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freeman-lab
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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 apache#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
freeman-lab
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The additions add the abstract BinaryFileInputFormat and BinaryRecordReader classes for reading in data as a byte stream and converting it to another format using the ```def parseByteArray(inArray: Array[Byte]): T``` function.
As a trivial example ```ByteInputFormat``` and ```ByteRecordReader``` are included which just return the Array[Byte] from a given file.
Finally a RDD for ```BinaryFileInputFormat``` (to allow for easier partitioning changes as was done for WholeFileInput) was added and the appropriate byteFiles to the ```SparkContext``` so the functions can be easily used by others.
A common use case might be to read in a folder
```
sc.byteFiles("s3://mydrive/tif/*.tif").map(rawData => ReadTiffFromByteArray(rawData))
```
Author: Kevin Mader <kevinmader@gmail.com>
Author: Kevin Mader <kmader@users.noreply.github.com>
Closes apache#1658 from kmader/master and squashes the following commits:
3c49a30 [Kevin Mader] fixing wholetextfileinput to it has the same setMinPartitions function as in BinaryData files
359a096 [Kevin Mader] making the final corrections suggested by @mateiz and renaming a few functions to make their usage clearer
6379be4 [Kevin Mader] reorganizing code
7b9d181 [Kevin Mader] removing developer API, cleaning up imports
8ac288b [Kevin Mader] fixed a single slightly over 100 character line
92bda0d [Kevin Mader] added new tests, renamed files, fixed several of the javaapi functions, formatted code more nicely
a32fef7 [Kevin Mader] removed unneeded classes added DeveloperApi note to portabledatastreams since the implementation might change
49174d9 [Kevin Mader] removed unneeded classes added DeveloperApi note to portabledatastreams since the implementation might change
c27a8f1 [Kevin Mader] jenkins crashed before running anything last time, so making minor change
b348ce1 [Kevin Mader] fixed order in check (prefix only appears on jenkins not when I run unit tests locally)
0588737 [Kevin Mader] filename check in "binary file input as byte array" test now ignores prefixes and suffixes which might get added by Hadoop
4163e38 [Kevin Mader] fixing line length and output from FSDataInputStream to DataInputStream to minimize sensitivity to Hadoop API changes
19812a8 [Kevin Mader] Fixed the serialization issue with PortableDataStream since neither CombineFileSplit nor TaskAttemptContext implement the Serializable interface, by using ByteArrays for storing both and then recreating the objects from these bytearrays as needed.
238c83c [Kevin Mader] fixed several scala-style issues, changed structure of binaryFiles, removed excessive classes added new tests. The caching tests still have a serialization issue, but that should be easily fixed as well.
932a206 [Kevin Mader] Update RawFileInput.scala
a01c9cf [Kevin Mader] Update RawFileInput.scala
441f79a [Kevin Mader] fixed a few small comments and dependency
12e7be1 [Kevin Mader] removing imglib from maven (definitely not ready yet)
5deb79e [Kevin Mader] added new portabledatastream to code so that it can be serialized correctly
f032bc0 [Kevin Mader] fixed bug in path name, renamed tests
bc5c0b9 [Kevin Mader] made minor stylistic adjustments from mateiz
df8e528 [Kevin Mader] fixed line lengths and changed java test
9a313d5 [Kevin Mader] making classes that needn't be public private, adding automatic file closure, adding new tests
edf5829 [Kevin Mader] fixing line lengths, adding new lines
f4841dc [Kevin Mader] un-optimizing imports, silly intellij
eacfaa6 [Kevin Mader] Added FixedLengthBinaryInputFormat and RecordReader from freeman-lab and added them to both the JavaSparkContext and the SparkContext as fixedLengthBinaryFile
1622935 [Kevin Mader] changing the line lengths to make jenkins happy
1cfa38a [Kevin Mader] added apache headers, added datainputstream directly as an output option for more complicated readers (HDF5 perhaps), and renamed several of the functions and files to be more consistent. Also added parallel functions to the java api
84035f1 [Kevin Mader] adding binary and byte file support spark
81c5f12 [Kevin Mader] Merge pull request #1 from apache/master
freeman-lab
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Dec 24, 2014
Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON. Metadata is preserved through simple operations like `SELECT`. marmbrus liancheng Author: Xiangrui Meng <meng@databricks.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#2701 from mengxr/structfield-metadata and squashes the following commits: dedda56 [Xiangrui Meng] merge remote 5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701 886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages. 589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 1e2abcf [Xiangrui Meng] change default value of metadata to None in python 611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility 3f49aab [Xiangrui Meng] remove StructField.toString 24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 473a7c5 [Xiangrui Meng] merge master c9d7301 [Xiangrui Meng] organize imports 1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java 60cc131 [Xiangrui Meng] add doc and header 60614c7 [Xiangrui Meng] add metadata e42c452 [Xiangrui Meng] merge master 93518fb [Xiangrui Meng] support metadata in python 905bb89 [Xiangrui Meng] java conversions 618e349 [Xiangrui Meng] make tests work in scala 61b8e0f [Xiangrui Meng] merge master 7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString c41a664 [Xiangrui Meng] merge master d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite 67fdebb [Xiangrui Meng] add test on join d65072e [Xiangrui Meng] remove Map.empty 367d237 [Xiangrui Meng] add test c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
freeman-lab
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Dec 24, 2014
In RDDSampler, it try use numpy to gain better performance for possion(), but the number of call of random() is only (1+faction) * N in the pure python implementation of possion(), so there is no much performance gain from numpy. numpy is not a dependent of pyspark, so it maybe introduce some problem, such as there is no numpy installed in slaves, but only installed master, as reported in SPARK-927. It also complicate the code a lot, so we may should remove numpy from RDDSampler. I also did some benchmark to verify that: ``` >>> from pyspark.mllib.random import RandomRDDs >>> rdd = RandomRDDs.uniformRDD(sc, 1 << 20, 1).cache() >>> rdd.count() # cache it >>> rdd.sample(True, 0.9).count() # measure this line ``` the results: |withReplacement | random | numpy.random | ------- | ------------ | ------- |True | 1.5 s| 1.4 s| |False| 0.6 s | 0.8 s| closes apache#2313 Note: this patch including some commits that not mirrored to github, it will be OK after it catches up. Author: Davies Liu <davies@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes apache#3351 from davies/numpy and squashes the following commits: 5c438d7 [Davies Liu] fix comment c5b9252 [Davies Liu] Merge pull request #1 from mengxr/SPARK-4477 98eb31b [Xiangrui Meng] make poisson sampling slightly faster ee17d78 [Davies Liu] remove = for float 13f7b05 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into numpy f583023 [Davies Liu] fix tests 51649f5 [Davies Liu] remove numpy in RDDSampler 78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain f5fdf63 [Davies Liu] fix bug with int in weights 4dfa2cd [Davies Liu] refactor f866bcf [Davies Liu] remove unneeded change c7a2007 [Davies Liu] switch to python implementation 95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit 0d9b256 [Davies Liu] refactor 1715ee3 [Davies Liu] address comments 41fce54 [Davies Liu] randomSplit()
freeman-lab
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…if sql has null
val jsc = new org.apache.spark.api.java.JavaSparkContext(sc)
val jhc = new org.apache.spark.sql.hive.api.java.JavaHiveContext(jsc)
val nrdd = jhc.hql("select null from spark_test.for_test")
println(nrdd.schema)
Then the error is thrown as follows:
scala.MatchError: NullType (of class org.apache.spark.sql.catalyst.types.NullType$)
at org.apache.spark.sql.types.util.DataTypeConversions$.asJavaDataType(DataTypeConversions.scala:43)
Author: YanTangZhai <hakeemzhai@tencent.com>
Author: yantangzhai <tyz0303@163.com>
Author: Michael Armbrust <michael@databricks.com>
Closes apache#3538 from YanTangZhai/MatchNullType and squashes the following commits:
e052dff [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
4b4bb34 [yantangzhai] [SPARK-4676] [SQL] JavaSchemaRDD.schema may throw NullType MatchError if sql has null
896c7b7 [yantangzhai] fix NullType MatchError in JavaSchemaRDD when sql has null
6e643f8 [YanTangZhai] Merge pull request apache#11 from apache/master
e249846 [YanTangZhai] Merge pull request apache#10 from apache/master
d26d982 [YanTangZhai] Merge pull request apache#9 from apache/master
76d4027 [YanTangZhai] Merge pull request apache#8 from apache/master
03b62b0 [YanTangZhai] Merge pull request apache#7 from apache/master
8a00106 [YanTangZhai] Merge pull request apache#6 from apache/master
cbcba66 [YanTangZhai] Merge pull request apache#3 from apache/master
cdef539 [YanTangZhai] Merge pull request #1 from apache/master
freeman-lab
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…the lineage The related JIRA is https://issues.apache.org/jira/browse/SPARK-4672 Iterative GraphX applications always have long lineage, while checkpoint() on EdgeRDD and VertexRDD themselves cannot shorten the lineage. In contrast, if we perform checkpoint() on their ParitionsRDD, the long lineage can be cut off. Moreover, the existing operations such as cache() in this code is performed on the PartitionsRDD, so checkpoint() should do the same way. More details and explanation can be found in the JIRA. Author: JerryLead <JerryLead@163.com> Author: Lijie Xu <csxulijie@gmail.com> Closes apache#3549 from JerryLead/my_graphX_checkpoint and squashes the following commits: d1aa8d8 [JerryLead] Perform checkpoint() on PartitionsRDD not VertexRDD and EdgeRDD themselves ff08ed4 [JerryLead] Merge branch 'master' of https://github.com/apache/spark c0169da [JerryLead] Merge branch 'master' of https://github.com/apache/spark 52799e3 [Lijie Xu] Merge pull request #1 from apache/master
freeman-lab
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Dec 24, 2014
…erflow error The related JIRA is https://issues.apache.org/jira/browse/SPARK-4672 In a nutshell, if `val partitionsRDD` in EdgeRDDImpl and VertexRDDImpl are non-transient, the serialization chain can become very long in iterative algorithms and finally lead to the StackOverflow error. More details and explanation can be found in the JIRA. Author: JerryLead <JerryLead@163.com> Author: Lijie Xu <csxulijie@gmail.com> Closes apache#3544 from JerryLead/my_graphX and squashes the following commits: 628f33c [JerryLead] set PartitionsRDD to be transient in EdgeRDDImpl and VertexRDDImpl c0169da [JerryLead] Merge branch 'master' of https://github.com/apache/spark 52799e3 [Lijie Xu] Merge pull request #1 from apache/master
freeman-lab
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…ation chain The related JIRA is https://issues.apache.org/jira/browse/SPARK-4672 The f closure of `PartitionsRDD(ZippedPartitionsRDD2)` contains a `$outer` that references EdgeRDD/VertexRDD, which causes task's serialization chain become very long in iterative GraphX applications. As a result, StackOverflow error will occur. If we set "f = null" in `clearDependencies()`, checkpoint() can cut off the long serialization chain. More details and explanation can be found in the JIRA. Author: JerryLead <JerryLead@163.com> Author: Lijie Xu <csxulijie@gmail.com> Closes apache#3545 from JerryLead/my_core and squashes the following commits: f7faea5 [JerryLead] checkpoint() should clear the f to avoid StackOverflow error c0169da [JerryLead] Merge branch 'master' of https://github.com/apache/spark 52799e3 [Lijie Xu] Merge pull request #1 from apache/master
freeman-lab
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…ins an empty AttributeSet() references The sql "select * from spark_test::for_test where abs(20141202) is not null" has predicates=List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)) and partitionKeyIds=AttributeSet(). PruningPredicates is List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)). Then the exception "java.lang.IllegalArgumentException: requirement failed: Partition pruning predicates only supported for partitioned tables." is thrown. The sql "select * from spark_test::for_test_partitioned_table where abs(20141202) is not null and type_id=11 and platform = 3" with partitioned key insert_date has predicates=List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202), (type_id#12 = 11), (platform#8 = 3)) and partitionKeyIds=AttributeSet(insert_date#24). PruningPredicates is List(IS NOT NULL HiveSimpleUdf#org.apache.hadoop.hive.ql.udf.UDFAbs(20141202)). Author: YanTangZhai <hakeemzhai@tencent.com> Author: yantangzhai <tyz0303@163.com> Closes apache#3556 from YanTangZhai/SPARK-4693 and squashes the following commits: 620ebe3 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references 37cfdf5 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references 70a3544 [yantangzhai] [SPARK-4693] [SQL] PruningPredicates may be wrong if predicates contains an empty AttributeSet() references efa9b03 [YanTangZhai] Update HiveQuerySuite.scala 72accf1 [YanTangZhai] Update HiveQuerySuite.scala e572b9a [YanTangZhai] Update HiveStrategies.scala 6e643f8 [YanTangZhai] Merge pull request apache#11 from apache/master e249846 [YanTangZhai] Merge pull request apache#10 from apache/master d26d982 [YanTangZhai] Merge pull request apache#9 from apache/master 76d4027 [YanTangZhai] Merge pull request apache#8 from apache/master 03b62b0 [YanTangZhai] Merge pull request apache#7 from apache/master 8a00106 [YanTangZhai] Merge pull request apache#6 from apache/master cbcba66 [YanTangZhai] Merge pull request apache#3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
freeman-lab
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…askTracker to reduce the chance of the communicating problem Using AkkaUtils.askWithReply in MapOutputTracker.askTracker to reduce the chance of the communicating problem Author: YanTangZhai <hakeemzhai@tencent.com> Author: yantangzhai <tyz0303@163.com> Closes apache#3785 from YanTangZhai/SPARK-4946 and squashes the following commits: 9ca6541 [yantangzhai] [SPARK-4946] [CORE] Using AkkaUtils.askWithReply in MapOutputTracker.askTracker to reduce the chance of the communicating problem e4c2c0a [YanTangZhai] Merge pull request apache#15 from apache/master 718afeb [YanTangZhai] Merge pull request apache#12 from apache/master 6e643f8 [YanTangZhai] Merge pull request apache#11 from apache/master e249846 [YanTangZhai] Merge pull request apache#10 from apache/master d26d982 [YanTangZhai] Merge pull request apache#9 from apache/master 76d4027 [YanTangZhai] Merge pull request apache#8 from apache/master 03b62b0 [YanTangZhai] Merge pull request apache#7 from apache/master 8a00106 [YanTangZhai] Merge pull request apache#6 from apache/master cbcba66 [YanTangZhai] Merge pull request apache#3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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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 apache#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
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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 apache#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
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Jan 30, 2015
Support ! boolean logic operator like NOT in sql as follows select * from for_test where !(col1 > col2) Author: YanTangZhai <hakeemzhai@tencent.com> Author: Michael Armbrust <michael@databricks.com> Closes apache#3555 from YanTangZhai/SPARK-4692 and squashes the following commits: 1a9f605 [YanTangZhai] Update HiveQuerySuite.scala 7c03c68 [YanTangZhai] Merge pull request apache#23 from apache/master 992046e [YanTangZhai] Update HiveQuerySuite.scala ea618f4 [YanTangZhai] Update HiveQuerySuite.scala 192411d [YanTangZhai] Merge pull request apache#17 from YanTangZhai/master e4c2c0a [YanTangZhai] Merge pull request apache#15 from apache/master 1e1ebb4 [YanTangZhai] Update HiveQuerySuite.scala efc4210 [YanTangZhai] Update HiveQuerySuite.scala bd2c444 [YanTangZhai] Update HiveQuerySuite.scala 1893956 [YanTangZhai] Merge pull request apache#14 from marmbrus/pr/3555 59e4de9 [Michael Armbrust] make hive test 718afeb [YanTangZhai] Merge pull request apache#12 from apache/master 950b21e [YanTangZhai] Update HiveQuerySuite.scala 74175b4 [YanTangZhai] Update HiveQuerySuite.scala 92242c7 [YanTangZhai] Update HiveQl.scala 6e643f8 [YanTangZhai] Merge pull request apache#11 from apache/master e249846 [YanTangZhai] Merge pull request apache#10 from apache/master d26d982 [YanTangZhai] Merge pull request apache#9 from apache/master 76d4027 [YanTangZhai] Merge pull request apache#8 from apache/master 03b62b0 [YanTangZhai] Merge pull request apache#7 from apache/master 8a00106 [YanTangZhai] Merge pull request apache#6 from apache/master cbcba66 [YanTangZhai] Merge pull request apache#3 from apache/master cdef539 [YanTangZhai] Merge pull request #1 from apache/master
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Jan 30, 2015
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 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
freeman-lab
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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 <zapletal-martin@email.cz> Author: Xiangrui Meng <meng@databricks.com> Author: Martin Zapletal <zapletal-martin@email.cz> Closes apache#3519 from zapletal-martin/SPARK-3278 and squashes the following commits: 5a54ea4 [Martin Zapletal] Merge pull request apache#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
freeman-lab
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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 <jacky.likun@huawei.com> Author: Jacky Li <jackylk@users.noreply.github.com> Author: Xiangrui Meng <meng@databricks.com> 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
freeman-lab
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Mar 15, 2015
…n LDAModel.scala Remove unicode characters from MLlib file. Author: Michael Griffiths <msjgriffiths@gmail.com> Author: Griffiths, Michael (NYC-RPM) <michael.griffiths@reprisemedia.com> 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 apache#2 from apache/master b08e865 [Michael Griffiths] Merge pull request #1 from apache/master
freeman-lab
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Mar 15, 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 <paul.power@peerside.com> 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
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Mar 15, 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 <meng@databricks.com> Author: DB Tsai <dbtsai@alpinenow.com> Author: DB Tsai <dbtsai@dbtsai.com> 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
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@freeman-lab I made the following changes: