diff options
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala | 6 | ||||
-rw-r--r-- | mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala | 27 |
2 files changed, 31 insertions, 2 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala index 9f3d2ca6db..28cbe1cb01 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala @@ -186,8 +186,10 @@ class MinMaxScalerModel private[ml] ( val size = values.length var i = 0 while (i < size) { - val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5 - values(i) = raw * scale + $(min) + if (!values(i).isNaN) { + val raw = if (originalRange(i) != 0) (values(i) - minArray(i)) / originalRange(i) else 0.5 + values(i) = raw * scale + $(min) + } i += 1 } Vectors.dense(values) diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala index 5da8471175..9f376b7003 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/feature/MinMaxScalerSuite.scala @@ -90,4 +90,31 @@ class MinMaxScalerSuite extends SparkFunSuite with MLlibTestSparkContext with De assert(newInstance.originalMin === instance.originalMin) assert(newInstance.originalMax === instance.originalMax) } + + test("MinMaxScaler should remain NaN value") { + val data = Array( + Vectors.dense(1, Double.NaN, 2.0, 2.0), + Vectors.dense(2, 2.0, 0.0, 3.0), + Vectors.dense(3, Double.NaN, 0.0, 1.0), + Vectors.dense(6, 2.0, 2.0, Double.NaN)) + + val expected: Array[Vector] = Array( + Vectors.dense(-5.0, Double.NaN, 5.0, 0.0), + Vectors.dense(-3.0, 0.0, -5.0, 5.0), + Vectors.dense(-1.0, Double.NaN, -5.0, -5.0), + Vectors.dense(5.0, 0.0, 5.0, Double.NaN)) + + val df = spark.createDataFrame(data.zip(expected)).toDF("features", "expected") + val scaler = new MinMaxScaler() + .setInputCol("features") + .setOutputCol("scaled") + .setMin(-5) + .setMax(5) + + val model = scaler.fit(df) + model.transform(df).select("expected", "scaled").collect() + .foreach { case Row(vector1: Vector, vector2: Vector) => + assert(vector1.equals(vector2), "Transformed vector is different with expected.") + } + } } |