## What changes were proposed in this pull request?
Use longs in calculating min hash to avoid bias due to int overflow.
## How was this patch tested?
Existing tests.
Author: Sean Owen <srowen@gmail.com>
Closes#21750 from srowen/SPARK-24754.
## What changes were proposed in this pull request?
Add locale support for `StopWordsRemover`.
## How was this patch tested?
[Scala|Python] unit tests.
Author: Lee Dongjin <dongjin@apache.org>
Closes#21501 from dongjinleekr/feature/SPARK-15064.
## What changes were proposed in this pull request?
add python api for VectorAssembler handleInvalid
## How was this patch tested?
Add doctest
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21003 from huaxingao/spark-23871.
## What changes were proposed in this pull request?
The Scala StringIndexerModel has an alternate constructor that will create the model from an array of label strings. Add the corresponding Python API:
model = StringIndexerModel.from_labels(["a", "b", "c"])
## How was this patch tested?
Add doctest and unit test.
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20968 from huaxingao/spark-23828.
## What changes were proposed in this pull request?
The maxDF parameter is for filtering out frequently occurring terms. This param was recently added to the Scala CountVectorizer and needs to be added to Python also.
## How was this patch tested?
add test
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#20777 from huaxingao/spark-23615.
## What changes were proposed in this pull request?
Added a class method to construct CountVectorizerModel from a list of vocabulary strings, equivalent to the Scala version. Introduced a common param base class `_CountVectorizerParams` to allow the Python model to also own the parameters. This now matches the Scala class hierarchy.
## How was this patch tested?
Added to CountVectorizer doctests to do a transform on a model constructed from vocab, and unit test to verify params and vocab are constructed correctly.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16770 from BryanCutler/pyspark-CountVectorizerModel-vocab_ctor-SPARK-15009.
The exit() builtin is only for interactive use. applications should use sys.exit().
## What changes were proposed in this pull request?
All usage of the builtin `exit()` function is replaced by `sys.exit()`.
## How was this patch tested?
I ran `python/run-tests`.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Benjamin Peterson <benjamin@python.org>
Closes#20682 from benjaminp/sys-exit.
## What changes were proposed in this pull request?
Murmur3 hash generates a different value from the original and other implementations (like Scala standard library and Guava or so) when the length of a bytes array is not multiple of 4.
## How was this patch tested?
Added a unit test.
**Note: When we merge this PR, please give all the credits to Shintaro Murakami.**
Author: Shintaro Murakami <mrkm4ntrgmail.com>
Author: gatorsmile <gatorsmile@gmail.com>
Author: Shintaro Murakami <mrkm4ntr@gmail.com>
Closes#20630 from gatorsmile/pr-20568.
## 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?
This syncs the ML Python API with Scala for differences found after the 2.3 QA audit.
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20354 from BryanCutler/pyspark-ml-doc-sync-23163.
## What changes were proposed in this pull request?
mark OneHotEncoder python API deprecated
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20241 from WeichenXu123/mark_ohe_deprecated.
## 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.
Previously, `FeatureHasher` always treats numeric type columns as numbers and never as categorical features. It is quite common to have categorical features represented as numbers or codes in data sources.
In order to hash these features as categorical, users must first explicitly convert them to strings which is cumbersome.
Add a new param `categoricalCols` which specifies the numeric columns that should be treated as categorical features.
## How was this patch tested?
New unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#19991 from MLnick/hasher-num-cat.
(Please fill in changes proposed in this fix)
Python API for VectorSizeHint Transformer.
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
doc-tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20112 from MrBago/vectorSizeHint-PythonAPI.
## What changes were proposed in this pull request?
Add python api for VectorIndexerModel support handle unseen categories via handleInvalid.
## How was this patch tested?
doctest added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19753 from WeichenXu123/vector_indexer_invalid_py.
https://issues.apache.org/jira/browse/SPARK-19866
## What changes were proposed in this pull request?
Add Python API for findSynonymsArray matching Scala API.
## How was this patch tested?
Manual test
`./python/run-tests --python-executables=python2.7 --modules=pyspark-ml`
Author: Xin Ren <iamshrek@126.com>
Author: Xin Ren <renxin.ubc@gmail.com>
Author: Xin Ren <keypointt@users.noreply.github.com>
Closes#17451 from keypointt/SPARK-19866.
Add Python API for `FeatureHasher` transformer.
## How was this patch tested?
New doc test.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#18970 from MLnick/SPARK-21468-pyspark-hasher.
## What changes were proposed in this pull request?
```RFormula``` should handle invalid for both features and label column.
#18496 only handle invalid values in features column. This PR add handling invalid values for label column and test cases.
## How was this patch tested?
Add test cases.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18613 from yanboliang/spark-20307.
## What changes were proposed in this pull request?
1, HasHandleInvaild support override
2, Make QuantileDiscretizer/Bucketizer/StringIndexer/RFormula inherit from HasHandleInvalid
## How was this patch tested?
existing tests
[JIRA](https://issues.apache.org/jira/browse/SPARK-18619)
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#18582 from zhengruifeng/heritate_HasHandleInvalid.
## What changes were proposed in this pull request?
This PR is to maintain API parity with changes made in SPARK-17498 to support a new option
'keep' in StringIndexer to handle unseen labels or NULL values with PySpark.
Note: This is updated version of #17237 , the primary author of this PR is VinceShieh .
## How was this patch tested?
Unit tests.
Author: VinceShieh <vincent.xie@intel.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18453 from yanboliang/spark-19852.
## What changes were proposed in this pull request?
PySpark supports stringIndexerOrderType in RFormula as in #17967.
## How was this patch tested?
docstring test
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#18122 from actuaryzhang/PythonRFormula.
## What changes were proposed in this pull request?
PySpark StringIndexer supports StringOrderType added in #17879.
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17978 from actuaryzhang/PythonStringIndexer.
Add Python wrapper for `Imputer` feature transformer.
## How was this patch tested?
New doc tests and tweak to PySpark ML `tests.py`
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17316 from MLnick/SPARK-15040-pyspark-imputer.
## What changes were proposed in this pull request?
The `keyword_only` decorator in PySpark is not thread-safe. It writes kwargs to a static class variable in the decorator, which is then retrieved later in the class method as `_input_kwargs`. If multiple threads are constructing the same class with different kwargs, it becomes a race condition to read from the static class variable before it's overwritten. See [SPARK-19348](https://issues.apache.org/jira/browse/SPARK-19348) for reproduction code.
This change will write the kwargs to a member variable so that multiple threads can operate on separate instances without the race condition. It does not protect against multiple threads operating on a single instance, but that is better left to the user to synchronize.
## How was this patch tested?
Added new unit tests for using the keyword_only decorator and a regression test that verifies `_input_kwargs` can be overwritten from different class instances.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#16782 from BryanCutler/pyspark-keyword_only-threadsafe-SPARK-19348.
## What changes were proposed in this pull request?
Updates the doc string to match up with the code
i.e. say dropLast instead of includeFirst
## How was this patch tested?
Not much, since it's a doc-like change. Will run unit tests via Jenkins job.
Author: Mark Grover <mark@apache.org>
Closes#17127 from markgrover/spark_19734.
## What changes were proposed in this pull request?
Remove `org.apache.spark.examples.` in
Add slash in one of the python doc.
## How was this patch tested?
Run examples using the commands in the comments.
Author: Yun Ni <yunn@uber.com>
Closes#17104 from Yunni/yunn_minor.
## What changes were proposed in this pull request?
This pull request includes python API and examples for LSH. The API changes was based on yanboliang 's PR #15768 and resolved conflicts and API changes on the Scala API. The examples are consistent with Scala examples of MinHashLSH and BucketedRandomProjectionLSH.
## How was this patch tested?
API and examples are tested using spark-submit:
`bin/spark-submit examples/src/main/python/ml/min_hash_lsh.py`
`bin/spark-submit examples/src/main/python/ml/bucketed_random_projection_lsh.py`
User guide changes are generated and manually inspected:
`SKIP_API=1 jekyll build`
Author: Yun Ni <yunn@uber.com>
Author: Yanbo Liang <ybliang8@gmail.com>
Author: Yunni <Euler57721@gmail.com>
Closes#16715 from Yunni/spark-18080.
## What changes were proposed in this pull request?
This PR is to document the changes on QuantileDiscretizer in pyspark for PR:
https://github.com/apache/spark/pull/15428
## How was this patch tested?
No test needed
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#16922 from VinceShieh/spark-19590.
## What changes were proposed in this pull request?
Add FDR test case in ml/feature/ChiSqSelectorSuite.
Improve some comments in the code.
This is a follow-up pr for #15212.
## How was this patch tested?
ut
Author: Peng, Meng <peng.meng@intel.com>
Closes#16434 from mpjlu/fdr_fwe_update.
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on univariate statistical tests.
FDR and FWE are a popular univariate statistical test for feature selection.
In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate.
In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests.
https://en.wikipedia.org/wiki/Family-wise_error_rate
We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
ut will be added soon
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: Peng <peng.meng@intel.com>
Author: Peng, Meng <peng.meng@intel.com>
Closes#15212 from mpjlu/fdr_fwe.
## What changes were proposed in this pull request?
Updated Scala param and Python param to have quotes around the options making it easier for users to read.
## How was this patch tested?
Manually checked the docstrings
Author: krishnakalyan3 <krishnakalyan3@gmail.com>
Closes#16242 from krishnakalyan3/doc-string.
## What changes were proposed in this pull request?
added the new handleInvalid param for these transformers to Python to maintain API parity.
## How was this patch tested?
existing tests
testing is done with new doctests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#15817 from techaddict/SPARK-18366.
## What changes were proposed in this pull request?
make a pass through the items marked as Experimental or DeveloperApi and see if any are stable enough to be unmarked. Also check for items marked final or sealed to see if they are stable enough to be opened up as APIs.
Some discussions in the jira: https://issues.apache.org/jira/browse/SPARK-18319
## How was this patch tested?
existing ut
Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#15972 from hhbyyh/experimental21.
## What changes were proposed in this pull request?
- Renamed kbest to numTopFeatures
- Renamed alpha to fpr
- Added missing Since annotations
- Doc cleanups
## How was this patch tested?
Added new standardized unit tests for spark.ml.
Improved existing unit test coverage a bit.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15647 from jkbradley/chisqselector-follow-ups.
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleNaN("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Author: Vincent Xie <vincent.xie@intel.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15428 from VinceShieh/spark-17219_followup.
## What changes were proposed in this pull request?
For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features.
So we change statistic to pValue for SelectKBest and SelectPercentile
## How was this patch tested?
change existing test
Author: Peng <peng.meng@intel.com>
Closes#15444 from mpjlu/chisqure-bug.
## What changes were proposed in this pull request?
Follow-up work of #13675, add Python API for ```RFormula forceIndexLabel```.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15430 from yanboliang/spark-15957-python.
## What changes were proposed in this pull request?
Partial revert of #15277 to instead sort and store input to model rather than require sorted input
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15299 from srowen/SPARK-17704.2.
## What changes were proposed in this pull request?
#14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed:
* We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```.
* Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also.
* If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620)
* We should use lower case of the selector type names to follow MLlib convention.
* Add ML Python API.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15214 from yanboliang/spark-17017.
## What changes were proposed in this pull request?
This PR fixes an issue when Bucketizer is called to handle a dataset containing NaN value.
Sometimes, null value might also be useful to users, so in these cases, Bucketizer should
reserve one extra bucket for NaN values, instead of throwing an illegal exception.
Before:
```
Bucketizer.transform on NaN value threw an illegal exception.
```
After:
```
NaN values will be grouped in an extra bucket.
```
## How was this patch tested?
New test cases added in `BucketizerSuite`.
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Closes#14858 from VinceShieh/spark-17219.
## What changes were proposed in this pull request?
General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Annotate Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.
Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new:
* Model Summary classes
* MLWriter, MLReader, MLWritable, MLReadable
* Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency.
* RFormula: Its behavior may need to change slightly to match R in edge cases.
* AFTSurvivalRegression
* MultilayerPerceptronClassifier
DeveloperApi changes:
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi
## How was this patch tested?
N/A
Note to reviewers:
* spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental.
* Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14147 from jkbradley/experimental-audit.
## 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.
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?
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?
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?
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?
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.
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?
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?
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?
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?
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?
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?
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 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?
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.
## 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?
#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?
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?
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?
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?
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?
This patch adds type conversion functionality for parameters in Pyspark. A `typeConverter` field is added to the constructor of `Param` class. This argument is a function which converts values passed to this param to the appropriate type if possible. This is beneficial so that the params can fail at set time if they are given inappropriate values, but even more so because coherent error messages are now provided when Py4J cannot cast the python type to the appropriate Java type.
This patch also adds a `TypeConverters` class with factory methods for common type conversions. Most of the changes involve adding these factory type converters to existing params. The previous solution to this issue, `expectedType`, is deprecated and can be removed in 2.1.0 as discussed on the Jira.
## How was this patch tested?
Unit tests were added in python/pyspark/ml/tests.py to test parameter type conversion. These tests check that values that should be convertible are converted correctly, and that the appropriate errors are thrown when invalid values are provided.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#11663 from sethah/SPARK-13068-tc.
Adds support for saving and loading nested ML Pipelines from Python. Pipeline and PipelineModel do not extend JavaWrapper, but they are able to utilize the JavaMLWriter, JavaMLReader implementations.
Also:
* Separates out interfaces from Java wrapper implementations for MLWritable, MLReadable, MLWriter, MLReader.
* Moves methods _stages_java2py, _stages_py2java into Pipeline, PipelineModel as _transfer_stage_from_java, _transfer_stage_to_java
Added new unit test for nested Pipelines. Abstracted validity check into a helper method for the 2 unit tests.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#11866 from jkbradley/nested-pipeline-io.
Closes#11835
Add save/load for feature.py. Meanwhile, add save/load for `ElementwiseProduct` in Scala side and fix a bug of missing `setDefault` in `VectorSlicer` and `StopWordsRemover`.
In this PR I ignore the `RFormula` and `RFormulaModel` because its Scala implementation is pending in https://github.com/apache/spark/pull/9884. I'll add them in this PR if https://github.com/apache/spark/pull/9884 gets merged first. Or add a follow-up JIRA for `RFormula`.
Author: Xusen Yin <yinxusen@gmail.com>
Closes#11203 from yinxusen/SPARK-13036.
This is to fix a long-time annoyance: Whenever we add a new algorithm to pyspark.ml, we have to add it to the ```__all__``` list at the top. Since we keep it alphabetized, it often creates a lot more changes than needed. It is also easy to add the Estimator and forget the Model. I'm going to switch it to have one algorithm per line.
This also alphabetizes a few out-of-place classes in pyspark.ml.feature. No changes have been made to the moved classes.
CC: thunterdb
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#10927 from jkbradley/ml-python-all-list.
## What changes were proposed in this pull request?
After SPARK-13028, we should add Python API for MaxAbsScaler.
## How was this patch tested?
unit test
Author: zlpmichelle <zlpmichelle@gmail.com>
Closes#11393 from zlpmichelle/master.
## What changes were proposed in this pull request?
QuantileDiscretizer in Python should also specify a random seed.
## How was this patch tested?
unit tests
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#11362 from yu-iskw/SPARK-13292 and squashes the following commits:
02ffa76 [Yu ISHIKAWA] [SPARK-13292][ML][PYTHON] QuantileDiscretizer should take random seed in PySpark
add support of arbitrary length sentence by using the nature representation of sentences in the input.
add new similarity functions and add normalization option for distances in synonym finding
add new accessor for internal structure(the vocabulary and wordindex) for convenience
need instructions about how to set value for the Since annotation for newly added public functions. 1.5.3?
jira link: https://issues.apache.org/jira/browse/SPARK-12153
Author: Yong Gang Cao <ygcao@amazon.com>
Author: Yong-Gang Cao <ygcao@users.noreply.github.com>
Closes#10152 from ygcao/improvementForSentenceBoundary.
The current python ml params require cut-and-pasting the param setup and description between the class & ```__init__``` methods. Remove this possible case of errors & simplify use of custom params by adding a ```_copy_new_parent``` method to param so as to avoid cut and pasting (and cut and pasting at different indentation levels urgh).
Author: Holden Karau <holden@us.ibm.com>
Closes#10216 from holdenk/SPARK-10509-excessive-param-boiler-plate-code.
Add Python API for ml.feature.QuantileDiscretizer.
One open question: Do we want to do this stuff to re-use the java model, create a new model, or use a different wrapper around the java model.
cc brkyvz & mengxr
Author: Holden Karau <holden@us.ibm.com>
Closes#10085 from holdenk/SPARK-11937-SPARK-11922-Python-API-for-ml.feature.QuantileDiscretizer.
```PCAModel``` can output ```explainedVariance``` at Python side.
cc mengxr srowen
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#10830 from yanboliang/spark-12905.
Add PySpark missing methods and params for ml.feature:
* ```RegexTokenizer``` should support setting ```toLowercase```.
* ```MinMaxScalerModel``` should support output ```originalMin``` and ```originalMax```.
* ```PCAModel``` should support output ```pc```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#9908 from yanboliang/spark-11925.
https://issues.apache.org/jira/browse/SPARK-10116
This is really trivial, just happened to notice it -- if `XORShiftRandom.hashSeed` is really supposed to have random bits throughout (as the comment implies), it needs to do something for the conversion to `long`.
mengxr mkolod
Author: Imran Rashid <irashid@cloudera.com>
Closes#8314 from squito/SPARK-10116.
This integrates the Interaction feature transformer with SparkR R formula support (i.e. support `:`).
To generate reasonable ML attribute names for feature interactions, it was necessary to add the ability to read attribute the original attribute names back from `StructField`, and also to specify custom group prefixes in `VectorAssembler`. This also has the side-benefit of cleaning up the double-underscores in the attributes generated for non-interaction terms.
mengxr
Author: Eric Liang <ekl@databricks.com>
Closes#8830 from ericl/interaction-2.
From JIRA: Add Python API, user guide and example for ml.feature.CountVectorizerModel
Author: Holden Karau <holden@pigscanfly.ca>
Closes#8561 from holdenk/SPARK-9769-add-python-api-for-countvectorizermodel.
Changes:
* Make Scala doc for StringIndexerInverse clearer. Also remove Scala doc from transformSchema, so that the doc is inherited.
* MetadataUtils.scala: “ Helper utilities for tree-based algorithms” —> not just trees anymore
CC: holdenk mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#8679 from jkbradley/doc-fixes-1.5.
Missing method of ml.feature are listed here:
```StringIndexer``` lacks of parameter ```handleInvalid```.
```StringIndexerModel``` lacks of method ```labels```.
```VectorIndexerModel``` lacks of methods ```numFeatures``` and ```categoryMaps```.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8313 from yanboliang/spark-10027.
Modified class-level docstrings to mark all feature transformers in pyspark.ml as experimental.
Author: noelsmith <mail@noelsmith.com>
Closes#8623 from noel-smith/SPARK-10094-mark-pyspark-ml-trans-exp.
Add Python API, user guide and example for ml.feature.ElementwiseProduct.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8061 from yanboliang/SPARK-9768.
Check and add miss docs for PySpark ML (this issue only check miss docs for o.a.s.ml not o.a.s.mllib).
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#8059 from yanboliang/SPARK-9766.
After https://github.com/apache/spark/pull/7263 it is pretty straightforward to Python wrappers.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#7930 from MechCoder/spark-9533 and squashes the following commits:
1bea394 [MechCoder] make getVectors a lazy val
5522756 [MechCoder] [SPARK-9533] [PySpark] [ML] Add missing methods in Word2Vec ML
