposed in this pull request?
This patch provides a first cut of python APIs for structured streaming. This PR provides the new classes:
- ContinuousQuery
- Trigger
- ProcessingTime
in pyspark under `pyspark.sql.streaming`.
In addition, it contains the new methods added under:
- `DataFrameWriter`
a) `startStream`
b) `trigger`
c) `queryName`
- `DataFrameReader`
a) `stream`
- `DataFrame`
a) `isStreaming`
This PR doesn't contain all methods exposed for `ContinuousQuery`, for example:
- `exception`
- `sourceStatuses`
- `sinkStatus`
They may be added in a follow up.
This PR also contains some very minor doc fixes in the Scala side.
## How was this patch tested?
Python doc tests
TODO:
- [ ] verify Python docs look good
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Burak Yavuz <burak@databricks.com>
Closes#12320 from brkyvz/stream-python.
## What changes were proposed in this pull request?
This issue aims to expose Scala `bround` function in Python/R API.
`bround` function is implemented in SPARK-14614 by extending current `round` function.
We used the following semantics from Hive.
```java
public static double bround(double input, int scale) {
if (Double.isNaN(input) || Double.isInfinite(input)) {
return input;
}
return BigDecimal.valueOf(input).setScale(scale, RoundingMode.HALF_EVEN).doubleValue();
}
```
After this PR, `pyspark` and `sparkR` also support `bround` function.
**PySpark**
```python
>>> from pyspark.sql.functions import bround
>>> sqlContext.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect()
[Row(r=2.0)]
```
**SparkR**
```r
> df = createDataFrame(sqlContext, data.frame(x = c(2.5, 3.5)))
> head(collect(select(df, bround(df$x, 0))))
bround(x, 0)
1 2
2 4
```
## How was this patch tested?
Pass the Jenkins tests (including new testcases).
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#12509 from dongjoon-hyun/SPARK-14639.
## What changes were proposed in this pull request?
Change unpersist blocking parameter default value to match Scala
## How was this patch tested?
unit tests, manual tests
jkbradley davies
Author: felixcheung <felixcheung_m@hotmail.com>
Closes#12507 from felixcheung/pyunpersist.
## What changes were proposed in this pull request?
PySpark Param constructors need to pass the TypeConverter argument by name, partly to make sure it is not mistaken for the expectedType arg and partly because we will remove the expectedType arg in 2.1. In several places, this is not being done correctly.
This PR changes all usages in pyspark/ml/ to keyword args.
## How was this patch tested?
Existing unit tests. I will not test type conversion for every Param unless we really think it necessary.
Also, if you start the PySpark shell and import classes (e.g., pyspark.ml.feature.StandardScaler), then you no longer get this warning:
```
/Users/josephkb/spark/python/pyspark/ml/param/__init__.py:58: UserWarning: expectedType is deprecated and will be removed in 2.1. Use typeConverter instead, as a keyword argument.
"Use typeConverter instead, as a keyword argument.")
```
That warning came from the typeConverter argument being passes as the expectedType arg by mistake.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12480 from jkbradley/typeconverter-fix.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14440
Remove
* PipelineMLWriter
* PipelineMLReader
* PipelineModelMLWriter
* PipelineModelMLReader
and modify comments.
## How was this patch tested?
test with unit test.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#12216 from yinxusen/SPARK-14440.
## What changes were proposed in this pull request?
Added windowSize getter/setter to ML/MLlib
## How was this patch tested?
Added test cases in tests.py under both ML and MLlib
Author: Jason Lee <cjlee@us.ibm.com>
Closes#12428 from jasoncl/SPARK-14564.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14306
Add PySpark OneVsRest save/load supports.
## How was this patch tested?
Test with Python unit test.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#12439 from yinxusen/SPARK-14306-0415.
## What changes were proposed in this pull request?
Python spark.ml Identifiable classes use UIDs of type str, but they should use unicode (in Python 2.x) to match Java. This could be a problem if someone created a class in Java with odd unicode characters, saved it, and loaded it in Python.
This PR: Use unicode everywhere in Python.
## How was this patch tested?
Updated persistence unit test to check uid type
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12368 from jkbradley/python-uid-unicode.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-7861
Add PySpark OneVsRest. I implement it with Python since it's a meta-pipeline.
## How was this patch tested?
Test with doctest.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#12124 from yinxusen/SPARK-14306-7861.
## What changes were proposed in this pull request?
Param setters in python previously accessed the _paramMap directly to update values. The `_set` method now implements type checking, so it should be used to update all parameters. This PR eliminates all direct accesses to `_paramMap` besides the one in the `_set` method to ensure type checking happens.
Additional changes:
* [SPARK-13068](https://github.com/apache/spark/pull/11663) missed adding type converters in evaluation.py so those are done here
* An incorrect `toBoolean` type converter was used for StringIndexer `handleInvalid` param in previous PR. This is fixed here.
## How was this patch tested?
Existing unit tests verify that parameters are still set properly. No new functionality is actually added in this PR.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11939 from sethah/SPARK-14104.
## What changes were proposed in this pull request?
The default stopwords were a Java object. They are no longer.
## How was this patch tested?
Unit test which failed before the fix
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12422 from jkbradley/pyspark-stopwords.
## What changes were proposed in this pull request?
PySpark ml GBTClassifier, Regressor support export/import.
## How was this patch tested?
Doc test.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12383 from yanboliang/spark-14374.
## What changes were proposed in this pull request?
This fix tries to add binary toggle Param to PySpark HashingTF in ML & MLlib. If this toggle is set, then all non-zero counts will be set to 1.
Note: This fix (SPARK-14238) is extended from SPARK-13963 where Scala implementation was done.
## How was this patch tested?
This fix adds two tests to cover the code changes. One for HashingTF in PySpark's ML and one for HashingTF in PySpark's MLLib.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#12079 from yongtang/SPARK-14238.
Added binary toggle param to CountVectorizer feature transformer in PySpark.
Created a unit test for using CountVectorizer with the binary toggle on.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#12308 from BryanCutler/binary-param-python-CountVectorizer-SPARK-13967.
## What changes were proposed in this pull request?
The PyDoc Makefile used "=" rather than "?=" for setting env variables so it overwrote the user values. This ignored the environment variables we set for linting allowing warnings through. This PR also fixes the warnings that had been introduced.
## How was this patch tested?
manual local export & make
Author: Holden Karau <holden@us.ibm.com>
Closes#12336 from holdenk/SPARK-14573-fix-pydoc-makefile.
Currently, JavaWrapper is only a wrapper class for pipeline classes that have Params and JavaCallable is a separate mixin that provides methods to make Java calls. This change simplifies the class structure and to define the Java wrapper in a plain base class along with methods to make Java calls. Also, renames Java wrapper classes to better reflect their purpose.
Ran existing Python ml tests and generated documentation to test this change.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#12304 from BryanCutler/pyspark-cleanup-JavaWrapper-SPARK-14472.
## What changes were proposed in this pull request?
- updated `OFF_HEAP` semantics for `StorageLevels.java`
- updated `OFF_HEAP` semantics for `storagelevel.py`
## How was this patch tested?
no need to test
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12126 from lw-lin/storagelevel.py.
## What changes were proposed in this pull request?
Python API for GeneralizedLinearRegression
JIRA: https://issues.apache.org/jira/browse/SPARK-13597
## How was this patch tested?
The patch is tested with Python doctest.
Author: Kai Jiang <jiangkai@gmail.com>
Closes#11468 from vectorijk/spark-13597.
## What changes were proposed in this pull request?
Eagerly cleanup PySpark's temporary parallelize cleanup files rather than waiting for shut down.
## How was this patch tested?
Unit tests
Author: Holden Karau <holden@us.ibm.com>
Closes#12233 from holdenk/SPARK-13687-cleanup-pyspark-temporary-files.
## What changes were proposed in this pull request?
Cleanups to documentation. No changes to code.
* GBT docs: Move Scala doc for private object GradientBoostedTrees to public docs for GBTClassifier,Regressor
* GLM regParam: needs doc saying it is for L2 only
* TrainValidationSplitModel: add .. versionadded:: 2.0.0
* Rename “_transformer_params_from_java” to “_transfer_params_from_java”
* LogReg Summary classes: “probability” col should not say “calibrated”
* LR summaries: coefficientStandardErrors —> document that intercept stderr comes last. Same for t,p-values
* approxCountDistinct: Document meaning of “rsd" argument.
* LDA: note which params are for online LDA only
## How was this patch tested?
Doc build
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#12266 from jkbradley/ml-doc-cleanups.
## What changes were proposed in this pull request?
A new column VarianceCol has been added to DecisionTreeRegressor in ML scala code.
This patch adds the corresponding Python API, HasVarianceCol, to class DecisionTreeRegressor.
## How was this patch tested?
./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 (12s)
Finished test(python2.7): pyspark.ml.clustering (18s)
Finished test(python2.7): pyspark.ml.classification (30s)
Finished test(python2.7): pyspark.ml.recommendation (28s)
Finished test(python2.7): pyspark.ml.feature (43s)
Finished test(python2.7): pyspark.ml.regression (31s)
Finished test(python2.7): pyspark.ml.tuning (19s)
Finished test(python2.7): pyspark.ml.tests (34s)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#12116 from wangmiao1981/fix_api.
## What changes were proposed in this pull request?
supporting `RandomForest{Classifier, Regressor}` save/load for Python API.
[JIRA](https://issues.apache.org/jira/browse/SPARK-14373)
## How was this patch tested?
doctest
Author: Kai Jiang <jiangkai@gmail.com>
Closes#12238 from vectorijk/spark-14373.
## What changes were proposed in this pull request?
Adding Python API for training summaries of LogisticRegression and LinearRegression in PySpark ML.
## How was this patch tested?
Added unit tests to exercise the api calls for the summary classes. Also, manually verified values are expected and match those from Scala directly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11621 from BryanCutler/pyspark-ml-summary-SPARK-13430.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13786
Add save/load for Python CrossValidator/Model and TrainValidationSplit/Model.
## How was this patch tested?
Test with Python doctest.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#12020 from yinxusen/SPARK-13786.
## What changes were proposed in this pull request?
Currently, Broaccast.unpersist() will remove the file of broadcast, which should be the behavior of destroy().
This PR added destroy() for Broadcast in Python, to match the sematics in Scala.
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#12189 from davies/py_unpersist.
## What changes were proposed in this pull request?
The `window` function was added to Dataset with [this PR](https://github.com/apache/spark/pull/12008).
This PR adds the Python, and SQL, API for this function.
With this PR, SQL, Java, and Scala will share the same APIs as in users can use:
- `window(timeColumn, windowDuration)`
- `window(timeColumn, windowDuration, slideDuration)`
- `window(timeColumn, windowDuration, slideDuration, startTime)`
In Python, users can access all APIs above, but in addition they can do
- In Python:
`window(timeColumn, windowDuration, startTime=...)`
that is, they can provide the startTime without providing the `slideDuration`. In this case, we will generate tumbling windows.
## How was this patch tested?
Unit tests + manual tests
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#12136 from brkyvz/python-windows.
## What changes were proposed in this pull request?
This fix tries to address the issue in PySpark where `spark.python.worker.memory`
could only be configured with a lower case unit (`k`, `m`, `g`, `t`). This fix
allows the upper case unit (`K`, `M`, `G`, `T`) to be used as well. This is to
conform to the JVM memory string as is specified in the documentation .
## How was this patch tested?
This fix adds additional test to cover the changes.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#12163 from yongtang/SPARK-14368.
This change modifies the "assembly/" module to just copy needed
dependencies to its build directory, and modifies the packaging
script to pick those up (and remove duplicate jars packages in the
examples module).
I also made some minor adjustments to dependencies to remove some
test jars from the final packaging, and remove jars that conflict with each
other when packaged separately (e.g. servlet api).
Also note that this change restores guava in applications' classpaths, even
though it's still shaded inside Spark. This is now needed for the Hadoop
libraries that are packaged with Spark, which now are not processed by
the shade plugin.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#11796 from vanzin/SPARK-13579.
## What changes were proposed in this pull request?
RDD.toLocalIterator() could be used to fetch one partition at a time to reduce the memory usage. Right now, for Dataset/Dataframe we have to use df.rdd.toLocalIterator, which is super slow also requires lots of memory (because of the Java serializer or even Kyro serializer).
This PR introduce an optimized toLocalIterator for Dataset/DataFrame, which is much faster and requires much less memory. For a partition with 5 millions rows, `df.rdd.toIterator` took about 100 seconds, but df.toIterator took less than 7 seconds. For 10 millions row, rdd.toIterator will crash (not enough memory) with 4G heap, but df.toLocalIterator could finished in 12 seconds.
The JDBC server has been updated to use DataFrame.toIterator.
## How was this patch tested?
Existing tests.
Author: Davies Liu <davies@databricks.com>
Closes#12114 from davies/local_iterator.
## What changes were proposed in this pull request?
Currently we extract Python UDFs into a special logical plan EvaluatePython in analyzer, But EvaluatePython is not part of catalyst, many rules have no knowledge of it , which will break many things (for example, filter push down or column pruning).
We should treat Python UDFs as normal expressions, until we want to evaluate in physical plan, we could extract them in end of optimizer, or physical plan.
This PR extract Python UDFs in physical plan.
Closes#10935
## How was this patch tested?
Added regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#12127 from davies/py_udf.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-14231
Currently, JSON data source supports to infer `DecimalType` for big numbers and `floatAsBigDecimal` option which reads floating-point values as `DecimalType`.
But there are few restrictions in Spark `DecimalType` below:
1. The precision cannot be bigger than 38.
2. scale cannot be bigger than precision.
Currently, both restrictions are not being handled.
This PR handles the cases by inferring them as `DoubleType`. Also, the option name was changed from `floatAsBigDecimal` to `prefersDecimal` as suggested [here](https://issues.apache.org/jira/browse/SPARK-14231?focusedCommentId=15215579&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15215579).
So, the codes below:
```scala
def doubleRecords: RDD[String] =
sqlContext.sparkContext.parallelize(
s"""{"a": 1${"0" * 38}, "b": 0.01}""" ::
s"""{"a": 2${"0" * 38}, "b": 0.02}""" :: Nil)
val jsonDF = sqlContext.read
.option("prefersDecimal", "true")
.json(doubleRecords)
jsonDF.printSchema()
```
produces below:
- **Before**
```scala
org.apache.spark.sql.AnalysisException: Decimal scale (2) cannot be greater than precision (1).;
at org.apache.spark.sql.types.DecimalType.<init>(DecimalType.scala:44)
at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:144)
at org.apache.spark.sql.execution.datasources.json.InferSchema$.org$apache$spark$sql$execution$datasources$json$InferSchema$$inferField(InferSchema.scala:108)
at
...
```
- **After**
```scala
root
|-- a: double (nullable = true)
|-- b: double (nullable = true)
```
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for coding style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#12030 from HyukjinKwon/SPARK-14231.
## What changes were proposed in this pull request?
Straggler references to Tachyon were removed:
- for docs, `tachyon` has been generalized as `off-heap memory`;
- for Mesos test suits, the key-value `tachyon:true`/`tachyon:false` has been changed to `os:centos`/`os:ubuntu`, since `os` is an example constrain used by the [Mesos official docs](http://mesos.apache.org/documentation/attributes-resources/).
## How was this patch tested?
Existing test suites.
Author: Liwei Lin <lwlin7@gmail.com>
Closes#12129 from lw-lin/tachyon-cleanup.
## What changes were proposed in this pull request?
PySpark ml.clustering BisectingKMeans support export/import
## How was this patch tested?
doc test.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12112 from yanboliang/spark-14305.
1.Implement LossFunction trait and implement squared error and cross entropy
loss with it
2.Implement unit test for gradient and loss
3.Implement InPlace trait and in-place layer evaluation
4.Refactor interface for ActivationFunction
5.Update of Layer and LayerModel interfaces
6.Fix random weights assignment
7.Implement memory allocation by MLP model instead of individual layers
These features decreased the memory usage and increased flexibility of
internal API.
Author: Alexander Ulanov <nashb@yandex.ru>
Author: avulanov <avulanov@gmail.com>
Closes#9229 from avulanov/mlp-refactoring.
## What changes were proposed in this pull request?
This PR support multiple Python UDFs within single batch, also improve the performance.
```python
>>> from pyspark.sql.types import IntegerType
>>> sqlContext.registerFunction("double", lambda x: x * 2, IntegerType())
>>> sqlContext.registerFunction("add", lambda x, y: x + y, IntegerType())
>>> sqlContext.sql("SELECT double(add(1, 2)), add(double(2), 1)").explain(True)
== Parsed Logical Plan ==
'Project [unresolvedalias('double('add(1, 2)), None),unresolvedalias('add('double(2), 1), None)]
+- OneRowRelation$
== Analyzed Logical Plan ==
double(add(1, 2)): int, add(double(2), 1): int
Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [double(add(1, 2))#14,add(double(2), 1)#15]
+- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Optimized Logical Plan ==
Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
+- EvaluatePython [add(pythonUDF1#17, 1)], [pythonUDF0#18]
+- EvaluatePython [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- OneRowRelation$
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF0#16 AS double(add(1, 2))#14,pythonUDF0#18 AS add(double(2), 1)#15]
: +- INPUT
+- !BatchPythonEvaluation [add(pythonUDF1#17, 1)], [pythonUDF0#16,pythonUDF1#17,pythonUDF0#18]
+- !BatchPythonEvaluation [double(add(1, 2)),double(2)], [pythonUDF0#16,pythonUDF1#17]
+- Scan OneRowRelation[]
```
## How was this patch tested?
Added new tests.
Using the following script to benchmark 1, 2 and 3 udfs,
```
df = sqlContext.range(1, 1 << 23, 1, 4)
double = F.udf(lambda x: x * 2, LongType())
print df.select(double(df.id)).count()
print df.select(double(df.id), double(df.id + 1)).count()
print df.select(double(df.id), double(df.id + 1), double(df.id + 2)).count()
```
Here is the results:
N | Before | After | speed up
---- |------------ | -------------|------
1 | 22 s | 7 s | 3.1X
2 | 38 s | 13 s | 2.9X
3 | 58 s | 16 s | 3.6X
This benchmark ran locally with 4 CPUs. For 3 UDFs, it launched 12 Python before before this patch, 4 process after this patch. After this patch, it will use less memory for multiple UDFs than before (less buffering).
Author: Davies Liu <davies@databricks.com>
Closes#12057 from davies/multi_udfs.
## What changes were proposed in this pull request?
Feature importances are exposed in the python API for GBTs.
Other changes:
* Update the random forest feature importance documentation to not repeat decision tree docstring and instead place a reference to it.
## How was this patch tested?
Python doc tests were updated to validate GBT feature importance.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#12056 from sethah/Pyspark_GBT_feature_importance.
### What changes were proposed in this pull request?
This PR removes the ANTLR3 based parser, and moves the new ANTLR4 based parser into the `org.apache.spark.sql.catalyst.parser package`.
### How was this patch tested?
Existing unit tests.
cc rxin andrewor14 yhuai
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#12071 from hvanhovell/SPARK-14211.
## What changes were proposed in this pull request?
```MultilayerPerceptronClassifier``` supports save/load for Python API.
## How was this patch tested?
doctest.
cc mengxr jkbradley yinxusen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11952 from yanboliang/spark-14152.
## What changes were proposed in this pull request?
This PR brings the support for chained Python UDFs, for example
```sql
select udf1(udf2(a))
select udf1(udf2(a) + 3)
select udf1(udf2(a) + udf3(b))
```
Also directly chained unary Python UDFs are put in single batch of Python UDFs, others may require multiple batches.
For example,
```python
>>> sqlContext.sql("select double(double(1))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#10 AS double(double(1))#9]
: +- INPUT
+- !BatchPythonEvaluation double(double(1)), [pythonUDF#10]
+- Scan OneRowRelation[]
>>> sqlContext.sql("select double(double(1) + double(2))").explain()
== Physical Plan ==
WholeStageCodegen
: +- Project [pythonUDF#19 AS double((double(1) + double(2)))#16]
: +- INPUT
+- !BatchPythonEvaluation double((pythonUDF#17 + pythonUDF#18)), [pythonUDF#17,pythonUDF#18,pythonUDF#19]
+- !BatchPythonEvaluation double(2), [pythonUDF#17,pythonUDF#18]
+- !BatchPythonEvaluation double(1), [pythonUDF#17]
+- Scan OneRowRelation[]
```
TODO: will support multiple unrelated Python UDFs in one batch (another PR).
## How was this patch tested?
Added new unit tests for chained UDFs.
Author: Davies Liu <davies@databricks.com>
Closes#12014 from davies/py_udfs.
Add property to MLWritable.write method, so we can use .write instead of .write()
Add a new test to ml/test.py to check whether the write is a property.
./python/run-tests --python-executables=python2.7 --modules=pyspark-ml
Will test against the following Python executables: ['python2.7']
Will test the following Python modules: ['pyspark-ml']
Finished test(python2.7): pyspark.ml.evaluation (11s)
Finished test(python2.7): pyspark.ml.clustering (16s)
Finished test(python2.7): pyspark.ml.classification (24s)
Finished test(python2.7): pyspark.ml.recommendation (24s)
Finished test(python2.7): pyspark.ml.feature (39s)
Finished test(python2.7): pyspark.ml.regression (26s)
Finished test(python2.7): pyspark.ml.tuning (15s)
Finished test(python2.7): pyspark.ml.tests (30s)
Tests passed in 55 seconds
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#11945 from wangmiao1981/fix_property.
## What changes were proposed in this pull request?
This PR replaces list comprehension in python_full_outer_join.dispatch with a generator expression.
## How was this patch tested?
PySpark-Core, PySpark-MLlib test suites against Python 2.7, 3.5.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#11998 from zero323/pyspark-join-generator-expr.
### What changes were proposed in this pull request?
The current ANTLR3 parser is quite complex to maintain and suffers from code blow-ups. This PR introduces a new parser that is based on ANTLR4.
This parser is based on the [Presto's SQL parser](https://github.com/facebook/presto/blob/master/presto-parser/src/main/antlr4/com/facebook/presto/sql/parser/SqlBase.g4). The current implementation can parse and create Catalyst and SQL plans. Large parts of the HiveQl DDL and some of the DML functionality is currently missing, the plan is to add this in follow-up PRs.
This PR is a work in progress, and work needs to be done in the following area's:
- [x] Error handling should be improved.
- [x] Documentation should be improved.
- [x] Multi-Insert needs to be tested.
- [ ] Naming and package locations.
### How was this patch tested?
Catalyst and SQL unit tests.
Author: Herman van Hovell <hvanhovell@questtec.nl>
Closes#11557 from hvanhovell/ngParser.
## What changes were proposed in this pull request?
This PR removes all docs about the old streaming-akka, streaming-zeromq, streaming-mqtt and streaming-twitter projects since I have already copied them to https://github.com/spark-packages
Also remove mqtt_wordcount.py that I forgot to remove previously.
## How was this patch tested?
Jenkins PR Build.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11824 from zsxwing/remove-doc.
## What changes were proposed in this pull request?
This PR moves flume back to Spark as per the discussion in the dev mail-list.
## How was this patch tested?
Existing Jenkins tests.
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11895 from zsxwing/move-flume-back.
## What changes were proposed in this pull request?
As we have `CreateArray` and `CreateStruct`, we should also have `CreateMap`. This PR adds the `CreateMap` expression, and the DataFrame API, and python API.
## How was this patch tested?
various new tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11879 from cloud-fan/create_map.
## What changes were proposed in this pull request?
This reopens#11836, which was merged but promptly reverted because it introduced flaky Hive tests.
## How was this patch tested?
See `CatalogTestCases`, `SessionCatalogSuite` and `HiveContextSuite`.
Author: Andrew Or <andrew@databricks.com>
Closes#11938 from andrewor14/session-catalog-again.
## What changes were proposed in this pull request?
unionAll has been deprecated in SPARK-14088.
## How was this patch tested?
Should be covered by all existing tests.
Author: Reynold Xin <rxin@databricks.com>
Closes#11946 from rxin/SPARK-14142.
## What changes were proposed in this pull request?
Added MLReadable and MLWritable to Decision Tree Classifier and Regressor. Added doctests.
## How was this patch tested?
Python Unit tests. Tests added to check persistence in DecisionTreeClassifier and DecisionTreeRegressor.
Author: GayathriMurali <gayathri.m.softie@gmail.com>
Closes#11892 from GayathriMurali/SPARK-13949.
## What changes were proposed in this pull request?
GBTs in pyspark previously had seed parameters, but they could not be passed as keyword arguments through the class constructor. This patch adds seed as a keyword argument and also sets default value.
## How was this patch tested?
Doc tests were updated to pass a random seed through the GBTClassifier and GBTRegressor constructors.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11944 from sethah/SPARK-14107.
Primary change:
* Removed spark.mllib.tree.DecisionTree implementation of tree and forest learning.
* spark.mllib now calls the spark.ml implementation.
* Moved unit tests (of tree learning internals) from spark.mllib to spark.ml as needed.
ml.tree.DecisionTreeModel
* Added toOld and made ```private[spark]```, implemented for Classifier and Regressor in subclasses. These methods now use OldInformationGainStats.invalidInformationGainStats for LeafNodes in order to mimic the spark.mllib implementation.
ml.tree.Node
* Added ```private[tree] def deepCopy```, used by unit tests
Copied developer comments from spark.mllib implementation to spark.ml one.
Moving unit tests
* Tree learning internals were tested by spark.mllib.tree.DecisionTreeSuite, or spark.mllib.tree.RandomForestSuite.
* Those tests were all moved to spark.ml.tree.impl.RandomForestSuite. The order in the file + the test names are the same, so you should be able to compare them by opening them in 2 windows side-by-side.
* I made minimal changes to each test to allow it to run. Each test makes the same checks as before, except for a few removed assertions which were checking irrelevant values.
* No new unit tests were added.
* mllib.tree.DecisionTreeSuite: I removed some checks of splits and bins which were not relevant to the unit tests they were in. Those same split calculations were already being tested in other unit tests, for each dataset type.
**Changes of behavior** (to be noted in SPARK-13448 once this PR is merged)
* spark.ml.tree.impl.RandomForest: Rather than throwing an error when maxMemoryInMB is set to too small a value (to split any node), we now allow 1 node to be split, even if its memory requirements exceed maxMemoryInMB. This involved removing the maxMemoryPerNode check in RandomForest.run, as well as modifying selectNodesToSplit(). Once this PR is merged, I will note the change of behavior on SPARK-13448.
* spark.mllib.tree.DecisionTree: When a tree only has one node (root = leaf node), the "stats" field will now be empty, rather than being set to InformationGainStats.invalidInformationGainStats. This does not remove information from the tree, and it will save a bit of storage.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#11855 from jkbradley/remove-mllib-tree-impl.
## What changes were proposed in this pull request?
`SessionCatalog`, introduced in #11750, is a catalog that keeps track of temporary functions and tables, and delegates metastore operations to `ExternalCatalog`. This functionality overlaps a lot with the existing `analysis.Catalog`.
As of this commit, `SessionCatalog` and `ExternalCatalog` will no longer be dead code. There are still things that need to be done after this patch, namely:
- SPARK-14013: Properly implement temporary functions in `SessionCatalog`
- SPARK-13879: Decide which DDL/DML commands to support natively in Spark
- SPARK-?????: Implement the ones we do want to support through `SessionCatalog`.
- SPARK-?????: Merge SQL/HiveContext
## How was this patch tested?
This is largely a refactoring task so there are no new tests introduced. The particularly relevant tests are `SessionCatalogSuite` and `ExternalCatalogSuite`.
Author: Andrew Or <andrew@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Closes#11836 from andrewor14/use-session-catalog.
## What changes were proposed in this pull request?
This patch adds type conversion functionality for parameters in Pyspark. A `typeConverter` field is added to the constructor of `Param` class. This argument is a function which converts values passed to this param to the appropriate type if possible. This is beneficial so that the params can fail at set time if they are given inappropriate values, but even more so because coherent error messages are now provided when Py4J cannot cast the python type to the appropriate Java type.
This patch also adds a `TypeConverters` class with factory methods for common type conversions. Most of the changes involve adding these factory type converters to existing params. The previous solution to this issue, `expectedType`, is deprecated and can be removed in 2.1.0 as discussed on the Jira.
## How was this patch tested?
Unit tests were added in python/pyspark/ml/tests.py to test parameter type conversion. These tests check that values that should be convertible are converted correctly, and that the appropriate errors are thrown when invalid values are provided.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11663 from sethah/SPARK-13068-tc.
## What changes were proposed in this pull request?
1. Deprecated unionAll. It is pretty confusing to have both "union" and "unionAll" when the two do the same thing in Spark but are different in SQL.
2. Rename reduce in KeyValueGroupedDataset to reduceGroups so it is more consistent with rest of the functions in KeyValueGroupedDataset. Also makes it more obvious what "reduce" and "reduceGroups" mean. Previously it was confusing because it could be reducing a Dataset, or just reducing groups.
3. Added a "name" function, which is more natural to name columns than "as" for non-SQL users.
4. Remove "subtract" function since it is just an alias for "except".
## How was this patch tested?
All changes should be covered by existing tests. Also added couple test cases to cover "name".
Author: Reynold Xin <rxin@databricks.com>
Closes#11908 from rxin/SPARK-14088.
Adds support for saving and loading nested ML Pipelines from Python. Pipeline and PipelineModel do not extend JavaWrapper, but they are able to utilize the JavaMLWriter, JavaMLReader implementations.
Also:
* Separates out interfaces from Java wrapper implementations for MLWritable, MLReadable, MLWriter, MLReader.
* Moves methods _stages_java2py, _stages_py2java into Pipeline, PipelineModel as _transfer_stage_from_java, _transfer_stage_to_java
Added new unit test for nested Pipelines. Abstracted validity check into a helper method for the 2 unit tests.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#11866 from jkbradley/nested-pipeline-io.
Closes#11835
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-13953
Currently, JSON data source creates a new field in `PERMISSIVE` mode for storing malformed string.
This field can be renamed via `spark.sql.columnNameOfCorruptRecord` option but it is a global configuration.
This PR make that option can be applied per read and can be specified via `option()`. This will overwrites `spark.sql.columnNameOfCorruptRecord` if it is set.
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for coding style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11881 from HyukjinKwon/SPARK-13953.
## What changes were proposed in this pull request?
Replaces current docstring ("Creates a :class:`WindowSpec` with the partitioning defined.") with "Creates a :class:`WindowSpec` with the ordering defined."
## How was this patch tested?
PySpark unit tests (no regression introduced). No changes to the code.
Author: zero323 <matthew.szymkiewicz@gmail.com>
Closes#11877 from zero323/order-by-description.
## What changes were proposed in this pull request?
Currently, there is no way to control the behaviour when fails to parse corrupt records in JSON data source .
This PR adds the support for parse modes just like CSV data source. There are three modes below:
- `PERMISSIVE` : When it fails to parse, this sets `null` to to field. This is a default mode when it has been this mode.
- `DROPMALFORMED`: When it fails to parse, this drops the whole record.
- `FAILFAST`: When it fails to parse, it just throws an exception.
This PR also make JSON data source share the `ParseModes` in CSV data source.
## How was this patch tested?
Unit tests were used and `./dev/run_tests` for code style tests.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#11756 from HyukjinKwon/SPARK-13764.
## What changes were proposed in this pull request?
In PySpark wrapper.py JavaWrapper change _java_obj from an unused static variable to a member variable that is consistent with usage in derived classes.
## How was this patch tested?
Ran python tests for ML and MLlib.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11767 from BryanCutler/JavaWrapper-static-_java_obj-SPARK-13937.
## What changes were proposed in this pull request?
Add export/import for all estimators and transformers(which have Scala implementation) under pyspark/ml/classification.py.
## How was this patch tested?
./python/run-tests
./dev/lint-python
Unit tests added to check persistence in Logistic Regression
Author: GayathriMurali <gayathri.m.softie@gmail.com>
Closes#11707 from GayathriMurali/SPARK-13034.
## What changes were proposed in this pull request?
JIRA issue: https://issues.apache.org/jira/browse/SPARK-13038
1. Add load/save to PySpark Pipeline and PipelineModel
2. Add `_transfer_stage_to_java()` and `_transfer_stage_from_java()` for `JavaWrapper`.
## How was this patch tested?
Test with doctest.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#11683 from yinxusen/SPARK-13038-only.
## What changes were proposed in this pull request?
We have seen users getting confused by the documentation for astype and drop_duplicates, because the examples in them do not use these functions (but do uses their aliases). This patch simply removes all examples for these functions, and say that they are aliases.
## How was this patch tested?
Existing PySpark unit tests.
Closes#11543.
Author: Reynold Xin <rxin@databricks.com>
Closes#11698 from rxin/SPARK-10380.
## What changes were proposed in this pull request?
Currently there are a few sub-projects, each for integrating with different external sources for Streaming. Now that we have better ability to include external libraries (spark packages) and with Spark 2.0 coming up, we can move the following projects out of Spark to https://github.com/spark-packages
- streaming-flume
- streaming-akka
- streaming-mqtt
- streaming-zeromq
- streaming-twitter
They are just some ancillary packages and considering the overhead of maintenance, running tests and PR failures, it's better to maintain them out of Spark. In addition, these projects can have their different release cycles and we can release them faster.
I have already copied these projects to https://github.com/spark-packages
## How was this patch tested?
Jenkins tests
Author: Shixiong Zhu <shixiong@databricks.com>
Closes#11672 from zsxwing/remove-external-pkg.
This patch upgrades Py4J from 0.9.1 to 0.9.2 in order to include a patch which modifies Py4J to use the current thread's ContextClassLoader when performing reflection / class loading. This is necessary in order to fix [SPARK-5185](https://issues.apache.org/jira/browse/SPARK-5185), a longstanding issue affecting the use of `--jars` and `--packages` in PySpark.
In order to demonstrate that the fix works, I removed the workarounds which were added as part of [SPARK-6027](https://issues.apache.org/jira/browse/SPARK-6027) / #4779 and other patches.
Py4J diff: https://github.com/bartdag/py4j/compare/0.9.1...0.9.2
/cc zsxwing tdas davies brkyvz
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11687 from JoshRosen/py4j-0.9.2.
## What changes were proposed in this pull request?
This PR split the PhysicalRDD into two classes, PhysicalRDD and PhysicalScan. PhysicalRDD is used for DataFrames that is created from existing RDD. PhysicalScan is used for DataFrame that is created from data sources. This enable use to apply different optimization on both of them.
Also fix the problem for sameResult() on two DataSourceScan.
Also fix the equality check to toString for `In`. It's better to use Seq there, but we can't break this public API (sad).
## How was this patch tested?
Existing tests. Manually tested with TPCDS query Q59 and Q64, all those duplicated exchanges can be re-used now, also saw there are 40+% performance improvement (saving half of the scan).
Author: Davies Liu <davies@databricks.com>
Closes#11514 from davies/existing_rdd.
This patch de-duplicates code in PySpark streaming which loads the `Python*Helper` classes. I also changed a few `raise e` statements to simply `raise` in order to preserve the full exception stacktrace when re-throwing.
Here's a link to the whitespace-change-free diff: https://github.com/apache/spark/compare/master...JoshRosen:pyspark-reflection-deduplication?w=0
Author: Josh Rosen <joshrosen@databricks.com>
Closes#11641 from JoshRosen/pyspark-reflection-deduplication.
## What changes were proposed in this pull request?
This patch adds a `featureImportance` property to the Pyspark API for `DecisionTreeRegressionModel`, `DecisionTreeClassificationModel`, `RandomForestRegressionModel` and `RandomForestClassificationModel`.
## How was this patch tested?
Python doc tests for the affected classes were updated to check feature importances.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11622 from sethah/SPARK-13787.
JIRA: https://issues.apache.org/jira/browse/SPARK-13672
## What changes were proposed in this pull request?
add two python examples of BisectingKMeans for ml and mllib
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#11515 from zhengruifeng/mllib_bkm_pe.
## What changes were proposed in this pull request?
This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`.
Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`).
There are several noticeable API changes related to those returning arrays:
1. `collect`/`take`
- Old APIs in class `DataFrame`:
```scala
def collect(): Array[Row]
def take(n: Int): Array[Row]
```
- New APIs in class `Dataset[T]`:
```scala
def collect(): Array[T]
def take(n: Int): Array[T]
def collectRows(): Array[Row]
def takeRows(n: Int): Array[Row]
```
Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side.
Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here).
1. `randomSplit`
- Old APIs in class `DataFrame`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame]
def randomSplit(weights: Array[Double]): Array[DataFrame]
```
- New APIs in class `Dataset[T]`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
def randomSplit(weights: Array[Double]): Array[Dataset[T]]
```
Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one.
1. `groupBy`
Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`.
Other noticeable changes:
1. Dataset always do eager analysis now
We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`.
## How was this patch tested?
Existing tests do the work.
## TODO
- [ ] Fix all tests
- [ ] Re-enable MiMA check
- [ ] Update ScalaDoc (`since`, `group`, and example code)
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes#11443 from liancheng/ds-to-df.
Minor typo: docstring for pyspark.sql.functions: hypot has extra characters
N/A
Author: Tristan Reid <treid@netflix.com>
Closes#11616 from tristanreid/master.
## What changes were proposed in this pull request?
Move `docker` dirs out of top level into `external/`; move `extras/*` into `external/`
## How was this patch tested?
This is tested with Jenkins tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#11523 from srowen/SPARK-13595.
## What changes were proposed in this pull request?
Added a check in pyspark.ml.param.Param.params() to see if an attribute is a property (decorated with `property`) before checking if it is a `Param` instance. This prevents the property from being invoked to 'get' this attribute, which could possibly cause an error.
## How was this patch tested?
Added a test case with a class has a property that will raise an error when invoked and then call`Param.params` to verify that the property is not invoked, but still able to find another property in the class. Also ran pyspark-ml test before fix that will trigger an error, and again after the fix to verify that the error was resolved and the method was working properly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11476 from BryanCutler/pyspark-ml-property-attr-SPARK-13625.
## What changes were proposed in this pull request?
This PR improves the `createDataFrame` method to make it also accept datatype string, then users can convert python RDD to DataFrame easily, for example, `df = rdd.toDF("a: int, b: string")`.
It also supports flat schema so users can convert an RDD of int to DataFrame directly, we will automatically wrap int to row for users.
If schema is given, now we checks if the real data matches the given schema, and throw error if it doesn't.
## How was this patch tested?
new tests in `test.py` and doc test in `types.py`
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11444 from cloud-fan/pyrdd.
## What changes were proposed in this pull request?
This PR adds null check in `_verify_type` according to the nullability information.
## How was this patch tested?
new doc tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11574 from cloud-fan/py-null-check.
## What changes were proposed in this pull request?
Move many top-level files in dev/ or other appropriate directory. In particular, put `make-distribution.sh` in `dev` and update docs accordingly. Remove deprecated `sbt/sbt`.
I was (so far) unable to figure out how to move `tox.ini`. `scalastyle-config.xml` should be movable but edits to the project `.sbt` files didn't work; config file location is updatable for compile but not test scope.
## How was this patch tested?
`./dev/run-tests` to verify RAT and checkstyle work. Jenkins tests for the rest.
Author: Sean Owen <sowen@cloudera.com>
Closes#11522 from srowen/SPARK-13596.
## What changes were proposed in this pull request?
In the Jenkins pull request builder, PySpark tests take around [962 seconds ](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/52530/console) of end-to-end time to run, despite the fact that we run four Python test suites in parallel. According to the log, the basic reason is that the long running test starts at the end due to FIFO queue. We first try to reduce the test time by just starting some long running tests first with simple priority queue.
```
========================================================================
Running PySpark tests
========================================================================
...
Finished test(python3.4): pyspark.streaming.tests (213s)
Finished test(pypy): pyspark.sql.tests (92s)
Finished test(pypy): pyspark.streaming.tests (280s)
Tests passed in 962 seconds
```
## How was this patch tested?
Manual check.
Check 'Running PySpark tests' part of the Jenkins log.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11551 from dongjoon-hyun/SPARK-12243.
