Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/12259#discussion_r60775523
--- Diff: mllib/src/main/scala/org/apache/spark/ml/linalg/MatrixUDT.scala
---
@@ -0,0 +1,112 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.linalg.udt
+
+import org.apache.spark.ml.linalg.{DenseMatrix, Matrix, SparseMatrix}
+import org.apache.spark.sql.catalyst.InternalRow
+import org.apache.spark.sql.catalyst.expressions.GenericMutableRow
+import org.apache.spark.sql.catalyst.util.GenericArrayData
+import org.apache.spark.sql.types._
+
+/**
+ * User-defined type for [[Matrix]] in [[mllib-local]] which allows easy
interaction with SQL
+ * via [[org.apache.spark.sql.Dataset]].
+ */
+private[ml] class MatrixUDT extends UserDefinedType[Matrix] {
+
+ override def sqlType: StructType = {
+ // type: 0 = sparse, 1 = dense
+ // the dense matrix is built by numRows, numCols, values and
isTransposed, all of which are
+ // set as not nullable, except values since in the future, support for
binary matrices might
+ // be added for which values are not needed.
+ // the sparse matrix needs colPtrs and rowIndices, which are set as
+ // null, while building the dense matrix.
+ StructType(Seq(
+ StructField("type", ByteType, nullable = false),
+ StructField("numRows", IntegerType, nullable = false),
+ StructField("numCols", IntegerType, nullable = false),
+ StructField("colPtrs", ArrayType(IntegerType, containsNull = false),
nullable = true),
+ StructField("rowIndices", ArrayType(IntegerType, containsNull =
false), nullable = true),
+ StructField("values", ArrayType(DoubleType, containsNull = false),
nullable = true),
+ StructField("isTransposed", BooleanType, nullable = false)
+ ))
+ }
+
+ override def serialize(obj: Matrix): InternalRow = {
+ val row = new GenericMutableRow(7)
+ obj match {
+ case sm: SparseMatrix =>
+ row.setByte(0, 0)
+ row.setInt(1, sm.numRows)
+ row.setInt(2, sm.numCols)
+ row.update(3, new
GenericArrayData(sm.colPtrs.map(_.asInstanceOf[Any])))
+ row.update(4, new
GenericArrayData(sm.rowIndices.map(_.asInstanceOf[Any])))
+ row.update(5, new
GenericArrayData(sm.values.map(_.asInstanceOf[Any])))
+ row.setBoolean(6, sm.isTransposed)
+
+ case dm: DenseMatrix =>
+ row.setByte(0, 1)
+ row.setInt(1, dm.numRows)
+ row.setInt(2, dm.numCols)
+ row.setNullAt(3)
+ row.setNullAt(4)
+ row.update(5, new
GenericArrayData(dm.values.map(_.asInstanceOf[Any])))
+ row.setBoolean(6, dm.isTransposed)
+ }
+ row
+ }
+
+ override def deserialize(datum: Any): Matrix = {
+ datum match {
+ case row: InternalRow =>
+ require(row.numFields == 7,
+ s"MatrixUDT.deserialize given row with length ${row.numFields}
but requires length == 7")
+ val tpe = row.getByte(0)
+ val numRows = row.getInt(1)
+ val numCols = row.getInt(2)
+ val values = row.getArray(5).toDoubleArray()
+ val isTransposed = row.getBoolean(6)
+ tpe match {
+ case 0 =>
+ val colPtrs = row.getArray(3).toIntArray()
+ val rowIndices = row.getArray(4).toIntArray()
+ new SparseMatrix(numRows, numCols, colPtrs, rowIndices,
values, isTransposed)
+ case 1 =>
+ new DenseMatrix(numRows, numCols, values, isTransposed)
+ }
+ }
+ }
+
+ override def userClass: Class[Matrix] = classOf[Matrix]
+
+ override def equals(o: Any): Boolean = {
+ o match {
+ case v: MatrixUDT => true
+ case _ => false
+ }
+ }
+
+ // see [SPARK-8647], this achieves the needed constant hash code without
constant no.
+ override def hashCode(): Int = classOf[MatrixUDT].getName.hashCode()
+
+ override def typeName: String = "matrix"
+
+ override def pyUDT: String = "pyspark.mllib.linalg.MatrixUDT"
--- End diff --
Let's change it to `pyspark.ml.linalg.MatrixUDT` for now. Since in python,
we don't have any code using the new UDT. We can add this in the followup PR.
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