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/*
* 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.SparkFunSuite
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Dataset
class MinHashLSHSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
@transient var dataset: Dataset[_] = _
override def beforeAll(): Unit = {
super.beforeAll()
val data = {
for (i <- 0 to 95) yield Vectors.sparse(100, (i until i + 5).map((_, 1.0)))
}
dataset = spark.createDataFrame(data.map(Tuple1.apply)).toDF("keys")
}
test("params") {
ParamsSuite.checkParams(new MinHashLSH)
val model = new MinHashLSHModel("mh", randCoefficients = Array((1, 0)))
ParamsSuite.checkParams(model)
}
test("MinHashLSH: default params") {
val rp = new MinHashLSH
assert(rp.getNumHashTables === 1.0)
}
test("read/write") {
def checkModelData(model: MinHashLSHModel, model2: MinHashLSHModel): Unit = {
assertResult(model.randCoefficients)(model2.randCoefficients)
}
val mh = new MinHashLSH()
val settings = Map("inputCol" -> "keys", "outputCol" -> "values")
testEstimatorAndModelReadWrite(mh, dataset, settings, settings, checkModelData)
}
test("hashFunction") {
val model = new MinHashLSHModel("mh", randCoefficients = Array((0, 1), (1, 2), (3, 0)))
val res = model.hashFunction(Vectors.sparse(10, Seq((2, 1.0), (3, 1.0), (5, 1.0), (7, 1.0))))
assert(res.length == 3)
assert(res(0).equals(Vectors.dense(1.0)))
assert(res(1).equals(Vectors.dense(5.0)))
assert(res(2).equals(Vectors.dense(9.0)))
}
test("hashFunction: empty vector") {
val model = new MinHashLSHModel("mh", randCoefficients = Array((0, 1), (1, 2), (3, 0)))
intercept[IllegalArgumentException] {
model.hashFunction(Vectors.sparse(10, Seq()))
}
}
test("keyDistance") {
val model = new MinHashLSHModel("mh", randCoefficients = Array((1, 0)))
val v1 = Vectors.sparse(10, Seq((2, 1.0), (3, 1.0), (5, 1.0), (7, 1.0)))
val v2 = Vectors.sparse(10, Seq((1, 1.0), (3, 1.0), (5, 1.0), (7, 1.0), (9, 1.0)))
val keyDist = model.keyDistance(v1, v2)
assert(keyDist === 0.5)
}
test("MinHashLSH: test of LSH property") {
val mh = new MinHashLSH()
.setInputCol("keys")
.setOutputCol("values")
.setSeed(12344)
val (falsePositive, falseNegative) = LSHTest.calculateLSHProperty(dataset, mh, 0.75, 0.5)
assert(falsePositive < 0.3)
assert(falseNegative < 0.3)
}
test("MinHashLSH: test of inputDim > prime") {
val mh = new MinHashLSH()
.setInputCol("keys")
.setOutputCol("values")
.setSeed(12344)
val data = {
for (i <- 0 to 2) yield Vectors.sparse(Int.MaxValue, (i until i + 5).map((_, 1.0)))
}
val badDataset = spark.createDataFrame(data.map(Tuple1.apply)).toDF("keys")
intercept[IllegalArgumentException] {
mh.fit(badDataset)
}
}
test("approxNearestNeighbors for min hash") {
val mh = new MinHashLSH()
.setNumHashTables(20)
.setInputCol("keys")
.setOutputCol("values")
.setSeed(12345)
val key: Vector = Vectors.sparse(100,
(0 until 100).filter(_.toString.contains("1")).map((_, 1.0)))
val (precision, recall) = LSHTest.calculateApproxNearestNeighbors(mh, dataset, key, 20,
singleProbe = true)
assert(precision >= 0.7)
assert(recall >= 0.7)
}
test("approxNearestNeighbors for numNeighbors <= 0") {
val model = new MinHashLSHModel("mh", randCoefficients = Array((1, 0)))
val key: Vector = Vectors.sparse(100,
(0 until 100).filter(_.toString.contains("1")).map((_, 1.0)))
intercept[IllegalArgumentException] {
model.approxNearestNeighbors(dataset, key, 0)
}
intercept[IllegalArgumentException] {
model.approxNearestNeighbors(dataset, key, -1)
}
}
test("approxSimilarityJoin for min hash on different dataset") {
val data1 = {
for (i <- 0 until 20) yield Vectors.sparse(100, (5 * i until 5 * i + 5).map((_, 1.0)))
}
val df1 = spark.createDataFrame(data1.map(Tuple1.apply)).toDF("keys")
val data2 = {
for (i <- 0 until 30) yield Vectors.sparse(100, (3 * i until 3 * i + 3).map((_, 1.0)))
}
val df2 = spark.createDataFrame(data2.map(Tuple1.apply)).toDF("keys")
val mh = new MinHashLSH()
.setNumHashTables(20)
.setInputCol("keys")
.setOutputCol("values")
.setSeed(12345)
val (precision, recall) = LSHTest.calculateApproxSimilarityJoin(mh, df1, df2, 0.5)
assert(precision == 1.0)
assert(recall >= 0.7)
}
}
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