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author | martinzapletal <zapletal-martin@email.cz> | 2015-07-30 15:57:14 -0700 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-07-30 15:57:14 -0700 |
commit | 7f7a319c4ce07f07a6bd68100cf0a4f1da66269e (patch) | |
tree | 01f64ff19785bfdb37fdc8174e9bd858086c3eb6 /mllib/src/test | |
parent | 0dbd6963d589a8f6ad344273f3da7df680ada515 (diff) | |
download | spark-7f7a319c4ce07f07a6bd68100cf0a4f1da66269e.tar.gz spark-7f7a319c4ce07f07a6bd68100cf0a4f1da66269e.tar.bz2 spark-7f7a319c4ce07f07a6bd68100cf0a4f1da66269e.zip |
[SPARK-8671] [ML] Added isotonic regression to the pipeline API.
Author: martinzapletal <zapletal-martin@email.cz>
Closes #7517 from zapletal-martin/SPARK-8671-isotonic-regression-api and squashes the following commits:
8c435c1 [martinzapletal] Review https://github.com/apache/spark/pull/7517 feedback update.
bebbb86 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b68efc0 [martinzapletal] Added tests for param validation.
07c12bd [martinzapletal] Comments and refactoring.
834fcf7 [martinzapletal] Merge remote-tracking branch 'upstream/master' into SPARK-8671-isotonic-regression-api
b611fee [martinzapletal] SPARK-8671. Added first version of isotonic regression to pipeline API
Diffstat (limited to 'mllib/src/test')
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala | 148 |
1 files changed, 148 insertions, 0 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.scala new file mode 100644 index 0000000000..66e4b170ba --- /dev/null +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/IsotonicRegressionSuite.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.regression + +import org.apache.spark.SparkFunSuite +import org.apache.spark.ml.param.ParamsSuite +import org.apache.spark.mllib.util.MLlibTestSparkContext +import org.apache.spark.sql.types.{DoubleType, StructField, StructType} +import org.apache.spark.sql.{DataFrame, Row} + +class IsotonicRegressionSuite extends SparkFunSuite with MLlibTestSparkContext { + private val schema = StructType( + Array( + StructField("label", DoubleType), + StructField("features", DoubleType), + StructField("weight", DoubleType))) + + private val predictionSchema = StructType(Array(StructField("features", DoubleType))) + + private def generateIsotonicInput(labels: Seq[Double]): DataFrame = { + val data = Seq.tabulate(labels.size)(i => Row(labels(i), i.toDouble, 1d)) + val parallelData = sc.parallelize(data) + + sqlContext.createDataFrame(parallelData, schema) + } + + private def generatePredictionInput(features: Seq[Double]): DataFrame = { + val data = Seq.tabulate(features.size)(i => Row(features(i))) + + val parallelData = sc.parallelize(data) + sqlContext.createDataFrame(parallelData, predictionSchema) + } + + test("isotonic regression predictions") { + val dataset = generateIsotonicInput(Seq(1, 2, 3, 1, 6, 17, 16, 17, 18)) + val trainer = new IsotonicRegression().setIsotonicParam(true) + + val model = trainer.fit(dataset) + + val predictions = model + .transform(dataset) + .select("prediction").map { + case Row(pred) => pred + }.collect() + + assert(predictions === Array(1, 2, 2, 2, 6, 16.5, 16.5, 17, 18)) + + assert(model.parentModel.boundaries === Array(0, 1, 3, 4, 5, 6, 7, 8)) + assert(model.parentModel.predictions === Array(1, 2, 2, 6, 16.5, 16.5, 17.0, 18.0)) + assert(model.parentModel.isotonic) + } + + test("antitonic regression predictions") { + val dataset = generateIsotonicInput(Seq(7, 5, 3, 5, 1)) + val trainer = new IsotonicRegression().setIsotonicParam(false) + + val model = trainer.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").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.getWeightCol === "weight") + assert(ir.getPredictionCol === "prediction") + assert(ir.getIsotonicParam === true) + + val model = ir.fit(dataset) + model.transform(dataset) + .select("label", "features", "prediction", "weight") + .collect() + + assert(model.getLabelCol === "label") + assert(model.getFeaturesCol === "features") + assert(model.getWeightCol === "weight") + assert(model.getPredictionCol === "prediction") + assert(model.getIsotonicParam === true) + assert(model.hasParent) + } + + test("set parameters") { + val isotonicRegression = new IsotonicRegression() + .setIsotonicParam(false) + .setWeightParam("w") + .setFeaturesCol("f") + .setLabelCol("l") + .setPredictionCol("p") + + assert(isotonicRegression.getIsotonicParam === false) + 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().setWeightParam("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) + } + } +} |