Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/4580#discussion_r29816089
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/feature/ElementwiseProductSuite.scala
---
@@ -0,0 +1,83 @@
+/*
+ * 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.mllib.linalg.{SparseVector, DenseVector, Vector,
Vectors}
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.scalatest.FunSuite
+import org.apache.spark.mllib.util.TestingUtils._
+
+class ElementwiseProductSuite extends FunSuite with MLlibTestSparkContext{
+
+ val denseData = Array(
+ Vectors.dense(1.0, 1.0, 0.0, 0.0),
+ Vectors.dense(1.0, 2.0, -3.0, 0.0),
+ Vectors.dense(1.0, 3.0, 0.0, 0.0),
+ Vectors.dense(1.0, 4.0, 1.9, -9.0),
+ Vectors.dense(1.0, 5.0, 0.0, 0.0)
+ )
+
+ val sparseData = Array(
+ Vectors.sparse(3, Seq((0, -2.0), (1, 2.3))),
+ Vectors.sparse(3, Seq((1, -1.0), (2, -3.0))),
+ Vectors.sparse(3, Seq((1, -5.1))),
+ Vectors.sparse(3, Seq((0, 3.8), (2, 1.9))),
+ Vectors.sparse(3, Seq((0, 1.7), (1, -0.6))),
+ Vectors.sparse(3, Seq((1, 1.9)))
+ )
+
+ val scalingVector = Vectors.dense(2.0, 0.5, 0.0, 0.25)
+
+ test("elementwise (hadamard) product should properly apply vector to
dense data set") {
+
+ val transformer = new ElementwiseProduct(scalingVector)
+ val transformedData = transformer.transform(sc.makeRDD(denseData))
+
+ val transformedVecs = transformedData.collect()
+
+ val fourthVec = transformedVecs.apply(3).toArray
+
+ assert(fourthVec.apply(0) === 2.0, "product by 2.0 should have been
applied")
+ assert(fourthVec.apply(1) === 2.0, "product by 0.5 should have been
applied")
+ assert(fourthVec.apply(2) === 0.0, "product by 0.0 should have been
applied")
+ assert(fourthVec.apply(3) === -2.25, "product by 0.25 should have been
applied")
+ }
+
+ test("elementwise (hadamard) product should properly apply vector to
sparse data set") {
+
+ 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((0, -2.0), (1, 0.0))) absTol
1E-5)
--- End diff --
same here: Test using per-row transform() instead of transforming the full
RDD
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