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[SPARK-19234][MLLib] AFTSurvivalRegression should fail fast when any labels are zero #16652

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[SPARK-19234][MLLib] AFTSurvivalRegression should fail fast when any labels are zero #16652

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f0e09c5
[SPARK-19234][MLLib] make sure label is positive for AFT regression
admackin Jan 20, 2017
0efbf0f
[SPARK-19234][MLLib] fix test suite to ensure no zero-labels get pass…
admackin Jan 20, 2017
cfdd286
[SPARK-19234][MLLib] added test case to ensure fast failure on zero l…
Jan 23, 2017
f645ab8
[SPARK-19234][MLLib] added a clue about test failure per code-review …
Jan 30, 2017
88aee2f
[SPARK-19234][MLLib] fixing indentation to match code guidelines
Jan 30, 2017
c855976
[SPARK-19234][MLLib] fixing imports to match scala style guidelines
Jan 30, 2017
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1 change: 1 addition & 0 deletions mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -644,4 +644,5 @@ private class AFTCostFun(
*/
private[regression] case class AFTPoint(features: Vector, label: Double, censor: Double) {
require(censor == 1.0 || censor == 0.0, "censor of class AFTPoint must be 1.0 or 0.0")
require(label > 0.0, "label of AFTPoint must be positive")
}
18 changes: 16 additions & 2 deletions mllib/src/test/scala/org/apache/spark/ml/regression/AFTSurvivalRegressionSuite.scala
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Expand Up @@ -19,14 +19,15 @@ package org.apache.spark.ml.regression

import scala.util.Random

import org.apache.spark.SparkFunSuite
import org.apache.spark.{SparkException, SparkFunSuite}
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.ml.util.TestingUtils._
import org.apache.spark.mllib.random.{ExponentialGenerator, WeibullGenerator}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Row}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}

class AFTSurvivalRegressionSuite
extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
Expand Down Expand Up @@ -400,6 +401,19 @@ class AFTSurvivalRegressionSuite
val trainer = new AFTSurvivalRegression()
trainer.fit(dataset)
}

test("SPARK-19234: Fail fast on zero-valued labels") {
val dataset = spark.createDataFrame(Seq(
(1.218, 1.0, Vectors.dense(1.560, -0.605)),
(0.000, 0.0, Vectors.dense(0.346, 2.158)), // ← generates error; zero labels invalid
(4.199, 0.0, Vectors.dense(0.795, -0.226)))).toDF("label", "censor", "features")
val aft = new AFTSurvivalRegression()
withClue("label of AFTPoint must be positive") {
intercept[SparkException] {
aft.fit(dataset)
}
}
}
}

object AFTSurvivalRegressionSuite {
Expand Down
3 changes: 1 addition & 2 deletions mllib/src/test/scala/org/apache/spark/ml/util/MLTestingUtils.scala
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Expand Up @@ -64,7 +64,7 @@ object MLTestingUtils extends SparkFunSuite {
actuals.foreach(actual => check(expected, actual))

val dfWithStringLabels = spark.createDataFrame(Seq(
("0", 1, Vectors.dense(0, 2, 3), 0.0)
("1", 1, Vectors.dense(0, 2, 3), 0.0)
)).toDF("label", "weight", "features", "censor")
val thrown = intercept[IllegalArgumentException] {
estimator.fit(dfWithStringLabels)
Expand Down Expand Up @@ -156,7 +156,6 @@ object MLTestingUtils extends SparkFunSuite {
featuresColName: String = "features",
censorColName: String = "censor"): Map[NumericType, DataFrame] = {
val df = spark.createDataFrame(Seq(
(0, Vectors.dense(0)),
(1, Vectors.dense(1)),
(2, Vectors.dense(2)),
(3, Vectors.dense(3)),
Expand Down