## What changes were proposed in this pull request?
The method calculateNumberOfPartitions() uses Int, not Long (unlike the MLlib version), so it is very easily to have an overflow in calculating the number of partitions for ML persistence.
This modifies the calculations to use Long.
## How was this patch tested?
New unit test. I verified that the test fails before this patch.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#18265 from jkbradley/word2vec-save-fix.
## What changes were proposed in this pull request?
JIRA: [SPARK-19762](https://issues.apache.org/jira/browse/SPARK-19762)
The larger changes in this patch are:
* Adds a `DifferentiableLossAggregator` trait which is intended to be used as a common parent trait to all Spark ML aggregator classes. It factors out the common methods: `merge, gradient, loss, weight` from the aggregator subclasses.
* Adds a `RDDLossFunction` which is intended to be the only implementation of Breeze's `DiffFunction` necessary in Spark ML, and can be used by all other algorithms. It takes the aggregator type as a type parameter, and maps the aggregator over an RDD. It additionally takes in a optional regularization loss function for applying the differentiable part of regularization.
* Factors out the regularization from the data part of the cost function, and treats regularization as a separate independent cost function which can be evaluated and added to the data cost function.
* Changes `LinearRegression` to use this new hierarchy as a proof of concept.
* Adds the following new namespaces `o.a.s.ml.optim.loss` and `o.a.s.ml.optim.aggregator`
Also note that none of these are public-facing changes. All of these classes are internal to Spark ML and remain that way.
**NOTE: The large majority of the "lines added" and "lines deleted" are simply code moving around or unit tests.**
BTW, I also converted LinearSVC to this framework as a way to prove that this new hierarchy is flexible enough for the other algorithms, but I backed those changes out because the PR is large enough as is.
## How was this patch tested?
Test suites are added for the new components, and some test suites are also added to provide coverage where there wasn't any before.
* DifferentiablLossAggregatorSuite
* LeastSquaresAggregatorSuite
* RDDLossFunctionSuite
* DifferentiableRegularizationSuite
Below are some performance testing numbers. Run on a 6 node virtual cluster with 44 cores and ~110G RAM, the dataset size is about 37G. These are not "large-scale" tests, but we really want to just make sure the iteration times don't increase with this patch. Notably we are doing the regularization a bit differently than before, but that should cost very little. I think there's very little risk otherwise, and these numbers don't show a difference. Of course I'm happy to add more tests as we think it's necessary, but I think the patch is ready for review now.
**Note:** timings are best of 3 runs.
| | numFeatures | numPoints | maxIter | regParam | elasticNetParam | SPARK-19762 (sec) | master (sec) |
|----|---------------|-------------|-----------|------------|-------------------|---------------------|----------------|
| 0 | 5000 | 1e+06 | 30 | 0 | 0 | 129.594 | 131.153 |
| 1 | 5000 | 1e+06 | 30 | 0.1 | 0 | 135.54 | 136.327 |
| 2 | 5000 | 1e+06 | 30 | 0.01 | 0.5 | 135.148 | 129.771 |
| 3 | 50000 | 100000 | 30 | 0 | 0 | 145.764 | 144.096 |
## Follow ups
If this design is accepted, we will convert the other ML algorithms that use this aggregator pattern to this new hierarchy in follow up PRs.
Author: sethah <seth.hendrickson16@gmail.com>
Author: sethah <shendrickson@cloudera.com>
Closes#17094 from sethah/ml_aggregators.
## What changes were proposed in this pull request?
Destroy broadcasted centers after computing cost
## How was this patch tested?
existing tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#18152 from zhengruifeng/destroy_kmeans_model.
## What changes were proposed in this pull request?
Remove extraneous logging.
## How was this patch tested?
Unit tests pass.
Author: David Eis <deis@bloomberg.net>
Closes#18188 from davideis/fix-test.
## What changes were proposed in this pull request?
The current conf setting logic is a little complex and has duplication, this PR simplifies it.
## How was this patch tested?
existing tests.
Author: Wenchen Fan <wenchen@databricks.com>
Closes#18172 from cloud-fan/session.
## What changes were proposed in this pull request?
- ~~I added the method `toBlockMatrixDense` to the IndexedRowMatrix class. The current implementation of `toBlockMatrix` is insufficient for users with relatively dense IndexedRowMatrix objects, since it assumes sparsity.~~
EDIT: Ended up deciding that there should be just a single `toBlockMatrix` method, which creates a BlockMatrix whose blocks may be dense or sparse depending on the sparsity of the rows. This method will work better on any current use case of `toBlockMatrix` and doesn't go through `CoordinateMatrix` like the old method.
## How was this patch tested?
~~I used the same tests already written for `toBlockMatrix()` to test this method. I also added a new additional unit test for an edge case that was not adequately tested by current test suite.~~
I ran the original `IndexedRowMatrix` tests, plus wrote more to better handle edge cases ignored by original tests.
Author: John Compitello <johnc@broadinstitute.org>
Closes#17459 from johnc1231/johnc-fix-ir-to-block.
## What changes were proposed in this pull request?
Revert the handling of negative values in ALS with implicit feedback, so that the confidence is the absolute value of the rating and the preference is 0 for negative ratings. This was the original behavior.
## How was this patch tested?
This patch was tested with the existing unit tests and an added unit test to ensure that negative ratings are not ignored.
mengxr
Author: David Eis <deis@bloomberg.net>
Closes#18022 from davideis/bugfix/negative-rating.
## What changes were proposed in this pull request?
When handling strings, the category dropped by RFormula and R are different:
- RFormula drops the least frequent level
- R drops the first level after ascending alphabetical ordering
This PR supports different string ordering types in StringIndexer #17879 so that RFormula can drop the same level as R when handling strings using`stringOrderType = "alphabetDesc"`.
## How was this patch tested?
new tests
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17967 from actuaryzhang/RFormula.
## What changes were proposed in this pull request?
Joint coefficients with intercept for SparkR linear SVM summary.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#18035 from yanboliang/svm-r.
## What changes were proposed in this pull request?
support decision tree in R
## How was this patch tested?
added tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17981 from zhengruifeng/dt_r.
## What changes were proposed in this pull request?
When two Breeze SparseMatrices are operated, the result matrix may contain provisional 0 values extra in rowIndices and data arrays. This causes an incoherence with the colPtrs data, but Breeze get away with this incoherence by keeping a counter of the valid data.
In spark, when this matrices are converted to SparseMatrices, Sparks relies solely on rowIndices, data, and colPtrs, but these might be incorrect because of breeze internal hacks. Therefore, we need to slice both rowIndices and data, using their counter of active data
This method is at least called by BlockMatrix when performing distributed block operations, causing exceptions on valid operations.
See http://stackoverflow.com/questions/33528555/error-thrown-when-using-blockmatrix-add
## How was this patch tested?
Added a test to MatricesSuite that verifies that the conversions are valid and that code doesn't crash. Originally the same code would crash on Spark.
Bugfix for https://issues.apache.org/jira/browse/SPARK-20687
Author: Ignacio Bermudez <ignaciobermudez@gmail.com>
Author: Ignacio Bermudez Corrales <icorrales@splunk.com>
Closes#17940 from ghoto/bug-fix/SPARK-20687.
Small clean ups from #17742 and #17845.
## How was this patch tested?
Existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17919 from MLnick/SPARK-20677-als-perf-followup.
## What changes were proposed in this pull request?
Review new Scala APIs introduced in 2.2.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17934 from yanboliang/spark-20501.
## What changes were proposed in this pull request?
Before 2.2, MLlib keep to remove APIs deprecated in last feature/minor release. But from Spark 2.2, we decide to remove deprecated APIs in a major release, so we need to change corresponding annotations to tell users those will be removed in 3.0.
Meanwhile, this fixed bugs in ML documents. The original ML docs can't show deprecated annotations in ```MLWriter``` and ```MLReader``` related class, we correct it in this PR.
Before:
After:
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17946 from yanboliang/spark-20707.
## What changes were proposed in this pull request?
make param `family` in LoR and `optimizer` in LDA case insensitive
## How was this patch tested?
updated tests
yanboliang
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#17910 from zhengruifeng/lr_family_lowercase.
## What changes were proposed in this pull request?
StringIndexer maps labels to numbers according to the descending order of label frequency. Other types of ordering (e.g., alphabetical) may be needed in feature ETL. For example, the ordering will affect the result in one-hot encoding and RFormula.
This PR proposes to support other ordering methods and we add a parameter `stringOrderType` that supports the following four options:
- 'frequencyDesc': descending order by label frequency (most frequent label assigned 0)
- 'frequencyAsc': ascending order by label frequency (least frequent label assigned 0)
- 'alphabetDesc': descending alphabetical order
- 'alphabetAsc': ascending alphabetical order
The default is still descending order of label frequency, so there should be no impact to existing programs.
## How was this patch tested?
new test
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17879 from actuaryzhang/stringIndexer.
## What changes were proposed in this pull request?
This pr added `withName` in `UserDefinedFunction` for printing UDF names in EXPLAIN
## How was this patch tested?
Added tests in `UDFSuite`.
Author: Takeshi Yamamuro <yamamuro@apache.org>
Closes#17712 from maropu/SPARK-20416.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-20670
As suggested by Sean Owen in https://github.com/apache/spark/pull/17130, the transform code in FPGrowthModel can be simplified.
As I tested on some public dataset http://fimi.ua.ac.be/data/, the performance of the new transform code is even or better than the old implementation.
## How was this patch tested?
Existing unit test.
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#17912 from hhbyyh/fpgrowthTransform.
## What changes were proposed in this pull request?
Remove ML methods we deprecated in 2.1.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17867 from yanboliang/spark-20606.
## What changes were proposed in this pull request?
Added a check for for the number of defined values. Previously the argmax function assumed that at least one value was defined if the vector size was greater than zero.
## How was this patch tested?
Tests were added to the existing VectorsSuite to cover this case.
Author: Jon McLean <jon.mclean@atsid.com>
Closes#17877 from jonmclean/vectorArgmaxIndexBug.
This PR is a `DataFrame` version of #17742 for [SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968), for improving the performance of `recommendAll` methods.
## How was this patch tested?
Existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17845 from MLnick/ml-als-perf.
The recommendForAll of MLLIB ALS is very slow.
GC is a key problem of the current method.
The task use the following code to keep temp result:
val output = new Array[(Int, (Int, Double))](m*n)
m = n = 4096 (default value, no method to set)
so output is about 4k * 4k * (4 + 4 + 8) = 256M. This is a large memory and cause serious GC problem, and it is frequently OOM.
Actually, we don't need to save all the temp result. Support we recommend topK (topK is about 10, or 20) product for each user, we only need 4k * topK * (4 + 4 + 8) memory to save the temp result.
The Test Environment:
3 workers: each work 10 core, each work 30G memory, each work 1 executor.
The Data: User 480,000, and Item 17,000
BlockSize: 1024 2048 4096 8192
Old method: 245s 332s 488s OOM
This solution: 121s 118s 117s 120s
The existing UT.
Author: Peng <peng.meng@intel.com>
Author: Peng Meng <peng.meng@intel.com>
Closes#17742 from mpjlu/OptimizeAls.
Existing test cases for `recommendForAllX` methods (added in [SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)) test `k < num items` and `k = num items`. Technically we should also test that `k > num items` returns the same results as `k = num items`.
## How was this patch tested?
Updated existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#17860 from MLnick/SPARK-20596-als-rec-tests.
## What changes were proposed in this pull request?
This PR adds documentation to the ALS code.
## How was this patch tested?
Existing tests were used.
mengxr srowen
This contribution is my original work. I have the license to work on this project under the Spark project’s open source license.
Author: Daniel Li <dan@danielyli.com>
Closes#17793 from danielyli/spark-20484.
## What changes were proposed in this pull request?
Bucketizer currently requires input column to be Double, but the logic should work on any numeric data types. Many practical problems have integer/float data types, and it could get very tedious to manually cast them into Double before calling bucketizer. This PR extends bucketizer to handle all numeric types.
## How was this patch tested?
New test.
Author: Wayne Zhang <actuaryzhang@uber.com>
Closes#17840 from actuaryzhang/bucketizer.
## What changes were proposed in this pull request?
Address some minor comments for #17715:
* Put bound-constrained optimization params under expertParams.
* Update some docs.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17829 from yanboliang/spark-20047-followup.
## What changes were proposed in this pull request?
Use midpoints for split values now, and maybe later to make it weighted.
## How was this patch tested?
+ [x] add unit test.
+ [x] revise Split's unit test.
Author: Yan Facai (颜发才) <facai.yan@gmail.com>
Author: 颜发才（Yan Facai） <facai.yan@gmail.com>
Closes#17556 from facaiy/ENH/decision_tree_overflow_and_precision_in_aggregation.
## What changes were proposed in this pull request?
Fix build warnings primarily related to Breeze 0.13 operator changes, Java style problems
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#17803 from srowen/SPARK-20523.
## What changes were proposed in this pull request?
MultilayerPerceptronClassifierWrapper model should be private.
LogisticRegressionWrapper.scala rFeatures and rCoefficients should be lazy.
## How was this patch tested?
Unit tests.
Author: wangmiao1981 <wm624@hotmail.com>
Closes#17808 from wangmiao1981/lazy.
## What changes were proposed in this pull request?
Add a new section for fpm
Add Example for FPGrowth in scala and Java
updated: Rewrite transform to be more compact.
## How was this patch tested?
local doc generation.
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#17130 from hhbyyh/fpmdoc.
## What changes were proposed in this pull request?
MLlib ```LogisticRegression``` should support bound constrained optimization (only for L2 regularization). Users can add bound constraints to coefficients to make the solver produce solution in the specified range.
Under the hood, we call Breeze [```L-BFGS-B```](https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/optimize/LBFGSB.scala) as the solver for bound constrained optimization. But in the current breeze implementation, there are some bugs in L-BFGS-B, and https://github.com/scalanlp/breeze/pull/633 fixed them. We need to upgrade dependent breeze later, and currently we use the workaround L-BFGS-B in this PR temporary for reviewing.
## How was this patch tested?
Unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17715 from yanboliang/spark-20047.
## What changes were proposed in this pull request?
Pregel-based iterative algorithms with more than ~50 iterations begin to slow down and eventually fail with a StackOverflowError due to Spark's lack of support for long lineage chains.
This PR causes Pregel to checkpoint the graph periodically if the checkpoint directory is set.
This PR moves PeriodicGraphCheckpointer.scala from mllib to graphx, moves PeriodicRDDCheckpointer.scala, PeriodicCheckpointer.scala from mllib to core
## How was this patch tested?
unit tests, manual tests
(Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
(If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
Author: ding <ding@localhost.localdomain>
Author: dding3 <ding.ding@intel.com>
Author: Michael Allman <michael@videoamp.com>
Closes#15125 from dding3/cp2_pregel.
## What changes were proposed in this pull request?
Upgrade breeze version to 0.13.1, which fixed some critical bugs of L-BFGS-B.
## How was this patch tested?
Existing unit tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#17746 from yanboliang/spark-20449.
## What changes were proposed in this pull request?
This is a follow-up PR of #17478.
## How was this patch tested?
Existing tests
Author: wangmiao1981 <wm624@hotmail.com>
Closes#17754 from wangmiao1981/followup.
## What changes were proposed in this pull request?
In MultivariateOnlineSummarizer,
`add` and `merge` have check for weights and feature sizes. The checks in LR are redundant, which are removed from this PR.
## How was this patch tested?
Existing tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes#17478 from wangmiao1981/logit.
## What changes were proposed in this pull request?
When reg == 0, MLOR has multiple solutions and we need to centralize the coeffs to get identical result.
BUT current implementation centralize the `coefficientMatrix` by the global coeffs means.
In fact the `coefficientMatrix` should be centralized on each feature index itself.
Because, according to the MLOR probability distribution function, it can be proven easily that:
suppose `{ w0, w1, .. w(K-1) }` make up the `coefficientMatrix`,
then `{ w0 + c, w1 + c, ... w(K - 1) + c}` will also be the equivalent solution.
`c` is an arbitrary vector of `numFeatures` dimension.
reference
https://core.ac.uk/download/pdf/6287975.pdf
So that we need to centralize the `coefficientMatrix` on each feature dimension separately.
**We can also confirm this through R library `glmnet`, that MLOR in `glmnet` always generate coefficients result that the sum of each dimension is all `zero`, when reg == 0.**
## How was this patch tested?
Tests added.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#17706 from WeichenXu123/mlor_center.
## What changes were proposed in this pull request?
Improve PrefixSpan pre-processing efficency by preventing sequences of zero in the cleaned database.
The efficiency gain is reflected in the following graph : https://postimg.org/image/9x6ireuvn/
## How was this patch tested?
Using MLlib's PrefixSpan existing tests and tests of my own on the 8 datasets shown in the graph. All
result obtained were stricly the same as the original implementation (without this change).
dev/run-tests was also runned, no error were found.
Author : Cyril de Vogelaere <cyril.devogelaeregmail.com>
Author: Syrux <pokcyril@hotmail.com>
Closes#17575 from Syrux/SPARK-20265.
## What changes were proposed in this pull request?
This PR proposes to run Spark unidoc to test Javadoc 8 build as Javadoc 8 is easily re-breakable.
There are several problems with it:
- It introduces little extra bit of time to run the tests. In my case, it took 1.5 mins more (`Elapsed :[94.8746569157]`). How it was tested is described in "How was this patch tested?".
- > One problem that I noticed was that Unidoc appeared to be processing test sources: if we can find a way to exclude those from being processed in the first place then that might significantly speed things up.
(see joshrosen's [comment](https://issues.apache.org/jira/browse/SPARK-18692?focusedCommentId=15947627&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-15947627))
To complete this automated build, It also suggests to fix existing Javadoc breaks / ones introduced by test codes as described above.
There fixes are similar instances that previously fixed. Please refer https://github.com/apache/spark/pull/15999 and https://github.com/apache/spark/pull/16013
Note that this only fixes **errors** not **warnings**. Please see my observation https://github.com/apache/spark/pull/17389#issuecomment-288438704 for spurious errors by warnings.
## How was this patch tested?
Manually via `jekyll build` for building tests. Also, tested via running `./dev/run-tests`.
This was tested via manually adding `time.time()` as below:
```diff
profiles_and_goals = build_profiles + sbt_goals
print("[info] Building Spark unidoc (w/Hive 1.2.1) using SBT with these arguments: ",
" ".join(profiles_and_goals))
+ import time
+ st = time.time()
exec_sbt(profiles_and_goals)
+ print("Elapsed :[%s]" % str(time.time() - st))
```
produces
```
...
========================================================================
Building Unidoc API Documentation
========================================================================
...
[info] Main Java API documentation successful.
...
Elapsed :[94.8746569157]
...
Author: hyukjinkwon <gurwls223@gmail.com>
Closes#17477 from HyukjinKwon/SPARK-18692.
## What changes were proposed in this pull request?
- made `numInstances` public in GLR
- made `degreesOfFreedom` public in LR
## How was this patch tested?
reran the concerned test suites
Author: Benjamin Fradet <benjamin.fradet@gmail.com>
Closes#17431 from BenFradet/SPARK-20097.
## What changes were proposed in this pull request?
Add Locale.ROOT to internal calls to String `toLowerCase`, `toUpperCase`, to avoid inadvertent locale-sensitive variation in behavior (aka the "Turkish locale problem").
The change looks large but it is just adding `Locale.ROOT` (the locale with no country or language specified) to every call to these methods.
## How was this patch tested?
Existing tests.
Author: Sean Owen <sowen@cloudera.com>
Closes#17527 from srowen/SPARK-20156.
## What changes were proposed in this pull request?
This error message doesn't get properly formatted because of a missing `s`. Currently the error looks like:
```
Caused by: java.lang.IllegalArgumentException: requirement failed: indices should be one-based and in ascending order; found current=$current, previous=$previous; line="$line"
```
(note the literal `$current` instead of the interpolated value)
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Vijay Ramesh <vramesh@demandbase.com>
Closes#17572 from vijaykramesh/master.
## What changes were proposed in this pull request?
The Dataframes-based support for the correlation statistics is added in #17108. This patch adds the Python interface for it.
## How was this patch tested?
Python unit test.
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#17494 from viirya/correlation-python-api.
## What changes were proposed in this pull request?
The ML `RandomForestClassificationModel` and `RandomForestRegressionModel` were not using the estimator parent UID when being fit. This change fixes that so the models can be properly be identified with their parents.
## How was this patch tested?Existing tests.
Added check to verify that model uid matches that of the parent, then renamed `checkCopy` to `checkCopyAndUids` and verified that it was called by one test for each ML algorithm.
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#17296 from BryanCutler/rfmodels-use-parent-uid-SPARK-19953.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-20003
I was doing some test and found the issue. ml.fpm.FPGrowthModel `setMinConfidence` should always affect rules generation and transform.
Currently associationRules in FPGrowthModel is a lazy val and `setMinConfidence` in FPGrowthModel has no impact once associationRules got computed .
I try to cache the associationRules to avoid re-computation if `minConfidence` is not changed, but this makes FPGrowthModel somehow stateful. Let me know if there's any concern.
## How was this patch tested?
new unit test and I strength the unit test for model save/load to ensure the cache mechanism.
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#17336 from hhbyyh/fpmodelminconf.
## What changes were proposed in this pull request?
This is a small piece from https://github.com/apache/spark/pull/16722 which ultimately will add sample weights to decision trees. This is to allow more flexibility in testing outliers since linear models and trees behave differently.
Note: The primary author when this is committed should be sethah since this is taken from his code.
## How was this patch tested?
Existing tests
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#17501 from jkbradley/SPARK-20183.
## What changes were proposed in this pull request?
Adds SparkR API for FPGrowth: [SPARK-19825](https://issues.apache.org/jira/browse/SPARK-19825):
- `spark.fpGrowth` -model training.
- `freqItemsets` and `associationRules` methods with new corresponding generics.
- Scala helper: `org.apache.spark.ml.r. FPGrowthWrapper`
- unit tests.
## How was this patch tested?
Feature specific unit tests.
Author: zero323 <zero323@users.noreply.github.com>
Closes#17170 from zero323/SPARK-19825.
## What changes were proposed in this pull request?
Add docs and examples for spark.ml.feature.Imputer. Currently scala and Java examples are included. Python example will be added after https://github.com/apache/spark/pull/17316
## How was this patch tested?
local doc generation and example execution
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#17324 from hhbyyh/imputerdoc.
