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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.clustering
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Dataset
class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext
with DefaultReadWriteTest {
final val k = 5
@transient var dataset: Dataset[_] = _
override def beforeAll(): Unit = {
super.beforeAll()
dataset = KMeansSuite.generateKMeansData(sqlContext, 50, 3, k)
}
test("default parameters") {
val gm = new GaussianMixture()
assert(gm.getK === 2)
assert(gm.getFeaturesCol === "features")
assert(gm.getPredictionCol === "prediction")
assert(gm.getMaxIter === 100)
assert(gm.getTol === 0.01)
}
test("set parameters") {
val gm = new GaussianMixture()
.setK(9)
.setFeaturesCol("test_feature")
.setPredictionCol("test_prediction")
.setProbabilityCol("test_probability")
.setMaxIter(33)
.setSeed(123)
.setTol(1e-3)
assert(gm.getK === 9)
assert(gm.getFeaturesCol === "test_feature")
assert(gm.getPredictionCol === "test_prediction")
assert(gm.getProbabilityCol === "test_probability")
assert(gm.getMaxIter === 33)
assert(gm.getSeed === 123)
assert(gm.getTol === 1e-3)
}
test("parameters validation") {
intercept[IllegalArgumentException] {
new GaussianMixture().setK(1)
}
}
test("fit, transform, and summary") {
val predictionColName = "gm_prediction"
val probabilityColName = "gm_probability"
val gm = new GaussianMixture().setK(k).setMaxIter(2).setPredictionCol(predictionColName)
.setProbabilityCol(probabilityColName).setSeed(1)
val model = gm.fit(dataset)
assert(model.hasParent)
assert(model.weights.length === k)
assert(model.gaussians.length === k)
val transformed = model.transform(dataset)
val expectedColumns = Array("features", predictionColName, probabilityColName)
expectedColumns.foreach { column =>
assert(transformed.columns.contains(column))
}
// Check validity of model summary
val numRows = dataset.count()
assert(model.hasSummary)
val summary: GaussianMixtureSummary = model.summary
assert(summary.predictionCol === predictionColName)
assert(summary.probabilityCol === probabilityColName)
assert(summary.featuresCol === "features")
assert(summary.predictions.count() === numRows)
for (c <- Array(predictionColName, probabilityColName, "features")) {
assert(summary.predictions.columns.contains(c))
}
assert(summary.cluster.columns === Array(predictionColName))
assert(summary.probability.columns === Array(probabilityColName))
val clusterSizes = summary.clusterSizes
assert(clusterSizes.length === k)
assert(clusterSizes.sum === numRows)
assert(clusterSizes.forall(_ >= 0))
}
test("read/write") {
def checkModelData(model: GaussianMixtureModel, model2: GaussianMixtureModel): Unit = {
assert(model.weights === model2.weights)
assert(model.gaussians.map(_.mu) === model2.gaussians.map(_.mu))
assert(model.gaussians.map(_.sigma) === model2.gaussians.map(_.sigma))
}
val gm = new GaussianMixture()
testEstimatorAndModelReadWrite(gm, dataset,
GaussianMixtureSuite.allParamSettings, checkModelData)
}
}
object GaussianMixtureSuite {
/**
* Mapping from all Params to valid settings which differ from the defaults.
* This is useful for tests which need to exercise all Params, such as save/load.
* This excludes input columns to simplify some tests.
*/
val allParamSettings: Map[String, Any] = Map(
"predictionCol" -> "myPrediction",
"probabilityCol" -> "myProbability",
"k" -> 3,
"maxIter" -> 2,
"tol" -> 0.01
)
}
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