[SPARK-29095][ML] add extractInstances

### What changes were proposed in this pull request? common methods support extract weights ### Why are the changes needed? today more and more ML algs support weighting, add this method will make impls simple ### Does this PR introduce any user-facing change? no ### How was this patch tested? existing testsuites Closes #25802 from zhengruifeng/add_extractInstances. Authored-by: zhengruifeng <[email protected]> Signed-off-by: Sean Owen <[email protected]>
apache · Sep 24, 2019 · fff2e84 · fff2e84
1 parent 7c02c14
commit fff2e84
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diff --git a/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala b/mllib/src/main/scala/org/apache/spark/ml/Predictor.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml
 
 import org.apache.spark.annotation.{DeveloperApi, Since}
-import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.feature.{Instance, LabeledPoint}
 import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
@@ -62,6 +62,39 @@ private[ml] trait PredictorParams extends Params
     }
     SchemaUtils.appendColumn(schema, $(predictionCol), DoubleType)
   }
+
+  /**
+   * Extract [[labelCol]], weightCol(if any) and [[featuresCol]] from the given dataset,
+   * and put it in an RDD with strong types.
+   */
+  protected def extractInstances(dataset: Dataset[_]): RDD[Instance] = {
+    val w = this match {
+      case p: HasWeightCol =>
+        if (isDefined(p.weightCol) && $(p.weightCol).nonEmpty) {
+          col($(p.weightCol)).cast(DoubleType)
+        } else {
+          lit(1.0)
+        }
+    }
+
+    dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
+      case Row(label: Double, weight: Double, features: Vector) =>
+        Instance(label, weight, features)
+    }
+  }
+
+  /**
+   * Extract [[labelCol]], weightCol(if any) and [[featuresCol]] from the given dataset,
+   * and put it in an RDD with strong types.
+   * Validate the output instances with the given function.
+   */
+  protected def extractInstances(dataset: Dataset[_],
+                                 validateInstance: Instance => Unit): RDD[Instance] = {
+    extractInstances(dataset).map { instance =>
+      validateInstance(instance)
+      instance
+    }
+  }
 }
 
 /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala
@@ -20,7 +20,7 @@ package org.apache.spark.ml.classification
 import org.apache.spark.SparkException
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.ml.{PredictionModel, Predictor, PredictorParams}
-import org.apache.spark.ml.feature.LabeledPoint
+import org.apache.spark.ml.feature.{Instance, LabeledPoint}
 import org.apache.spark.ml.linalg.{Vector, VectorUDT}
 import org.apache.spark.ml.param.shared.HasRawPredictionCol
 import org.apache.spark.ml.util.{MetadataUtils, SchemaUtils}
@@ -42,6 +42,22 @@ private[spark] trait ClassifierParams
     val parentSchema = super.validateAndTransformSchema(schema, fitting, featuresDataType)
     SchemaUtils.appendColumn(parentSchema, $(rawPredictionCol), new VectorUDT)
   }
+
+  /**
+   * Extract [[labelCol]], weightCol(if any) and [[featuresCol]] from the given dataset,
+   * and put it in an RDD with strong types.
+   * Validates the label on the classifier is a valid integer in the range [0, numClasses).
+   */
+  protected def extractInstances(dataset: Dataset[_],
+                                 numClasses: Int): RDD[Instance] = {
+    val validateInstance = (instance: Instance) => {
+      val label = instance.label
+      require(label.toLong == label && label >= 0 && label < numClasses, s"Classifier was given" +
+        s" dataset with invalid label $label.  Labels must be integers in range" +
+        s" [0, $numClasses).")
+    }
+    extractInstances(dataset, validateInstance)
+  }
 }
 
 /**

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/DecisionTreeClassifier.scala
@@ -22,7 +22,7 @@ import org.json4s.{DefaultFormats, JObject}
 import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.Since
-import org.apache.spark.ml.feature.{Instance, LabeledPoint}
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector, Vectors}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree._
@@ -34,9 +34,8 @@ import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit, udf}
-import org.apache.spark.sql.types.DoubleType
+import org.apache.spark.sql.{DataFrame, Dataset}
+import org.apache.spark.sql.functions.{col, udf}
 
 /**
  * Decision tree learning algorithm (http://en.wikipedia.org/wiki/Decision_tree_learning)
@@ -116,23 +115,16 @@ class DecisionTreeClassifier @Since("1.4.0") (
       dataset: Dataset[_]): DecisionTreeClassificationModel = instrumented { instr =>
     instr.logPipelineStage(this)
     instr.logDataset(dataset)
-    val categoricalFeatures: Map[Int, Int] =
-      MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val numClasses: Int = getNumClasses(dataset)
+    val categoricalFeatures = MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
+    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}")
     }
     validateNumClasses(numClasses)
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances =
-      dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          validateLabel(label, numClasses)
-          Instance(label, weight, features)
-      }
+    val instances = extractInstances(dataset, numClasses)
     val strategy = getOldStrategy(categoricalFeatures, numClasses)
     instr.logNumClasses(numClasses)
     instr.logParams(this, labelCol, featuresCol, predictionCol, rawPredictionCol,

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/LinearSVC.scala
@@ -36,9 +36,7 @@ import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit}
 
 /** Params for linear SVM Classifier. */
 private[classification] trait LinearSVCParams extends ClassifierParams with HasRegParam
@@ -161,12 +159,7 @@ class LinearSVC @Since("2.2.0") (
   override def copy(extra: ParamMap): LinearSVC = defaultCopy(extra)
 
   override protected def train(dataset: Dataset[_]): LinearSVCModel = instrumented { instr =>
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances: RDD[Instance] =
-      dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight, features)
-      }
+    val instances = extractInstances(dataset)
 
     instr.logPipelineStage(this)
     instr.logDataset(dataset)

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
@@ -40,9 +40,8 @@ import org.apache.spark.mllib.evaluation.{BinaryClassificationMetrics, Multiclas
 import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.functions.col
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.util.VersionUtils
@@ -492,12 +491,7 @@ class LogisticRegression @Since("1.2.0") (
   protected[spark] def train(
       dataset: Dataset[_],
       handlePersistence: Boolean): LogisticRegressionModel = instrumented { instr =>
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances: RDD[Instance] =
-      dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight, features)
-      }
+    val instances = extractInstances(dataset)
 
     if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
 

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/MultilayerPerceptronClassifier.scala
@@ -24,7 +24,7 @@ import org.apache.hadoop.fs.Path
 import org.apache.spark.annotation.Since
 import org.apache.spark.ml.ann.{FeedForwardTopology, FeedForwardTrainer}
 import org.apache.spark.ml.feature.OneHotEncoderModel
-import org.apache.spark.ml.linalg.{Vector, Vectors}
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param._
 import org.apache.spark.ml.param.shared._
 import org.apache.spark.ml.util._

diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/NaiveBayes.scala
@@ -21,14 +21,15 @@ import org.apache.hadoop.fs.Path
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.ml.PredictorParams
+import org.apache.spark.ml.feature.Instance
 import org.apache.spark.ml.linalg._
 import org.apache.spark.ml.param.{DoubleParam, Param, ParamMap, ParamValidators}
 import org.apache.spark.ml.param.shared.HasWeightCol
 import org.apache.spark.ml.util._
 import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.{Dataset, Row}
-import org.apache.spark.sql.functions.{col, lit}
+import org.apache.spark.sql.functions.col
 
 /**
  * Params for Naive Bayes Classifiers.
@@ -137,35 +138,30 @@ class NaiveBayes @Since("1.5.0") (
         s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
     }
 
-    val modelTypeValue = $(modelType)
-    val requireValues: Vector => Unit = {
-      modelTypeValue match {
-        case Multinomial =>
-          requireNonnegativeValues
-        case Bernoulli =>
-          requireZeroOneBernoulliValues
-        case _ =>
-          // This should never happen.
-          throw new IllegalArgumentException(s"Invalid modelType: ${$(modelType)}.")
-      }
+    val validateInstance = $(modelType) match {
+      case Multinomial =>
+        (instance: Instance) => requireNonnegativeValues(instance.features)
+      case Bernoulli =>
+        (instance: Instance) => requireZeroOneBernoulliValues(instance.features)
+      case _ =>
+        // This should never happen.
+        throw new IllegalArgumentException(s"Invalid modelType: ${$(modelType)}.")
     }
 
     instr.logParams(this, labelCol, featuresCol, weightCol, predictionCol, rawPredictionCol,
       probabilityCol, modelType, smoothing, thresholds)
 
     val numFeatures = dataset.select(col($(featuresCol))).head().getAs[Vector](0).size
     instr.logNumFeatures(numFeatures)
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
 
     // Aggregates term frequencies per label.
     // TODO: Calling aggregateByKey and collect creates two stages, we can implement something
     // TODO: similar to reduceByKeyLocally to save one stage.
-    val aggregated = dataset.select(col($(labelCol)), w, col($(featuresCol))).rdd
-      .map { row => (row.getDouble(0), (row.getDouble(1), row.getAs[Vector](2)))
-      }.aggregateByKey[(Double, DenseVector, Long)]((0.0, Vectors.zeros(numFeatures).toDense, 0L))(
+    val aggregated = extractInstances(dataset, validateInstance).map { instance =>
+      (instance.label, (instance.weight, instance.features))
+    }.aggregateByKey[(Double, DenseVector, Long)]((0.0, Vectors.zeros(numFeatures).toDense, 0L))(
       seqOp = {
          case ((weightSum, featureSum, count), (weight, features)) =>
-           requireValues(features)
            BLAS.axpy(weight, features, featureSum)
            (weightSum + weight, featureSum, count + 1)
       },

diff --git a/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala b/mllib/src/main/scala/org/apache/spark/ml/regression/DecisionTreeRegressor.scala
@@ -23,7 +23,7 @@ import org.json4s.JsonDSL._
 
 import org.apache.spark.annotation.Since
 import org.apache.spark.ml.{PredictionModel, Predictor}
-import org.apache.spark.ml.feature.{Instance, LabeledPoint}
+import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.tree._
@@ -34,9 +34,8 @@ import org.apache.spark.ml.util.Instrumentation.instrumented
 import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo, Strategy => OldStrategy}
 import org.apache.spark.mllib.tree.model.{DecisionTreeModel => OldDecisionTreeModel}
 import org.apache.spark.rdd.RDD
-import org.apache.spark.sql.{Column, DataFrame, Dataset, Row}
+import org.apache.spark.sql.{Column, DataFrame, Dataset}
 import org.apache.spark.sql.functions._
-import org.apache.spark.sql.types.DoubleType
 
 
 /**
@@ -118,12 +117,7 @@ class DecisionTreeRegressor @Since("1.4.0") (@Since("1.4.0") override val uid: S
       dataset: Dataset[_]): DecisionTreeRegressionModel = instrumented { instr =>
     val categoricalFeatures: Map[Int, Int] =
       MetadataUtils.getCategoricalFeatures(dataset.schema($(featuresCol)))
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
-    val instances =
-      dataset.select(col($(labelCol)).cast(DoubleType), w, col($(featuresCol))).rdd.map {
-        case Row(label: Double, weight: Double, features: Vector) =>
-          Instance(label, weight, features)
-      }
+    val instances = extractInstances(dataset)
     val strategy = getOldStrategy(categoricalFeatures)
 
     instr.logPipelineStage(this)

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
@@ -43,7 +43,6 @@ import org.apache.spark.mllib.linalg.VectorImplicits._
 import org.apache.spark.mllib.regression.{LinearRegressionModel => OldLinearRegressionModel}
 import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
 import org.apache.spark.mllib.util.MLUtils
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Dataset, Row, SparkSession}
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types.{DataType, DoubleType, StructType}
@@ -320,13 +319,8 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
   override protected def train(dataset: Dataset[_]): LinearRegressionModel = instrumented { instr =>
     // Extract the number of features before deciding optimization solver.
     val numFeatures = dataset.select(col($(featuresCol))).first().getAs[Vector](0).size
-    val w = if (!isDefined(weightCol) || $(weightCol).isEmpty) lit(1.0) else col($(weightCol))
 
-    val instances: RDD[Instance] = dataset.select(
-      col($(labelCol)), w, col($(featuresCol))).rdd.map {
-      case Row(label: Double, weight: Double, features: Vector) =>
-        Instance(label, weight, features)
-    }
+    val instances = extractInstances(dataset)
 
     instr.logPipelineStage(this)
     instr.logDataset(dataset)