I am fairly new to ScalaCheck and somehow I want to generate a list of distinct values (i.e. a set). With my approach the values are not unique.
val valueList: Gen[List[Int]] = Gen.ListOf(Arbitrary.arbitrary[Int])
What are the possible ways to create a list with unique values/a set? Maybe with using suchThat or distinct?
Crude but effective:
Gen.listOf(Arbitrary.arbitrary[Int]).map(_.toSet)
If you wanted a set of a particular size, you could do something like
def setOfN[A](n: Int, gen: Gen[A]): Gen[Set[A]] =
Gen.listOfN(n, gen).flatMap { lst =>
val set = lst.toSet
if (set.size == n) Gen.const(set)
else if (set.size > n) Gen.const(set.take(n))
else setOfN(n - set.size, gen.retryUntil(x => !set(x))).flatMap(_ ++ set)
}
(It's worth noting that retryUntil can be fragile, especially as n grows relative to the number of commonly generated values (e.g. for Int Scalacheck generates 0, +/- 1, etc. fairly frequently))
And of course, since there's an org.scalacheck.util.Buildable instance for Set
Gen.containerOf[Set, Int](Arbitrary.arbitrary[Int])
Following up on what Levi Ramsey said, you might want to check out https://github.com/rallyhealth/scalacheck-ops for the ability to generate sets of a specific size.
import org.scalacheck.Arbitrary.arbitrary
import org.scalacheck.Gen
import org.scalacheck.ops._
Gen.setOfN(5, arbitrary[Int]).map(_.toVector)
Related
I've been breaking my head about this one for a couple of days now. It feels like it should be intuitively easy... Really hope someone can help!
I've built an org.nd4j.linalg.api.ndarray.INDArray of word occurrence from some semi-structured data like this:
import org.nd4j.linalg.factory.Nd4j
import org.nd4s.Implicits._
val docMap = collection.mutable.Map[Int,Map[Int,Int]] //of the form Map(phrase -> Map(phrasePosition -> word)
val words = ArrayBuffer("word_1","word_2","word_3",..."word_n")
val windows = ArrayBuffer("$phrase,$phrasePosition_1","$phrase,$phrasePosition_2",..."$phrase,$phrasePosition_n")
var matrix = Nd4j.create(windows.length*words.length).reshape(windows.length,words.length)
for (row <- matrix.shape(0)){
for(column <- matrix.shape(1){
//+1 to (row,column) if word occurs at phrase, phrasePosition indicated by window_n.
}
}
val finalmatrix = matrix.T.dot(matrix) // to get co-occurrence matrix
So far so good...
Downstream of this point I need to integrate the data into an existing pipeline in Spark, and use that implementation of pca etc, so I need to create a DataFrame, or at least an RDD. If I knew the number of words and/or windows in advance I could do something like:
case class Row(window : String, word_1 : Double, word_2 : Double, ...etc)
val dfSeq = ArrayBuffer[Row]()
for (row <- matrix.shape(0)){
dfSeq += Row(windows(row),matrix.get(NDArrayIndex.point(row), NDArrayIndex.all()))
}
sc.parallelize(dfSeq).toDF("window","word_1","word_2",...etc)
but the number of windows and words is determined at runtime. I'm looking for a WindowsxWords org.apache.spark.sql.DataFrame as output, input is a WindowsxWords org.nd4j.linalg.api.ndarray.INDArray
Thanks in advance for any help you can offer.
Ok, so after several days work it looks like the simple answer is: there isn't one. In fact, it looks like trying to use Nd4j in this context at all is a bad idea for several reasons:
It's (really) hard to get data out of the native INDArray format once you've put it in.
Even using something like guava, the .data() method brings everything on heap which will quickly become expensive.
You've got the added hassle of having to compile an assembly jar or use hdfs etc to handle the library itself.
I did also consider using Breeze which may actually provide a viable solution but carries some of the same problems and can't be used on distributed data structures.
Unfortunately, using native Spark / Scala datatypes, although easier once you know how, is - for someone like me coming from Python + numpy + pandas heaven at least - painfully convoluted and ugly.
Nevertheless, I did implement this solution successfully:
import org.apache.spark.mllib.linalg.{Vectors,Vector,Matrix,DenseMatrix,DenseVector}
import org.apache.spark.mllib.linalg.distributed.RowMatrix
//first make a pseudo-matrix from Scala Array[Double]:
var rowSeq = Seq.fill(windows.length)(Array.fill(words.length)(0d))
//iterate through 'rows' and 'columns' to fill it:
for (row 0 until windows.length){
for (column 0 until words.length){
// rowSeq(row)(column) += 1 if word occurs at phrase, phrasePosition indicated by window_n.
}
}
//create Spark DenseMatrix
val rows : Array[Double] = rowSeq.transpose.flatten.toArray
val matrix = new DenseMatrix(windows.length,words.length,rows)
One of the main operations that I needed Nd4J for was matrix.T.dot(matrix) but it turns out that you can't multiply 2 matrices of Type org.apache.spark.mllib.linalg.DenseMatrix together, one of them (A) has to be a org.apache.spark.mllib.linalg.distributed.RowMatrix and - you guessed it - you can't call matrix.transpose() on a RowMatrix, only on a DenseMatrix! Since it's not really relevant to the question, I'll leave that part out, except to explain that what comes out of that step is a RowMatrix. Credit is also due here and here for the final part of the solution:
val rowMatrix : [RowMatrix] = transposeAndDotDenseMatrix(matrix)
// get DataFrame from RowMatrix via DenseMatrix
val newdense = new DenseMatrix(rowMatrix.numRows().toInt,rowMatrix.numCols().toInt,rowMatrix.rows.collect.flatMap(x => x.toArray)) // the call to collect() here is undesirable...
val matrixRows = newdense.rowIter.toSeq.map(_.toArray)
val df = spark.sparkContext.parallelize(matrixRows).toDF("Rows")
// then separate columns:
val df2 = (0 until words.length).foldLeft(df)((df, num) =>
df.withColumn(words(num), $"Rows".getItem(num)))
.drop("Rows")
Would love to hear improvements and suggestions on this, thanks.
I have a comparator like this:
lazy val seq = mapping.toSeq.sortWith { case ((_, set1), (_, set2)) =>
// Just propose all the most connected nodes first to the users
// But also allow less connected nodes to pop out sometimes
val popOutChance = random.nextDouble <= 0.1D && set2.size > 5
if (popOutChance) set1.size < set2.size else set1.size > set2.size
}
It is my intention to compare sets sizes such that smaller sets may appear higher in a sorted list with 10% chance.
But compiler does not let me do that and throws an Exception: java.lang.IllegalArgumentException: Comparison method violates its general contract! once I try to use it in runtime. How can I override it?
I think the problem here is that, every time two elements are compared, the outcome is random, thus violating the transitive property required of a comparator function in any sorting algorithm.
For example, let's say that some instance a compares as less than b, and then b compares as less than c. These results should imply that a compares as less than c. However, since your comparisons are stochastic, you can't guarantee that outcome. In fact, you can't even guarantee that a will be less than b next time they're compared.
So don't do that. No sort algorithm can handle it. (Such an approach also violates the referential transparency principle of functional programming and will make your program much harder to reason about.)
Instead, what you need to do is to decorate your map's members with a randomly assigned weighting - before attempting to sort them - so that they can be sorted consistently. However, since this happens at the start of a sort operation, the result of the sort will be different each time, which I think is what you're looking for.
It's not clear what type mapping has in your example, but it appears to be something like: Map[Any, Set[_]]. (You can replace the types as required - it's not that important to this approach. For example, say mapping actually has the type Map[String, Set[SomeClass]], then you would replace references below to Any with String and Set[_] to Set[SomeClass].)
First, we'll create a case class that we'll use to score and compare the map elements. Then we'll map the contents of mapping to a sequence of elements of this case class. Next, we sort those elements. Finally, we extract the tuple from the decorated class. The result should look something like this:
final case class Decorated(x: (Any, Set[_]), rand: Double = random.nextDouble)
extends Ordered[Decorated] {
// Calculate a rank for this element. You'll need to change this to suit your precise
// requirements. Here, if rand is less than 0.1 (a 10% chance), I'm adding 5 to the size;
// otherwise, I'll report the actual size. This allows transitive comparisons, since
// rand doesn't change once defined. Values are negated so bigger sets come to the fore
// when sorted.
private def rank: Int = {
if(rand < 0.1) -(x._2.size + 5)
else -x._2.size
}
// Compare this element with another, by their ranks.
override def compare(that: Decorated): Int = rank.compare(that.rank)
}
// Now sort your mapping elements as follows and convert back to tuples.
lazy val seq = mapping.map(x => Decorated(x)).toSeq.sorted.map(_.x)
This should put the elements with larger sets towards the front, but there's 10% chance that sets appear 5 bigger and so move up the list. The result will be different each time the last line is re-executed, since map will create new random values for each element. However, during sorting, the ranks will be fixed and will not change.
(Note that I'm setting the rank to a negative value. The Ordered[T] trait sorts elements in ascending order, so that - if we sorted purely by set size - smaller sets would come before larger sets. By negating the rank value, sorting will put larger sets before smaller sets. If you don't want this behavior, remove the negations.)
If I have a custom generator then the shrinker will remember my suchThat clause and not shrink with invalid values:
val myGen = Gen.identifier.suchThat { _.length > 3 }
// all shrinks have > 3 characters
property("failing case") = forAll (myGen) { (a: String) =>
println(s"Gen suchThat Value: $a")
a == "Impossible"
}
If I do something further to the generated value (ie map it) then the shrinker "forgets" my suchThat clause:
// the shrinker will shrink all the way down to ""
property("failing case") = forAll (myGen.map{_ + "bbb"}) { (a: String) =>
println(s"Gen with map Value: $a")
a == "Impossible"
}
Is it possible to have suchThat values propagate through generators. In my real project I am doing more than a simple map but that seems to be the simplest example of the limitation I am hitting.
I'm fairly certain the answer is no (at least at this point in time).
This is quite annoying although perhaps not as trivial as it seems. The generator result does attempt to keep track of the sieve although it gets lost in map and flatMap. Apart from applying the sieve to the result of the shrink there isn't any other connection back to the generator. Even if there were all the intermediate results would need to be retained and applied to each sieve at the correct points. That then raises the question of: What exactly is being shrunk? The generated result or the original generator(s)?
The only solution that I have found so far is to either:
Disable shrinking, or
Implement a custom Shrink, or
Add a whenever clause that rechecks the generated value.
This can be quite challenging, especially when composing multiple generators.
I am looking through Rng sources to see how they generate a list of random values.
They define a function fill:
def fill(n: Int): Rng[List[A]] = sequence(List.fill(n)(this))
where sequence is just an invocation of Traverse.sequence from scalaz:
def sequence[T[_], A](x: T[Rng[A]])(implicit T: Traverse[T]): Rng[T[A]] =
T.sequence(x)
In other words they create a temporary list List[Rang[A]] and then apply sequence: List[Rng[A]] => Rng[List[A]]. I see how it works but the temporary list looks list a waste of memory to me. Is it absolutely necessary ? Can it be improved ?
This is a slightly faster implementation. I didn't profile to see if there was a noticeable impact on the heap. I did a rough timing test and it took roughly 70% of the time Rng.fill took to fill a 1M item list with random Ints. I didn't attempt to find out how these scaled with different size lists. See https://gist.github.com/drstevens/77db6bab6b1e995dac13
def fill[A](a: Rng[A], count: Int): Rng[List[A]] =
Stream.from(0).take(count).traverseU(_ => a).map(_.toList)
The interesting thing about this is that the toList isn't evaluated until unsafePerformIO.
I want to create a parallel scanLeft(computes prefix sums for an associative operator) function for Hadoop (scalding in particular; see below for how this is done).
Given a sequence of numbers in a hdfs file (one per line) I want to calculate a new sequence with the sums of consecutive even/odd pairs. For example:
input sequence:
0,1,2,3,4,5,6,7,8,9,10
output sequence:
0+1, 2+3, 4+5, 6+7, 8+9, 10
i.e.
1,5,9,13,17,10
I think in order to do this, I need to write an InputFormat and InputSplits classes for Hadoop, but I don't know how to do this.
See this section 3.3 here. Below is an example algorithm in Scala:
// for simplicity assume input length is a power of 2
def scanadd(input : IndexedSeq[Int]) : IndexedSeq[Int] =
if (input.length == 1)
input
else {
//calculate a new collapsed sequence which is the sum of sequential even/odd pairs
val collapsed = IndexedSeq.tabulate(input.length/2)(i => input(2 * i) + input(2*i+1))
//recursively scan collapsed values
val scancollapse = scanadd(collapse)
//now we can use the scan of the collapsed seq to calculate the full sequence
val output = IndexedSeq.tabulate(input.length)(
i => i.evenOdd match {
//if an index is even then we can just look into the collapsed sequence and get the value
// otherwise we can look just before it and add the value at the current index
case Even => scancollapse(i/2)
case Odd => scancollapse((i-1)/2) + input(i)
}
output
}
I understand that this might need a fair bit of optimization for it to work nicely with Hadoop. Translating this directly I think would lead to pretty inefficient Hadoop code. For example, Obviously in Hadoop you can't use an IndexedSeq. I would appreciate any specific problems you see. I think it can probably be made to work well, though.
Superfluous. You meant this code?
val vv = (0 to 1000000).grouped(2).toVector
vv.par.foldLeft((0L, 0L, false))((a, v) =>
if (a._3) (a._1, a._2 + v.sum, !a._3) else (a._1 + v.sum, a._2, !a._3))
This was the best tutorial I found for writing an InputFormat and RecordReader. I ended up reading the whole split as one ArrayWritable record.