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simplify FPTree and update FPGrowth #1

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@mengxr mengxr commented Feb 1, 2015

@jackylk This PR contains the following updates:

  1. simplify fp-tree and extract transactions and frequent itemsets as iterators
  2. take a generic item type
  3. use ranks instead of the raw item to build trees
  4. update FP-Growth to send at most one conditional transactions per (transaction, partition)

Now this should implement the same algorithm as described in the PFP paper.

jackylk added a commit that referenced this pull request Feb 2, 2015
simplify FPTree and update FPGrowth
@jackylk jackylk merged commit bee3093 into jackylk:apriori Feb 2, 2015
jackylk pushed a commit that referenced this pull request Feb 3, 2015
This implements the functionality for SPARK-4749 and provides units tests in Scala and PySpark

Author: nate.crosswhite <[email protected]>
Author: nxwhite-str <[email protected]>
Author: Xiangrui Meng <[email protected]>

Closes apache#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
jackylk pushed a commit that referenced this pull request Feb 3, 2015
…l adjacent violators algorithm

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](https://bitbucket.org/sreangsu/libpav/src/f744bc1b0fea257f0cacaead1c922eab201ba91b/src/pav.h?at=default) and [Gustav Larsson](https://gist.github.com/gustavla/9499068).

Author: martinzapletal <[email protected]>
Author: Xiangrui Meng <[email protected]>
Author: Martin Zapletal <[email protected]>

Closes apache#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 apache#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 apache#3519. Styling and comment fixes.
d93c8f9 [martinzapletal] SPARK-3278 changes after PR comments apache#3519. Change to IsotonicRegression api. Isotonic parameter now follows api of other mllib algorithms
1fff77d [martinzapletal] SPARK-3278 changes after PR comments apache#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 apache#3519. Binary search used for isotonic regression model predictions
fad4bf9 [martinzapletal] SPARK-3278 changes after PR comments apache#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
jackylk added a commit that referenced this pull request Feb 3, 2015
…pattern mining in MLlib

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 <[email protected]>
Author: Jacky Li <[email protected]>
Author: Xiangrui Meng <[email protected]>

Closes apache#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
jackylk pushed a commit that referenced this pull request Mar 5, 2015
…n LDAModel.scala

Remove unicode characters from MLlib file.

Author: Michael Griffiths <[email protected]>
Author: Griffiths, Michael (NYC-RPM) <[email protected]>

Closes apache#4815 from msjgriffiths/SPARK-6063 and squashes the following commits:

bcd7de1 [Griffiths, Michael (NYC-RPM)] Change \u201D quote marks around 'theta' to standard single apostrophe (\x27)
38eb535 [Michael Griffiths] Merge pull request #2 from apache/master
b08e865 [Michael Griffiths] Merge pull request #1 from apache/master
jackylk pushed a commit that referenced this pull request Mar 5, 2015
…ering

The API signatire for join requires the JoinType to be the third parameter. The code examples provided for join show JoinType being provided as the 2nd parater resuling in errors (i.e. "df1.join(df2, "outer", $"df1Key" === $"df2Key") ). The correct sample code is df1.join(df2, $"df1Key" === $"df2Key", "outer")

Author: Paul Power <[email protected]>

Closes apache#4847 from peerside/master and squashes the following commits:

ebc1efa [Paul Power] Merge pull request #1 from peerside/peerside-patch-1
e353340 [Paul Power] Updated comments use correct sample code for Dataframe joins
jackylk pushed a commit that referenced this pull request Mar 5, 2015
…ce bug

LBFGS and OWLQN in Breeze 0.10 has convergence check bug.
This is fixed in 0.11, see the description in Breeze project for detail:

scalanlp/breeze#373 (comment)

Author: Xiangrui Meng <[email protected]>
Author: DB Tsai <[email protected]>
Author: DB Tsai <[email protected]>

Closes apache#4879 from dbtsai/breeze and squashes the following commits:

d848f65 [DB Tsai] Merge pull request #1 from mengxr/AlpineNow-breeze
c2ca6ac [Xiangrui Meng] upgrade to breeze-0.11.1
35c2f26 [Xiangrui Meng] fix LRSuite
397a208 [DB Tsai] upgrade breeze
jackylk pushed a commit that referenced this pull request Apr 1, 2015
…ering

The API signatire for join requires the JoinType to be the third parameter. The code examples provided for join show JoinType being provided as the 2nd parater resuling in errors (i.e. "df1.join(df2, "outer", $"df1Key" === $"df2Key") ). The correct sample code is df1.join(df2, $"df1Key" === $"df2Key", "outer")

Author: Paul Power <[email protected]>

Closes apache#4847 from peerside/master and squashes the following commits:

ebc1efa [Paul Power] Merge pull request #1 from peerside/peerside-patch-1
e353340 [Paul Power] Updated comments use correct sample code for Dataframe joins

(cherry picked from commit d9a8bae)
Signed-off-by: Michael Armbrust <[email protected]>
jackylk pushed a commit that referenced this pull request Apr 1, 2015
…ce bug

LBFGS and OWLQN in Breeze 0.10 has convergence check bug.
This is fixed in 0.11, see the description in Breeze project for detail:

scalanlp/breeze#373 (comment)

Author: Xiangrui Meng <[email protected]>
Author: DB Tsai <[email protected]>
Author: DB Tsai <[email protected]>

Closes apache#4879 from dbtsai/breeze and squashes the following commits:

d848f65 [DB Tsai] Merge pull request #1 from mengxr/AlpineNow-breeze
c2ca6ac [Xiangrui Meng] upgrade to breeze-0.11.1
35c2f26 [Xiangrui Meng] fix LRSuite
397a208 [DB Tsai] upgrade breeze

(cherry picked from commit 76e20a0)
Signed-off-by: Xiangrui Meng <[email protected]>
jackylk pushed a commit that referenced this pull request Apr 1, 2015
…ve path.

when i run cmd like that sc.addFile("../test.txt"), it did not work and throwed an exception:
java.lang.IllegalArgumentException: java.net.URISyntaxException: Relative path in absolute URI: file:../test.txt
at org.apache.hadoop.fs.Path.initialize(Path.java:206)
at org.apache.hadoop.fs.Path.<init>(Path.java:172)
........
.......
Caused by: java.net.URISyntaxException: Relative path in absolute URI: file:../test.txt
at java.net.URI.checkPath(URI.java:1804)
at java.net.URI.<init>(URI.java:752)
at org.apache.hadoop.fs.Path.initialize(Path.java:203)

Author: DoingDone9 <[email protected]>

Closes apache#4993 from DoingDone9/relativePath and squashes the following commits:

ee375cd [DoingDone9] Update SparkContextSuite.scala
d594e16 [DoingDone9] Update SparkContext.scala
0ff3fa8 [DoingDone9] test for add file
dced8eb [DoingDone9] Update SparkContext.scala
e4a13fe [DoingDone9] getCanonicalPath
161cae3 [DoingDone9] Merge pull request apache#4 from apache/master
c87e8b6 [DoingDone9] Merge pull request apache#3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
jackylk pushed a commit that referenced this pull request Apr 1, 2015
…, because this will make some UDAF can not work.

spark avoid old inteface of hive, then some udaf can not work like "org.apache.hadoop.hive.ql.udf.generic.GenericUDAFAverage"

Author: DoingDone9 <[email protected]>

Closes apache#5131 from DoingDone9/udaf and squashes the following commits:

9de08d0 [DoingDone9] Update HiveUdfSuite.scala
49c62dc [DoingDone9] Update hiveUdfs.scala
98b134f [DoingDone9] Merge pull request apache#5 from apache/master
161cae3 [DoingDone9] Merge pull request apache#4 from apache/master
c87e8b6 [DoingDone9] Merge pull request apache#3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
jackylk pushed a commit that referenced this pull request Apr 1, 2015
…her duplicate in HiveQl

Author: DoingDone9 <[email protected]>

Closes apache#4973 from DoingDone9/sort_token and squashes the following commits:

855fa10 [DoingDone9] Update HiveQl.scala
c7080b3 [DoingDone9] Sort these tokens in alphabetic order to avoid further duplicate in HiveQl
c87e8b6 [DoingDone9] Merge pull request apache#3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
jackylk pushed a commit that referenced this pull request Apr 1, 2015
… failed!!

wrong code : val tmpDir = Files.createTempDir()
not Files should Utils

Author: DoingDone9 <[email protected]>

Closes apache#5198 from DoingDone9/FilesBug and squashes the following commits:

6e0140d [DoingDone9] Update InsertIntoHiveTableSuite.scala
e57d23f [DoingDone9] Update InsertIntoHiveTableSuite.scala
802261c [DoingDone9] Merge pull request apache#7 from apache/master
d00303b [DoingDone9] Merge pull request apache#6 from apache/master
98b134f [DoingDone9] Merge pull request apache#5 from apache/master
161cae3 [DoingDone9] Merge pull request apache#4 from apache/master
c87e8b6 [DoingDone9] Merge pull request apache#3 from apache/master
cb1852d [DoingDone9] Merge pull request #2 from apache/master
c3f046f [DoingDone9] Merge pull request #1 from apache/master
jackylk pushed a commit that referenced this pull request Apr 1, 2015
Added optional model type parameter for  NaiveBayes training. Can be either Multinomial or Bernoulli.

When Bernoulli is given the Bernoulli smoothing is used for fitting and for prediction as per: http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html.

 Default for model is original Multinomial fit and predict.

Added additional testing for Bernoulli and Multinomial models.

Author: leahmcguire <[email protected]>
Author: Joseph K. Bradley <[email protected]>
Author: Leah McGuire <[email protected]>

Closes apache#4087 from leahmcguire/master and squashes the following commits:

f3c8994 [leahmcguire] changed checks on model type to requires
acb69af [leahmcguire] removed enum type and replaces all modelType parameters with strings
2224b15 [Leah McGuire] Merge pull request #2 from jkbradley/leahmcguire-master
9ad89ca [Joseph K. Bradley] removed old code
6a8f383 [Joseph K. Bradley] Added new model save/load format 2.0 for NaiveBayesModel after modelType parameter was added.  Updated tests.  Also updated ModelType enum-like type.
852a727 [leahmcguire] merged with upstream master
a22d670 [leahmcguire] changed NaiveBayesModel modelType parameter back to NaiveBayes.ModelType, made NaiveBayes.ModelType serializable, fixed getter method in NavieBayes
18f3219 [leahmcguire] removed private from naive bayes constructor for lambda only
bea62af [leahmcguire] put back in constructor for NaiveBayes
01baad7 [leahmcguire] made fixes from code review
fb0a5c7 [leahmcguire] removed typo
e2d925e [leahmcguire] fixed nonserializable error that was causing naivebayes test failures
2d0c1ba [leahmcguire] fixed typo in NaiveBayes
c298e78 [leahmcguire] fixed scala style errors
b85b0c9 [leahmcguire] Merge remote-tracking branch 'upstream/master'
900b586 [leahmcguire] fixed model call so that uses type argument
ea09b28 [leahmcguire] Merge remote-tracking branch 'upstream/master'
e016569 [leahmcguire] updated test suite with model type fix
85f298f [leahmcguire] Merge remote-tracking branch 'upstream/master'
dc65374 [leahmcguire] integrated model type fix
7622b0c [leahmcguire] added comments and fixed style as per rb
b93aaf6 [Leah McGuire] Merge pull request #1 from jkbradley/nb-model-type
3730572 [Joseph K. Bradley] modified NB model type to be more Java-friendly
b61b5e2 [leahmcguire] added back compatable constructor to NaiveBayesModel to fix MIMA test failure
5a4a534 [leahmcguire] fixed scala style error in NaiveBayes
3891bf2 [leahmcguire] synced with apache spark and resolved merge conflict
d9477ed [leahmcguire] removed old inaccurate comment from test suite for mllib naive bayes
76e5b0f [leahmcguire] removed unnecessary sort from test
0313c0c [leahmcguire] fixed style error in NaiveBayes.scala
4a3676d [leahmcguire] Updated changes re-comments. Got rid of verbose populateMatrix method. Public api now has string instead of enumeration. Docs are updated."
ce73c63 [leahmcguire] added Bernoulli option to niave bayes model in mllib, added optional model type parameter for training. When Bernoulli is given the Bernoulli smoothing is used for fitting and for prediction http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
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