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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.mllib.clustering
import org.apache.spark.SparkFunSuite
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._
class BisectingKMeansSuite extends SparkFunSuite with MLlibTestSparkContext {
test("default values") {
val bkm0 = new BisectingKMeans()
assert(bkm0.getK === 4)
assert(bkm0.getMaxIterations === 20)
assert(bkm0.getMinDivisibleClusterSize === 1.0)
val bkm1 = new BisectingKMeans()
assert(bkm0.getSeed === bkm1.getSeed, "The default seed should be constant.")
}
test("setter/getter") {
val bkm = new BisectingKMeans()
val k = 10
assert(bkm.getK !== k)
assert(bkm.setK(k).getK === k)
val maxIter = 100
assert(bkm.getMaxIterations !== maxIter)
assert(bkm.setMaxIterations(maxIter).getMaxIterations === maxIter)
val minSize = 2.0
assert(bkm.getMinDivisibleClusterSize !== minSize)
assert(bkm.setMinDivisibleClusterSize(minSize).getMinDivisibleClusterSize === minSize)
val seed = 10L
assert(bkm.getSeed !== seed)
assert(bkm.setSeed(seed).getSeed === seed)
intercept[IllegalArgumentException] {
bkm.setK(0)
}
intercept[IllegalArgumentException] {
bkm.setMaxIterations(0)
}
intercept[IllegalArgumentException] {
bkm.setMinDivisibleClusterSize(0.0)
}
}
test("1D data") {
val points = Vectors.sparse(1, Array.empty, Array.empty) +:
(1 until 8).map(i => Vectors.dense(i))
val data = sc.parallelize(points, 2)
val bkm = new BisectingKMeans()
.setK(4)
.setMaxIterations(1)
.setSeed(1L)
// The clusters should be
// (0, 1, 2, 3, 4, 5, 6, 7)
// - (0, 1, 2, 3)
// - (0, 1)
// - (2, 3)
// - (4, 5, 6, 7)
// - (4, 5)
// - (6, 7)
val model = bkm.run(data)
assert(model.k === 4)
// The total cost should be 8 * 0.5 * 0.5 = 2.0.
assert(model.computeCost(data) ~== 2.0 relTol 1e-12)
val predictions = data.map(v => (v(0), model.predict(v))).collectAsMap()
Range(0, 8, 2).foreach { i =>
assert(predictions(i) === predictions(i + 1),
s"$i and ${i + 1} should belong to the same cluster.")
}
val root = model.root
assert(root.center(0) ~== 3.5 relTol 1e-12)
assert(root.height ~== 2.0 relTol 1e-12)
assert(root.children.length === 2)
assert(root.children(0).height ~== 1.0 relTol 1e-12)
assert(root.children(1).height ~== 1.0 relTol 1e-12)
}
test("points are the same") {
val data = sc.parallelize(Seq.fill(8)(Vectors.dense(1.0, 1.0)), 2)
val bkm = new BisectingKMeans()
.setK(2)
.setMaxIterations(1)
.setSeed(1L)
val model = bkm.run(data)
assert(model.k === 1)
}
test("more desired clusters than points") {
val data = sc.parallelize(Seq.tabulate(4)(i => Vectors.dense(i)), 2)
val bkm = new BisectingKMeans()
.setK(8)
.setMaxIterations(2)
.setSeed(1L)
val model = bkm.run(data)
assert(model.k === 4)
}
test("min divisible cluster") {
val data = sc.parallelize(
Seq.tabulate(16)(i => Vectors.dense(i)) ++ Seq.tabulate(4)(i => Vectors.dense(-100.0 - i)),
2)
val bkm = new BisectingKMeans()
.setK(4)
.setMinDivisibleClusterSize(10)
.setMaxIterations(1)
.setSeed(1L)
val model = bkm.run(data)
assert(model.k === 3)
assert(model.predict(Vectors.dense(-100)) === model.predict(Vectors.dense(-97)))
assert(model.predict(Vectors.dense(7)) !== model.predict(Vectors.dense(8)))
bkm.setMinDivisibleClusterSize(0.5)
val sameModel = bkm.run(data)
assert(sameModel.k === 3)
}
test("larger clusters get selected first") {
val data = sc.parallelize(
Seq.tabulate(16)(i => Vectors.dense(i)) ++ Seq.tabulate(4)(i => Vectors.dense(-100.0 - i)),
2)
val bkm = new BisectingKMeans()
.setK(3)
.setMaxIterations(1)
.setSeed(1L)
val model = bkm.run(data)
assert(model.k === 3)
assert(model.predict(Vectors.dense(-100)) === model.predict(Vectors.dense(-97)))
assert(model.predict(Vectors.dense(7)) !== model.predict(Vectors.dense(8)))
}
test("2D data") {
val points = Seq(
(11, 10), (9, 10), (10, 9), (10, 11),
(11, -10), (9, -10), (10, -9), (10, -11),
(0, 1), (0, -1)
).map { case (x, y) =>
if (x == 0) {
Vectors.sparse(2, Array(1), Array(y))
} else {
Vectors.dense(x, y)
}
}
val data = sc.parallelize(points, 2)
val bkm = new BisectingKMeans()
.setK(3)
.setMaxIterations(4)
.setSeed(1L)
val model = bkm.run(data)
assert(model.k === 3)
assert(model.root.center ~== Vectors.dense(8, 0) relTol 1e-12)
model.root.leafNodes.foreach { node =>
if (node.center(0) < 5) {
assert(node.size === 2)
assert(node.center ~== Vectors.dense(0, 0) relTol 1e-12)
} else if (node.center(1) > 0) {
assert(node.size === 4)
assert(node.center ~== Vectors.dense(10, 10) relTol 1e-12)
} else {
assert(node.size === 4)
assert(node.center ~== Vectors.dense(10, -10) relTol 1e-12)
}
}
}
}
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