## What changes were proposed in this pull request?
In regression/clustering/ovr/als, if an output column name is empty, igore it. And if all names are empty, log a warning msg, then do nothing.
## How was this patch tested?
existing tests
Closes#24793 from zhengruifeng/aft_iso_check_empty_outputCol.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This upgraded to a newer version of Pyrolite. Most updates [1] in the newer version are for dotnot. For java, it includes a bug fix to Unpickler regarding cleaning up Unpickler memo, and support of protocol 5.
After upgrading, we can remove the fix at SPARK-27629 for the bug in Unpickler.
[1] https://github.com/irmen/Pyrolite/compare/pyrolite-4.23...master
## How was this patch tested?
Manually tested on Python 3.6 in local on existing tests.
Closes#25143 from viirya/upgrade-pyrolite.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
fix typo in spark-28159
`transfromWithMean` -> `transformWithMean`
## How was this patch tested?
existing test
Closes#25129 from zhengruifeng/to_ml_vec_cleanup.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
In both cases, the input `DataFrame` schema must contain only the information that's required for the matrix object, so a vector column in the case of `RowMatrix` and long and vector columns for `IndexedRowMatrix`.
## How was this patch tested?
Unit tests that verify:
- `RowMatrix` and `IndexedRowMatrix` can be created from `DataFrame`s
- If the schema does not match expectations, we throw an `IllegalArgumentException`
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24953 from henrydavidge/row-matrix-df.
Authored-by: Henry D <henrydavidge@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Make the transform natively in ml framework to avoid extra conversion.
There are many TODOs in current ml module, like `// TODO: Make the transformer natively in ml framework to avoid extra conversion.` in ChiSqSelector.
This PR is to make ml algs no longer need to convert ml-vector to mllib-vector in transforms.
Including: LDA/ChiSqSelector/ElementwiseProduct/HashingTF/IDF/Normalizer/PCA/StandardScaler.
## How was this patch tested?
existing testsuites
Closes#24963 from zhengruifeng/to_ml_vector.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
if the input dataset is alreadly cached, then we do not need to cache the internal rdd (like kmeans)
## How was this patch tested?
existing test
Closes#24919 from zhengruifeng/gmm_fix_double_caching.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
cache dataset in BisectingKMeans
cache dataset in LDA if Online solver is chosen.
## How was this patch tested?
existing test
Closes#24920 from zhengruifeng/bikm_cache.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add missing RankingEvaluator
## How was this patch tested?
added testsuites
Closes#24869 from zhengruifeng/ranking_eval.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Provide a way to recursively load data from datasource.
I add a "recursiveFileLookup" option.
When "recursiveFileLookup" option turn on, then partition inferring is turned off and all files from the directory will be loaded recursively.
If some datasource explicitly specify the partitionSpec, then if user turn on "recursive" option, then exception will be thrown.
## How was this patch tested?
Unit tests.
Please review https://spark.apache.org/contributing.html before opening a pull request.
Closes#24830 from WeichenXu123/recursive_ds.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Modifies the HuberAggregator class so that a copy of the coefficients vector isn't created every time that an instance is added. Follows the approach of LeastSquaresAggregator and uses transient lazy class variable to store the reused quantities. (See https://github.com/apache/spark/pull/14109 for explanation of the use of transient lazy variables)
On the test case in the linked JIRA, this change gives an order of magnitude performance improvement reducing the time taken to fit the model from 540 to 47 seconds.
## How was this patch tested?
Existing unit tests.
See https://issues.apache.org/jira/browse/SPARK-28062 for results from running a benchmark script.
Closes#24880 from Andrew-Crosby/spark-28062.
Authored-by: Andrew-Crosby <andrew.crosby@autotrader.co.uk>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
expose more metrics in evaluator: weightedTruePositiveRate/weightedFalsePositiveRate/weightedFMeasure/truePositiveRateByLabel/falsePositiveRateByLabel/precisionByLabel/recallByLabel/fMeasureByLabel
## How was this patch tested?
existing cases and add cases
Closes#24868 from zhengruifeng/multi_class_support_bylabel.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The word2vec logic fails if a corpora has a word with count > 1e9. We should be able to handle very large counts generally better here by using longs to count.
This takes over https://github.com/apache/spark/pull/24814
## How was this patch tested?
Existing tests.
Closes#24893 from srowen/SPARK-28081.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add MultilabelClassificationEvaluator
## How was this patch tested?
added testsuites
Closes#24777 from zhengruifeng/multi_label_eval.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added support for `*` and `^` operators, along with expressions within parentheses. New operators just expand to already supported terms, such as;
- y ~ a * b = y ~ a + b + a : b
- y ~ (a+b+c)^3 = y ~ a + b + c + a : b + a : c + a :b : c
## How was this patch tested?
Added new unit tests to RFormulaParserSuite
mengxr yanboliang
Closes#24764 from ozancicek/rformula.
Authored-by: ozan <ozancancicekci@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add missing since annotation of meanAveragePrecision
## How was this patch tested?
existing tests
Closes#24778 from zhengruifeng/ranking_missing_since.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
compute all metrics with only one pass
## How was this patch tested?
existing tests
Closes#24717 from zhengruifeng/multi_label_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Single-point clusters should have silhouette score of 0, according to the original paper and scikit implementation.
## How was this patch tested?
Existing test suite + new test case.
Closes#24756 from srowen/SPARK-27896.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
## What changes were proposed in this pull request?
This pr wrap all `PrintWriter` with `Utils.tryWithResource` to prevent resource leak.
## How was this patch tested?
Existing test
Closes#24739 from wangyum/SPARK-27875.
Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
…lure
## What changes were proposed in this pull request?
The failure log format is fixed according to the jdk implementation.
## How was this patch tested?
Manual tests have been done. The new failure log format would be like:
java.lang.RuntimeException: Failed to finish the task
at com.xxx.Test.test(Test.java:106)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.testng.internal.MethodInvocationHelper.invokeMethod(MethodInvocationHelper.java:124)
at org.testng.internal.Invoker.invokeMethod(Invoker.java:571)
at org.testng.internal.Invoker.invokeTestMethod(Invoker.java:707)
at org.testng.internal.Invoker.invokeTestMethods(Invoker.java:979)
at org.testng.internal.TestMethodWorker.invokeTestMethods(TestMethodWorker.java:125)
at org.testng.internal.TestMethodWorker.run(TestMethodWorker.java:109)
at org.testng.TestRunner.privateRun(TestRunner.java:648)
at org.testng.TestRunner.run(TestRunner.java:505)
at org.testng.SuiteRunner.runTest(SuiteRunner.java:455)
at org.testng.SuiteRunner.runSequentially(SuiteRunner.java:450)
at org.testng.SuiteRunner.privateRun(SuiteRunner.java:415)
at org.testng.SuiteRunner.run(SuiteRunner.java:364)
at org.testng.SuiteRunnerWorker.runSuite(SuiteRunnerWorker.java:52)
at org.testng.SuiteRunnerWorker.run(SuiteRunnerWorker.java:84)
at org.testng.TestNG.runSuitesSequentially(TestNG.java:1187)
at org.testng.TestNG.runSuitesLocally(TestNG.java:1116)
at org.testng.TestNG.runSuites(TestNG.java:1028)
at org.testng.TestNG.run(TestNG.java:996)
at org.testng.IDEARemoteTestNG.run(IDEARemoteTestNG.java:72)
at org.testng.RemoteTestNGStarter.main(RemoteTestNGStarter.java:123)
Caused by: java.io.FileNotFoundException: File is not found
at com.xxx.Test.test(Test.java:105)
... 24 more
Closes#24684 from breakdawn/master.
Authored-by: MJ Tang <mingjtang@ebay.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
compute AUC on one pass
## How was this patch tested?
existing tests
performance tests:
```
import org.apache.spark.mllib.evaluation._
val scoreAndLabels = sc.parallelize(Array.range(0, 100000).map{ i => (i.toDouble / 100000, (i % 2).toDouble) }, 4)
scoreAndLabels.persist()
scoreAndLabels.count()
val tic = System.currentTimeMillis
(0 until 100).foreach{i => val metrics = new BinaryClassificationMetrics(scoreAndLabels, 0); val auc = metrics.areaUnderROC; metrics.unpersist}
val toc = System.currentTimeMillis
toc - tic
```
|New| Existing|
|------|----------|
|87532|103644|
One-pass AUC saves about 16% computation time.
Closes#24648 from zhengruifeng/auc_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Eliminate uncessary job to compute SSreg
Compute SSreg based on the summary of predictions
## How was this patch tested?
existing tests
Closes#24656 from zhengruifeng/RegressionMetrics_opt.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
avoid hardcoded configs in `SparkConf` and `SparkSubmit` and test
## How was this patch tested?
N/A
Closes#24631 from wenxuanguan/minor-fix.
Authored-by: wenxuanguan <choose_home@126.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
This replaces use of collection classes like `MutableList` and `ArrayStack` with workalikes that are available in 2.12, as they will be removed in 2.13. It also removes use of `.to[Collection]` as its uses was superfluous anyway. Removing `collection.breakOut` will have to wait until 2.13
## How was this patch tested?
Existing tests
Closes#24586 from srowen/SPARK-27682.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Test steps to reproduce:
1) bin/spark-shell
```
val dataset = spark.createDataFrame(Seq(
(0L, 1L, 1.0),
(1L,2L,1.0),
(3L, 4L,1.0),
(4L,0L,0.1))).toDF("src", "dst", "weight")
val model = new PowerIterationClustering().
setMaxIter(10).
setInitMode("degree").
setWeightCol("weight")
val prediction = model.assignClusters(dataset).select("id", "cluster")
```
2) Open storage tab of the UI. We can see many RDD block cached, even after running the PIC.
In this PR, basically materializes the new graph before unpersisting the old ones.
## How was this patch tested?
Manually tested and existing UTs.
Before patch:
After patch:
Closes#24531 from shahidki31/SPARK-27636.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Added method 'meanAveragePrecisionAt' k to RankingMetrics.
This branch is rebased with squashed commits from https://github.com/apache/spark/pull/24458
## How was this patch tested?
Added code in the existing test RankingMetricsSuite.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24543 from qb-tarushg/SPARK-27540-REBASE.
Authored-by: qb-tarushg <tarush.grover@quantumblack.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fixed the `spark-<version>-yarn-shuffle.jar` artifact packaging to shade the native netty libraries:
- shade the `META-INF/native/libnetty_*` native libraries when packagin
the yarn shuffle service jar. This is required as netty library loader
derives that based on shaded package name.
- updated the `org/spark_project` shade package prefix to `org/sparkproject`
(i.e. removed underscore) as the former breaks the netty native lib loading.
This was causing the yarn external shuffle service to fail
when spark.shuffle.io.mode=EPOLL
## How was this patch tested?
Manual tests
Closes#24502 from amuraru/SPARK-27610_master.
Authored-by: Adi Muraru <amuraru@adobe.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
Choose the last record in chunks when calculating metrics with downsampling in `BinaryClassificationMetrics`.
## How was this patch tested?
A new unit test is added to verify thresholds from downsampled records.
Closes#24470 from shishaochen/spark-mllib-binary-metrics.
Authored-by: Shaochen Shi <shishaochen@bytedance.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In SPARK-27612, one correctness issue was reported. When protocol 4 is used to pickle Python objects, we found that unpickled objects were wrong. A temporary fix was proposed by not using highest protocol.
It was found that Opcodes.MEMOIZE was appeared in the opcodes in protocol 4. It is suspect to this issue.
A deeper dive found that Opcodes.MEMOIZE stores objects into internal map of Unpickler object. We use single Unpickler object to unpickle serialized Python bytes. Stored objects intervenes next round of unpickling, if the map is not cleared.
We has two options:
1. Continues to reuse Unpickler, but calls its close after each unpickling.
2. Not to reuse Unpickler and create new Unpickler object in each unpickling.
This patch takes option 1.
## How was this patch tested?
Passing the test added in SPARK-27612 (#24519).
Closes#24521 from viirya/SPARK-27629.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
When transform(...) method is called on a LinearRegressionModel created directly with the coefficients and intercepts, the following exception is encountered.
```
java.util.NoSuchElementException: Failed to find a default value for loss
at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780)
at org.apache.spark.ml.param.Params$$anonfun$getOrDefault$2.apply(params.scala:780)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.ml.param.Params$class.getOrDefault(params.scala:779)
at org.apache.spark.ml.PipelineStage.getOrDefault(Pipeline.scala:42)
at org.apache.spark.ml.param.Params$class.$(params.scala:786)
at org.apache.spark.ml.PipelineStage.$(Pipeline.scala:42)
at org.apache.spark.ml.regression.LinearRegressionParams$class.validateAndTransformSchema(LinearRegression.scala:111)
at org.apache.spark.ml.regression.LinearRegressionModel.validateAndTransformSchema(LinearRegression.scala:637)
at org.apache.spark.ml.PredictionModel.transformSchema(Predictor.scala:192)
at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311)
at org.apache.spark.ml.PipelineModel$$anonfun$transformSchema$5.apply(Pipeline.scala:311)
at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57)
at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66)
at scala.collection.mutable.ArrayOps$ofRef.foldLeft(ArrayOps.scala:186)
at org.apache.spark.ml.PipelineModel.transformSchema(Pipeline.scala:311)
at org.apache.spark.ml.PipelineStage.transformSchema(Pipeline.scala:74)
at org.apache.spark.ml.PipelineModel.transform(Pipeline.scala:305)
```
This is because validateAndTransformSchema() is called both during training and scoring phases, but the checks against the training related params like loss should really be performed during training phase only, I think, please correct me if I'm missing anything :)
This issue was first reported for mleap (https://github.com/combust/mleap/issues/455) because basically when we serialize the Spark transformers for mleap, we only serialize the params that are relevant for scoring. We do have the option to de-serialize the serialized transformers back into Spark for scoring again, but in that case, we no longer have all the training params.
## How was this patch tested?
Added a unit test to check this scenario.
Please let me know if there's anything additional required, this is the first PR that I've raised in this project.
Closes#24509 from ancasarb/linear_regression_params_fix.
Authored-by: asarb <asarb@expedia.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
There is a MemorySink v2 already so v1 can be removed. In this PR I've removed it completely.
What this PR contains:
* V1 memory sink removal
* V2 memory sink renamed to become the only implementation
* Since DSv2 sends exceptions in a chained format (linking them with cause field) I've made python side compliant
* Adapted all the tests
## How was this patch tested?
Existing unit tests.
Closes#24403 from gaborgsomogyi/SPARK-23014.
Authored-by: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Signed-off-by: Marcelo Vanzin <vanzin@cloudera.com>
## What changes were proposed in this pull request?
I want to get rid of as much use of `scala.language.existentials` as possible for 3.0. It's a complicated language feature that generates warnings unless this value is imported. It might even be on the way out of Scala: https://contributors.scala-lang.org/t/proposal-to-remove-existential-types-from-the-language/2785
For Spark, it comes up mostly where the code plays fast and loose with generic types, not the advanced situations you'll often see referenced where this feature is explained. For example, it comes up in cases where a function returns something like `(String, Class[_])`. Scala doesn't like matching this to any other instance of `(String, Class[_])` because doing so requires inferring the existence of some type that satisfies both. Seems obvious if the generic type is a wildcard, but, not technically something Scala likes to let you get away with.
This is a large PR, and it only gets rid of _most_ instances of `scala.language.existentials`. The change should be all compile-time and shouldn't affect APIs or logic.
Many of the changes simply touch up sloppiness about generic types, making the known correct value explicit in the code.
Some fixes involve being more explicit about the existence of generic types in methods. For instance, `def foo(arg: Class[_])` seems innocent enough but should really be declared `def foo[T](arg: Class[T])` to let Scala select and fix a single type when evaluating calls to `foo`.
For kind of surprising reasons, this comes up in places where code evaluates a tuple of things that involve a generic type, but is OK if the two parts of the tuple are evaluated separately.
One key change was altering `Utils.classForName(...): Class[_]` to the more correct `Utils.classForName[T](...): Class[T]`. This caused a number of small but positive changes to callers that otherwise had to cast the result.
In several tests, `Dataset[_]` was used where `DataFrame` seems to be the clear intent.
Finally, in a few cases in MLlib, the return type `this.type` was used where there are no subclasses of the class that uses it. This really isn't needed and causes issues for Scala reasoning about the return type. These are just changed to be concrete classes as return types.
After this change, we have only a few classes that still import `scala.language.existentials` (because modifying them would require extensive rewrites to fix) and no build warnings.
## How was this patch tested?
Existing tests.
Closes#24431 from srowen/SPARK-27536.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Kind of related to https://github.com/gatorsmile/spark/pull/5 - let's update genjavadoc to see if it generates fewer spurious javadoc errors to begin with.
## How was this patch tested?
Existing docs build
Closes#24443 from srowen/genjavadoc013.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Otherwise, tests that use tables from multiple sessions will run into issues if they access the same table. The correct location is in shared state.
A couple other minor test improvements.
cc gatorsmile srinathshankar
## How was this patch tested?
Existing unit tests.
Closes#24302 from ericl/test-conflicts.
Lead-authored-by: Eric Liang <ekl@databricks.com>
Co-authored-by: Eric Liang <ekhliang@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix in Spark image datasource fail when encounter some illegal images.
This related to bugs inside `ImageIO.read` so in spark code I add exception handling for it.
## How was this patch tested?
N/A
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24362 from WeichenXu123/fix_image_ds_bug.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
This should reduce the total runtime of these tests from about 2 minutes to about 25 seconds.
## How was this patch tested?
Existing tests
Closes#24360 from srowen/SpeedQDS.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Loosen some tolerances in the ML regression-related tests, as they seem to account for some of the top slow tests in https://spark-tests.appspot.com/slow-tests
These changes are good for about a 25 second speedup on my laptop.
## How was this patch tested?
Existing tests
Closes#24351 from srowen/SpeedReg.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix build warnings -- see some details below.
But mostly, remove use of postfix syntax where it causes warnings without the `scala.language.postfixOps` import. This is mostly in expressions like "120000 milliseconds". Which, I'd like to simplify to things like "2.minutes" anyway.
## How was this patch tested?
Existing tests.
Closes#24314 from srowen/SPARK-27404.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Remove deprecated / no-op mllib.KMeans getRuns, setRuns
mllib.KMeans has getRuns, setRuns methods which haven't done anything since Spark 2.1. They're deprecated, and no-ops, and should be removed for Spark 3.
## How was this patch tested?
Existing tests.
Closes#24320 from srowen/SPARK-27410.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Changes proposed:
- Adding method to compute treeAggregate depth required to avoid exceeding driver max result size (first commit)
- Using it in the computation of grammian of RowMatrix (second commit)
Tests:
- Unit Test wise, one unit test checking the behavior of the depth computation method
- Tested at scale on hadoop cluster by doing PCA on a large dataset (needed depth 3 to succeed)
Debatable choice:
I'm not sure if RDD API is the right place to put the depth computation method. The advantage of it is that it allows to access driver max result size, and rdd number of partitions, to set default arguments for the method. Semantically, such a method might belong to something like org.apache.spark.util.Utils though.
Closes#23983 from gagafunctor/Heuristic_for_treeAggregate_depth.
Authored-by: Rafael Renaudin <renaudin.rafael@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Followup to PR https://github.com/apache/spark/pull/17085
This PR adds the weight column to the pyspark side, which was already added to the scala API.
The PR also undoes a name change in the scala side corresponding to a change in another similar PR as noted here:
https://github.com/apache/spark/pull/17084#discussion_r259648639
## How was this patch tested?
This patch adds python tests for the changes to the pyspark API.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#24197 from imatiach-msft/ilmat/regressor-eval-python.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The hashSeed method allocates 64 bytes instead of 8. Other bytes are always zeros (thanks to default behavior of ByteBuffer). And they could be excluded from hash calculation because they don't differentiate inputs.
## How was this patch tested?
By running the existing tests - XORShiftRandomSuite
Closes#20793 from MaxGekk/hash-buff-size.
Lead-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Co-authored-by: Maxim Gekk <max.gekk@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
`Saveable` interface introduces `formatVersion` which is protected and it is used nowhere. So the PR proposes to remove it.
## How was this patch tested?
existing tests
Closes#22830 from mgaido91/SPARK-25838.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add 'Recall_at_k' metric to RankingMetrics
## How was this patch tested?
Add test to RankingMetricsSuite.
Closes#23881 from masa3141/SPARK-26981.
Authored-by: masa3141 <masahiro@kazama.tv>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Redundant `get` when getting a value from `Map` given a key.
## How was this patch tested?
N/A
Closes#23901 from 10110346/removegetfrommap.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add sample weights to decision trees
## How was this patch tested?
updated testsuites
Closes#23818 from zhengruifeng/py_tree_support_sample_weight.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Comparing whether Boolean expression is equal to true is redundant
For example:
The datatype of `a` is boolean.
Before:
if (a == true)
After:
if (a)
## How was this patch tested?
N/A
Closes#23884 from 10110346/simplifyboolean.
Authored-by: liuxian <liu.xian3@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add reference JAXB impl for Java 9+ from Glassfish. Right now it's only apparently necessary in MLlib but can be expanded later.
## How was this patch tested?
Existing tests particularly PMML-related ones, which use JAXB.
This works on Java 11.
Closes#23890 from srowen/SPARK-26986.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
## How was this patch tested?
I added tests to the metrics and evaluators classes.
Closes#17084 from imatiach-msft/ilmat/binary-evalute.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
JPMML apparently only supports Java 9 in 1.4.2+. We are seeing text failures from JPMML relating to JAXB when running on Java 11. It's shaded and not a big change, so should be safe.
## How was this patch tested?
Existing tests.
Closes#23868 from srowen/SPARK-26966.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
expose method `predict` in KMeans/BiKMeans/GMM
## How was this patch tested?
added testsuites
Closes#22087 from zhengruifeng/clu_pre_instance.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This patch aims to address flakiness I've observed in MLEventsSuite in these tests:
* test("pipeline read/write events")
* test("pipeline model read/write events")
The issue is in the "read/write events" tests, which work as follows:
* write
* wait until we see at least 1 write-related SparkListenerEvent
* read
* wait until we see at least 1 read-related SparkListenerEvent
The problem is that the last step does NOT allow any write-related SparkListenerEvents, but some of those events may be delayed enough that they are seen in this last step. We should ideally add logic before "read" to wait until the listener events are cleared/complete. Looking into other SparkListener tests, we need to use `sc.listenerBus.waitUntilEmpty(TIMEOUT)`.
This patch adds the waitUntilEmpty() call.
## How was this patch tested?
It's a test!
Closes#23863 from jkbradley/SPARK-26960.
Authored-by: Joseph K. Bradley <joseph@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Our feature importance calculation is taken from sklearn's one, which has been recently fixed (in https://github.com/scikit-learn/scikit-learn/pull/11176). Citing the description of that PR:
> Because the feature importances are (currently, by default) normalized and then averaged, feature importances from later stages are overweighted.
The PR performs a fix similar to sklearn's one. The per-tree normalization of the feature importance is skipped and GBT.
Credits for pointing out clearly the issue and the sklearn's PR to Daniel Jumper.
## How was this patch tested?
modified UT, checked that the computed `featureImportance` in that test is similar to sklearn's one (ti can't be the same, because the trees may be slightly different)
Closes#23773 from mgaido91/SPARK-26721.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
In the PR, I propose to use `System.nanoTime()` instead of `System.currentTimeMillis()` in measurements of time intervals.
`System.currentTimeMillis()` returns current wallclock time and will follow changes to the system clock. Thus, negative wallclock adjustments can cause timeouts to "hang" for a long time (until wallclock time has caught up to its previous value again). This can happen when ntpd does a "step" after the network has been disconnected for some time. The most canonical example is during system bootup when DHCP takes longer than usual. This can lead to failures that are really hard to understand/reproduce. `System.nanoTime()` is guaranteed to be monotonically increasing irrespective of wallclock changes.
## How was this patch tested?
By existing test suites.
Closes#23727 from MaxGekk/system-nanotime.
Lead-authored-by: Maxim Gekk <max.gekk@gmail.com>
Co-authored-by: Maxim Gekk <maxim.gekk@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Make .unpersist(), .destroy() non-blocking by default and adjust callers to request blocking only where important.
This also adds an optional blocking argument to Pyspark's RDD.unpersist(), which never had one.
## How was this patch tested?
Existing tests.
Closes#23685 from srowen/SPARK-26771.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
Error message falsely states standardization=True is causing a problem, even when standardization=False. The real issue is standardizeLabels=True, which is set automatically in LinearRegression and not currently available in the Public API.
## What changes were proposed in this pull request?
A simple change to an error message. More details here: https://jira.apache.org/jira/browse/SPARK-26787
## How was this patch tested?
This does not change any functionality.
Closes#23705 from bscan/bscan-errormsg-1.
Authored-by: bscan <brianjscannell@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
This is a follow-up to PR:
https://github.com/apache/spark/pull/21632
## What changes were proposed in this pull request?
This PR tunes the tolerance used for deciding whether to add zero feature values to a value-count map (where the key is the feature value and the value is the weighted count of those feature values).
In the previous PR the tolerance scaled by the square of the unweighted number of samples, which is too aggressive for a large number of unweighted samples. Unfortunately using just "Utils.EPSILON * unweightedNumSamples" is not enough either, so I multiplied that by a factor tuned by the testing procedure below.
## How was this patch tested?
This involved manually running the sample weight tests for decision tree regressor to see whether the tolerance was large enough to exclude zero feature values.
Eg in SBT:
```
./build/sbt
> project mllib
> testOnly *DecisionTreeRegressorSuite -- -z "training with sample weights"
```
For validation, I added a print inside the if in the code below and validated that the tolerance was large enough so that we would not include zero features (which don't exist in that test):
```
val valueCountMap = if (weightedNumSamples - partNumSamples > tolerance) {
print("should not print this")
partValueCountMap + (0.0 -> (weightedNumSamples - partNumSamples))
} else {
partValueCountMap
}
```
Closes#23682 from imatiach-msft/ilmat/sample-weights-tol.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This takes over #19621 to add multi-column support to StringIndexer:
1. Supports encoding multiple columns.
2. Previously, when specifying `frequencyDesc` or `frequencyAsc` as `stringOrderType` param in `StringIndexer`, in case of equal frequency, the order of strings is undefined. After this change, the strings with equal frequency are further sorted alphabetically.
## How was this patch tested?
Added tests.
Closes#20146 from viirya/SPARK-11215.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR proposes to add ML events to Instrumentation, and use it in Pipeline so that other developers can track and add some actions for them.
## Introduction
ML events (like SQL events) can be quite useful when people want to track and make some actions for corresponding ML operations. For instance, I have been working on integrating
Apache Spark with [Apache Atlas](https://atlas.apache.org/QuickStart.html). With some custom changes with this PR, I can visualise ML pipeline as below:
Another good thing that might have to be considered is, that we can interact this with other SQL/Streaming events. For instance, where the input `Dataset` is originated. For instance, with current Apache Spark, I can visualise SQL operations as below:
I think we can combine those existing lineages together to easily understand where the data comes and goes. Currently, ML side is a hole so the lineages can't be connected for the current Apache Spark ..
To add up, I think it's not to mention how useful it is to track the SQL/Streaming operations. Likewise, I would like to propose ML events as well (as lowest stability `Unstable` APIs for now - no guarantee about stability).
## Implementation Details
### Sends event (but not expose ML specific listener)
**`mllib/src/main/scala/org/apache/spark/ml/events.scala`**
```scala
Unstable
case class ...StartEvent(caller, input)
Unstable
case class ...EndEvent(caller, output)
trait MLEvents {
// Wrappers to send events:
// def with...Event(body) = {
// body()
// SparkContext.getOrCreate().listenerBus.post(event)
// }
}
```
This trait is used by `Instrumentation`.
```scala
class Instrumentation ... with MLEvents {
```
and used as below:
```scala
instrumented { instr =>
instr.with...Event(...) {
...
}
}
```
This way mimics both:
**1. Catalog events (see `org/apache/spark/sql/catalyst/catalog/events.scala`)**
- This allows a Catalog specific listener to be added `ExternalCatalogEventListener`
- It's implemented in a way of wrapping whole `ExternalCatalog` named `ExternalCatalogWithListener`
which delegates the operations to `ExternalCatalog`
This is not quite possible in this case because most of instances (like `Pipeline`) will be directly created in most of cases. We might be able to do that via extending `ListenerBus` for all possible instances but IMHO it's too invasive. Also, exposing another ML specific listener sounds a bit too much at this stage. Therefore, I simply borrowed file name and structures here
**2. SQL execution events (see `org/apache/spark/sql/execution/SQLExecution.scala`)**
- Add an object that wraps a body to send events
Current apporach is rather close to this. It has a `with...` wrapper to send events. I borrowed this approach to be consistent.
## Usage
It needs a custom implementation for a query listener. For instance,
with the custom listener below:
```scala
class CustomMLListener extends SparkListener
def onOtherEvents(e) = e match {
case e: MLEvent => // do something
case _ => // pass
}
}
```
There are two (existing) ways to use this.
```scala
spark.sparkContext.addSparkListener(new CustomMLListener)
```
```bash
spark-submit ...\
--conf spark.extraListeners=CustomMLListener\
...
```
It's also similar with other existing implementation in SQL side.
## Target users
1. I think someone in general would likely utilise this feature like other event listeners. At least, I can see some interests going on outside.
- SQL Listener
- https://stackoverflow.com/questions/46409339/spark-listener-to-an-sql-query
- http://apache-spark-user-list.1001560.n3.nabble.com/spark-sql-Custom-Query-Execution-listener-via-conf-properties-td30979.html
- Streaming Query Listener
- https://jhui.github.io/2017/01/15/Apache-Spark-Streaming/
- http://apache-spark-developers-list.1001551.n3.nabble.com/Structured-Streaming-with-Watermark-td25413.html#a25416
2. Someone would likely run this via Atlas. The plugin mirror intentionally is exposed at [spark-atlas-connector](https://github.com/hortonworks-spark/spark-atlas-connector) so that anyone could do something about lineage and governance in Atlas. I'm trying to show integrated lineages in Apache Spark but this is a missing hole.
## How was this patch tested?
Manually tested and unit tests were added.
Closes#23263 from HyukjinKwon/SPARK-23674-1.
Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
This is updated PR https://github.com/apache/spark/pull/16722 to latest master
## What changes were proposed in this pull request?
This patch adds support for sample weights to DecisionTreeRegressor and DecisionTreeClassifier.
Note: This patch does not add support for sample weights to RandomForest. As discussed in the JIRA, we would like to add sample weights into the bagging process. This patch is large enough as is, and there are some additional considerations to be made for random forests. Since the machinery introduced here needs to be present regardless, I have opted to leave random forests for a follow up pr.
## How was this patch tested?
The algorithms are tested to ensure that:
1. Arbitrary scaling of constant weights has no effect
2. Outliers with small weights do not affect the learned model
3. Oversampling and weighting are equivalent
Unit tests are also added to test other smaller components.
## Summary of changes
- Impurity aggregators now store weighted sufficient statistics. They also store a raw count, however, since this is needed to use minInstancesPerNode.
- Impurity aggregators now also hold the raw count.
- This patch maintains the meaning of minInstancesPerNode, in that the parameter still corresponds to raw, unweighted counts. It also adds a new parameter minWeightFractionPerNode which requires that nodes must contain at least minWeightFractionPerNode * weightedNumExamples total weight.
- This patch modifies findSplitsForContinuousFeatures to use weighted sums. Unit tests are added.
- TreePoint is modified to hold a sample weight
- BaggedPoint is modified from:
``` Scala
private[spark] class BaggedPoint[Datum](val datum: Datum, val subsampleWeights: Array[Double]) extends Serializable
```
to
``` Scala
private[spark] class BaggedPoint[Datum](
val datum: Datum,
val subsampleCounts: Array[Int],
val sampleWeight: Double) extends Serializable
```
We do not simply multiply the counts by the weight and store that because we need the raw counts and the weight in order to use both minInstancesPerNode and minWeightPerNode
**Note**: many of the changed files are due simply to using Instance instead of LabeledPoint
Closes#21632 from imatiach-msft/ilmat/sample-weights.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Avoid memory problems in closure cleaning when handling large Gramians (>= 16K rows/cols) by using null as zeroValue
## How was this patch tested?
Existing tests.
Note that it's hard to test the case that triggers this issue as it would require a large amount of memory and run a while. I confirmed locally that a 16K x 16K Gramian failed with tons of driver memory before, and didn't fail upfront after this change.
Closes#23600 from srowen/SPARK-26228.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.dynamicAllocation`, `spark.scheduler`, `spark.rpc`, `spark.task`, `spark.speculation`, and `spark.cleaner` configs to use `ConfigEntry`.
## How was this patch tested?
Existing tests
Closes#23416 from kiszk/SPARK-26463.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This change exposes the `df` (document frequency) as a public val along with the number of documents (`m`) as part of the IDF model.
* The document frequency is returned as an `Array[Long]`
* If the minimum document frequency is set, this is considered in the df calculation. If the count is less than minDocFreq, the df is 0 for such terms
* numDocs is not very required. But it can be useful, if we plan to provide a provision in future for user to give their own idf function, instead of using a default (log((1+m)/(1+df))). In such cases, the user can provide a function taking input of `m` and `df` and returning the idf value
* Pyspark changes
## How was this patch tested?
The existing test case was edited to also check for the document frequency values.
I am not very good with python or pyspark. I have committed and run tests based on my understanding. Kindly let me know if I have missed anything
Reviewer request: mengxr zjffdu yinxusen
Closes#23549 from purijatin/master.
Authored-by: Jatin Puri <purijatin@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, there are some minor inconsistencies in doc compared to the code. In this PR, I am correcting those inconsistencies.
1) Links related to the evaluation metrics in the docs are not working
2) Minor correction in the evaluation metrics formulas in docs.
## How was this patch tested?
NA
Closes#23589 from shahidki31/docCorrection.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR makes hardcoded `spark.unsafe` configs to use ConfigEntry and put them in the `config` package.
## How was this patch tested?
Existing UTs
Closes#23412 from kiszk/SPARK-26477.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR makes hardcoded configs below to use `ConfigEntry`.
* spark.kryo
* spark.kryoserializer
* spark.serializer
* spark.jars
* spark.files
* spark.submit
* spark.deploy
* spark.worker
This patch doesn't change configs which are not relevant to SparkConf (e.g. system properties).
## How was this patch tested?
Existing tests.
Closes#23532 from HeartSaVioR/SPARK-26466-v2.
Authored-by: Jungtaek Lim (HeartSaVioR) <kabhwan@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
If users set equivalent values to spark.network.timeout and spark.executor.heartbeatInterval, they get the following message:
```
java.lang.IllegalArgumentException: requirement failed: The value of spark.network.timeout=120s must be no less than the value of spark.executor.heartbeatInterval=120s.
```
But it's misleading since it can be read as they could be equal. So this PR replaces "no less than" with "greater than". Also, it fixes similar inconsistencies found in MLlib and SQL components.
## How was this patch tested?
Ran Spark with equivalent values for them manually and confirmed that the revised message was displayed.
Closes#23488 from sekikn/SPARK-26564.
Authored-by: Kengo Seki <sekikn@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The test case will result the following failure. currently in ml.PIC, there is no check for the data type of weight column.
```
test("invalid input types for weight") {
val invalidWeightData = spark.createDataFrame(Seq(
(0L, 1L, "a"),
(2L, 3L, "b")
)).toDF("src", "dst", "weight")
val pic = new PowerIterationClustering()
.setWeightCol("weight")
val result = pic.assignClusters(invalidWeightData)
}
```
```
Job aborted due to stage failure: Task 0 in stage 8077.0 failed 1 times, most recent failure: Lost task 0.0 in stage 8077.0 (TID 882, localhost, executor driver): scala.MatchError: [0,1,null] (of class org.apache.spark.sql.catalyst.expressions.GenericRowWithSchema)
at org.apache.spark.ml.clustering.PowerIterationClustering$$anonfun$3.apply(PowerIterationClustering.scala:178)
at org.apache.spark.ml.clustering.PowerIterationClustering$$anonfun$3.apply(PowerIterationClustering.scala:178)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:107)
at org.apache.spark.graphx.EdgeRDD$$anonfun$1.apply(EdgeRDD.scala:105)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$26.apply(RDD.scala:847)
```
In this PR, added check types for weight column.
## How was this patch tested?
UT added
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#21509 from shahidki31/testCasePic.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Holden Karau <holden@pigscanfly.ca>
## What changes were proposed in this pull request?
This PR upgrades Mockito from 1.10.19 to 2.23.4. The following changes are required.
- Replace `org.mockito.Matchers` with `org.mockito.ArgumentMatchers`
- Replace `anyObject` with `any`
- Replace `getArgumentAt` with `getArgument` and add type annotation.
- Use `isNull` matcher in case of `null` is invoked.
```scala
saslHandler.channelInactive(null);
- verify(handler).channelInactive(any(TransportClient.class));
+ verify(handler).channelInactive(isNull());
```
- Make and use `doReturn` wrapper to avoid [SI-4775](https://issues.scala-lang.org/browse/SI-4775)
```scala
private def doReturn(value: Any) = org.mockito.Mockito.doReturn(value, Seq.empty: _*)
```
## How was this patch tested?
Pass the Jenkins with the existing tests.
Closes#23452 from dongjoon-hyun/SPARK-26536.
Authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
The PR adds the `trainingCost` value to the `BisectingKMeansSummary`, in order to expose the information retrievable by running `computeCost` on the training dataset. This fills the gap with `KMeans` implementation.
## How was this patch tested?
improved UTs
Closes#22764 from mgaido91/SPARK-25765.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add Instrumentation to PrefixSpan
## How was this patch tested?
existing tests
Closes#22971 from zhengruifeng/log_PrefixSpan.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Add a trait HasTrainingSummary to avoid code duplicate related to training summary.
Currently all the training summary use the similar pattern which can be generalized,
```
private[ml] final var trainingSummary: Option[T] = None
def hasSummary: Boolean = trainingSummary.isDefined
def summary: T = trainingSummary.getOrElse...
private[ml] def setSummary(summary: Option[T]): ...
```
Classes with the trait need to override `setSummry`. And for Java compatibility, they will also have to override `summary` method, otherwise the java code will regard all the summary class as Object due to a known issue with Scala.
## How was this patch tested?
existing Java and Scala unit tests
Closes#17654 from hhbyyh/hassummary.
Authored-by: Yuhao Yang <yuhao.yang@intel.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
The updates to the regression metrics were based on (and updated with new changes based on comments):
https://issues.apache.org/jira/browse/SPARK-11520
("RegressionMetrics should support instance weights")
but the pull request was closed as the changes were never checked in.
## How was this patch tested?
I added tests to the metrics class.
Closes#17085 from imatiach-msft/ilmat/regression-evaluate.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Add PowerIterationCluster (PIC) in R
## How was this patch tested?
Add test case
Closes#23072 from huaxingao/spark-19827.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
… incorrect.
## What changes were proposed in this pull request?
In the reported heartbeat information, the unit of the memory data is bytes, which is converted by the formatBytes() function in the utils.js file before being displayed in the interface. The cardinality of the unit conversion in the formatBytes function is 1000, which should be 1024.
Change the cardinality of the unit conversion in the formatBytes function to 1024.
## How was this patch tested?
manual tests
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22683 from httfighter/SPARK-25696.
Lead-authored-by: 韩田田00222924 <han.tiantian@zte.com.cn>
Co-authored-by: han.tiantian@zte.com.cn <han.tiantian@zte.com.cn>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Enhance accuracy of the covariance logic in RowMatrix for function computeCovariance
## How was this patch tested?
Unit test
Accuracy test
Closes#23126 from KyleLi1985/master.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
add following param to instr:
GBTC: validationTol
GBTR: validationTol, validationIndicatorCol
colnames in LiR, LinearSVC, etc
## How was this patch tested?
existing tests
Closes#23122 from zhengruifeng/instr_append_missing_params.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
We have deprecated `OneHotEncoder` at Spark 2.3.0 and introduced `OneHotEncoderEstimator`. At 3.0.0, we remove deprecated `OneHotEncoder` and rename `OneHotEncoderEstimator` to `OneHotEncoder`.
TODO: According to ML migration guide, we need to keep `OneHotEncoderEstimator` as an alias after renaming. This is not done at this patch in order to facilitate review.
## How was this patch tested?
Existing tests.
Closes#23100 from viirya/remove_one_hot_encoder.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
ignore empty output columns
## How was this patch tested?
added tests
Closes#22991 from zhengruifeng/ovrm_empty_outcol.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
use base models' `numFeature` instead of `first` job
## How was this patch tested?
existing tests
Closes#23123 from zhengruifeng/avoid_first_job.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The DOI foundation recommends [this new resolver](https://www.doi.org/doi_handbook/3_Resolution.html#3.8). Accordingly, this PR re`sed`s all static DOI links ;-)
## How was this patch tested?
It wasn't, since it seems as safe as a "[typo fix](https://spark.apache.org/contributing.html)".
In case any of the files is included from other projects, and should be updated there, please let me know.
Closes#23129 from katrinleinweber/resolve-DOIs-securely.
Authored-by: Katrin Leinweber <9948149+katrinleinweber@users.noreply.github.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Optimization [SPARK-12869] was made for dense matrices but caused great performance issue for sparse matrices because manipulating them is very inefficient. When manipulating sparse matrices in Breeze we better use VectorBuilder.
## How was this patch tested?
checked it against a use case that we have that after moving to Spark 2 took 6.5 hours instead of 20 mins. After the change it is back to 20 mins again.
Closes#16732 from uzadude/SparseVector_optimization.
Authored-by: oraviv <oraviv@paypal.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR removes the deprecated method `computeCost` of `KMeans`.
## How was this patch tested?
NA
Closes#22875 from mgaido91/SPARK-25867.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The setter methods are deprecated since 2.1 for the models of regression and classification using trees. The deprecation was stating that the method would have been removed in 3.0. Hence the PR removes the deprecated method.
## How was this patch tested?
NA
Closes#23093 from mgaido91/SPARK-26127.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The build has a lot of deprecation warnings. Some are new in Scala 2.12 and Java 11. We've fixed some, but I wanted to take a pass at fixing lots of easy miscellaneous ones here.
They're too numerous and small to list here; see the pull request. Some highlights:
- `BeanInfo` is deprecated in 2.12, and BeanInfo classes are pretty ancient in Java. Instead, case classes can explicitly declare getters
- Eta expansion of zero-arg methods; foo() becomes () => foo() in many cases
- Floating-point Range is inexact and deprecated, like 0.0 to 100.0 by 1.0
- finalize() is finally deprecated (just needs to be suppressed)
- StageInfo.attempId was deprecated and easiest to remove here
I'm not now going to touch some chunks of deprecation warnings:
- Parquet deprecations
- Hive deprecations (particularly serde2 classes)
- Deprecations in generated code (mostly Thriftserver CLI)
- ProcessingTime deprecations (we may need to revive this class as internal)
- many MLlib deprecations because they concern methods that may be removed anyway
- a few Kinesis deprecations I couldn't figure out
- Mesos get/setRole, which I don't know well
- Kafka/ZK deprecations (e.g. poll())
- Kinesis
- a few other ones that will probably resolve by deleting a deprecated method
## How was this patch tested?
Existing tests, including manual testing with the 2.11 build and Java 11.
Closes#23065 from srowen/SPARK-26090.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This restores scaladoc artifact generation, which got dropped with the Scala 2.12 update. The change looks large, but is almost all due to needing to make the InterfaceStability annotations top-level classes (i.e. `InterfaceStability.Stable` -> `Stable`), unfortunately. A few inner class references had to be qualified too.
Lots of scaladoc warnings now reappear. We can choose to disable generation by default and enable for releases, later.
## How was this patch tested?
N/A; build runs scaladoc now.
Closes#23069 from srowen/SPARK-26026.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Our `GBTClassifier` supports only `variance` impurity. But unfortunately, its `impurity` param by default contains the value `gini`: it is not even modifiable by the user and it differs from the actual impurity used, which is `variance`. This issue does not limit to a wrong value returned for it if the user queries by `getImpurity`, but it also affect the load of a saved model, as its `impurityStats` are created as `gini` (since this is the value stored for the model impurity) which leads to wrong `featureImportances` in model loaded from saved ones.
The PR changes the `impurity` param used to one which allows only the value `variance`.
## How was this patch tested?
modified UT
Closes#22986 from mgaido91/SPARK-25959.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Mllib's Prefixspan - run method - cached RDD stays in cache. After run is comlpeted , rdd remain in cache.
We need to unpersist the cached RDD after run method.
## How was this patch tested?
Existing tests
Closes#23016 from shahidki31/SPARK-26006.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
register following classes in Kryo:
"org.apache.spark.ml.stat.distribution.MultivariateGaussian",
"org.apache.spark.mllib.stat.distribution.MultivariateGaussian"
## How was this patch tested?
added tests
Due to existing module dependency, I can not import spark-core in mllib-local's testsuits, so I do not add testsuite in `org.apache.spark.ml.stat.distribution.MultivariateGaussianSuite`.
And I notice that class `ClusterStats` in `ClusteringEvaluator` is registered in a different way, should it be modified to keep in line with others in ML? srowen
Closes#22974 from zhengruifeng/kryo_MultivariateGaussian.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This PR makes Spark's default Scala version as 2.12, and Scala 2.11 will be the alternative version. This implies that Scala 2.12 will be used by our CI builds including pull request builds.
We'll update the Jenkins to include a new compile-only jobs for Scala 2.11 to ensure the code can be still compiled with Scala 2.11.
## How was this patch tested?
existing tests
Closes#22967 from dbtsai/scala2.12.
Authored-by: DB Tsai <d_tsai@apple.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Add scala and java lint check rules to ban the usage of `throw new xxxErrors` and fix up all exists instance followed by https://github.com/apache/spark/pull/22989#issuecomment-437939830. See more details in https://github.com/apache/spark/pull/22969.
## How was this patch tested?
Local test with lint-scala and lint-java.
Closes#22989 from xuanyuanking/SPARK-25986.
Authored-by: Yuanjian Li <xyliyuanjian@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
…. Other related changes to get JDK 11 working, to test
## What changes were proposed in this pull request?
- Access `sun.misc.Cleaner` (Java 8) and `jdk.internal.ref.Cleaner` (JDK 9+) by reflection (note: the latter only works if illegal reflective access is allowed)
- Access `sun.misc.Unsafe.invokeCleaner` in Java 9+ instead of `sun.misc.Cleaner` (Java 8)
In order to test anything on JDK 11, I also fixed a few small things, which I include here:
- Fix minor JDK 11 compile issues
- Update scala plugin, Jetty for JDK 11, to facilitate tests too
This doesn't mean JDK 11 tests all pass now, but lots do. Note also that the JDK 9+ solution for the Cleaner has a big caveat.
## How was this patch tested?
Existing tests. Manually tested JDK 11 build and tests, and tests covering this change appear to pass. All Java 8 tests should still pass, but this change alone does not achieve full JDK 11 compatibility.
Closes#22993 from srowen/SPARK-24421.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Fix fastSquaredDistance to calculate dense-dense situation calculation performance problem and meanwhile enhance the calculation accuracy.
## How was this patch tested?
From different point to test after add this patch, the dense-dense calculation situation performance is enhanced and will do influence other calculation situation like (sparse-sparse, sparse-dense)
**For calculation logic test**
There is my test for sparse-sparse, dense-dense, sparse-dense case
There is test result:
First we need define some branch path logic for sparse-sparse and sparse-dense case
if meet precisionBound1, we define it as LOGIC1
if not meet precisionBound1, and not meet precisionBound2, we define it as LOGIC2
if not meet precisionBound1, but meet precisionBound2, we define it as LOGIC3
(There is a trick, you can manually change the precision value to meet above situation)
sparse- sparse case time cost situation (milliseconds)
LOGIC1
Before add patch: 7786, 7970, 8086
After add patch: 7729, 7653, 7903
LOGIC2
Before add patch: 8412, 9029, 8606
After add patch: 8603, 8724, 9024
LOGIC3
Before add patch: 19365, 19146, 19351
After add patch: 18917, 19007, 19074
sparse-dense case time cost situation (milliseconds)
LOGIC1
Before add patch: 4195, 4014, 4409
After add patch: 4081,3971, 4151
LOGIC2
Before add patch: 4968, 5579, 5080
After add patch: 4980, 5472, 5148
LOGIC3
Before add patch: 11848, 12077, 12168
After add patch: 11718, 11874, 11743
And for dense-dense case like we already discussed in comment, only use sqdist to calculate distance
dense-dense case time cost situation (milliseconds)
Before add patch: 7340, 7816, 7672
After add patch: 5752, 5800, 5753
**For real world data test**
There is my test data situation
I use the data
http://archive.ics.uci.edu/ml/datasets/Condition+monitoring+of+hydraulic+systems
extract file (PS1, PS2, PS3, PS4, PS5, PS6) to form the test data
total instances are 13230
the attributes for line are 6000
Result for sparse-sparse situation time cost (milliseconds)
Before Enhance: 7670, 7704, 7652
After Enhance: 7634, 7729, 7645
Closes#22893 from KyleLi1985/updatekmeanpatch.
Authored-by: 李亮 <liang.li.work@outlook.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Clarify Bucketizer handleInvalid docs. Just a resubmit of https://github.com/apache/spark/pull/17169
## How was this patch tested?
N/A
Closes#23003 from srowen/SPARK-19714.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Deprecated in Java 11, replace Class.newInstance with Class.getConstructor.getInstance, and primtive wrapper class constructors with valueOf or equivalent
## How was this patch tested?
Existing tests.
Closes#22988 from srowen/SPARK-25984.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The evaluators BinaryClassificationEvaluator, RegressionEvaluator, and MulticlassClassificationEvaluator and the corresponding metrics classes BinaryClassificationMetrics, RegressionMetrics and MulticlassMetrics should use sample weight data.
I've closed the PR: https://github.com/apache/spark/pull/16557
as recommended in favor of creating three pull requests, one for each of the evaluators (binary/regression/multiclass) to make it easier to review/update.
Note: I've updated the JIRA to:
https://issues.apache.org/jira/browse/SPARK-24101
Which is a child of JIRA:
https://issues.apache.org/jira/browse/SPARK-18693
## How was this patch tested?
I added tests to the metrics class.
Closes#17086 from imatiach-msft/ilmat/multiclass-evaluate.
Authored-by: Ilya Matiach <ilmat@microsoft.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
- Remove some AccumulableInfo .apply() methods
- Remove non-label-specific multiclass precision/recall/fScore in favor of accuracy
- Remove toDegrees/toRadians in favor of degrees/radians (SparkR: only deprecated)
- Remove approxCountDistinct in favor of approx_count_distinct (SparkR: only deprecated)
- Remove unused Python StorageLevel constants
- Remove Dataset unionAll in favor of union
- Remove unused multiclass option in libsvm parsing
- Remove references to deprecated spark configs like spark.yarn.am.port
- Remove TaskContext.isRunningLocally
- Remove ShuffleMetrics.shuffle* methods
- Remove BaseReadWrite.context in favor of session
- Remove Column.!== in favor of =!=
- Remove Dataset.explode
- Remove Dataset.registerTempTable
- Remove SQLContext.getOrCreate, setActive, clearActive, constructors
Not touched yet
- everything else in MLLib
- HiveContext
- Anything deprecated more recently than 2.0.0, generally
## How was this patch tested?
Existing tests
Closes#22921 from srowen/SPARK-25908.
Lead-authored-by: Sean Owen <sean.owen@databricks.com>
Co-authored-by: hyukjinkwon <gurwls223@apache.org>
Co-authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
JVMs can't allocate arrays of length exactly Int.MaxValue, so ensure we never try to allocate an array that big. This commit changes some defaults & configs to gracefully fallover to something that doesn't require one large array in some cases; in other cases it simply improves an error message for cases which will still fail.
Closes#22818 from squito/SPARK-25827.
Authored-by: Imran Rashid <irashid@cloudera.com>
Signed-off-by: Imran Rashid <irashid@cloudera.com>
## What changes were proposed in this pull request?
When we added the `distanceMeasure`, we didn't update the `formatVersion` for `KMeans`. Despite this is not a big issue, as that information is used nowhere, we are returning a wrong information.
## How was this patch tested?
NA
Closes#22873 from mgaido91/SPARK-25866.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Fix typos and misspellings, per https://github.com/apache/spark-website/pull/158#issuecomment-435790366
## How was this patch tested?
Existing tests.
Closes#22950 from srowen/Typos.
Authored-by: Sean Owen <sean.owen@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR proposes to deprecate the `computeCost` method on `BisectingKMeans` in favor of the adoption of `ClusteringEvaluator` in order to evaluate the clustering.
## How was this patch tested?
NA
Closes#22869 from mgaido91/SPARK-25758_3.0.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Spark PCA supports maximum only ~65,535 columns matrix. This is due to the fact that, it computes the Covariance matrix first, then compute principle components. The main bottle neck was computing **covariance matrix.** The limit 65,500 came due to the integer size limit. Because we are passing an array of size n*(n+1)/2 to the breeze library and the size cannot be more than INT_MAX. so, the maximum column size we can give is 65,500.
Currently we don't have such limitation for computing SVD in spark. So, we can make use of Spark SVD to compute the PCA, if the number of columns exceeds the limit.
Computation of PCA can be done directly using SVD of matrix, instead of finding the covariance matrix.
Following are the papers/links for the reference.
https://arxiv.org/pdf/1404.1100.pdfhttps://en.wikipedia.org/wiki/Principal_component_analysis#Singular_value_decompositionhttp://www.ifis.uni-luebeck.de/~moeller/Lectures/WS-16-17/Web-Mining-Agents/PCA-SVD.pdf
## How was this patch tested?
added UT, also manually verified with the existing test for pca, by removing the limit condition in the fit method.
Closes#22784 from shahidki31/PCA.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Set main args correctly in BenchmarkBase, to make it accessible for its subclass.
It will benefit:
- BuiltInDataSourceWriteBenchmark
- AvroWriteBenchmark
## How was this patch tested?
manual tests
Closes#22872 from yucai/main_args.
Authored-by: yucai <yyu1@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
The following code in BisectingKMeansModel.load calls the wrong version of load.
```
case (SaveLoadV2_0.thisClassName, SaveLoadV2_0.thisFormatVersion) =>
val model = SaveLoadV1_0.load(sc, path)
```
Closes#22790 from huaxingao/spark-25793.
Authored-by: Huaxin Gao <huaxing@us.ibm.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
add R API for PrefixSpan
## How was this patch tested?
add test in test_mllib_fpm.R
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#21710 from huaxingao/spark-24207.
## What changes were proposed in this pull request?
The PR proposes to deprecate the `computeCost` method on `BisectingKMeans` in favor of the adoption of `ClusteringEvaluator` in order to evaluate the clustering.
## How was this patch tested?
NA
Closes#22756 from mgaido91/SPARK-25758.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
...with intercept with L1 regularization
## What changes were proposed in this pull request?
In the test, "multinomial logistic regression with intercept with L1 regularization" in the "LogisticRegressionSuite", taking more than a minute due to training of 2 logistic regression model.
However after analysing the training cost over iteration, we can reduce the computation time by 50%.
Training cost vs iteration for model 1
So, model1 is converging after iteration 150.
Training cost vs iteration for model 2
After around 100 iteration, model2 is converging.
So, if we give maximum iteration for model1 and model2 as 175 and 125 respectively, we can reduce the computation time by half.
## How was this patch tested?
Computation time in local setup :
Before change:
~53 sec
After change:
~26 sec
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#22659 from shahidki31/SPARK-25623.
Authored-by: Shahid <shahidki31@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
This is the same as #22492 but for master branch. Revert SPARK-14681 to avoid API breaking changes.
cc: WeichenXu123
## How was this patch tested?
Existing unit tests.
Closes#22618 from mengxr/SPARK-25321.master.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
Rename method `benchmark` in `BenchmarkBase` as `runBenchmarkSuite `. Also add comments.
Currently the method name `benchmark` is a bit confusing. Also the name is the same as instances of `Benchmark`:
f246813afb/sql/hive/src/test/scala/org/apache/spark/sql/hive/orc/OrcReadBenchmark.scala (L330-L339)
## How was this patch tested?
Unit test.
Closes#22599 from gengliangwang/renameBenchmarkSuite.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
This patch is to bump the master branch version to 3.0.0-SNAPSHOT.
## How was this patch tested?
N/A
Closes#22606 from gatorsmile/bump3.0.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
This PR adds a rule to force `.toLowerCase(Locale.ROOT)` or `toUpperCase(Locale.ROOT)`.
It produces an error as below:
```
[error] Are you sure that you want to use toUpperCase or toLowerCase without the root locale? In most cases, you
[error] should use toUpperCase(Locale.ROOT) or toLowerCase(Locale.ROOT) instead.
[error] If you must use toUpperCase or toLowerCase without the root locale, wrap the code block with
[error] // scalastyle:off caselocale
[error] .toUpperCase
[error] .toLowerCase
[error] // scalastyle:on caselocale
```
This PR excludes the cases above for SQL code path for external calls like table name, column name and etc.
For test suites, or when it's clear there's no locale problem like Turkish locale problem, it uses `Locale.ROOT`.
One minor problem is, `UTF8String` has both methods, `toLowerCase` and `toUpperCase`, and the new rule detects them as well. They are ignored.
## How was this patch tested?
Manually tested, and Jenkins tests.
Closes#22581 from HyukjinKwon/SPARK-25565.
Authored-by: hyukjinkwon <gurwls223@apache.org>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Refactor `UDTSerializationBenchmark` to use main method and print the output as a separate file.
Run blow command to generate benchmark results:
```
SPARK_GENERATE_BENCHMARK_FILES=1 build/sbt "mllib/test:runMain org.apache.spark.mllib.linalg.UDTSerializationBenchmark"
```
## How was this patch tested?
Manual tests.
Closes#22499 from seancxmao/SPARK-25489.
Authored-by: seancxmao <seancxmao@gmail.com>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
## What changes were proposed in this pull request?
change back the constructor to:
```
class LocalLDAModel private[ml] (
uid: String,
vocabSize: Int,
private[clustering] val oldLocalModel : OldLocalLDAModel,
sparkSession: SparkSession)
```
Although it is marked `private[ml]`, it is used in `mleap` and the master change breaks `mleap` building.
See mleap code [here](c7860af328/mleap-spark/src/main/scala/org/apache/spark/ml/bundle/ops/clustering/LDAModelOp.scala (L57))
## How was this patch tested?
Manual.
Closes#22510 from WeichenXu123/LDA_fix.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
Currently there are two classes with the same naming BenchmarkBase:
1. `org.apache.spark.util.BenchmarkBase`
2. `org.apache.spark.sql.execution.benchmark.BenchmarkBase`
This is very confusing. And the benchmark object `org.apache.spark.sql.execution.benchmark.FilterPushdownBenchmark` is using the one in `org.apache.spark.util.BenchmarkBase`, while there is another class `BenchmarkBase` in the same package of it...
Here I propose:
1. the package `org.apache.spark.util.BenchmarkBase` should be in test package of core module. Move it to package `org.apache.spark.benchmark` .
2. Move `org.apache.spark.util.Benchmark` to test package of core module. Move it to package `org.apache.spark.benchmark` .
3. Rename the class `org.apache.spark.sql.execution.benchmark.BenchmarkBase` as `BenchmarkWithCodegen`
## How was this patch tested?
Unit test
Closes#22513 from gengliangwang/refactorBenchmarkBase.
Authored-by: Gengliang Wang <gengliang.wang@databricks.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Improve testcase "image datasource test: read non image" to tolerate different schema representation.
Because file:/path and file:///path are both valid URI-ifications so in some environment the testcase will fail.
## How was this patch tested?
Manual.
Closes#22449 from WeichenXu123/image_url.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
In the dev list, we can still discuss whether the next version is 2.5.0 or 3.0.0. Let us first bump the master branch version to `2.5.0-SNAPSHOT`.
## How was this patch tested?
N/A
Closes#22426 from gatorsmile/bumpVersionMaster.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
SPARK-21281 introduced a check for the inputs of `CreateStructLike` to be non-empty. This means that `struct()`, which was previously considered valid, now throws an Exception. This behavior change was introduced in 2.3.0. The change may break users' application on upgrade and it causes `VectorAssembler` to fail when an empty `inputCols` is defined.
The PR removes the added check making `struct()` valid again.
## How was this patch tested?
added UT
Closes#22373 from mgaido91/SPARK-25371.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Remove `BisectingKMeansModel.setDistanceMeasure` method.
In `BisectingKMeansModel` set this param is meaningless.
## How was this patch tested?
N/A
Closes#22360 from WeichenXu123/bkmeans_update.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
When running TPC-DS benchmarks on 2.4 release, npoggi and winglungngai saw more than 10% performance regression on the following queries: q67, q24a and q24b. After we applying the PR https://github.com/apache/spark/pull/22338, the performance regression still exists. If we revert the changes in https://github.com/apache/spark/pull/19222, npoggi and winglungngai found the performance regression was resolved. Thus, this PR is to revert the related changes for unblocking the 2.4 release.
In the future release, we still can continue the investigation and find out the root cause of the regression.
## How was this patch tested?
The existing test cases
Closes#22361 from gatorsmile/revertMemoryBlock.
Authored-by: gatorsmile <gatorsmile@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
Deprecate public APIs from ImageSchema.
## How was this patch tested?
N/A
Closes#22349 from WeichenXu123/image_api_deprecate.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
In SharedSparkSession and TestHive, we need to disable the rule ConvertToLocalRelation for better test case coverage.
## How was this patch tested?
Identify the failures after excluding "ConvertToLocalRelation" rule.
Closes#22270 from dilipbiswal/SPARK-25267-final.
Authored-by: Dilip Biswal <dbiswal@us.ibm.com>
Signed-off-by: gatorsmile <gatorsmile@gmail.com>
## What changes were proposed in this pull request?
Upgrade chill to 0.9.3, Kryo to 4.0.2, to get bug fixes and improvements.
The resolved tickets includes:
- SPARK-25258 Upgrade kryo package to version 4.0.2
- SPARK-23131 Kryo raises StackOverflow during serializing GLR model
- SPARK-25176 Kryo fails to serialize a parametrised type hierarchy
More details:
https://github.com/twitter/chill/releases/tag/v0.9.3cc3910d501
## How was this patch tested?
Existing tests.
Closes#22179 from wangyum/SPARK-23131.
Lead-authored-by: Yuming Wang <yumwang@ebay.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Implement an image schema datasource.
This image datasource support:
- partition discovery (loading partitioned images)
- dropImageFailures (the same behavior with `ImageSchema.readImage`)
- path wildcard matching (the same behavior with `ImageSchema.readImage`)
- loading recursively from directory (different from `ImageSchema.readImage`, but use such path: `/path/to/dir/**`)
This datasource **NOT** support:
- specify `numPartitions` (it will be determined by datasource automatically)
- sampling (you can use `df.sample` later but the sampling operator won't be pushdown to datasource)
## How was this patch tested?
Unit tests.
## Benchmark
I benchmark and compare the cost time between old `ImageSchema.read` API and my image datasource.
**cluster**: 4 nodes, each with 64GB memory, 8 cores CPU
**test dataset**: Flickr8k_Dataset (about 8091 images)
**time cost**:
- My image datasource time (automatically generate 258 partitions): 38.04s
- `ImageSchema.read` time (set 16 partitions): 68.4s
- `ImageSchema.read` time (set 258 partitions): 90.6s
**time cost when increase image number by double (clone Flickr8k_Dataset and loads double number images)**:
- My image datasource time (automatically generate 515 partitions): 95.4s
- `ImageSchema.read` (set 32 partitions): 109s
- `ImageSchema.read` (set 515 partitions): 105s
So we can see that my image datasource implementation (this PR) bring some performance improvement compared against old`ImageSchema.read` API.
Closes#22328 from WeichenXu123/image_datasource.
Authored-by: WeichenXu <weichen.xu@databricks.com>
Signed-off-by: Xiangrui Meng <meng@databricks.com>
## What changes were proposed in this pull request?
The PR adds the lift measure to Association rules.
## How was this patch tested?
existing and modified UTs
Closes#22236 from mgaido91/SPARK-10697.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
Currently, when FDR is used for `ChiSqSelector` and no feature is selected an exception is thrown because the max operation fails.
The PR fixes the problem by handling this case and returning an empty array in that case, as sklearn (which was the reference for the initial implementation of FDR) does.
## How was this patch tested?
added UT
Closes#22303 from mgaido91/SPARK-25289.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The PR moves the compilation of the regexp for code formatting outside the method which is called for each code block when splitting expressions, in order to avoid recompiling the regexp every time.
Credit should be given to Izek Greenfield.
## How was this patch tested?
existing UTs
Closes#22135 from mgaido91/SPARK-25093.
Authored-by: Marco Gaido <marcogaido91@gmail.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
## What changes were proposed in this pull request?
In MultilayerPerceptronClassifier, we use RDD operation to encode labels for now. I think we should use ML's OneHotEncoderEstimator/Model to do the encoding.
## How was this patch tested?
Existing tests.
Closes#20232 from viirya/SPARK-23042.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
logNumExamples in KMeans/BiKM/GMM/AFT/NB
## How was this patch tested?
existing tests
Closes#21561 from zhengruifeng/alg_logNumExamples.
Authored-by: zhengruifeng <ruifengz@foxmail.com>
Signed-off-by: Sean Owen <sean.owen@databricks.com>
## What changes were proposed in this pull request?
The output column from `CountVectorModel` lacks attribute. So a later transformer like `Interaction` can raise error because no attribute available.
## How was this patch tested?
Added test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Closes#20313 from viirya/SPARK-22974.
Authored-by: Liang-Chi Hsieh <viirya@gmail.com>
Signed-off-by: DB Tsai <d_tsai@apple.com>
## What changes were proposed in this pull request?
Fixing typos is sometimes very hard. It's not so easy to visually review them. Recently, I discovered a very useful tool for it, [misspell](https://github.com/client9/misspell).
This pull request fixes minor typos detected by [misspell](https://github.com/client9/misspell) except for the false positives. If you would like me to work on other files as well, let me know.
## How was this patch tested?
### before
```
$ misspell . | grep -v '.js'
R/pkg/R/SQLContext.R:354:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:424:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:445:43: "definiton" is a misspelling of "definition"
R/pkg/R/SQLContext.R:495:43: "definiton" is a misspelling of "definition"
NOTICE-binary:454:16: "containd" is a misspelling of "contained"
R/pkg/R/context.R:46:43: "definiton" is a misspelling of "definition"
R/pkg/R/context.R:74:43: "definiton" is a misspelling of "definition"
R/pkg/R/DataFrame.R:591:48: "persistance" is a misspelling of "persistence"
R/pkg/R/streaming.R:166:44: "occured" is a misspelling of "occurred"
R/pkg/inst/worker/worker.R:65:22: "ouput" is a misspelling of "output"
R/pkg/tests/fulltests/test_utils.R:106:25: "environemnt" is a misspelling of "environment"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/InMemoryStoreSuite.java:38:39: "existant" is a misspelling of "existent"
common/kvstore/src/test/java/org/apache/spark/util/kvstore/LevelDBSuite.java:83:39: "existant" is a misspelling of "existent"
common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:243:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:234:19: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:238:63: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:244:46: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:276:39: "transfered" is a misspelling of "transferred"
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
common/unsafe/src/test/scala/org/apache/spark/unsafe/types/UTF8StringPropertyCheckSuite.scala:195:15: "orgin" is a misspelling of "origin"
core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala:621:39: "gauranteed" is a misspelling of "guaranteed"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/main/scala/org/apache/spark/storage/DiskStore.scala:282:18: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/util/ListenerBus.scala:64:17: "overriden" is a misspelling of "overridden"
core/src/test/scala/org/apache/spark/ShuffleSuite.scala:211:7: "substracted" is a misspelling of "subtracted"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:2468:84: "truely" is a misspelling of "truly"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:25:18: "persistance" is a misspelling of "persistence"
core/src/test/scala/org/apache/spark/storage/FlatmapIteratorSuite.scala:26:69: "persistance" is a misspelling of "persistence"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
dev/run-pip-tests:55:28: "enviroments" is a misspelling of "environments"
dev/run-pip-tests:91:37: "virutal" is a misspelling of "virtual"
dev/merge_spark_pr.py:377:72: "accross" is a misspelling of "across"
dev/merge_spark_pr.py:378:66: "accross" is a misspelling of "across"
dev/run-pip-tests:126:25: "enviroments" is a misspelling of "environments"
docs/configuration.md:1830:82: "overriden" is a misspelling of "overridden"
docs/structured-streaming-programming-guide.md:525:45: "processs" is a misspelling of "processes"
docs/structured-streaming-programming-guide.md:1165:61: "BETWEN" is a misspelling of "BETWEEN"
docs/sql-programming-guide.md:1891:810: "behaivor" is a misspelling of "behavior"
examples/src/main/python/sql/arrow.py:98:8: "substract" is a misspelling of "subtract"
examples/src/main/python/sql/arrow.py:103:27: "substract" is a misspelling of "subtract"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala:230:24: "inital" is a misspelling of "initial"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala:237:26: "descripiton" is a misspelling of "descriptions"
python/pyspark/find_spark_home.py:30:13: "enviroment" is a misspelling of "environment"
python/pyspark/context.py:937:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:938:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:939:12: "supress" is a misspelling of "suppress"
python/pyspark/context.py:940:12: "supress" is a misspelling of "suppress"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:713:8: "probabilty" is a misspelling of "probability"
python/pyspark/ml/clustering.py:1038:8: "Currenlty" is a misspelling of "Currently"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/ml/regression.py:1378:20: "paramter" is a misspelling of "parameter"
python/pyspark/mllib/stat/_statistics.py:262:8: "probabilty" is a misspelling of "probability"
python/pyspark/rdd.py:1363:32: "paramter" is a misspelling of "parameter"
python/pyspark/streaming/tests.py:825:42: "retuns" is a misspelling of "returns"
python/pyspark/sql/tests.py:768:29: "initalization" is a misspelling of "initialization"
python/pyspark/sql/tests.py:3616:31: "initalize" is a misspelling of "initialize"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackendUtil.scala:120:39: "arbitary" is a misspelling of "arbitrary"
resource-managers/mesos/src/test/scala/org/apache/spark/deploy/mesos/MesosClusterDispatcherArgumentsSuite.scala:26:45: "sucessfully" is a misspelling of "successfully"
resource-managers/mesos/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerUtils.scala:358:27: "constaints" is a misspelling of "constraints"
resource-managers/yarn/src/test/scala/org/apache/spark/deploy/yarn/YarnClusterSuite.scala:111:24: "senstive" is a misspelling of "sensitive"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/catalog/SessionCatalog.scala:1063:5: "overwirte" is a misspelling of "overwrite"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/datetimeExpressions.scala:1348:17: "compatability" is a misspelling of "compatibility"
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/logical/basicLogicalOperators.scala:77:36: "paramter" is a misspelling of "parameter"
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala:1374:22: "precendence" is a misspelling of "precedence"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:238:27: "unnecassary" is a misspelling of "unnecessary"
sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ConditionalExpressionSuite.scala:212:17: "whn" is a misspelling of "when"
sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/StreamingSymmetricHashJoinHelper.scala:147:60: "timestmap" is a misspelling of "timestamp"
sql/core/src/test/scala/org/apache/spark/sql/TPCDSQuerySuite.scala:150:45: "precentage" is a misspelling of "percentage"
sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchemaSuite.scala:135:29: "infered" is a misspelling of "inferred"
sql/hive/src/test/resources/golden/udf_instr-1-2e76f819563dbaba4beb51e3a130b922:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_instr-2-32da357fc754badd6e3898dcc8989182:1:52: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-1-6e41693c9c6dceea4d7fab4c02884e4e:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_locate-2-d9b5934457931447874d6bb7c13de478:1:63: "occurance" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:9:79: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/golden/udf_translate-2-f7aa38a33ca0df73b7a1e6b6da4b7fe8:13:110: "occurence" is a misspelling of "occurrence"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/annotate_stats_join.q:46:105: "distint" is a misspelling of "distinct"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/auto_sortmerge_join_11.q:29:3: "Currenly" is a misspelling of "Currently"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/avro_partitioned.q:72:15: "existant" is a misspelling of "existent"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/decimal_udf.q:25:3: "substraction" is a misspelling of "subtraction"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby2_map_multi_distinct.q:16:51: "funtion" is a misspelling of "function"
sql/hive/src/test/resources/ql/src/test/queries/clientpositive/groupby_sort_8.q:15:30: "issueing" is a misspelling of "issuing"
sql/hive/src/test/scala/org/apache/spark/sql/sources/HadoopFsRelationTest.scala:669:52: "wiht" is a misspelling of "with"
sql/hive-thriftserver/src/main/java/org/apache/hive/service/cli/session/HiveSessionImpl.java:474:9: "Refering" is a misspelling of "Referring"
```
### after
```
$ misspell . | grep -v '.js'
common/network-common/src/main/java/org/apache/spark/network/util/AbstractFileRegion.java:27:20: "transfered" is a misspelling of "transferred"
core/src/main/scala/org/apache/spark/status/storeTypes.scala:113:29: "ect" is a misspelling of "etc"
core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala:1922:49: "agriculteur" is a misspelling of "agriculture"
data/streaming/AFINN-111.txt:1219:0: "humerous" is a misspelling of "humorous"
licenses/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:5:63: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:6:2: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:262:29: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:262:39: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:269:49: "Stichting" is a misspelling of "Stitching"
licenses-binary/LICENSE-heapq.txt:269:59: "Mathematisch" is a misspelling of "Mathematics"
licenses-binary/LICENSE-heapq.txt:274:2: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:274:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
licenses-binary/LICENSE-heapq.txt:276:29: "STICHTING" is a misspelling of "STITCHING"
licenses-binary/LICENSE-heapq.txt:276:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/hungarian.txt:170:0: "teh" is a misspelling of "the"
mllib/src/main/resources/org/apache/spark/ml/feature/stopwords/portuguese.txt:53:0: "eles" is a misspelling of "eels"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:99:20: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/ml/stat/Summarizer.scala:539:11: "Euclidian" is a misspelling of "Euclidean"
mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAOptimizer.scala:77:36: "Teh" is a misspelling of "The"
mllib/src/main/scala/org/apache/spark/mllib/stat/MultivariateOnlineSummarizer.scala:276:9: "Euclidian" is a misspelling of "Euclidean"
python/pyspark/heapq3.py:6:63: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:7:2: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:263:29: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:263:39: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:270:49: "Stichting" is a misspelling of "Stitching"
python/pyspark/heapq3.py:270:59: "Mathematisch" is a misspelling of "Mathematics"
python/pyspark/heapq3.py:275:2: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:275:12: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/heapq3.py:277:29: "STICHTING" is a misspelling of "STITCHING"
python/pyspark/heapq3.py:277:39: "MATHEMATISCH" is a misspelling of "MATHEMATICS"
python/pyspark/ml/stat.py:339:23: "Euclidian" is a misspelling of "Euclidean"
```
Closes#22070 from seratch/fix-typo.
Authored-by: Kazuhiro Sera <seratch@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This is a follow-up pr of #22014 and #22039
We still have some more compilation errors in mllib with scala-2.12 with sbt:
```
[error] [warn] /home/ishizaki/Spark/PR/scala212/spark/mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala:116: match may not be exhaustive.
[error] It would fail on the following inputs: ("silhouette", _), (_, "cosine"), (_, "squaredEuclidean"), (_, String()), (_, _)
[error] [warn] ($(metricName), $(distanceMeasure)) match {
[error] [warn]
```
## How was this patch tested?
Existing UTs
Closes#22058 from kiszk/SPARK-25036c.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
Convert two function fields in ML classes to simple functions to avoi…d odd SerializedLambda deserialization problem
## How was this patch tested?
Existing tests.
Closes#22032 from srowen/SPARK-25047.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
This PR fixes typo regarding `auxiliary verb + verb[s]`. This is a follow-on of #21956.
## How was this patch tested?
N/A
Closes#22040 from kiszk/spellcheck1.
Authored-by: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Signed-off-by: hyukjinkwon <gurwls223@apache.org>
## What changes were proposed in this pull request?
Fixes for test issues that arose after Scala 2.12 support was added -- ones that only affect the 2.12 build.
## How was this patch tested?
Existing tests.
Closes#22004 from srowen/SPARK-25029.
Authored-by: Sean Owen <srowen@gmail.com>
Signed-off-by: Sean Owen <srowen@gmail.com>
## What changes were proposed in this pull request?
There are many warnings in the current build (for instance see https://amplab.cs.berkeley.edu/jenkins/job/spark-master-test-sbt-hadoop-2.7/4734/console).
**common**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/kvstore/src/main/java/org/apache/spark/util/kvstore/LevelDB.java:237: warning: [rawtypes] found raw type: LevelDBIterator
[warn] void closeIterator(LevelDBIterator it) throws IOException {
[warn] ^
[warn] missing type arguments for generic class LevelDBIterator<T>
[warn] where T is a type-variable:
[warn] T extends Object declared in class LevelDBIterator
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:151: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:152: warning: [deprecation] group() in AbstractBootstrap has been deprecated
[warn] bootstrap.group().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:154: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportServer.java:155: warning: [deprecation] childGroup() in ServerBootstrap has been deprecated
[warn] bootstrap.childGroup().shutdownGracefully();
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/util/NettyUtils.java:112: warning: [deprecation] PooledByteBufAllocator(boolean,int,int,int,int,int,int,int) in PooledByteBufAllocator has been deprecated
[warn] return new PooledByteBufAllocator(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportClient.java:321: warning: [rawtypes] found raw type: Future
[warn] public void operationComplete(Future future) throws Exception {
[warn] ^
[warn] missing type arguments for generic class Future<V>
[warn] where V is a type-variable:
[warn] V extends Object declared in interface Future
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/client/TransportResponseHandler.java:215: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, resp.streamId, resp.byteCount,
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [rawtypes] found raw type: StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] missing type arguments for generic class StreamInterceptor<T>
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/server/TransportRequestHandler.java:255: warning: [unchecked] unchecked call to StreamInterceptor(MessageHandler<T>,String,long,StreamCallback) as a member of the raw type StreamInterceptor
[warn] StreamInterceptor interceptor = new StreamInterceptor(this, wrappedCallback.getID(),
[warn] ^
[warn] where T is a type-variable:
[warn] T extends Message declared in class StreamInterceptor
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/crypto/TransportCipher.java:270: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteRawChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/main/java/org/apache/spark/network/sasl/SaslEncryption.java:304: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] region.transferTo(byteChannel, region.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:119: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] while (in.transfered() < in.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/network-common/src/test/java/org/apache/spark/network/ProtocolSuite.java:120: warning: [deprecation] transfered() in FileRegion has been deprecated
[warn] in.transferTo(channel, in.transfered());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:80: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-300363099, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:84: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-1210324667, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/common/unsafe/src/test/java/org/apache/spark/unsafe/hash/Murmur3_x86_32Suite.java:88: warning: [static] static method should be qualified by type name, Murmur3_x86_32, instead of by an expression
[warn] Assert.assertEquals(-634919701, hasher.hashUnsafeWords(bytes, offset, 16, 42));
[warn] ^
```
**launcher**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/launcher/src/main/java/org/apache/spark/launcher/AbstractLauncher.java:31: warning: [rawtypes] found raw type: AbstractLauncher
[warn] public abstract class AbstractLauncher<T extends AbstractLauncher> {
[warn] ^
[warn] missing type arguments for generic class AbstractLauncher<T>
[warn] where T is a type-variable:
[warn] T extends AbstractLauncher declared in class AbstractLauncher
```
**core**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:99: method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.group() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala💯 method group in class AbstractBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.group().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:102: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] if (bootstrap != null && bootstrap.childGroup() != null) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/main/scala/org/apache/spark/api/r/RBackend.scala:103: method childGroup in class ServerBootstrap is deprecated: see corresponding Javadoc for more information.
[warn] bootstrap.childGroup().shutdownGracefully()
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:151: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] This can be achieved by adding the import clause 'import scala.language.reflectiveCalls'
[warn] or by setting the compiler option -language:reflectiveCalls.
[warn] See the Scaladoc for value scala.language.reflectiveCalls for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member value innerObject2 should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/util/ClosureCleanerSuite.scala:175: reflective access of structural type member method getData should be enabled
[warn] by making the implicit value scala.language.reflectiveCalls visible.
[warn] val rdd = sc.parallelize(1 to 1).map(concreteObject.innerObject2.getData)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/LocalSparkContext.scala:32: constructor Slf4JLoggerFactory in class Slf4JLoggerFactory is deprecated: see corresponding Javadoc for more information.
[warn] InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory())
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:218: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] assert(wrapper.stageAttemptId === stages.head.attemptId)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:261: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:287: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.head.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:471: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] stageAttemptId = stages.last.attemptId))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:966: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskStart(SparkListenerTaskStart(dropped.stageId, dropped.attemptId, task))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:972: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] listener.onTaskEnd(SparkListenerTaskEnd(dropped.stageId, dropped.attemptId,
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:976: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] .taskSummary(dropped.stageId, dropped.attemptId, Array(0.25d, 0.50d, 0.75d))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1146: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(1), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/status/AppStatusListenerSuite.scala:1150: value attemptId in class StageInfo is deprecated: Use attemptNumber instead
[warn] SparkListenerTaskEnd(stage1.stageId, stage1.attemptId, "taskType", Success, tasks(0), null))
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:197: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] while (region.transfered() < region.count()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/core/src/test/scala/org/apache/spark/storage/DiskStoreSuite.scala:198: method transfered in trait FileRegion is deprecated: see corresponding Javadoc for more information.
[warn] region.transferTo(byteChannel, region.transfered())
[warn] ^
```
**sql**:
```
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/analysis/AnalysisSuite.scala:534: abstract type T is unchecked since it is eliminated by erasure
[warn] assert(partitioning.isInstanceOf[T])
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ObjectExpressionsSuite.scala:323: inferred existential type Option[Class[_$1]]( forSome { type _$1 }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val optClass = Option(collectionCls)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:226: warning: [deprecation] ParquetFileReader(Configuration,FileMetaData,Path,List<BlockMetaData>,List<ColumnDescriptor>) in ParquetFileReader has been deprecated
[warn] this.reader = new ParquetFileReader(
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:178: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT32 ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:179: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] (descriptor.getType() == PrimitiveType.PrimitiveTypeName.INT64 &&
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:181: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.FLOAT ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:182: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.DOUBLE ||
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:183: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType() == PrimitiveType.PrimitiveTypeName.BINARY))) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:198: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:221: warning: [deprecation] getTypeLength() in ColumnDescriptor has been deprecated
[warn] readFixedLenByteArrayBatch(rowId, num, column, descriptor.getTypeLength());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:224: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new IOException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:246: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] descriptor.getType().toString(),
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:258: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] switch (descriptor.getType()) {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/execution/datasources/parquet/VectorizedColumnReader.java:384: warning: [deprecation] getType() in ColumnDescriptor has been deprecated
[warn] throw new UnsupportedOperationException("Unsupported type: " + descriptor.getType());
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:458: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int index = rowId * accessor.OFFSET_WIDTH;
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/main/java/org/apache/spark/sql/vectorized/ArrowColumnVector.java:460: warning: [static] static variable should be qualified by type name, BaseRepeatedValueVector, instead of by an expression
[warn] int end = offsets.getInt(index + accessor.OFFSET_WIDTH);
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/BenchmarkQueryTest.scala:57: a pure expression does nothing in statement position; you may be omitting necessary parentheses
[warn] case s => s
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:182: inferred existential type org.apache.parquet.column.statistics.Statistics[?0]( forSome { type ?0 <: Comparable[?0] }), which cannot be expressed by wildcards, should be enabled
[warn] by making the implicit value scala.language.existentials visible.
[warn] This can be achieved by adding the import clause 'import scala.language.existentials'
[warn] or by setting the compiler option -language:existentials.
[warn] See the Scaladoc for value scala.language.existentials for a discussion
[warn] why the feature should be explicitly enabled.
[warn] val columnStats = oneBlockColumnMeta.getStatistics
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/streaming/sources/ForeachBatchSinkSuite.scala:146: implicit conversion method conv should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] This can be achieved by adding the import clause 'import scala.language.implicitConversions'
[warn] or by setting the compiler option -language:implicitConversions.
[warn] See the Scaladoc for value scala.language.implicitConversions for a discussion
[warn] why the feature should be explicitly enabled.
[warn] implicit def conv(x: (Int, Long)): KV = KV(x._1, x._2)
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/streaming/continuous/shuffle/ContinuousShuffleSuite.scala:48: implicit conversion method unsafeRow should be enabled
[warn] by making the implicit value scala.language.implicitConversions visible.
[warn] private implicit def unsafeRow(value: Int) = {
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetInteroperabilitySuite.scala:178: method getType in class ColumnDescriptor is deprecated: see corresponding Javadoc for more information.
[warn] assert(oneFooter.getFileMetaData.getSchema.getColumns.get(0).getType() ===
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetTest.scala:154: method readAllFootersInParallel in object ParquetFileReader is deprecated: see corresponding Javadoc for more information.
[warn] ParquetFileReader.readAllFootersInParallel(configuration, fs.getFileStatus(path)).asScala.toSeq
[warn] ^
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/sql/hive/src/test/java/org/apache/spark/sql/hive/test/Complex.java:679: warning: [cast] redundant cast to Complex
[warn] Complex typedOther = (Complex)other;
[warn] ^
```
**mllib**:
```
[warn] Pruning sources from previous analysis, due to incompatible CompileSetup.
[warn] /home/jenkins/workspace/spark-master-test-maven-hadoop-2.7/mllib/src/test/scala/org/apache/spark/ml/recommendation/ALSSuite.scala:597: match may not be exhaustive.
[warn] It would fail on the following inputs: None, Some((x: Tuple2[?, ?] forSome x not in (?, ?)))
[warn] val df = dfs.find {
[warn] ^
```
This PR does not target fix all of them since some look pretty tricky to fix and there look too many warnings including false positive (like deprecated API but it's used in its test, etc.)
## How was this patch tested?
Existing tests should cover this.
Author: hyukjinkwon <gurwls223@apache.org>
Closes#21975 from HyukjinKwon/remove-build-warnings.
## What changes were proposed in this pull request?
This PR addresses issues 2,3 in this [document](https://docs.google.com/document/d/1fbkjEL878witxVQpOCbjlvOvadHtVjYXeB-2mgzDTvk).
* We modified the closure cleaner to identify closures that are implemented via the LambdaMetaFactory mechanism (serializedLambdas) (issue2).
* We also fix the issue due to scala/bug#11016. There are two options for solving the Unit issue, either add () at the end of the closure or use the trick described in the doc. Otherwise overloading resolution does not work (we are not going to eliminate either of the methods) here. Compiler tries to adapt to Unit and makes these two methods candidates for overloading, when there is polymorphic overloading there is no ambiguity (that is the workaround implemented). This does not look that good but it serves its purpose as we need to support two different uses for method: `addTaskCompletionListener`. One that passes a TaskCompletionListener and one that passes a closure that is wrapped with a TaskCompletionListener later on (issue3).
Note: regarding issue 1 in the doc the plan is:
> Do Nothing. Don’t try to fix this as this is only a problem for Java users who would want to use 2.11 binaries. In that case they can cast to MapFunction to be able to utilize lambdas. In Spark 3.0.0 the API should be simplified so that this issue is removed.
## How was this patch tested?
This was manually tested:
```./dev/change-scala-version.sh 2.12
./build/mvn -DskipTests -Pscala-2.12 clean package
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.serializer.ProactiveClosureSerializationSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.util.ClosureCleanerSuite -Dtest=None
./build/mvn -Pscala-2.12 clean package -DwildcardSuites=org.apache.spark.streaming.DStreamClosureSuite -Dtest=None```
Author: Stavros Kontopoulos <stavros.kontopoulos@lightbend.com>
Closes#21930 from skonto/scala2.12-sup.
## What changes were proposed in this pull request?
ClusteringEvaluator support array input
## How was this patch tested?
added tests
Author: zhengruifeng <ruifengz@foxmail.com>
Closes#21563 from zhengruifeng/clu_eval_support_array.
## What changes were proposed in this pull request?
In most cases, we should use `spark.sessionState.newHadoopConf()` instead of `sparkContext.hadoopConfiguration`, so that the hadoop configurations specified in Spark session
configuration will come into effect.
Add a rule matching `spark.sparkContext.hadoopConfiguration` or `spark.sqlContext.sparkContext.hadoopConfiguration` to prevent the usage.
## How was this patch tested?
Unit test
Author: Gengliang Wang <gengliang.wang@databricks.com>
Closes#21873 from gengliangwang/linterRule.
## What changes were proposed in this pull request?
Followup for #21719.
Update spark.ml training code to fully wrap instrumented methods and remove old instrumentation APIs.
## How was this patch tested?
existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#21799 from MrBago/new-instrumentation-apis2.
## What changes were proposed in this pull request?
Deprecate `KMeans.computeCost` which was introduced as a temp fix and now it is not needed anymore, since we introduced `ClusteringEvaluator`.
## How was this patch tested?
manual test (deprecation warning displayed)
Scala
```
...
scala> model.computeCost(dataset)
warning: there was one deprecation warning; re-run with -deprecation for details
res1: Double = 0.0
```
Python
```
>>> import warnings
>>> warnings.simplefilter('always', DeprecationWarning)
...
>>> model.computeCost(df)
/Users/mgaido/apache/spark/python/pyspark/ml/clustering.py:330: DeprecationWarning: Deprecated in 2.4.0. It will be removed in 3.0.0. Use ClusteringEvaluator instead.
" instead.", DeprecationWarning)
```
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20629 from mgaido91/SPARK-23451.
## What changes were proposed in this pull request?
As stated in https://github.com/apache/spark/pull/21321, in the error messages we should use `catalogString`. This is not the case, as SPARK-22893 used `simpleString` in order to have the same representation everywhere and it missed some places.
The PR unifies the messages using alway the `catalogString` representation of the dataTypes in the messages.
## How was this patch tested?
existing/modified UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21804 from mgaido91/SPARK-24268_catalog.
## What changes were proposed in this pull request?
This PR updates the Instrumentation class to make it more flexible and a little bit easier to use. When these APIs are merged, I'll followup with a PR to update the training code to use these new APIs so we can remove the old APIs. These changes are all to private APIs so this PR doesn't make any user facing changes.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#21719 from MrBago/new-instrumentation-apis.
When invoking MatrixFactorizationModel.recommendProducts(Int, Int) with a non-existing user, a java.util.NoSuchElementException is thrown:
> java.util.NoSuchElementException: next on empty iterator
at scala.collection.Iterator$$anon$2.next(Iterator.scala:39)
at scala.collection.Iterator$$anon$2.next(Iterator.scala:37)
at scala.collection.IndexedSeqLike$Elements.next(IndexedSeqLike.scala:63)
at scala.collection.IterableLike$class.head(IterableLike.scala:107)
at scala.collection.mutable.WrappedArray.scala$collection$IndexedSeqOptimized$$super$head(WrappedArray.scala:35)
at scala.collection.IndexedSeqOptimized$class.head(IndexedSeqOptimized.scala:126)
at scala.collection.mutable.WrappedArray.head(WrappedArray.scala:35)
at org.apache.spark.mllib.recommendation.MatrixFactorizationModel.recommendProducts(MatrixFactorizationModel.scala:169)
## What changes were proposed in this pull request?
Throw a better exception, like "user-id/product-id doesn't found in the model", for a non-existent user/product
## How was this patch tested?
Added UT
Author: Shahid <shahidki31@gmail.com>
Closes#21740 from shahidki31/checkInvalidUserProduct.
## What changes were proposed in this pull request?
unpersist `instances` after training
## How was this patch tested?
existing tests
Author: 郑瑞峰 <zhengruifeng@ZBMAC-C02VX5XWH.local>
Closes#21562 from zhengruifeng/gmm_unpersist.
## 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?
Added the number of iterations in `ClusteringSummary`. This is an helpful information in evaluating how to eventually modify the parameters in order to get a better model.
## How was this patch tested?
modified existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20701 from mgaido91/SPARK-23528.
## What changes were proposed in this pull request?
This PR enables a Java bytecode check tool [spotbugs](https://spotbugs.github.io/) to avoid possible integer overflow at multiplication. When an violation is detected, the build process is stopped.
Due to the tool limitation, some other checks will be enabled. In this PR, [these patterns](http://spotbugs-in-kengo-toda.readthedocs.io/en/lqc-list-detectors/detectors.html#findpuzzlers) in `FindPuzzlers` can be detected.
This check is enabled at `compile` phase. Thus, `mvn compile` or `mvn package` launches this check.
## How was this patch tested?
Existing UTs
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#21542 from kiszk/SPARK-24529.
## What changes were proposed in this pull request?
SPARK-22893 tried to unify error messages about dataTypes. Unfortunately, still many places were missing the `simpleString` method in other to have the same representation everywhere.
The PR unified the messages using alway the simpleString representation of the dataTypes in the messages.
## How was this patch tested?
existing/modified UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#21321 from mgaido91/SPARK-24268.
## What changes were proposed in this pull request?
Currently the power iteration clustering test in spark ml, maps the results to the labels 0 and 1 for assertion. Since the clustering outputs need not be the same as the mapped labels, it may cause failure in the test case. Even if it correctly maps, theoretically we cannot guarantee which set belongs to which cluster label. KMeans can assign label 0 to either of the set.
PowerIterationClusteringSuite in the MLLib checks the clustering results without mapping to the particular cluster label, as shown below.
`` val predictions = Array.fill(2)(mutable.Set.empty[Long])
model.assignments.collect().foreach { a =>
predictions(a.cluster) += a.id
}
assert(predictions.toSet == Set((0 until n1).toSet, (n1 until n).toSet))
``
## How was this patch tested?
Existing tests
Author: Shahid <shahidki31@gmail.com>
Closes#21689 from shahidki31/picTestSuiteMinorCorrection.
## What changes were proposed in this pull request?
[SPARK-14712](https://issues.apache.org/jira/browse/SPARK-14712)
spark.mllib LogisticRegressionModel overrides toString to print a little model info. We should do the same in spark.ml and override repr in pyspark.
## How was this patch tested?
LogisticRegressionSuite.scala
Python doctest in pyspark.ml.classification.py
Author: bravo-zhang <mzhang1230@gmail.com>
Closes#18826 from bravo-zhang/spark-14712.
## What changes were proposed in this pull request?
When user create a aggregator object in scala and pass the aggregator to Spark Dataset's agg() method, Spark's will initialize TypedAggregateExpression with the nodeName field as aggregator.getClass.getSimpleName. However, getSimpleName is not safe in scala environment, depending on how user creates the aggregator object. For example, if the aggregator class full qualified name is "com.my.company.MyUtils$myAgg$2$", the getSimpleName will throw java.lang.InternalError "Malformed class name". This has been reported in scalatest https://github.com/scalatest/scalatest/pull/1044 and discussed in many scala upstream jiras such as SI-8110, SI-5425.
To fix this issue, we follow the solution in https://github.com/scalatest/scalatest/pull/1044 to add safer version of getSimpleName as a util method, and TypedAggregateExpression will invoke this util method rather than getClass.getSimpleName.
## How was this patch tested?
added unit test
Author: Fangshi Li <fli@linkedin.com>
Closes#21276 from fangshil/SPARK-24216.
## 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?
According to the discussion on JIRA. I rewrite the Power Iteration Clustering API in `spark.ml`.
## How was this patch tested?
Unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21493 from WeichenXu123/pic_api.
## What changes were proposed in this pull request?
Using different RNG in all different partitions.
## How was this patch tested?
manually
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21492 from ludatabricks/SPARK-24300.
## What changes were proposed in this pull request?
Extend instrumentation.logNamedValue to support Array input
change the logging for "clusterSizes" to new method
## How was this patch tested?
N/A
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21347 from ludatabricks/SPARK-24290.
## What changes were proposed in this pull request?
spark.ml parity for sequential pattern mining - PrefixSpan: Python API
## How was this patch tested?
doctests
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21265 from WeichenXu123/prefix_span_py.
## What changes were proposed in this pull request?
Add fit with validation set to spark.ml GBT
## How was this patch tested?
Will add later.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21129 from WeichenXu123/gbt_fit_validation.
## What changes were proposed in this pull request?
Converting clustering tests to also check code with structured streaming, using the ML testing infrastructure implemented in SPARK-22882.
This PR is a new version of https://github.com/apache/spark/pull/20319
Author: Sandor Murakozi <smurakozi@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#21358 from jkbradley/smurakozi-SPARK-22884.
## What changes were proposed in this pull request?
Have FPGrowth keep track of model training using the Instrumentation class.
## How was this patch tested?
manually
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#21344 from MrBago/fpgrowth-instr.
## What changes were proposed in this pull request?
Fixes to tuning instrumentation.
## How was this patch tested?
Existing tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#21340 from MrBago/tunning-instrumentation.
## What changes were proposed in this pull request?
- Add seed parameter for variationalTopicInference
- Add seed for calling variationalTopicInference in submitMiniBatch
- Add var seed in LDAModel so that it can take the seed from LDA and use it for the function call of variationalTopicInference in logLikelihoodBound, topicDistributions, getTopicDistributionMethod, and topicDistribution.
## How was this patch tested?
Check the test result in mllib.clustering.LDASuite to make sure the result is repeatable with the seed.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21183 from ludatabricks/SPARK-22210.
## What changes were proposed in this pull request?
changed the instrument for all of the clustering methods
## How was this patch tested?
N/A
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21218 from ludatabricks/SPARK-23686-1.
## What changes were proposed in this pull request?
I think the ‘n_t+t’ in the following code may be wrong, it shoud be ‘n_t+1’ that means is the number of points to the cluster after it finish the no.t+1 min-batch.
* <blockquote>
* $$
* \begin{align}
* c_t+1 &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\
* n_t+t &= n_t * a + m_t
* \end{align}
* $$
* </blockquote>
Author: Fan Donglai <ddna_1022@163.com>
Closes#21179 from ddna1021/master.
## What changes were proposed in this pull request?
Provide evaluateEachIteration method or equivalent for spark.ml GBTs.
## How was this patch tested?
UT.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21097 from WeichenXu123/GBTeval.
## What changes were proposed in this pull request?
- Add OptionalInstrumentation as argument for getNumClasses in ml.classification.Classifier
- Change the function call for getNumClasses in train() in ml.classification.DecisionTreeClassifier, ml.classification.RandomForestClassifier, and ml.classification.NaiveBayes
- Modify the instrumentation creation in ml.classification.LinearSVC
- Change the log call in ml.classification.OneVsRest and ml.classification.LinearSVC
## How was this patch tested?
Manual.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21204 from ludatabricks/SPARK-23686.
## What changes were proposed in this pull request?
Add support for all of the clustering methods
## How was this patch tested?
unit tests added
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21195 from ludatabricks/SPARK-23975-1.
## What changes were proposed in this pull request?
PrefixSpan API for spark.ml. New implementation instead of #20810
## How was this patch tested?
TestSuite added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20973 from WeichenXu123/prefixSpan2.
## What changes were proposed in this pull request?
ML test for StructuredStreaming: spark.ml.tuning
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20261 from WeichenXu123/ml_stream_tuning_test.
## What changes were proposed in this pull request?
Change FPGrowth from private to private[spark]. If no numPartitions is specified, then default value -1 is used. But -1 is only valid in the construction function of FPGrowth, but not in setNumPartitions. So I make this change and use the constructor directly rather than using set method.
## How was this patch tested?
Unit test is added
Author: Jeff Zhang <zjffdu@apache.org>
Closes#13493 from zjffdu/SPARK-15750.
## What changes were proposed in this pull request?
Instruments logging improvements - ML regression package
I add an `OptionalInstrument` class which used in `WeightLeastSquares` and `IterativelyReweightedLeastSquares`.
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21078 from WeichenXu123/inst_reg.
## What changes were proposed in this pull request?
We save ML's user-supplied params and default params as one entity in metadata. During loading the saved models, we set all the loaded params into created ML model instances as user-supplied params.
It causes some problems, e.g., if we strictly disallow some params to be set at the same time, a default param can fail the param check because it is treated as user-supplied param after loading.
The loaded default params should not be set as user-supplied params. We should save ML default params separately in metadata.
For backward compatibility, when loading metadata, if it is a metadata file from previous Spark, we shouldn't raise error if we can't find the default param field.
## How was this patch tested?
Pass existing tests and added tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20633 from viirya/save-ml-default-params.
## What changes were proposed in this pull request?
- Multiple possible input types is added in validateAndTransformSchema() and computeCost() while checking column type
- Add if statement in transform() to support array type as featuresCol
- Add the case statement in fit() while selecting columns from dataset
These changes will be applied to KMeans first, then to other clustering method
## How was this patch tested?
unit test is added
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21081 from ludatabricks/SPARK-23975.
## What changes were proposed in this pull request?
Adding PMML export to Spark ML's KMeans Model.
## How was this patch tested?
New unit test for Spark ML PMML export based on the old Spark MLlib unit test.
Author: Holden Karau <holden@pigscanfly.ca>
Closes#20907 from holdenk/SPARK-11237-Add-PMML-Export-for-KMeans.
## What changes were proposed in this pull request?
It is reported by Spark users that the deviance calculation for poisson regression does not handle y = 0. Thus, the correct model summary cannot be obtained. The user has confirmed the the issue is in
```
override def deviance(y: Double, mu: Double, weight: Double): Double =
{ 2.0 * weight * (y * math.log(y / mu) - (y - mu)) }
when y = 0.
```
The user also mentioned there are many other places he believe we should check the same thing. However, no other changes are needed, including Gamma distribution.
## How was this patch tested?
Add a comparison with R deviance calculation to the existing unit test.
Author: Teng Peng <josephtengpeng@gmail.com>
Closes#21125 from tengpeng/Spark24024GLM.
## What changes were proposed in this pull request?
This PR adds PowerIterationClustering as a Transformer to spark.ml. In the transform method, it calls spark.mllib's PowerIterationClustering.run() method and transforms the return value assignments (the Kmeans output of the pseudo-eigenvector) as a DataFrame (id: LongType, cluster: IntegerType).
This PR is copied and modified from https://github.com/apache/spark/pull/15770 The primary author is wangmiao1981
## How was this patch tested?
This PR has 2 types of tests:
* Copies of tests from spark.mllib's PIC tests
* New tests specific to the spark.ml APIs
Author: wm624@hotmail.com <wm624@hotmail.com>
Author: wangmiao1981 <wm624@hotmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#21090 from jkbradley/wangmiao1981-pic.
## 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.
add RawPrediction as output column
add numClasses and numFeatures to OneVsRestModel
## What changes were proposed in this pull request?
- Add two val numClasses and numFeatures in OneVsRestModel so that we can inherit from Classifier in the future
- Add rawPrediction output column in transform, the prediction label in calculated by the rawPrediciton like raw2prediction
## How was this patch tested?
(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)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21044 from ludatabricks/SPARK-9312.
## What changes were proposed in this pull request?
Many suites currently leak Spark sessions (sometimes with stopped SparkContexts) via the thread-local active Spark session and default Spark session. We should attempt to clean these up and detect when this happens to improve the reproducibility of tests.
## How was this patch tested?
Existing tests
Author: Eric Liang <ekl@databricks.com>
Closes#21058 from ericl/clear-session.
## What changes were proposed in this pull request?
fix build for scala-2.12
## How was this patch tested?
Manual.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#21051 from WeichenXu123/fix_build212.
## What changes were proposed in this pull request?
Adds structured streaming tests using testTransformer for these suites:
* IDF
* Imputer
* Interaction
* MaxAbsScaler
* MinHashLSH
* MinMaxScaler
* NGram
## How was this patch tested?
It is a bunch of tests!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#20964 from jkbradley/SPARK-22883-part2.
## What changes were proposed in this pull request?
Add two set method for LSHModel in LSH.scala, BucketedRandomProjectionLSH.scala, and MinHashLSH.scala
## How was this patch tested?
New test for the param setup was added into
- BucketedRandomProjectionLSHSuite.scala
- MinHashLSHSuite.scala
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Lu WANG <lu.wang@databricks.com>
Closes#21015 from ludatabricks/SPARK-23944.
## 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?
Kolmogorov-Smirnoff test Python API in `pyspark.ml`
**Note** API with `CDF` is a little difficult to support in python. We can add it in following PR.
## How was this patch tested?
doctest
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20904 from WeichenXu123/ks-test-py.
## What changes were proposed in this pull request?
unpersist the last cached nodeIdsForInstances in `deleteAllCheckpoints`
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#20956 from zhengruifeng/NodeIdCache_cleanup.
## What changes were proposed in this pull request?
This is a follow-up pr of #19108 which broke Scala 2.12 build.
```
[error] /Users/ueshin/workspace/apache-spark/spark/mllib/src/main/scala/org/apache/spark/ml/stat/KolmogorovSmirnovTest.scala:86: overloaded method value test with alternatives:
[error] (dataset: org.apache.spark.sql.DataFrame,sampleCol: String,cdf: org.apache.spark.api.java.function.Function[java.lang.Double,java.lang.Double])org.apache.spark.sql.DataFrame <and>
[error] (dataset: org.apache.spark.sql.DataFrame,sampleCol: String,cdf: scala.Double => scala.Double)org.apache.spark.sql.DataFrame
[error] cannot be applied to (org.apache.spark.sql.DataFrame, String, scala.Double => java.lang.Double)
[error] test(dataset, sampleCol, (x: Double) => cdf.call(x))
[error] ^
[error] one error found
```
## How was this patch tested?
Existing tests.
Author: Takuya UESHIN <ueshin@databricks.com>
Closes#20994 from ueshin/issues/SPARK-21898/fix_scala-2.12.
## What changes were proposed in this pull request?
Initial PR for Instrumentation improvements: UUID and logging levels.
This PR takes over #20837Closes#20837
## How was this patch tested?
Manual.
Author: Bago Amirbekian <bago@databricks.com>
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20982 from WeichenXu123/better-instrumentation.
## What changes were proposed in this pull request?
`handleInvalid` Param was forwarded to the VectorAssembler used by RFormula.
## How was this patch tested?
added a test and ran all tests for RFormula and VectorAssembler
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Closes#20970 from yogeshg/spark_23562.
## What changes were proposed in this pull request?
This PR allows us to use one of several types of `MemoryBlock`, such as byte array, int array, long array, or `java.nio.DirectByteBuffer`. To use `java.nio.DirectByteBuffer` allows to have off heap memory which is automatically deallocated by JVM. `MemoryBlock` class has primitive accessors like `Platform.getInt()`, `Platform.putint()`, or `Platform.copyMemory()`.
This PR uses `MemoryBlock` for `OffHeapColumnVector`, `UTF8String`, and other places. This PR can improve performance of operations involving memory accesses (e.g. `UTF8String.trim`) by 1.8x.
For now, this PR does not use `MemoryBlock` for `BufferHolder` based on cloud-fan's [suggestion](https://github.com/apache/spark/pull/11494#issuecomment-309694290).
Since this PR is a successor of #11494, close#11494. Many codes were ported from #11494. Many efforts were put here. **I think this PR should credit to yzotov.**
This PR can achieve **1.1-1.4x performance improvements** for operations in `UTF8String` or `Murmur3_x86_32`. Other operations are almost comparable performances.
Without this PR
```
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Hash byte arrays with length 268435487: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Murmur3_x86_32 526 / 536 0.0 131399881.5 1.0X
UTF8String benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hashCode 525 / 552 1022.6 1.0 1.0X
substring 414 / 423 1298.0 0.8 1.3X
```
With this PR
```
OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-22-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Hash byte arrays with length 268435487: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Murmur3_x86_32 474 / 488 0.0 118552232.0 1.0X
UTF8String benchmark: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
hashCode 476 / 480 1127.3 0.9 1.0X
substring 287 / 291 1869.9 0.5 1.7X
```
Benchmark program
```
test("benchmark Murmur3_x86_32") {
val length = 8192 * 32768 + 31
val seed = 42L
val iters = 1 << 2
val random = new Random(seed)
val arrays = Array.fill[MemoryBlock](numArrays) {
val bytes = new Array[Byte](length)
random.nextBytes(bytes)
new ByteArrayMemoryBlock(bytes, Platform.BYTE_ARRAY_OFFSET, length)
}
val benchmark = new Benchmark("Hash byte arrays with length " + length,
iters * numArrays, minNumIters = 20)
benchmark.addCase("HiveHasher") { _: Int =>
var sum = 0L
for (_ <- 0L until iters) {
sum += HiveHasher.hashUnsafeBytesBlock(
arrays(i), Platform.BYTE_ARRAY_OFFSET, length)
}
}
benchmark.run()
}
test("benchmark UTF8String") {
val N = 512 * 1024 * 1024
val iters = 2
val benchmark = new Benchmark("UTF8String benchmark", N, minNumIters = 20)
val str0 = new java.io.StringWriter() { { for (i <- 0 until N) { write(" ") } } }.toString
val s0 = UTF8String.fromString(str0)
benchmark.addCase("hashCode") { _: Int =>
var h: Int = 0
for (_ <- 0L until iters) { h += s0.hashCode }
}
benchmark.addCase("substring") { _: Int =>
var s: UTF8String = null
for (_ <- 0L until iters) { s = s0.substring(N / 2 - 5, N / 2 + 5) }
}
benchmark.run()
}
```
I run [this benchmark program](https://gist.github.com/kiszk/94f75b506c93a663bbbc372ffe8f05de) using [the commit](ee5a79861c). I got the following results:
```
OpenJDK 64-Bit Server VM 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12 on Linux 4.4.0-66-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Memory access benchmarks: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
ByteArrayMemoryBlock get/putInt() 220 / 221 609.3 1.6 1.0X
Platform get/putInt(byte[]) 220 / 236 610.9 1.6 1.0X
Platform get/putInt(Object) 492 / 494 272.8 3.7 0.4X
OnHeapMemoryBlock get/putLong() 322 / 323 416.5 2.4 0.7X
long[] 221 / 221 608.0 1.6 1.0X
Platform get/putLong(long[]) 321 / 321 418.7 2.4 0.7X
Platform get/putLong(Object) 561 / 563 239.2 4.2 0.4X
```
I also run [this benchmark program](https://gist.github.com/kiszk/5fdb4e03733a5d110421177e289d1fb5) for comparing performance of `Platform.copyMemory()`.
```
OpenJDK 64-Bit Server VM 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12 on Linux 4.4.0-66-generic
Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz
Platform copyMemory: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative
------------------------------------------------------------------------------------------------
Object to Object 1961 / 1967 8.6 116.9 1.0X
System.arraycopy Object to Object 1917 / 1921 8.8 114.3 1.0X
byte array to byte array 1961 / 1968 8.6 116.9 1.0X
System.arraycopy byte array to byte array 1909 / 1937 8.8 113.8 1.0X
int array to int array 1921 / 1990 8.7 114.5 1.0X
double array to double array 1918 / 1923 8.7 114.3 1.0X
Object to byte array 1961 / 1967 8.6 116.9 1.0X
Object to short array 1965 / 1972 8.5 117.1 1.0X
Object to int array 1910 / 1915 8.8 113.9 1.0X
Object to float array 1971 / 1978 8.5 117.5 1.0X
Object to double array 1919 / 1944 8.7 114.4 1.0X
byte array to Object 1959 / 1967 8.6 116.8 1.0X
int array to Object 1961 / 1970 8.6 116.9 1.0X
double array to Object 1917 / 1924 8.8 114.3 1.0X
```
These results show three facts:
1. According to the second/third or sixth/seventh results in the first experiment, if we use `Platform.get/putInt(Object)`, we achieve more than 2x worse performance than `Platform.get/putInt(byte[])` with concrete type (i.e. `byte[]`).
2. According to the second/third or fourth/fifth/sixth results in the first experiment, the fastest way to access an array element on Java heap is `array[]`. **Cons of `array[]` is that it is not possible to support unaligned-8byte access.**
3. According to the first/second/third or fourth/sixth/seventh results in the first experiment, `getInt()/putInt() or getLong()/putLong()` in subclasses of `MemoryBlock` can achieve comparable performance to `Platform.get/putInt()` or `Platform.get/putLong()` with concrete type (second or sixth result). There is no overhead regarding virtual call.
4. According to results in the second experiment, for `Platform.copy()`, to pass `Object` can achieve the same performance as to pass any type of primitive array as source or destination.
5. According to second/fourth results in the second experiment, `Platform.copy()` can achieve the same performance as `System.arrayCopy`. **It would be good to use `Platform.copy()` since `Platform.copy()` can take any types for src and dst.**
We are incrementally replace `Platform.get/putXXX` with `MemoryBlock.get/putXXX`. This is because we have two advantages.
1) Achieve better performance due to having a concrete type for an array.
2) Use simple OO design instead of passing `Object`
It is easy to use `MemoryBlock` in `InternalRow`, `BufferHolder`, `TaskMemoryManager`, and others that are already abstracted. It is not easy to use `MemoryBlock` in utility classes related to hashing or others.
Other candidates are
- UnsafeRow, UnsafeArrayData, UnsafeMapData, SpecificUnsafeRowJoiner
- UTF8StringBuffer
- BufferHolder
- TaskMemoryManager
- OnHeapColumnVector
- BytesToBytesMap
- CachedBatch
- classes for hash
- others.
## How was this patch tested?
Added `UnsafeMemoryAllocator`
Author: Kazuaki Ishizaki <ishizaki@jp.ibm.com>
Closes#19222 from kiszk/SPARK-10399.
## What changes were proposed in this pull request?
Introduce `handleInvalid` parameter in `VectorAssembler` that can take in `"keep", "skip", "error"` options. "error" throws an error on seeing a row containing a `null`, "skip" filters out all such rows, and "keep" adds relevant number of NaN. "keep" figures out an example to find out what this number of NaN s should be added and throws an error when no such number could be found.
## How was this patch tested?
Unit tests are added to check the behavior of `assemble` on specific rows and the transformer is called on `DataFrame`s of different configurations to test different corner cases.
Author: Yogesh Garg <yogesh(dot)garg()databricks(dot)com>
Author: Bago Amirbekian <bago@databricks.com>
Author: Yogesh Garg <1059168+yogeshg@users.noreply.github.com>
Closes#20829 from yogeshg/rformula_handleinvalid.
## 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?
Adds PMML export support to Spark ML pipelines in the style of Spark's DataSource API to allow library authors to add their own model export formats.
Includes a specific implementation for Spark ML linear regression PMML export.
In addition to adding PMML to reach parity with our current MLlib implementation, this approach will allow other libraries & formats (like PFA) to implement and export models with a unified API.
## How was this patch tested?
Basic unit test.
Author: Holden Karau <holdenkarau@google.com>
Author: Holden Karau <holden@pigscanfly.ca>
Closes#19876 from holdenk/SPARK-11171-SPARK-11237-Add-PMML-export-for-ML-KMeans-r2.
## What changes were proposed in this pull request?
Fix lint-java from https://github.com/apache/spark/pull/19108 addition of JavaKolmogorovSmirnovTestSuite
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#20875 from jkbradley/kstest-lint-fix.
## What changes were proposed in this pull request?
Support prediction on single instance for regression and classification related models (i.e., PredictionModel, ClassificationModel and their sub classes).
Add corresponding test cases.
## How was this patch tested?
Test cases added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19381 from WeichenXu123/single_prediction.
## What changes were proposed in this pull request?
Silhouette need to know the number of features. This was taken using `first` and checking the size of the vector. Despite this works fine, if the number of attributes is present in metadata, we can avoid to trigger a job for this and use the metadata value. This can help improving performances of course.
## How was this patch tested?
existing UTs + added UT
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20719 from mgaido91/SPARK-23568.
## What changes were proposed in this pull request?
Feature parity for KolmogorovSmirnovTest in MLlib.
Implement `DataFrame` interface for `KolmogorovSmirnovTest` in `mllib.stat`.
## How was this patch tested?
Test suite added.
Author: WeichenXu <weichen.xu@databricks.com>
Author: jkbradley <joseph.kurata.bradley@gmail.com>
Closes#19108 from WeichenXu123/ml-ks-test.
## What changes were proposed in this pull request?
The PR adds the option to specify a distance measure in BisectingKMeans. Moreover, it introduces the ability to use the cosine distance measure in it.
## How was this patch tested?
added UTs + existing UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20600 from mgaido91/SPARK-23412.
## What changes were proposed in this pull request?
As I mentioned in [SPARK-22751](https://issues.apache.org/jira/browse/SPARK-22751?jql=project%20%3D%20SPARK%20AND%20component%20%3D%20ML%20AND%20text%20~%20randomforest), there is a shuffle performance problem in ML Randomforest when train a RF in high dimensional data.
The reason is that, in _org.apache.spark.tree.impl.RandomForest_, the function _findSplitsBySorting_ will actually flatmap a sparse vector into a dense vector, then in groupByKey there will be a huge shuffle write size.
To avoid this, we can add a filter in flatmap, to filter out zero value. And in function _findSplitsForContinuousFeature_, we can infer the number of zero value by _metadata_.
In addition, if a feature only contains zero value, _continuousSplits_ will not has the key of feature id. So I add a check when using _continuousSplits_.
## How was this patch tested?
Ran model locally using spark-submit.
Author: lucio <576632108@qq.com>
Closes#20472 from lucio-yz/master.
## What changes were proposed in this pull request?
adding Structured Streaming tests for all Models/Transformers in spark.ml.classification
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20121 from WeichenXu123/ml_stream_test_classification.
## What changes were proposed in this pull request?
Added subtree pruning in the translation from LearningNode to Node: a learning node having a single prediction value for all the leaves in the subtree rooted at it is translated into a LeafNode, instead of a (redundant) InternalNode
## How was this patch tested?
Added two unit tests under "mllib/src/test/scala/org/apache/spark/ml/tree/impl/RandomForestSuite.scala":
- test("SPARK-3159 tree model redundancy - classification")
- test("SPARK-3159 tree model redundancy - regression")
4 existing unit tests relying on the tree structure (existence of a specific redundant subtree) had to be adapted as the tested components in the output tree are now pruned (fixed by adding an extra _prune_ parameter which can be used to disable pruning for testing)
Author: Alessandro Solimando <18898964+asolimando@users.noreply.github.com>
Closes#20632 from asolimando/master.
## What changes were proposed in this pull request?
Adds structured streaming tests using testTransformer for these suites:
* BinarizerSuite
* BucketedRandomProjectionLSHSuite
* BucketizerSuite
* ChiSqSelectorSuite
* CountVectorizerSuite
* DCTSuite.scala
* ElementwiseProductSuite
* FeatureHasherSuite
* HashingTFSuite
## How was this patch tested?
It tests itself because it is a bunch of tests!
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#20111 from jkbradley/SPARK-22883-streaming-featureAM.
## What changes were proposed in this pull request?
Converting spark.ml.recommendation tests to also check code with structured streaming, using the ML testing infrastructure implemented in SPARK-22882.
## How was this patch tested?
Automated: Pass the Jenkins.
Author: Gabor Somogyi <gabor.g.somogyi@gmail.com>
Closes#20362 from gaborgsomogyi/SPARK-22886.
## 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?
#### Problem:
Since 2.3, `Bucketizer` supports multiple input/output columns. We will check if exclusive params are set during transformation. E.g., if `inputCols` and `outputCol` are both set, an error will be thrown.
However, when we write `Bucketizer`, looks like the default params and user-supplied params are merged during writing. All saved params are loaded back and set to created model instance. So the default `outputCol` param in `HasOutputCol` trait will be set in `paramMap` and become an user-supplied param. That makes the check of exclusive params failed.
#### Fix:
This changes the saving logic of Bucketizer to handle this case. This is a quick fix to catch the time of 2.3. We should consider modify the persistence mechanism later.
Please see the discussion in the JIRA.
Note: The multi-column `QuantileDiscretizer` also has the same issue.
## How was this patch tested?
Modified tests.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#20594 from viirya/SPARK-23377-2.
## What changes were proposed in this pull request?
The PR provided an implementation of ClusteringEvaluator using the cosine distance measure.
This allows to evaluate clustering results created using the cosine distance, introduced in SPARK-22119.
In the corresponding JIRA, there is a design document for the algorithm implemented here.
## How was this patch tested?
Added UT which compares the result to the one provided by python sklearn.
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20396 from mgaido91/SPARK-23217.
## What changes were proposed in this pull request?
Add some test cases for images feature
## How was this patch tested?
Add some test cases in ImageSchemaSuite
Author: xubo245 <601450868@qq.com>
Closes#20583 from xubo245/CARBONDATA23392_AddTestForImage.
## What changes were proposed in this pull request?
Cache the RDD of items in ml.FPGrowth before passing it to mllib.FPGrowth. Cache only when the user did not cache the input dataset of transactions. This fixes the warning about uncached data emerging from mllib.FPGrowth.
## How was this patch tested?
Manually:
1. Run ml.FPGrowthExample - warning is there
2. Apply the fix
3. Run ml.FPGrowthExample again - no warning anymore
Author: Arseniy Tashoyan <tashoyan@gmail.com>
Closes#20578 from tashoyan/SPARK-23318.
## What changes were proposed in this pull request?
In #19340 some comments considered needed to use spherical KMeans when cosine distance measure is specified, as Matlab does; instead of the implementation based on the behavior of other tools/libraries like Rapidminer, nltk and ELKI, ie. the centroids are computed as the mean of all the points in the clusters.
The PR introduce the approach used in spherical KMeans. This behavior has the nice feature to minimize the within-cluster cosine distance.
## How was this patch tested?
existing/improved UTs
Author: Marco Gaido <marcogaido91@gmail.com>
Closes#20518 from mgaido91/SPARK-22119_followup.
## What changes were proposed in this pull request?
Audit new APIs and docs in 2.3.0.
## How was this patch tested?
No test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#20459 from yanboliang/SPARK-23107.
## What changes were proposed in this pull request?
Currently, the CountVectorizer has a minDF parameter.
It might be useful to also have a maxDF parameter.
It will be used as a threshold for filtering all the terms that occur very frequently in a text corpus, because they are not very informative or could even be stop-words.
This is analogous to scikit-learn, CountVectorizer, max_df.
Other changes:
- Refactored code to invoke "filter()" conditioned on maxDF or minDF set.
- Refactored code to unpersist input after counting is done.
## How was this patch tested?
Unit tests.
Author: Yacine Mazari <y.mazari@gmail.com>
Closes#20367 from ymazari/SPARK-23166.
## What changes were proposed in this pull request?
Currently shuffle repartition uses RoundRobinPartitioning, the generated result is nondeterministic since the sequence of input rows are not determined.
The bug can be triggered when there is a repartition call following a shuffle (which would lead to non-deterministic row ordering), as the pattern shows below:
upstream stage -> repartition stage -> result stage
(-> indicate a shuffle)
When one of the executors process goes down, some tasks on the repartition stage will be retried and generate inconsistent ordering, and some tasks of the result stage will be retried generating different data.
The following code returns 931532, instead of 1000000:
```
import scala.sys.process._
import org.apache.spark.TaskContext
val res = spark.range(0, 1000 * 1000, 1).repartition(200).map { x =>
x
}.repartition(200).map { x =>
if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 2) {
throw new Exception("pkill -f java".!!)
}
x
}
res.distinct().count()
```
In this PR, we propose a most straight-forward way to fix this problem by performing a local sort before partitioning, after we make the input row ordering deterministic, the function from rows to partitions is fully deterministic too.
The downside of the approach is that with extra local sort inserted, the performance of repartition() will go down, so we add a new config named `spark.sql.execution.sortBeforeRepartition` to control whether this patch is applied. The patch is default enabled to be safe-by-default, but user may choose to manually turn it off to avoid performance regression.
This patch also changes the output rows ordering of repartition(), that leads to a bunch of test cases failure because they are comparing the results directly.
## How was this patch tested?
Add unit test in ExchangeSuite.
With this patch(and `spark.sql.execution.sortBeforeRepartition` set to true), the following query returns 1000000:
```
import scala.sys.process._
import org.apache.spark.TaskContext
spark.conf.set("spark.sql.execution.sortBeforeRepartition", "true")
val res = spark.range(0, 1000 * 1000, 1).repartition(200).map { x =>
x
}.repartition(200).map { x =>
if (TaskContext.get.attemptNumber == 0 && TaskContext.get.partitionId < 2) {
throw new Exception("pkill -f java".!!)
}
x
}
res.distinct().count()
res7: Long = 1000000
```
Author: Xingbo Jiang <xingbo.jiang@databricks.com>
Closes#20393 from jiangxb1987/shuffle-repartition.
## What changes were proposed in this pull request?
Currently there is a mixed situation when both single- and multi-column are supported. In some cases exceptions are thrown, in others only a warning log is emitted. In this discussion https://issues.apache.org/jira/browse/SPARK-8418?focusedCommentId=16275049&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-16275049, the decision was to throw an exception.
The PR throws an exception in `Bucketizer`, instead of logging a warning.
## How was this patch tested?
modified UT
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#19993 from mgaido91/SPARK-22799.
## What changes were proposed in this pull request?
When parsing raw image data in ImageSchema.decode(), we use a [java.awt.Color](https://docs.oracle.com/javase/7/docs/api/java/awt/Color.html#Color(int)) constructor that sets alpha = 255, even for four-channel images (which may have different alpha values). This PR fixes this issue & adds a unit test to verify correctness of reading four-channel images.
## How was this patch tested?
Updates an existing unit test ("readImages pixel values test" in `ImageSchemaSuite`) to also verify correctness when reading a four-channel image.
Author: Sid Murching <sid.murching@databricks.com>
Closes#20389 from smurching/image-schema-bugfix.
## What changes were proposed in this pull request?
Correctly guard against empty datasets in `org.apache.spark.ml.classification.Classifier`
## How was this patch tested?
existing tests
Author: Matthew Tovbin <mtovbin@salesforce.com>
Closes#20321 from tovbinm/SPARK-23152.
## What changes were proposed in this pull request?
Currently, KMeans assumes the only possible distance measure to be used is the Euclidean. This PR aims to add the cosine distance support to the KMeans algorithm.
## How was this patch tested?
existing and added UTs.
Author: Marco Gaido <marcogaido91@gmail.com>
Author: Marco Gaido <mgaido@hortonworks.com>
Closes#19340 from mgaido91/SPARK-22119.
## What changes were proposed in this pull request?
`ML.Vectors#sparse(size: Int, elements: Seq[(Int, Double)])` support zero-length
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#20275 from zhengruifeng/SparseVector_size.
## What changes were proposed in this pull request?
Fixed some typos found in ML scaladocs
## How was this patch tested?
NA
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#20300 from BryanCutler/ml-doc-typos-MINOR.
## What changes were proposed in this pull request?
RFormula should use VectorSizeHint & OneHotEncoderEstimator in its pipeline to avoid using the deprecated OneHotEncoder & to ensure the model produced can be used in streaming.
## How was this patch tested?
Unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20229 from MrBago/rFormula.
## What changes were proposed in this pull request?
This patch bumps the master branch version to `2.4.0-SNAPSHOT`.
## How was this patch tested?
N/A
Author: gatorsmile <gatorsmile@gmail.com>
Closes#20222 from gatorsmile/bump24.
## What changes were proposed in this pull request?
Including VectorSizeHint in RFormula piplelines will allow them to be applied to streaming dataframes.
## How was this patch tested?
Unit tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20238 from MrBago/rFormulaVectorSize.
## 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?
Follow-up cleanups for the OneHotEncoderEstimator PR. See some discussion in the original PR: https://github.com/apache/spark/pull/19527 or read below for what this PR includes:
* configedCategorySize: I reverted this to return an Array. I realized the original setup (which I had recommended in the original PR) caused the whole model to be serialized in the UDF.
* encoder: I reorganized the logic to show what I meant in the comment in the previous PR. I think it's simpler but am open to suggestions.
I also made some small style cleanups based on IntelliJ warnings.
## How was this patch tested?
Existing unit tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#20132 from jkbradley/viirya-SPARK-13030.
## What changes were proposed in this pull request?
Avoid holding all models in memory for `TrainValidationSplit`.
## How was this patch tested?
Existing tests.
Author: Bago Amirbekian <bago@databricks.com>
Closes#20143 from MrBago/trainValidMemoryFix.
## What changes were proposed in this pull request?
This patch adds a new class `OneHotEncoderEstimator` which extends `Estimator`. The `fit` method returns `OneHotEncoderModel`.
Common methods between existing `OneHotEncoder` and new `OneHotEncoderEstimator`, such as transforming schema, are extracted and put into `OneHotEncoderCommon` to reduce code duplication.
### Multi-column support
`OneHotEncoderEstimator` adds simpler multi-column support because it is new API and can be free from backward compatibility.
### handleInvalid Param support
`OneHotEncoderEstimator` supports `handleInvalid` Param. It supports `error` and `keep`.
## How was this patch tested?
Added new test suite `OneHotEncoderEstimatorSuite`.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#19527 from viirya/SPARK-13030.
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.
## What changes were proposed in this pull request?
add multi columns support to QuantileDiscretizer.
When calculating the splits, we can either merge together all the probabilities into one array by calculating approxQuantiles on multiple columns at once, or compute approxQuantiles separately for each column. After doing the performance comparision, we found it’s better to calculating approxQuantiles on multiple columns at once.
Here is how we measuring the performance time:
```
var duration = 0.0
for (i<- 0 until 10) {
val start = System.nanoTime()
discretizer.fit(df)
val end = System.nanoTime()
duration += (end - start) / 1e9
}
println(duration/10)
```
Here is the performance test result:
|numCols |NumRows | compute each approxQuantiles separately|compute multiple columns approxQuantiles at one time|
|--------|----------|--------------------------------|-------------------------------------------|
|10 |60 |0.3623195839 |0.1626658607 |
|10 |6000 |0.7537239841 |0.3869370046 |
|22 |6000 |1.6497598557 |0.4767903059 |
|50 |6000 |3.2268305752 |0.7217818396 |
## How was this patch tested?
add UT in QuantileDiscretizerSuite to test multi columns supports
Author: Huaxin Gao <huaxing@us.ibm.com>
Closes#19715 from huaxingao/spark_22397.
## What changes were proposed in this pull request?
ML regression package testsuite add StructuredStreaming test
In order to make testsuite easier to modify, new helper function added in `MLTest`:
```
def testTransformerByGlobalCheckFunc[A : Encoder](
dataframe: DataFrame,
transformer: Transformer,
firstResultCol: String,
otherResultCols: String*)
(globalCheckFunction: Seq[Row] => Unit): Unit
```
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Author: Bago Amirbekian <bago@databricks.com>
Closes#19979 from WeichenXu123/ml_stream_test.
(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?
make sure model data is stored in order. WeichenXu123
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#20113 from zhengruifeng/gmm_save.
## What changes were proposed in this pull request?
Currently, in `ChiSqSelectorModel`, save:
```
spark.createDataFrame(dataArray).repartition(1).write...
```
The default partition number used by createDataFrame is "defaultParallelism",
Current RoundRobinPartitioning won't guarantee the "repartition" generating the same order result with local array. We need fix it.
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20088 from WeichenXu123/fix_chisq_model_save.
## What changes were proposed in this pull request?
Fix OneVsRestModel transform on streaming data failed.
## How was this patch tested?
UT will be added soon, once #19979 merged. (Need a helper test method there)
Author: WeichenXu <weichen.xu@databricks.com>
Closes#20077 from WeichenXu123/fix_ovs_model_transform.
## What changes were proposed in this pull request?
Via some test I found CrossValidator still exists memory issue, it will still occupy `O(n*sizeof(model))` memory for holding models when fitting, if well optimized, it should be `O(parallelism*sizeof(model))`
This is because modelFutures will hold the reference to model object after future is complete (we can use `future.value.get.get` to fetch it), and the `Future.sequence` and the `modelFutures` array holds references to each model future. So all model object are keep referenced. So it will still occupy `O(n*sizeof(model))` memory.
I fix this by merging the `modelFuture` and `foldMetricFuture` together, and use `atomicInteger` to statistic complete fitting tasks and when all done, trigger `trainingDataset.unpersist`.
I ever commented this issue on the old PR [SPARK-19357]
https://github.com/apache/spark/pull/16774#pullrequestreview-53674264
unfortunately, at that time I do not realize that the issue still exists, but now I confirm it and create this PR to fix it.
## Discussion
I give 3 approaches which we can compare, after discussion I realized none of them is ideal, we have to make a trade-off.
**After discussion with jkbradley , choose approach 3**
### Approach 1
~~The approach proposed by MrBago at~~ https://github.com/apache/spark/pull/19904#discussion_r156751569
~~This approach resolve the model objects referenced issue, allow the model objects to be GCed in time. **BUT, in some cases, it still do not resolve the O(N) model memory occupation issue**. Let me use an extreme case to describe it:~~
~~suppose we set `parallelism = 1`, and there're 100 paramMaps. So we have 100 fitting & evaluation tasks. In this approach, because of `parallelism = 1`, the code have to wait 100 fitting tasks complete, **(at this time the memory occupation by models already reach 100 * sizeof(model) )** and then it will unpersist training dataset and then do 100 evaluation tasks.~~
### Approach 2
~~This approach is my PR old version code~~ 2cc7c28f38
~~This approach can make sure at any case, the peak memory occupation by models to be `O(numParallelism * sizeof(model))`, but, it exists an issue that, in some extreme case, the "unpersist training dataset" will be delayed until most of the evaluation tasks complete. Suppose the case
`parallelism = 1`, and there're 100 fitting & evaluation tasks, each fitting&evaluation task have to be executed one by one, so only after the first 99 fitting&evaluation tasks and the 100th fitting task complete, the "unpersist training dataset" will be triggered.~~
### Approach 3
After I compared approach 1 and approach 2, I realized that, in the case which parallelism is low but there're many fitting & evaluation tasks, we cannot achieve both of the following two goals:
- Make the peak memory occupation by models(driver-side) to be O(parallelism * sizeof(model))
- unpersist training dataset before most of the evaluation tasks started.
So I vote for a simpler approach, move the unpersist training dataset to the end (Does this really matters ?)
Because the goal 1 is more important, we must make sure the peak memory occupation by models (driver-side) to be O(parallelism * sizeof(model)), otherwise it will bring high risk of OOM.
Like following code:
```
val foldMetricFutures = epm.zipWithIndex.map { case (paramMap, paramIndex) =>
Future[Double] {
val model = est.fit(trainingDataset, paramMap).asInstanceOf[Model[_]]
//...other minor codes
val metric = eval.evaluate(model.transform(validationDataset, paramMap))
logDebug(s"Got metric metricformodeltrainedwithparamMap.")
metric
} (executionContext)
}
val foldMetrics = foldMetricFutures.map(ThreadUtils.awaitResult(_, Duration.Inf))
trainingDataset.unpersist() // <------- unpersist at the end
validationDataset.unpersist()
```
## How was this patch tested?
N/A
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19904 from WeichenXu123/fix_cross_validator_memory_issue.
## What changes were proposed in this pull request?
A new VectorSizeHint transformer was added. This transformer is meant to be used as a pipeline stage ahead of VectorAssembler, on vector columns, so that VectorAssembler can join vectors in a streaming context where the size of the input vectors is otherwise not known.
## How was this patch tested?
Unit tests.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Bago Amirbekian <bago@databricks.com>
Closes#19746 from MrBago/vector-size-hint.
## What changes were proposed in this pull request?
register following classes in Kryo:
`org.apache.spark.mllib.regression.LabeledPoint`
`org.apache.spark.mllib.clustering.VectorWithNorm`
`org.apache.spark.ml.feature.LabeledPoint`
`org.apache.spark.ml.tree.impl.TreePoint`
`org.apache.spark.ml.tree.impl.BaggedPoint` seems also need to be registered, but I don't know how to do it in this safe way.
WeichenXu123 cloud-fan
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19950 from zhengruifeng/labeled_kryo.
## What changes were proposed in this pull request?
Make several improvements in dataframe vectorized summarizer.
1. Make the summarizer return `Vector` type for all metrics (except "count").
It will return "WrappedArray" type before which won't be very convenient.
2. Make `MetricsAggregate` inherit `ImplicitCastInputTypes` trait. So it can check and implicitly cast input values.
3. Add "weight" parameter for all single metric method.
4. Update doc and improve the example code in doc.
5. Simplified test cases.
## How was this patch tested?
Test added and simplified.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19156 from WeichenXu123/improve_vec_summarizer.
## What changes were proposed in this pull request?
unpersist unused datasets
## How was this patch tested?
existing tests and local check in Spark-Shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#20017 from zhengruifeng/bkm_unpersist.
## What changes were proposed in this pull request?
MLlib ```LinearRegression``` supports _huber_ loss addition to _leastSquares_ loss. The huber loss objective function is:
Refer Eq.(6) and Eq.(8) in [A robust hybrid of lasso and ridge regression](http://statweb.stanford.edu/~owen/reports/hhu.pdf). This objective is jointly convex as a function of (w, σ) ∈ R × (0,∞), we can use L-BFGS-B to solve it.
The current implementation is a straight forward porting for Python scikit-learn [```HuberRegressor```](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.HuberRegressor.html). There are some differences:
* We use mean loss (```lossSum/weightSum```), but sklearn uses total loss (```lossSum```).
* We multiply the loss function and L2 regularization by 1/2. It does not affect the result if we multiply the whole formula by a factor, we just keep consistent with _leastSquares_ loss.
So if fitting w/o regularization, MLlib and sklearn produce the same output. If fitting w/ regularization, MLlib should set ```regParam``` divide by the number of instances to match the output of sklearn.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19020 from yanboliang/spark-3181.
## What changes were proposed in this pull request?
only drops the rows containing NaN in the input columns
## How was this patch tested?
existing tests and added tests
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#19894 from zhengruifeng/bucketizer_nan.
## What changes were proposed in this pull request?
We need to add some helper code to make testing ML transformers & models easier with streaming data. These tests might help us catch any remaining issues and we could encourage future PRs to use these tests to prevent new Models & Transformers from having issues.
I add a `MLTest` trait which extends `StreamTest` trait, and override `createSparkSession`. So ML testsuite can only extend `MLTest`, to use both ML & Stream test util functions.
I only modify one testcase in `LinearRegressionSuite`, for first pass review.
Link to #19746
## How was this patch tested?
`MLTestSuite` added.
Author: WeichenXu <weichen.xu@databricks.com>
Closes#19843 from WeichenXu123/ml_stream_test_helper.
## What changes were proposed in this pull request?
#19208 modified ```sharedParams.scala```, but didn't generated by ```SharedParamsCodeGen.scala```. This involves mismatch between them.
## How was this patch tested?
Existing test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#19958 from yanboliang/spark-21087.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-22289
add JSON encoding/decoding for Param[Matrix].
The issue was reported by Nic Eggert during saving LR model with LowerBoundsOnCoefficients.
There're two ways to resolve this as I see:
1. Support save/load on LogisticRegressionParams, and also adjust the save/load in LogisticRegression and LogisticRegressionModel.
2. Directly support Matrix in Param.jsonEncode, similar to what we have done for Vector.
After some discussion in jira, we prefer the fix to support Matrix as a valid Param type, for simplicity and convenience for other classes.
Note that in the implementation, I added a "class" field in the JSON object to match different JSON converters when loading, which is for preciseness and future extension.
## How was this patch tested?
new unit test to cover the LR case and JsonMatrixConverter
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#19525 from hhbyyh/lrsave.
