Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/5731#discussion_r36050382
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/VectorSlicer.scala ---
@@ -0,0 +1,153 @@
+/*
+ * 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 org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.attribute.AttributeGroup
+import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
+import org.apache.spark.ml.param.{IntArrayParam, ParamMap,
StringArrayParam}
+import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
+import org.apache.spark.mllib.linalg._
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.StructType
+
+/**
+ * :: Experimental ::
+ * Given either indices or names, it takes a vector column and output a
vector column with the
+ * specified subset of features. Note that the vector column should
contain ML [[Attribute]].
+ */
+@Experimental
+final class VectorSlicer(override val uid: String)
+ extends Transformer with HasInputCol with HasOutputCol {
+
+ def this() = this(Identifiable.randomUID("vectorSlicer"))
+
+ /**
+ * An array of indices to select features from a vector column.
+ * @group param
+ */
+ val selectedIndices = new IntArrayParam(this, "selectedIndices",
+ "An array of indices to select features from a vector column",
+ (x: Array[Int]) => if (x.isEmpty) true else x.min >= 0)
+
+ setDefault(selectedIndices -> Array.empty[Int])
+
+ /** @group getParam */
+ def getSelectedIndices: Array[Int] = getOrDefault(selectedIndices)
+
+ /** @group setParam */
+ def setSelectedIndices(value: Array[Int]): this.type =
set(selectedIndices, value)
+
+ /**
+ * An array of feature names to select features from a vector column.
+ * @group param
+ */
+ val selectedNames = new StringArrayParam(this, "selectedNames",
+ "An array of feature names to select features from a vector column")
+
+ setDefault(selectedNames -> Array.empty[String])
+
+ /** @group getParam */
+ def getSelectedNames: Array[String] = getOrDefault(selectedNames)
+
+ /** @group setParam */
+ def setSelectedNames(value: Array[String]): this.type =
set(selectedNames, value)
+
+ /** @group setParam */
+ def setInputCol(value: String): this.type = set(inputCol, value)
+
+ /** @group setParam */
+ def setOutputCol(value: String): this.type = set(outputCol, value)
+
+ /**
+ * Slice a dense vector with an array of indices.
+ */
+ private def selectColumns(indices: Array[Int], features: DenseVector):
Vector = {
+ Vectors.dense(indices.map(features.apply))
+ }
+
+ /**
+ * Slice a sparse vector with a set of indices.
+ */
+ private def selectColumns(indices: Set[Int], features: SparseVector):
Vector = {
+ Vectors.sparse(
+ indices.size, features.indices.zip(features.values).filter(x =>
indices.contains(x._1)))
+ }
+
+ private def getFeatureIndicesFromNames(inputAttr: AttributeGroup):
Array[Int] = {
+ $(selectedNames).map(name => inputAttr.getAttr(name).index.get)
+ }
+
+ private def merge(xs: List[Int], ys: List[Int]): List[Int] = {
+ (xs, ys) match {
+ case (Nil, ys) => ys
+ case (xs, Nil) => xs
+ case (x :: xs1, y :: ys1) =>
+ if (x < y) x :: merge(xs1, ys)
+ else y :: merge(xs, ys1)
+ }
+ }
+
+ /**
+ * Union feature indices from user specified indices and indices that
transformed from user
+ * specified feature names. After the union process, indices are sorted
ASC and any potential
+ * duplicates are removed.
+ */
+ private def unionFeatureIndices(first: Array[Int], second: Array[Int]):
Array[Int] = {
+ merge(first.sorted.toList, second.sorted.toList).distinct.toArray
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ transformSchema(dataset.schema)
+
+ val indices = $(selectedIndices)
+ val slicer = udf { vec: Vector =>
+ vec match {
+ case features: DenseVector => selectColumns(indices, features)
+ case features: SparseVector => selectColumns(indices.toSet,
features)
+ }
+ }
+ dataset.withColumn($(outputCol), slicer(dataset($(inputCol))))
+ }
+
+ override def transformSchema(schema: StructType): StructType = {
+ SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
+ val inputAttr = AttributeGroup.fromStructField(schema($(inputCol)))
+ $(selectedNames).foreach { name =>
+ assert(inputAttr.hasAttr(name), s"Selected feature $name does not
belong in the vector.")
+ }
+ assert($(selectedIndices).max < inputAttr.size, s"Selected index out of
bound.")
--- End diff --
same for this check
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