added train() to Predictor subclasses which does not take a ParamMap.

nxwhite-str · Feb 5, 2015 · 58802e3 · 58802e3
1 parent 57d54ab
commit 58802e3
Show file tree

Hide file tree

Showing 2 changed files with 20 additions and 6 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
@@ -70,12 +70,12 @@ class LogisticRegression extends Classifier[LogisticRegression, LogisticRegressi
 
   /**
    * Same as [[fit()]], but using strong types.
-   *
-   * @param dataset  Training data.  WARNING: This does not yet handle instance weights.
+   * NOTE: This does NOT support instance weights.
+   * @param dataset  Training data.  Instance weights are ignored.
    * @param paramMap  Parameters for training.
    *                  These values override any specified in this Estimator's embedded ParamMap.
    */
-  def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LogisticRegressionModel = {
+  override def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LogisticRegressionModel = {
     val map = this.paramMap ++ paramMap
     val oldDataset = dataset.map { case LabeledPoint(label: Double, features: Vector, weight) =>
       org.apache.spark.mllib.regression.LabeledPoint(label, features)
@@ -96,6 +96,13 @@ class LogisticRegression extends Classifier[LogisticRegression, LogisticRegressi
     }
     lrm
   }
+
+  /**
+   * Same as [[fit()]], but using strong types.
+   * NOTE: This does NOT support instance weights.
+   * @param dataset  Training data.  Instance weights are ignored.
+   */
+  def train(dataset: RDD[LabeledPoint]): LogisticRegressionModel = train(dataset, new ParamMap())
 }
 
 

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
@@ -47,12 +47,12 @@ class LinearRegression extends Regressor[LinearRegression, LinearRegressionModel
 
   /**
    * Same as [[fit()]], but using strong types.
-   *
-   * @param dataset  Training data.  WARNING: This does not yet handle instance weights.
+   * NOTE: This does NOT support instance weights.
+   * @param dataset  Training data.  Instance weights are ignored.
    * @param paramMap  Parameters for training.
    *                  These values override any specified in this Estimator's embedded ParamMap.
    */
-  def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LinearRegressionModel = {
+  override def train(dataset: RDD[LabeledPoint], paramMap: ParamMap): LinearRegressionModel = {
     val oldDataset = dataset.map { case LabeledPoint(label: Double, features: Vector, weight) =>
       org.apache.spark.mllib.regression.LabeledPoint(label, features)
     }
@@ -71,6 +71,13 @@ class LinearRegression extends Regressor[LinearRegression, LinearRegressionModel
     }
     lrm
   }
+
+  /**
+   * Same as [[fit()]], but using strong types.
+   * NOTE: This does NOT support instance weights.
+   * @param dataset  Training data.  Instance weights are ignored.
+   */
+  def train(dataset: RDD[LabeledPoint]): LinearRegressionModel = train(dataset, new ParamMap())
 }
 
 /**