rename model to modelParams

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

@@ -64,7 +64,7 @@ abstract class Estimator[M <: Model] extends PipelineStage with Params {
   }
 
   /**
-   * Parameter for the output model.
+   * Parameters for the output model.
    */
-  def model: Params = Params.empty
+  def modelParams: Params = Params.empty
 }

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

@@ -35,7 +35,9 @@ abstract class Transformer extends PipelineStage with Params {
    */
   @varargs
   def transform(dataset: SchemaRDD, paramPairs: ParamPair[_]*): SchemaRDD = {
-    transform(dataset, ParamMap.empty)
+    val map = new ParamMap()
+    paramPairs.foreach(map.put(_))
+    transform(dataset, map)
   }
 
   /**

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

@@ -42,7 +42,7 @@ class LogisticRegression extends Estimator[LogisticRegressionModel]
   override def setLabelCol(labelCol: String): this.type = super.setLabelCol(labelCol)
   override def setFeaturesCol(featuresCol: String): this.type = super.setFeaturesCol(featuresCol)
 
-  override final val model: LogisticRegressionModelParams = new LogisticRegressionModelParams {}
+  override final val modelParams: LogisticRegressionModelParams = new LogisticRegressionModelParams {}
 
   override def fit(dataset: SchemaRDD, paramMap: ParamMap): LogisticRegressionModel = {
     import dataset.sqlContext._
@@ -58,7 +58,7 @@ class LogisticRegression extends Estimator[LogisticRegressionModel]
       .setNumIterations(maxIter)
     val lrm = new LogisticRegressionModel(lr.run(instances).weights)
     instances.unpersist()
-    this.model.params.foreach { param =>
+    this.modelParams.params.foreach { param =>
       if (map.contains(param)) {
         lrm.paramMap.put(lrm.getParam(param.name), map(param))
       }
@@ -71,6 +71,11 @@ class LogisticRegression extends Estimator[LogisticRegressionModel]
 }
 
 trait LogisticRegressionModelParams extends Params with HasThreshold with HasFeaturesCol
+  with HasScoreCol {
+  override def setThreshold(threshold: Double): this.type = super.setThreshold(threshold)
+  override def setFeaturesCol(featuresCol: String): this.type = super.setFeaturesCol(featuresCol)
+  override def setScoreCol(scoreCol: String): this.type = super.setScoreCol(scoreCol)
+}
 
 class LogisticRegressionModel(
     val weights: Vector)
@@ -81,6 +86,7 @@ class LogisticRegressionModel(
   override def transform(dataset: SchemaRDD, paramMap: ParamMap): SchemaRDD = {
     import dataset.sqlContext._
     val map = this.paramMap ++ paramMap
+    println(s"transform called with $map")
     import map.implicitMapping
     val score: Vector => Double = (v) => {
       val margin = BLAS.dot(v, weights)
@@ -92,7 +98,7 @@ class LogisticRegressionModel(
     }
     dataset.select(
       Star(None),
-      score.call((featuresCol: String).attr) as 'score,
+      score.call((featuresCol: String).attr) as scoreCol,
       predict.call((featuresCol: String).attr) as 'prediction)
   }
 }

mllib/src/test/java/org/apache/spark/ml/example/JavaLogisticRegressionSuite.java

@@ -60,10 +60,21 @@ public void tearDown() {
 
   @Test
   public void logisticRegression() {
+    LogisticRegression lr = new LogisticRegression();
+    LogisticRegressionModel model = lr.fit(dataset.schemaRDD());
+    model.transform(dataset.schemaRDD()).registerTempTable("prediction");
+    JavaSchemaRDD predictions = jsql.sql("SELECT label, score, prediction FROM prediction");
+    for (Row r: predictions.collect()) {
+      System.out.println(r);
+    }
+  }
+
+  @Test
+  public void logisticRegressionWithSetters() {
     LogisticRegression lr = new LogisticRegression()
       .setMaxIter(10)
       .setRegParam(1.0);
-    lr.model().setThreshold(0.8);
+    lr.modelParams().setThreshold(0.8);
     LogisticRegressionModel model = lr.fit(dataset.schemaRDD());
     model.transform(dataset.schemaRDD()).registerTempTable("prediction");
     JavaSchemaRDD predictions = jsql.sql("SELECT label, score, prediction FROM prediction");
@@ -72,6 +83,15 @@ public void logisticRegression() {
     }
   }
 
+  @Test
+  public void chainModelParams() {
+    LogisticRegression lr = new LogisticRegression();
+    lr.modelParams()
+      .setFeaturesCol("features")
+      .setScoreCol("score")
+      .setThreshold(0.5);
+  }
+
   @Test
   public void logisticRegressionFitWithVarargs() {
     LogisticRegression lr = new LogisticRegression();

mllib/src/test/scala/org/apache/spark/ml/example/LogisticRegressionSuite.scala

@@ -30,19 +30,39 @@ class LogisticRegressionSuite extends FunSuite {
     .loadLibSVMFile(sparkContext, "../data/mllib/sample_binary_classification_data.txt")
     .cache()
 
-  test("logistic regression alone") {
+  test("logistic regression") {
+    val lr = new LogisticRegression
+    val model = lr.fit(dataset)
+    model.transform(dataset)
+      .select('label, 'prediction)
+      .collect()
+      .foreach(println)
+  }
+
+  test("logistic regression with setters") {
     val lr = new LogisticRegression()
       .setMaxIter(10)
       .setRegParam(1.0)
     val model = lr.fit(dataset)
-    model.transform(dataset, lr.model.threshold -> 0.8) // overwrite threshold
+    model.transform(dataset, lr.modelParams.threshold -> 0.8) // overwrite threshold
       .select('label, 'score, 'prediction).collect()
       .foreach(println)
   }
 
-  test("logistic regression fit with varargs") {
+  test("chain model parameters") {
     val lr = new LogisticRegression
-    lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0)
+    lr.modelParams
+      .setFeaturesCol("features")
+      .setScoreCol("score")
+      .setThreshold(0.5)
+  }
+
+  test("logistic regression fit and transform with varargs") {
+    val lr = new LogisticRegression
+    val model = lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0)
+    model.transform(dataset, model.threshold -> 0.8, model.scoreCol -> "probability")
+      .select('label, 'probability, 'prediction)
+      .foreach(println)
   }
 
   test("logistic regression with cross validation") {