## 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>
