## What changes were proposed in this pull request?
The current tests assumes that `impurity.calculate()` returns the variance correctly. It should be better to make the tests independent of this assumption. In other words verify that the variance computed equals the variance computed manually on a small tree.
## How was this patch tested?
The patch is a test....
Author: MechCoder <mks542@nyu.edu>
Closes#13981 from MechCoder/dt_variance.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16249
Change visibility of Object ml.clustering.LDA to public for loading, thus users can invoke LDA.load("path").
## How was this patch tested?
existing ut and manually test for load ( saved with current code)
Author: Yuhao Yang <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#13941 from hhbyyh/ldapublic.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-14608
PipelineStage.transformSchema currently has minimal documentation. It should have more to explain it can:
check schema
check parameter interactions
## How was this patch tested?
unit test
Author: Yuhao Yang <hhbyyh@gmail.com>
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#12384 from hhbyyh/transformSchemaDoc.
## What changes were proposed in this pull request?
model loading backward compatibility for ml NaiveBayes
## How was this patch tested?
existing ut and manual test for loading models saved by Spark 1.6.
Author: zlpmichelle <zlpmichelle@gmail.com>
Closes#13940 from zlpmichelle/naivebayes.
## What changes were proposed in this pull request?
What changes were proposed in this pull request?
Improving evaluateEachIteration function in mllib as it fails when trying to calculate error by tree for a model that has more than 500 trees
## How was this patch tested?
the batch tested on productions data set (2K rows x 2K features) training a gradient boosted model without validation with 1000 maxIteration settings, then trying to produce the error by tree, the new patch was able to perform the calculation within 30 seconds, while previously it was take hours then fail.
**PS**: It would be better if this PR can be cherry picked into release branches 1.6.1 and 2.0
Author: Mahmoud Rawas <mhmoudr@gmail.com>
Author: Mahmoud Rawas <Mahmoud.Rawas@quantium.com.au>
Closes#13624 from mhmoudr/SPARK-15858.master.
## What changes were proposed in this pull request?
model loading backward compatibility for ml.feature.PCA.
## How was this patch tested?
existing ut and manual test for loading models saved by Spark 1.6.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13937 from yanboliang/spark-16245.
## What changes were proposed in this pull request?
This PR implements python wrappers for #13888 to convert old/new matrix columns in a DataFrame.
## How was this patch tested?
Doctest in python.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13935 from yanboliang/spark-16242.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16187
This is to provide conversion utils between old/new vector columns in a DataFrame. So users can use it to migrate their datasets and pipelines manually.
## How was this patch tested?
java and scala ut
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#13888 from hhbyyh/matComp.
## What changes were proposed in this pull request?
Just adjust the size of an array in line 58 so it does not cause an ArrayOutOfBoundsException in line 66.
## How was this patch tested?
Manual tests. I have recompiled the entire project with the fix, it has been built successfully and I have run the code, also with good results.
line 66: val yD = blas.ddot(trueWeights.length, x, 1, trueWeights, 1) + rnd.nextGaussian() * 0.1
crashes because trueWeights has length "nfeatures + 1" while "x" has length "features", and they should have the same length.
To fix this just make trueWeights be the same length as x.
I have recompiled the project with the change and it is working now:
[spark-1.6.1]$ spark-submit --master local[*] --class org.apache.spark.mllib.util.SVMDataGenerator mllib/target/spark-mllib_2.11-1.6.1.jar local /home/user/test
And it generates the data successfully now in the specified folder.
Author: José Antonio <joseanmunoz@gmail.com>
Closes#13895 from j4munoz/patch-2.
## What changes were proposed in this pull request?
model loading backward compatibility for ml.feature,
## How was this patch tested?
existing ut and manual test for loading 1.6 models.
Author: Yuhao Yang <yuhao.yang@intel.com>
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#13844 from hhbyyh/featureComp.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16177
model loading backward compatibility for ml.regression
## How was this patch tested?
existing ut and manual test for loading 1.6 models.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#13879 from hhbyyh/regreComp.
## What changes were proposed in this pull request?
jira: https://issues.apache.org/jira/browse/SPARK-16130
model loading backward compatibility for ml.classfication.LogisticRegression
## How was this patch tested?
existing ut and manual test for loading old models.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#13841 from hhbyyh/lrcomp.
## What changes were proposed in this pull request?
Since we decided to switch spark.mllib package into maintenance mode in 2.0, it would be nice to update the package docs to reflect this change.
## How was this patch tested?
Manually checked generated APIs.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13859 from mengxr/SPARK-16154.
## What changes were proposed in this pull request?
We recently deprecated setLabelCol in ChiSqSelectorModel (#13823):
~~~scala
/** group setParam */
Since("1.6.0")
deprecated("labelCol is not used by ChiSqSelectorModel.", "2.0.0")
def setLabelCol(value: String): this.type = set(labelCol, value)
~~~
This unfortunately hit a genjavadoc bug and broken doc generation. This is the generated Java code:
~~~java
/** group setParam */
public org.apache.spark.ml.feature.ChiSqSelectorModel setOutputCol (java.lang.String value) { throw new RuntimeException(); }
*
* deprecated labelCol is not used by ChiSqSelectorModel. Since 2.0.0.
*/
public org.apache.spark.ml.feature.ChiSqSelectorModel setLabelCol (java.lang.String value) { throw new RuntimeException(); }
~~~
Switching to multiline is a workaround.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13855 from mengxr/SPARK-16153.
## What changes were proposed in this pull request?
`DefaultParamsReadable/Writable` are not user-facing. Only developers who implement `Transformer/Estimator` would use it. So this PR changes the annotation to `DeveloperApi`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13828 from mengxr/default-readable-should-be-developer-api.
[SPARK-14615](https://issues.apache.org/jira/browse/SPARK-14615) and #12627 changed `spark.ml` pipelines to use the new `ml.linalg` classes for `Vector`/`Matrix`. Some `Since` annotations for public methods/vals have not been updated accordingly to be `2.0.0`. This PR updates them.
## How was this patch tested?
Existing unit tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13840 from MLnick/SPARK-16127-ml-linalg-since.
## What changes were proposed in this pull request?
Mark ml.classification algorithms as experimental to match Scala algorithms, update PyDoc for for thresholds on `LogisticRegression` to have same level of info as Scala, and enable mathjax for PyDoc.
## How was this patch tested?
Built docs locally & PySpark SQL tests
Author: Holden Karau <holden@us.ibm.com>
Closes#12938 from holdenk/SPARK-15162-SPARK-15164-update-some-pydocs.
#### What changes were proposed in this pull request?
This PR is to use the latest `SparkSession` to replace the existing `SQLContext` in `MLlib`. `SQLContext` is removed from `MLlib`.
Also fix a test case issue in `BroadcastJoinSuite`.
BTW, `SQLContext` is not being used in the `MLlib` test suites.
#### How was this patch tested?
Existing test cases.
Author: gatorsmile <gatorsmile@gmail.com>
Author: xiaoli <lixiao1983@gmail.com>
Author: Xiao Li <xiaoli@Xiaos-MacBook-Pro.local>
Closes#13380 from gatorsmile/sqlContextML.
## What changes were proposed in this pull request?
Deprecate `labelCol`, which is not used by ChiSqSelectorModel.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13823 from mengxr/deprecate-setLabelCol-in-ChiSqSelectorModel.
## What changes were proposed in this pull request?
We forgot the getter of `dropLast` in `OneHotEncoder`
## How was this patch tested?
unit test
Author: Xiangrui Meng <meng@databricks.com>
Closes#13821 from mengxr/SPARK-16118.
## What changes were proposed in this pull request?
LibSVMFileFormat implements data source for LIBSVM format. However, users do not really need to call its APIs to use it. So we should hide it in the public API docs. The main issue is that we still need to put the documentation and example code somewhere. The proposal it to have a dummy class to hold the documentation, as a workaround to https://issues.scala-lang.org/browse/SI-8124.
## How was this patch tested?
Manually checked the generated API doc and tested loading LIBSVM data.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13819 from mengxr/SPARK-16117.
## What changes were proposed in this pull request?
The `checkpointInterval` is a non-expert param. This PR moves its setter to non-expert group.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13813 from mengxr/checkpoint-non-expert.
## What changes were proposed in this pull request?
This PR is a subset of #13023 by yanboliang to make SparkR model param names and default values consistent with MLlib. I tried to avoid other changes from #13023 to keep this PR minimal. I will send a follow-up PR to improve the documentation.
Main changes:
* `spark.glm`: epsilon -> tol, maxit -> maxIter
* `spark.kmeans`: default k -> 2, default maxIter -> 20, default initMode -> "k-means||"
* `spark.naiveBayes`: laplace -> smoothing, default 1.0
## How was this patch tested?
Existing unit tests.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13801 from mengxr/SPARK-15177.1.
This PR adds missing `Since` annotations to `ml.feature` package.
Closes#8505.
## How was this patch tested?
Existing tests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13641 from MLnick/add-since-annotations.
## What changes were proposed in this pull request?
Both VectorUDT and MatrixUDT are private APIs, because UserDefinedType itself is private in Spark. However, in order to let developers implement their own transformers and estimators, we should expose both types in a public API to simply the implementation of transformSchema, transform, etc. Otherwise, they need to get the data types using reflection.
## How was this patch tested?
Unit tests in Scala and Java.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13789 from mengxr/SPARK-16074.
## What changes were proposed in this pull request?
This PR implements python wrappers for #13662 to convert old/new vector columns in a DataFrame.
## How was this patch tested?
doctest in Python
cc: yanboliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#13731 from mengxr/SPARK-15946.
JIRA: [SPARK-16008](https://issues.apache.org/jira/browse/SPARK-16008)
## What changes were proposed in this pull request?
`LogisticAggregator` stores references to two arrays of dimension `numFeatures` which are serialized before the combine op, unnecessarily. This results in the shuffle write being ~3x (for multiclass logistic regression, this number will go up) larger than it should be (in MLlib, for instance, it is 3x smaller).
This patch modifies `LogisticAggregator.add` to accept the two arrays as method parameters which avoids the serialization.
## How was this patch tested?
I tested this locally and verified the serialization reduction.
Additionally, I ran some tests of a 4 node cluster (4x48 cores, 4x128 GB RAM). Data set size of 2M rows and 10k features showed >2x iteration speedup.
Author: sethah <seth.hendrickson16@gmail.com>
Closes#13729 from sethah/lr_improvement.
## What changes were proposed in this pull request?
SPARK-15922 reports the following scenario throwing an exception due to the mismatched vector sizes. This PR handles the exceptional case, `cols < (offset + colsPerBlock)`.
**Before**
```scala
scala> import org.apache.spark.mllib.linalg.distributed._
scala> import org.apache.spark.mllib.linalg._
scala> val rows = IndexedRow(0L, new DenseVector(Array(1,2,3))) :: IndexedRow(1L, new DenseVector(Array(1,2,3))):: IndexedRow(2L, new DenseVector(Array(1,2,3))):: Nil
scala> val rdd = sc.parallelize(rows)
scala> val matrix = new IndexedRowMatrix(rdd, 3, 3)
scala> val bmat = matrix.toBlockMatrix
scala> val imat = bmat.toIndexedRowMatrix
scala> imat.rows.collect
... // java.lang.IllegalArgumentException: requirement failed: Vectors must be the same length!
```
**After**
```scala
...
scala> imat.rows.collect
res0: Array[org.apache.spark.mllib.linalg.distributed.IndexedRow] = Array(IndexedRow(0,[1.0,2.0,3.0]), IndexedRow(1,[1.0,2.0,3.0]), IndexedRow(2,[1.0,2.0,3.0]))
```
## How was this patch tested?
Pass the Jenkins tests (including the above case)
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#13643 from dongjoon-hyun/SPARK-15922.
## What changes were proposed in this pull request?
Interface method `FileFormat.prepareRead()` was added in #12088 to handle a special case in the LibSVM data source.
However, the semantics of this interface method isn't intuitive: it returns a modified version of the data source options map. Considering that the LibSVM case can be easily handled using schema metadata inside `inferSchema`, we can remove this interface method to keep the `FileFormat` interface clean.
## How was this patch tested?
Existing tests.
Author: Cheng Lian <lian@databricks.com>
Closes#13698 from liancheng/remove-prepare-read.
## What changes were proposed in this pull request?
This patch renames various Parquet support classes from CatalystAbc to ParquetAbc. This new naming makes more sense for two reasons:
1. These are not optimizer related (i.e. Catalyst) classes.
2. We are in the Spark code base, and as a result it'd be more clear to call out these are Parquet support classes, rather than some Spark classes.
## How was this patch tested?
Renamed test cases as well.
Author: Reynold Xin <rxin@databricks.com>
Closes#13696 from rxin/parquet-rename.
The PR changes outdated scaladocs for Gini and Entropy classes. Since PR #886 Spark supports multiclass classification, but the docs tell only about binary classification.
Author: Wojciech Jurczyk <wojciech.jurczyk@codilime.com>
Closes#11252 from wjur/wjur/docs_multiclass.
## What changes were proposed in this pull request?
This PR provides conversion utils between old/new vector columns in a DataFrame. So users can use it to migrate their datasets and pipelines manually. The methods are implemented under `MLUtils` and called `convertVectorColumnsToML` and `convertVectorColumnsFromML`. Both take a DataFrame and a list of vector columns to be converted. It is a no-op on vector columns that are already converted. A warning message is logged if actual conversion happens.
This is the first sub-task under SPARK-15944 to make it easier to migrate existing pipelines to Spark 2.0.
## How was this patch tested?
Unit tests in Scala and Java.
cc: yanboliang
Author: Xiangrui Meng <meng@databricks.com>
Closes#13662 from mengxr/SPARK-15945.
## What changes were proposed in this pull request?
Now we have PySpark picklers for new and old vector/matrix, individually. However, they are all implemented under `PythonMLlibAPI`. To separate spark.mllib from spark.ml, we should implement the picklers of new vector/matrix under `spark.ml.python` instead.
## How was this patch tested?
Existing tests.
Author: Liang-Chi Hsieh <simonh@tw.ibm.com>
Closes#13219 from viirya/pyspark-pickler-ml.
## What changes were proposed in this pull request?
Currently, `AFTAggregator` is not being merged correctly. For example, if there is any single empty partition in the data, this creates an `AFTAggregator` with zero total count which causes the exception below:
```
IllegalArgumentException: u'requirement failed: The number of instances should be greater than 0.0, but got 0.'
```
Please see [AFTSurvivalRegression.scala#L573-L575](6ecedf39b4/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala (L573-L575)) as well.
Just to be clear, the python example `aft_survival_regression.py` seems using 5 rows. So, if there exist partitions more than 5, it throws the exception above since it contains empty partitions which results in an incorrectly merged `AFTAggregator`.
Executing `bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py` on a machine with CPUs more than 5 is being failed because it creates tasks with some empty partitions with defualt configurations (AFAIK, it sets the parallelism level to the number of CPU cores).
## How was this patch tested?
An unit test in `AFTSurvivalRegressionSuite.scala` and manually tested by `bin/spark-submit examples/src/main/python/ml/aft_survival_regression.py`.
Author: hyukjinkwon <gurwls223@gmail.com>
Author: Hyukjin Kwon <gurwls223@gmail.com>
Closes#13619 from HyukjinKwon/SPARK-15892.
## What changes were proposed in this pull request?
Currently, we always split the files when it's bigger than maxSplitBytes, but Hadoop LineRecordReader does not respect the splits for compressed files correctly, we should have a API for FileFormat to check whether the file could be splitted or not.
This PR is based on #13442, closes#13442
## How was this patch tested?
add regression tests.
Author: Davies Liu <davies@databricks.com>
Closes#13531 from davies/fix_split.
## What changes were proposed in this pull request?
In scala, immutable.List.length is an expensive operation so we should
avoid using Seq.length == 0 or Seq.lenth > 0, and use Seq.isEmpty and Seq.nonEmpty instead.
## How was this patch tested?
existing tests
Author: wangyang <wangyang@haizhi.com>
Closes#13601 from yangw1234/isEmpty.
## What changes were proposed in this pull request?
Adding __str__ to RFormula and model that will show the set formula param and resolved formula. This is currently present in the Scala API, found missing in PySpark during Spark 2.0 coverage review.
## How was this patch tested?
run pyspark-ml tests locally
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#13481 from BryanCutler/pyspark-ml-rformula_str-SPARK-15738.
## What changes were proposed in this pull request?
https://issues.apache.org/jira/browse/SPARK-15793
Word2vec in ML package should have maxSentenceLength method for feature parity.
## How was this patch tested?
Tested with Spark unit test.
Author: yinxusen <yinxusen@gmail.com>
Closes#13536 from yinxusen/SPARK-15793.
## What changes were proposed in this pull request?
When fitting ```LinearRegressionModel```(by "l-bfgs" solver) and ```LogisticRegressionModel``` w/o intercept on dataset with constant nonzero column, spark.ml produce same model as R glmnet but different from LIBSVM.
When fitting ```AFTSurvivalRegressionModel``` w/o intercept on dataset with constant nonzero column, spark.ml produce different model compared with R survival::survreg.
We should output a warning message and clarify in document for this condition.
## How was this patch tested?
Document change, no unit test.
cc mengxr
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12731 from yanboliang/spark-13590.
## What changes were proposed in this pull request?
Made DefaultParamsReadable, DefaultParamsWritable public. Also added relevant doc and annotations. Added UnaryTransformerExample to demonstrate use of UnaryTransformer and DefaultParamsReadable,Writable.
## How was this patch tested?
Wrote example making use of the now-public APIs. Compiled and ran locally
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#13461 from jkbradley/defaultparamswritable.
## What changes were proposed in this pull request?
`an -> a`
Use cmds like `find . -name '*.R' | xargs -i sh -c "grep -in ' an [^aeiou]' {} && echo {}"` to generate candidates, and review them one by one.
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13515 from zhengruifeng/an_a.
`PartitionStatistics` uses `foldLeft` and list concatenation (`++`) to flatten an iterator of lists, but this is extremely inefficient compared to simply doing `flatMap`/`flatten` because it performs many unnecessary object allocations. Simply replacing this `foldLeft` by a `flatMap` results in decent performance gains when constructing PartitionStatistics instances for tables with many columns.
This patch fixes this and also makes two similar changes in MLlib and streaming to try to fix all known occurrences of this pattern.
Author: Josh Rosen <joshrosen@databricks.com>
Closes#13491 from JoshRosen/foldleft-to-flatmap.
## What changes were proposed in this pull request?
1, remove comments `:: Experimental ::` for non-experimental API
2, add comments `:: Experimental ::` for experimental API
3, add comments `:: DeveloperApi ::` for developerApi API
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13514 from zhengruifeng/del_experimental.
## What changes were proposed in this pull request?
1, del precision,recall in `ml.MulticlassClassificationEvaluator`
2, update user guide for `mlllib.weightedFMeasure`
## How was this patch tested?
local build
Author: Ruifeng Zheng <ruifengz@foxmail.com>
Closes#13390 from zhengruifeng/clarify_f1.
## What changes were proposed in this pull request?
Our encoder framework has been evolved a lot, this PR tries to clean up the code to make it more readable and emphasise the concept that encoder should be used as a container of serde expressions.
1. move validation logic to analyzer instead of encoder
2. only have a `resolveAndBind` method in encoder instead of `resolve` and `bind`, as we don't have the encoder life cycle concept anymore.
3. `Dataset` don't need to keep a resolved encoder, as there is no such concept anymore. bound encoder is still needed to do serialization outside of query framework.
4. Using `BoundReference` to represent an unresolved field in deserializer expression is kind of weird, this PR adds a `GetColumnByOrdinal` for this purpose. (serializer expression still use `BoundReference`, we can replace it with `GetColumnByOrdinal` in follow-ups)
## How was this patch tested?
existing test
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <lian@databricks.com>
Closes#13269 from cloud-fan/clean-encoder.
## What changes were proposed in this pull request?
andrewor14 noticed some OOM errors caused by "test big model load / save" in Word2VecSuite, e.g., https://amplab.cs.berkeley.edu/jenkins/view/Spark%20QA%20Test/job/spark-master-test-maven-hadoop-2.2/1168/consoleFull. It doesn't show up in the test result because it was OOMed.
This PR disables the test. I will leave the JIRA open for a proper fix
## How was this patch tested?
No new features.
Author: Xiangrui Meng <meng@databricks.com>
Closes#13478 from mengxr/SPARK-15740.
## What changes were proposed in this pull request?
ml.feature: update check schema to avoid confusion when user use MLlib.vector as input type
## How was this patch tested?
existing ut
Author: Yuhao Yang <yuhao.yang@intel.com>
Closes#13411 from hhbyyh/schemaCheck.
Clean up style for param setter methods in ALS to match standard style and the other setter in class (this is an artefact of one of my previous PRs that wasn't cleaned up).
## How was this patch tested?
Existing tests - no functionality change.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13480 from MLnick/als-param-minor-cleanup.
## What changes were proposed in this pull request?
ML 2.0 QA: Scala APIs audit for ml.feature. Mainly include:
* Remove seed for ```QuantileDiscretizer```, since we use ```approxQuantile``` to produce bins and ```seed``` is useless.
* Scala API docs update.
* Sync Scala and Python API docs for these changes.
## How was this patch tested?
Exist tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#13410 from yanboliang/spark-15587.
## What changes were proposed in this pull request?
if sparkContext.set CheckpointDir to another Dir that is not default FileSystem, it will throw exception when removing CheckpointFile in MLlib.
So we should always get the FileSystem from Path to avoid wrong FS problem.
## How was this patch tested?
N/A
Author: Lianhui Wang <lianhuiwang09@gmail.com>
Closes#13408 from lianhuiwang/SPARK-15664.
