I have a row that is obtained in the following way:
val row_name = df.collect()(i)
how can I convert this row to a type vector so that it can be passed to fromML() as follows ?
val vector_name=org.apache.spark.mllib.linalg.Vectors.fromML(row_name)
thanks in advance!
You can use vectorAssembler:
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.ml.feature.VectorAssembler
val df = Seq((1,2),(3,4)).toDF("col1","col2")
val va = new VectorAssembler().setInputCols(Array("col1","col2")).setOutputCol("vector")
val row0 = va.transform(df).select("vector").collect()(0).getAs[Vector](0)
val vector0 = Vectors.fromML(row0)
// vector0: org.apache.spark.mllib.linalg.Vector = [1.0,2.0]
Related
I have a dataframe which is created from parquet files that has 512 columns(all float values).
I am trying to calculate euclidean distance of each row in my dataframe to a constant reference array.
My development environment is Zeppelin 0.7.3 with spark 2.1 and Scala. Here is the zeppelin paragraphs I run:
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors
//Create dataframe from parquet file
val filePath = "/tmp/vector.parquet/*.parquet"
val df = spark.read.parquet(filePath)
//Create assembler and vectorize df
val assembler = new VectorAssembler()
.setInputCols(df.columns)
.setOutputCol("features")
val training = assembler.transform(df)
//Create udf
val eucDisUdf = udf((features: Vector,
myvec:Vector)=>Vectors.sqdist(features, myvec))
//Cretae ref vector
val myScalaVec = Vectors.dense( Array.fill(512)(25.44859))
val distDF =
training2.withColumn("euc",eucDisUdf($"features",myScalaVec))
This code gives the following error for eucDisUdf call:
error: type mismatch; found : org.apache.spark.ml.linalg.Vector
required: org.apache.spark.sql.Column
I appreciate any idea how to eliminate this error and compute distances properly in scala.
I think you can use currying to achieve that:
def eucDisUdf(myvec:Vector) = udf((features: Vector) => Vectors.sqdist(features, myvec))
val myScalaVec = Vectors.dense(Array.fill(512)(25.44859))
val distDF = training2.withColumn( "euc", eucDisUdf(myScalaVec)($"features") )
I have a Spark 2.2.0 DataFrame of currency prices where I add the returns to.
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.functions._
val spark = SparkSession.builder.getOrCreate()
val prices = spark.read.json("prices.json")
// make a window function and convert prices to returns
val window = Window.partitionBy("currency").orderBy("time")
val lagPrice = lag(col("close"), 1).over(window)
val percentReturn = col("close") / col("lastClose") - 1d
val logReturn = log(col("close") / col("lastClose"))
val returns = prices.withColumn("lastClose", lagPrice)
.withColumn("return", percentReturn)
.withColumn("logReturn", logReturn)
Now I want to calculate a rolling Covarance Matrix (like a moving average) of all currencies using a window function. But I can not find any documentation or examples.
I tried to apply PCA to my data and then apply RandomForest to the transformed data. However, PCA.transform(data) gave me a DataFrame but I need a mllib LabeledPoints to feed my RandomForest. How can I do that?
My code:
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.ml.feature.PCA
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
val dataset = MLUtils.loadLibSVMFile(sc, "data/mnist/mnist.bz2")
val splits = dataset.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
val trainingDf = trainingData.toDF()
val pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(100)
.fit(trainingDf)
val pcaTrainingData = pca.transform(trainingDf)
val numClasses = 10
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 10 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 20
val maxBins = 32
val model = RandomForest.trainClassifier(pcaTrainingData, numClasses, categoricalFeaturesInfo,
numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)
error: type mismatch;
found : org.apache.spark.sql.DataFrame
required: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint]
I tried the following two possible solutions but they didn't work:
scala> val pcaTrainingData = trainingData.map(p => p.copy(features = pca.transform(p.features)))
<console>:39: error: overloaded method value transform with alternatives:
(dataset: org.apache.spark.sql.DataFrame)org.apache.spark.sql.DataFrame <and>
(dataset: org.apache.spark.sql.DataFrame,paramMap: org.apache.spark.ml.param.ParamMap)org.apache.spark.sql.DataFrame <and>
(dataset: org.apache.spark.sql.DataFrame,firstParamPair: org.apache.spark.ml.param.ParamPair[_],otherParamPairs: org.apache.spark.ml.param.ParamPair[_]*)org.apache.spark.sql.DataFrame
cannot be applied to (org.apache.spark.mllib.linalg.Vector)
And:
val labeled = pca
.transform(trainingDf)
.map(row => LabeledPoint(row.getDouble(0), row(4).asInstanceOf[Vector[Int]]))
error: type mismatch;
found : scala.collection.immutable.Vector[Int]
required: org.apache.spark.mllib.linalg.Vector
(I have imported org.apache.spark.mllib.linalg.Vectors in the above case)
Any help?
The correct approach here is the second one you tried - mapping each Row into a LabeledPoint to get an RDD[LabeledPoint]. However, it has two mistakes:
The correct Vector class (org.apache.spark.mllib.linalg.Vector) does NOT take type arguments (e.g. Vector[Int]) - so even though you had the right import, the compiler concluded that you meant scala.collection.immutable.Vector which DOES.
The DataFrame returned from PCA.fit() has 3 columns, and you tried to extract column number 4. For example, showing first 4 lines:
+-----+--------------------+--------------------+
|label| features| pcaFeatures|
+-----+--------------------+--------------------+
| 5.0|(780,[152,153,154...|[880.071111851977...|
| 1.0|(780,[158,159,160...|[-41.473039034112...|
| 2.0|(780,[155,156,157...|[931.444898405036...|
| 1.0|(780,[124,125,126...|[25.5114585648411...|
+-----+--------------------+--------------------+
To make this easier - I prefer using the column names instead of their indices.
So here's the transformation you need:
val labeled = pca.transform(trainingDf).rdd.map(row => LabeledPoint(
row.getAs[Double]("label"),
row.getAs[org.apache.spark.mllib.linalg.Vector]("pcaFeatures")
))
I have a very simple code to try Cosine Similarity:
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.distributed.{MatrixEntry, CoordinateMatrix, RowMatrix}
val rows= Array(((1,2,3,4,5),(1,2,3,4,5),(1,2,4,5,8),(3,4,1,2,7),(7,7,7,7,7)))
val mat = new RowMatrix(rows)
val simsPerfect = mat.columnSimilarities()
val simsEstimate = mat.columnSimilarities(0.8)
I run this code on Amazon AWS which has Spark 1.5 however I got the following message for the last two lines:
"Erroe: value columnSimilarities is not a memeber of org.apache.spark.rdd.RDD[(int,int)]"
Could you please help to resolve this issue?
I found the answer. I need to convert the matrix to RDD. Here is the right code:
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.linalg.distributed.{MatrixEntry, CoordinateMatrix, RowMatrix}
import org.apache.spark.rdd._
import org.apache.spark.mllib.linalg._
def matrixToRDD(m: Matrix): RDD[Vector] = {
val columns = m.toArray.grouped(m.numRows)
val rows = columns.toSeq.transpose // Skip this if you want a column-major RDD.
val vectors = rows.map(row => new DenseVector(row.toArray))
sc.parallelize(vectors)
}
val dm: Matrix = Matrices.dense(5, 5,Array(1,2,3,4,5,1,2,3,4,5,1,2,4,5,8,3,4,1,2,7,7,7,7,7,7))
val rows = matrixToRDD(dm)
val mat = new RowMatrix(rows)
val simsPerfect = mat.columnSimilarities()
val simsEstimate = mat.columnSimilarities(0.8)
println("Pairwise similarities are: " + simsPerfect.entries.collect.mkString(", "))
println("Estimated pairwise similarities are: " + simsEstimate.entries.collect.mkString(", "))
Cheers
How do I convert csv to Rdd[Double]? I have the error: cannot be applied to (org.apache.spark.rdd.RDD[Unit]) at this line:
val kd = new KernelDensity().setSample(rows)
My full code is here:
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.stat.KernelDensity
import org.apache.spark.rdd.RDD
import org.apache.spark.{SparkContext, SparkConf}
class KdeAnalysis {
val conf = new SparkConf().setAppName("sample").setMaster("local")
val sc = new SparkContext(conf)
val DATAFILE: String = "C:\\Users\\ajohn\\Desktop\\spark_R\\data\\mass_cytometry\\mass.csv"
val rows = sc.textFile(DATAFILE).map {
line => val values = line.split(',').map(_.toDouble)
Vectors.dense(values)
}.cache()
// Construct the density estimator with the sample data and a standard deviation for the Gaussian
// kernels
val rdd : RDD[Double] = sc.parallelize(rows)
val kd = new KernelDensity().setSample(rdd)
.setBandwidth(3.0)
// Find density estimates for the given values
val densities = kd.estimate(Array(-1.0, 2.0, 5.0))
}
Since rows is a RDD[org.apache.spark.mllib.linalg.Vector] following line cannot work:
val rdd : RDD[Double] = sc.parallelize(rows)
parallelize expects Seq[T] and RDD is not a Seq.
Even if this part worked as you expect your input is simply wrong. A correct argument for KernelDensity.setSample is either RDD[Double] or JavaRDD[java.lang.Double]. It looks like it doesn't support a multivariate data at this moment.
Regarding a question from the tile you can flatMap
rows.flatMap(_.toArray)
or even better when you create rows
val rows = sc.textFile(DATAFILE).flatMap(_.split(',').map(_.toDouble)).cache()
but I doubt it is really what you need.
Have prepared this code, please evaluate if it can help you out ->
val doubleRDD = rows.map(_.toArray).flatMap(x => x)