[SPARK-2479][MLlib] Comparing floating-point numbers using relative e…

…rror in UnitTests

Floating point math is not exact, and most floating-point numbers end up being slightly imprecise due to rounding errors.

Simple values like 0.1 cannot be precisely represented using binary floating point numbers, and the limited precision of floating point numbers means that slight changes in the order of operations or the precision of intermediates can change the result.

That means that comparing two floats to see if they are equal is usually not what we want. As long as this imprecision stays small, it can usually be ignored.

Based on discussion in the community, we have implemented two different APIs for relative tolerance, and absolute tolerance. It makes sense that test writers should know which one they need depending on their circumstances.

Developers also need to explicitly specify the eps, and there is no default value which will sometimes cause confusion.

When comparing against zero using relative tolerance, a exception will be raised to warn users that it's meaningless.

For relative tolerance, users can now write

    assert(23.1 ~== 23.52 relTol 0.02)
    assert(23.1 ~== 22.74 relTol 0.02)
    assert(23.1 ~= 23.52 relTol 0.02)
    assert(23.1 ~= 22.74 relTol 0.02)
    assert(!(23.1 !~= 23.52 relTol 0.02))
    assert(!(23.1 !~= 22.74 relTol 0.02))

    // This will throw exception with the following message.
    // "Did not expect 23.1 and 23.52 to be within 0.02 using relative tolerance."
    assert(23.1 !~== 23.52 relTol 0.02)

    // "Expected 23.1 and 22.34 to be within 0.02 using relative tolerance."
    assert(23.1 ~== 22.34 relTol 0.02)

For absolute error,

    assert(17.8 ~== 17.99 absTol 0.2)
    assert(17.8 ~== 17.61 absTol 0.2)
    assert(17.8 ~= 17.99 absTol 0.2)
    assert(17.8 ~= 17.61 absTol 0.2)
    assert(!(17.8 !~= 17.99 absTol 0.2))
    assert(!(17.8 !~= 17.61 absTol 0.2))

    // This will throw exception with the following message.
    // "Did not expect 17.8 and 17.99 to be within 0.2 using absolute error."
    assert(17.8 !~== 17.99 absTol 0.2)

    // "Expected 17.8 and 17.59 to be within 0.2 using absolute error."
    assert(17.8 ~== 17.59 absTol 0.2)

Authors:
  DB Tsai <dbtsaialpinenow.com>
  Marek Kolodziej <marekalpinenow.com>

Author: DB Tsai <[email protected]>

Closes apache#1425 from dbtsai/SPARK-2479_comparing_floating_point and squashes the following commits:

8c7cbcc [DB Tsai] Alpine Data Labs

mllib/src/test/scala/org/apache/spark/mllib/classification/LogisticRegressionSuite.scala

@@ -26,6 +26,7 @@ import org.scalatest.Matchers
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 object LogisticRegressionSuite {
 
@@ -81,9 +82,8 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
     val model = lr.run(testRDD)
 
     // Test the weights
-    val weight0 = model.weights(0)
-    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
-    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    assert(model.weights(0) ~== -1.52 relTol 0.01)
+    assert(model.intercept ~== 2.00 relTol 0.01)
 
     val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)
@@ -113,9 +113,9 @@ class LogisticRegressionSuite extends FunSuite with LocalSparkContext with Match
 
     val model = lr.run(testRDD, initialWeights)
 
-    val weight0 = model.weights(0)
-    assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
-    assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
+    // Test the weights
+    assert(model.weights(0) ~== -1.50 relTol 0.01)
+    assert(model.intercept ~== 1.97 relTol 0.01)
 
     val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
     val validationRDD = sc.parallelize(validationData, 2)

mllib/src/test/scala/org/apache/spark/mllib/clustering/KMeansSuite.scala

@@ -21,8 +21,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
-import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 class KMeansSuite extends FunSuite with LocalSparkContext {
 
@@ -41,26 +42,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // centered at the mean of the points
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(
       data, k = 1, maxIterations = 1, runs = 1, initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
   }
 
   test("no distinct points") {
@@ -104,26 +105,26 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
     assert(model.clusterCenters.size === 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
       initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
   }
 
   test("single cluster with sparse data") {
@@ -149,31 +150,39 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     val center = Vectors.sparse(n, Seq((0, 1.0), (1, 3.0), (2, 4.0)))
 
     var model = KMeans.train(data, k = 1, maxIterations = 1)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 2)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 5)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1, initializationMode = RANDOM)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     model = KMeans.train(data, k = 1, maxIterations = 1, runs = 1,
       initializationMode = K_MEANS_PARALLEL)
-    assert(model.clusterCenters.head === center)
+    assert(model.clusterCenters.head ~== center absTol 1E-5)
 
     data.unpersist()
   }
 
   test("k-means|| initialization") {
+
+    case class VectorWithCompare(x: Vector) extends Ordered[VectorWithCompare] {
+      @Override def compare(that: VectorWithCompare): Int = {
+        if(this.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x) >
+          that.x.toArray.foldLeft[Double](0.0)((acc, x) => acc + x * x)) -1 else 1
+      }
+    }
+
     val points = Seq(
       Vectors.dense(1.0, 2.0, 6.0),
       Vectors.dense(1.0, 3.0, 0.0),
@@ -188,15 +197,19 @@ class KMeansSuite extends FunSuite with LocalSparkContext {
     // unselected point as long as it hasn't yet selected all of them
 
     var model = KMeans.train(rdd, k = 5, maxIterations = 1)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
 
     // Iterations of Lloyd's should not change the answer either
     model = KMeans.train(rdd, k = 5, maxIterations = 10)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
 
     // Neither should more runs
     model = KMeans.train(rdd, k = 5, maxIterations = 10, runs = 5)
-    assert(Set(model.clusterCenters: _*) === Set(points: _*))
+    assert(model.clusterCenters.sortBy(VectorWithCompare(_))
+      .zip(points.sortBy(VectorWithCompare(_))).forall(x => x._1 ~== (x._2) absTol 1E-5))
   }
 
   test("two clusters") {

mllib/src/test/scala/org/apache/spark/mllib/evaluation/AreaUnderCurveSuite.scala

@@ -20,27 +20,28 @@ package org.apache.spark.mllib.evaluation
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
+import org.apache.spark.mllib.util.TestingUtils._
 
 class AreaUnderCurveSuite extends FunSuite with LocalSparkContext {
   test("auc computation") {
     val curve = Seq((0.0, 0.0), (1.0, 1.0), (2.0, 3.0), (3.0, 0.0))
     val auc = 4.0
-    assert(AreaUnderCurve.of(curve) === auc)
+    assert(AreaUnderCurve.of(curve) ~== auc absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) == auc)
+    assert(AreaUnderCurve.of(rddCurve) ~== auc absTol 1E-5)
   }
 
   test("auc of an empty curve") {
     val curve = Seq.empty[(Double, Double)]
-    assert(AreaUnderCurve.of(curve) === 0.0)
+    assert(AreaUnderCurve.of(curve) ~== 0.0 absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) === 0.0)
+    assert(AreaUnderCurve.of(rddCurve) ~== 0.0 absTol 1E-5)
   }
 
   test("auc of a curve with a single point") {
     val curve = Seq((1.0, 1.0))
-    assert(AreaUnderCurve.of(curve) === 0.0)
+    assert(AreaUnderCurve.of(curve) ~== 0.0 absTol 1E-5)
     val rddCurve = sc.parallelize(curve, 2)
-    assert(AreaUnderCurve.of(rddCurve) === 0.0)
+    assert(AreaUnderCurve.of(rddCurve) ~== 0.0 absTol 1E-5)
   }
 }

mllib/src/test/scala/org/apache/spark/mllib/evaluation/BinaryClassificationMetricsSuite.scala

@@ -20,25 +20,14 @@ package org.apache.spark.mllib.evaluation
 import org.scalatest.FunSuite
 
 import org.apache.spark.mllib.util.LocalSparkContext
-import org.apache.spark.mllib.util.TestingUtils.DoubleWithAlmostEquals
+import org.apache.spark.mllib.util.TestingUtils._
 
 class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
 
-  // TODO: move utility functions to TestingUtils.
+  def cond1(x: (Double, Double)): Boolean = x._1 ~= (x._2) absTol 1E-5
 
-  def elementsAlmostEqual(actual: Seq[Double], expected: Seq[Double]): Boolean = {
-    actual.zip(expected).forall { case (x1, x2) =>
-      x1.almostEquals(x2)
-    }
-  }
-
-  def elementsAlmostEqual(
-      actual: Seq[(Double, Double)],
-      expected: Seq[(Double, Double)])(implicit dummy: DummyImplicit): Boolean = {
-    actual.zip(expected).forall { case ((x1, y1), (x2, y2)) =>
-      x1.almostEquals(x2) && y1.almostEquals(y2)
-    }
-  }
+  def cond2(x: ((Double, Double), (Double, Double))): Boolean =
+    (x._1._1 ~= x._2._1 absTol 1E-5) && (x._1._2 ~= x._2._2 absTol 1E-5)
 
   test("binary evaluation metrics") {
     val scoreAndLabels = sc.parallelize(
@@ -57,16 +46,17 @@ class BinaryClassificationMetricsSuite extends FunSuite with LocalSparkContext {
     val rocCurve = Seq((0.0, 0.0)) ++ fpr.zip(recall) ++ Seq((1.0, 1.0))
     val pr = recall.zip(precision)
     val prCurve = Seq((0.0, 1.0)) ++ pr
-    val f1 = pr.map { case (r, p) => 2.0 * (p * r) / (p + r) }
+    val f1 = pr.map { case (r, p) => 2.0 * (p * r) / (p + r)}
     val f2 = pr.map { case (r, p) => 5.0 * (p * r) / (4.0 * p + r)}
-    assert(elementsAlmostEqual(metrics.thresholds().collect(), threshold))
-    assert(elementsAlmostEqual(metrics.roc().collect(), rocCurve))
-    assert(metrics.areaUnderROC().almostEquals(AreaUnderCurve.of(rocCurve)))
-    assert(elementsAlmostEqual(metrics.pr().collect(), prCurve))
-    assert(metrics.areaUnderPR().almostEquals(AreaUnderCurve.of(prCurve)))
-    assert(elementsAlmostEqual(metrics.fMeasureByThreshold().collect(), threshold.zip(f1)))
-    assert(elementsAlmostEqual(metrics.fMeasureByThreshold(2.0).collect(), threshold.zip(f2)))
-    assert(elementsAlmostEqual(metrics.precisionByThreshold().collect(), threshold.zip(precision)))
-    assert(elementsAlmostEqual(metrics.recallByThreshold().collect(), threshold.zip(recall)))
+
+    assert(metrics.thresholds().collect().zip(threshold).forall(cond1))
+    assert(metrics.roc().collect().zip(rocCurve).forall(cond2))
+    assert(metrics.areaUnderROC() ~== AreaUnderCurve.of(rocCurve) absTol 1E-5)
+    assert(metrics.pr().collect().zip(prCurve).forall(cond2))
+    assert(metrics.areaUnderPR() ~== AreaUnderCurve.of(prCurve) absTol 1E-5)
+    assert(metrics.fMeasureByThreshold().collect().zip(threshold.zip(f1)).forall(cond2))
+    assert(metrics.fMeasureByThreshold(2.0).collect().zip(threshold.zip(f2)).forall(cond2))
+    assert(metrics.precisionByThreshold().collect().zip(threshold.zip(precision)).forall(cond2))
+    assert(metrics.recallByThreshold().collect().zip(threshold.zip(recall)).forall(cond2))
   }
 }

mllib/src/test/scala/org/apache/spark/mllib/optimization/GradientDescentSuite.scala

@@ -25,6 +25,7 @@ import org.scalatest.{FunSuite, Matchers}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression._
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 object GradientDescentSuite {
 
@@ -126,19 +127,14 @@ class GradientDescentSuite extends FunSuite with LocalSparkContext with Matchers
     val (newWeights1, loss1) = GradientDescent.runMiniBatchSGD(
       dataRDD, gradient, updater, 1, 1, regParam1, 1.0, initialWeightsWithIntercept)
 
-    def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
-      math.abs(x - y) / (math.abs(y) + 1e-15) < tol
-    }
-
-    assert(compareDouble(
-      loss1(0),
-      loss0(0) + (math.pow(initialWeightsWithIntercept(0), 2) +
-        math.pow(initialWeightsWithIntercept(1), 2)) / 2),
+    assert(
+      loss1(0) ~= (loss0(0) + (math.pow(initialWeightsWithIntercept(0), 2) +
+        math.pow(initialWeightsWithIntercept(1), 2)) / 2) absTol 1E-5,
       """For non-zero weights, the regVal should be \frac{1}{2}\sum_i w_i^2.""")
 
     assert(
-      compareDouble(newWeights1(0) , newWeights0(0) - initialWeightsWithIntercept(0)) &&
-      compareDouble(newWeights1(1) , newWeights0(1) - initialWeightsWithIntercept(1)),
+      (newWeights1(0) ~= (newWeights0(0) - initialWeightsWithIntercept(0)) absTol 1E-5) &&
+      (newWeights1(1) ~= (newWeights0(1) - initialWeightsWithIntercept(1)) absTol 1E-5),
       "The different between newWeights with/without regularization " +
         "should be initialWeightsWithIntercept.")
   }

mllib/src/test/scala/org/apache/spark/mllib/optimization/LBFGSSuite.scala

@@ -24,6 +24,7 @@ import org.scalatest.{FunSuite, Matchers}
 import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.regression.LabeledPoint
 import org.apache.spark.mllib.util.{LocalClusterSparkContext, LocalSparkContext}
+import org.apache.spark.mllib.util.TestingUtils._
 
 class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
@@ -49,10 +50,6 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
 
   lazy val dataRDD = sc.parallelize(data, 2).cache()
 
-  def compareDouble(x: Double, y: Double, tol: Double = 1E-3): Boolean = {
-    math.abs(x - y) / (math.abs(y) + 1e-15) < tol
-  }
-
   test("LBFGS loss should be decreasing and match the result of Gradient Descent.") {
     val regParam = 0
 
@@ -126,15 +123,15 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
       miniBatchFrac,
       initialWeightsWithIntercept)
 
-    assert(compareDouble(lossGD(0), lossLBFGS(0)),
+    assert(lossGD(0) ~= lossLBFGS(0) absTol 1E-5,
       "The first losses of LBFGS and GD should be the same.")
 
     // The 2% difference here is based on observation, but is not theoretically guaranteed.
-    assert(compareDouble(lossGD.last, lossLBFGS.last, 0.02),
+    assert(lossGD.last ~= lossLBFGS.last relTol 0.02,
       "The last losses of LBFGS and GD should be within 2% difference.")
 
-    assert(compareDouble(weightLBFGS(0), weightGD(0), 0.02) &&
-      compareDouble(weightLBFGS(1), weightGD(1), 0.02),
+    assert(
+      (weightLBFGS(0) ~= weightGD(0) relTol 0.02) && (weightLBFGS(1) ~= weightGD(1) relTol 0.02),
       "The weight differences between LBFGS and GD should be within 2%.")
   }
 
@@ -226,8 +223,8 @@ class LBFGSSuite extends FunSuite with LocalSparkContext with Matchers {
       initialWeightsWithIntercept)
 
     // for class LBFGS and the optimize method, we only look at the weights
-    assert(compareDouble(weightLBFGS(0), weightGD(0), 0.02) &&
-      compareDouble(weightLBFGS(1), weightGD(1), 0.02),
+    assert(
+      (weightLBFGS(0) ~= weightGD(0) relTol 0.02) && (weightLBFGS(1) ~= weightGD(1) relTol 0.02),
       "The weight differences between LBFGS and GD should be within 2%.")
   }
 }

mllib/src/test/scala/org/apache/spark/mllib/optimization/NNLSSuite.scala

@@ -21,7 +21,9 @@ import scala.util.Random
 
 import org.scalatest.FunSuite
 
-import org.jblas.{DoubleMatrix, SimpleBlas, NativeBlas}
+import org.jblas.{DoubleMatrix, SimpleBlas}
+
+import org.apache.spark.mllib.util.TestingUtils._
 
 class NNLSSuite extends FunSuite {
   /** Generate an NNLS problem whose optimal solution is the all-ones vector. */
@@ -73,7 +75,7 @@ class NNLSSuite extends FunSuite {
     val ws = NNLS.createWorkspace(n)
     val x = NNLS.solve(ata, atb, ws)
     for (i <- 0 until n) {
-      assert(Math.abs(x(i) - goodx(i)) < 1e-3)
+      assert(x(i) ~== goodx(i) absTol 1E-3)
       assert(x(i) >= 0)
     }
   }