WIP

mllib/src/main/scala/org/apache/spark/mllib/classification/BinaryClassificationModel.scala

@@ -21,7 +21,6 @@ import org.apache.spark.mllib.linalg.Vector
 import org.apache.spark.rdd.RDD
 import org.apache.spark.mllib.regression.GeneralizedLinearModel
 import org.apache.spark.api.java.JavaRDD
-import scala.deprecated
 
 /**
  * Represents a classification model that predicts to which of a set of categories an example
@@ -33,7 +32,8 @@ class BinaryClassificationModel (
   extends GeneralizedLinearModel(weights, intercept) with ClassificationModel with Serializable {
 
   protected var threshold: Double = 0.0
-  @deprecated
+
+  // this is only used to ensure prior behaviour of deprecated `predict``
   protected var useThreshold: Boolean = true
 
   /**
@@ -61,7 +61,7 @@ class BinaryClassificationModel (
   }
 
   /**
-   * :: Deprecated ::
+   * DEPRECATED: Use predictScore(...) or predictClass(...) instead
    * Clears the threshold so that `predict` will output raw prediction scores.
    */
   @Deprecated
@@ -71,38 +71,50 @@ class BinaryClassificationModel (
   }
 
   /**
-   * :: Deprecated ::
+   * DEPRECATED: Use predictScore(...) or predictClass(...) instead
+   */
+  @Deprecated
+  override protected def predictPoint(
+                                       dataMatrix: Vector,
+                                       weightMatrix: Vector,
+                                       intercept: Double) = {
+    if (useThreshold) predictClass(dataMatrix)
+    else predictScore(dataMatrix)
+  }
+
+  /**
+   * DEPRECATED: Use predictScore(...) or predictClass(...) instead
    * Predict values for the given data set using the model trained.
    *
    * @param testData RDD representing data points to be predicted
    * @return an RDD[Double] where each entry contains the corresponding prediction
    */
-  @deprecated
+  @Deprecated
   override def predict(testData: RDD[Vector]): RDD[Double] = {
     if (useThreshold) predictClass(testData)
     else predictScore(testData)
   }
 
   /**
-   * :: Deprecated ::
+   * DEPRECATED: Use predictScore(...) or predictClass(...) instead
    * Predict values for a single data point using the model trained.
    *
    * @param testData array representing a single data point
    * @return predicted category from the trained model
    */
-  @deprecated
-  def predict(testData: Vector): Double = {
+  @Deprecated
+  override def predict(testData: Vector): Double = {
     if (useThreshold) predictClass(testData)
     else predictScore(testData)
   }
 
   /**
-   * :: Deprecated ::
+   * DEPRECATED: Use predictScore(...) or predictClass(...) instead
    * Predict values for examples stored in a JavaRDD.
    * @param testData JavaRDD representing data points to be predicted
    * @return a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
    */
-  @deprecated
+  @Deprecated
   def predict(testData: JavaRDD[Vector]): JavaRDD[java.lang.Double] =
     predict(testData.rdd).toJavaRDD().asInstanceOf[JavaRDD[java.lang.Double]]
 }

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

@@ -46,7 +46,10 @@ class LogisticRegressionModel (
     computeProbability(predictScore(testData))
   }
 
-  @deprecated
+  /**
+   * DEPRECATED: Use predictProbability(...) or predictClass(...) instead
+   */
+  @Deprecated
   override protected def predictPoint(dataMatrix: Vector, weightMatrix: Vector,
                                       intercept: Double) = {
     if (useThreshold) predictClass(dataMatrix)

mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala

@@ -66,8 +66,15 @@ class NaiveBayesModel private[mllib] (
     labels(brzArgmax(brzPi + brzTheta * testData.toBreeze))
   }
 
-  @deprecated
+  /**
+   * DEPRECATED: Use predictClass(...) instead
+   */
+  @Deprecated
   def predict(testData: RDD[Vector]): RDD[Double] = predictClass(testData)
+
+  /**
+   * DEPRECATED: Use predictClass(...) instead
+   */
   @deprecated
   def predict(testData: Vector): Double = predictClass(testData)
 }

mllib/src/main/scala/org/apache/spark/mllib/classification/SVM.scala

@@ -33,15 +33,6 @@ class SVMModel (
                  override val weights: Vector,
                  override val intercept: Double)
   extends BinaryClassificationModel(weights, intercept) {
-
-  @deprecated
-  override protected def predictPoint(
-                                       dataMatrix: Vector,
-                                       weightMatrix: Vector,
-                                       intercept: Double) = {
-    if (useThreshold) predictClass(dataMatrix)
-    else predictScore(dataMatrix)
-  }
 }
 
 /**

mllib/src/main/scala/org/apache/spark/mllib/regression/GeneralizedLinearAlgorithm.scala

@@ -86,7 +86,7 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param weightMatrix Column vector containing the weights of the model
    * @param intercept Intercept of the model.
    */
-  @deprecated
+  @Deprecated
   protected def predictPoint(dataMatrix: Vector, weightMatrix: Vector, intercept: Double): Double
 
   /**
@@ -96,7 +96,7 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData RDD representing data points to be predicted
    * @return RDD[Double] where each entry contains the corresponding prediction
    */
-  @deprecated
+  @Deprecated
   def predict(testData: RDD[Vector]): RDD[Double] = {
     // A small optimization to avoid serializing the entire model. Only the weightsMatrix
     // and intercept is needed.
@@ -116,7 +116,7 @@ abstract class GeneralizedLinearModel(val weights: Vector, val intercept: Double
    * @param testData array representing a single data point
    * @return Double prediction from the trained model
    */
-  @deprecated
+  @Deprecated
   def predict(testData: Vector): Double = {
     predictPoint(testData, weights, intercept)
   }

mllib/src/main/scala/org/apache/spark/mllib/regression/Lasso.scala

@@ -34,12 +34,16 @@ class LassoModel (
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable {
 
-  override protected def computeScore(
-      dataMatrix: Vector,
-      weightMatrix: Vector,
-      intercept: Double): Double = {
-    weightMatrix.toBreeze.dot(dataMatrix.toBreeze) + intercept
-  }
+  /**
+   * DEPRECATED: Use predictScore(...) instead
+   */
+  @Deprecated
+  override protected def predictPoint(
+                                       dataMatrix: Vector,
+                                       weightMatrix: Vector,
+                                       intercept: Double): Double =
+    predictScore(dataMatrix)
+
 }
 
 /**

mllib/src/main/scala/org/apache/spark/mllib/regression/LinearRegression.scala

@@ -32,7 +32,11 @@ class LinearRegressionModel (
     override val intercept: Double)
   extends GeneralizedLinearModel(weights, intercept) with RegressionModel with Serializable {
 
-  @deprecated
+
+  /**
+   * DEPRECATED: Use predictScore(...) instead
+   */
+  @Deprecated
   override protected def predictPoint(
                                        dataMatrix: Vector,
                                        weightMatrix: Vector,

mllib/src/main/scala/org/apache/spark/mllib/regression/RidgeRegression.scala

@@ -33,11 +33,14 @@ class RidgeRegressionModel (
   extends GeneralizedLinearModel(weights, intercept)
   with RegressionModel with Serializable {
 
-  @deprecated
+  /**
+   * DEPRECATED: Use predictScore(...) instead
+   */
+  @Deprecated
   override protected def predictPoint(
                                        dataMatrix: Vector,
                                        weightMatrix: Vector,
-                                       intercept: Double) =
+                                       intercept: Double): Double =
     predictScore(dataMatrix)
 
 }