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Merge pull request twitter#221 from krishnanraman/develop
Added combinations(n,k) to RichPipe
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src/main/scala/com/twitter/scalding/mathematics/Combinatorics.scala
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package com.twitter.scalding.mathematics | ||
import com.twitter.scalding._ | ||
import com.twitter.scalding.Dsl._ | ||
import cascading.flow.FlowDef | ||
import cascading.tuple.{Fields, TupleEntry} | ||
import cascading.pipe.Pipe | ||
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/** | ||
Serve as a repo for self-contained combinatorial functions with no dependencies | ||
such as | ||
combinations, aka n choose k, nCk | ||
permutations , aka nPk | ||
subset sum : numbers that add up to a finite sum | ||
weightedSum: For weights (a,b,c, ...), want integers (x,y,z,...) to satisfy constraint |ax + by + cz + ... - result | < error | ||
... | ||
@author : Krishnan Raman, kraman@twitter.com | ||
*/ | ||
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object Combinatorics { | ||
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/** | ||
Given an int k, and an input of size n, | ||
return a pipe with nCk combinations, with k columns per row | ||
Computes nCk = n choose k, for large values of nCk | ||
Use-case: Say you have 100 hashtags sitting in an array | ||
You want a table with 5 hashtags per row, all possible combinations | ||
If the hashtags are sitting in a string array, then | ||
combinations[String]( hashtags, 5) | ||
will create the 100 chose 5 combinations. | ||
Algorithm: Use k pipes, cross pipes two at a time, filter out non-monotonic entries | ||
eg. 10C2 = 10 choose 2 | ||
Use 2 pipes. | ||
Pipe1 = (1,2,3,...10) | ||
Pipe2 = (2,3,4....10) | ||
Cross Pipe1 with Pipe2 for 10*9 = 90 tuples | ||
Filter out tuples that are non-monotonic | ||
For (t1,t2) we want t1<t2, otherwise reject. | ||
This brings down 90 tuples to the desired 45 tuples = 10C2 | ||
*/ | ||
def combinations[T](input:IndexedSeq[T], k:Int)(implicit flowDef:FlowDef):Pipe = { | ||
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// make k pipes with 1 column each | ||
// pipe 1 = 1 to n | ||
// pipe 2 = 2 to n | ||
// pipe 3 = 3 to n etc | ||
val n = input.size | ||
val allc = (1 to k).toList.map( x=> Symbol("n"+x)) // all column names | ||
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val pipes = allc.zipWithIndex.map( x=> { | ||
val num = x._2 + 1 | ||
val pipe = IterableSource( (num to n), x._1 ).read | ||
(pipe, num) | ||
}) | ||
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val res = pipes.reduceLeft( (a,b) => { | ||
val num = b._2 | ||
val prevname = Symbol("n" + (num - 1)) | ||
val myname = Symbol( "n" + num) | ||
val mypipe = a._1 | ||
.crossWithSmaller(b._1) | ||
.filter( prevname, myname ){ | ||
foo:(Int, Int) => | ||
val( nn1, nn2) = foo | ||
nn1 < nn2 | ||
} | ||
(mypipe, -1) | ||
})._1 | ||
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(1 to k).foldLeft(res)((a,b)=>{ | ||
val myname = Symbol( "n" + b) | ||
val newname = Symbol("k" + b) | ||
a.map(myname->newname){ | ||
inpc:Int => input(inpc-1) | ||
}.discard(myname) | ||
}) | ||
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} | ||
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/** | ||
Return a pipe with all nCk combinations, with k columns per row | ||
*/ | ||
def combinations(n:Int, k:Int)(implicit flowDef:FlowDef) = combinations[Int]((1 to n).toArray, k) | ||
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/** | ||
Return a pipe with all nPk permutations, with k columns per row | ||
For details, see combinations(...) above | ||
*/ | ||
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def permutations[T](input:IndexedSeq[T], k:Int)(implicit flowDef:FlowDef):Pipe = { | ||
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val n = input.size | ||
val allc = (1 to k).toList.map( x=> Symbol("n"+x)) // all column names | ||
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val pipes = allc.map( x=> IterableSource(1 to n, x).read) | ||
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// on a given row, we cannot have duplicate columns in a permutation | ||
val res = pipes | ||
.reduceLeft( (a,b) => { a.crossWithSmaller(b) }) | ||
.filter( allc ) { | ||
x: TupleEntry => Boolean | ||
val values = (0 until allc.size).map( i=> x.getInteger( i.asInstanceOf[java.lang.Integer])) | ||
values.size == values.distinct.size | ||
} | ||
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// map numerals to actual data | ||
(1 to k).foldLeft(res)((a,b)=>{ | ||
val myname = Symbol( "n" + b) | ||
val newname = Symbol("k" + b) | ||
a.map(myname->newname){ | ||
inpc:Int => input(inpc-1) | ||
}.discard(myname) | ||
}) | ||
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} | ||
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/** | ||
Return a pipe with all nPk permutations, with k columns per row | ||
*/ | ||
def permutations(n:Int, k:Int)(implicit flowDef:FlowDef) = permutations[Int]((1 to n).toArray, k) | ||
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/** | ||
Goal: Given weights (a,b,c, ...), we seek integers (x,y,z,...) to satisft | ||
the constraint |ax + by + cz + ... - result | < error | ||
Parameters: The weights (a,b,c,...) must be non-negative doubles. | ||
Our search space is 0 to result/min(weights) | ||
The returned pipe will contain integer tuples (x,y,z,...) that satisfy ax+by+cz +... = result | ||
Note: This is NOT Simplex | ||
WE use a slughtly-improved brute-force algorithm that performs well on account of parallelization. | ||
Algorithm: | ||
Create as many pipes as the number of weights | ||
Each pipe copntains integral multiples of the weight w ie. (0,1w,2w,3w,4w,....) | ||
Iterate as below - | ||
Cross two pipes | ||
Create a temp column that stores intermediate results | ||
Apply progressive filtering on the temp column | ||
Discard the temp column | ||
Once all pipes are crossed, test for temp column within error bounds of result | ||
Discard duplicates at end of process | ||
Usecase: We'd like to generate all integer tuples for typical usecases like | ||
0. How many ways can you invest $1000 in facebook, microsoft, hp ? | ||
val cash = 1000.0 | ||
val error = 5.0 // max error $5, so its ok if we cannot invest the last $5 or less | ||
val (FB, MSFT, HP) = (23.3,27.4,51.2) // share prices | ||
val stocks = IndexedSeq( FB,MSFT,HP ) | ||
weightedSum( stocks, cash, error).write( Tsv("invest.txt")) | ||
1. find all (x,y,z) such that 2x+3y+5z = 23, with max error 1 | ||
weightedSum( IndexedSeq(2.0,3.0,5.0), 23.0, 1.0) | ||
2. find all (a,b,c,d) such that 2a+12b+12.5c+34.7d = 3490 with max error 3 | ||
weightedSum( IndexedSeq(2.0,12.0,2.5,34.7),3490.0,3.0) | ||
This is at the heart of portfolio mgmt( Markowitz optimization), subset-sum, operations-research LP problems. | ||
*/ | ||
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def weightedSum( weights:IndexedSeq[Double], result:Double, error:Double)(implicit flowDef:FlowDef):Pipe = { | ||
val numWeights = weights.size | ||
val allColumns = (1 to numWeights).map( x=> Symbol("k"+x)) | ||
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// create as many single-column pipes as the number of weights | ||
val pipes = allColumns.zip(weights).map( x=> { | ||
val (name,wt) = x | ||
IterableSource( (0.0 to result by wt), name).read | ||
}).zip( allColumns ) | ||
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val first = pipes.head | ||
val accum = (first._1, List[Symbol](first._2)) | ||
val rest = pipes.tail | ||
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val res = rest.foldLeft(accum)((a,b)=>{ | ||
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val (apipe, aname) = a | ||
val (bpipe, bname) = b | ||
val allc = (List(aname)).flatten ++ List[Symbol](bname) | ||
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// Algorithm: | ||
// Cross two pipes | ||
// Create a temp column that stores intermediate results | ||
// Apply progressive filtering on the temp column | ||
// Discard the temp column | ||
// Once all pipes are crossed, test for temp column within error bounds of result | ||
// Discard duplicates at end of process | ||
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( apipe.crossWithSmaller(bpipe) | ||
.map(allc->'temp){ | ||
x:TupleEntry => | ||
val values = (0 until allc.size).map( i=> x.getDouble( i.asInstanceOf[java.lang.Integer])) | ||
values.sum | ||
}.filter('temp){ | ||
x:Double => if( allc.size == numWeights) (math.abs(x-result)<= error) else (x <= result) | ||
}.discard('temp), allc ) | ||
})._1.unique(allColumns) | ||
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(1 to numWeights).zip(weights).foldLeft( res) ((a,b) => { | ||
val (num,wt) = b | ||
val myname = Symbol("k"+num) | ||
a.map( myname->myname){ x:Int => (x/wt).toInt } | ||
}) | ||
} | ||
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/** | ||
Does the exact same thing as weightedSum, but filters out tuples with a weight of 0 | ||
The returned pipe contain only positive non-zero weights. | ||
*/ | ||
def positiveWeightedSum( weights:IndexedSeq[Double], result:Double, error:Double)(implicit flowDef:FlowDef):Pipe = { | ||
val allColumns = (1 to weights.size).map( x=> Symbol("k"+x)) | ||
weightedSum( weights, result, error).filter( allColumns ){ | ||
x:TupleEntry => (0 until allColumns.size).map( i=> x.getDouble(i.asInstanceOf[java.lang.Integer])!=0.0).reduceLeft(_&&_) | ||
} | ||
} | ||
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} |
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src/test/scala/com/twitter/scalding/mathematics/CombinatoricsTest.scala
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package com.twitter.scalding.mathematics | ||
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import org.specs._ | ||
import com.twitter.scalding._ | ||
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class CombinatoricsJob(args : Args) extends Job(args) { | ||
val C = Combinatorics | ||
C.permutations( 10,3 ).write(Tsv("perms.txt")) | ||
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C.combinations( 5,2 ).write(Tsv("combs.txt")) | ||
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// how many ways can you invest $10000 in KR,ABT,DLTR,MNST ? | ||
val cash = 1000.0 | ||
val error = 1.0 // max error $1, so its ok if we cannot invest the last dollar | ||
val (kr,abt,dltr,mnst) = (27.0,64.0,41.0,52.0) // share prices | ||
val stocks = IndexedSeq( kr,abt,dltr,mnst) | ||
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C.weightedSum( stocks, cash,error).write( Tsv("invest.txt")) | ||
C.positiveWeightedSum( stocks, cash,error).write( Tsv("investpos.txt")) | ||
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} | ||
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class CombinatoricsJobTest extends Specification { | ||
noDetailedDiffs() | ||
import Dsl._ | ||
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"A Combinatorics Job" should { | ||
JobTest( new CombinatoricsJob(_)) | ||
.sink[(Int,Int)](Tsv("perms.txt")) { pbuf => | ||
val psize = pbuf.toList.size | ||
"correctly compute 10 permute 3 equals 720" in { | ||
psize must be_==(720) | ||
} | ||
} | ||
.sink[(Int,Int)](Tsv("combs.txt")) { buf => | ||
val csize = buf.toList.size | ||
"correctly compute 5 choose 2 equals 10" in { | ||
csize must be_==(10) | ||
} | ||
} | ||
.sink[(Int,Int,Int,Int)](Tsv("invest.txt")) { buf => | ||
val isize = buf.toList.size | ||
"correctly compute 169 tuples that allow you to invest $1000 among the 4 given stocks" in { | ||
isize must be_==(169) | ||
} | ||
} | ||
.sink[(Int,Int,Int,Int)](Tsv("investpos.txt")) { buf => | ||
val ipsize = buf.toList.size | ||
"correctly compute 101 non-zero tuples that allow you to invest $1000 among the 4 given stocks" in { | ||
ipsize must be_==(101) | ||
} | ||
} | ||
.run | ||
.finish | ||
} | ||
} |