Name changes

xiliu82 · Aug 1, 2014 · 14b801e · 14b801e
1 parent c7d38a3
commit 14b801e
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Showing 3 changed files with 12 additions and 13 deletions.
diff --git a/...llib/regression/StreamingRegression.scala → ...regression/StreamingLinearAlgorithm.scala b/...llib/regression/StreamingRegression.scala → ...regression/StreamingLinearAlgorithm.scala
@@ -23,9 +23,9 @@ import org.apache.spark.streaming.dstream.DStream
 
 /**
  * :: DeveloperApi ::
- * StreamingRegression implements methods for training
- * a linear regression model on streaming data, and using it
- * for prediction on streaming data.
+ * StreamingLinearAlgorithm implements methods for continuously
+ * training a generalized linear model model on streaming data,
+ * and using it for prediction on streaming data.
  *
  * This class takes as type parameters a GeneralizedLinearModel,
  * and a GeneralizedLinearAlgorithm, making it easy to extend to construct
@@ -34,7 +34,7 @@ import org.apache.spark.streaming.dstream.DStream
  *
  */
 @DeveloperApi
-abstract class StreamingRegression[
+abstract class StreamingLinearAlgorithm[
     M <: GeneralizedLinearModel,
     A <: GeneralizedLinearAlgorithm[M]] extends Logging {
 
@@ -45,7 +45,7 @@ abstract class StreamingRegression[
   val algorithm: A
 
   /** Return the latest model. */
-  def latest(): M = {
+  def latestModel(): M = {
     model
   }
 
@@ -58,8 +58,7 @@ abstract class StreamingRegression[
    * @param data DStream containing labeled data
    */
   def trainOn(data: DStream[LabeledPoint]) {
-    data.foreachRDD{
-      rdd =>
+    data.foreachRDD { rdd =>
         model = algorithm.run(rdd, model.weights)
         logInfo("Model updated")
         logInfo("Current model: weights, %s".format(model.weights.toString))

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegression.scala b/mllib/src/main/scala/org/apache/spark/mllib/regression/StreamingLinearRegression.scala
@@ -35,7 +35,7 @@ class StreamingLinearRegressionWithSGD private (
     private var numIterations: Int,
     private var miniBatchFraction: Double,
     private var numFeatures: Int)
-  extends StreamingRegression[LinearRegressionModel, LinearRegressionWithSGD] with Serializable {
+  extends StreamingLinearAlgorithm[LinearRegressionModel, LinearRegressionWithSGD] with Serializable {
 
   val algorithm = new LinearRegressionWithSGD(stepSize, numIterations, miniBatchFraction)
 

diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala
@@ -75,13 +75,13 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
     Utils.deleteRecursively(testDir)
 
     // check accuracy of final parameter estimates
-    assertEqual(model.latest().intercept, 0.0, 0.1)
-    assertEqual(model.latest().weights(0), 10.0, 0.1)
-    assertEqual(model.latest().weights(1), 10.0, 0.1)
+    assertEqual(model.latestModel().intercept, 0.0, 0.1)
+    assertEqual(model.latestModel().weights(0), 10.0, 0.1)
+    assertEqual(model.latestModel().weights(1), 10.0, 0.1)
 
     // check accuracy of predictions
     val validationData = LinearDataGenerator.generateLinearInput(0.0, Array(10.0, 10.0), 100, 17)
-    validatePrediction(validationData.map(row => model.latest().predict(row.features)), validationData)
+    validatePrediction(validationData.map(row => model.latestModel().predict(row.features)), validationData)
   }
 
   // Test that parameter estimates improve when learning Y = 10*X1 on streaming data
@@ -107,7 +107,7 @@ class StreamingLinearRegressionSuite extends FunSuite with LocalSparkContext {
       Thread.sleep(batchDuration.milliseconds)
       // wait an extra few seconds to make sure the update finishes before new data arrive
       Thread.sleep(4000)
-      history.append(math.abs(model.latest().weights(0) - 10.0))
+      history.append(math.abs(model.latestModel().weights(0) - 10.0))
     }
 
     ssc.stop(stopSparkContext=false)