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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