[ML-177][Native Bayes] Fix error when converting Vector to CSRNumericTable

mllib-dal/src/main/scala/com/intel/oap/mllib/OneDAL.scala

@@ -236,8 +236,8 @@ object OneDAL {
     matrixLabel
   }
 
-  private def vectorsToSparseNumericTable(vectors: Array[Vector],
-                                          nFeatures: Long): CSRNumericTable = {
+  def vectorsToSparseNumericTable(vectors: Array[Vector],
+                                  nFeatures: Long): CSRNumericTable = {
     require(vectors(0).isInstanceOf[SparseVector], "vectors should be sparse")
 
     println(s"Features row x column: ${vectors.length} x ${vectors(0).size}")
@@ -250,10 +250,9 @@ object OneDAL {
     val columnIndices = Array.fill(ratingsNum) {
       0L
     }
-    val rowOffsets = ArrayBuffer[Long](1L)
+    val rowOffsets = ArrayBuffer[Long]()
 
     var indexValues = 0
-    var curRow = 0L
 
     // Converted to one CSRNumericTable
     for (row <- 0 until vectors.length) {
@@ -263,20 +262,22 @@ object OneDAL {
         // one-based indexValues
         columnIndices(indexValues) = column + 1
 
-        if (row > curRow) {
-          curRow = row
-          // one-based indexValues
-          rowOffsets += indexValues + 1
-        }
-
         indexValues = indexValues + 1
       }
+      // one-based row indexValues
+      rowOffsets += indexValues + 1
     }
-    // one-based row indexValues
-    rowOffsets += indexValues + 1
 
     val contextLocal = new DaalContext()
 
+    // check CSR encoding
+    assert(values.length == ratingsNum,
+      "the length of values should be equal to the number of non-zero elements")
+    assert(columnIndices.length == ratingsNum,
+      "the length of columnIndices should be equal to the number of non-zero elements")
+    assert(rowOffsets.size == (csrRowNum + 1),
+      "the size of rowOffsets should be equal to the number of rows + 1")
+
     val cTable = OneDAL.cNewCSRNumericTableDouble(values, columnIndices, rowOffsets.toArray,
       nFeatures, csrRowNum)
     val table = new CSRNumericTable(contextLocal, cTable)

mllib-dal/src/test/scala/org/apache/spark/ml/oneDALSuite.scala

@@ -0,0 +1,40 @@
+package org.apache.spark.ml
+
+import com.intel.oap.mllib.OneDAL
+import org.apache.spark.internal.Logging
+import org.apache.spark.ml.linalg.{Matrices, Vector, Vectors}
+import org.apache.spark.sql.Row
+
+class oneDALSuite extends FunctionsSuite with Logging {
+
+  import testImplicits._
+
+  test("test sparse vector to CSRNumericTable") {
+    val data = Seq(
+      Vectors.sparse(3, Seq((0, 1.0), (1, 2.0), (2, 3.0))),
+      Vectors.sparse(3, Seq((0, 10.0), (1, 20.0), (2, 30.0))),
+      Vectors.sparse(3, Seq.empty),
+      Vectors.sparse(3, Seq.empty),
+      Vectors.sparse(3, Seq((0, 1.0), (1, 2.0))),
+      Vectors.sparse(3, Seq((0, 10.0), (2, 20.0))),
+    )
+    val df = data.map(Tuple1.apply).toDF("features")
+    df.show()
+    val rowsRDD = df.rdd.map {
+      case Row(features: Vector) => features
+    }
+    val results = rowsRDD.coalesce(1).mapPartitions { it: Iterator[Vector] =>
+      val vectors: Array[Vector] = it.toArray
+      val numColumns = vectors(0).size
+      val CSRNumericTable = {
+        OneDAL.vectorsToSparseNumericTable(vectors, numColumns)
+      }
+      Iterator(CSRNumericTable.getCNumericTable)
+    }.collect()
+    val csr = OneDAL.makeNumericTable(results(0))
+    val resultMatrix = OneDAL.numericTableToMatrix(csr)
+    val matrix = Matrices.fromVectors(data)
+
+    assert((resultMatrix.toArray sameElements matrix.toArray) === true)
+  }
+}

mllib-dal/test.sh

@@ -53,7 +53,8 @@ suiteArray=(
   "classification.MLlibNaiveBayesSuite" \
   "regression.MLlibLinearRegressionSuite" \
   "stat.MLlibCorrelationSuite" \
-  "stat.MultivariateOnlineSummarizerSuite"
+  "stat.MultivariateOnlineSummarizerSuite" \
+  "oneDALSuite"
 )
 
 MVN_NO_TRANSFER_PROGRESS=