Refactor initializers

README.md

@@ -61,7 +61,7 @@ val cp = CP(tensor, rank)
     .withMaxIterations(50) // the maximum number of iterations (default 25)
     .withMinFms(0.95) // The Factor Match Score threshold used to stop the iterations (default 0.99)
     .withNorm(Norms.L1) // The norm to use on the factor matrices (default L2)
-    .withInitializer(Initializers.hosvd) // The method used to initialize the factor matrices (default gaussian)
+    .withInitializer(ALS.Initializers.hosvd) // The method used to initialize the factor matrices (default gaussian)
     .withComputeCorcondia(true) // To decide if CORCONDIA must be computed on the result (default false)
 ```
 CORCONDIA is the [core consistency diagnostic](https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/cem.801), and can be used to evaluate the quality of the decomposition.
@@ -93,7 +93,7 @@ Some optional parameters are available:
 val tucker = HOOI(tensor, ranks)
     .withMaxIterations(50) // the maximum number of iterations (default 25)
     .withMinFrobenius(10E-6) // The Frobenius threshold used to stop the iterations (default 10E-5)
-    .withInitializer(Initializers.hosvd) // The method used to initialize the factor matrices (default hosvd)
+    .withInitializer(HOOI.Initializers.hosvd) // The method used to initialize the factor matrices (default hosvd)
 ```
 
 Once the decomposition is set, it is run with:

distributed/src/main/scala/mulot/distributed/tensordecomposition/cp/ALS.scala

@@ -12,28 +12,28 @@ object ALS {
 	def apply(tensor: Tensor, rank: Int)(implicit spark: SparkSession): ALS =  {
 		new ALS(tensor, rank)(spark)
 	}
-}
-
-class ALS private(val tensor: Tensor, val rank: Int)(implicit spark: SparkSession)
-	extends cp.ALS[Tensor, ExtendedBlockMatrix, Map[String, DataFrame]]
-		with Logging {
 
 	object Initializers {
-		def gaussian(tensor: Tensor, rank: Int): Array[ExtendedBlockMatrix] = {
+		def gaussian(tensor: Tensor, rank: Int)(implicit spark: SparkSession): Array[ExtendedBlockMatrix] = {
 			(for (i <- 1 until tensor.order) yield {
 				ExtendedBlockMatrix.gaussian(tensor.dimensionsSize(i), rank)
 			}).toArray
 		}
 
-		def hosvd(tensor: Tensor, rank: Int): Array[ExtendedBlockMatrix] = {
+		def hosvd(tensor: Tensor, rank: Int)(implicit spark: SparkSession): Array[ExtendedBlockMatrix] = {
 			(for (i <- 1 until tensor.order) yield {
 				ExtendedBlockMatrix.hosvd(tensor, i, rank)
 			}).toArray
 		}
 	}
+}
+
+class ALS private(val tensor: Tensor, val rank: Int)(implicit spark: SparkSession)
+	extends cp.ALS[Tensor, ExtendedBlockMatrix, Map[String, DataFrame]]
+		with Logging {
 
 	protected var highRank: Option[Boolean] = None
-	override var initializer: (Tensor, Int) => Array[ExtendedBlockMatrix] = Initializers.gaussian
+	override var initializer: (Tensor, Int) => Array[ExtendedBlockMatrix] = ALS.Initializers.gaussian
 
 	override protected def copy(): ALS = {
 		val newObject = new ALS(tensor, rank)

distributed/src/main/scala/mulot/distributed/tensordecomposition/tucker/HOOI.scala

@@ -8,29 +8,38 @@ import scribe.Logging
 
 object HOOI {
 	def apply(tensor: Tensor, ranks: Array[Int])(implicit spark: SparkSession): HOOI = {
-		new HOOI(tensor, ranks)(spark)
+		var columnsName = (for (i <- 0 until tensor.order) yield s"row_$i") :+ tensor.valueColumnName
+		var newTensor = new Tensor(
+			tensor.data.select(columnsName(0), columnsName.tail: _*).cache(),
+			tensor.order,
+			tensor.dimensionsSize,
+			tensor.dimensionsName,
+			tensor.dimensionsIndex,
+			tensor.valueColumnName
+		)
+		new HOOI(newTensor, ranks)(spark)
 	}
-}
-
-class HOOI private[tucker](val tensor: Tensor, val ranks: Array[Int])(implicit spark: SparkSession)
-	extends tucker.HOOI[Tensor, ExtendedIndexedRowMatrix, Map[String, DataFrame]]
-		with Logging {
 
 	object Initializers {
-		def gaussian(tensor: Tensor, ranks: Array[Int]): Array[ExtendedIndexedRowMatrix] = {
+		def gaussian(tensor: Tensor, ranks: Array[Int])(implicit spark: SparkSession): Array[ExtendedIndexedRowMatrix] = {
 			(for (i <- 0 until tensor.order) yield {
 				ExtendedIndexedRowMatrix.gaussian(tensor.dimensionsSize(i), ranks(i))
 			}).toArray
 		}
 
-		def hosvd(tensor: Tensor, ranks: Array[Int]): Array[ExtendedIndexedRowMatrix] = {
+		def hosvd(tensor: Tensor, ranks: Array[Int])(implicit spark: SparkSession): Array[ExtendedIndexedRowMatrix] = {
 			(for (i <- 0 until tensor.order) yield {
 				ExtendedIndexedRowMatrix.fromIndexedRowMatrix(tensor.matricization(i, true)).VofSVD(ranks(i))
 			}).toArray
 		}
 	}
+}
+
+class HOOI private[tucker](val tensor: Tensor, val ranks: Array[Int])(implicit spark: SparkSession)
+	extends tucker.HOOI[Tensor, ExtendedIndexedRowMatrix, Map[String, DataFrame]]
+		with Logging {
 
-	override var initializer: (Tensor, Array[Int]) => Array[ExtendedIndexedRowMatrix] = Initializers.hosvd
+	override var initializer: (Tensor, Array[Int]) => Array[ExtendedIndexedRowMatrix] = HOOI.Initializers.hosvd
 
 	override protected def copy(): HOOI = {
 		val newObject = new HOOI(tensor, ranks)
@@ -80,6 +89,7 @@ class HOOI private[tucker](val tensor: Tensor, val ranks: Array[Int])(implicit s
 			// Compute the new factor matrices
 			for (dimensionIndice <- dimensionsOrder.indices) {
 				val dimension = dimensionsOrder(dimensionIndice)
+
 				// Prepare the core tensor for the iteration
 				var coreTensor = new Tensor(
 					previousCoreTensor.data.cache(),
@@ -141,6 +151,19 @@ class HOOI private[tucker](val tensor: Tensor, val ranks: Array[Int])(implicit s
 			}
 		}
 
+		var finalData = finalCoreTensor.data
+		for (dimension <- 0 until finalCoreTensor.order) {
+			finalData = finalData.withColumnRenamed(s"row_$dimension", finalCoreTensor.dimensionsName(dimension))
+		}
+		finalCoreTensor = new Tensor(
+			finalData,
+			tensor.order,
+			tensor.dimensionsSize,
+			tensor.dimensionsName,
+			tensor.dimensionsIndex,
+			tensor.valueColumnName
+		)
+
 		HOOIResult(factorMatrices, finalCoreTensor)
 	}
 }

distributed/src/test/scala/mulot/distributed/tensordecomposition/tucker/HOOITest.scala

@@ -22,7 +22,7 @@ class HOOITest extends FunSuite {
 		val ranks = Array(3, 3, 3)
 		val valueColumnName = "val"
 		val tensor = Tensor.fromIndexedDataFrame(
-			data.toDF("d0", "d1", "d2", valueColumnName),
+			data.toDF("d0", "d1", "d2", valueColumnName).select(valueColumnName, "d0", "d1", "d2"),
 			sizes,
 			valueColumnName = valueColumnName)
 		println("Tensor created")