Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/15148#discussion_r85407083
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/feature/RandomProjectionSuite.scala ---
@@ -0,0 +1,148 @@
+/*
+ * 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 breeze.numerics.{cos, sin}
+import breeze.numerics.constants.Pi
+
+import org.apache.spark.SparkFunSuite
+import org.apache.spark.ml.linalg.Vectors
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+
+class RandomProjectionSuite extends SparkFunSuite with
MLlibTestSparkContext {
+ test("RandomProjection") {
+ val data = {
+ for (i <- -5 until 5; j <- -5 until 5) yield
Vectors.dense(i.toDouble, j.toDouble)
+ }
+ val df = spark.createDataFrame(data.map(Tuple1.apply)).toDF("keys")
+
+ // Project from 2 dimensional Euclidean Space to 1 dimensions
+ val rp = new RandomProjection()
+ .setOutputDim(1)
+ .setInputCol("keys")
+ .setOutputCol("values")
+ .setBucketLength(1.0)
+ .setSeed(12345)
+
+ val (falsePositive, falseNegative) = LSHTest.calculateLSHProperty(df,
rp, 8.0, 2.0)
+ assert(falsePositive < 0.05)
+ assert(falseNegative < 0.06)
+ }
+
+ test("RandomProjection with high dimension data") {
+ val numDim = 100
+ val data = {
+ for (i <- 0 until numDim; j <- Seq(-2, -1, 1, 2))
+ yield Vectors.sparse(numDim, Seq((i, j.toDouble)))
+ }
+ val df = spark.createDataFrame(data.map(Tuple1.apply)).toDF("keys")
+
+ // Project from 100 dimensional Euclidean Space to 10 dimensions
+ val rp = new RandomProjection()
+ .setOutputDim(10)
+ .setInputCol("keys")
+ .setOutputCol("values")
+ .setBucketLength(2.5)
+ .setSeed(12345)
+
+ val (falsePositive, falseNegative) = LSHTest.calculateLSHProperty(df,
rp, 3.0, 2.0)
+ assert(falsePositive == 0.0)
+ assert(falseNegative < 0.05)
+ }
+
+ test("approxNearestNeighbors for random projection") {
+ val data = {
+ for (i <- -10 until 10; j <- -10 until 10) yield
Vectors.dense(i.toDouble, j.toDouble)
+ }
+ val df = spark.createDataFrame(data.map(Tuple1.apply)).toDF("keys")
+ val key = Vectors.dense(1.2, 3.4)
+
+ val rp = new RandomProjection()
+ .setOutputDim(2)
+ .setInputCol("keys")
+ .setOutputCol("values")
+ .setBucketLength(4.0)
+ .setSeed(12345)
+
+ val (precision, recall) = LSHTest.calculateApproxNearestNeighbors(rp,
df, key, 100,
+ singleProbing = true)
+ assert(precision >= 0.6)
+ assert(recall >= 0.6)
+ }
+
+ test("approxNearestNeighbors with multiple probing") {
--- End diff --
Ohh, I see. Makes sense.
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