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[SPARK-24557][ML] ClusteringEvaluator support array input #21563

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[SPARK-24557][ML] ClusteringEvaluator support array input #21563

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15 changes: 9 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/evaluation/ClusteringEvaluator.scala
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Expand Up @@ -21,11 +21,10 @@ import org.apache.spark.SparkContext
import org.apache.spark.annotation.{Experimental, Since}
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.ml.attribute.AttributeGroup
import org.apache.spark.ml.linalg.{BLAS, DenseVector, SparseVector, Vector, Vectors, VectorUDT}
import org.apache.spark.ml.linalg.{BLAS, DenseVector, SparseVector, Vector, Vectors}
import org.apache.spark.ml.param.{Param, ParamMap, ParamValidators}
import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasPredictionCol}
import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable,
SchemaUtils}
import org.apache.spark.ml.util._
import org.apache.spark.sql.{Column, DataFrame, Dataset}
import org.apache.spark.sql.functions.{avg, col, udf}
import org.apache.spark.sql.types.DoubleType
Expand Down Expand Up @@ -107,15 +106,19 @@ class ClusteringEvaluator @Since("2.3.0") (@Since("2.3.0") override val uid: Str

@Since("2.3.0")
override def evaluate(dataset: Dataset[_]): Double = {
SchemaUtils.checkColumnType(dataset.schema, $(featuresCol), new VectorUDT)
SchemaUtils.validateVectorCompatibleColumn(dataset.schema, $(featuresCol))
SchemaUtils.checkNumericType(dataset.schema, $(predictionCol))

val vectorCol = DatasetUtils.columnToVector(dataset, $(featuresCol))
val df = dataset.select(col($(predictionCol)),
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@mgaido91 mgaido91 Jun 14, 2018

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not sure this is the right way. Probably we can face the same issue everywhere we are using DatasetUtils.columnToVector. Probably it is better to fix the problem there.

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@zhengruifeng zhengruifeng Jun 15, 2018

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@mgaido91 Thanks for your reviewing!
I have considered this, however there exists a problem:
if we want to append metadata into the transformed column (like using method .as(alias: String, metadata: Metadata)) in DatasetUtils.columnToVector, how can we get the name of transformed column?
The only way to do this I know is:

val metadata = ...
val vectorCol = ..
val vectorName = dataset.select(vectorCol) .schema.head.name
vectorCol.as(vectorName, metadata)

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@mgaido91 mgaido91 Jun 15, 2018 •
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we have the new column we are returning, so we can get its name with .expr.sql

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@zhengruifeng zhengruifeng Jun 23, 2018

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@mgaido91 I think it maybe nice to first add a name getter for column

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@mgaido91 mgaido91 Jun 23, 2018

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we can propose that

vectorCol.as($(featuresCol), dataset.schema($(featuresCol)).metadata))

($(metricName), $(distanceMeasure)) match {
case ("silhouette", "squaredEuclidean") =>
SquaredEuclideanSilhouette.computeSilhouetteScore(
dataset, $(predictionCol), $(featuresCol))
df, $(predictionCol), $(featuresCol))
case ("silhouette", "cosine") =>
CosineSilhouette.computeSilhouetteScore(dataset, $(predictionCol), $(featuresCol))
CosineSilhouette.computeSilhouetteScore(df, $(predictionCol), $(featuresCol))
}
}
}
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15 changes: 14 additions & 1 deletion mllib/src/test/scala/org/apache/spark/ml/evaluation/ClusteringEvaluatorSuite.scala
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Expand Up @@ -21,7 +21,7 @@ import org.apache.spark.{SparkException, SparkFunSuite}
import org.apache.spark.ml.attribute.AttributeGroup
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.util.DefaultReadWriteTest
import org.apache.spark.ml.util.{DefaultReadWriteTest, MLTestingUtils}
import org.apache.spark.ml.util.TestingUtils._
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.Dataset
Expand All @@ -33,10 +33,17 @@ class ClusteringEvaluatorSuite
import testImplicits._

@transient var irisDataset: Dataset[_] = _
@transient var newIrisDataset: Dataset[_] = _
@transient var newIrisDatasetD: Dataset[_] = _
@transient var newIrisDatasetF: Dataset[_] = _

override def beforeAll(): Unit = {
super.beforeAll()
irisDataset = spark.read.format("libsvm").load("../data/mllib/iris_libsvm.txt")
val datasets = MLTestingUtils.generateArrayFeatureDataset(irisDataset)
newIrisDataset = datasets._1
newIrisDatasetD = datasets._2
newIrisDatasetF = datasets._3
}

test("params") {
Expand Down Expand Up @@ -66,6 +73,9 @@ class ClusteringEvaluatorSuite
.setPredictionCol("label")

assert(evaluator.evaluate(irisDataset) ~== 0.6564679231 relTol 1e-5)
assert(evaluator.evaluate(newIrisDataset) ~== 0.6564679231 relTol 1e-5)
assert(evaluator.evaluate(newIrisDatasetD) ~== 0.6564679231 relTol 1e-5)
assert(evaluator.evaluate(newIrisDatasetF) ~== 0.6564679231 relTol 1e-5)
}

/*
Expand All @@ -85,6 +95,9 @@ class ClusteringEvaluatorSuite
.setDistanceMeasure("cosine")

assert(evaluator.evaluate(irisDataset) ~== 0.7222369298 relTol 1e-5)
assert(evaluator.evaluate(newIrisDataset) ~== 0.7222369298 relTol 1e-5)
assert(evaluator.evaluate(newIrisDatasetD) ~== 0.7222369298 relTol 1e-5)
assert(evaluator.evaluate(newIrisDatasetF) ~== 0.7222369298 relTol 1e-5)
}

test("number of clusters must be greater than one") {
Expand Down