I have an RDD of type RDD[(Int,Double)] in which the first element of the pair is the index and the second is the value and I'd like to convert this RDD to a Vector to use for classification. Could someone help me with that?
I have the following code but it's not working
def vectorize(x:RDD[(Int,Double)], size: Int):Vector = {
val vec = Vectors.sparse(size,x)
}
Since org.apache.spark.mllib.linalg.Vector is a local data structure you have to collect your data.
def vectorize(x:RDD[(Int,Double)], size: Int):Vector = {
Vectors.sparse(size, x.collect)
}
Since there is no data distribution you have to be sure output will fit in a driver memory.
In general this operation is not particularly useful. If your data can be easily handled using local data structures then it probably shouldn't be stored inside RDD in the first place.
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 want to convert CSV coordinate format (COO) data into a local matrix. Currently I'm first converting them to CoordinateMatrix and then converting to LocalMatrix. But is there a better way to do this?
Example data:
0,5,5.486978435
0,3,0.438472867
0,0,6.128832321
0,7,5.295923198
0,1,7.738270234
Code:
var loadG = sqlContext.read.option("header", "false").csv("file.csv").rdd.map("mapfunctionCreatingMatrixEntryOutOfRow")
var G = new CoordinateMatrix(loadG)
var matrixG = G.toBlockMatrix().toLocalMatrix()
A LocalMatrix will be stored on a single machine and hence not make use of Spark's strengths. In other words, using Spark seems a bit wasteful, although still possible.
The easiest way to get the CSV file to a LocalMatrix is to first read the CSV with Scala, not Spark:
val entries = Source.fromFile("data.csv").getLines()
.map(_.split(","))
.map(a => (a(0).toInt, a(1).toInt, a(2).toDouble))
.toSeq
The SparseMatrix variant of the LocalMatrix has a method for reading COO formatted data. The number of rows and columns need to be specified to use this. Since the matrix is sparse this should in most cases be done by hand but it's possible to get the highest values in the data as follows:
val numRows = entries.map(_._1).max + 1
val numCols = entries.map(_._2).max + 1
Then create the matrix:
val matrixG = SparseMatrix.fromCOO(numRows, numCols, entries)
The matrix will be stored in CSC format on the machine. Printing the example input above will yield the following output:
1 x 8 CSCMatrix
(0,0) 6.128832321
(0,1) 7.738270234
(0,3) 0.438472867
(0,5) 5.486978435
(0,7) 5.295923198
Despite existing a lot of seemingly similar questions none answers my question.
I have a DataFrame already processed in order to be fed to a DecisionTreeClassifier and it contains a column label which is filled with either 0.0 or 1.0.
I need to bootstrap my data set, by randomly selecting with replacement the same amount of rows for each values of my label column.
I've looked at all the doc and all I could find are DataFrame.sample(...) and DataFrameStatFunctions.sampleBy(...) but the issue with those are that the number of sample retained is not guaranteed and the second one doesn't allow replacement! This wouldn't be an issue on larger data set but in around 50% of my cases I'll have one of the label values that have less than a hundred rows and I really don't want skewed data.
Despite my best efforts, I was unable to find a clean solution to this problem and I resolved myself. to collecting the whole DataFrame and doing the sampling "manually" in Scala before recreating a new DataFrame to train my DecisionTreeClassifier on. But this seem highly inefficient and cumbersome, I would much rather stay with DataFrame and keep all the benefits coming from that structure.
Here is my current implementation for reference and so you know exactly what I'd like to do:
val nbSamplePerClass = /* some int value currently ranging between 50 and 10000 */
val onesDataFrame = inputDataFrame.filter("label > 0.0")
val zeros = inputDataFrame.except(onesDataFrame).collect()
val ones = onesDataFrame.collect()
val nbZeros = zeros.count().toInt
val nbOnes = ones.count().toInt
def randomIndexes(maxIndex: Int) = (0 until nbSamplePerClass).map(
_ => new scala.util.Random().nextInt(maxIndex)).toSeq
val zerosSample = randomIndexes(nbZeros).map(idx => zeros(idx))
val onesSample = randomIndexes(nbOnes).map(idx => ones(idx))
val samples = scala.collection.JavaConversions.seqAsJavaList(zerosSample ++ onesSample)
val resDf = sqlContext.createDataFrame(samples, inputDataFrame.schema)
Does anyone know how I could implement such a sampling while only working with DataFrames?
I'm pretty sure that it would significantly speed up my code!
Thank you for your time.
I have the following code:
val blueCount = sc.accumulator[Long](0)
val output = input.map { data =>
for (value <- data.getValues()) {
if (record.getEnum() == DataEnum.BLUE) {
blueCount += 1
println("Enum = BLUE : " + value.toString()
}
}
data
}.persist(StorageLevel.MEMORY_ONLY_SER)
output.saveAsTextFile("myOutput")
Then the blueCount is not zero, but I got no println() output! Am I missing anything here? Thanks!
This is a conceptual question...
Imagine You have a big cluster, composed of many workers let's say n workers and those workers store a partition of an RDD or DataFrame, imagine You start a map task across that data, and inside that map you have a print statement, first of all:
Where will that data be printed out?
What node has priority and what partition?
If all nodes are running in parallel, who will be printed first?
How will be this print queue created?
Those are too many questions, thus the designers/maintainers of apache-spark decided logically to drop any support to print statements inside any map-reduce operation (this include accumulators and even broadcast variables).
This also makes sense because Spark is a language designed for very large datasets. While printing can be useful for testing and debugging, you wouldn't want to print every line of a DataFrame or RDD because they are built to have millions or billions of rows! So why deal with these complicated questions when you wouldn't even want to print in the first place?
In order to prove this you can run this scala code for example:
// Let's create a simple RDD
val rdd = sc.parallelize(1 to 10000)
def printStuff(x:Int):Int = {
println(x)
x + 1
}
// It doesn't print anything! because of a logic design limitation!
rdd.map(printStuff)
// But you can print the RDD by doing the following:
rdd.take(10).foreach(println)
I was able to work it around by making a utility function:
object PrintUtiltity {
def print(data:String) = {
println(data)
}
}
TL;DR; I am trying to train off of an existing data set (Seq[Words] with corresponding categories), and use that trained dataset to filter another dataset using category similarity.
I am trying to train a corpus of data and then use it for text analysis*. I've tried using NaiveBayes, but that seems to only work with the data you have, so it's predict algorithm will always return something, even if it doesn't match anything.
So, I am now trying to use TFIDF and passing that output into a RowMatrix and computing the similarities. But, I'm not sure how to run my query (one word for now). Here's what I've tried:
val rddOfTfidfFromCorpus : RDD[Vector]
val query = "word"
val tf = new HashingTF().transform(List(query))
val tfIDF = new IDF().fit(sc.makeRDD(List(tf))).transform(tf)
val mergedVectors = rddOfTfidfFromCorpus.union(sc.makeRDD(List(tfIDF)))
val similarities = new RowMatrix(mergedVectors).columnSimilarities(1.0)
Here is where I'm stuck (if I've even done everything right until here). I tried filtering the similarities i and j down to the parts of my query's TFIDF and end up with an empty collection.
The gist is that I want to train on a corpus of data and find what category it falls in. The above code is at least trying to get it down to one category and checking if I can get a prediction from that at least....
*Note that this is a toy example, so I only need something that works well enough
*I am using Spark 1.4.0
Using columnSimilarities doesn't make sense here. Since each column in your matrix represents a set of terms you'll get a matrix of similarities between tokens not documents. You could transpose the matrix and then use columnSimilarities but as far as I understand what you want is a similarity between query and corpus. You can express that using matrix multiplication as follows:
For starters you'll need an IDFModel you've trained on a corpus. Lets assume it is called idf:
import org.apache.spark.mllib.feature.IDFModel
val idf: IDFModel = ??? // Trained using corpus data
and a small helper:
def toBlockMatrix(rdd: RDD[Vector]) = new IndexedRowMatrix(
rdd.zipWithIndex.map{case (v, i) => IndexedRow(i, v)}
).toCoordinateMatrix.toBlockMatrix
First lets convert query to an RDD and compute TF:
val query: Seq[String] = ???
val queryTf = new HashingTF().transform(query)
Next we can apply IDF model and convert result to matrix:
val queryTfidf = idf.transform(queryTf)
val queryMatrix = toBlockMatrix(queryTfidf)
We'll need a corpus matrix as well:
val corpusMatrix = toBlockMatrix(rddOfTfidfFromCorpus)
If you multiple both we get a matrix with number of rows equal to the number of docs in the query and number of columns equal to the number of documents in the corpus.
val dotProducts = queryMatrix.multiply(corpusMatrix.transpose)
To get a proper cosine similarity you have to divide by a product of magnitudes but if you can handle that.
There are two problems here. First of all it is rather expensive. Moreover I am not sure if it really useful. To reduce cost you can apply some dimensionality reduction algorithm first but lets leave it for now.
Judging from a following statement
NaiveBayes (...) seems to only work with the data you have, so it's predict algorithm will always return something, even if it doesn't match anything.
I guess you want some kind of unsupervised learning method. The simplest thing you can try is K-means:
import org.apache.spark.mllib.clustering.{KMeans, KMeansModel}
val numClusters: Int = ???
val numIterations = 20
val model = KMeans.train(rddOfTfidfFromCorpus, numClusters, numIterations)
val predictions = model.predict(queryTfidf)