Github user petro-rudenko commented on a diff in the pull request:
https://github.com/apache/spark/pull/5196#discussion_r27739585
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/VectorAssembler.scala ---
@@ -0,0 +1,101 @@
+/*
+ * 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.feature
+
+import scala.collection.mutable.ArrayBuilder
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.AlphaComponent
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.{HasInputCols, HasOutputCol, ParamMap}
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT, Vectors}
+import org.apache.spark.sql.{Column, DataFrame, Row}
+import org.apache.spark.sql.catalyst.analysis.UnresolvedAttribute
+import org.apache.spark.sql.catalyst.expressions.CreateStruct
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * :: AlphaComponent ::
+ * A feature transformer than merge multiple columns into a vector column.
+ */
+@AlphaComponent
+class VectorAssembler extends Transformer with HasInputCols with
HasOutputCol {
+
+ /** @group setParam */
+ def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ override def transform(dataset: DataFrame, paramMap: ParamMap):
DataFrame = {
+ val map = this.paramMap ++ paramMap
+ val assembleFunc = udf { r: Row =>
+ VectorAssembler.assemble(r.toSeq: _*)
+ }
+ val args = map(inputCols).map(c => UnresolvedAttribute(c))
+ dataset.select(col("*"), assembleFunc(new
Column(CreateStruct(args))).as(map(outputCol)))
+ }
+
+ override def transformSchema(schema: StructType, paramMap: ParamMap):
StructType = {
+ val map = this.paramMap ++ paramMap
+ val inputColNames = map(inputCols)
+ val outputColName = map(outputCol)
+ val inputDataTypes = inputColNames.map(name => schema(name).dataType)
+ for (dataType <- inputDataTypes) {
+ if (!(dataType == DoubleType || dataType.isInstanceOf[VectorUDT])) {
+ throw new IllegalArgumentException(s"Data type $dataType is not
supported.")
+ }
+ }
+ if (schema.fieldNames.contains(outputColName)) {
+ throw new IllegalArgumentException(s"Output column $outputColName
already exists.")
+ }
+ StructType(schema.fields :+ new StructField(outputColName, new
VectorUDT, false))
+ }
+}
+
+@AlphaComponent
+object VectorAssembler {
+
+ private[feature] def assemble(vv: Any*): Vector = {
+ val indices = ArrayBuilder.make[Int]
+ val values = ArrayBuilder.make[Double]
+ var cur = 0
+ vv.foreach {
+ case v: Double =>
--- End diff --
Would be good to support Integers also and just convert them to double.
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