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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.regression
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}
class IsotonicRegressionSuite
extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
private def generateIsotonicInput(labels: Seq[Double]): DataFrame = {
sqlContext.createDataFrame(
labels.zipWithIndex.map { case (label, i) => (label, i.toDouble, 1.0) }
).toDF("label", "features", "weight")
}
private def generatePredictionInput(features: Seq[Double]): DataFrame = {
sqlContext.createDataFrame(features.map(Tuple1.apply))
.toDF("features")
}
test("isotonic regression predictions") {
val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18))
val ir = new IsotonicRegression().setIsotonic(true)
val model = ir.fit(dataset)
val predictions = model
.transform(dataset)
.select("prediction").rdd.map { case Row(pred) =>
pred
}.collect()
assert(predictions === Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18))
assert(model.boundaries === Vectors.dense(0, 1, 3, 4, 5, 6, 7, 8))
assert(model.predictions === Vectors.dense(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0))
assert(model.getIsotonic)
}
test("antitonic regression predictions") {
val dataset = generateIsotonicInput(Seq(7, 5, 3, 5, 1))
val ir = new IsotonicRegression().setIsotonic(false)
val model = ir.fit(dataset)
val features = generatePredictionInput(Seq(-2.0, -1.0, 0.5, 0.75, 1.0, 2.0, 9.0))
val predictions = model
.transform(features)
.select("prediction").rdd.map {
case Row(pred) => pred
}.collect()
assert(predictions === Array(7, 7, 6, 5.5, 5, 4, 1))
}
test("params validation") {
val dataset = generateIsotonicInput(Seq(1, 2, 3))
val ir = new IsotonicRegression
ParamsSuite.checkParams(ir)
val model = ir.fit(dataset)
ParamsSuite.checkParams(model)
}
test("default params") {
val dataset = generateIsotonicInput(Seq(1, 2, 3))
val ir = new IsotonicRegression()
assert(ir.getLabelCol === "label")
assert(ir.getFeaturesCol === "features")
assert(ir.getPredictionCol === "prediction")
assert(!ir.isDefined(ir.weightCol))
assert(ir.getIsotonic)
assert(ir.getFeatureIndex === 0)
val model = ir.fit(dataset)
// copied model must have the same parent.
MLTestingUtils.checkCopy(model)
model.transform(dataset)
.select("label", "features", "prediction", "weight")
.collect()
assert(model.getLabelCol === "label")
assert(model.getFeaturesCol === "features")
assert(model.getPredictionCol === "prediction")
assert(!model.isDefined(model.weightCol))
assert(model.getIsotonic)
assert(model.getFeatureIndex === 0)
assert(model.hasParent)
}
test("set parameters") {
val isotonicRegression = new IsotonicRegression()
.setIsotonic(false)
.setWeightCol("w")
.setFeaturesCol("f")
.setLabelCol("l")
.setPredictionCol("p")
assert(!isotonicRegression.getIsotonic)
assert(isotonicRegression.getWeightCol === "w")
assert(isotonicRegression.getFeaturesCol === "f")
assert(isotonicRegression.getLabelCol === "l")
assert(isotonicRegression.getPredictionCol === "p")
}
test("missing column") {
val dataset = generateIsotonicInput(Seq(1, 2, 3))
intercept[IllegalArgumentException] {
new IsotonicRegression().setWeightCol("w").fit(dataset)
}
intercept[IllegalArgumentException] {
new IsotonicRegression().setFeaturesCol("f").fit(dataset)
}
intercept[IllegalArgumentException] {
new IsotonicRegression().setLabelCol("l").fit(dataset)
}
intercept[IllegalArgumentException] {
new IsotonicRegression().fit(dataset).setFeaturesCol("f").transform(dataset)
}
}
test("vector features column with feature index") {
val dataset = sqlContext.createDataFrame(Seq(
(4.0, Vectors.dense(0.0, 1.0)),
(3.0, Vectors.dense(0.0, 2.0)),
(5.0, Vectors.sparse(2, Array(1), Array(3.0))))
).toDF("label", "features")
val ir = new IsotonicRegression()
.setFeatureIndex(1)
val model = ir.fit(dataset)
val features = generatePredictionInput(Seq(2.0, 3.0, 4.0, 5.0))
val predictions = model
.transform(features)
.select("prediction").rdd.map {
case Row(pred) => pred
}.collect()
assert(predictions === Array(3.5, 5.0, 5.0, 5.0))
}
test("read/write") {
val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18))
def checkModelData(model: IsotonicRegressionModel, model2: IsotonicRegressionModel): Unit = {
assert(model.boundaries === model2.boundaries)
assert(model.predictions === model2.predictions)
assert(model.isotonic === model2.isotonic)
}
val ir = new IsotonicRegression()
testEstimatorAndModelReadWrite(ir, dataset, IsotonicRegressionSuite.allParamSettings,
checkModelData)
}
test("should support all NumericType labels and not support other types") {
val ir = new IsotonicRegression()
MLTestingUtils.checkNumericTypes[IsotonicRegressionModel, IsotonicRegression](
ir, isClassification = false, sqlContext) { (expected, actual) =>
assert(expected.boundaries === actual.boundaries)
assert(expected.predictions === actual.predictions)
}
}
}
object IsotonicRegressionSuite {
/**
* 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",
"isotonic" -> true,
"featureIndex" -> 0
)
}
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