[SPARK-5726] [MLLIB] Elementwise (Hadamard) Vector Product Transformer

docs/mllib-feature-extraction.md

@@ -477,3 +477,57 @@ sc.stop();
 </div>
 </div>
 
+## ElementwiseProduct
+
+ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier.  This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `w`, to yield a result vector.
+
+`\[ \begin{pmatrix}
+v_1 \\
+\vdots \\
+v_N
+\end{pmatrix} \circ \begin{pmatrix}
+                    w_1 \\
+                    \vdots \\
+                    w_N
+                    \end{pmatrix}
+= \begin{pmatrix}
+  v_1 w_1 \\
+  \vdots \\
+  v_N w_N
+  \end{pmatrix}
+\]`
+
+[`ElementwiseProduct`](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct) has the following parameter in the constructor:
+
+* `w`: the transforming vector.
+
+`ElementwiseProduct` implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer) which can apply the weighting on a `Vector` to produce a transformed `Vector` or on an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
+
+### Example
+
+This example below demonstrates how to load a simple vectors file, extract a set of vectors, then transform those vectors using a transforming vector value.
+
+
+<div class="codetabs">
+<div data-lang="scala">
+{% highlight scala %}
+import org.apache.spark.SparkContext._
+import org.apache.spark.mllib.feature.ElementwiseProduct
+import org.apache.spark.mllib.linalg.Vectors
+
+// Load and parse the data:
+val data = sc.textFile("data/mllib/kmeans_data.txt")
+val parsedData = data.map(s => Vectors.dense(s.split(' ').map(_.toDouble)))
+
+val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
+val transformer = new ElementwiseProduct(transformingVector)
+
+// Batch transform and per-row transform give the same results:
+val transformedData = transformer.transform(parsedData)
+val transformedData2 = parsedData.map(x => transformer.transform(x))
+
+{% endhighlight %}
+</div>
+</div>
+
+

mllib/src/main/scala/org/apache/spark/ml/feature/ElementwiseProduct.scala

@@ -0,0 +1,55 @@
+/*
+ * 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.AlphaComponent
+import org.apache.spark.ml.UnaryTransformer
+import org.apache.spark.ml.param.Param
+import org.apache.spark.mllib.feature
+import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
+import org.apache.spark.sql.types.DataType
+
+/**
+ * :: AlphaComponent ::
+ * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
+ * provided "weight" vector.  In other words, it scales each column of the dataset by a scalar
+ * multiplier.
+ */
+@AlphaComponent
+class ElementwiseProduct extends UnaryTransformer[Vector, Vector, ElementwiseProduct] {
+
+  /**
+    * the vector to multiply with input vectors
+    * @group param
+    */
+  val scalingVec: Param[Vector] = new Param(this, "scalingVector", "vector for hadamard product")
+
+  /** @group setParam */
+  def setScalingVec(value: Vector): this.type = set(scalingVec, value)
+
+  /** @group getParam */
+  def getScalingVec: Vector = getOrDefault(scalingVec)
+
+  override protected def createTransformFunc: Vector => Vector = {
+    require(params.contains(scalingVec), s"transformation requires a weight vector")
+    val elemScaler = new feature.ElementwiseProduct($(scalingVec))
+    elemScaler.transform
+  }
+
+  override protected def outputDataType: DataType = new VectorUDT()
+}

mllib/src/main/scala/org/apache/spark/mllib/feature/ElementwiseProduct.scala

@@ -0,0 +1,64 @@
+/*
+ * 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.mllib.feature
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.mllib.linalg._
+
+/**
+ * :: Experimental ::
+ * Outputs the Hadamard product (i.e., the element-wise product) of each input vector with a
+ * provided "weight" vector. In other words, it scales each column of the dataset by a scalar
+ * multiplier.
+ * @param scalingVector The values used to scale the reference vector's individual components.
+ */
+@Experimental
+class ElementwiseProduct(val scalingVector: Vector) extends VectorTransformer {
+
+  /**
+   * Does the hadamard product transformation.
+   *
+   * @param vector vector to be transformed.
+   * @return transformed vector.
+   */
+  override def transform(vector: Vector): Vector = {
+    require(vector.size == scalingVector.size,
+      s"vector sizes do not match: Expected ${scalingVector.size} but found ${vector.size}")
+    vector match {
+      case dv: DenseVector =>
+        val values: Array[Double] = dv.values.clone()
+        val dim = scalingVector.size
+        var i = 0
+        while (i < dim) {
+          values(i) *= scalingVector(i)
+          i += 1
+        }
+        Vectors.dense(values)
+      case SparseVector(size, indices, vs) =>
+        val values = vs.clone()
+        val dim = values.length
+        var i = 0
+        while (i < dim) {
+          values(i) *= scalingVector(indices(i))
+          i += 1
+        }
+        Vectors.sparse(size, indices, values)
+      case v => throw new IllegalArgumentException("Does not support vector type " + v.getClass)
+    }
+  }
+}

mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala

@@ -0,0 +1,61 @@
+/*
+ * 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.mllib.feature
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.linalg.{DenseVector, SparseVector, Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
+
+class ElementwiseProductSuite extends FunSuite with MLlibTestSparkContext {
+
+  test("elementwise (hadamard) product should properly apply vector to dense data set") {
+    val denseData = Array(
+      Vectors.dense(1.0, 4.0, 1.9, -9.0)
+    )
+    val scalingVec = Vectors.dense(2.0, 0.5, 0.0, 0.25)
+    val transformer = new ElementwiseProduct(scalingVec)
+    val transformedData = transformer.transform(sc.makeRDD(denseData))
+    val transformedVecs = transformedData.collect()
+    val transformedVec = transformedVecs(0)
+    val expectedVec = Vectors.dense(2.0, 2.0, 0.0, -2.25)
+    assert(transformedVec ~== expectedVec absTol 1E-5,
+      s"Expected transformed vector $expectedVec but found $transformedVec")
+  }
+
+  test("elementwise (hadamard) product should properly apply vector to sparse data set") {
+    val sparseData = Array(
+      Vectors.sparse(3, Seq((1, -1.0), (2, -3.0)))
+    )
+    val dataRDD = sc.parallelize(sparseData, 3)
+    val scalingVec = Vectors.dense(1.0, 0.0, 0.5)
+    val transformer = new ElementwiseProduct(scalingVec)
+    val data2 = sparseData.map(transformer.transform)
+    val data2RDD = transformer.transform(dataRDD)
+
+    assert((sparseData, data2, data2RDD.collect()).zipped.forall {
+      case (v1: DenseVector, v2: DenseVector, v3: DenseVector) => true
+      case (v1: SparseVector, v2: SparseVector, v3: SparseVector) => true
+      case _ => false
+    }, "The vector type should be preserved after hadamard product")
+
+    assert((data2, data2RDD.collect()).zipped.forall((v1, v2) => v1 ~== v2 absTol 1E-5))
+    assert(data2(0) ~== Vectors.sparse(3, Seq((1, 0.0), (2, -1.5))) absTol 1E-5)
+  }
+}