This method survived the code review and it has been there since v1.1.0. It exposes jblas types. Let's remove it from the public API. I think no one calls it directly.
Author: Xiangrui Meng <meng@databricks.com>
Closes#4318 from mengxr/SPARK-5540 and squashes the following commits:
586ade6 [Xiangrui Meng] hide ALS.solveLeastSquares
#1379 is automatically closed by asfgit, and github can not reopen it once it's closed, so this will be the new PR.
Binary Logistic Regression can be extended to Multinomial Logistic Regression by running K-1 independent Binary Logistic Regression models. The following formula is implemented.
http://www.slideshare.net/dbtsai/2014-0620-mlor-36132297/25
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3833 from dbtsai/mlor and squashes the following commits:
4e2f354 [DB Tsai] triger jenkins
697b7c9 [DB Tsai] address some feedback
4ce4d33 [DB Tsai] refactoring
ff843b3 [DB Tsai] rebase
f114135 [DB Tsai] refactoring
4348426 [DB Tsai] Addressed feedback from Sean Owen
a252197 [DB Tsai] first commit
This PR ports the NNLS solver to the new ALS implementation.
CC: coderxiang
Author: Xiangrui Meng <meng@databricks.com>
Closes#4302 from mengxr/SPARK-5513 and squashes the following commits:
4cbdab0 [Xiangrui Meng] fix serialization
88de634 [Xiangrui Meng] add NNLS to ml's ALS
The following is implemented:
1) generic traits for feature selection and filtering
2) trait for feature selection of LabeledPoint with discrete data
3) traits for calculation of contingency table and chi squared
4) class for chi-squared feature selection
5) tests for the above
Needs some optimization in matrix operations.
This request is a try to implement feature selection for MLLIB, the previous work by the issue author izendejas was not finished (https://issues.apache.org/jira/browse/SPARK-1473). This request is also related to data discretization issues: https://issues.apache.org/jira/browse/SPARK-1303 and https://issues.apache.org/jira/browse/SPARK-1216 that weren't merged.
Author: Alexander Ulanov <nashb@yandex.ru>
Closes#1484 from avulanov/featureselection and squashes the following commits:
755d358 [Alexander Ulanov] Addressing reviewers comments @mengxr
a6ad82a [Alexander Ulanov] Addressing reviewers comments @mengxr
714b878 [Alexander Ulanov] Addressing reviewers comments @mengxr
010acff [Alexander Ulanov] Rebase
427ca4e [Alexander Ulanov] Addressing reviewers comments: implement VectorTransformer interface, use Statistics.chiSqTest
f9b070a [Alexander Ulanov] Adding Apache header in tests...
80363ca [Alexander Ulanov] Tests, comments, apache headers and scala style
150a3e0 [Alexander Ulanov] Scala style fix
f356365 [Alexander Ulanov] Chi Squared by contingency table. Refactoring
2bacdc7 [Alexander Ulanov] Combinations and chi-squared values test
66e0333 [Alexander Ulanov] Feature selector, fix of lazyness
aab9b73 [Alexander Ulanov] Feature selection redesign with vigdorchik
e24eee4 [Alexander Ulanov] Traits for FeatureSelection, CombinationsCalculator and FeatureFilter
ca49e80 [Alexander Ulanov] Feature selection filter
2ade254 [Alexander Ulanov] Code style
0bd8434 [Alexander Ulanov] Chi Squared feature selection: initial version
Apriori is the classic algorithm for frequent item set mining in a transactional data set. It will be useful if Apriori algorithm is added to MLLib in Spark. This PR add an implementation for it.
There is a point I am not sure wether it is most efficient. In order to filter out the eligible frequent item set, currently I am using a cartesian operation on two RDDs to calculate the degree of support of each item set, not sure wether it is better to use broadcast variable to achieve the same.
I will add an example to use this algorithm if requires
Author: Jacky Li <jacky.likun@huawei.com>
Author: Jacky Li <jackylk@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#2847 from jackylk/apriori and squashes the following commits:
bee3093 [Jacky Li] Merge pull request #1 from mengxr/SPARK-4001
7e69725 [Xiangrui Meng] simplify FPTree and update FPGrowth
ec21f7d [Jacky Li] fix scalastyle
93f3280 [Jacky Li] create FPTree class
d110ab2 [Jacky Li] change test case to use MLlibTestSparkContext
a6c5081 [Jacky Li] Add Parallel FPGrowth algorithm
eb3e4ca [Jacky Li] add FPGrowth
03df2b6 [Jacky Li] refactory according to comments
7b77ad7 [Jacky Li] fix scalastyle check
f68a0bd [Jacky Li] add 2 apriori implemenation and fp-growth implementation
889b33f [Jacky Li] modify per scalastyle check
da2cba7 [Jacky Li] adding apriori algorithm for frequent item set mining in Spark
JIRA link: https://issues.apache.org/jira/browse/SPARK-5406
The code in breeze svd imposes the upper bound for LocalLAPACK in RowMatrix.computeSVD
code from breeze svd (https://github.com/scalanlp/breeze/blob/master/math/src/main/scala/breeze/linalg/functions/svd.scala)
val workSize = ( 3
* scala.math.min(m, n)
* scala.math.min(m, n)
+ scala.math.max(scala.math.max(m, n), 4 * scala.math.min(m, n)
* scala.math.min(m, n) + 4 * scala.math.min(m, n))
)
val work = new Array[Double](workSize)
As a result, 7 * n * n + 4 * n < Int.MaxValue at least (depends on JVM)
In some worse cases, like n = 25000, work size will become positive again (80032704) and bring wired behavior.
The PR is only the beginning, to support Genbase ( an important biological benchmark that would help promote Spark to genetic applications, http://www.paradigm4.com/wp-content/uploads/2014/06/Genomics-Benchmark-Technical-Report.pdf),
which needs to compute svd for matrix up to 60K * 70K. I found many potential issues and would like to know if there's any plan undergoing that would expand the range of matrix computation based on Spark.
Thanks.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#4200 from hhbyyh/rowMatrix and squashes the following commits:
f7864d0 [Yuhao Yang] update auto logic for rowMatrix svd
23860e4 [Yuhao Yang] fix comment style
e48a6e4 [Yuhao Yang] make latent svd computation constraint clear
This PR makes the ALS implementation take generic ID types, e.g., Long and String, and expose it as a developer API.
TODO:
- [x] make sure that specialization works (validated in profiler)
srowen You may like this change:) I hit a Scala compiler bug with specialization. It compiles now but users and items must have the same type. I'm going to check whether specialization really works.
Author: Xiangrui Meng <meng@databricks.com>
Closes#4281 from mengxr/generic-als and squashes the following commits:
96072c3 [Xiangrui Meng] merge master
135f741 [Xiangrui Meng] minor update
c2db5e5 [Xiangrui Meng] make test pass
86588e1 [Xiangrui Meng] use a single ID type for both users and items
74f1f73 [Xiangrui Meng] compile but runtime error at test
e36469a [Xiangrui Meng] add classtags and make it compile
7a5aeb3 [Xiangrui Meng] UserType -> User, ItemType -> Item
c8ee0bc [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into generic-als
72b5006 [Xiangrui Meng] remove generic from pipeline interface
8bbaea0 [Xiangrui Meng] make ALS take generic IDs
This seems complete, the duplication of tests for provided means/variances might be overkill, would appreciate some feedback.
Author: Octavian Geagla <ogeagla@gmail.com>
Closes#4140 from ogeagla/SPARK-5207 and squashes the following commits:
fa64dfa [Octavian Geagla] [SPARK-5207] [MLLIB] [WIP] change StandardScalerModel to take stddev instead of variance
9078fe0 [Octavian Geagla] [SPARK-5207] [MLLIB] [WIP] Incorporate code review feedback: change arg ordering, add dev api annotations, do better null checking, add another test and some doc for this.
997d2e0 [Octavian Geagla] [SPARK-5207] [MLLIB] [WIP] make withMean and withStd public, add constructor which uses defaults, un-refactor test class
64408a4 [Octavian Geagla] [SPARK-5207] [MLLIB] [WIP] change StandardScalerModel contructor to not be private to mllib, added tests for newly-exposed functionality
These are more `javadoc` 8-related changes I spotted while investigating. These should be helpful in any event, but this does not nearly resolve SPARK-3359, which may never be feasible while using `unidoc` and `javadoc` 8.
Author: Sean Owen <sowen@cloudera.com>
Closes#4193 from srowen/SPARK-3359 and squashes the following commits:
5b33f66 [Sean Owen] Additional scaladoc fixes for javadoc 8; still not going to be javadoc 8 compatible
This PR introduces an API for Isotonic regression and one algorithm implementing it, Pool adjacent violators.
The Isotonic regression problem is sufficiently described in [Floudas, Pardalos, Encyclopedia of Optimization](http://books.google.co.uk/books?id=gtoTkL7heS0C&pg=RA2-PA87&lpg=RA2-PA87&dq=pooled+adjacent+violators+code&source=bl&ots=ZzQbZXVJnn&sig=reH_hBV6yIb9BeZNTF9092vD8PY&hl=en&sa=X&ei=WmF2VLiOIZLO7Qa-t4Bo&ved=0CD8Q6AEwBA#v=onepage&q&f=false), [Wikipedia](http://en.wikipedia.org/wiki/Isotonic_regression) or [Stat Wiki](http://stat.wikia.com/wiki/Isotonic_regression).
Pool adjacent violators was introduced by M. Ayer et al. in 1955. A history and development of isotonic regression algorithms is in [Leeuw, Hornik, Mair, Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods](http://www.jstatsoft.org/v32/i05/paper) and list of available algorithms including their complexity is listed in [Stout, Fastest Isotonic Regression Algorithms](http://web.eecs.umich.edu/~qstout/IsoRegAlg_140812.pdf).
An approach to parallelize the computation of PAV was presented in [Kearsley, Tapia, Trosset, An Approach to Parallelizing Isotonic Regression](http://softlib.rice.edu/pub/CRPC-TRs/reports/CRPC-TR96640.pdf).
The implemented Pool adjacent violators algorithm is based on [Floudas, Pardalos, Encyclopedia of Optimization](http://books.google.co.uk/books?id=gtoTkL7heS0C&pg=RA2-PA87&lpg=RA2-PA87&dq=pooled+adjacent+violators+code&source=bl&ots=ZzQbZXVJnn&sig=reH_hBV6yIb9BeZNTF9092vD8PY&hl=en&sa=X&ei=WmF2VLiOIZLO7Qa-t4Bo&ved=0CD8Q6AEwBA#v=onepage&q&f=false) (Chapter Isotonic regression problems, p. 86) and [Leeuw, Hornik, Mair, Isotone Optimization in R: Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods](http://www.jstatsoft.org/v32/i05/paper), also nicely formulated in [Tibshirani, Hoefling, Tibshirani, Nearly-Isotonic Regression](http://www.stat.cmu.edu/~ryantibs/papers/neariso.pdf). Implementation itself inspired by R implementations [Klaus, Strimmer, 2008, fdrtool: Estimation of (Local) False Discovery Rates and Higher Criticism](http://cran.r-project.org/web/packages/fdrtool/index.html) and [R Development Core Team, stats, 2009](https://github.com/lgautier/R-3-0-branch-alt/blob/master/src/library/stats/R/isoreg.R). I ran tests with both these libraries and confirmed they yield the same results. More R implementations referenced in aforementioned [Leeuw, Hornik, Mair, Isotone Optimization in R: Pool-Adjacent-Violators
Algorithm (PAVA) and Active Set Methods](http://www.jstatsoft.org/v32/i05/paper). The implementation is also inspired and cross checked with other implementations: [Ted Harding, 2007](https://stat.ethz.ch/pipermail/r-help/2007-March/127981.html), [scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/_isotonic.pyx), [Andrew Tulloch, 2014, Julia](https://github.com/ajtulloch/Isotonic.jl/blob/master/src/pooled_pava.jl), [Andrew Tulloch, 2014, c++](https://gist.github.com/ajtulloch/9499872), described in [Andrew Tulloch, Speeding up isotonic regression in scikit-learn by 5,000x](http://tullo.ch/articles/speeding-up-isotonic-regression/), [Fabian Pedregosa, 2012](https://gist.github.com/fabianp/3081831), [Sreangsu Acharyya. libpav](f744bc1b0f/src/pav.h?at=default) and [Gustav Larsson](https://gist.github.com/gustavla/9499068).
Author: martinzapletal <zapletal-martin@email.cz>
Author: Xiangrui Meng <meng@databricks.com>
Author: Martin Zapletal <zapletal-martin@email.cz>
Closes#3519 from zapletal-martin/SPARK-3278 and squashes the following commits:
5a54ea4 [Martin Zapletal] Merge pull request #2 from mengxr/isotonic-fix-java
37ba24e [Xiangrui Meng] fix java tests
e3c0e44 [martinzapletal] Merge remote-tracking branch 'origin/SPARK-3278' into SPARK-3278
d8feb82 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
ded071c [Martin Zapletal] Merge pull request #1 from mengxr/SPARK-3278
4dfe136 [Xiangrui Meng] add cache back
0b35c15 [Xiangrui Meng] compress pools and update tests
35d044e [Xiangrui Meng] update paraPAVA
077606b [Xiangrui Meng] minor
05422a8 [Xiangrui Meng] add unit test for model construction
5925113 [Xiangrui Meng] Merge remote-tracking branch 'zapletal-martin/SPARK-3278' into SPARK-3278
80c6681 [Xiangrui Meng] update IRModel
3da56e5 [martinzapletal] SPARK-3278 fixed indentation error
75eac55 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
88eb4e2 [martinzapletal] SPARK-3278 changes after PR comments https://github.com/apache/spark/pull/3519. Isotonic parameter removed from algorithm, defined behaviour for multiple data points with the same feature value, added tests to verify it
e60a34f [martinzapletal] SPARK-3278 changes after PR comments https://github.com/apache/spark/pull/3519. Styling and comment fixes.
d93c8f9 [martinzapletal] SPARK-3278 changes after PR comments https://github.com/apache/spark/pull/3519. Change to IsotonicRegression api. Isotonic parameter now follows api of other mllib algorithms
1fff77d [martinzapletal] SPARK-3278 changes after PR comments https://github.com/apache/spark/pull/3519. Java api changes, test refactoring, comments and citations, isotonic regression model validations, linear interpolation for predictions
12151e6 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
7aca4cc [martinzapletal] SPARK-3278 comment spelling
9ae9d53 [martinzapletal] SPARK-3278 changes after PR feedback https://github.com/apache/spark/pull/3519. Binary search used for isotonic regression model predictions
fad4bf9 [martinzapletal] SPARK-3278 changes after PR comments https://github.com/apache/spark/pull/3519
ce0e30c [martinzapletal] SPARK-3278 readability refactoring
f90c8c7 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
0d14bd3 [martinzapletal] SPARK-3278 changed Java api to match Scala api's (Double, Double, Double)
3c2954b [martinzapletal] SPARK-3278 Isotonic regression java api
45aa7e8 [martinzapletal] SPARK-3278 Isotonic regression java api
e9b3323 [martinzapletal] Merge branch 'SPARK-3278-weightedLabeledPoint' into SPARK-3278
823d803 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
941fd1f [martinzapletal] SPARK-3278 Isotonic regression java api
a24e29f [martinzapletal] SPARK-3278 refactored weightedlabeledpoint to (double, double, double) and updated api
deb0f17 [martinzapletal] SPARK-3278 refactored weightedlabeledpoint to (double, double, double) and updated api
8cefd18 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278-weightedLabeledPoint
cab5a46 [martinzapletal] SPARK-3278 PR 3519 refactoring WeightedLabeledPoint to tuple as per comments
b8b1620 [martinzapletal] Removed WeightedLabeledPoint. Replaced by tuple of doubles
34760d5 [martinzapletal] Removed WeightedLabeledPoint. Replaced by tuple of doubles
089bf86 [martinzapletal] Removed MonotonicityConstraint, Isotonic and Antitonic constraints. Replced by simple boolean
c06f88c [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
6046550 [martinzapletal] SPARK-3278 scalastyle errors resolved
8f5daf9 [martinzapletal] SPARK-3278 added comments and cleaned up api to consistently handle weights
629a1ce [martinzapletal] SPARK-3278 added isotonic regression for weighted data. Added tests for Java api
05d9048 [martinzapletal] SPARK-3278 isotonic regression refactoring and api changes
961aa05 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-3278
3de71d0 [martinzapletal] SPARK-3278 added initial version of Isotonic regression algorithm including proposed API
Decoupling the model and the algorithm
Author: Travis Galoppo <tjg2107@columbia.edu>
Closes#4290 from tgaloppo/spark-5400 and squashes the following commits:
9c1534c [Travis Galoppo] Fixed invokation instructions in comments
d848076 [Travis Galoppo] SPARK-5400 Changed name of GaussianMixtureEM to GaussianMixture to separate model from algorithm
Add single pseudo-eigenvector PIC
Including documentations and updated pom.xml with the following codes:
mllib/src/main/scala/org/apache/spark/mllib/clustering/PIClustering.scala
mllib/src/test/scala/org/apache/spark/mllib/clustering/PIClusteringSuite.scala
Author: sboeschhuawei <stephen.boesch@huawei.com>
Author: Fan Jiang <fanjiang.sc@huawei.com>
Author: Jiang Fan <fjiang6@gmail.com>
Author: Stephen Boesch <stephen.boesch@huawei.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#4254 from fjiang6/PIC and squashes the following commits:
4550850 [sboeschhuawei] Removed pic test data
f292f31 [Stephen Boesch] Merge pull request #44 from mengxr/SPARK-4259
4b78aaf [Xiangrui Meng] refactor PIC
24fbf52 [sboeschhuawei] Updated API to be similar to KMeans plus other changes requested by Xiangrui on the PR
c12dfc8 [sboeschhuawei] Removed examples files and added pic_data.txt. Revamped testcases yet to come
92d4752 [sboeschhuawei] Move the Guassian/ Affinity matrix calcs out of PIC. Presently in the test suite
7ebd149 [sboeschhuawei] Incorporate Xiangrui's first set of PR comments except restructure PIC.run to take Graph but do not remove Gaussian
121e4d5 [sboeschhuawei] Remove unused testing data files
1c3a62e [sboeschhuawei] removed matplot.py and reordered all private methods to bottom of PIC
218a49d [sboeschhuawei] Applied Xiangrui's comments - especially removing RDD/PICLinalg classes and making noncritical methods private
43ab10b [sboeschhuawei] Change last two println's to log4j logger
88aacc8 [sboeschhuawei] Add assert to testcase on cluster sizes
24f438e [sboeschhuawei] fixed incorrect markdown in clustering doc
060e6bf [sboeschhuawei] Added link to PIC doc from the main clustering md doc
be659e3 [sboeschhuawei] Added mllib specific log4j
90e7fa4 [sboeschhuawei] Converted from custom Linalg routines to Breeze: added JavaDoc comments; added Markdown documentation
bea48ea [sboeschhuawei] Converted custom Linear Algebra datatypes/routines to use Breeze.
b29c0db [Fan Jiang] Update PIClustering.scala
ace9749 [Fan Jiang] Update PIClustering.scala
a112f38 [sboeschhuawei] Added graphx main and test jars as dependencies to mllib/pom.xml
f656c34 [sboeschhuawei] Added iris dataset
b7dbcbe [sboeschhuawei] Added axes and combined into single plot for matplotlib
a2b1e57 [sboeschhuawei] Revert inadvertent update to KMeans
9294263 [sboeschhuawei] Added visualization/plotting of input/output data
e5df2b8 [sboeschhuawei] First end to end working PIC
0700335 [sboeschhuawei] First end to end working version: but has bad performance issue
32a90dc [sboeschhuawei] Update circles test data values
0ef163f [sboeschhuawei] Added ConcentricCircles data generation and KMeans clustering
3fd5bc8 [sboeschhuawei] PIClustering is running in new branch (up to the pseudo-eigenvector convergence step)
d5aae20 [Jiang Fan] Adding Power Iteration Clustering and Suite test
a3c5fbe [Jiang Fan] Adding Power Iteration Clustering
The `validate` method will allow users to debug their `BlockMatrix`, if operations like `add` or `multiply` return unexpected results. It checks the following properties in a `BlockMatrix`:
- Are the dimensions of the `BlockMatrix` consistent with what the user entered: (`nRows`, `nCols`)
- Are the dimensions of each `MatrixBlock` consistent with what the user entered: (`rowsPerBlock`, `colsPerBlock`)
- Are there blocks with duplicate indices
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#4279 from brkyvz/SPARK-5486 and squashes the following commits:
c152a73 [Burak Yavuz] addressed code review v2
598c583 [Burak Yavuz] merged master
b55ac5c [Burak Yavuz] addressed code review v1
25f083b [Burak Yavuz] simplify implementation
0aa519a [Burak Yavuz] [SPARK-5486] Added validate method to BlockMatrix
to be backward compatible.
Author: Xiangrui Meng <meng@databricks.com>
Closes#4287 from mengxr/SPARK-5496 and squashes the following commits:
a025c53 [Xiangrui Meng] Allow both classification and Classification in Algo for trees.
When the vocabSize\*vectorSize is larger than Int.MaxValue/8, we try to throw a RuntimeException. Because under this circumstance it would definitely throw an OOM when allocating memory to serialize the arrays syn0Global&syn1Global. syn0Global&syn1Global are float arrays. Serializing them should need a byte array of more than 8 times of syn0Global's size.
Also if we catch an OOM even if vocabSize\*vectorSize is less than Int.MaxValue/8, we should give users hints to increase the minCount or decrease the vectorSize.
Author: Joseph J.C. Tang <jinntrance@gmail.com>
Closes#4247 from jinntrance/w2v-fix and squashes the following commits:
b5eb71f [Joseph J.C. Tang] throw a RuntimeException and give users hints regarding the vectorSize&minCount
This PR is implementing the Gradient Boosted Trees for Python API.
Author: Kazuki Taniguchi <kazuki.t.1018@gmail.com>
Closes#3951 from kazk1018/gbt_for_py and squashes the following commits:
620d247 [Kazuki Taniguchi] [SPARK-5094][MLlib] Add Python API for Gradient Boosted Trees
BlockMatrices can now be transposed!
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#4275 from brkyvz/SPARK-5322 and squashes the following commits:
33806ed [Burak Yavuz] added lazy comment
33e9219 [Burak Yavuz] made transpose lazy
5a274cd [Burak Yavuz] added cached tests
5dcf85c [Burak Yavuz] [SPARK-5322] Added transpose functionality to BlockMatrix
looks unnecessary 😀
Author: Yoshihiro Shimizu <shimizu@amoad.com>
Closes#4268 from y-shimizu/remove-return and squashes the following commits:
12be0e9 [Yoshihiro Shimizu] remove 'return'
Turns out Scala does generate static methods for ones defined in a companion object. Finally no need to separate api.java.dsl and api.scala.dsl.
Author: Reynold Xin <rxin@databricks.com>
Closes#4276 from rxin/dsl and squashes the following commits:
30aa611 [Reynold Xin] Add all files.
1a9d215 [Reynold Xin] [SPARK-5445][SQL] Consolidate Java and Scala DSL static methods.
There is only a single `stat.py` file for the `mllib.stat` package. We recently added `MultivariateGaussian` under `mllib.stat.distribution` in Scala/Java. It would be nice to refactor `stat.py` and make it easy to expand. Note that `ChiSqTestResult` is moved from `mllib.stat` to `mllib.stat.test`. The latter is used in Scala/Java. It is only used in the return value of `Statistics.chiSqTest`, so this should be an okay change.
davies
Author: Xiangrui Meng <meng@databricks.com>
Closes#4266 from mengxr/py-stat-refactor and squashes the following commits:
1a5e1db [Xiangrui Meng] refactor stat.py
1. Added foreach, foreachPartition, flatMap to DataFrame.
2. Added col() in dsl.
3. Support renaming columns in toDataFrame.
4. Support type inference on arrays (in addition to Seq).
5. Updated mllib to use the new DSL.
Author: Reynold Xin <rxin@databricks.com>
Closes#4260 from rxin/sql-dsl-update and squashes the following commits:
73466c1 [Reynold Xin] Fixed LogisticRegression. Also added better error message for resolve.
fab3ccc [Reynold Xin] Bug fix.
d31fcd2 [Reynold Xin] Style fix.
62608c4 [Reynold Xin] [SQL] Various DataFrame DSL update.
The conversion methods for `BlockMatrix`. Conversions go through `CoordinateMatrix` in order to cause a shuffle so that intermediate operations will be stored on disk and the expensive initial computation will be mitigated.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#4256 from brkyvz/SPARK-3977PR and squashes the following commits:
4df37fe [Burak Yavuz] moved TODO inside code block
b049c07 [Burak Yavuz] addressed code review feedback v1
66cb755 [Burak Yavuz] added default toBlockMatrix conversion
851f2a2 [Burak Yavuz] added better comments and checks
cdb9895 [Burak Yavuz] [SPARK-3977] Conversion methods for BlockMatrix to other Distributed Matrices
Also removed the literal implicit transformation since it is pretty scary for API design. Instead, created a new lit method for creating literals. This doesn't break anything from a compatibility perspective because Literal was added two days ago.
Author: Reynold Xin <rxin@databricks.com>
Closes#4241 from rxin/df-docupdate and squashes the following commits:
c0f4810 [Reynold Xin] Fix Python merge conflict.
094c7d7 [Reynold Xin] Minor style fix. Reset Python tests.
3c89f4a [Reynold Xin] Package.
dfe6962 [Reynold Xin] Updated Python aggregate.
5dd4265 [Reynold Xin] Made dsl Java callable.
14b3c27 [Reynold Xin] Fix literal expression for symbols.
68b31cb [Reynold Xin] Literal.
4cfeb78 [Reynold Xin] [SPARK-5097][SQL] Address DataFrame code review feedback.
We have seen many use cases of `treeAggregate`/`treeReduce` outside the ML domain. Maybe it is time to move them to Core. pwendell
Author: Xiangrui Meng <meng@databricks.com>
Closes#4228 from mengxr/SPARK-5430 and squashes the following commits:
20ad40d [Xiangrui Meng] exclude tree* from mima
e89a43e [Xiangrui Meng] fix compile and update java doc
3ae1a4b [Xiangrui Meng] add treeReduce/treeAggregate to Python
6f948c5 [Xiangrui Meng] add treeReduce/treeAggregate to JavaRDDLike
d600b6c [Xiangrui Meng] move treeReduce and treeAggregate to core
This PR adds Python API for ML pipeline and parameters. The design doc can be found on the JIRA page. It includes transformers and an estimator to demo the simple text classification example code.
TODO:
- [x] handle parameters in LRModel
- [x] unit tests
- [x] missing some docs
CC: davies jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Author: Davies Liu <davies@databricks.com>
Closes#4151 from mengxr/SPARK-4586 and squashes the following commits:
415268e [Xiangrui Meng] remove inherit_doc from __init__
edbd6fe [Xiangrui Meng] move Identifiable to ml.util
44c2405 [Xiangrui Meng] Merge pull request #2 from davies/ml
dd1256b [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
14ae7e2 [Davies Liu] fix docs
54ca7df [Davies Liu] fix tests
78638df [Davies Liu] Merge branch 'SPARK-4586' of github.com:mengxr/spark into ml
fc59a02 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
1dca16a [Davies Liu] refactor
090b3a3 [Davies Liu] Merge branch 'master' of github.com:apache/spark into ml
0882513 [Xiangrui Meng] update doc style
a4f4dbf [Xiangrui Meng] add unit test for LR
7521d1c [Xiangrui Meng] add unit tests to HashingTF and Tokenizer
ba0ba1e [Xiangrui Meng] add unit tests for pipeline
0586c7b [Xiangrui Meng] add more comments to the example
5153cff [Xiangrui Meng] simplify java models
036ca04 [Xiangrui Meng] gen numFeatures
46fa147 [Xiangrui Meng] update mllib/pom.xml to include python files in the assembly
1dcc17e [Xiangrui Meng] update code gen and make param appear in the doc
f66ba0c [Xiangrui Meng] make params a property
d5efd34 [Xiangrui Meng] update doc conf and move embedded param map to instance attribute
f4d0fe6 [Xiangrui Meng] use LabeledDocument and Document in example
05e3e40 [Xiangrui Meng] update example
d3e8dbe [Xiangrui Meng] more docs optimize pipeline.fit impl
56de571 [Xiangrui Meng] fix style
d0c5bb8 [Xiangrui Meng] a working copy
bce72f4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4586
17ecfb9 [Xiangrui Meng] code gen for shared params
d9ea77c [Xiangrui Meng] update doc
c18dca1 [Xiangrui Meng] make the example working
dadd84e [Xiangrui Meng] add base classes and docs
a3015cf [Xiangrui Meng] add Estimator and Transformer
46eea43 [Xiangrui Meng] a pipeline in python
33b68e0 [Xiangrui Meng] a working LR
and
[SPARK-5448][SQL] Make CacheManager a concrete class and field in SQLContext
Author: Reynold Xin <rxin@databricks.com>
Closes#4242 from rxin/sqlCleanup and squashes the following commits:
e351cb2 [Reynold Xin] Fixed toDataFrame.
6545c42 [Reynold Xin] More changes.
728c017 [Reynold Xin] [SPARK-5447][SQL] Replaced reference to SchemaRDD with DataFrame.
This pull request includes the abstractions for the distributed BlockMatrix representation.
`BlockMatrix` will allow users to store very large matrices in small blocks of local matrices. Specific partitioners, such as `RowBasedPartitioner` and `ColumnBasedPartitioner`, are implemented in order to optimize addition and multiplication operations that will be added in a following PR.
This work is based on the ml-matrix repo developed at the AMPLab at UC Berkeley, CA.
https://github.com/amplab/ml-matrix
Additional thanks to rezazadeh, shivaram, and mengxr for guidance on the design.
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Burak Yavuz <brkyvz@dn51t42l.sunet>
Author: Burak Yavuz <brkyvz@dn51t4rd.sunet>
Author: Burak Yavuz <brkyvz@dn0a221430.sunet>
Closes#3200 from brkyvz/SPARK-3974 and squashes the following commits:
a8eace2 [Burak Yavuz] Merge pull request #2 from mengxr/brkyvz-SPARK-3974
feb32a7 [Xiangrui Meng] update tests
e1d3ee8 [Xiangrui Meng] minor updates
24ec7b8 [Xiangrui Meng] update grid partitioner
5eecd48 [Burak Yavuz] fixed gridPartitioner and added tests
140f20e [Burak Yavuz] Merge branch 'master' of github.com:apache/spark into SPARK-3974
1694c9e [Burak Yavuz] almost finished addressing comments
f9d664b [Burak Yavuz] updated API and modified partitioning scheme
eebbdf7 [Burak Yavuz] preliminary changes addressing code review
1a63b20 [Burak Yavuz] [SPARK-3974] Remove setPartition method. Isn't required
1e8bb2a [Burak Yavuz] [SPARK-3974] Change return type of cache and persist
239ab4b [Burak Yavuz] [SPARK-3974] Addressed @jkbradley's comments
ba414d2 [Burak Yavuz] [SPARK-3974] fixed frobenius norm
ab6cde0 [Burak Yavuz] [SPARK-3974] Modifications cleaning code up, making size calculation more robust
9ae85aa [Burak Yavuz] [SPARK-3974] Made partitioner a variable inside BlockMatrix instead of a constructor variable
d033861 [Burak Yavuz] [SPARK-3974] Removed SubMatrixInfo and added constructor without partitioner
49b9586 [Burak Yavuz] [SPARK-3974] Updated testing utils from master
645afbe [Burak Yavuz] [SPARK-3974] Pull latest master
b05aabb [Burak Yavuz] [SPARK-3974] Updated tests to reflect changes
19c17e8 [Burak Yavuz] [SPARK-3974] Changed blockIdRow and blockIdCol
589fbb6 [Burak Yavuz] [SPARK-3974] Code review feedback addressed
aa8f086 [Burak Yavuz] [SPARK-3974] Additional comments added
f378e16 [Burak Yavuz] [SPARK-3974] Block Matrix Abstractions ready
b693209 [Burak Yavuz] Ready for Pull request
Support for transposing local matrices added. The `.transpose` function creates a new object re-using the backing array(s) but switches `numRows` and `numCols`. Operations check the flag `.isTransposed` to see whether the indexing in `values` should be modified.
This PR will pave the way for transposing `BlockMatrix`.
Author: Burak Yavuz <brkyvz@gmail.com>
Closes#4109 from brkyvz/SPARK-5321 and squashes the following commits:
87ab83c [Burak Yavuz] fixed scalastyle
caf4438 [Burak Yavuz] addressed code review v3
c524770 [Burak Yavuz] address code review comments 2
77481e8 [Burak Yavuz] fixed MiMa
f1c1742 [Burak Yavuz] small refactoring
ccccdec [Burak Yavuz] fixed failed test
dd45c88 [Burak Yavuz] addressed code review
a01bd5f [Burak Yavuz] [SPARK-5321] Fixed MiMa issues
2a63593 [Burak Yavuz] [SPARK-5321] fixed bug causing failed gemm test
c55f29a [Burak Yavuz] [SPARK-5321] Support for transposing local matrices cleaned up
c408c05 [Burak Yavuz] [SPARK-5321] Support for transposing local matrices added
The current implementation in Vectors.sqdist is not efficient because of allocating temp arrays. There is also a bug in the code `v1.indices.length / v1.size < 0.5`. This pr fixes the bug and refactors sqdist without allocating new arrays.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4217 from viirya/fix_sqdist and squashes the following commits:
e8b0b3d [Liang-Chi Hsieh] For review comments.
314c424 [Liang-Chi Hsieh] Fix sqdist bug.
I've added support for sampling_rate not equal to 1.0 . I have two major questions.
1. A Scala style test is failing, since the number of parameters now exceed 10.
2. I would like suggestions to understand how to test this.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#4073 from MechCoder/spark-3726 and squashes the following commits:
8012fb2 [MechCoder] Add test in Strategy
e0e0d9c [MechCoder] TST: Add better test
d1df1b2 [MechCoder] Add test to verify subsampling behavior
a7bfc70 [MechCoder] [SPARK-3726] Allow sampling_rate not equal to 1.0
... decision tree model
Labels loaded from libsvm files are mapped to 0.0 if they are negative labels because they should be nonnegative value.
Author: lewuathe <lewuathe@me.com>
Closes#3975 from Lewuathe/map-negative-label-to-positive and squashes the following commits:
12d1d59 [lewuathe] [SPARK-5119] Fix code styles
6d9a18a [lewuathe] [SPARK-5119] Organize test codes
62a150c [lewuathe] [SPARK-5119] Modify Impurities throw exceptions with negatie labels
3336c21 [lewuathe] [SPARK-5119] java.lang.ArrayIndexOutOfBoundsException on trying to train decision tree model
JIRA issue: https://issues.apache.org/jira/browse/SPARK-5384
Currently `Vectors.sqdist` return inconsistent result for sparse/dense vectors when the vectors have different lengths, please refer to JIRA for sample
PR scope:
Unify the sqdist logic for dense/sparse vectors and fix the inconsistency, also remove the possible sparse to dense conversion in the original code.
For reviewers:
Maybe we should first discuss what's the correct behavior.
1. Vectors for sqdist must have the same length, like in breeze?
2. If they can have different lengths, what's the correct result for sqdist? (should the extra part get into calculation?)
I'll update PR with more optimization and additional ut afterwards. Thanks.
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#4183 from hhbyyh/fixDouble and squashes the following commits:
1f17328 [Yuhao Yang] limit PR scope to size constraints only
54cbf97 [Yuhao Yang] fix Vectors.sqdist inconsistence
This PR adds a new ALS implementation to `spark.ml` using the pipeline API, which should be able to scale to billions of ratings. Compared with the ALS under `spark.mllib`, the new implementation
1. uses the same algorithm,
2. uses float type for ratings,
3. uses primitive arrays to avoid GC,
4. sorts and compresses ratings on each block so that we can solve least squares subproblems one by one using only one normal equation instance.
The following figure shows performance comparison on copies of the Amazon Reviews dataset using a 16-node (m3.2xlarge) EC2 cluster (the same setup as in http://databricks.com/blog/2014/07/23/scalable-collaborative-filtering-with-spark-mllib.html):
I keep the `spark.mllib`'s ALS untouched for easy comparison. If the new implementation works well, I'm going to match the features of the ALS under `spark.mllib` and then make it a wrapper of the new implementation, in a separate PR.
TODO:
- [X] Add unit tests for implicit preferences.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3720 from mengxr/SPARK-3541 and squashes the following commits:
1b9e852 [Xiangrui Meng] fix compile
5129be9 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3541
dd0d0e8 [Xiangrui Meng] simplify test code
c627de3 [Xiangrui Meng] add tests for implicit feedback
b84f41c [Xiangrui Meng] address comments
a76da7b [Xiangrui Meng] update ALS tests
2a8deb3 [Xiangrui Meng] add some ALS tests
857e876 [Xiangrui Meng] add tests for rating block and encoded block
d3c1ac4 [Xiangrui Meng] rename some classes for better code readability add more doc and comments
213d163 [Xiangrui Meng] org imports
771baf3 [Xiangrui Meng] chol doc update
ca9ad9d [Xiangrui Meng] add unit tests for chol
b4fd17c [Xiangrui Meng] add unit tests for NormalEquation
d0f99d3 [Xiangrui Meng] add tests for LocalIndexEncoder
80b8e61 [Xiangrui Meng] fix imports
4937fd4 [Xiangrui Meng] update ALS example
56c253c [Xiangrui Meng] rename product to item
bce8692 [Xiangrui Meng] doc for parameters and project the output columns
3f2d81a [Xiangrui Meng] add doc
1efaecf [Xiangrui Meng] add example code
8ae86b5 [Xiangrui Meng] add a working copy of the new ALS implementation
If a point is selected as new centers for many runs, it would collect many redundant data. This pr refactors it.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#4159 from viirya/small_refactor_kmeans and squashes the following commits:
25487e6 [Liang-Chi Hsieh] Refactor codes to reduce redundant data.
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-5317
When setting the BoostingStrategy.defaultParams("Classification"), It's more straightforward to set it with the Enumeration Algo.Classification, just like BoostingStragety.defaultParams(Algo.Classification).
I overload the method BoostingStragety.defaultParams().
Author: Basin <jpsachilles@gmail.com>
Closes#4103 from Peishen-Jia/stragetyAlgo and squashes the following commits:
87bab1c [Basin] Docs and Code documentations updated.
3b72875 [Basin] defaultParams(algoStr: String) call defaultParams(algo: Algo).
7c1e6ee [Basin] Doc of Java updated. algo -> algoStr instead.
d5c8a2e [Basin] Merge branch 'stragetyAlgo' of github.com:Peishen-Jia/spark into stragetyAlgo
65f96ce [Basin] mllib-ensembles doc modified.
e04a5aa [Basin] boostingstrategy.defaultParam string algo to enumeration.
68cf544 [Basin] mllib-ensembles doc modified.
a4aea51 [Basin] boostingstrategy.defaultParam string algo to enumeration.
This PR ports the following feature implemented in #2634 by derrickburns:
* During k-means|| initialization, we should cache costs (squared distances) previously computed.
It also contains the following optimization:
* aggregate sumCosts directly
* ran multiple (#runs) k-means++ in parallel
I compared the performance locally on mnist-digit. Before this patch:
with this patch:
It is clear that each k-means|| iteration takes about the same amount of time with this patch.
Authors:
Derrick Burns <derrickburns@gmail.com>
Xiangrui Meng <meng@databricks.com>
Closes#4144 from mengxr/SPARK-3424-kmeans-parallel and squashes the following commits:
0a875ec [Xiangrui Meng] address comments
4341bb8 [Xiangrui Meng] do not re-compute point distances during k-means||
This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark
Author: nate.crosswhite <nate.crosswhite@stresearch.com>
Author: nxwhite-str <nxwhite-str@users.noreply.github.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3610 from nxwhite-str/master and squashes the following commits:
a2ebbd3 [nxwhite-str] Merge pull request #1 from mengxr/SPARK-4749-kmeans-seed
7668124 [Xiangrui Meng] minor updates
f8d5928 [nate.crosswhite] Addressing PR issues
277d367 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
9156a57 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
5d087b4 [nate.crosswhite] Adding KMeans train with seed and Scala unit test
616d111 [nate.crosswhite] Merge remote-tracking branch 'upstream/master'
35c1884 [nate.crosswhite] Add kmeans initial seed to pyspark API
* Transpose is missing from CoordinateMatrix (this is cheap to compute, so it should be there)
* IndexedRowMatrix should be convertable to CoordinateMatrix (conversion added)
Tests for both added.
Author: Reza Zadeh <reza@databricks.com>
Closes#4089 from rezazadeh/matutils and squashes the following commits:
ec5238b [Reza Zadeh] Array -> Iterator to avoid temp array
3ce0b5d [Reza Zadeh] Array -> Iterator
bbc907a [Reza Zadeh] Use 'i' for index, and zipWithIndex
cb10ae5 [Reza Zadeh] remove unnecessary import
a7ae048 [Reza Zadeh] Missing linear algebra utilities
JIRA Issue: https://issues.apache.org/jira/browse/SPARK-5186
Currently SparseVector is using the inherited equals from Vector, which will create a full-size array for even the sparse vector. The pull request contains a specialized equals optimization that improves on both time and space.
1. The implementation will be consistent with the original. Especially it will keep equality comparison between SparseVector and DenseVector.
Author: Yuhao Yang <hhbyyh@gmail.com>
Author: Yuhao Yang <yuhao@yuhaodevbox.sh.intel.com>
Closes#3997 from hhbyyh/master and squashes the following commits:
0d9d130 [Yuhao Yang] function name change and ut update
93f0d46 [Yuhao Yang] unify sparse vs dense vectors
985e160 [Yuhao Yang] improve locality for equals
bdf8789 [Yuhao Yang] improve equals and rewrite hashCode for Vector
a6952c3 [Yuhao Yang] fix scala style for comments
50abef3 [Yuhao Yang] fix ut for sparse vector with explicit 0
f41b135 [Yuhao Yang] iterative equals for sparse vector
5741144 [Yuhao Yang] Specialized equals for SparseVector
This PR modifies GaussianMixtureModel to expose instances of MutlivariateGaussian rather than separate mean and covariance arrays.
Author: Travis Galoppo <tjg2107@columbia.edu>
Closes#4088 from tgaloppo/spark-5019 and squashes the following commits:
3ef6c7f [Travis Galoppo] In GaussianMixtureModel: Changed name of weight, gaussian to weights, gaussians. Other sources modified accordingly.
091e8da [Travis Galoppo] SPARK-5019 - GaussianMixtureModel exposes instances of MultivariateGaussian rather than mean/covariance matrices
JIRA: https://issues.apache.org/jira/browse/SPARK-5282
fix the possible int overflow in the memory computation warning
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes#4069 from hhbyyh/addscStop and squashes the following commits:
e54e5c8 [Yuhao Yang] change to MB based number
7afac23 [Yuhao Yang] 5282: fix int overflow in the warning
Having two versions of the data type APIs (one for Java, one for Scala) requires downstream libraries to also have two versions of the APIs if the library wants to support both Java and Scala. I took a look at the Scala version of the data type APIs - it can actually work out pretty well for Java out of the box.
As part of the PR, I created a sql.types package and moved all type definitions there. I then removed the Java specific data type API along with a lot of the conversion code.
This subsumes https://github.com/apache/spark/pull/3925
Author: Reynold Xin <rxin@databricks.com>
Closes#3958 from rxin/SPARK-5123-datatype-2 and squashes the following commits:
66505cc [Reynold Xin] [SPARK-5123] Expose only one version of the data type APIs (i.e. remove the Java-specific API).
Moving MutlivariateGaussian from private[mllib] to public. The class uses Breeze vectors internally, so this involves creating a public interface using MLlib vectors and matrices.
This initial commit provides public construction, accessors for mean/covariance, density and log-density.
Other potential methods include entropy and sample generation.
Author: Travis Galoppo <tjg2107@columbia.edu>
Closes#3923 from tgaloppo/spark-5018 and squashes the following commits:
2b15587 [Travis Galoppo] Style correction
b4121b4 [Travis Galoppo] Merge remote-tracking branch 'upstream/master' into spark-5018
e30a100 [Travis Galoppo] Made mu, sigma private[mllib] members of MultivariateGaussian Moved MultivariateGaussian (and test suite) from stat.impl to stat.distribution (required updates in GaussianMixture{EM,Model}.scala) Marked MultivariateGaussian as @DeveloperApi Fixed style error
9fa3bb7 [Travis Galoppo] Style improvements
91a5fae [Travis Galoppo] Rearranged equation for part of density function
8c35381 [Travis Galoppo] Fixed accessor methods to match member variable names. Modified calculations to avoid log(pow(x,y)) calculations
0943dc4 [Travis Galoppo] SPARK-5018
4dee9e1 [Travis Galoppo] SPARK-5018
Raise exception when k is non-positive in SVD
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes#3945 from MechCoder/spark-4406 and squashes the following commits:
64e6d2d [MechCoder] TST: Add better test errors and messages
12dae73 [MechCoder] [SPARK-4406] FIX: Validate k in SVD
Issues:
* From JIRA: GaussianMixtureEM uses randomness but does not take a random seed. It should take one as a parameter.
* This also makes the test suite flaky since initialization can fail due to stochasticity.
Fix:
* Add random seed
* Use it in test suite
CC: mengxr tgaloppo
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3981 from jkbradley/gmm-seed and squashes the following commits:
f0df4fd [Joseph K. Bradley] Added seed parameter to GMM. Updated test suite to use seed to prevent flakiness
This pr uses BLAS.dsyr to replace few implementations in GaussianMixtureEM.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#3949 from viirya/blas_dsyr and squashes the following commits:
4e4d6cf [Liang-Chi Hsieh] Add unit test. Rename function name, modify doc and style.
3f57fd2 [Liang-Chi Hsieh] Add BLAS.dsyr and use it in GaussianMixtureEM.
This change does a few things to make the hadoop-provided profile more useful:
- Create new profiles for other libraries / services that might be provided by the infrastructure
- Simplify and fix the poms so that the profiles are only activated while building assemblies.
- Fix tests so that they're able to run when the profiles are activated
- Add a new env variable to be used by distributions that use these profiles to provide the runtime
classpath for Spark jobs and daemons.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#2982 from vanzin/SPARK-4048 and squashes the following commits:
82eb688 [Marcelo Vanzin] Add a comment.
eb228c0 [Marcelo Vanzin] Fix borked merge.
4e38f4e [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
9ef79a3 [Marcelo Vanzin] Alternative way to propagate test classpath to child processes.
371ebee [Marcelo Vanzin] Review feedback.
52f366d [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
83099fc [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
7377e7b [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
322f882 [Marcelo Vanzin] Fix merge fail.
f24e9e7 [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
8b00b6a [Marcelo Vanzin] Merge branch 'master' into SPARK-4048
9640503 [Marcelo Vanzin] Cleanup child process log message.
115fde5 [Marcelo Vanzin] Simplify a comment (and make it consistent with another pom).
e3ab2da [Marcelo Vanzin] Fix hive-thriftserver profile.
7820d58 [Marcelo Vanzin] Fix CliSuite with provided profiles.
1be73d4 [Marcelo Vanzin] Restore flume-provided profile.
d1399ed [Marcelo Vanzin] Restore jetty dependency.
82a54b9 [Marcelo Vanzin] Remove unused profile.
5c54a25 [Marcelo Vanzin] Fix HiveThriftServer2Suite with *-provided profiles.
1fc4d0b [Marcelo Vanzin] Update dependencies for hive-thriftserver.
f7b3bbe [Marcelo Vanzin] Add snappy to hadoop-provided list.
9e4e001 [Marcelo Vanzin] Remove duplicate hive profile.
d928d62 [Marcelo Vanzin] Redirect child stderr to parent's log.
4d67469 [Marcelo Vanzin] Propagate SPARK_DIST_CLASSPATH on Yarn.
417d90e [Marcelo Vanzin] Introduce "SPARK_DIST_CLASSPATH".
2f95f0d [Marcelo Vanzin] Propagate classpath to child processes during testing.
1adf91c [Marcelo Vanzin] Re-enable maven-install-plugin for a few projects.
284dda6 [Marcelo Vanzin] Rework the "hadoop-provided" profile, add new ones.
...ySpark MLlib
This is a follow up to PR3680 https://github.com/apache/spark/pull/3680 .
Author: RJ Nowling <rnowling@gmail.com>
Closes#3955 from rnowling/spark4891 and squashes the following commits:
1236a01 [RJ Nowling] Fix Python style issues
7a01a78 [RJ Nowling] Fix Python style issues
174beab [RJ Nowling] [SPARK-4891][PySpark][MLlib] Add gamma/log normal/exp dist sampling to PySpark MLlib
Add extractor for SparseVector and DenseVector in MLlib to save some code while performing pattern matching on Vectors. For example, previously we may use:
vec match {
case dv: DenseVector =>
val values = dv.values
...
case sv: SparseVector =>
val indices = sv.indices
val values = sv.values
val size = sv.size
...
}
with extractor it is:
vec match {
case DenseVector(values) =>
...
case SparseVector(size, indices, values) =>
...
}
Author: Shuo Xiang <shuoxiangpub@gmail.com>
Closes#3919 from coderxiang/extractor and squashes the following commits:
359e8d5 [Shuo Xiang] merge master
ca5fc3e [Shuo Xiang] merge master
0b1e190 [Shuo Xiang] use extractor for vectors in RowMatrix.scala
e961805 [Shuo Xiang] use extractor for vectors in StandardScaler.scala
c2bbdaf [Shuo Xiang] use extractor for vectors in IDFscala
8433922 [Shuo Xiang] use extractor for vectors in NaiveBayes.scala and Normalizer.scala
d83c7ca [Shuo Xiang] use extractor for vectors in Vectors.scala
5523dad [Shuo Xiang] Add extractor for SparseVector and DenseVector
When `x` is positive and large, computing `math.log(1 + math.exp(x))` will lead to arithmetic
overflow. This will happen when `x > 709.78` which is not a very large number.
It can be addressed by rewriting the formula into `x + math.log1p(math.exp(-x))` when `x > 0`.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3915 from dbtsai/mathutil and squashes the following commits:
bec6a84 [DB Tsai] remove empty line
3239541 [DB Tsai] revert part of patch into another PR
23144f3 [DB Tsai] doc
49f3658 [DB Tsai] temp
6c29ed3 [DB Tsai] formating
f8447f9 [DB Tsai] address another overflow issue in gradientMultiplier in LOR gradient code
64eefd0 [DB Tsai] first commit
This is a minor pr where I think that we can simply take minus of `margin`, instead of subtracting `margin`.
Mathematically, they are equal. But the modified equation is the common form of logistic loss function and so more readable. It also computes more accurate value as some quick tests show.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#3899 from viirya/logit_func and squashes the following commits:
91a3860 [Liang-Chi Hsieh] Modified for comment.
0aa51e4 [Liang-Chi Hsieh] Further simplified.
72a295e [Liang-Chi Hsieh] Revert LogLoss back and add more considerations in Logistic Loss.
a3f83ca [Liang-Chi Hsieh] Fix a bug.
2bc5712 [Liang-Chi Hsieh] Simplify loss function.
Related to #3643. Follow the previous suggestion to add unit test for `sqdist` in `VectorsSuite`.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#3869 from viirya/sqdist_test and squashes the following commits:
fb743da [Liang-Chi Hsieh] Modified for comment and fix bug.
90a08f3 [Liang-Chi Hsieh] Modified for comment.
39a3ca6 [Liang-Chi Hsieh] Take care of special case.
b789f42 [Liang-Chi Hsieh] More proper unit test with random sparsity pattern.
c36be68 [Liang-Chi Hsieh] Add unit test for sqdist.
MultivariateGaussian was calling both pinv() and det() on the covariance matrix, effectively performing two matrix decompositions. Both values are now computed using the singular value decompositon. Both the pseudo-inverse and the pseudo-determinant are used to guard against singular matrices.
Author: Travis Galoppo <tjg2107@columbia.edu>
Closes#3871 from tgaloppo/spark-5017 and squashes the following commits:
383b5b3 [Travis Galoppo] MultivariateGaussian - minor optimization in density calculation
a5b8bc5 [Travis Galoppo] Added additional points to tests in test suite. Fixed comment in MultivariateGaussian
629d9d0 [Travis Galoppo] Moved some test values from var to val.
dc3d0f7 [Travis Galoppo] Catch potential exception calculating pseudo-determinant. Style improvements.
d448137 [Travis Galoppo] Added test suite for MultivariateGaussian, including test for degenerate case.
1989be0 [Travis Galoppo] SPARK-5017 - Fixed to use SVD to compute determinant and inverse of covariance matrix. Previous code called both pinv() and det(), effectively performing two matrix decompositions. Additionally, the pinv() implementation in Breeze is known to fail for singular matrices.
b4415ea [Travis Galoppo] Merge branch 'spark-5017' of https://github.com/tgaloppo/spark into spark-5017
6f11b6d [Travis Galoppo] SPARK-5017 - Use SVD to compute determinant and inverse of covariance matrix. Code was calling both det() and pinv(), effectively performing two matrix decompositions. Futhermore, Breeze pinv() currently fails for singular matrices.
fd9784c [Travis Galoppo] SPARK-5017 - Use SVD to compute determinant and inverse of covariance matrix
This PR:
- Reenables `surefire`, and copies config from `scalatest` (which is itself an old fork of `surefire`, so similar)
- Tells `surefire` to test only Java tests
- Enables `surefire` and `scalatest` for all children, and in turn eliminates some duplication.
For me this causes the Scala and Java tests to be run once each, it seems, as desired. It doesn't affect the SBT build but works for Maven. I still need to verify that all of the Scala tests and Java tests are being run.
Author: Sean Owen <sowen@cloudera.com>
Closes#3651 from srowen/SPARK-4159 and squashes the following commits:
2e8a0af [Sean Owen] Remove specialized SPARK_HOME setting for REPL, YARN tests as it appears to be obsolete
12e4558 [Sean Owen] Append to unit-test.log instead of overwriting, so that both surefire and scalatest output is preserved. Also standardize/correct comments a bit.
e6f8601 [Sean Owen] Reenable Java tests by reenabling surefire with config cloned from scalatest; centralize test config in the parent
Now that I've implemented the basics here, I'm less convinced there is a need for this change, somehow. Callers can downsample before or after. Really the OOM is not in the ROC curve code, but in code that might `collect()` it for local analysis. Still, might be useful to down-sample since the ROC curve probably never needs millions of points.
This is a first pass. Since the `(score,label)` are already grouped and sorted, I think it's sufficient to just take every Nth such pair, in order to downsample by a factor of N? this is just like retaining every Nth point on the curve, which I think is the goal. All of the data is still used to build the curve of course.
What do you think about the API, and usefulness?
Author: Sean Owen <sowen@cloudera.com>
Closes#3702 from srowen/SPARK-4547 and squashes the following commits:
1d34d05 [Sean Owen] Indent and reorganize numBins scaladoc
692d825 [Sean Owen] Change handling of large numBins, make 2nd consturctor instead of optional param, style change
a03610e [Sean Owen] Add downsamplingFactor to BinaryClassificationMetrics
This PR replaces slow breezeSquaredDistance.
Author: Liang-Chi Hsieh <viirya@gmail.com>
Closes#3643 from viirya/faster_squareddistance and squashes the following commits:
f28b275 [Liang-Chi Hsieh] Move the implementation to linalg.Vectors and rename as sqdist.
0bc48ee [Liang-Chi Hsieh] Merge branch 'master' into faster_squareddistance
ba34422 [Liang-Chi Hsieh] Fix bug.
91849d0 [Liang-Chi Hsieh] Modified for comment.
44a65ad [Liang-Chi Hsieh] Modified for comments.
35db395 [Liang-Chi Hsieh] Fix bug and some modifications for comments.
f4f5ebb [Liang-Chi Hsieh] Follow BLAS.dot pattern to replace intersect, diff with while-loop.
a36e09f [Liang-Chi Hsieh] Use while-loop to replace foreach for better performance.
d3e0628 [Liang-Chi Hsieh] Make the methods private.
dd415bc [Liang-Chi Hsieh] Consider different cases of SparseVector and DenseVector.
13669db [Liang-Chi Hsieh] Replace breezeSquaredDistance.
To make the functions with the same in "object" effective, specially when using java reflection.
As the "train" function defined in "class DecisionTree" will hide the functions with the same name in "object DecisionTree".
JIRA[SPARK-4998]
Author: Liu Jiongzhou <ljzzju@163.com>
Closes#3836 from ljzzju/master and squashes the following commits:
4e13133 [Liu Jiongzhou] [MLlib]delete the "train" function
New foreachActive method of vector was introduced by SPARK-4431 as more efficient alternative to vector.toBreeze.activeIterator. There are some parts of codebase where it was not yet replaced.
dbtsai
Author: Jakub Dubovsky <dubovsky@avast.com>
Closes#3846 from james64/SPARK-4995-foreachActive and squashes the following commits:
3eb7e37 [Jakub Dubovsky] Scalastyle fix
32fe6c6 [Jakub Dubovsky] activeIterator removed - IndexedRowMatrix.toBreeze
47a4777 [Jakub Dubovsky] activeIterator removed in RowMatrix.toBreeze
90a7d98 [Jakub Dubovsky] activeIterator removed in MLUtils.saveAsLibSVMFile
In #SPARK-4907, we added factor of 2 into the LeastSquaresGradient. We updated the scala doc for lasso and ridge regression here.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3808 from dbtsai/doc and squashes the following commits:
ec3c989 [DB Tsai] first commit
Wanted to customize the private minCount variable in the Word2Vec class. Added
a method to do so.
Author: ganonp <ganonp@gmail.com>
Closes#3693 from ganonp/my-custom-spark and squashes the following commits:
ad534f2 [ganonp] made norm method public
5110a6f [ganonp] Reorganized
854958b [ganonp] Fixed Indentation for setMinCount
12ed8f9 [ganonp] Update Word2Vec.scala
76bdf5a [ganonp] Update Word2Vec.scala
ffb88bb [ganonp] Update Word2Vec.scala
5eb9100 [ganonp] Added setMinCount to Word2Vec.scala
Implementation of Expectation-Maximization for Gaussian Mixture Models.
This is my maiden contribution to Apache Spark, so I apologize now if I have done anything incorrectly; having said that, this work is my own, and I offer it to the project under the project's open source license.
Author: Travis Galoppo <tjg2107@columbia.edu>
Author: Travis Galoppo <travis@localhost.localdomain>
Author: tgaloppo <tjg2107@columbia.edu>
Author: FlytxtRnD <meethu.mathew@flytxt.com>
Closes#3022 from tgaloppo/master and squashes the following commits:
aaa8f25 [Travis Galoppo] MLUtils: changed privacy of EPSILON from [util] to [mllib]
709e4bf [Travis Galoppo] fixed usage line to include optional maxIterations parameter
acf1fba [Travis Galoppo] Fixed parameter comment in GaussianMixtureModel Made maximum iterations an optional parameter to DenseGmmEM
9b2fc2a [Travis Galoppo] Style improvements Changed ExpectationSum to a private class
b97fe00 [Travis Galoppo] Minor fixes and tweaks.
1de73f3 [Travis Galoppo] Removed redundant array from array creation
578c2d1 [Travis Galoppo] Removed unused import
227ad66 [Travis Galoppo] Moved prediction methods into model class.
308c8ad [Travis Galoppo] Numerous changes to improve code
cff73e0 [Travis Galoppo] Replaced accumulators with RDD.aggregate
20ebca1 [Travis Galoppo] Removed unusued code
42b2142 [Travis Galoppo] Added functionality to allow setting of GMM starting point. Added two cluster test to testing suite.
8b633f3 [Travis Galoppo] Style issue
9be2534 [Travis Galoppo] Style issue
d695034 [Travis Galoppo] Fixed style issues
c3b8ce0 [Travis Galoppo] Merge branch 'master' of https://github.com/tgaloppo/spark Adds predict() method
2df336b [Travis Galoppo] Fixed style issue
b99ecc4 [tgaloppo] Merge pull request #1 from FlytxtRnD/predictBranch
f407b4c [FlytxtRnD] Added predict() to return the cluster labels and membership values
97044cf [Travis Galoppo] Fixed style issues
dc9c742 [Travis Galoppo] Moved MultivariateGaussian utility class
e7d413b [Travis Galoppo] Moved multivariate Gaussian utility class to mllib/stat/impl Improved comments
9770261 [Travis Galoppo] Corrected a variety of style and naming issues.
8aaa17d [Travis Galoppo] Added additional train() method to companion object for cluster count and tolerance parameters.
676e523 [Travis Galoppo] Fixed to no longer ignore delta value provided on command line
e6ea805 [Travis Galoppo] Merged with master branch; update test suite with latest context changes. Improved cluster initialization strategy.
86fb382 [Travis Galoppo] Merge remote-tracking branch 'upstream/master'
719d8cc [Travis Galoppo] Added scala test suite with basic test
c1a8e16 [Travis Galoppo] Made GaussianMixtureModel class serializable Modified sum function for better performance
5c96c57 [Travis Galoppo] Merge remote-tracking branch 'upstream/master'
c15405c [Travis Galoppo] SPARK-4156
Addition of a very limited number of local matrix manipulation and generation methods that would be helpful in the further development for algorithms on top of BlockMatrix (SPARK-3974), such as Randomized SVD, and Multi Model Training (SPARK-1486).
The proposed methods for addition are:
For `Matrix`
- map: maps the values in the matrix with a given function. Produces a new matrix.
- update: the values in the matrix are updated with a given function. Occurs in place.
Factory methods for `DenseMatrix`:
- *zeros: Generate a matrix consisting of zeros
- *ones: Generate a matrix consisting of ones
- *eye: Generate an identity matrix
- *rand: Generate a matrix consisting of i.i.d. uniform random numbers
- *randn: Generate a matrix consisting of i.i.d. gaussian random numbers
- *diag: Generate a diagonal matrix from a supplied vector
*These methods already exist in the factory methods for `Matrices`, however for cases where we require a `DenseMatrix`, you constantly have to add `.asInstanceOf[DenseMatrix]` everywhere, which makes the code "dirtier". I propose moving these functions to factory methods for `DenseMatrix` where the putput will be a `DenseMatrix` and the factory methods for `Matrices` will call these functions directly and output a generic `Matrix`.
Factory methods for `SparseMatrix`:
- speye: Identity matrix in sparse format. Saves a ton of memory when dimensions are large, especially in Multi Model Training, where each row requires being multiplied by a scalar.
- sprand: Generate a sparse matrix with a given density consisting of i.i.d. uniform random numbers.
- sprandn: Generate a sparse matrix with a given density consisting of i.i.d. gaussian random numbers.
- diag: Generate a diagonal matrix from a supplied vector, but is memory efficient, because it just stores the diagonal. Again, very helpful in Multi Model Training.
Factory methods for `Matrices`:
- Include all the factory methods given above, but return a generic `Matrix` rather than `SparseMatrix` or `DenseMatrix`.
- horzCat: Horizontally concatenate matrices to form one larger matrix. Very useful in both Multi Model Training, and for the repartitioning of BlockMatrix.
- vertCat: Vertically concatenate matrices to form one larger matrix. Very useful for the repartitioning of BlockMatrix.
The names for these methods were selected from MATLAB
Author: Burak Yavuz <brkyvz@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3319 from brkyvz/SPARK-4409 and squashes the following commits:
b0354f6 [Burak Yavuz] [SPARK-4409] Incorporated mengxr's code
04c4829 [Burak Yavuz] Merge pull request #1 from mengxr/SPARK-4409
80cfa29 [Xiangrui Meng] minor changes
ecc937a [Xiangrui Meng] update sprand
4e95e24 [Xiangrui Meng] simplify fromCOO implementation
10a63a6 [Burak Yavuz] [SPARK-4409] Fourth pass of code review
f62d6c7 [Burak Yavuz] [SPARK-4409] Modified genRandMatrix
3971c93 [Burak Yavuz] [SPARK-4409] Third pass of code review
75239f8 [Burak Yavuz] [SPARK-4409] Second pass of code review
e4bd0c0 [Burak Yavuz] [SPARK-4409] Modified horzcat and vertcat
65c562e [Burak Yavuz] [SPARK-4409] Hopefully fixed Java Test
d8be7bc [Burak Yavuz] [SPARK-4409] Organized imports
065b531 [Burak Yavuz] [SPARK-4409] First pass after code review
a8120d2 [Burak Yavuz] [SPARK-4409] Finished updates to API according to SPARK-4614
f798c82 [Burak Yavuz] [SPARK-4409] Updated API according to SPARK-4614
c75f3cd [Burak Yavuz] [SPARK-4409] Added JavaAPI Tests, and fixed a couple of bugs
d662f9d [Burak Yavuz] [SPARK-4409] Modified according to remote repo
83dfe37 [Burak Yavuz] [SPARK-4409] Scalastyle error fixed
a14c0da [Burak Yavuz] [SPARK-4409] Initial commit to add methods
There is only one implicit function `toPairDStreamFunctions` in `StreamingContext`. This PR did similar reorganization like [SPARK-4397](https://issues.apache.org/jira/browse/SPARK-4397).
Compiled the following codes with Spark Streaming 1.1.0 and ran it with this PR. Everything is fine.
```Scala
import org.apache.spark._
import org.apache.spark.streaming._
import org.apache.spark.streaming.StreamingContext._
object StreamingApp {
def main(args: Array[String]) {
val conf = new SparkConf().setMaster("local[2]").setAppName("FileWordCount")
val ssc = new StreamingContext(conf, Seconds(10))
val lines = ssc.textFileStream("/some/path")
val words = lines.flatMap(_.split(" "))
val pairs = words.map(word => (word, 1))
val wordCounts = pairs.reduceByKey(_ + _)
wordCounts.print()
ssc.start()
ssc.awaitTermination()
}
}
```
Author: zsxwing <zsxwing@gmail.com>
Closes#3464 from zsxwing/SPARK-4608 and squashes the following commits:
aa6d44a [zsxwing] Fix a copy-paste error
f74c190 [zsxwing] Merge branch 'master' into SPARK-4608
e6f9cc9 [zsxwing] Update the docs
27833bb [zsxwing] Remove `import StreamingContext._`
c15162c [zsxwing] Reorganize StreamingContext implicit to improve API convenience
There are a number of warnings generated in a normal, successful build right now. They're mostly Java unchecked cast warnings, which can be suppressed. But there's a grab bag of other Scala language warnings and so on that can all be easily fixed. The forthcoming PR fixes about 90% of the build warnings I see now.
Author: Sean Owen <sowen@cloudera.com>
Closes#3157 from srowen/SPARK-4297 and squashes the following commits:
8c9e469 [Sean Owen] Suppress unchecked cast warnings, and several other build warning fixes
In most of the academic paper and algorithm implementations,
people use L = 1/2n ||A weights-y||^2 instead of L = 1/n ||A weights-y||^2
for least-squared loss. See Eq. (1) in http://web.stanford.edu/~hastie/Papers/glmnet.pdf
Since MLlib uses different convention, this will result different residuals and
all the stats properties will be different from GLMNET package in R.
The model coefficients will be still the same under this change.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3746 from dbtsai/lir and squashes the following commits:
19c2e85 [DB Tsai] make stepsize twice to converge to the same solution
0b2c29c [DB Tsai] first commit
...ta generators
This patch adds:
* Exponential, gamma, and log normal generators that wrap Apache Commons math3 to the private API
* Functions for generating exponential, gamma, and log normal RDDs and vector RDDs
* Tests for the above
Author: RJ Nowling <rnowling@gmail.com>
Closes#3680 from rnowling/spark4728 and squashes the following commits:
455f50a [RJ Nowling] Add tests for exponential, gamma, and log normal samplers to JavaRandomRDDsSuite
3e1134a [RJ Nowling] Fix val/var, unncessary creation of Distribution objects when setting seeds, and import line longer than line wrap limits
58f5b97 [RJ Nowling] Fix bounds in tests so they scale with variance, not stdev
84fd98d [RJ Nowling] Add more values for testing distributions.
9f96232 [RJ Nowling] [SPARK-4728] Add exponential, gamma, and log normal sampling to MLlib data generators
The original test doesn't make sense since if you step in, the lossSum is already NaN,
and the coefficients are diverging. That's because the step size is too large for SGD,
so it doesn't work.
The correct behavior is that you should get smaller coefficients than the one
without regularization. Comparing the values using 20000.0 relative error doesn't
make sense as well.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3735 from dbtsai/mlortestfix and squashes the following commits:
b1a3c42 [DB Tsai] first commit
I improved `IDFModel.transform` to allow using a single vector.
[[SPARK-4494] IDFModel.transform() add support for single vector - ASF JIRA](https://issues.apache.org/jira/browse/SPARK-4494)
Author: Yuu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes#3603 from yu-iskw/idf and squashes the following commits:
256ff3d [Yuu ISHIKAWA] Fix typo
a3bf566 [Yuu ISHIKAWA] - Fix typo - Optimize import order - Aggregate the assertion tests - Modify `IDFModel.transform` API for pyspark
d25e49b [Yuu ISHIKAWA] Add the implementation of `IDFModel.transform` for a term frequency vector
and some minor changes in ScalaDoc.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3601 from mengxr/SPARK-4575-fix and squashes the following commits:
c559768 [Xiangrui Meng] minor code update
ce94da8 [Xiangrui Meng] Java Bean -> JavaBean
0b5c182 [Xiangrui Meng] fix links in ml-guide
Documentation:
* Added ml-guide.md, linked from mllib-guide.md
* Updated mllib-guide.md with small section pointing to ml-guide.md
Examples:
* CrossValidatorExample
* SimpleParamsExample
* (I copied these + the SimpleTextClassificationPipeline example into the ml-guide.md)
Bug fixes:
* PipelineModel: did not use ParamMaps correctly
* UnaryTransformer: issues with TypeTag serialization (Thanks to mengxr for that fix!)
CC: mengxr shivaram etrain Documentation for Pipelines: I know the docs are not complete, but the goal is to have enough to let interested people get started using spark.ml and to add more docs once the package is more established/complete.
Author: Joseph K. Bradley <joseph@databricks.com>
Author: jkbradley <joseph.kurata.bradley@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#3588 from jkbradley/ml-package-docs and squashes the following commits:
d393b5c [Joseph K. Bradley] fixed bug in Pipeline (typo from last commit). updated examples for CV and Params for spark.ml
c38469c [Joseph K. Bradley] Updated ml-guide with CV examples
99f88c2 [Joseph K. Bradley] Fixed bug in PipelineModel.transform* with usage of params. Updated CrossValidatorExample to use more training examples so it is less likely to get a 0-size fold.
ea34dc6 [jkbradley] Merge pull request #4 from mengxr/ml-package-docs
3b83ec0 [Xiangrui Meng] replace TypeTag with explicit datatype
41ad9b1 [Joseph K. Bradley] Added examples for spark.ml: SimpleParamsExample + Java version, CrossValidatorExample + Java version. CrossValidatorExample not working yet. Added programming guide for spark.ml, but need to add CrossValidatorExample to it once CrossValidatorExample works.
Major changes:
* Added programming guide sections for tree ensembles
* Added examples for tree ensembles
* Updated DecisionTree programming guide with more info on parameters
* **API change**: Standardized the tree parameter for the number of classes (for classification)
Minor changes:
* Updated decision tree documentation
* Updated existing tree and tree ensemble examples
* Use train/test split, and compute test error instead of training error.
* Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix)
Note: I know this is a lot of lines, but most is covered by:
* Programming guide sections for gradient boosting and random forests. (The changes are probably best viewed by generating the docs locally.)
* New examples (which were copied from the programming guide)
* The "numClasses" renaming
I have run all examples and relevant unit tests.
CC: mengxr manishamde codedeft
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#3461 from jkbradley/ensemble-docs and squashes the following commits:
70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison
d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide
8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide
6fab846 [Joseph K. Bradley] small fixes based on review
b9f8576 [Joseph K. Bradley] updated decision tree doc
375204c [Joseph K. Bradley] fixed python style
2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file. added header. Fixed small bug in same example in the programming guide.
706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small
c76c823 [Joseph K. Bradley] added migration guide for mllib
abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder
07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification).
cdfdfbc [Joseph K. Bradley] added examples for GBT
6372a2b [Joseph K. Bradley] updated decision tree examples to use random split. tested all of them.
ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide. still need to update their examples
Have a local reference to `values` and `indices` array in the `Vector` object
so JVM can locate the value with one operation call. See `SPARK-4581`
for similar optimization, and the bytecode analysis.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3577 from dbtsai/blasopt and squashes the following commits:
62d38c4 [DB Tsai] formating
0316cef [DB Tsai] first commit
Note that the usage of `breezeSquaredDistance` in
`org.apache.spark.mllib.util.MLUtils.fastSquaredDistance`
is in the critical path, and `breezeSquaredDistance` is slow.
We should replace it with our own implementation.
Here is the benchmark against mnist8m dataset.
Before
DenseVector: 70.04secs
SparseVector: 59.05secs
With this PR
DenseVector: 30.58secs
SparseVector: 21.14secs
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3565 from dbtsai/kmean and squashes the following commits:
08bc068 [DB Tsai] restyle
de24662 [DB Tsai] address feedback
b185a77 [DB Tsai] cleanup
4554ddd [DB Tsai] first commit
The vector norm in breeze is implemented by `activeIterator` which is known to be very slow.
In this PR, an efficient vector norm is implemented, and with this API, `Normalizer` and
`k-means` have big performance improvement.
Here is the benchmark against mnist8m dataset.
a) `Normalizer`
Before
DenseVector: 68.25secs
SparseVector: 17.01secs
With this PR
DenseVector: 12.71secs
SparseVector: 2.73secs
b) `k-means`
Before
DenseVector: 83.46secs
SparseVector: 61.60secs
With this PR
DenseVector: 70.04secs
SparseVector: 59.05secs
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3462 from dbtsai/norm and squashes the following commits:
63c7165 [DB Tsai] typo
0c3637f [DB Tsai] add import org.apache.spark.SparkContext._ back
6fa616c [DB Tsai] address feedback
9b7cb56 [DB Tsai] move norm to static method
0b632e6 [DB Tsai] kmeans
dbed124 [DB Tsai] style
c1a877c [DB Tsai] first commit
Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes.
brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#3468 from mengxr/SPARK-4614 and squashes the following commits:
3b0e4e2 [Xiangrui Meng] add mima excludes
6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
User could construct an MF model directly. I added a note about the performance.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3459 from mengxr/SPARK-4604 and squashes the following commits:
f64bcd3 [Xiangrui Meng] organize imports
ed08214 [Xiangrui Meng] check preconditions and unit tests
a624c12 [Xiangrui Meng] make MatrixFactorizationModel public
Currently, the LogLoss used by GradientBoostedTrees has 2 issues:
* the gradient (and therefore loss) does not match that used by Friedman (1999)
* the error computation uses 0/1 accuracy, not log loss
This PR updates LogLoss.
It also adds some doc for boosting and forests.
I tested it on sample data and made sure the log loss is monotonically decreasing with each boosting iteration.
CC: mengxr manishamde codedeft
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3439 from jkbradley/gbt-loss-fix and squashes the following commits:
cfec17e [Joseph K. Bradley] removed forgotten temp comments
a27eb6d [Joseph K. Bradley] corrections to last log loss commit
ed5da2c [Joseph K. Bradley] updated LogLoss (boosting) for numerical stability
5e52bff [Joseph K. Bradley] * Removed the 1/2 from SquaredError. This also required updating the test suite since it effectively doubles the gradient and loss. * Added doc for developers within RandomForest. * Small cleanup in test suite (generating data only once)
e57897a [Joseph K. Bradley] Fixed LogLoss for GradientBoostedTrees, and updated doc for losses, forests, and boosting
The following optimizations are done to improve the StandardScaler model
transformation performance.
1) Covert Breeze dense vector to primitive vector to reduce the overhead.
2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector.
3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call.
4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to
make the codebase cleaner.
Benchmark with mnist8m dataset:
Before,
DenseVector withMean and withStd: 50.97secs
DenseVector withMean and withoutStd: 42.11secs
DenseVector withoutMean and withStd: 8.75secs
SparseVector withoutMean and withStd: 5.437secs
With this PR,
DenseVector withMean and withStd: 5.76secs
DenseVector withMean and withoutStd: 5.28secs
DenseVector withoutMean and withStd: 5.30secs
SparseVector withoutMean and withStd: 1.27secs
Note that without the local reference copy of `factor` and `shift` arrays,
the runtime is almost three time slower.
DenseVector withMean and withStd: 18.15secs
DenseVector withMean and withoutStd: 18.05secs
DenseVector withoutMean and withStd: 18.54secs
SparseVector withoutMean and withStd: 2.01secs
The following code,
```scala
while (i < size) {
values(i) = (values(i) - shift(i)) * factor(i)
i += 1
}
```
will generate the bytecode
```
L13
LINENUMBER 106 L13
FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] []
ILOAD 7
ILOAD 6
IF_ICMPGE L14
L15
LINENUMBER 107 L15
ALOAD 5
ILOAD 7
ALOAD 5
ILOAD 7
DALOAD
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D
ILOAD 7
DALOAD
DSUB
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
ILOAD 7
DALOAD
DMUL
DASTORE
L16
LINENUMBER 108 L16
ILOAD 7
ICONST_1
IADD
ISTORE 7
GOTO L13
```
, while with the local reference of the `shift` and `factor` arrays, the bytecode will be
```
L14
LINENUMBER 107 L14
ALOAD 0
INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D
ASTORE 9
L15
LINENUMBER 108 L15
FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] []
ILOAD 8
ILOAD 7
IF_ICMPGE L16
L17
LINENUMBER 109 L17
ALOAD 6
ILOAD 8
ALOAD 6
ILOAD 8
DALOAD
ALOAD 2
ILOAD 8
DALOAD
DSUB
ALOAD 9
ILOAD 8
DALOAD
DMUL
DASTORE
L18
LINENUMBER 110 L18
ILOAD 8
ICONST_1
IADD
ISTORE 8
GOTO L15
```
You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3435 from dbtsai/standardscaler and squashes the following commits:
85885a9 [DB Tsai] revert to have lazy in shift array.
daf2b06 [DB Tsai] Address the feedback
cdb5cef [DB Tsai] small change
9c51eef [DB Tsai] style
fc795e4 [DB Tsai] update
5bffd3d [DB Tsai] first commit
This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed.
cc mengxr
Author: GuoQiang Li <witgo@qq.com>
Closes#3399 from witgo/GradientDescent and squashes the following commits:
13cb228 [GuoQiang Li] review commit
668ab66 [GuoQiang Li] Double to Long
b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0
0b5c3e3 [GuoQiang Li] Minor fix
12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.
In this refactoring, the performance will be slightly increased due to removing
the overhead from breeze vector. The bottleneck is still in breeze norm
which is implemented by activeIterator.
This inefficiency of breeze norm will be addressed in next PR. At least,
this PR makes the code more consistent in the codebase.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3446 from dbtsai/normalizer and squashes the following commits:
e20a2b9 [DB Tsai] first commit
This is #3309 for the master branch.
e.g. clustering
Author: tkaessmann <tobias.kaessmanns24.com>
Closes#3309 from tkaessmann/branch-1.2 and squashes the following commits:
e3a3142 [tkaessmann] changes the comment for getVectors
58d3d83 [tkaessmann] removes sign from comment
a5be213 [tkaessmann] fixes getVectors to fit code guidelines
3782fa9 [tkaessmann] get raw vectors for further processing
Author: tkaessmann <tobias.kaessmann@s24.com>
Closes#3437 from mengxr/SPARK-4582 and squashes the following commits:
6c666b4 [tkaessmann] get raw vectors for further processing in Word2Vec
This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array.
It also improve the serialization of DenseVector.
Before this change:
trial | trainingTime | testTime
-------|--------|--------
0 | 5.126 | 1.786
1 |2.698 |1.693
After the change:
trial | trainingTime | testTime
-------|--------|--------
0 |4.692 |0.554
1 |2.307 |0.525
This could partially fix the performance regression during test.
Author: Davies Liu <davies@databricks.com>
Closes#3420 from davies/ser2 and squashes the following commits:
0e1e6f3 [Davies Liu] fix tests
426f5db [Davies Liu] impove toArray()
44707ec [Davies Liu] add name for ISO-8859-1
fa7d791 [Davies Liu] address comments
1cfb137 [Davies Liu] handle zero sparse vector
2548ee2 [Davies Liu] fix tests
9e6389d [Davies Liu] bugfix
470f702 [Davies Liu] speed up DenseMatrix
f0d3c40 [Davies Liu] speedup SparseVector
ef6ce70 [Davies Liu] speed up dense vector
Previously, we were using Breeze's activeIterator to access the non-zero elements
in dense/sparse vector. Due to the overhead, we switched back to native `while loop`
in #SPARK-4129.
However, #SPARK-4129 requires de-reference the dv.values/sv.values in
each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer,
we're using Breeze's dense vector to store the partial stats, and this is very expensive compared
with using primitive scala array.
In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse
vector operation which makes codebase easier to maintain. Breeze dense vector is replaced
by primitive array to reduce the overhead further.
Benchmarking with mnist8m dataset on single JVM
with first 200 samples loaded in memory, and repeating 5000 times.
Before change:
Sparse Vector - 30.02
Dense Vector - 38.27
With this PR:
Sparse Vector - 6.29
Dense Vector - 11.72
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#3288 from dbtsai/activeIterator and squashes the following commits:
844b0e6 [DB Tsai] formating
03dd693 [DB Tsai] futher performance tunning.
1907ae1 [DB Tsai] address feedback
98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation.
c0cbd5a [DB Tsai] fix a bug
6441f92 [DB Tsai] Finished SPARK-4431
The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step.
This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster.
Author: Davies Liu <davies@databricks.com>
Closes#3397 from davies/cache and squashes the following commits:
7f6e6ce [Davies Liu] Update -> Updater
4b52edd [Davies Liu] using named argument
63b984e [Davies Liu] fix
7da0332 [Davies Liu] add unpersist()
dff33e1 [Davies Liu] address comments
c2bdfc2 [Davies Liu] refactor
d572f00 [Davies Liu] Merge branch 'master' into cache
f1063e1 [Davies Liu] cache serialized java object
```
class RandomForestModel
| A model trained by RandomForest
|
| numTrees(self)
| Get number of trees in forest.
|
| predict(self, x)
| Predict values for a single data point or an RDD of points using the model trained.
|
| toDebugString(self)
| Full model
|
| totalNumNodes(self)
| Get total number of nodes, summed over all trees in the forest.
|
class RandomForest
| trainClassifier(cls, data, numClassesForClassification, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for binary or multiclass classification.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels should take values {0, 1, ..., numClasses-1}.
| :param numClassesForClassification: number of classes for classification.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "sqrt".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "gini" (recommended) or "entropy".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes. (default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
| trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32, seed=None):
| Method to train a decision tree model for regression.
|
| :param data: Training dataset: RDD of LabeledPoint.
| Labels are real numbers.
| :param categoricalFeaturesInfo: Map storing arity of categorical features.
| E.g., an entry (n -> k) indicates that feature n is categorical
| with k categories indexed from 0: {0, 1, ..., k-1}.
| :param numTrees: Number of trees in the random forest.
| :param featureSubsetStrategy: Number of features to consider for splits at each node.
| Supported: "auto" (default), "all", "sqrt", "log2", "onethird".
| If "auto" is set, this parameter is set based on numTrees:
| if numTrees == 1, set to "all";
| if numTrees > 1 (forest) set to "onethird".
| :param impurity: Criterion used for information gain calculation.
| Supported values: "variance".
| :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means
| 1 internal node + 2 leaf nodes.(default: 4)
| :param maxBins: maximum number of bins used for splitting features (default: 100)
| :param seed: Random seed for bootstrapping and choosing feature subsets.
| :return: RandomForestModel that can be used for prediction
|
```
Author: Davies Liu <davies@databricks.com>
Closes#3320 from davies/forest and squashes the following commits:
8003dfc [Davies Liu] reorder
53cf510 [Davies Liu] fix docs
4ca593d [Davies Liu] fix docs
e0df852 [Davies Liu] fix docs
0431746 [Davies Liu] rebased
2b6f239 [Davies Liu] Merge branch 'master' of github.com:apache/spark into forest
885abee [Davies Liu] address comments
dae7fc0 [Davies Liu] address comments
89a000f [Davies Liu] fix docs
565d476 [Davies Liu] add python api for random forest
There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent.
1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly.
1. GradientBoosting -> GradientBoostedTrees
1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy
1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.)
1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train`
1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method.
1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting.
1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError
1. doc updates
manishamde jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#3374 from mengxr/SPARK-4486 and squashes the following commits:
7097251 [Xiangrui Meng] address joseph's comments
98dea09 [Xiangrui Meng] address manish's comments
4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy
ea4c467 [Xiangrui Meng] fix unit tests
751da4e [Xiangrui Meng] rename class method train -> run
19030a5 [Xiangrui Meng] update boosting public APIs
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#3277 from vanzin/version-1.3 and squashes the following commits:
7c3c396 [Marcelo Vanzin] Added temp repo to sbt build.
5f404ff [Marcelo Vanzin] Add another exclusion.
19457e7 [Marcelo Vanzin] Update old version to 1.2, add temporary 1.2 repo.
3c8d705 [Marcelo Vanzin] Workaround for MIMA checks.
e940810 [Marcelo Vanzin] Bumping version to 1.3.0-SNAPSHOT.
```
class LogisticRegressionWithLBFGS
| train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False)
| Train a logistic regression model on the given data.
|
| :param data: The training data, an RDD of LabeledPoint.
| :param iterations: The number of iterations (default: 100).
| :param initialWeights: The initial weights (default: None).
| :param regParam: The regularizer parameter (default: 0.01).
| :param regType: The type of regularizer used for training
| our model.
| :Allowed values:
| - "l1" for using L1 regularization
| - "l2" for using L2 regularization
| - None for no regularization
| (default: "l2")
| :param intercept: Boolean parameter which indicates the use
| or not of the augmented representation for
| training data (i.e. whether bias features
| are activated or not).
| :param corrections: The number of corrections used in the LBFGS update (default: 10).
| :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4).
|
| >>> data = [
| ... LabeledPoint(0.0, [0.0, 1.0]),
| ... LabeledPoint(1.0, [1.0, 0.0]),
| ... ]
| >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data))
| >>> lrm.predict([1.0, 0.0])
| 1
| >>> lrm.predict([0.0, 1.0])
| 0
| >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect()
| [1, 0]
```
Author: Davies Liu <davies@databricks.com>
Closes#3307 from davies/lbfgs and squashes the following commits:
34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs
5a945a6 [Davies Liu] address comments
941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs
03e5543 [Davies Liu] add it to docs
ed2f9a8 [Davies Liu] add regType
76cd1b6 [Davies Liu] reorder arguments
4429a74 [Davies Liu] Update classification.py
9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS
This PR add setThrehold() and clearThreshold() for LogisticRegressionModel and SVMModel, also support RDD of vector in LogisticRegressionModel.predict(), SVNModel.predict() and NaiveBayes.predict()
Author: Davies Liu <davies@databricks.com>
Closes#3305 from davies/setThreshold and squashes the following commits:
d0b835f [Davies Liu] Merge branch 'master' of github.com:apache/spark into setThreshold
e4acd76 [Davies Liu] address comments
2231a5f [Davies Liu] bugfix
7bd9009 [Davies Liu] address comments
0b0a8a7 [Davies Liu] address comments
c1e5573 [Davies Liu] improve classification
Author: Felix Maximilian Möller <felixmaximilian.moeller@immobilienscout24.de>
Closes#3343 from felixmaximilian/fix-documentation and squashes the following commits:
43dcdfb [Felix Maximilian Möller] Removed the information about the switch implicitPrefs. The parameter implicitPrefs cannot be set in this context because it is inherent true when calling the trainImplicit method.
7d172ba [Felix Maximilian Möller] added missing parameter alpha in doc string.
cc mengxr
Author: GuoQiang Li <witgo@qq.com>
Closes#3281 from witgo/SPARK-4422 and squashes the following commits:
5f1fa5e [GuoQiang Li] import order
50783bd [GuoQiang Li] review commits
7a10123 [GuoQiang Li] In some cases, Vectors.fromBreeze get wrong results.
The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy.
In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default.
davies atalwalkar
Author: Xiangrui Meng <meng@databricks.com>
Closes#3232 from mengxr/SPARK-4372 and squashes the following commits:
9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments
d3ba096 [Xiangrui Meng] change 'none' back to None
1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
Add Java-friendly version of `run` and `predict`, and use bulk prediction in Java unit tests. The user guide update will come later (though we may not save many lines of code there). srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes#3240 from mengxr/SPARK-4378 and squashes the following commits:
6581503 [Xiangrui Meng] check number of predictions
6c8bbd1 [Xiangrui Meng] make ALS more Java-friendly
...e 0 positive or 0 negative examples.
Author: Andrew Bullen <andrew.bullen@workday.com>
Closes#3118 from abull/master and squashes the following commits:
c2bf2b1 [Andrew Bullen] [SPARK-4256] Update Code formatting for BinaryClassificationMetricsSpec
36b0533 [Andrew Bullen] [SYMAN-4256] Extract BinaryClassificationMetricsSuite assertions into private method
4d2f79a [Andrew Bullen] [SPARK-4256] Refactor classification metrics tests - extract comparison functions in test
f411e70 [Andrew Bullen] [SPARK-4256] Define precision as 1.0 when there are no positive examples; update code formatting per pull request comments
d9a09ef [Andrew Bullen] Make Binary Evaluation Metrics functions defined in cases where there are 0 positive or 0 negative examples.
We want to make sure there is at most one spark context inside the same jvm. JoshRosen
Author: Xiangrui Meng <meng@databricks.com>
Closes#3235 from mengxr/SPARK-4373 and squashes the following commits:
6574b69 [Xiangrui Meng] rename LocalSparkContext to MLlibTestSparkContext
913d48d [Xiangrui Meng] make sure there is at most one spark context inside the same jvm
Fix TreeModel.predict() with RDD, added tests for it.
(Also checked that other models don't have this issue)
Author: Davies Liu <davies@databricks.com>
Closes#3230 from davies/predict and squashes the following commits:
81172aa [Davies Liu] fix predict
This PR adds package "org.apache.spark.ml" with pipeline and parameters, as discussed on the JIRA. This is a joint work of jkbradley etrain shivaram and many others who helped on the design, also with help from marmbrus and liancheng on the Spark SQL side. The design doc can be found at:
https://docs.google.com/document/d/1rVwXRjWKfIb-7PI6b86ipytwbUH7irSNLF1_6dLmh8o/edit?usp=sharing
**org.apache.spark.ml**
This is a new package with new set of ML APIs that address practical machine learning pipelines. (Sorry for taking so long!) It will be an alpha component, so this is definitely not something set in stone. The new set of APIs, inspired by the MLI project from AMPLab and scikit-learn, takes leverage on Spark SQL's schema support and execution plan optimization. It introduces the following components that help build a practical pipeline:
1. Transformer, which transforms a dataset into another
2. Estimator, which fits models to data, where models are transformers
3. Evaluator, which evaluates model output and returns a scalar metric
4. Pipeline, a simple pipeline that consists of transformers and estimators
Parameters could be supplied at fit/transform or embedded with components.
1. Param: a strong-typed parameter key with self-contained doc
2. ParamMap: a param -> value map
3. Params: trait for components with parameters
For any component that implements `Params`, user can easily check the doc by calling `explainParams`:
~~~
> val lr = new LogisticRegression
> lr.explainParams
maxIter: max number of iterations (default: 100)
regParam: regularization constant (default: 0.1)
labelCol: label column name (default: label)
featuresCol: features column name (default: features)
~~~
or user can check individual param:
~~~
> lr.maxIter
maxIter: max number of iterations (default: 100)
~~~
**Please start with the example code in test suites and under `org.apache.spark.examples.ml`, where I put several examples:**
1. run a simple logistic regression job
~~~
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(1.0)
val model = lr.fit(dataset)
model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
.select('label, 'score, 'prediction).collect()
.foreach(println)
~~~
2. run logistic regression with cross-validation and grid search using areaUnderROC (default) as the metric
~~~
val lr = new LogisticRegression
val lrParamMaps = new ParamGridBuilder()
.addGrid(lr.regParam, Array(0.1, 100.0))
.addGrid(lr.maxIter, Array(0, 5))
.build()
val eval = new BinaryClassificationEvaluator
val cv = new CrossValidator()
.setEstimator(lr)
.setEstimatorParamMaps(lrParamMaps)
.setEvaluator(eval)
.setNumFolds(3)
val bestModel = cv.fit(dataset)
~~~
3. run a pipeline that consists of a standard scaler and a logistic regression component
~~~
val scaler = new StandardScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
val lr = new LogisticRegression()
.setFeaturesCol(scaler.getOutputCol)
val pipeline = new Pipeline()
.setStages(Array(scaler, lr))
val model = pipeline.fit(dataset)
val predictions = model.transform(dataset)
.select('label, 'score, 'prediction)
.collect()
.foreach(println)
~~~
4. a simple text classification pipeline, which recognizes "spark":
~~~
val training = sparkContext.parallelize(Seq(
LabeledDocument(0L, "a b c d e spark", 1.0),
LabeledDocument(1L, "b d", 0.0),
LabeledDocument(2L, "spark f g h", 1.0),
LabeledDocument(3L, "hadoop mapreduce", 0.0)))
val tokenizer = new Tokenizer()
.setInputCol("text")
.setOutputCol("words")
val hashingTF = new HashingTF()
.setInputCol(tokenizer.getOutputCol)
.setOutputCol("features")
val lr = new LogisticRegression()
.setMaxIter(10)
val pipeline = new Pipeline()
.setStages(Array(tokenizer, hashingTF, lr))
val model = pipeline.fit(training)
val test = sparkContext.parallelize(Seq(
Document(4L, "spark i j k"),
Document(5L, "l m"),
Document(6L, "mapreduce spark"),
Document(7L, "apache hadoop")))
model.transform(test)
.select('id, 'text, 'prediction, 'score)
.collect()
.foreach(println)
~~~
Java examples are very similar. I put example code that creates a simple text classification pipeline in Scala and Java, where a simple tokenizer is defined as a transformer outside `org.apache.spark.ml`.
**What are missing now and will be added soon:**
1. ~~Runtime check of schemas. So before we touch the data, we will go through the schema and make sure column names and types match the input parameters.~~
2. ~~Java examples.~~
3. ~~Store training parameters in trained models.~~
4. (later) Serialization and Python API.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3099 from mengxr/SPARK-3530 and squashes the following commits:
2cc93fd [Xiangrui Meng] hide APIs as much as I can
34319ba [Xiangrui Meng] use local instead local[2] for unit tests
2524251 [Xiangrui Meng] rename PipelineStage.transform to transformSchema
c9daab4 [Xiangrui Meng] remove mockito version
1397ab5 [Xiangrui Meng] use sqlContext from LocalSparkContext instead of TestSQLContext
6ffc389 [Xiangrui Meng] try to fix unit test
a59d8b7 [Xiangrui Meng] doc updates
977fd9d [Xiangrui Meng] add scala ml package object
6d97fe6 [Xiangrui Meng] add AlphaComponent annotation
731f0e4 [Xiangrui Meng] update package doc
0435076 [Xiangrui Meng] remove ;this from setters
fa21d9b [Xiangrui Meng] update extends indentation
f1091b3 [Xiangrui Meng] typo
228a9f4 [Xiangrui Meng] do not persist before calling binary classification metrics
f51cd27 [Xiangrui Meng] rename default to defaultValue
b3be094 [Xiangrui Meng] refactor schema transform in lr
8791e8e [Xiangrui Meng] rename copyValues to inheritValues and make it do the right thing
51f1c06 [Xiangrui Meng] remove leftover code in Transformer
494b632 [Xiangrui Meng] compure score once
ad678e9 [Xiangrui Meng] more doc for Transformer
4306ed4 [Xiangrui Meng] org imports in text pipeline
6e7c1c7 [Xiangrui Meng] update pipeline
4f9e34f [Xiangrui Meng] more doc for pipeline
aa5dbd4 [Xiangrui Meng] fix typo
11be383 [Xiangrui Meng] fix unit tests
3df7952 [Xiangrui Meng] clean up
986593e [Xiangrui Meng] re-org java test suites
2b11211 [Xiangrui Meng] remove external data deps
9fd4933 [Xiangrui Meng] add unit test for pipeline
2a0df46 [Xiangrui Meng] update tests
2d52e4d [Xiangrui Meng] add @AlphaComponent to package-info
27582a4 [Xiangrui Meng] doc changes
73a000b [Xiangrui Meng] add schema transformation layer
6736e87 [Xiangrui Meng] more doc / remove HasMetricName trait
80a8b5e [Xiangrui Meng] rename SimpleTransformer to UnaryTransformer
62ca2bb [Xiangrui Meng] check param parent in set/get
1622349 [Xiangrui Meng] add getModel to PipelineModel
a0e0054 [Xiangrui Meng] update StandardScaler to use SimpleTransformer
d0faa04 [Xiangrui Meng] remove implicit mapping from ParamMap
c7f6921 [Xiangrui Meng] move ParamGridBuilder test to ParamGridBuilderSuite
e246f29 [Xiangrui Meng] re-org:
7772430 [Xiangrui Meng] remove modelParams add a simple text classification pipeline
b95c408 [Xiangrui Meng] remove implicits add unit tests to params
bab3e5b [Xiangrui Meng] update params
fe0ee92 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
6e86d98 [Xiangrui Meng] some code clean-up
2d040b3 [Xiangrui Meng] implement setters inside each class, add Params.copyValues [ci skip]
fd751fc [Xiangrui Meng] add java-friendly versions of fit and tranform
3f810cd [Xiangrui Meng] use multi-model training api in cv
5b8f413 [Xiangrui Meng] rename model to modelParams
9d2d35d [Xiangrui Meng] test varargs and chain model params
f46e927 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530
1ef26e0 [Xiangrui Meng] specialize methods/types for Java
df293ed [Xiangrui Meng] switch to setter/getter
376db0a [Xiangrui Meng] pipeline and parameters
See inline comment about the bug. I also did some code clean-up. dbtsai I moved `update` to a private method of `MultivariateOnlineSummarizer`. I don't think it will cause performance regression, but it would be great if you have some time to test.
Author: Xiangrui Meng <meng@databricks.com>
Closes#3220 from mengxr/SPARK-4355 and squashes the following commits:
5ef601f [Xiangrui Meng] fix OnlineSummarizer.merge when other.mean is zero and some code clean-up
SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API. This pull request resolves that.
Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>
Closes#3095 from mdagost/python_nmf and squashes the following commits:
a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI. Remove the new static methods I added. Set seed in tests. Change ratings to ratingsRDD in both train and trainImplicit for consistency.
7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places.
3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list.
bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null.
cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes. Also made the python ALS tests more exact.
a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.
### Summary
* Made it easier to construct default Strategy and BoostingStrategy and to set parameters using simple types.
* Added Scala and Java examples for GradientBoostedTrees
* small cleanups and fixes
### Details
GradientBoosting bug fixes (“bug” = bad default options)
* Force boostingStrategy.weakLearnerParams.algo = Regression
* Force boostingStrategy.weakLearnerParams.impurity = impurity.Variance
* Only persist data if not yet persisted (since it causes an error if persisted twice)
BoostingStrategy
* numEstimators: renamed to numIterations
* removed subsamplingRate (duplicated by Strategy)
* removed categoricalFeaturesInfo since it belongs with the weak learner params (since boosting can be oblivious to feature type)
* Changed algo to var (not val) and added BeanProperty, with overload taking String argument
* Added assertValid() method
* Updated defaultParams() method and eliminated defaultWeakLearnerParams() since that belongs in Strategy
Strategy (for DecisionTree)
* Changed algo to var (not val) and added BeanProperty, with overload taking String argument
* Added setCategoricalFeaturesInfo method taking Java Map.
* Cleaned up assertValid
* Changed val’s to def’s since parameters can now be changed.
CC: manishamde mengxr codedeft
Author: Joseph K. Bradley <joseph@databricks.com>
Closes#3094 from jkbradley/gbt-api and squashes the following commits:
7a27e22 [Joseph K. Bradley] scalastyle fix
52013d5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into gbt-api
e9b8410 [Joseph K. Bradley] Summary of changes
```
pyspark.mllib.stat.StatisticschiSqTest(observed, expected=None)
:: Experimental ::
If `observed` is Vector, conduct Pearson's chi-squared goodness
of fit test of the observed data against the expected distribution,
or againt the uniform distribution (by default), with each category
having an expected frequency of `1 / len(observed)`.
(Note: `observed` cannot contain negative values)
If `observed` is matrix, conduct Pearson's independence test on the
input contingency matrix, which cannot contain negative entries or
columns or rows that sum up to 0.
If `observed` is an RDD of LabeledPoint, conduct Pearson's independence
test for every feature against the label across the input RDD.
For each feature, the (feature, label) pairs are converted into a
contingency matrix for which the chi-squared statistic is computed.
All label and feature values must be categorical.
:param observed: it could be a vector containing the observed categorical
counts/relative frequencies, or the contingency matrix
(containing either counts or relative frequencies),
or an RDD of LabeledPoint containing the labeled dataset
with categorical features. Real-valued features will be
treated as categorical for each distinct value.
:param expected: Vector containing the expected categorical counts/relative
frequencies. `expected` is rescaled if the `expected` sum
differs from the `observed` sum.
:return: ChiSquaredTest object containing the test statistic, degrees
of freedom, p-value, the method used, and the null hypothesis.
```
Author: Davies Liu <davies@databricks.com>
Closes#3091 from davies/his and squashes the following commits:
145d16c [Davies Liu] address comments
0ab0764 [Davies Liu] fix float
5097d54 [Davies Liu] add Hypothesis test Python API
Author: Niklas Wilcke <1wilcke@informatik.uni-hamburg.de>
Closes#2907 from numbnut/master and squashes the following commits:
7f7c767 [Niklas Wilcke] [Spark-4060] [MLlib] exposing special rdd functions to the public, #2907
This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1.
Author: Davies Liu <davies@databricks.com>
This patch had conflicts when merged, resolved by
Committer: Josh Rosen <joshrosen@databricks.com>
Closes#2920 from davies/fix_autobatch and squashes the following commits:
e544ef9 [Davies Liu] revert unrelated change
6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
1d557fc [Davies Liu] fix tests
8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
76abdce [Davies Liu] clean up
53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch
b4292ce [Davies Liu] fix bug in master
d79744c [Davies Liu] recover hive tests
be37ece [Davies Liu] refactor
eb3938d [Davies Liu] refactor serializer in scala
8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley.
~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~
marmbrus jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#3070 from mengxr/SPARK-3573 and squashes the following commits:
3a0b6e5 [Xiangrui Meng] organize imports
236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
Saw Jenkins test failures due to random seeds.
jkbradley manishamde
Author: Xiangrui Meng <meng@databricks.com>
Closes#3084 from mengxr/fix-baggedpoint-suite and squashes the following commits:
f735a43 [Xiangrui Meng] fix seed in BaggedPointSuite
...sion trees. jkbradley mengxr chouqin Please review this.
Author: Sung Chung <schung@alpinenow.com>
Closes#2868 from codedeft/SPARK-3161 and squashes the following commits:
5f5a156 [Sung Chung] [SPARK-3161][MLLIB] Adding a node Id caching mechanism for training decision trees.
In #2928 , we shade commons-math3 to prevent future conflicts with hadoop. It caused problems with our Jenkins master build with maven. Some tests used local-cluster mode, where the assembly jar contains relocated math3 classes, while mllib test code still compiles with core and the untouched math3 classes.
This PR sets commons-math3 version based on hadoop profiles.
pwendell JoshRosen srowen
Author: Xiangrui Meng <meng@databricks.com>
Closes#3023 from mengxr/SPARK-4121-alt and squashes the following commits:
580f6d9 [Xiangrui Meng] replace tab by spaces
7f71f08 [Xiangrui Meng] revert changes to PoissonSampler to avoid conflicts
d3353d9 [Xiangrui Meng] do not shade commons-math3
b4180dc [Xiangrui Meng] temp work
This adds a Streaming KMeans algorithm to MLlib. It uses an update rule that generalizes the mini-batch KMeans update to incorporate a decay factor, which allows past data to be forgotten. The decay factor can be specified explicitly, or via a more intuitive "fractional decay" setting, in units of either data points or batches.
The PR includes:
- StreamingKMeans algorithm with decay factor settings
- Usage example
- Additions to documentation clustering page
- Unit tests of basic behavior and decay behaviors
tdas mengxr rezazadeh
Author: freeman <the.freeman.lab@gmail.com>
Author: Jeremy Freeman <the.freeman.lab@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#2942 from freeman-lab/streaming-kmeans and squashes the following commits:
b2e5b4a [freeman] Fixes to docs / examples
078617c [Jeremy Freeman] Merge pull request #1 from mengxr/SPARK-3254
2e682c0 [Xiangrui Meng] take discount on previous weights; use BLAS; detect dying clusters
0411bf5 [freeman] Change decay parameterization
9f7aea9 [freeman] Style fixes
374a706 [freeman] Formatting
ad9bdc2 [freeman] Use labeled points and predictOnValues in examples
77dbd3f [freeman] Make initialization check an assertion
9cfc301 [freeman] Make random seed an argument
44050a9 [freeman] Simpler constructor
c7050d5 [freeman] Fix spacing
2899623 [freeman] Use pattern matching for clarity
a4a316b [freeman] Use collect
1472ec5 [freeman] Doc formatting
ea22ec8 [freeman] Fix imports
2086bdc [freeman] Log cluster center updates
ea9877c [freeman] More documentation
9facbe3 [freeman] Bug fix
5db7074 [freeman] Example usage for StreamingKMeans
f33684b [freeman] Add explanation and example to docs
b5b5f8d [freeman] Add better documentation
a0fd790 [freeman] Merge remote-tracking branch 'upstream/master' into streaming-kmeans
9fd9c15 [freeman] Merge remote-tracking branch 'upstream/master' into streaming-kmeans
b93350f [freeman] Streaming KMeans with decay
Given the popular demand for gradient boosting and AdaBoost in MLlib, I am creating a WIP branch for early feedback on gradient boosting with AdaBoost to follow soon after this PR is accepted. This is based on work done along with hirakendu that was pending due to decision tree optimizations and random forests work.
Ideally, boosting algorithms should work with any base learners. This will soon be possible once the MLlib API is finalized -- we want to ensure we use a consistent interface for the underlying base learners. In the meantime, this PR uses decision trees as base learners for the gradient boosting algorithm. The current PR allows "pluggable" loss functions and provides least squares error and least absolute error by default.
Here is the task list:
- [x] Gradient boosting support
- [x] Pluggable loss functions
- [x] Stochastic gradient boosting support – Re-use the BaggedPoint approach used for RandomForest.
- [x] Binary classification support
- [x] Support configurable checkpointing – This approach will avoid long lineage chains.
- [x] Create classification and regression APIs
- [x] Weighted Ensemble Model -- created a WeightedEnsembleModel class that can be used by ensemble algorithms such as random forests and boosting.
- [x] Unit Tests
Future work:
+ Multi-class classification is currently not supported by this PR since it requires discussion on the best way to support "deviance" as a loss function.
+ BaggedRDD caching -- Avoid repeating feature to bin mapping for each tree estimator after standard API work is completed.
cc: jkbradley hirakendu mengxr etrain atalwalkar chouqin
Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Closes#2607 from manishamde/gbt and squashes the following commits:
991c7b5 [Manish Amde] public api
ff2a796 [Manish Amde] addressing comments
b4c1318 [Manish Amde] removing spaces
8476b6b [Manish Amde] fixing line length
0183cb9 [Manish Amde] fixed naming and formatting issues
1c40c33 [Manish Amde] add newline, removed spaces
e33ab61 [Manish Amde] minor comment
eadbf09 [Manish Amde] parameter renaming
035a2ed [Manish Amde] jkbradley formatting suggestions
9f7359d [Manish Amde] simplified gbt logic and added more tests
49ba107 [Manish Amde] merged from master
eff21fe [Manish Amde] Added gradient boosting tests
3fd0528 [Manish Amde] moved helper methods to new class
a32a5ab [Manish Amde] added test for subsampling without replacement
781542a [Manish Amde] added support for fractional subsampling with replacement
3a18cc1 [Manish Amde] cleaned up api for conversion to bagged point and moved tests to it's own test suite
0e81906 [Manish Amde] improving caching unpersisting logic
d971f73 [Manish Amde] moved RF code to use WeightedEnsembleModel class
fee06d3 [Manish Amde] added weighted ensemble model
1b01943 [Manish Amde] add weights for base learners
9bc6e74 [Manish Amde] adding random seed as parameter
d2c8323 [Manish Amde] Merge branch 'master' into gbt
2ae97b7 [Manish Amde] added documentation for the loss classes
9366b8f [Manish Amde] minor: using numTrees instead of trees.size
3b43896 [Manish Amde] added learning rate for prediction
9b2e35e [Manish Amde] Merge branch 'master' into gbt
6a11c02 [manishamde] fixing formatting
823691b [Manish Amde] fixing RF test
1f47941 [Manish Amde] changing access modifier
5b67102 [Manish Amde] shortened parameter list
5ab3796 [Manish Amde] minor reformatting
9155a9d [Manish Amde] consolidated boosting configuration and added public API
631baea [Manish Amde] Merge branch 'master' into gbt
2cb1258 [Manish Amde] public API support
3b8ffc0 [Manish Amde] added documentation
8e10c63 [Manish Amde] modified unpersist strategy
f62bc48 [Manish Amde] added unpersist
bdca43a [Manish Amde] added timing parameters
2fbc9c7 [Manish Amde] fixing binomial classification prediction
6dd4dd8 [Manish Amde] added support for log loss
9af0231 [Manish Amde] classification attempt
62cc000 [Manish Amde] basic checkpointing
4784091 [Manish Amde] formatting
78ed452 [Manish Amde] added newline and fixed if statement
3973dd1 [Manish Amde] minor indicating subsample is double during comparison
aa8fae7 [Manish Amde] minor refactoring
1a8031c [Manish Amde] sampling with replacement
f1c9ef7 [Manish Amde] Merge branch 'master' into gbt
cdceeef [Manish Amde] added documentation
6251fd5 [Manish Amde] modified method name
5538521 [Manish Amde] disable checkpointing for now
0ae1c0a [Manish Amde] basic gradient boosting code from earlier branches
Implementation of various multi-label classification measures, including: Hamming-loss, strict and default Accuracy, macro-averaged Precision, Recall and F1-measure based on documents and labels, micro-averaged measures: https://issues.apache.org/jira/browse/SPARK-2329
Multi-class measures are currently in the following pull request: https://github.com/apache/spark/pull/1155
Author: Alexander Ulanov <nashb@yandex.ru>
Author: avulanov <nashb@yandex.ru>
Closes#1270 from avulanov/multilabelmetrics and squashes the following commits:
fc8175e [Alexander Ulanov] Merge with previous updates
43a613e [Alexander Ulanov] Addressing reviewers comments: change Set to Array
517a594 [avulanov] Addressing reviewers comments: Scala style
cf4222bc [avulanov] Addressing reviewers comments: renaming. Added label method that returns the list of labels
1843f73 [Alexander Ulanov] Scala style fix
79e8476 [Alexander Ulanov] Replacing fold(_ + _) with sum as suggested by srowen
ca46765 [Alexander Ulanov] Cosmetic changes: Apache header and parameter explanation
40593f5 [Alexander Ulanov] Multi-label metrics: Hamming-loss, strict and normal accuracy, fix to macro measures, bunch of tests
ad62df0 [Alexander Ulanov] Comments and scala style check
154164b [Alexander Ulanov] Multilabel evaluation metics and tests: macro precision and recall averaged by docs, micro and per-class precision and recall averaged by class
More efficient sampling, based on Gap Sampling optimization:
http://erikerlandson.github.io/blog/2014/09/11/faster-random-samples-with-gap-sampling/
Author: Erik Erlandson <eerlands@redhat.com>
Closes#2455 from erikerlandson/spark-3250-pr and squashes the following commits:
72496bc [Erik Erlandson] [SPARK-3250] Implement Gap Sampling optimization for random sampling
Create several helper functions to call MLlib Java API, convert the arguments to Java type and convert return value to Python object automatically, this simplify serialization in MLlib Python API very much.
After this, the MLlib Python API does not need to deal with serialization details anymore, it's easier to add new API.
cc mengxr
Author: Davies Liu <davies@databricks.com>
Closes#2995 from davies/cleanup and squashes the following commits:
8fa6ec6 [Davies Liu] address comments
16b85a0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into cleanup
43743e5 [Davies Liu] bugfix
731331f [Davies Liu] simplify serialization in MLlib Python API
Add RegressionMetrics.scala as regression metrics used for evaluation and corresponding test case RegressionMetricsSuite.scala.
Author: Yanbo Liang <yanbohappy@gmail.com>
Author: liangyanbo <liangyanbo@meituan.com>
Closes#2978 from yanbohappy/regression_metrics and squashes the following commits:
730d0a9 [Yanbo Liang] more clearly annotation
3d0bec1 [Yanbo Liang] rename and keep code style
a8ad3e3 [Yanbo Liang] simplify code for keeping style
d454909 [Yanbo Liang] rename parameter and function names, delete unused columns, add reference
2e56282 [liangyanbo] rename r2_score() and remove unused column
43bb12b [liangyanbo] add regression metrics
This simple patch filters out extra whitespace entries.
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Author: Joey <joseph.e.gonzalez@gmail.com>
Closes#2996 from jegonzal/loadLibSVM and squashes the following commits:
e0227ab [Joey] improving readability
e028e84 [Joseph E. Gonzalez] fixing whitespace bug in loadLibSVMFile when parsing libSVM files
In MultivariateOnlineSummarizer, breeze's activeIterator is used
to loop through the nonZero elements in the vector. However,
activeIterator doesn't perform well due to lots of overhead.
In this PR, native while loop is used for both DenseVector and SparseVector.
The benchmark result with 20 executors using mnist8m dataset:
Before:
DenseVector: 48.2 seconds
SparseVector: 16.3 seconds
After:
DenseVector: 17.8 seconds
SparseVector: 11.2 seconds
Since MultivariateOnlineSummarizer is used in several places,
the overall performance gain in mllib library will be significant with this PR.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#2992 from dbtsai/SPARK-4129 and squashes the following commits:
b99db6c [DB Tsai] fixed java.lang.ArrayIndexOutOfBoundsException
2b5e882 [DB Tsai] small refactoring
ebe3e74 [DB Tsai] First commit
Also update step parameter to pass the proposed test
Author: coderxiang <shuoxiangpub@gmail.com>
Closes#2965 from coderxiang/nnls-test and squashes the following commits:
24b06f9 [coderxiang] add test case on objective value for NNLS; update step parameter to pass the test
This change replaces usages of colt with commons-math3 equivalents, and makes some minor necessary adjustments to related code and tests to match.
Author: Sean Owen <sowen@cloudera.com>
Closes#2928 from srowen/SPARK-4022 and squashes the following commits:
61a232f [Sean Owen] Fix failure due to different sampling in JavaAPISuite.sample()
16d66b8 [Sean Owen] Simplify seeding with call to reseedRandomGenerator
a1a78e0 [Sean Owen] Use Well19937c
31c7641 [Sean Owen] Fix Python Poisson test by choosing a different seed; about 88% of seeds should work but 1 didn't, it seems
5c9c67f [Sean Owen] Additional test fixes from review
d8f88e0 [Sean Owen] Replace colt with commons-math3. Some tests do not pass yet.
This follows https://github.com/apache/spark/pull/2893 , but does not completely fix SPARK-3359 either. This fixes minor scaladoc/javadoc issues that Javadoc 8 will treat as errors.
Author: Sean Owen <sowen@cloudera.com>
Closes#2909 from srowen/SPARK-3359 and squashes the following commits:
f62c347 [Sean Owen] Fix some javadoc issues that javadoc 8 considers errors. This is not all of the errors turned up when javadoc 8 runs on output of genjavadoc.
Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2903 from sarutak/SPARK-4055 and squashes the following commits:
b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
Add common metrics for ranking algorithms (http://www-nlp.stanford.edu/IR-book/), including:
- Mean Average Precision
- Precisionn: top-n precision
- Discounted cumulative gain (DCG) and NDCG
The following methods and the corresponding tests are implemented:
```
class RankingMetrics[T](predictionAndLabels: RDD[(Array[T], Array[T])]) {
/* Returns the precsionk for each query */
lazy val precAtK: RDD[Array[Double]]
/**
* param k the position to compute the truncated precision
* return the average precision at the first k ranking positions
*/
def precision(k: Int): Double
/* Returns the average precision for each query */
lazy val avePrec: RDD[Double]
/*Returns the mean average precision (MAP) of all the queries*/
lazy val meanAvePrec: Double
/*Returns the normalized discounted cumulative gain for each query */
lazy val ndcgAtK: RDD[Array[Double]]
/**
* param k the position to compute the truncated ndcg
* return the average ndcg at the first k ranking positions
*/
def ndcg(k: Int): Double
}
```
Author: coderxiang <shuoxiangpub@gmail.com>
Closes#2667 from coderxiang/rankingmetrics and squashes the following commits:
d881097 [coderxiang] update doc
14d9cd9 [coderxiang] remove unexpected files
d7fb93f [coderxiang] style change and remove ignored files
f113ee1 [coderxiang] modify doc for displaying superscript and subscript
f626896 [coderxiang] improve doc and remove unnecessary computation while labSet is empty
be6645e [coderxiang] set the precision of empty labset to 0.0
d64c120 [coderxiang] add logWarning for empty ground truth set
dfae292 [coderxiang] handle empty labSet for map. add test
62047c4 [coderxiang] style change and add documentation
f66612d [coderxiang] add additional test of precisionAt
b794cb2 [coderxiang] move private members precAtK, ndcgAtK into public methods. style change
77c9e5d [coderxiang] set precAtK and ndcgAtK as private member. Improve documentation
5f87bce [coderxiang] add API to calculate precision and ndcg at each ranking position
b7851cc [coderxiang] Use generic type to represent IDs
e443fee [coderxiang] change style and use alternative builtin methods
3a5a6ff [coderxiang] add ranking metrics
https://issues.apache.org/jira/browse/SPARK-3770
We need access to the underlying latent user features from python. However, the userFeatures RDD from the MatrixFactorizationModel isn't accessible from the python bindings. I've added a method to the underlying scala class to turn the RDD[(Int, Array[Double])] to an RDD[String]. This is then accessed from the python recommendation.py
Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com>
Closes#2636 from mdagost/mf_user_features and squashes the following commits:
c98f9e2 [Michelangelo D'Agostino] Added unit tests for userFeatures and productFeatures and merged master.
d5eadf8 [Michelangelo D'Agostino] Merge branch 'master' into mf_user_features
2481a2a [Michelangelo D'Agostino] Merged master and resolved conflict.
a6ffb96 [Michelangelo D'Agostino] Eliminated a function from our first approach to this problem that is no longer needed now that we added the fromTuple2RDD function.
2aa1bf8 [Michelangelo D'Agostino] Implemented a function called fromTuple2RDD in PythonMLLibAPI and used it to expose the MF userFeatures and productFeatures in python.
34cb2a2 [Michelangelo D'Agostino] A couple of lint cleanups and a comment.
cdd98e3 [Michelangelo D'Agostino] It's working now.
e1fbe5e [Michelangelo D'Agostino] Added scala function to stringify userFeatures for access in python.
DecisionTree splits on continuous features by choosing an array of values from a subsample of the data.
Currently, it does not check for identical values in the subsample, so it could end up having multiple copies of the same split. In this PR, we choose splits for a continuous feature in 3 steps:
1. Sort sample values for this feature
2. Get number of occurrence of each distinct value
3. Iterate the value count array computed in step 2 to choose splits.
After find splits, `numSplits` and `numBins` in metadata will be updated.
CC: mengxr manishamde jkbradley, please help me review this, thanks.
Author: Qiping Li <liqiping1991@gmail.com>
Author: chouqin <liqiping1991@gmail.com>
Author: liqi <liqiping1991@gmail.com>
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Closes#2780 from chouqin/dt-findsplits and squashes the following commits:
18d0301 [Qiping Li] check explicitly findsplits return distinct splits
8dc28ab [chouqin] remove blank lines
ffc920f [chouqin] adjust code based on comments and add more test cases
9857039 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits
d353596 [qiping.lqp] fix pyspark doc test
9e64699 [Qiping Li] fix random forest unit test
3c72913 [Qiping Li] fix random forest unit test
092efcb [Qiping Li] fix bug
f69f47f [Qiping Li] fix bug
ab303a4 [Qiping Li] fix bug
af6dc97 [Qiping Li] fix bug
2a8267a [Qiping Li] fix bug
c339a61 [Qiping Li] fix bug
369f812 [Qiping Li] fix style
8f46af6 [Qiping Li] add comments and unit test
9e7138e [Qiping Li] Merge branch 'dt-findsplits' of https://github.com/chouqin/spark into dt-findsplits
1b25a35 [Qiping Li] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits
0cd744a [liqi] fix bug
3652823 [Qiping Li] fix bug
af7cb79 [Qiping Li] Choose splits for continuous features in DecisionTree more adaptively
SPARK-3934: When run with a mix of unordered categorical and continuous features, on multiclass classification, RandomForest fails. The bug is in the sanity checks in getFeatureOffset and getLeftRightFeatureOffsets, which use the wrong indices for checking whether features are unordered.
Fix: Remove the sanity checks since they are not really needed, and since they would require DTStatsAggregator to keep track of an extra set of indices (for the feature subset).
Added test to RandomForestSuite which failed with old version but now works.
SPARK-3918: Added baggedInput.unpersist at end of training.
Also:
* I removed DTStatsAggregator.isUnordered since it is no longer used.
* DecisionTreeMetadata: Added logWarning when maxBins is automatically reduced.
* Updated DecisionTreeRunner to explicitly fix the test data to have the same number of features as the training data. This is a temporary fix which should eventually be replaced by pre-indexing both datasets.
* RandomForestModel: Updated toString to print total number of nodes in forest.
* Changed Predict class to be public DeveloperApi. This was necessary to allow users to create their own trees by hand (for testing).
CC: mengxr manishamde chouqin codedeft Just notifying you of these small bug fixes.
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2785 from jkbradley/dtrunner-update and squashes the following commits:
9132321 [Joseph K. Bradley] merged with master, fixed imports
9dbd000 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
e116473 [Joseph K. Bradley] Changed Predict class to be public DeveloperApi.
f502e65 [Joseph K. Bradley] bug fix for SPARK-3934
7f3d60f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
ba567ab [Joseph K. Bradley] Changed DTRunner to load test data using same number of features as in training data.
4e88c1f [Joseph K. Bradley] changed RF toString to print total number of nodes
Customized pickler should be registered before unpickling, but in executor, there is no way to register the picklers before run the tasks.
So, we need to register the picklers in the tasks itself, duplicate the javaToPython() and pythonToJava() in MLlib, call SerDe.initialize() before pickling or unpickling.
Author: Davies Liu <davies.liu@gmail.com>
Closes#2830 from davies/fix_pickle and squashes the following commits:
0c85fb9 [Davies Liu] revert the privacy change
6b94e15 [Davies Liu] use JavaConverters instead of JavaConversions
0f02050 [Davies Liu] hotfix: Customized pickler does not work in cluster
Avoid overflow in computing n*(n+1)/2 as much as possible; throw explicit error when Gramian computation will fail due to negative array size; warn about large result when computing Gramian too
Author: Sean Owen <sowen@cloudera.com>
Closes#2801 from srowen/SPARK-3803 and squashes the following commits:
b4e6d92 [Sean Owen] Avoid overflow in computing n*(n+1)/2 as much as possible; throw explicit error when Gramian computation will fail due to negative array size; warn about large result when computing Gramian too
During trainning Gradient Boosting Decision Tree on large-scale sparse data, spark spill hundreds of data onto disk. And find the bug below:
In version 1.1.0 DecisionTree.scala, train Method, treeInput has been persisted in Memory, but without unpersist. It caused heavy DISK usage.
In github version(1.2.0 maybe), RandomForest.scala, train Method, baggedInput has been persisted but without unpersisted too.
After added unpersist, it works right.
https://issues.apache.org/jira/browse/SPARK-3918
Author: omgteam <Kimlong.Liu@gmail.com>
Closes#2775 from omgteam/master and squashes the following commits:
815d543 [omgteam] adjust tab to spaces
1a36f83 [omgteam] Bug: fix without unpersist baggedInput in RandomForest.scala
I noticed a few issues with how temp directories are created and deleted:
*Minor*
* Guava's `Files.createTempDir()` plus `File.deleteOnExit()` is used in many tests to make a temp dir, but `Utils.createTempDir()` seems to be the standard Spark mechanism
* Call to `File.deleteOnExit()` could be pushed into `Utils.createTempDir()` as well, along with this replacement
* _I messed up the message in an exception in `Utils` in SPARK-3794; fixed here_
*Bit Less Minor*
* `Utils.deleteRecursively()` fails immediately if any `IOException` occurs, instead of trying to delete any remaining files and subdirectories. I've observed this leave temp dirs around. I suggest changing it to continue in the face of an exception and throw one of the possibly several exceptions that occur at the end.
* `Utils.createTempDir()` will add a JVM shutdown hook every time the method is called. Even if the subdir is the parent of another parent dir, since this check is inside the hook. However `Utils` manages a set of all dirs to delete on shutdown already, called `shutdownDeletePaths`. A single hook can be registered to delete all of these on exit. This is how Tachyon temp paths are cleaned up in `TachyonBlockManager`.
I noticed a few other things that might be changed but wanted to ask first:
* Shouldn't the set of dirs to delete be `File`, not just `String` paths?
* `Utils` manages the set of `TachyonFile` that have been registered for deletion, but the shutdown hook is managed in `TachyonBlockManager`. Should this logic not live together, and not in `Utils`? it's more specific to Tachyon, and looks a slight bit odd to import in such a generic place.
Author: Sean Owen <sowen@cloudera.com>
Closes#2670 from srowen/SPARK-3811 and squashes the following commits:
071ae60 [Sean Owen] Update per @vanzin's review
da0146d [Sean Owen] Make Utils.deleteRecursively try to delete all paths even when an exception occurs; use one shutdown hook instead of one per method call to delete temp dirs
3a0faa4 [Sean Owen] Standardize on Utils.createTempDir instead of Files.createTempDir
cc mengxr
Author: GuoQiang Li <witgo@qq.com>
Closes#2730 from witgo/SPARK-3856 and squashes the following commits:
2cffce1 [GuoQiang Li] use norm operator after breeze 0.10 upgrade
Currently, the implementation does one unnecessary aggregation step. The aggregation step for level L (to choose splits) gives enough information to set the predictions of any leaf nodes at level L+1. We can use that info and skip the aggregation step for the last level of the tree (which only has leaf nodes).
### Implementation Details
Each node now has a `impurity` field and the `predict` is changed from type `Double` to type `Predict`(this can be used to compute predict probability in the future) When compute best splits for each node, we also compute impurity and predict for the child nodes, which is used to constructed newly allocated child nodes. So at level L, we have set impurity and predict for nodes at level L +1.
If level L+1 is the last level, then we can avoid aggregation. What's more, calculation of parent impurity in
Top nodes for each tree needs to be treated differently because we have to compute impurity and predict for them first. In `binsToBestSplit`, if current node is top node(level == 0), we calculate impurity and predict first.
after finding best split, top node's predict and impurity is set to the calculated value. Non-top nodes's impurity and predict are already calculated and don't need to be recalculated again. I have considered to add a initialization step to set top nodes' impurity and predict and then we can treat all nodes in the same way, but this will need a lot of duplication of code(all the code to do seq operation(BinSeqOp) needs to be duplicated), so I choose the current way.
CC mengxr manishamde jkbradley, please help me review this, thanks.
Author: Qiping Li <liqiping1991@gmail.com>
Closes#2708 from chouqin/avoid-agg and squashes the following commits:
8e269ea [Qiping Li] adjust code and comments
eefeef1 [Qiping Li] adjust comments and check child nodes' impurity
c41b1b6 [Qiping Li] fix pyspark unit test
7ad7a71 [Qiping Li] fix unit test
822c912 [Qiping Li] add comments and unit test
e41d715 [Qiping Li] fix bug in test suite
6cc0333 [Qiping Li] SPARK-3158: Avoid 1 extra aggregation for DecisionTree training
Got warning msg:
~~~
[warn] /Users/meng/src/spark/mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala:50: method norm in trait NumericOps is deprecated: Use norm(XXX) instead of XXX.norm
[warn] var norm = vector.toBreeze.norm(p)
~~~
dbtsai
Author: Xiangrui Meng <meng@databricks.com>
Closes#2718 from mengxr/SPARK-3856 and squashes the following commits:
4f38169 [Xiangrui Meng] use norm operator
In Breeze 0.10, the L1regParam can be configured through anonymous function in OWLQN, and each component can be penalized differently. This is required for GLMNET in MLlib with L1/L2 regularization.
2570911026
Author: DB Tsai <dbtsai@dbtsai.com>
Closes#2693 from dbtsai/breeze0.10 and squashes the following commits:
7a0c45c [DB Tsai] In Breeze 0.10, the L1regParam can be configured through anonymous function in OWLQN, and each component can be penalized differently. This is required for GLMNET in MLlib with L1/L2 regularization. 2570911026
mengxr
Added PySpark support for Word2Vec
Change list
(1) PySpark support for Word2Vec
(2) SerDe support of string sequence both on python side and JVM side
(3) Test for SerDe of string sequence on JVM side
Author: Liquan Pei <liquanpei@gmail.com>
Closes#2356 from Ishiihara/Word2Vec-python and squashes the following commits:
476ea34 [Liquan Pei] style fixes
b13a0b9 [Liquan Pei] resolve merge conflicts and minor fixes
8671eba [Liquan Pei] Merge remote-tracking branch 'upstream/master' into Word2Vec-python
daf88a6 [Liquan Pei] modification according to feedback
a73fa19 [Liquan Pei] clean up
3d8007b [Liquan Pei] fix findSynonyms for vector
1bdcd2e [Liquan Pei] minor fixes
cdef9f4 [Liquan Pei] add missing comments
b7447eb [Liquan Pei] modify according to feedback
b9a7383 [Liquan Pei] cache words RDD in fit
89490bf [Liquan Pei] add tests and Word2VecModelWrapper
78bbb53 [Liquan Pei] use pickle for seq string SerDe
a264b08 [Liquan Pei] Merge remote-tracking branch 'upstream/master' into Word2Vec-python
ca1e5ff [Liquan Pei] fix test
68e7276 [Liquan Pei] minor style fixes
48d5e72 [Liquan Pei] Functionality improvement
0ad3ac1 [Liquan Pei] minor fix
c867fdf [Liquan Pei] add Word2Vec to pyspark
Add a toString method to GeneralizedLinearModel, also change `__str__` to `__repr__` for some classes, to provide better message in repr.
This PR is based on #1388, thanks to sryza!
closes#1388
Author: Sandy Ryza <sandy@cloudera.com>
Author: Davies Liu <davies.liu@gmail.com>
Closes#2625 from davies/string and squashes the following commits:
3544aad [Davies Liu] fix LinearModel
0bcd642 [Davies Liu] Merge branch 'sandy-spark-2461' of github.com:sryza/spark
1ce5c2d [Sandy Ryza] __repr__ back to __str__ in a couple places
aa9e962 [Sandy Ryza] Switch __str__ to __repr__
a0c5041 [Sandy Ryza] Add labels back in
1aa17f5 [Sandy Ryza] Match existing conventions
fac1bc4 [Sandy Ryza] Fix PEP8 error
f7b58ed [Sandy Ryza] SPARK-2461. Add a toString method to GeneralizedLinearModel
Currently, all best splits are computed on the driver, which makes the driver a bottleneck for both communication and computation. This PR fix this problem by computed best splits on executors.
Instead of send all aggregate stats to the driver node, we can send aggregate stats for a node to a particular executor, using `reduceByKey` operation, then we can compute best split for this node there.
Implementation details:
Each node now has a nodeStatsAggregator, which save aggregate stats for all features and bins.
First use mapPartition to compute node aggregate stats for all nodes in each partition.
Then transform node aggregate stats to (nodeIndex, nodeStatsAggregator) pairs and use to `reduceByKey` operation to combine nodeStatsAggregator for the same node.
After all stats have been combined, best splits can be computed for each node based on the node aggregate stats. Best split result is collected to driver to construct the decision tree.
CC: mengxr manishamde jkbradley, please help me review this, thanks.
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2595 from chouqin/dt-dist-agg and squashes the following commits:
db0d24a [chouqin] fix a minor bug and adjust code
a0d9de3 [chouqin] adjust code based on comments
9f201a6 [chouqin] fix bug: statsSize -> allStatsSize
a8a7ed0 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-dist-agg
f13b346 [chouqin] adjust randomforest comments
c32636e [chouqin] adjust code based on comments
ac6a505 [chouqin] adjust code based on comments
7bbb787 [chouqin] add comments
bdd2a63 [qiping.lqp] fix test suite
a75df27 [qiping.lqp] fix test suite
b5b0bc2 [qiping.lqp] fix style
e76414f [qiping.lqp] fix testsuite
748bd45 [qiping.lqp] fix type-mismatch bug
24eacd8 [qiping.lqp] fix type-mismatch bug
5f63d6c [qiping.lqp] add multiclassification using One-Vs-All strategy
4f56496 [qiping.lqp] fix bug
f00fc22 [qiping.lqp] fix bug
532993a [qiping.lqp] Compute best splits distributively in decision tree
Thread names are useful for correlating failures.
Author: Reynold Xin <rxin@apache.org>
Closes#2600 from rxin/log4j and squashes the following commits:
83ffe88 [Reynold Xin] [SPARK-3748] Log thread name in unit test logs
DecisionTreeRunner functionality additions:
* Allow user to pass in a test dataset
* Do not print full model if the model is too large.
As part of this, modify DecisionTreeModel and RandomForestModel to allow printing less info. Proposed updates:
* toString: prints model summary
* toDebugString: prints full model (named after RDD.toDebugString)
Similar update to Python API:
* __repr__() now prints a model summary
* toDebugString() now prints the full model
CC: mengxr chouqin manishamde codedeft Small update (whomever can take a look). Thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2604 from jkbradley/dtrunner-update and squashes the following commits:
b2b3c60 [Joseph K. Bradley] re-added python sql doc test, temporarily removed before
07b1fae [Joseph K. Bradley] repr() now prints a model summary toDebugString() now prints the full model
1d0d93d [Joseph K. Bradley] Updated DT and RF to print less when toString is called. Added toDebugString for verbose printing.
22eac8c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
e007a95 [Joseph K. Bradley] Updated DecisionTreeRunner to accept a test dataset.
1. doc updates
2. simple checks on vector dimensions
3. use column major for matrices
davies jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#2548 from mengxr/mllib-py-clean and squashes the following commits:
6dce2df [Xiangrui Meng] address comments
116b5db [Xiangrui Meng] use np.dot instead of array.dot
75f2fcc [Xiangrui Meng] fix python style
fefce00 [Xiangrui Meng] better check of vector size with more tests
067ef71 [Xiangrui Meng] majored -> major
ef853f9 [Xiangrui Meng] update python linalg api and small fixes
# All-pairs similarity via DIMSUM
Compute all pairs of similar vectors using brute force approach, and also DIMSUM sampling approach.
Laying down some notation: we are looking for all pairs of similar columns in an m x n RowMatrix whose entries are denoted a_ij, with the i’th row denoted r_i and the j’th column denoted c_j. There is an oversampling parameter labeled ɣ that should be set to 4 log(n)/s to get provably correct results (with high probability), where s is the similarity threshold.
The algorithm is stated with a Map and Reduce, with proofs of correctness and efficiency in published papers [1] [2]. The reducer is simply the summation reducer. The mapper is more interesting, and is also the heart of the scheme. As an exercise, you should try to see why in expectation, the map-reduce below outputs cosine similarities.
[1] Bosagh-Zadeh, Reza and Carlsson, Gunnar (2013), Dimension Independent Matrix Square using MapReduce, arXiv:1304.1467 http://arxiv.org/abs/1304.1467
[2] Bosagh-Zadeh, Reza and Goel, Ashish (2012), Dimension Independent Similarity Computation, arXiv:1206.2082 http://arxiv.org/abs/1206.2082
# Testing
Tests for all invocations included.
Added L1 and L2 norm computation to MultivariateStatisticalSummary since it was needed. Added tests for both of them.
Author: Reza Zadeh <rizlar@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#1778 from rezazadeh/dimsumv2 and squashes the following commits:
404c64c [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2
4eb71c6 [Reza Zadeh] Add excludes for normL1 and normL2
ee8bd65 [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2
976ddd4 [Reza Zadeh] Broadcast colMags. Avoid div by zero.
3467cff [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2
aea0247 [Reza Zadeh] Allow large thresholds to promote sparsity
9fe17c0 [Xiangrui Meng] organize imports
2196ba5 [Xiangrui Meng] Merge branch 'rezazadeh-dimsumv2' into dimsumv2
254ca08 [Reza Zadeh] Merge remote-tracking branch 'upstream/master' into dimsumv2
f2947e4 [Xiangrui Meng] some optimization
3c4cf41 [Xiangrui Meng] Merge branch 'master' into rezazadeh-dimsumv2
0e4eda4 [Reza Zadeh] Use partition index for RNG
251bb9c [Reza Zadeh] Documentation
25e9d0d [Reza Zadeh] Line length for style
fb296f6 [Reza Zadeh] renamed to normL1 and normL2
3764983 [Reza Zadeh] Documentation
e9c6791 [Reza Zadeh] New interface and documentation
613f261 [Reza Zadeh] Column magnitude summary
75a0b51 [Reza Zadeh] Use Ints instead of Longs in the shuffle
0f12ade [Reza Zadeh] Style changes
eb1dc20 [Reza Zadeh] Use Double.PositiveInfinity instead of Double.Max
f56a882 [Reza Zadeh] Remove changes to MultivariateOnlineSummarizer
dbc55ba [Reza Zadeh] Make colMagnitudes a method in RowMatrix
41e8ece [Reza Zadeh] style changes
139c8e1 [Reza Zadeh] Syntax changes
029aa9c [Reza Zadeh] javadoc and new test
75edb25 [Reza Zadeh] All tests passing!
05e59b8 [Reza Zadeh] Add test
502ce52 [Reza Zadeh] new interface
654c4fb [Reza Zadeh] default methods
3726ca9 [Reza Zadeh] Remove MatrixAlgebra
6bebabb [Reza Zadeh] remove changes to MatrixSuite
5b8cd7d [Reza Zadeh] Initial files
This PR adds RandomForest to MLlib. The implementation is basic, and future performance optimizations will be important. (Note: RFs = Random Forests.)
# Overview
## RandomForest
* trains multiple trees at once to reduce the number of passes over the data
* allows feature subsets at each node
* uses a queue of nodes instead of fixed groups for each level
This implementation is based an implementation by manishamde and the [Alpine Labs Sequoia Forest](https://github.com/AlpineNow/SparkML2) by codedeft (in particular, the TreePoint, BaggedPoint, and node queue implementations). Thank you for your inputs!
## Testing
Correctness: This has been tested for correctness with the test suites and with DecisionTreeRunner on example datasets.
Performance: This has been performance tested using [this branch of spark-perf](https://github.com/jkbradley/spark-perf/tree/rfs). Results below.
### Regression tests for DecisionTree
Summary: For training 1 tree, there are small regressions, especially from feature subsampling.
In the table below, each row is a single (random) dataset. The 2 different sets of result columns are for 2 different RF implementations:
* (numTrees): This is from an earlier commit, after implementing RandomForest to train multiple trees at once. It does not include any code for feature subsampling.
* (feature subsets): This is from this current PR's code, after implementing feature subsampling.
These tests were to identify regressions in DecisionTree, so they are training 1 tree with all of the features (i.e., no feature subsampling).
These were run on an EC2 cluster with 15 workers, training 1 tree with maxDepth = 5 (= 6 levels). Speedup values < 1 indicate slowdowns from the old DecisionTree implementation.
numInstances | numFeatures | runtime (sec) | speedup | runtime (sec) | speedup
---- | ---- | ---- | ---- | ---- | ----
| | (numTrees) | (numTrees) | (feature subsets) | (feature subsets)
20000 | 100 | 4.051 | 1.044433473 | 4.478 | 0.9448414471
20000 | 500 | 8.472 | 1.104461756 | 9.315 | 1.004508857
20000 | 1500 | 19.354 | 1.05854087 | 20.863 | 0.9819776638
20000 | 3500 | 43.674 | 1.072033704 | 45.887 | 1.020332556
200000 | 100 | 4.196 | 1.171830315 | 4.848 | 1.014232673
200000 | 500 | 8.926 | 1.082791844 | 9.771 | 0.989151571
200000 | 1500 | 20.58 | 1.068415938 | 22.134 | 0.9934038131
200000 | 3500 | 48.043 | 1.075203464 | 52.249 | 0.9886505005
2000000 | 100 | 4.944 | 1.01355178 | 5.796 | 0.8645617667
2000000 | 500 | 11.11 | 1.016831683 | 12.482 | 0.9050632911
2000000 | 1500 | 31.144 | 1.017852556 | 35.274 | 0.8986789136
2000000 | 3500 | 79.981 | 1.085382778 | 101.105 | 0.8586123337
20000000 | 100 | 8.304 | 0.9270231214 | 9.073 | 0.8484514494
20000000 | 500 | 28.174 | 1.083268262 | 34.236 | 0.8914592826
20000000 | 1500 | 143.97 | 0.9579634646 | 159.275 | 0.8659111599
### Tests for forests
I have run other tests with numTrees=10 and with sqrt(numFeatures), and those indicate that multi-model training and feature subsets can speed up training for forests, especially when training deeper trees.
# Details on specific classes
## Changes to DecisionTree
* Main train() method is now in RandomForest.
* findBestSplits() is no longer needed. (It split levels into groups, but we now use a queue of nodes.)
* Many small changes to support RFs. (Note: These methods should be moved to RandomForest.scala in a later PR, but are in DecisionTree.scala to make code comparison easier.)
## RandomForest
* Main train() method is from old DecisionTree.
* selectNodesToSplit: Note that it selects nodes and feature subsets jointly to track memory usage.
## RandomForestModel
* Stores an Array[DecisionTreeModel]
* Prediction:
* For classification, most common label. For regression, mean.
* We could support other methods later.
## examples/.../DecisionTreeRunner
* This now takes numTrees and featureSubsetStrategy, to support RFs.
## DTStatsAggregator
* 2 types of functionality (w/ and w/o subsampling features): These require different indexing methods. (We could treat both as subsampling, but this is less efficient
DTStatsAggregator is now abstract, and 2 child classes implement these 2 types of functionality.
## impurities
* These now take instance weights.
## Node
* Some vals changed to vars.
* This is unfortunately a public API change (DeveloperApi). This could be avoided by creating a LearningNode struct, but would be awkward.
## RandomForestSuite
Please let me know if there are missing tests!
## BaggedPoint
This wraps TreePoint and holds bootstrap weights/counts.
# Design decisions
* BaggedPoint: BaggedPoint is separate from TreePoint since it may be useful for other bagging algorithms later on.
* RandomForest public API: What options should be easily supported by the train* methods? Should ALL options be in the Java-friendly constructors? Should there be a constructor taking Strategy?
* Feature subsampling options: What options should be supported? scikit-learn supports the same options, except for "onethird." One option would be to allow users to specific fractions ("0.1"): the current options could be supported, and any unrecognized values would be parsed as Doubles in [0,1].
* Splits and bins are computed before bootstrapping, so all trees use the same discretization.
* One queue, instead of one queue per tree.
CC: mengxr manishamde codedeft chouqin Please let me know if you have suggestions---thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2435 from jkbradley/rfs-new and squashes the following commits:
c694174 [Joseph K. Bradley] Fixed typo
cc59d78 [Joseph K. Bradley] fixed imports
e25909f [Joseph K. Bradley] Simplified node group maps. Specifically, created NodeIndexInfo to store node index in agg and feature subsets, and no longer create extra maps in findBestSplits
fbe9a1e [Joseph K. Bradley] Changed default featureSubsetStrategy to be sqrt for classification, onethird for regression. Updated docs with references.
ef7c293 [Joseph K. Bradley] Updates based on code review. Most substantial changes: * Simplified DTStatsAggregator * Made RandomForestModel.trees public * Added test for regression to RandomForestSuite
593b13c [Joseph K. Bradley] Fixed bug in metadata for computing log2(num features). Now it checks >= 1.
a1a08df [Joseph K. Bradley] Removed old comments
866e766 [Joseph K. Bradley] Changed RandomForestSuite randomized tests to use multiple fixed random seeds.
ff8bb96 [Joseph K. Bradley] removed usage of null from RandomForest and replaced with Option
bf1a4c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
6b79c07 [Joseph K. Bradley] Added RandomForestSuite, and fixed small bugs, style issues.
d7753d4 [Joseph K. Bradley] Added numTrees and featureSubsetStrategy to DecisionTreeRunner (to support RandomForest). Fixed bugs so that RandomForest now runs.
746d43c [Joseph K. Bradley] Implemented feature subsampling. Tested DecisionTree but not RandomForest.
6309d1d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new. Added RandomForestModel.toString
b7ae594 [Joseph K. Bradley] Updated docs. Small fix for bug which does not cause errors: No longer allocate unused child nodes for leaf nodes.
121c74e [Joseph K. Bradley] Basic random forests are implemented. Random features per node not yet implemented. Test suite not implemented.
325d18a [Joseph K. Bradley] Merge branch 'chouqin-dt-preprune' into rfs-new
4ef9bf1 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy.
a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
6da8571 [Joseph K. Bradley] RFs partly implemented, not done yet
eddd1eb [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1
0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes
e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py
306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc
eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix
d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated
d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training.
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
This PR for [SPARK-3614](https://issues.apache.org/jira/browse/SPARK-3614) adds functionality for filtering out terms which do not appear in at least a minimum number of documents.
This is implemented using a minimumOccurence parameter (default 0). When terms' document frequencies are less than minimumOccurence, their IDFs are set to 0, just like when the DF is 0. As a result, the TF-IDFs for the terms are found to be 0, as if the terms were not present in the documents.
This PR makes the following changes:
* Add a minimumOccurence parameter to the IDF and DocumentFrequencyAggregator classes.
* Create a parameter-less constructor for IDF with a default minimumOccurence value of 0 to remain backwards-compatibility with the original IDF API.
* Sets the IDFs to 0 for terms which DFs are less than minimumOccurence
* Add tests to the Spark IDFSuite and Java JavaTfIdfSuite test suites
* Updated the MLLib Feature Extraction programming guide to describe the new feature
Author: RJ Nowling <rnowling@gmail.com>
Closes#2494 from rnowling/spark-3614-idf-filter and squashes the following commits:
0aa3c63 [RJ Nowling] Fix identation
e6523a8 [RJ Nowling] Remove unnecessary toDouble's from IDFSuite
bfa82ec [RJ Nowling] Add space after if
30d20b3 [RJ Nowling] Add spaces around equals signs
9013447 [RJ Nowling] Add space before division operator
79978fc [RJ Nowling] Remove unnecessary semi-colon
40fd70c [RJ Nowling] Change minimumOccurence to minDocFreq in code and docs
47850ab [RJ Nowling] Changed minimumOccurence to Int from Long
9fb4093 [RJ Nowling] Remove unnecessary lines from IDF class docs
1fc09d8 [RJ Nowling] Add backwards-compatible constructor to DocumentFrequencyAggregator
1801fd2 [RJ Nowling] Fix style errors in IDF.scala
6897252 [RJ Nowling] Preface minimumOccurence members with val to make them final and immutable
a200bab [RJ Nowling] Remove unnecessary else statement
4b974f5 [RJ Nowling] Remove accidentally-added import from testing
c0cc643 [RJ Nowling] Add minimumOccurence filtering to IDF
Add warnings to KMeans, GeneralizedLinearAlgorithm, and computeSVD when called with input data that is not cached. KMeans is implemented iteratively, and I believe that GeneralizedLinearAlgorithm’s current optimizers are iterative and its future optimizers are also likely to be iterative. RowMatrix’s computeSVD is iterative against an RDD when run in DistARPACK mode. ALS and DecisionTree are iterative as well, but they implement RDD caching internally so do not require a warning.
I added a warning to GeneralizedLinearAlgorithm rather than inside its optimizers, where the iteration actually occurs, because internally GeneralizedLinearAlgorithm maps its input data to an uncached RDD before passing it to an optimizer. (In other words, the warning would be printed for every GeneralizedLinearAlgorithm run, regardless of whether its input is cached, if the warning were in GradientDescent or other optimizer.) I assume that use of an uncached RDD by GeneralizedLinearAlgorithm is intentional, and that the mapping there (adding label, intercepts and scaling) is a lightweight operation. Arguably a user calling an optimizer such as GradientDescent will be knowledgable enough to cache their data without needing a log warning, so lack of a warning in the optimizers may be ok.
Some of the documentation examples making use of these iterative algorithms did not cache their training RDDs (while others did). I updated the examples to always cache. I also fixed some (unrelated) minor errors in the documentation examples.
Author: Aaron Staple <aaron.staple@gmail.com>
Closes#2347 from staple/SPARK-1484 and squashes the following commits:
bd49701 [Aaron Staple] Address review comments.
ab2d4a4 [Aaron Staple] Disable warnings on python code path.
a7a0f99 [Aaron Staple] Change code comments per review comments.
7cca1dc [Aaron Staple] Change warning message text.
c77e939 [Aaron Staple] [SPARK-1484][MLLIB] Warn when running an iterative algorithm on uncached data.
3b6c511 [Aaron Staple] Minor doc example fixes.
Currently, we serialize the data between JVM and Python case by case manually, this cannot scale to support so many APIs in MLlib.
This patch will try to address this problem by serialize the data using pickle protocol, using Pyrolite library to serialize/deserialize in JVM. Pickle protocol can be easily extended to support customized class.
All the modules are refactored to use this protocol.
Known issues: There will be some performance regression (both CPU and memory, the serialized data increased)
Author: Davies Liu <davies.liu@gmail.com>
Closes#2378 from davies/pickle_mllib and squashes the following commits:
dffbba2 [Davies Liu] Merge branch 'master' of github.com:apache/spark into pickle_mllib
810f97f [Davies Liu] fix equal of matrix
032cd62 [Davies Liu] add more type check and conversion for user_product
bd738ab [Davies Liu] address comments
e431377 [Davies Liu] fix cache of rdd, refactor
19d0967 [Davies Liu] refactor Picklers
2511e76 [Davies Liu] cleanup
1fccf1a [Davies Liu] address comments
a2cc855 [Davies Liu] fix tests
9ceff73 [Davies Liu] test size of serialized Rating
44e0551 [Davies Liu] fix cache
a379a81 [Davies Liu] fix pickle array in python2.7
df625c7 [Davies Liu] Merge commit '154d141' into pickle_mllib
154d141 [Davies Liu] fix autobatchedpickler
44736d7 [Davies Liu] speed up pickling array in Python 2.7
e1d1bfc [Davies Liu] refactor
708dc02 [Davies Liu] fix tests
9dcfb63 [Davies Liu] fix style
88034f0 [Davies Liu] rafactor, address comments
46a501e [Davies Liu] choose batch size automatically
df19464 [Davies Liu] memorize the module and class name during pickleing
f3506c5 [Davies Liu] Merge branch 'master' into pickle_mllib
722dd96 [Davies Liu] cleanup _common.py
0ee1525 [Davies Liu] remove outdated tests
b02e34f [Davies Liu] remove _common.py
84c721d [Davies Liu] Merge branch 'master' into pickle_mllib
4d7963e [Davies Liu] remove muanlly serialization
6d26b03 [Davies Liu] fix tests
c383544 [Davies Liu] classification
f2a0856 [Davies Liu] mllib/regression
d9f691f [Davies Liu] mllib/util
cccb8b1 [Davies Liu] mllib/tree
8fe166a [Davies Liu] Merge branch 'pickle' into pickle_mllib
aa2287e [Davies Liu] random
f1544c4 [Davies Liu] refactor clustering
52d1350 [Davies Liu] use new protocol in mllib/stat
b30ef35 [Davies Liu] use pickle to serialize data for mllib/recommendation
f44f771 [Davies Liu] enable tests about array
3908f5c [Davies Liu] Merge branch 'master' into pickle
c77c87b [Davies Liu] cleanup debugging code
60e4e2f [Davies Liu] support unpickle array.array for Python 2.6
Local `SparseMatrix` support added in Compressed Column Storage (CCS) format in addition to Level-2 and Level-3 BLAS operations such as dgemv and dgemm respectively.
BLAS doesn't support sparse matrix operations, therefore support for `SparseMatrix`-`DenseMatrix` multiplication and `SparseMatrix`-`DenseVector` implementations have been added. I will post performance comparisons in the comments momentarily.
Author: Burak <brkyvz@gmail.com>
Closes#2294 from brkyvz/SPARK-3418 and squashes the following commits:
88814ed [Burak] Hopefully fixed MiMa this time
47e49d5 [Burak] really fixed MiMa issue
f0bae57 [Burak] [SPARK-3418] Fixed MiMa compatibility issues (excluded from check)
4b7dbec [Burak] 9/17 comments addressed
7af2f83 [Burak] sealed traits Vector and Matrix
d3a8a16 [Burak] [SPARK-3418] Squashed missing alpha bug.
421045f [Burak] [SPARK-3418] New code review comments addressed
f35a161 [Burak] [SPARK-3418] Code review comments addressed and multiplication further optimized
2508577 [Burak] [SPARK-3418] Fixed one more style issue
d16e8a0 [Burak] [SPARK-3418] Fixed style issues and added documentation for methods
204a3f7 [Burak] [SPARK-3418] Fixed failing Matrix unit test
6025297 [Burak] [SPARK-3418] Fixed Scala-style errors
dc7be71 [Burak] [SPARK-3418][MLlib] Matrix unit tests expanded with indexing and updating
d2d5851 [Burak] [SPARK-3418][MLlib] Sparse Matrix support and additional native BLAS operations added
