Improve tests

core/src/main/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScaler.scala

@@ -62,11 +62,13 @@ class OpScalarStandardScaler
 
  val std = scalerModel.std.toArray
  val mean = scalerModel.mean.toArray
- val stdMean = std.sum / std.length
- val meanMean = mean.sum / mean.length
- val scalingArgs = LinearScalerArgs(1 / stdMean, meanMean / stdMean)
- val meta = ScalerMetadata(ScalingType.Linear, scalingArgs).toMetadata()
- setMetadata(meta)
+ if (std.length == 1) {
+ val stdMean = std.head
+ val meanMean = mean.head
+ val scalingArgs = LinearScalerArgs(1 / stdMean, - meanMean / stdMean)
+ val meta = ScalerMetadata(ScalingType.Linear, scalingArgs).toMetadata()
+ setMetadata(meta)
+ }
 
  new OpScalarStandardScalerModel(
  std = std,

core/src/test/scala/com/salesforce/op/stages/impl/feature/OpScalarStandardScalerTest.scala

@@ -61,6 +61,8 @@ class OpScalarStandardScalerTest extends OpEstimatorSpec[RealNN, UnaryModel[Real
  1.150792911137501.toRealNN
  )
 
+ val descaleValues = Seq(10.0, 100.0, 1000.0)
+ val (descaleData, testD) = TestFeatureBuilder(descaleValues.map(_.toRealNN))
 
  val (inputData, testF) = TestFeatureBuilder(Seq(10, 100, 1000).map(_.toRealNN))
 
@@ -151,31 +153,35 @@ class OpScalarStandardScalerTest extends OpEstimatorSpec[RealNN, UnaryModel[Real
  }
 
  it should "descale and work in standardized workflow" in {
- val featureNormalizer = new OpScalarStandardScaler().setInput(testF)
+ val featureNormalizer = new OpScalarStandardScaler().setInput(testD)
  val normedOutput = featureNormalizer.getOutput()
- val metadata = featureNormalizer.fit(inputData).getMetadata()
- val expectedStd = 90.0 * math.sqrt(37.0)
- val expectedMean = 370.0
+ val metadata = featureNormalizer.fit(descaleData).getMetadata()
+
+ val expectedMean = descaleValues.sum / descaleValues.length
+ val expectedStd = math.sqrt(descaleValues.map(value => math.pow(expectedMean - value, 2)).sum
+ / (descaleValues.length - 1))
  val expectedSlope = 1 / expectedStd
- val expectedIntercept = expectedMean / expectedStd
+ val expectedIntercept = - expectedMean / expectedStd
  ScalerMetadata(metadata) match {
  case Failure(err) => fail(err)
  case Success(meta) =>
  meta shouldBe a[ScalerMetadata]
  meta.scalingType shouldBe ScalingType.Linear
  meta.scalingArgs shouldBe a[LinearScalerArgs]
- meta.scalingArgs.asInstanceOf[LinearScalerArgs].slope - expectedSlope should be < 0.001
- meta.scalingArgs.asInstanceOf[LinearScalerArgs].intercept - expectedIntercept should be < 0.001
+ math.abs((meta.scalingArgs.asInstanceOf[LinearScalerArgs].slope - expectedSlope)
+ / expectedSlope) should be < 0.001
+ math.abs((meta.scalingArgs.asInstanceOf[LinearScalerArgs].intercept - expectedIntercept)
+ / expectedIntercept) should be < 0.001
  }
 
  val descaledResponse = new DescalerTransformer[RealNN, RealNN, RealNN]()
  .setInput(normedOutput, normedOutput).getOutput()
  val workflow = new OpWorkflow().setResultFeatures(descaledResponse)
- val wfModel = workflow.setInputDataset(inputData).train()
+ val wfModel = workflow.setInputDataset(descaleData).train()
  val transformed = wfModel.score()
 
  val actual = transformed.collect().map(_.getAs[Double](1))
- val expected = Array(-730.0, -640.0, 260.0)
+ val expected : Seq[Double] = descaleValues
  all(actual.zip(expected).map(x => math.abs(x._2 - x._1))) should be < 0.0001
  }