Github user rotationsymmetry commented on a diff in the pull request:
https://github.com/apache/spark/pull/8611#discussion_r39090742
--- Diff:
mllib/src/test/scala/org/apache/spark/ml/regression/AFTRegressionSuite.scala ---
@@ -0,0 +1,279 @@
+/*
+ * 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.feature.OneHotEncoder
+import org.apache.spark.ml.param.ParamsSuite
+import org.apache.spark.ml.util.MLTestingUtils
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+import org.apache.spark.mllib.random.{ExponentialGenerator,
WeibullGenerator}
+import org.apache.spark.mllib.util.TestingUtils._
+import org.apache.spark.mllib.util.MLlibTestSparkContext
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+
+import scala.util.Random
+
+case class AFTExamplePoint(stage: Double, time: Double, age: Int, year:
Int, censored: Double)
+
+case class AFTPoint(features: Vector, censored: Double, label: Double)
+
+class AFTRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
+
+ @transient var datasetUnivariate: DataFrame = _
+ @transient var datasetMultivariate: DataFrame = _
+
+ override def beforeAll(): Unit = {
+ super.beforeAll()
+ datasetUnivariate = sqlContext.createDataFrame(
+ sc.parallelize(generateAFTInput(
+ 1, Array(5.5), Array(0.8), 1000, 42, 1.0, 2.0)))
+ datasetMultivariate = sqlContext.createDataFrame(
+ sc.parallelize(generateAFTInput(
+ 2, Array(0.9, -1.3), Array(0.7, 1.2), 1000, 42, 1.5, 2.5)))
+ }
+
+ test("params") {
+ ParamsSuite.checkParams(new AFTRegression)
+ val model = new AFTRegressionModel("aftReg", Vectors.dense(0.0), 0.0,
0.0)
+ ParamsSuite.checkParams(model)
+ }
+
+ test("aft regression: default params") {
+ val aftr = new AFTRegression
+ assert(aftr.getLabelCol === "label")
+ assert(aftr.getFeaturesCol === "features")
+ assert(aftr.getPredictionCol === "prediction")
+ assert(aftr.getFitIntercept)
+ val model = aftr.fit(datasetUnivariate)
+
+ // copied model must have the same parent.
+ MLTestingUtils.checkCopy(model)
+
+ assert(model.getFeaturesCol === "features")
+ assert(model.getPredictionCol === "prediction")
+ assert(model.getQuantileCol == "quantile")
+ assert(model.intercept !== 0.0)
+ assert(model.hasParent)
+ }
+
+ def generateAFTInput(
+ numFeatures: Int,
+ xMean: Array[Double],
+ xVariance: Array[Double],
+ nPoints: Int,
+ seed: Int,
+ alpha: Double,
+ beta: Double): Seq[AFTPoint] = {
+
+ def censored(x: Double, y: Double): Double = {
+ if (x <= y) 1.0 else 0.0
+ }
+
+ val weibull = new WeibullGenerator(alpha, beta)
+ weibull.setSeed(seed)
+
+ val exponential = new ExponentialGenerator(2.0)
+ exponential.setSeed(seed)
+
+ val rnd = new Random(seed)
+ val x = Array.fill[Array[Double]](nPoints)(
+ Array.fill[Double](numFeatures)(rnd.nextDouble()))
+
+ x.foreach { v =>
+ var i = 0
+ val len = v.length
+ while (i < len) {
+ v(i) = (v(i) - 0.5) * math.sqrt(12.0 * xVariance(i)) + xMean(i)
+ i += 1
+ }
+ }
+ val y = (1 to nPoints).map { i =>
+ (weibull.nextValue(), exponential.nextValue())
+ }
+
+ y.zip(x).map { p =>
+ AFTPoint(Vectors.dense(p._2), censored(p._1._1, p._1._2), p._1._1)
+ }
+ }
+
+ test("aft regression with univariate") {
+ val trainer = new AFTRegression
+ val model = trainer.fit(datasetUnivariate)
+
+ /*
+ Using the following R code to load the data and train the model
using survival package.
+
+ > library("survival")
+ > data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
+ > features <- as.matrix(data.frame(as.numeric(data$V1)))
+ > censored <- as.numeric(data$V2)
+ > label <- as.numeric(data$V3)
+ > sr.fit <- survreg(Surv(label, censored)~features, dist='weibull')
+ > summary(sr.fit)
+
+ survreg(formula = Surv(label, censored) ~ features, dist =
"weibull")
+ Value Std. Error z p
+ (Intercept) 1.759 0.4141 4.247 2.16e-05
+ features -0.039 0.0735 -0.531 5.96e-01
+ Log(scale) 0.344 0.0379 9.073 1.16e-19
+
+ Scale= 1.41
+
+ Weibull distribution
+ Loglik(model)= -1152.2 Loglik(intercept only)= -1152.3
+ Chisq= 0.28 on 1 degrees of freedom, p= 0.6
+ Number of Newton-Raphson Iterations: 5
+ n= 1000
+ */
+ val weightsR = Vectors.dense(-0.039)
+ val interceptR = 1.759
+ val scaleR = 1.41
+
+ assert(model.intercept ~== interceptR relTol 1E-3)
+ assert(model.weights ~= weightsR relTol 1E-3)
+ assert(model.scale ~= scaleR relTol 1E-3)
+
+ val features = Vectors.dense(4.675290165370009)
+ val quantile = Vectors.dense(Array(0.1, 0.5, 0.9))
+ val expected = model.predict(features, quantile)
+ }
+
+ test("aft regression with multivariate") {
+ val trainer = new AFTRegression
+ val model = trainer.fit(datasetMultivariate)
+
+ /*
+ Using the following R code to load the data and train the model
using survival package.
+
+ > library("survival")
+ > data <- read.csv("path", header=FALSE, stringsAsFactors=FALSE)
+ > features <- as.matrix(data.frame(as.numeric(data$V1),
as.numeric(data$V2)))
+ > censored <- as.numeric(data$V3)
+ > label <- as.numeric(data$V4)
+ > sr.fit <- survreg(Surv(label, censored)~features, dist='weibull')
+ > summary(sr.fit)
+
+ Value Std. Error z p
+ (Intercept) 1.9206 0.1057 18.171 8.78e-74
+ featuresas.numeric.data.V1. -0.0844 0.0611 -1.381 1.67e-01
+ featuresas.numeric.data.V2. 0.0677 0.0468 1.447 1.48e-01
+ Log(scale) -0.0236 0.0436 -0.542 5.88e-01
+
+ Scale= 0.977
+
+ Weibull distribution
+ Loglik(model)= -1070.7 Loglik(intercept only)= -1072.7
+ Chisq= 3.91 on 2 degrees of freedom, p= 0.14
+ Number of Newton-Raphson Iterations: 5
+ n= 1000
+ */
+ val weightsR = Vectors.dense(-0.0844, 0.0677)
+ val interceptR = 1.9206
+ val scaleR = 0.977
+
+ assert(model.intercept ~== interceptR relTol 1E-3)
+ assert(model.weights ~= weightsR relTol 1E-3)
+ assert(model.scale ~= scaleR relTol 1E-3)
+
+ val features = Vectors.dense(1.109175828579902, -0.5315711415960551)
+ val quantile = Vectors.dense(Array(0.1, 0.5, 0.9))
+ val expected = model.predict(features, quantile)
--- End diff --
put an `assert` statement for `expected`?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
