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author | sethah <seth.hendrickson16@gmail.com> | 2016-11-21 05:36:49 -0800 |
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committer | Yanbo Liang <ybliang8@gmail.com> | 2016-11-21 05:36:49 -0800 |
commit | e811fbf9ed131bccbc46f3c5701c4ff317222fd9 (patch) | |
tree | 36026581dec2887f946fe15ad50e92ee69c69395 /mllib | |
parent | 658547974915ebcaae83e13e4c3bdf68d5426fda (diff) | |
download | spark-e811fbf9ed131bccbc46f3c5701c4ff317222fd9.tar.gz spark-e811fbf9ed131bccbc46f3c5701c4ff317222fd9.tar.bz2 spark-e811fbf9ed131bccbc46f3c5701c4ff317222fd9.zip |
[SPARK-18282][ML][PYSPARK] Add python clustering summaries for GMM and BKM
## What changes were proposed in this pull request?
Add model summary APIs for `GaussianMixtureModel` and `BisectingKMeansModel` in pyspark.
## How was this patch tested?
Unit tests.
Author: sethah <seth.hendrickson16@gmail.com>
Closes #15777 from sethah/pyspark_cluster_summaries.
Diffstat (limited to 'mllib')
12 files changed, 44 insertions, 34 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala index f58efd36a1..d07b4adebb 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala @@ -648,7 +648,7 @@ class LogisticRegression @Since("1.2.0") ( $(labelCol), $(featuresCol), objectiveHistory) - model.setSummary(logRegSummary) + model.setSummary(Some(logRegSummary)) } else { model } @@ -790,9 +790,9 @@ class LogisticRegressionModel private[spark] ( } } - private[classification] def setSummary( - summary: LogisticRegressionTrainingSummary): this.type = { - this.trainingSummary = Some(summary) + private[classification] + def setSummary(summary: Option[LogisticRegressionTrainingSummary]): this.type = { + this.trainingSummary = summary this } @@ -887,8 +887,7 @@ class LogisticRegressionModel private[spark] ( override def copy(extra: ParamMap): LogisticRegressionModel = { val newModel = copyValues(new LogisticRegressionModel(uid, coefficientMatrix, interceptVector, numClasses, isMultinomial), extra) - if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get) - newModel.setParent(parent) + newModel.setSummary(trainingSummary).setParent(parent) } override protected def raw2prediction(rawPrediction: Vector): Double = { 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 f8a606d60b..e6ca3aedff 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 @@ -95,8 +95,7 @@ class BisectingKMeansModel private[ml] ( @Since("2.0.0") override def copy(extra: ParamMap): BisectingKMeansModel = { val copied = copyValues(new BisectingKMeansModel(uid, parentModel), extra) - if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get) - copied.setParent(this.parent) + copied.setSummary(trainingSummary).setParent(this.parent) } @Since("2.0.0") @@ -132,8 +131,8 @@ class BisectingKMeansModel private[ml] ( private var trainingSummary: Option[BisectingKMeansSummary] = None - private[clustering] def setSummary(summary: BisectingKMeansSummary): this.type = { - this.trainingSummary = Some(summary) + private[clustering] def setSummary(summary: Option[BisectingKMeansSummary]): this.type = { + this.trainingSummary = summary this } @@ -265,7 +264,7 @@ class BisectingKMeans @Since("2.0.0") ( val model = copyValues(new BisectingKMeansModel(uid, parentModel).setParent(this)) val summary = new BisectingKMeansSummary( model.transform(dataset), $(predictionCol), $(featuresCol), $(k)) - model.setSummary(summary) + model.setSummary(Some(summary)) instr.logSuccess(model) model } 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 c6035cc4c9..92d0b7d085 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 @@ -90,8 +90,7 @@ class GaussianMixtureModel private[ml] ( @Since("2.0.0") override def copy(extra: ParamMap): GaussianMixtureModel = { val copied = copyValues(new GaussianMixtureModel(uid, weights, gaussians), extra) - if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get) - copied.setParent(this.parent) + copied.setSummary(trainingSummary).setParent(this.parent) } @Since("2.0.0") @@ -150,8 +149,8 @@ class GaussianMixtureModel private[ml] ( private var trainingSummary: Option[GaussianMixtureSummary] = None - private[clustering] def setSummary(summary: GaussianMixtureSummary): this.type = { - this.trainingSummary = Some(summary) + private[clustering] def setSummary(summary: Option[GaussianMixtureSummary]): this.type = { + this.trainingSummary = summary this } @@ -340,7 +339,7 @@ class GaussianMixture @Since("2.0.0") ( .setParent(this) val summary = new GaussianMixtureSummary(model.transform(dataset), $(predictionCol), $(probabilityCol), $(featuresCol), $(k)) - model.setSummary(summary) + model.setSummary(Some(summary)) instr.logNumFeatures(model.gaussians.head.mean.size) instr.logSuccess(model) model 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 26505b4cc1..152bd13b7a 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 @@ -110,8 +110,7 @@ class KMeansModel private[ml] ( @Since("1.5.0") override def copy(extra: ParamMap): KMeansModel = { val copied = copyValues(new KMeansModel(uid, parentModel), extra) - if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get) - copied.setParent(this.parent) + copied.setSummary(trainingSummary).setParent(this.parent) } /** @group setParam */ @@ -165,8 +164,8 @@ class KMeansModel private[ml] ( private var trainingSummary: Option[KMeansSummary] = None - private[clustering] def setSummary(summary: KMeansSummary): this.type = { - this.trainingSummary = Some(summary) + private[clustering] def setSummary(summary: Option[KMeansSummary]): this.type = { + this.trainingSummary = summary this } @@ -325,7 +324,7 @@ class KMeans @Since("1.5.0") ( val model = copyValues(new KMeansModel(uid, parentModel).setParent(this)) val summary = new KMeansSummary( model.transform(dataset), $(predictionCol), $(featuresCol), $(k)) - model.setSummary(summary) + model.setSummary(Some(summary)) instr.logSuccess(model) model } diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala index 736fd3b9e0..3f9de1fe74 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala @@ -270,7 +270,7 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val .setParent(this)) val trainingSummary = new GeneralizedLinearRegressionTrainingSummary(dataset, model, wlsModel.diagInvAtWA.toArray, 1, getSolver) - return model.setSummary(trainingSummary) + return model.setSummary(Some(trainingSummary)) } // Fit Generalized Linear Model by iteratively reweighted least squares (IRLS). @@ -284,7 +284,7 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val .setParent(this)) val trainingSummary = new GeneralizedLinearRegressionTrainingSummary(dataset, model, irlsModel.diagInvAtWA.toArray, irlsModel.numIterations, getSolver) - model.setSummary(trainingSummary) + model.setSummary(Some(trainingSummary)) } @Since("2.0.0") @@ -761,8 +761,8 @@ class GeneralizedLinearRegressionModel private[ml] ( def hasSummary: Boolean = trainingSummary.nonEmpty private[regression] - def setSummary(summary: GeneralizedLinearRegressionTrainingSummary): this.type = { - this.trainingSummary = Some(summary) + def setSummary(summary: Option[GeneralizedLinearRegressionTrainingSummary]): this.type = { + this.trainingSummary = summary this } @@ -778,8 +778,7 @@ class GeneralizedLinearRegressionModel private[ml] ( override def copy(extra: ParamMap): GeneralizedLinearRegressionModel = { val copied = copyValues(new GeneralizedLinearRegressionModel(uid, coefficients, intercept), extra) - if (trainingSummary.isDefined) copied.setSummary(trainingSummary.get) - copied.setParent(parent) + copied.setSummary(trainingSummary).setParent(parent) } /** diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala index da7ce6b46f..8ea5e1e6c4 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala @@ -225,7 +225,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String model.diagInvAtWA.toArray, model.objectiveHistory) - return lrModel.setSummary(trainingSummary) + return lrModel.setSummary(Some(trainingSummary)) } val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE @@ -278,7 +278,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String model, Array(0D), Array(0D)) - return model.setSummary(trainingSummary) + return model.setSummary(Some(trainingSummary)) } else { require($(regParam) == 0.0, "The standard deviation of the label is zero. " + "Model cannot be regularized.") @@ -400,7 +400,7 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String model, Array(0D), objectiveHistory) - model.setSummary(trainingSummary) + model.setSummary(Some(trainingSummary)) } @Since("1.4.0") @@ -446,8 +446,9 @@ class LinearRegressionModel private[ml] ( throw new SparkException("No training summary available for this LinearRegressionModel") } - private[regression] def setSummary(summary: LinearRegressionTrainingSummary): this.type = { - this.trainingSummary = Some(summary) + private[regression] + def setSummary(summary: Option[LinearRegressionTrainingSummary]): this.type = { + this.trainingSummary = summary this } @@ -490,8 +491,7 @@ class LinearRegressionModel private[ml] ( @Since("1.4.0") override def copy(extra: ParamMap): LinearRegressionModel = { val newModel = copyValues(new LinearRegressionModel(uid, coefficients, intercept), extra) - if (trainingSummary.isDefined) newModel.setSummary(trainingSummary.get) - newModel.setParent(parent) + newModel.setSummary(trainingSummary).setParent(parent) } /** diff --git a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala index 2877285eb4..e360542eae 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala @@ -147,6 +147,8 @@ class LogisticRegressionSuite assert(model.hasSummary) val copiedModel = model.copy(ParamMap.empty) assert(copiedModel.hasSummary) + model.setSummary(None) + assert(!model.hasSummary) } test("empty probabilityCol") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala index 49797d938d..fc491cd616 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/BisectingKMeansSuite.scala @@ -109,6 +109,9 @@ class BisectingKMeansSuite assert(clusterSizes.length === k) assert(clusterSizes.sum === numRows) assert(clusterSizes.forall(_ >= 0)) + + model.setSummary(None) + assert(!model.hasSummary) } test("read/write") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala index 7165b63ed3..07299123f8 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/GaussianMixtureSuite.scala @@ -111,6 +111,9 @@ class GaussianMixtureSuite extends SparkFunSuite with MLlibTestSparkContext assert(clusterSizes.length === k) assert(clusterSizes.sum === numRows) assert(clusterSizes.forall(_ >= 0)) + + model.setSummary(None) + assert(!model.hasSummary) } test("read/write") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala index 73972557d2..c1b7242e11 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/clustering/KMeansSuite.scala @@ -123,6 +123,9 @@ class KMeansSuite extends SparkFunSuite with MLlibTestSparkContext with DefaultR assert(clusterSizes.length === k) assert(clusterSizes.sum === numRows) assert(clusterSizes.forall(_ >= 0)) + + model.setSummary(None) + assert(!model.hasSummary) } test("KMeansModel transform with non-default feature and prediction cols") { diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala index 6a4ac1735b..9b0fa67630 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/GeneralizedLinearRegressionSuite.scala @@ -197,6 +197,8 @@ class GeneralizedLinearRegressionSuite assert(model.hasSummary) val copiedModel = model.copy(ParamMap.empty) assert(copiedModel.hasSummary) + model.setSummary(None) + assert(!model.hasSummary) assert(model.getFeaturesCol === "features") assert(model.getPredictionCol === "prediction") diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala index df97d0b2ae..0be82742a3 100644 --- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala @@ -146,6 +146,8 @@ class LinearRegressionSuite assert(model.hasSummary) val copiedModel = model.copy(ParamMap.empty) assert(copiedModel.hasSummary) + model.setSummary(None) + assert(!model.hasSummary) model.transform(datasetWithDenseFeature) .select("label", "prediction") |