### What changes were proposed in this pull request?
jira link: https://issues.apache.org/jira/browse/SPARK-30930
Remove ML/MLLIB DeveloperApi annotations.
### Why are the changes needed?
The Developer APIs in ML/MLLIB have been there for a long time. They are stable now and are very unlikely to be changed or removed, so I unmark these Developer APIs in this PR.
### Does this PR introduce any user-facing change?
Yes. DeveloperApi annotations are removed from docs.
### How was this patch tested?
existing tests
Closes#27859 from huaxingao/spark-30930.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
Add ```HasBlockSize``` in shared Params in both Scala and Python.
Make ALS/MLP extend ```HasBlockSize```
### Why are the changes needed?
Add ```HasBlockSize ``` in ALS, so user can specify the blockSize.
Make ```HasBlockSize``` a shared param so both ALS and MLP can use it.
### Does this PR introduce any user-facing change?
Yes
```ALS.setBlockSize/getBlockSize```
```ALSModel.setBlockSize/getBlockSize```
### How was this patch tested?
Manually tested. Also added doctest.
Closes#27501 from huaxingao/spark_30662.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Revert
#27360#27396#27374#27389
### Why are the changes needed?
BLAS need more performace tests, specially on sparse datasets.
Perfermance test of LogisticRegression (https://github.com/apache/spark/pull/27374) on sparse dataset shows that blockify vectors to matrices and use BLAS will cause performance regression.
LinearSVC and LinearRegression were also updated in the same way as LogisticRegression, so we need to revert them to make sure no regression.
### Does this PR introduce any user-facing change?
remove newly added param blockSize
### How was this patch tested?
reverted testsuites
Closes#27487 from zhengruifeng/revert_blockify_ii.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
1, use blocks instead of vectors
2, use Level-2 BLAS for binary, use Level-3 BLAS for multinomial
### Why are the changes needed?
1, less RAM to persist training data; (save ~40%)
2, faster than existing impl; (40% ~ 92%)
### Does this PR introduce any user-facing change?
add a new expert param `blockSize`
### How was this patch tested?
updated testsuites
Closes#27374 from zhengruifeng/blockify_lor.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
### What changes were proposed in this pull request?
1, stack input vectors to blocks (like ALS/MLP);
2, add new param `blockSize`;
3, add a new class `InstanceBlock`
4, standardize the input outside of optimization procedure;
### Why are the changes needed?
1, reduce RAM to persist traing dataset; (save ~40% in test)
2, use Level-2 BLAS routines; (12% ~ 28% faster, without native BLAS)
### Does this PR introduce any user-facing change?
a new param `blockSize`
### How was this patch tested?
existing and updated testsuites
Closes#27360 from zhengruifeng/blockify_svc.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
modify Param._copyValues to check valid Param objects supplied as extra
### What changes were proposed in this pull request?
Estimator.fit() and Model.transform() accept a dictionary of extra parameters whose values are used to overwrite those supplied at initialization or by default. Additionally, the ParamGridBuilder.addGrid accepts a parameter and list of values. The keys are presumed to be valid Param objects. This change adds a check that only Param objects are supplied as keys.
### Why are the changes needed?
Param objects are created by and bound to an instance of Params (Estimator, Model, or Transformer). They may be obtained from their parent as attributes, or by name through getParam.
The documentation does not state that keys must be valid Param objects, nor describe how one may be obtained. The current behavior is to silently ignore keys which are not valid Param objects.
### Does this PR introduce any user-facing change?
If the user does not pass in a Param object as required for keys in `extra` for Estimator.fit() and Model.transform(), and `param` for ParamGridBuilder.addGrid, an error will be raised indicating it is an invalid object.
### How was this patch tested?
Added method test_copy_param_extras_check to test_param.py. Tested with Python 3.7
Closes#26527 from JohnHBauer/paramExtra.
Authored-by: John Bauer <john.h.bauer@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
### What changes were proposed in this pull request?
1, add shared param `relativeError`
2, `Imputer`/`RobusterScaler`/`QuantileDiscretizer` extend `HasRelativeError`
### Why are the changes needed?
It makes sense to expose RelativeError to end users, since it controls both the precision and memory overhead.
`QuantileDiscretizer` had already added this param, while other algs not yet.
### Does this PR introduce any user-facing change?
yes, new param is added in `Imputer`/`RobusterScaler`
### How was this patch tested?
existing testsutes
Closes#26305 from zhengruifeng/add_relative_err.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Remove automatically generated param setters in _shared_params_code_gen.py
### Why are the changes needed?
To keep parity between scala and python
### Does this PR introduce any user-facing change?
Yes
Add some setters in Python ML XXXModels
### How was this patch tested?
unit tests
Closes#26232 from huaxingao/spark-29093.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
change PySpark ml ```Params._clear``` to ```Params.clear```
### Why are the changes needed?
PySpark ML currently has a private _clear() method that will unset a param. This should be made public to match the Scala API and give users a way to unset a user supplied param.
### Does this PR introduce any user-facing change?
Yes. PySpark ml ```Params._clear``` ---> ```Params.clear```
### How was this patch tested?
Add test.
Closes#26130 from huaxingao/spark-29464.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
### What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
### Why are the changes needed?
Bucketizer should support multi-column like the scala side.
### Does this PR introduce any user-facing change?
yes, this PR add new Python API
### How was this patch tested?
added testsuites
Closes#25801 from zhengruifeng/20542_py.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
### What changes were proposed in this pull request?
Add HasNumFeatures in the scala side, with `1<<18` as the default value
### Why are the changes needed?
HasNumFeatures is already added in the py side, it is reasonable to keep them in sync.
I don't find other similar place.
### Does this PR introduce any user-facing change?
No
### How was this patch tested?
Existing testsuites
Closes#25671 from zhengruifeng/add_HasNumFeatures.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: zhengruifeng <ruifengz@foxmail.com>
## What changes were proposed in this pull request?
Leave ```shared.py``` untouched. Move Python ```DecisionTreeParams``` to ```regression.py```
## How was this patch tested?
Use existing tests
Closes#25406 from huaxingao/spark-28243.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove deprecated setFeatureSubsetStrategy and setSubsamplingRate from Python TreeEnsembleParams
## How was this patch tested?
Use existing tests.
Closes#25046 from huaxingao/spark-28243.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add validationIndicatorCol and validationTol to GBT Python.
## How was this patch tested?
Add test in doctest to test the new API.
Closes#21465 from huaxingao/spark-24333.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
## What changes were proposed in this pull request?
add distanceMeasure to BisectingKMeans in Python.
## How was this patch tested?
added doctest and also manually tested it.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21557 from huaxingao/spark-24439.
## What changes were proposed in this pull request?
Add python API for collecting sub-models during CrossValidator/TrainValidationSplit fitting.
## How was this patch tested?
UT added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19627 from WeichenXu123/expose-model-list-py.
## What changes were proposed in this pull request?
Bucketizer support multi-column in the python side
## How was this patch tested?
existing tests and added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19892 from zhengruifeng/20542_py.
## What changes were proposed in this pull request?
OnehotEncoderEstimator python API.
## How was this patch tested?
doctest
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20209 from WeichenXu123/ohe_py.
## What changes were proposed in this pull request?
Add a note to the `HasCheckpointInterval` parameter doc that clarifies that this setting is ignored when no checkpoint directory has been set on the spark context.
## How was this patch tested?
No tests necessary, just a doc update.
Author: sethah <shendrickson@cloudera.com>
Closes#20188 from sethah/als_checkpoint_doc.
## What changes were proposed in this pull request?
Expose Python API for _LinearRegression_ with _huber_ loss.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19994 from yanboliang/spark-22810.
# What changes were proposed in this pull request?
Added tunable parallelism to the pyspark implementation of one vs. rest classification. Added a parallelism parameter to the Scala implementation of one vs. rest along with functionality for using the parameter to tune the level of parallelism.
I take this PR #18281 over because the original author is busy but we need merge this PR soon.
After this been merged, we can close#18281 .
## How was this patch tested?
Test suite added.
Author: Ajay Saini <ajays725@gmail.com>
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19110 from WeichenXu123/spark-21027.
## What changes were proposed in this pull request?
This PR proposes to support unicodes in Param methods in ML, other missed functions in DataFrame.
For example, this causes a `ValueError` in Python 2.x when param is a unicode string:
```python
>>> from pyspark.ml.classification import LogisticRegression
>>> lr = LogisticRegression()
>>> lr.hasParam("threshold")
True
>>> lr.hasParam(u"threshold")
Traceback (most recent call last):
...
raise TypeError("hasParam(): paramName must be a string")
TypeError: hasParam(): paramName must be a string
```
This PR is based on https://github.com/apache/spark/pull/13036
## How was this patch tested?
Unit tests in `python/pyspark/ml/tests.py` and `python/pyspark/sql/tests.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: sethah <seth.hendrickson16@gmail.com>
Closes#17096 from HyukjinKwon/SPARK-15243.
## What changes were proposed in this pull request?
Added DefaultParamsWriteable, DefaultParamsReadable, DefaultParamsWriter, and DefaultParamsReader to Python to support Python-only persistence of Json-serializable parameters.
## How was this patch tested?
Instantiated an estimator with Json-serializable parameters (ex. LogisticRegression), saved it using the added helper functions, and loaded it back, and compared it to the original instance to make sure it is the same. This test was both done in the Python REPL and implemented in the unit tests.
Note to reviewers: there are a few excess comments that I left in the code for clarity but will remove before the code is merged to master.
Author: Ajay Saini <ajays725@gmail.com>
Closes#18742 from ajaysaini725/PythonPersistenceHelperFunctions.
## What changes were proposed in this pull request?
Python API for Constrained Logistic Regression based on #17922 , thanks for the original contribution from zero323 .
## How was this patch tested?
Unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18759 from yanboliang/SPARK-20601.
## What changes were proposed in this pull request?
Fixed the PySpark Params.copy method to behave like the Scala implementation. The main issue was that it did not account for the _defaultParamMap and merged it into the explicitly created param map.
## How was this patch tested?
Added new unit test to verify the copy method behaves correctly for copying uid, explicitly created params, and default params.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16772 from BryanCutler/pyspark-ml-param_copy-Scala_sync-SPARK-14772.
## What changes were proposed in this pull request?
* Removed Since tags in Python Params since they are inherited by other classes
* Fixed doc links for LinearSVC
## How was this patch tested?
* doc tests
* generating docs locally and checking manually
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#16723 from jkbradley/pyparam-fix-doc.
## What changes were proposed in this pull request?
Add Python API for the newly added LinearSVC algorithm.
## How was this patch tested?
Add new doc string test.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#16694 from wangmiao1981/ser.
## What changes were proposed in this pull request?
Match ProbabilisticClassifer.thresholds requirements to R randomForest cutoff, requiring all > 0
## How was this patch tested?
Jenkins tests plus new test cases
Author: Sean Owen <sowen@cloudera.com>
Closes#15149 from srowen/SPARK-17057.
## 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?
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?
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?
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?
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?
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?
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.
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?
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?
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.
Pyspark Params class has a method `hasParam(paramName)` which returns `True` if the class has a parameter by that name, but throws an `AttributeError` otherwise. There is not currently a way of getting a Boolean to indicate if a class has a parameter. With Spark 2.0 we could modify the existing behavior of `hasParam` or add an additional method with this functionality.
In Python:
```python
from pyspark.ml.classification import NaiveBayes
nb = NaiveBayes()
print nb.hasParam("smoothing")
print nb.hasParam("notAParam")
```
produces:
> True
> AttributeError: 'NaiveBayes' object has no attribute 'notAParam'
However, in Scala:
```scala
import org.apache.spark.ml.classification.NaiveBayes
val nb = new NaiveBayes()
nb.hasParam("smoothing")
nb.hasParam("notAParam")
```
produces:
> true
> false
cc holdenk
Author: sethah <seth.hendrickson16@gmail.com>
Closes#10962 from sethah/SPARK-13047.
* Implement ```MLWriter/MLWritable/MLReader/MLReadable``` for PySpark.
* Making ```LinearRegression``` to support ```save/load``` as example. After this merged, the work for other transformers/estimators will be easy, then we can list and distribute the tasks to the community.
cc mengxr jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10469 from yanboliang/spark-11939.
The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh).
Author: Holden Karau <holden@us.ibm.com>
Closes#10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.
From JIRA:
Currently, PySpark wrappers for spark.ml Scala classes are brittle when accepting Param types. E.g., Normalizer's "p" param cannot be set to "2" (an integer); it must be set to "2.0" (a float). Fixing this is not trivial since there does not appear to be a natural place to insert the conversion before Python wrappers call Java's Params setter method.
A possible fix will be to include a method "_checkType" to PySpark's Param class which checks the type, prints an error if needed, and converts types when relevant (e.g., int to float, or scipy matrix to array). The Java wrapper method which copies params to Scala can call this method when available.
This fix instead checks the types at set time since I think failing sooner is better, but I can switch it around to check at copy time if that would be better. So far this only converts int to float and other conversions (like scipymatrix to array) are left for the future.
Author: Holden Karau <holden@us.ibm.com>
Closes#9581 from holdenk/SPARK-7675-PySpark-sparkml-Params-type-conversion.
* Update doc for PySpark ```HasCheckpointInterval``` that users can understand how to disable checkpoint.
* Update doc for PySpark ```cacheNodeIds``` of ```DecisionTreeParams``` to notify the relationship between ```cacheNodeIds``` and ```checkpointInterval```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9856 from yanboliang/spark-11875.
[SPARK-10668](https://issues.apache.org/jira/browse/SPARK-10668) has provided ```WeightedLeastSquares``` solver("normal") in ```LinearRegression``` with L2 regularization in Scala and R, Python ML ```LinearRegression``` should also support setting solver("auto", "normal", "l-bfgs")
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9328 from yanboliang/spark-11367.
Namely "." shows up in some places in the template when using the param docstring and not in others
Author: Holden Karau <holden@pigscanfly.ca>
Closes#9017 from holdenk/SPARK-10767-Make-pyspark-shared-params-codegen-more-consistent.
Add the Python API for isotonicregression.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8214 from holdenk/SPARK-9774-add-python-api-for-ml-regression-isotonicregression.
