Repository: spark Updated Branches: refs/heads/master d1d5069aa -> 0557a4545
[SPARK-16750][ML] Fix GaussianMixture training failed due to feature column type mistake ## What changes were proposed in this pull request? ML ```GaussianMixture``` training failed due to feature column type mistake. The feature column type should be ```ml.linalg.VectorUDT``` but got ```mllib.linalg.VectorUDT``` by mistake. See [SPARK-16750](https://issues.apache.org/jira/browse/SPARK-16750) for how to reproduce this bug. Why the unit tests did not complain this errors? Because some estimators/transformers missed calling ```transformSchema(dataset.schema)``` firstly during ```fit``` or ```transform```. I will also add this function to all estimators/transformers who missed in this PR. ## How was this patch tested? No new tests, should pass existing ones. Author: Yanbo Liang <[email protected]> Closes #14378 from yanboliang/spark-16750. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/0557a454 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/0557a454 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/0557a454 Branch: refs/heads/master Commit: 0557a45452f6e73877e5ec972110825ce8f3fbc5 Parents: d1d5069 Author: Yanbo Liang <[email protected]> Authored: Fri Jul 29 04:40:20 2016 -0700 Committer: Sean Owen <[email protected]> Committed: Fri Jul 29 04:40:20 2016 -0700 ---------------------------------------------------------------------- .../org/apache/spark/ml/clustering/BisectingKMeans.scala | 2 ++ .../org/apache/spark/ml/clustering/GaussianMixture.scala | 8 +++++--- .../main/scala/org/apache/spark/ml/clustering/KMeans.scala | 2 ++ .../main/scala/org/apache/spark/ml/feature/Interaction.scala | 1 + .../scala/org/apache/spark/ml/feature/MinMaxScaler.scala | 1 + .../org/apache/spark/ml/feature/QuantileDiscretizer.scala | 3 ++- .../main/scala/org/apache/spark/ml/feature/RFormula.scala | 1 + .../scala/org/apache/spark/ml/feature/SQLTransformer.scala | 1 + .../apache/spark/ml/regression/AFTSurvivalRegression.scala | 4 ++-- .../org/apache/spark/ml/regression/IsotonicRegression.scala | 3 ++- 10 files changed, 19 insertions(+), 7 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala index afb1080..a97bd0f 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/BisectingKMeans.scala @@ -99,6 +99,7 @@ class BisectingKMeansModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val predictUDF = udf((vector: Vector) => predict(vector)) dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol)))) } @@ -222,6 +223,7 @@ class BisectingKMeans @Since("2.0.0") ( @Since("2.0.0") override def fit(dataset: Dataset[_]): BisectingKMeansModel = { + transformSchema(dataset.schema, logging = true) val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => OldVectors.fromML(point) } http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala index 8174905..69f060a 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/GaussianMixture.scala @@ -30,7 +30,7 @@ import org.apache.spark.ml.stat.distribution.MultivariateGaussian import org.apache.spark.ml.util._ import org.apache.spark.mllib.clustering.{GaussianMixture => MLlibGM} import org.apache.spark.mllib.linalg.{Matrices => OldMatrices, Matrix => OldMatrix, - Vector => OldVector, Vectors => OldVectors, VectorUDT => OldVectorUDT} + Vector => OldVector, Vectors => OldVectors} import org.apache.spark.rdd.RDD import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession} import org.apache.spark.sql.functions.{col, udf} @@ -61,9 +61,9 @@ private[clustering] trait GaussianMixtureParams extends Params with HasMaxIter w * @return output schema */ protected def validateAndTransformSchema(schema: StructType): StructType = { - SchemaUtils.checkColumnType(schema, $(featuresCol), new OldVectorUDT) + SchemaUtils.checkColumnType(schema, $(featuresCol), new VectorUDT) SchemaUtils.appendColumn(schema, $(predictionCol), IntegerType) - SchemaUtils.appendColumn(schema, $(probabilityCol), new OldVectorUDT) + SchemaUtils.appendColumn(schema, $(probabilityCol), new VectorUDT) } } @@ -95,6 +95,7 @@ class GaussianMixtureModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val predUDF = udf((vector: Vector) => predict(vector)) val probUDF = udf((vector: Vector) => predictProbability(vector)) dataset.withColumn($(predictionCol), predUDF(col($(featuresCol)))) @@ -317,6 +318,7 @@ class GaussianMixture @Since("2.0.0") ( @Since("2.0.0") override def fit(dataset: Dataset[_]): GaussianMixtureModel = { + transformSchema(dataset.schema, logging = true) val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => OldVectors.fromML(point) } http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala index 9fb7d6a..6c46be7 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/clustering/KMeans.scala @@ -120,6 +120,7 @@ class KMeansModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val predictUDF = udf((vector: Vector) => predict(vector)) dataset.withColumn($(predictionCol), predictUDF(col($(featuresCol)))) } @@ -304,6 +305,7 @@ class KMeans @Since("1.5.0") ( @Since("2.0.0") override def fit(dataset: Dataset[_]): KMeansModel = { + transformSchema(dataset.schema, logging = true) val rdd: RDD[OldVector] = dataset.select(col($(featuresCol))).rdd.map { case Row(point: Vector) => OldVectors.fromML(point) } http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala index 7b11f86..96d0bde 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/Interaction.scala @@ -68,6 +68,7 @@ class Interaction @Since("1.6.0") (@Since("1.6.0") override val uid: String) ext @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val inputFeatures = $(inputCols).map(c => dataset.schema(c)) val featureEncoders = getFeatureEncoders(inputFeatures) val featureAttrs = getFeatureAttrs(inputFeatures) http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/feature/MinMaxScaler.scala ---------------------------------------------------------------------- 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 9ed8d83..068f11a 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 @@ -170,6 +170,7 @@ class MinMaxScalerModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val originalRange = (originalMax.asBreeze - originalMin.asBreeze).toArray val minArray = originalMin.toArray http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala index 9a636bd..558a7bb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/QuantileDiscretizer.scala @@ -97,7 +97,7 @@ final class QuantileDiscretizer @Since("1.6.0") (@Since("1.6.0") override val ui @Since("1.6.0") override def transformSchema(schema: StructType): StructType = { - SchemaUtils.checkColumnType(schema, $(inputCol), DoubleType) + SchemaUtils.checkNumericType(schema, $(inputCol)) val inputFields = schema.fields require(inputFields.forall(_.name != $(outputCol)), s"Output column ${$(outputCol)} already exists.") @@ -108,6 +108,7 @@ final class QuantileDiscretizer @Since("1.6.0") (@Since("1.6.0") override val ui @Since("2.0.0") override def fit(dataset: Dataset[_]): Bucketizer = { + transformSchema(dataset.schema, logging = true) val splits = dataset.stat.approxQuantile($(inputCol), (0.0 to 1.0 by 1.0/$(numBuckets)).toArray, $(relativeError)) splits(0) = Double.NegativeInfinity http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala index c95dacf..2ee899b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala @@ -112,6 +112,7 @@ class RFormula @Since("1.5.0") (@Since("1.5.0") override val uid: String) @Since("2.0.0") override def fit(dataset: Dataset[_]): RFormulaModel = { + transformSchema(dataset.schema, logging = true) require(isDefined(formula), "Formula must be defined first.") val parsedFormula = RFormulaParser.parse($(formula)) val resolvedFormula = parsedFormula.resolve(dataset.schema) http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala index 2890376..259be26 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/feature/SQLTransformer.scala @@ -63,6 +63,7 @@ class SQLTransformer @Since("1.6.0") (@Since("1.6.0") override val uid: String) @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val tableName = Identifiable.randomUID(uid) dataset.createOrReplaceTempView(tableName) val realStatement = $(statement).replace(tableIdentifier, tableName) http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala index 2b99126..d4ae59d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/AFTSurvivalRegression.scala @@ -196,7 +196,7 @@ class AFTSurvivalRegression @Since("1.6.0") (@Since("1.6.0") override val uid: S @Since("2.0.0") override def fit(dataset: Dataset[_]): AFTSurvivalRegressionModel = { - validateAndTransformSchema(dataset.schema, fitting = true) + transformSchema(dataset.schema, logging = true) val instances = extractAFTPoints(dataset) val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK) @@ -326,7 +326,7 @@ class AFTSurvivalRegressionModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { - transformSchema(dataset.schema) + transformSchema(dataset.schema, logging = true) val predictUDF = udf { features: Vector => predict(features) } val predictQuantilesUDF = udf { features: Vector => predictQuantiles(features)} if (hasQuantilesCol) { http://git-wip-us.apache.org/repos/asf/spark/blob/0557a454/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala ---------------------------------------------------------------------- diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala index 3539644..cd7b4f2 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/IsotonicRegression.scala @@ -164,7 +164,7 @@ class IsotonicRegression @Since("1.5.0") (@Since("1.5.0") override val uid: Stri @Since("2.0.0") override def fit(dataset: Dataset[_]): IsotonicRegressionModel = { - validateAndTransformSchema(dataset.schema, fitting = true) + transformSchema(dataset.schema, logging = true) // Extract columns from data. If dataset is persisted, do not persist oldDataset. val instances = extractWeightedLabeledPoints(dataset) val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE @@ -234,6 +234,7 @@ class IsotonicRegressionModel private[ml] ( @Since("2.0.0") override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) val predict = dataset.schema($(featuresCol)).dataType match { case DoubleType => udf { feature: Double => oldModel.predict(feature) } --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
