## What changes were proposed in this pull request?
Reviewing SparkR ML wrappers API for 2.1 release, mainly two issues:
* Remove ```probabilityCol``` from the argument list of ```spark.logit``` and ```spark.randomForest```. Since it was used when making prediction and should be an argument of ```predict```, and we will work on this at [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) in the next release cycle.
* Fix ```spark.als``` params to make it consistent with MLlib.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16169 from yanboliang/spark-18326.
Poisson GLM fails for many standard data sets (see example in test or JIRA). The issue is incorrect initialization leading to almost zero probability and weights. Specifically, the mean is initialized as the response, which could be zero. Applying the log link results in very negative numbers (protected against -Inf), which again leads to close to zero probability and weights in the weighted least squares. Fix and test are included in the commits.
## What changes were proposed in this pull request?
Update initialization in Poisson GLM
## How was this patch tested?
Add test in GeneralizedLinearRegressionSuite
srowen sethah yanboliang HyukjinKwon mengxr
Author: actuaryzhang <actuaryzhang10@gmail.com>
Closes#16131 from actuaryzhang/master.
## What changes were proposed in this pull request?
Several cleanup and improvements for ```spark.logit```:
* ```summary``` should return coefficients matrix, and should output labels for each class if the model is multinomial logistic regression model.
* ```summary``` should not return ```areaUnderROC, roc, pr, ...```, since most of them are DataFrame which are less important for R users. Meanwhile, these metrics ignore instance weights (setting all to 1.0) which will be changed in later Spark version. In case it will introduce breaking changes, we do not expose them currently.
* SparkR test improvement: comparing the training result with native R glmnet.
* Remove argument ```aggregationDepth``` from ```spark.logit```, since it's an expert Param(related with Spark architecture and job execution) that would be used rarely by R users.
## How was this patch tested?
Unit tests.
The ```summary``` output after this change:
multinomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> model <- spark.logit(df, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
versicolor virginica setosa
(Intercept) 1.514031 -2.609108 1.095077
Sepal_Length 0.02511006 0.2649821 -0.2900921
Sepal_Width -0.5291215 -0.02016446 0.549286
Petal_Length 0.03647411 0.1544119 -0.190886
Petal_Width 0.000236092 0.4195804 -0.4198165
```
binomial logistic regression:
```
> df <- suppressWarnings(createDataFrame(iris))
> training <- df[df$Species %in% c("versicolor", "virginica"), ]
> model <- spark.logit(training, Species ~ ., regParam = 0.5)
> summary(model)
$coefficients
Estimate
(Intercept) -6.053815
Sepal_Length 0.2449379
Sepal_Width 0.1648321
Petal_Length 0.4730718
Petal_Width 1.031947
```
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16117 from yanboliang/spark-18686.
## What changes were proposed in this pull request?
Currently English stop words list in MLlib contains only the argumented words after removing all the apostrophes, so "wouldn't" become "wouldn" and "t". Yet by default Tokenizer and RegexTokenizer don't split on apostrophes or quotes.
Adding original form to stop words list to match the behavior of Tokenizer and StopwordsRemover. Also remove "won" from list.
see more discussion in the jira: https://issues.apache.org/jira/browse/SPARK-18374
## How was this patch tested?
existing ut
Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#16103 from hhbyyh/addstopwords.
## What changes were proposed in this pull request?
add `setFeaturesCol` and `setPredictionCol` for `OneVsRestModel`
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#16059 from zhengruifeng/ovrm_setCol.
## What changes were proposed in this pull request?
This patch bumps master branch version to 2.2.0-SNAPSHOT.
## How was this patch tested?
N/A
Author: Reynold Xin <rxin@databricks.com>
Closes#16126 from rxin/SPARK-18695.
## What changes were proposed in this pull request?
It's better we can fix this issue by providing an option ```type``` for users to change the ```predict``` output schema, then they could output probabilities, log-space predictions, or original labels. In order to not involve breaking API change for 2.1, so revert this change firstly and will add it back after [SPARK-18618](https://issues.apache.org/jira/browse/SPARK-18618) resolved.
## How was this patch tested?
Existing unit tests.
This reverts commit daa975f4bf.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16118 from yanboliang/spark-18291-revert.
## What changes were proposed in this pull request?
This replaces uses of `TextOutputFormat` with an `OutputStream`, which will either write directly to the filesystem or indirectly via a compressor (if so configured). This avoids intermediate buffering.
The inverse of this (reading directly from a stream) is necessary for streaming large JSON records (when `wholeFile` is enabled) so I wanted to keep the read and write paths symmetric.
## How was this patch tested?
Existing unit tests.
Author: Nathan Howell <nhowell@godaddy.com>
Closes#16089 from NathanHowell/SPARK-18658.
## What changes were proposed in this pull request?
Similar to SPARK-18401, as a classification algorithm, logistic regression should support output original label instead of supporting index label.
In this PR, original label output is supported and test cases are modified and added. Document is also modified.
## How was this patch tested?
Unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15910 from wangmiao1981/audit.
## What changes were proposed in this pull request?
API review for 2.1, except ```LSH``` related classes which are still under development.
## How was this patch tested?
Only doc changes, no new tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16009 from yanboliang/spark-18318.
## What changes were proposed in this pull request?
Fix a broadcasted variable leak occurring at each invocation of CostFun in L-BFGS.
## How was this patch tested?
UTests + check that fixed fatal memory consumption on Criteo's use cases.
This contribution is made on behalf of Criteo S.A.
(http://labs.criteo.com/) under the terms of the Apache v2 License.
Author: Anthony Truchet <a.truchet@criteo.com>
Closes#16040 from AnthonyTruchet/SPARK-18612-lbfgs-cost-fun.
## What changes were proposed in this pull request?
make a pass through the items marked as Experimental or DeveloperApi and see if any are stable enough to be unmarked. Also check for items marked final or sealed to see if they are stable enough to be opened up as APIs.
Some discussions in the jira: https://issues.apache.org/jira/browse/SPARK-18319
## How was this patch tested?
existing ut
Author: Yuhao <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#15972 from hhbyyh/experimental21.
## What changes were proposed in this pull request?
Mainly two changes:
* Move DT/RF/GBT Param setter methods to subclasses.
* Deprecate corresponding setter methods in the model classes.
See discussion here https://github.com/apache/spark/pull/15913#discussion_r89662469.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#16017 from yanboliang/spark-18592.
## What changes were proposed in this pull request?
This PR make `sbt unidoc` complete with Java 8.
This PR roughly includes several fixes as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```diff
- * A column that will be computed based on the data in a [[DataFrame]].
+ * A column that will be computed based on the data in a `DataFrame`.
```
- Fix throws annotations so that they are recognisable in javadoc
- Fix URL links to `<a href="http..."></a>`.
```diff
- * [[http://en.wikipedia.org/wiki/Decision_tree_learning Decision tree]] model for regression.
+ * <a href="http://en.wikipedia.org/wiki/Decision_tree_learning">
+ * Decision tree (Wikipedia)</a> model for regression.
```
```diff
- * see http://en.wikipedia.org/wiki/Receiver_operating_characteristic
+ * see <a href="http://en.wikipedia.org/wiki/Receiver_operating_characteristic">
+ * Receiver operating characteristic (Wikipedia)</a>
```
- Fix < to > to
- `greater than`/`greater than or equal to` or `less than`/`less than or equal to` where applicable.
- Wrap it with `{{{...}}}` to print them in javadoc or use `{code ...}` or `{literal ..}`. Please refer https://github.com/apache/spark/pull/16013#discussion_r89665558
- Fix `</p>` complaint
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#16013 from HyukjinKwon/SPARK-3359-errors-more.
## What changes were proposed in this pull request?
(1) Change output schema to `Array of Vector` instead of `Vectors`
(2) Use `numHashTables` as the dimension of Array
(3) Rename `RandomProjection` to `BucketedRandomProjectionLSH`, `MinHash` to `MinHashLSH`
(4) Make `randUnitVectors/randCoefficients` private
(5) Make Multi-Probe NN Search and `hashDistance` private for future discussion
Saved for future PRs:
(1) AND-amplification and `numHashFunctions` as the dimension of Vector are saved for a future PR.
(2) `hashDistance` and MultiProbe NN Search needs more discussion. The current implementation is just a backward compatible one.
## How was this patch tested?
Related unit tests are modified to make sure the performance of LSH are ensured, and the outputs of the APIs meets expectation.
Author: Yun Ni <yunn@uber.com>
Author: Yunni <Euler57721@gmail.com>
Closes#15874 from Yunni/SPARK-18408-yunn-api-improvements.
## What changes were proposed in this pull request?
Remove deprecated methods for ML.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15913 from yanboliang/spark-18481.
## What changes were proposed in this pull request?
Spark Kmeans fit() doesn't cache the RDD which generates a lot of warnings :
WARN KMeans: The input data is not directly cached, which may hurt performance if its parent RDDs are also uncached.
So, Kmeans should cache the internal rdd before calling the Mllib.Kmeans algo, this helped to improve spark kmeans performance by 14%
a9cf905cf7
hhbyyh
## How was this patch tested?
Pass Kmeans tests and existing tests
Author: Zakaria_Hili <zakahili@gmail.com>
Author: HILI Zakaria <zakahili@gmail.com>
Closes#15965 from ZakariaHili/zakbranch.
## What changes were proposed in this pull request?
This PR only tries to fix things that looks pretty straightforward and were fixed in other previous PRs before.
This PR roughly fixes several things as below:
- Fix unrecognisable class and method links in javadoc by changing it from `[[..]]` to `` `...` ``
```
[error] .../spark/sql/core/target/java/org/apache/spark/sql/streaming/DataStreamReader.java:226: error: reference not found
[error] * Loads text files and returns a {link DataFrame} whose schema starts with a string column named
```
- Fix an exception annotation and remove code backticks in `throws` annotation
Currently, sbt unidoc with Java 8 complains as below:
```
[error] .../java/org/apache/spark/sql/streaming/StreamingQuery.java:72: error: unexpected text
[error] * throws StreamingQueryException, if <code>this</code> query has terminated with an exception.
```
`throws` should specify the correct class name from `StreamingQueryException,` to `StreamingQueryException` without backticks. (see [JDK-8007644](https://bugs.openjdk.java.net/browse/JDK-8007644)).
- Fix `[[http..]]` to `<a href="http..."></a>`.
```diff
- * [[https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https Oracle
- * blog page]].
+ * <a href="https://blogs.oracle.com/java-platform-group/entry/diagnosing_tls_ssl_and_https">
+ * Oracle blog page</a>.
```
`[[http...]]` link markdown in scaladoc is unrecognisable in javadoc.
- It seems class can't have `return` annotation. So, two cases of this were removed.
```
[error] .../java/org/apache/spark/mllib/regression/IsotonicRegression.java:27: error: invalid use of return
[error] * return New instance of IsotonicRegression.
```
- Fix < to `<` and > to `>` according to HTML rules.
- Fix `</p>` complaint
- Exclude unrecognisable in javadoc, `constructor`, `todo` and `groupname`.
## How was this patch tested?
Manually tested by `jekyll build` with Java 7 and 8
```
java version "1.7.0_80"
Java(TM) SE Runtime Environment (build 1.7.0_80-b15)
Java HotSpot(TM) 64-Bit Server VM (build 24.80-b11, mixed mode)
```
```
java version "1.8.0_45"
Java(TM) SE Runtime Environment (build 1.8.0_45-b14)
Java HotSpot(TM) 64-Bit Server VM (build 25.45-b02, mixed mode)
```
Note: this does not yet make sbt unidoc suceed with Java 8 yet but it reduces the number of errors with Java 8.
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#15999 from HyukjinKwon/SPARK-3359-errors.
## What changes were proposed in this pull request?
add `setFeaturesCol` and `setPredictionCol` for BiKModel and GMModel
add `setProbabilityCol` for GMModel
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15957 from zhengruifeng/bikm_set.
## What changes were proposed in this pull request?
* Fix SparkR ```spark.glm``` errors when fitting on collinear data, since ```standard error of coefficients, t value and p value``` are not available in this condition.
* Scala/Python GLM summary should throw exception if users get ```standard error of coefficients, t value and p value``` but the underlying WLS was solved by local "l-bfgs".
## How was this patch tested?
Add unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15930 from yanboliang/spark-18501.
## What changes were proposed in this pull request?
Add model summary APIs for `GaussianMixtureModel` and `BisectingKMeansModel` in pyspark.
## How was this patch tested?
Unit tests.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15777 from sethah/pyspark_cluster_summaries.
## What changes were proposed in this pull request?
This is a follow up to some of the discussion [here](https://github.com/apache/spark/pull/15593). During LogisticRegression training, we store the coefficients combined with intercepts as a flat vector, but a more natural abstraction is a matrix. Here, we refactor the code to use matrix where possible, which makes the code more readable and greatly simplifies the indexing.
Note: We do not use a Breeze matrix for the cost function as was mentioned in the linked PR. This is because LBFGS/OWLQN require an implicit `MutableInnerProductModule[DenseMatrix[Double], Double]` which is not natively defined in Breeze. We would need to extend Breeze in Spark to define it ourselves. Also, we do not modify the `regParamL1Fun` because OWLQN in Breeze requires a `MutableEnumeratedCoordinateField[(Int, Int), DenseVector[Double]]` (since we still use a dense vector for coefficients). Here again we would have to extend Breeze inside Spark.
## How was this patch tested?
This is internal code refactoring - the current unit tests passing show us that the change did not break anything. No added functionality in this patch.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15893 from sethah/logreg_refactor.
## What changes were proposed in this pull request?
1, There are two `[Graph.partitionBy]` in `graphx-programming-guide.md`, the first one had no effert.
2, `DataFrame`, `Transformer`, `Pipeline` and `Parameter` in `ml-pipeline.md` were linked to `ml-guide.html` by mistake.
3, `PythonMLLibAPI` in `mllib-linear-methods.md` was not accessable, because class `PythonMLLibAPI` is private.
4, Other link updates.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15912 from zhengruifeng/md_fix.
## What changes were proposed in this pull request?
Several places in MLlib use custom regexes or other approaches to parse Spark versions.
Those should be fixed to use the VersionUtils. This PR replaces custom regexes with
VersionUtils to get Spark version numbers.
## How was this patch tested?
Existing tests.
Signed-off-by: VinceShieh vincent.xieintel.com
Author: VinceShieh <vincent.xie@intel.com>
Closes#15055 from VinceShieh/SPARK-17462.
## What changes were proposed in this pull request?
Add missing ParamValidations for ML algos
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15881 from zhengruifeng/arg_checking.
## What changes were proposed in this pull request?
```spark.mlp``` should support ```RFormula``` like other ML algorithm wrappers.
BTW, I did some cleanup and improvement for ```spark.mlp```.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15883 from yanboliang/spark-18438.
## What changes were proposed in this pull request?
The current implementation of Poisson GLM seems to allow only positive values. This is incorrect since the support of Poisson includes the origin. The bug is easily fixed by changing the test of the Poisson variable from 'require(y **>** 0.0' to 'require(y **>=** 0.0'.
mengxr srowen
Author: actuaryzhang <actuaryzhang10@gmail.com>
Author: actuaryzhang <actuaryzhang@uber.com>
Closes#15683 from actuaryzhang/master.
## What changes were proposed in this pull request?
* Fix the following exceptions which throws when ```spark.randomForest```(classification), ```spark.gbt```(classification), ```spark.naiveBayes``` and ```spark.glm```(binomial family) were fitted on libsvm data.
```
java.lang.IllegalArgumentException: requirement failed: If label column already exists, forceIndexLabel can not be set with true.
```
See [SPARK-18412](https://issues.apache.org/jira/browse/SPARK-18412) for more detail about how to reproduce this bug.
* Refactor out ```getFeaturesAndLabels``` to RWrapperUtils, since lots of ML algorithm wrappers use this function.
* Drop some unwanted columns when making prediction.
## How was this patch tested?
Add unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15851 from yanboliang/spark-18412.
## What changes were proposed in this pull request?
* Refactor out ```trainWithLabelCheck``` and make ```mllib.NaiveBayes``` call into it.
* Avoid capturing the outer object for ```modelType```.
* Move ```requireNonnegativeValues``` and ```requireZeroOneBernoulliValues``` to companion object.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15826 from yanboliang/spark-14077-2.
## What changes were proposed in this pull request?
Before this patch, the gradient updates for multinomial logistic regression were computed by an outer loop over the number of classes and an inner loop over the number of features. Inside the inner loop, we standardized the feature value (`value / featuresStd(index)`), which means we performed the computation `numFeatures * numClasses` times. We only need to perform that computation `numFeatures` times, however. If we re-order the inner and outer loop, we can avoid this, but then we lose sequential memory access. In this patch, we instead lay out the coefficients in column major order while we train, so that we can avoid the extra computation and retain sequential memory access. We convert back to row-major order when we create the model.
## How was this patch tested?
This is an implementation detail only, so the original behavior should be maintained. All tests pass. I ran some performance tests to verify speedups. The results are below, and show significant speedups.
## Performance Tests
**Setup**
3 node bare-metal cluster
120 cores total
384 gb RAM total
**Results**
NOTE: The `currentMasterTime` and `thisPatchTime` are times in seconds for a single iteration of L-BFGS or OWL-QN.
| | numPoints | numFeatures | numClasses | regParam | elasticNetParam | currentMasterTime (sec) | thisPatchTime (sec) | pctSpeedup |
|----|-------------|---------------|--------------|------------|-------------------|---------------------------|-----------------------|--------------|
| 0 | 1e+07 | 100 | 500 | 0.5 | 0 | 90 | 18 | 80 |
| 1 | 1e+08 | 100 | 50 | 0.5 | 0 | 90 | 19 | 78 |
| 2 | 1e+08 | 100 | 50 | 0.05 | 1 | 72 | 19 | 73 |
| 3 | 1e+06 | 100 | 5000 | 0.5 | 0 | 93 | 53 | 43 |
| 4 | 1e+07 | 100 | 5000 | 0.5 | 0 | 900 | 390 | 56 |
| 5 | 1e+08 | 100 | 500 | 0.5 | 0 | 840 | 174 | 79 |
| 6 | 1e+08 | 100 | 200 | 0.5 | 0 | 360 | 72 | 80 |
| 7 | 1e+08 | 1000 | 5 | 0.5 | 0 | 9 | 3 | 66 |
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15593 from sethah/MLOR_PERF_COL_MAJOR_COEF.
## What changes were proposed in this pull request?
SparkR ```spark.randomForest``` classification prediction should output original label rather than the indexed label. This issue is very similar with [SPARK-18291](https://issues.apache.org/jira/browse/SPARK-18291).
## How was this patch tested?
Add unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15842 from yanboliang/spark-18401.
## What changes were proposed in this pull request?
ALS.run fail with better message if ratings is empty rdd
ALS.train and ALS.trainImplicit are also affected
## How was this patch tested?
added new tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#15809 from techaddict/SPARK-18268.
## What changes were proposed in this pull request?
Gradient Boosted Tree in R.
With a few minor improvements to RandomForest in R.
Since this is relatively isolated I'd like to target this for branch-2.1
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15746 from felixcheung/rgbt.
## What changes were proposed in this pull request?
* Made SingularMatrixException private ml
* WeightedLeastSquares: Changed to allow tol >= 0 instead of only tol > 0
## How was this patch tested?
existing tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15779 from jkbradley/wls-cleanups.
## What changes were proposed in this pull request?
Close `FileStreams`, `ZipFiles` etc to release the resources after using. Not closing the resources will cause IO Exception to be raised while deleting temp files.
## How was this patch tested?
Existing tests
Author: U-FAREAST\tl <tl@microsoft.com>
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Tao LI <tl@microsoft.com>
Closes#15618 from HyukjinKwon/SPARK-14914-1.
## What changes were proposed in this pull request?
Motivation:
`org.apache.spark.ml.Pipeline.copy(extra: ParamMap)` does not create an instance with the same UID. It does not conform to the method specification from its base class `org.apache.spark.ml.param.Params.copy(extra: ParamMap)`
Solution:
- fix for Pipeline UID
- introduced new tests for `org.apache.spark.ml.Pipeline.copy`
- minor improvements in test for `org.apache.spark.ml.PipelineModel.copy`
## How was this patch tested?
Introduced new unit test: `org.apache.spark.ml.PipelineSuite."Pipeline.copy"`
Improved existing unit test: `org.apache.spark.ml.PipelineSuite."PipelineModel.copy"`
Author: Wojciech Szymanski <wk.szymanski@gmail.com>
Closes#15759 from wojtek-szymanski/SPARK-18210.
## What changes were proposed in this pull request?
Only some of the models which contain a training summary currently set the summaries in the copy method. Linear/Logistic regression do, GLR, GMM, KM, and BKM do not. Additionally, these copy methods did not set the parent pointer of the copied model. This patch modifies the copy methods of the four models mentioned above to copy the training summary and set the parent.
## How was this patch tested?
Add unit tests in Linear/Logistic/GeneralizedLinear regression and GaussianMixture/KMeans/BisectingKMeans to check the parent pointer of the copied model and check that the copied model has a summary.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#15773 from sethah/SPARK-18276.
## What changes were proposed in this pull request?
Fix `Locale.US` for all usages of `DateFormat`, `NumberFormat`
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#15610 from srowen/SPARK-18076.
## What changes were proposed in this pull request?
- Renamed kbest to numTopFeatures
- Renamed alpha to fpr
- Added missing Since annotations
- Doc cleanups
## How was this patch tested?
Added new standardized unit tests for spark.ml.
Improved existing unit test coverage a bit.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15647 from jkbradley/chisqselector-follow-ups.
## What changes were proposed in this pull request?
1, move cast to `Predictor`
2, and then, remove unnecessary cast
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15414 from zhengruifeng/move_cast.
## What changes were proposed in this pull request?
This patch introduces an internal commit protocol API that is used by the batch data source to do write commits. It currently has only one implementation that uses Hadoop MapReduce's OutputCommitter API. In the future, this commit API can be used to unify streaming and batch commits.
## How was this patch tested?
Should be covered by existing write tests.
Author: Reynold Xin <rxin@databricks.com>
Author: Eric Liang <ekl@databricks.com>
Closes#15707 from rxin/SPARK-18024-2.
## What changes were proposed in this pull request?
Random Forest Regression and Classification for R
Clean-up/reordering generics.R
## How was this patch tested?
manual tests, unit tests
Author: Felix Cheung <felixcheung_m@hotmail.com>
Closes#15607 from felixcheung/rrandomforest.
## What changes were proposed in this pull request?
Return potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#15450 from srowen/SPARK-3261.
## What changes were proposed in this pull request?
Implement Locality Sensitive Hashing along with approximate nearest neighbors and approximate similarity join based on the [design doc](https://docs.google.com/document/d/1D15DTDMF_UWTTyWqXfG7y76iZalky4QmifUYQ6lH5GM/edit).
Detailed changes are as follows:
(1) Implement abstract LSH, LSHModel classes as Estimator-Model
(2) Implement approxNearestNeighbors and approxSimilarityJoin in the abstract LSHModel
(3) Implement Random Projection as LSH subclass for Euclidean distance, Min Hash for Jaccard Distance
(4) Implement unit test utility methods including checkLshProperty, checkNearestNeighbor and checkSimilarityJoin
Things that will be implemented in a follow-up PR:
- Bit Sampling for Hamming Distance, SignRandomProjection for Cosine Distance
- PySpark Integration for the scala classes and methods.
## How was this patch tested?
Unit test is implemented for all the implemented classes and algorithms. A scalability test on Uber's dataset was performed internally.
Tested the methods on [WEX dataset](https://aws.amazon.com/items/2345) from AWS, with the steps and results [here](https://docs.google.com/document/d/19BXg-67U83NVB3M0I84HVBVg3baAVaESD_mrg_-vLro/edit).
## References
Gionis, Aristides, Piotr Indyk, and Rajeev Motwani. "Similarity search in high dimensions via hashing." VLDB 7 Sep. 1999: 518-529.
Wang, Jingdong et al. "Hashing for similarity search: A survey." arXiv preprint arXiv:1408.2927 (2014).
Author: Yunni <Euler57721@gmail.com>
Author: Yun Ni <yunn@uber.com>
Closes#15148 from Yunni/SPARK-5992-yunn-lsh.
## What changes were proposed in this pull request?
Add instrumentation to GMM
## How was this patch tested?
Test in spark-shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#15636 from zhengruifeng/gmm_instr.
## What changes were proposed in this pull request?
This PR is an enhancement of PR with commit ID:57dc326bd00cf0a49da971e9c573c48ae28acaa2.
NaN is a special type of value which is commonly seen as invalid. But We find that there are certain cases where NaN are also valuable, thus need special handling. We provided user when dealing NaN values with 3 options, to either reserve an extra bucket for NaN values, or remove the NaN values, or report an error, by setting handleNaN "keep", "skip", or "error"(default) respectively.
'''Before:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
'''After:
val bucketizer: Bucketizer = new Bucketizer()
.setInputCol("feature")
.setOutputCol("result")
.setSplits(splits)
.setHandleNaN("keep")
## How was this patch tested?
Tests added in QuantileDiscretizerSuite, BucketizerSuite and DataFrameStatSuite
Signed-off-by: VinceShieh <vincent.xieintel.com>
Author: VinceShieh <vincent.xie@intel.com>
Author: Vincent Xie <vincent.xie@intel.com>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#15428 from VinceShieh/spark-17219_followup.
## What changes were proposed in this pull request?
As we discussed in #14818, I added a separate R wrapper spark.logit for logistic regression.
This single interface supports both binary and multinomial logistic regression. It also has "predict" and "summary" for binary logistic regression.
## How was this patch tested?
New unit tests are added.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#15365 from wangmiao1981/glm.
