## What changes were proposed in this pull request?
API documentation and collaborative filtering documentation page changes to clarify inconsistent description of ALS rank parameter.
- [DOCS] was previously: "rank is the number of latent factors in the model."
- [API] was previously: "rank - number of features to use"
This change describes rank in both places consistently as:
- "Number of features to use (also referred to as the number of latent factors)"
Author: Chris Snow <chris.snowuk.ibm.com>
Author: christopher snow <chsnow123@gmail.com>
Closes#17345 from snowch/SPARK-20011.
## What changes were proposed in this pull request?
Add FDR test case in ml/feature/ChiSqSelectorSuite.
Improve some comments in the code.
This is a follow-up pr for #15212.
## How was this patch tested?
ut
Author: Peng, Meng <peng.meng@intel.com>
Closes#16434 from mpjlu/fdr_fwe_update.
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on univariate statistical tests.
FDR and FWE are a popular univariate statistical test for feature selection.
In 2005, the Benjamini and Hochberg paper on FDR was identified as one of the 25 most-cited statistical papers. The FDR uses the Benjamini-Hochberg procedure in this PR. https://en.wikipedia.org/wiki/False_discovery_rate.
In statistics, FWE is the probability of making one or more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests.
https://en.wikipedia.org/wiki/Family-wise_error_rate
We add FDR and FWE methods for ChiSqSelector in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
ut will be added soon
(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: Peng <peng.meng@intel.com>
Author: Peng, Meng <peng.meng@intel.com>
Closes#15212 from mpjlu/fdr_fwe.
## 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?
For feature selection method ChiSquareSelector, it is based on the ChiSquareTestResult.statistic (ChiSqure value) to select the features. It select the features with the largest ChiSqure value. But the Degree of Freedom (df) of ChiSqure value is different in Statistics.chiSqTest(RDD), and for different df, you cannot base on ChiSqure value to select features.
So we change statistic to pValue for SelectKBest and SelectPercentile
## How was this patch tested?
change existing test
Author: Peng <peng.meng@intel.com>
Closes#15444 from mpjlu/chisqure-bug.
## What changes were proposed in this pull request?
Replaces` ValueError` with `IndexError` when index passed to `ml` / `mllib` `SparseVector.__getitem__` is out of range. This ensures correct iteration behavior.
Replaces `ValueError` with `IndexError` for `DenseMatrix` and `SparkMatrix` in `ml` / `mllib`.
## How was this patch tested?
PySpark `ml` / `mllib` unit tests. Additional unit tests to prove that the problem has been resolved.
Author: zero323 <zero323@users.noreply.github.com>
Closes#15144 from zero323/SPARK-17587.
## What changes were proposed in this pull request?
#14597 modified ```ChiSqSelector``` to support ```fpr``` type selector, however, it left some issue need to be addressed:
* We should allow users to set selector type explicitly rather than switching them by using different setting function, since the setting order will involves some unexpected issue. For example, if users both set ```numTopFeatures``` and ```percentile```, it will train ```kbest``` or ```percentile``` model based on the order of setting (the latter setting one will be trained). This make users confused, and we should allow users to set selector type explicitly. We handle similar issues at other place of ML code base such as ```GeneralizedLinearRegression``` and ```LogisticRegression```.
* Meanwhile, if there are more than one parameter except ```alpha``` can be set for ```fpr``` model, we can not handle it elegantly in the existing framework. And similar issues for ```kbest``` and ```percentile``` model. Setting selector type explicitly can solve this issue also.
* If setting selector type explicitly by users is allowed, we should handle param interaction such as if users set ```selectorType = percentile``` and ```alpha = 0.1```, we should notify users the parameter ```alpha``` will take no effect. We should handle complex parameter interaction checks at ```transformSchema```. (FYI #11620)
* We should use lower case of the selector type names to follow MLlib convention.
* Add ML Python API.
## How was this patch tested?
Unit test.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15214 from yanboliang/spark-17017.
## What changes were proposed in this pull request?
Univariate feature selection works by selecting the best features based on univariate statistical tests. False Positive Rate (FPR) is a popular univariate statistical test for feature selection. We add a chiSquare Selector based on False Positive Rate (FPR) test in this PR, like it is implemented in scikit-learn.
http://scikit-learn.org/stable/modules/feature_selection.html#univariate-feature-selection
## How was this patch tested?
Add Scala ut
Author: Peng, Meng <peng.meng@intel.com>
Closes#14597 from mpjlu/fprChiSquare.
## What changes were proposed in this pull request?
This pull request changes the behavior of `Word2VecModel.findSynonyms` so that it will not spuriously reject the best match when invoked with a vector that does not correspond to a word in the model's vocabulary. Instead of blindly discarding the best match, the changed implementation discards a match that corresponds to the query word (in cases where `findSynonyms` is invoked with a word) or that has an identical angle to the query vector.
## How was this patch tested?
I added a test to `Word2VecSuite` to ensure that the word with the most similar vector from a supplied vector would not be spuriously rejected.
Author: William Benton <willb@redhat.com>
Closes#15105 from willb/fix/findSynonyms.
## What changes were proposed in this pull request?
#14956 reduced default k-means|| init steps to 2 from 5 only for spark.mllib package, we should also do same change for spark.ml and PySpark.
## How was this patch tested?
Existing tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#15050 from yanboliang/spark-17389.
## What changes were proposed in this pull request?
Related to https://github.com/apache/spark/pull/14524 -- just the 'fix' rather than a behavior change.
- PythonMLlibAPI methods that take a seed now always take a `java.lang.Long` consistently, allowing the Python API to specify "no seed"
- .mllib's Word2VecModel seemed to be an odd man out in .mllib in that it picked its own random seed. Instead it defaults to None, meaning, letting the Scala implementation pick a seed
- BisectingKMeansModel arguably should not hard-code a seed for consistency with .mllib, I think. However I left it.
## How was this patch tested?
Existing tests
Author: Sean Owen <sowen@cloudera.com>
Closes#14826 from srowen/SPARK-16832.2.
## What changes were proposed in this pull request?
Allow centering / mean scaling of sparse vectors in StandardScaler, if requested. This is for compatibility with `VectorAssembler` in common usages.
## How was this patch tested?
Jenkins tests, including new caes to reflect the new behavior.
Author: Sean Owen <sowen@cloudera.com>
Closes#14663 from srowen/SPARK-17001.
JIRA issue link:
https://issues.apache.org/jira/browse/SPARK-16961
Changed one line of Utils.randomizeInPlace to allow elements to stay in place.
Created a unit test that runs a Pearson's chi squared test to determine whether the output diverges significantly from a uniform distribution.
Author: Nick Lavers <nick.lavers@videoamp.com>
Closes#14551 from nicklavers/SPARK-16961-randomizeInPlace.
## What changes were proposed in this pull request?
Made DataFrame-based API primary
* Spark doc menu bar and other places now link to ml-guide.html, not mllib-guide.html
* mllib-guide.html keeps RDD-specific list of features, with a link at the top redirecting people to ml-guide.html
* ml-guide.html includes a "maintenance mode" announcement about the RDD-based API
* **Reviewers: please check this carefully**
* (minor) Titles for DF API no longer include "- spark.ml" suffix. Titles for RDD API have "- RDD-based API" suffix
* Moved migration guide to ml-guide from mllib-guide
* Also moved past guides from mllib-migration-guides to ml-migration-guides, with a redirect link on mllib-migration-guides
* **Reviewers**: I did not change any of the content of the migration guides.
Reorganized DataFrame-based guide:
* ml-guide.html mimics the old mllib-guide.html page in terms of content: overview, migration guide, etc.
* Moved Pipeline description into ml-pipeline.html and moved tuning into ml-tuning.html
* **Reviewers**: I did not change the content of these guides, except some intro text.
* Sidebar remains the same, but with pipeline and tuning sections added
Other:
* ml-classification-regression.html: Moved text about linear methods to new section in page
## How was this patch tested?
Generated docs locally
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14213 from jkbradley/ml-guide-2.0.
## What changes were proposed in this pull request?
General decisions to follow, except where noted:
* spark.mllib, pyspark.mllib: Remove all Experimental annotations. Leave DeveloperApi annotations alone.
* spark.ml, pyspark.ml
** Annotate Estimator-Model pairs of classes and companion objects the same way.
** For all algorithms marked Experimental with Since tag <= 1.6, remove Experimental annotation.
** For all algorithms marked Experimental with Since tag = 2.0, leave Experimental annotation.
* DeveloperApi annotations are left alone, except where noted.
* No changes to which types are sealed.
Exceptions where I am leaving items Experimental in spark.ml, pyspark.ml, mainly because the items are new:
* Model Summary classes
* MLWriter, MLReader, MLWritable, MLReadable
* Evaluator and subclasses: There is discussion of changes around evaluating multiple metrics at once for efficiency.
* RFormula: Its behavior may need to change slightly to match R in edge cases.
* AFTSurvivalRegression
* MultilayerPerceptronClassifier
DeveloperApi changes:
* ml.tree.Node, ml.tree.Split, and subclasses should no longer be DeveloperApi
## How was this patch tested?
N/A
Note to reviewers:
* spark.ml.clustering.LDA underwent significant changes (additional methods), so let me know if you want me to leave it Experimental.
* Be careful to check for cases where a class should no longer be Experimental but has an Experimental method, val, or other feature. I did not find such cases, but please verify.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14147 from jkbradley/experimental-audit.
## What changes were proposed in this pull request?
Issue: Omitting the full classpath can cause problems when calling JVM methods or classes from pyspark.
This PR: Changed all uses of jvm.X in pyspark.ml and pyspark.mllib to use full classpath for X
## How was this patch tested?
Existing unit tests. Manual testing in an environment where this was an issue.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#14023 from jkbradley/SPARK-16348.
The move to `ml.linalg` created `asML`/`fromML` utility methods in Scala/Java for converting between representations. These are missing in Python, this PR adds them.
## How was this patch tested?
New doctests.
Author: Nick Pentreath <nickp@za.ibm.com>
Closes#13997 from MLnick/SPARK-16328-python-linalg-convert.
## 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?
The check on the end parenthesis of the expression to parse was using the wrong variable. I corrected that.
## How was this patch tested?
Manual test
Author: andreapasqua <andrea@radius.com>
Closes#13750 from andreapasqua/sparse-vector-parser-assertion-fix.
## 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.
## What changes were proposed in this pull request?
`accuracy` should be decorated with `property` to keep step with other methods in `pyspark.MulticlassMetrics`, like `weightedPrecision`, `weightedRecall`, etc
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13560 from zhengruifeng/add_accuracy_property.
## What changes were proposed in this pull request?
1, add accuracy for MulticlassMetrics
2, deprecate overall precision,recall,f1 and recommend accuracy usage
## How was this patch tested?
manual tests in pyspark shell
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13511 from zhengruifeng/deprecate_py_precisonrecall.
## 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.
## What changes were proposed in this pull request?
`a` -> `an`
I use regex to generate potential error lines:
`grep -in ' a [aeiou]' mllib/src/main/scala/org/apache/spark/ml/*/*scala`
and review them line by line.
## How was this patch tested?
local build
`lint-java` checking
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#13317 from zhengruifeng/a_an.
## What changes were proposed in this pull request?
Replace SQLContext and SparkContext with SparkSession using builder pattern in python test code.
## How was this patch tested?
Existing test.
Author: WeichenXu <WeichenXu123@outlook.com>
Closes#13242 from WeichenXu123/python_doctest_update_sparksession.
## What changes were proposed in this pull request?
Use SparkSession instead of SQLContext in Python TestSuites
## How was this patch tested?
Existing tests
Author: Sandeep Singh <sandeep@techaddict.me>
Closes#13044 from techaddict/SPARK-15037-python.
## What changes were proposed in this pull request?
According to the [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829), deprecate API of LogisticRegression and LinearRegression using SGD
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#12596 from zhengruifeng/deprecate_sgd.
This PR adds the remaining group of methods to PySpark's distributed linear algebra classes as follows:
* `RowMatrix` <sup>**[1]**</sup>
1. `computeGramianMatrix`
2. `computeCovariance`
3. `computeColumnSummaryStatistics`
4. `columnSimilarities`
5. `tallSkinnyQR` <sup>**[2]**</sup>
* `IndexedRowMatrix` <sup>**[3]**</sup>
1. `computeGramianMatrix`
* `CoordinateMatrix`
1. `transpose`
* `BlockMatrix`
1. `validate`
2. `cache`
3. `persist`
4. `transpose`
**[1]**: Note: `multiply`, `computeSVD`, and `computePrincipalComponents` are already part of PR #7963 for SPARK-6227.
**[2]**: Implementing `tallSkinnyQR` uncovered a bug with our PySpark `RowMatrix` constructor. As discussed on the dev list [here](http://apache-spark-developers-list.1001551.n3.nabble.com/K-Means-And-Class-Tags-td10038.html), there appears to be an issue with type erasure with RDDs coming from Java, and by extension from PySpark. Although we are attempting to construct a `RowMatrix` from an `RDD[Vector]` in [PythonMLlibAPI](https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala#L1115), the `Vector` type is erased, resulting in an `RDD[Object]`. Thus, when calling Scala's `tallSkinnyQR` from PySpark, we get a Java `ClassCastException` in which an `Object` cannot be cast to a Spark `Vector`. As noted in the aforementioned dev list thread, this issue was also encountered with `DecisionTrees`, and the fix involved an explicit `retag` of the RDD with a `Vector` type. Thus, this PR currently contains that fix applied to the `createRowMatrix` helper function in `PythonMLlibAPI`. `IndexedRowMatrix` and `CoordinateMatrix` do not appear to have this issue likely due to their related helper functions in `PythonMLlibAPI` creating the RDDs explicitly from DataFrames with pattern matching, thus preserving the types. However, this fix may be out of scope for this single PR, and it may be better suited in a separate JIRA/PR. Therefore, I have marked this PR as WIP and am open to discussion.
**[3]**: Note: `multiply` and `computeSVD` are already part of PR #7963 for SPARK-6227.
Author: Mike Dusenberry <mwdusenb@us.ibm.com>
Closes#9441 from dusenberrymw/SPARK-9656_Add_Missing_Methods_to_PySpark_Distributed_Linear_Algebra.
## What changes were proposed in this pull request?
We deprecated ```runs``` of mllib.KMeans in Spark 1.6 (SPARK-11358). In 2.0, we will make it no effect (with warning messages). We did not remove ```setRuns/getRuns``` for better binary compatibility.
This PR change `runs` which are appeared at the public API. Usage inside of ```KMeans.runAlgorithm()``` will be resolved at #10806.
## How was this patch tested?
Existing unit tests.
cc jkbradley
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#12608 from yanboliang/spark-11559.
## What changes were proposed in this pull request?
The PySpark deserialization has a bug that shows while deserializing all zero sparse vectors. This fix filters out empty string tokens before casting, hence properly stringified SparseVectors successfully get parsed.
## How was this patch tested?
Standard unit-tests similar to other methods.
Author: Arash Parsa <arash@ip-192-168-50-106.ec2.internal>
Author: Arash Parsa <arashpa@gmail.com>
Author: Vishnu Prasad <vishnu667@gmail.com>
Author: Vishnu Prasad S <vishnu667@gmail.com>
Closes#12516 from arashpa/SPARK-14739.
## What changes were proposed in this pull request?
Added windowSize getter/setter to ML/MLlib
## How was this patch tested?
Added test cases in tests.py under both ML and MLlib
Author: Jason Lee <cjlee@us.ibm.com>
Closes#12428 from jasoncl/SPARK-14564.
## What changes were proposed in this pull request?
This fix tries to add binary toggle Param to PySpark HashingTF in ML & MLlib. If this toggle is set, then all non-zero counts will be set to 1.
Note: This fix (SPARK-14238) is extended from SPARK-13963 where Scala implementation was done.
## How was this patch tested?
This fix adds two tests to cover the code changes. One for HashingTF in PySpark's ML and one for HashingTF in PySpark's MLLib.
Author: Yong Tang <yong.tang.github@outlook.com>
Closes#12079 from yongtang/SPARK-14238.
JIRA: https://issues.apache.org/jira/browse/SPARK-13672
## What changes were proposed in this pull request?
add two python examples of BisectingKMeans for ml and mllib
## How was this patch tested?
manual tests
Author: Zheng RuiFeng <ruifengz@foxmail.com>
Closes#11515 from zhengruifeng/mllib_bkm_pe.
## What changes were proposed in this pull request?
This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`.
Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`).
There are several noticeable API changes related to those returning arrays:
1. `collect`/`take`
- Old APIs in class `DataFrame`:
```scala
def collect(): Array[Row]
def take(n: Int): Array[Row]
```
- New APIs in class `Dataset[T]`:
```scala
def collect(): Array[T]
def take(n: Int): Array[T]
def collectRows(): Array[Row]
def takeRows(n: Int): Array[Row]
```
Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side.
Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here).
1. `randomSplit`
- Old APIs in class `DataFrame`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame]
def randomSplit(weights: Array[Double]): Array[DataFrame]
```
- New APIs in class `Dataset[T]`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
def randomSplit(weights: Array[Double]): Array[Dataset[T]]
```
Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one.
1. `groupBy`
Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`.
Other noticeable changes:
1. Dataset always do eager analysis now
We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`.
## How was this patch tested?
Existing tests do the work.
## TODO
- [ ] Fix all tests
- [ ] Re-enable MiMA check
- [ ] Update ScalaDoc (`since`, `group`, and example code)
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes#11443 from liancheng/ds-to-df.
## What changes were proposed in this pull request?
This PR fixes typos in comments and testcase name of code.
## How was this patch tested?
manual.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes#11481 from dongjoon-hyun/minor_fix_typos_in_code.
## What changes were proposed in this pull request?
Remove `map`, `flatMap`, `mapPartitions` from python DataFrame, to prepare for Dataset API in the future.
## How was this patch tested?
existing tests
Author: Wenchen Fan <wenchen@databricks.com>
Closes#11445 from cloud-fan/python-clean.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the regression module. Also, updated 2 params in classification to read as `Supported values:` to be consistent.
closes#10600
Author: vijaykiran <mail@vijaykiran.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11404 from BryanCutler/param-desc-consistent-regression-SPARK-12633.
## What changes were proposed in this pull request?
* The default value of ```regParam``` of PySpark MLlib ```LogisticRegressionWithLBFGS``` should be consistent with Scala which is ```0.0```. (This is also consistent with ML ```LogisticRegression```.)
* BTW, if we use a known updater(L1 or L2) for binary classification, ```LogisticRegressionWithLBFGS``` will call the ML implementation. We should update the API doc to clarifying ```numCorrections``` will have no effect if we fall into that route.
* Make a pass for all parameters of ```LogisticRegressionWithLBFGS```, others are set properly.
cc mengxr dbtsai
## How was this patch tested?
No new tests, it should pass all current tests.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11424 from yanboliang/spark-13545.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the tree module.
closes#10601
Author: Bryan Cutler <cutlerb@gmail.com>
Author: vijaykiran <mail@vijaykiran.com>
Closes#11353 from BryanCutler/param-desc-consistent-tree-SPARK-12634.
## What changes were proposed in this pull request?
In order to provide better and consistent result, let's change the default value of MLlib ```LogisticRegressionWithLBFGS convergenceTol``` from ```1E-4``` to ```1E-6``` which will be equal to ML ```LogisticRegression```.
cc dbtsai
## How was the this patch tested?
unit tests
Author: Yanbo Liang <ybliang8@gmail.com>
Closes#11299 from yanboliang/spark-13429.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules.
Closes#10602Closes#10897
Author: Bryan Cutler <cutlerb@gmail.com>
Author: somideshmukh <somilde@us.ibm.com>
Closes#11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
There's a small typo in the SparseVector.parse docstring (which says that it returns a DenseVector rather than a SparseVector), which seems to be incorrect.
Author: Miles Yucht <miles@databricks.com>
Closes#11213 from mgyucht/fix-sparsevector-docs.
JIRA: https://issues.apache.org/jira/browse/SPARK-12363
This issue is pointed by yanboliang. When `setRuns` is removed from PowerIterationClustering, one of the tests will be failed. I found that some `dstAttr`s of the normalized graph are not correct values but 0.0. By setting `TripletFields.All` in `mapTriplets` it can work.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#10539 from viirya/fix-poweriter.
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the classification module.
Author: vijaykiran <mail@vijaykiran.com>
Author: Bryan Cutler <cutlerb@gmail.com>
Closes#11183 from BryanCutler/pyspark-consistent-param-classification-SPARK-12630.
