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Fix rawPrediction of OpXGBoostClassifcationModel for binary classification #229

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Feb 19, 2019
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Fix rawPrediction of OpXGBoostClassifcationModel for binary classification #229

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3 changes: 2 additions & 1 deletion core/src/main/scala/com/salesforce/op/stages/impl/classification/OpXGBoostClassifier.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -366,9 +366,10 @@ class OpXGBoostClassificationModel
val data = removeMissingValues(Iterator(features.value.asXGB), missing)
val dm = new DMatrix(dataIter = data)
val rawPred = booster.predict(dm, outPutMargin = true, treeLimit = treeLimit)(0).map(_.toDouble)
val rawPrediction = if (model.numClasses == 2) Array(-rawPred(0), rawPred(0)) else rawPred
val prob = booster.predict(dm, outPutMargin = false, treeLimit = treeLimit)(0).map(_.toDouble)
val probability = if (model.numClasses == 2) Array(1.0 - prob(0), prob(0)) else prob
val prediction = probability2predictionMirror(Vectors.dense(probability)).asInstanceOf[Double]
Prediction(prediction = prediction, rawPrediction = rawPred, probability = probability)
Prediction(prediction = prediction, rawPrediction = rawPrediction, probability = probability)
}
}
12 changes: 9 additions & 3 deletions core/src/test/scala/com/salesforce/op/stages/impl/classification/OpClassifierModelTest.scala
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Expand Up @@ -134,10 +134,15 @@ class OpClassifierModelTest extends FlatSpec with TestSparkContext with OpXGBoos
.setLabelCol(labelF.name)
val spk = cl.fit(rawDF)
val op = toOP(spk, spk.uid).setInput(labelF, featureV)
compareOutputs(spk.transform(rawDF), op.transform(rawDF))
compareOutputs(spk.transform(rawDF), op.transform(rawDF), false)
}

def compareOutputs(df1: DataFrame, df2: DataFrame)(implicit arrayEquality: Equality[Array[Double]]): Unit = {
def compareOutputs
(
df1: DataFrame,
df2: DataFrame,
fullRawPred: Boolean = true
)(implicit arrayEquality: Equality[Array[Double]]): Unit = {
def keysStartsWith(name: String, value: Map[String, Double]): Array[Double] = {
val names = value.keys.filter(_.startsWith(name)).toArray.sorted
names.map(value)
Expand All @@ -148,7 +153,8 @@ class OpClassifierModelTest extends FlatSpec with TestSparkContext with OpXGBoos
val map = r2.getAs[Map[String, Double]](2)
r1.getAs[Double](4) shouldEqual map(Prediction.Keys.PredictionName)
r1.getAs[Vector](3).toArray shouldEqual keysStartsWith(Prediction.Keys.ProbabilityName, map)
r1.getAs[Vector](2).toArray shouldEqual keysStartsWith(Prediction.Keys.RawPredictionName, map)
if (fullRawPred) r1.getAs[Vector](2).toArray shouldEqual keysStartsWith(Prediction.Keys.RawPredictionName, map)
else r1.getAs[Vector](2).toArray shouldEqual keysStartsWith(Prediction.Keys.RawPredictionName, map).tail
}
}

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16 changes: 8 additions & 8 deletions ...src/test/scala/com/salesforce/op/stages/impl/classification/OpXGBoostClassifierTest.scala
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Expand Up @@ -64,14 +64,14 @@ class OpXGBoostClassifierTest extends OpEstimatorSpec[Prediction, OpPredictorWra
estimator.setSilent(1)

val expectedResult = Seq(
Prediction(1.0, Array(0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(0.0, Array(0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(1.0, Array(0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(1.0, Array(0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(1.0, Array(0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284))
Prediction(1.0, Array(-0.6200000047683716, 0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(-0.3799999952316284, 0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(0.0, Array(-0.3799999952316284, 0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(1.0, Array(-0.6200000047683716, 0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(1.0, Array(-0.6200000047683716, 0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(-0.3799999952316284, 0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284)),
Prediction(1.0, Array(-0.6200000047683716, 0.6200000047683716), Array(0.3799999952316284, 0.6200000047683716)),
Prediction(0.0, Array(-0.3799999952316284, 0.3799999952316284), Array(0.6200000047683716, 0.3799999952316284))
)

it should "allow the user to set the desired spark parameters" in {
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