removing everything except for simple class hierarchy for classification

Author: jkbradley

mllib/src/main/scala/org/apache/spark/ml/LabeledPoint.scala

@@ -11,14 +11,12 @@ import org.apache.spark.mllib.linalg.Vector
  */
 case class LabeledPoint(label: Double, features: Vector, weight: Double) {
 
-  /** Default constructor which sets instance weight to 1.0 */
-  def this(label: Double, features: Vector) = this(label, features, 1.0)
-
   override def toString: String = {
     "(%s,%s,%s)".format(label, features, weight)
   }
 }
 
 object LabeledPoint {
-  def apply(label: Double, features: Vector) = new LabeledPoint(label, features)
+  /** Constructor which sets instance weight to 1.0 */
+  def apply(label: Double, features: Vector) = new LabeledPoint(label, features, 1.0)
 }

mllib/src/main/scala/org/apache/spark/ml/classification/AdaBoost.scala

@@ -18,12 +18,8 @@
 package org.apache.spark.ml.classification
 
 import org.apache.spark.annotation.AlphaComponent
-import org.apache.spark.ml.evaluation.ClassificationEvaluator
+import org.apache.spark.ml.impl.estimator.{PredictionModel, Predictor, PredictorParams}
 import org.apache.spark.mllib.linalg.Vector
-import org.apache.spark.ml._
-import org.apache.spark.ml.impl.estimator.{HasDefaultEvaluator, PredictionModel, Predictor,
-  PredictorParams}
-import org.apache.spark.rdd.RDD
 
 @AlphaComponent
 private[classification] trait ClassifierParams extends PredictorParams
@@ -33,10 +29,9 @@ private[classification] trait ClassifierParams extends PredictorParams
  */
 abstract class Classifier[Learner <: Classifier[Learner, M], M <: ClassificationModel[M]]
   extends Predictor[Learner, M]
-  with ClassifierParams
-  with HasDefaultEvaluator {
+  with ClassifierParams {
 
-  override def defaultEvaluator: Evaluator = new ClassificationEvaluator
+  // TODO: defaultEvaluator (follow-up PR)
 }
 
 
@@ -60,14 +55,6 @@ private[ml] abstract class ClassificationModel[M <: ClassificationModel[M]]
    */
   def predictRaw(features: Vector): Vector
 
-  /**
-   * Compute this model's accuracy on the given dataset.
-   */
-  def accuracy(dataset: RDD[LabeledPoint]): Double = {
-    // TODO: Handle instance weights.
-    val predictionsAndLabels = dataset.map(lp => predict(lp.features))
-      .zip(dataset.map(_.label))
-    ClassificationEvaluator.computeMetric(predictionsAndLabels, "accuracy")
-  }
+  // TODO: accuracy(dataset: RDD[LabeledPoint]): Double (follow-up PR)
 
 }

mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala

@@ -58,8 +58,6 @@ private[classification] trait LogisticRegressionParams extends ClassifierParams
 class LogisticRegression extends Classifier[LogisticRegression, LogisticRegressionModel]
   with LogisticRegressionParams {
 
-  // TODO: Extend IterativeEstimator
-
   setRegParam(0.1)
   setMaxIter(100)
   setThreshold(0.5)

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -21,7 +21,6 @@ import org.apache.spark.annotation.AlphaComponent
 import org.apache.spark.ml._
 import org.apache.spark.ml.param._
 import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
-import org.apache.spark.rdd.RDD
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.types.DoubleType
 
@@ -57,16 +56,8 @@ class BinaryClassificationEvaluator extends Evaluator with Params
       .map { case Row(score: Double, label: Double) =>
         (score, label)
       }
-    BinaryClassificationEvaluator.computeMetric(scoreAndLabels, map(metricName))
-  }
-
-}
-
-private[ml] object BinaryClassificationEvaluator {
-
-  def computeMetric(scoreAndLabels: RDD[(Double, Double)], metricName: String): Double = {
     val metrics = new BinaryClassificationMetrics(scoreAndLabels)
-    val metric = metricName match {
+    val metric = map(metricName) match {
       case "areaUnderROC" =>
         metrics.areaUnderROC()
       case "areaUnderPR" =>
@@ -77,5 +68,4 @@ private[ml] object BinaryClassificationEvaluator {
     metrics.unpersist()
     metric
   }
-
 }