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[SPARK-16863][ML] ProbabilisticClassifier.fit check threshoulds' length #14470

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[SPARK-16863][ML] ProbabilisticClassifier.fit check threshoulds' length #14470

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79a72e0
add length check for ProbabilisticClassifier
zhengruifeng Aug 2, 2016
df5af72
del Int
zhengruifeng Aug 4, 2016
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7 changes: 7 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
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Expand Up @@ -84,6 +84,13 @@ class DecisionTreeClassifier @Since("1.4.0") (
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val numClasses: Int = getNumClasses(dataset)

if (isDefined(thresholds)) {
require($(thresholds).length == numClasses, this.getClass.getSimpleName +
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Nit, but why not use string interpolation for the class name bit?

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Because ProbabilisticClassificationModel.transform first check like this, so I just follow this style.

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Hm, OK. I suppose I'm indifferent then.

".train() called with non-matching numClasses and thresholds.length." +
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
val strategy = getOldStrategy(categoricalFeatures, numClasses)

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6 changes: 6 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala
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Expand Up @@ -292,6 +292,12 @@ class LogisticRegression @Since("1.2.0") (
val numClasses = histogram.length
val numFeatures = summarizer.mean.size

if (isDefined(thresholds)) {
require($(thresholds).length == numClasses, this.getClass.getSimpleName +
".train() called with non-matching numClasses and thresholds.length." +
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

instr.logNumClasses(numClasses)
instr.logNumFeatures(numFeatures)

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8 changes: 8 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
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Expand Up @@ -101,6 +101,14 @@ class NaiveBayes @Since("1.5.0") (
setDefault(modelType -> OldNaiveBayes.Multinomial)

override protected def train(dataset: Dataset[_]): NaiveBayesModel = {
val numClasses = getNumClasses(dataset)

if (isDefined(thresholds)) {
require($(thresholds).length == numClasses, this.getClass.getSimpleName +
".train() called with non-matching numClasses and thresholds.length." +
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

val oldDataset: RDD[OldLabeledPoint] =
extractLabeledPoints(dataset).map(OldLabeledPoint.fromML)
val oldModel = OldNaiveBayes.train(oldDataset, $(smoothing), $(modelType))
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7 changes: 7 additions & 0 deletions mllib/src/main/scala/org/apache/spark/ml/classification/RandomForestClassifier.scala
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Expand Up @@ -100,6 +100,13 @@ class RandomForestClassifier @Since("1.4.0") (
val categoricalFeatures: Map[Int, Int] =
MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
val numClasses: Int = getNumClasses(dataset)

if (isDefined(thresholds)) {
require($(thresholds).length == numClasses, this.getClass.getSimpleName +
".train() called with non-matching numClasses and thresholds.length." +
s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
}

val oldDataset: RDD[LabeledPoint] = extractLabeledPoints(dataset, numClasses)
val strategy =
super.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, getOldImpurity)
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