update params

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

@@ -25,29 +25,28 @@ import org.apache.spark.sql.SchemaRDD
 import org.apache.spark.sql.api.java.JavaSchemaRDD
 
 /**
- * Abstract class for estimators that fits models to data.
+ * Abstract class for estimators that fit models to data.
  */
 abstract class Estimator[M <: Model] extends PipelineStage with Params {
 
   /**
    * Fits a single model to the input data with optional parameters.
    *
    * @param dataset input dataset
-   * @param paramPairs optional list of param pairs, overwrite embedded params
+   * @param paramPairs optional list of param pairs (overwrite embedded params)
    * @return fitted model
    */
   @varargs
   def fit(dataset: SchemaRDD, paramPairs: ParamPair[_]*): M = {
-    val map = new ParamMap()
-    paramPairs.foreach(map.put(_))
+    val map = new ParamMap().put(paramPairs: _*)
     fit(dataset, map)
   }
 
   /**
    * Fits a single model to the input data with provided parameter map.
    *
    * @param dataset input dataset
-   * @param paramMap parameters
+   * @param paramMap parameter map
    * @return fitted model
    */
   def fit(dataset: SchemaRDD, paramMap: ParamMap): M
@@ -61,27 +60,48 @@ abstract class Estimator[M <: Model] extends PipelineStage with Params {
    * @param paramMaps an array of parameter maps
    * @return fitted models, matching the input parameter maps
    */
-  def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[M] = { // how to return an array?
+  def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Seq[M] = {
     paramMaps.map(fit(dataset, _))
   }
 
   // Java-friendly versions of fit.
 
+  /**
+   * Fits a single model to the input data with optional parameters.
+   *
+   * @param dataset input dataset
+   * @param paramPairs optional list of param pairs (overwrite embedded params)
+   * @return fitted model
+   */
   @varargs
   def fit(dataset: JavaSchemaRDD, paramPairs: ParamPair[_]*): M = {
     fit(dataset.schemaRDD, paramPairs: _*)
   }
 
+  /**
+   * Fits a single model to the  input data with provided parameter map.
+   *
+   * @param dataset input dataset
+   * @param paramMap parameter map
+   * @return fitted model
+   */
   def fit(dataset: JavaSchemaRDD, paramMap: ParamMap): M = {
     fit(dataset.schemaRDD, paramMap)
   }
 
+  /**
+   * Fits multiple models to the input data with multiple sets of parameters.
+   *
+   * @param dataset input dataset
+   * @param paramMaps an array of parameter maps
+   * @return fitted models, matching the input parameter maps
+   */
   def fit(dataset: JavaSchemaRDD, paramMaps: Array[ParamMap]): java.util.List[M] = {
     fit(dataset.schemaRDD, paramMaps).asJava
   }
 
   /**
    * Parameters for the output model.
    */
-  def modelParams: Params = Params.empty
+  val modelParams: Params = Params.empty
 }

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

@@ -20,7 +20,7 @@ package org.apache.spark.ml.example
 import com.github.fommil.netlib.F2jBLAS
 
 import org.apache.spark.ml._
-import org.apache.spark.ml.param.{Param, ParamMap, Params}
+import org.apache.spark.ml.param.{IntParam, Param, ParamMap, Params}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.sql.SchemaRDD
 
@@ -44,7 +44,8 @@ class CrossValidator extends Estimator[CrossValidatorModel] with Params {
   def setEvaluator(value: Evaluator): this.type = { set(evaluator, value); this }
   def getEvaluator: Evaluator = get(evaluator)
 
-  val numFolds: Param[Int] = new Param(this, "numFolds", "number of folds for cross validation", 3)
+  val numFolds: Param[Int] =
+    new IntParam(this, "numFolds", "number of folds for cross validation", Some(3))
   def setNumFolds(value: Int): this.type = { set(numFolds, value); this }
   def getNumFolds: Int = get(numFolds)
 

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

@@ -58,8 +58,9 @@ class LogisticRegression extends Estimator[LogisticRegressionModel]
       .setNumIterations(maxIter)
     val lrm = new LogisticRegressionModel(lr.run(instances).weights)
     instances.unpersist()
+    // copy model params
     Params.copyValues(modelParams, lrm)
-    if (!lrm.paramMap.contains(lrm.featuresCol) && map.contains(lrm.featuresCol)) {
+    if (!lrm.isSet(lrm.featuresCol) && map.contains(lrm.featuresCol)) {
       lrm.setFeaturesCol(featuresCol)
     }
     lrm
@@ -69,8 +70,8 @@ class LogisticRegression extends Estimator[LogisticRegressionModel]
    * Validates parameters specified by the input parameter map.
    * Raises an exception if any parameter belongs to this object is invalid.
    */
-  override def validateParams(paramMap: ParamMap): Unit = {
-    super.validateParams(paramMap)
+  override def validate(paramMap: ParamMap): Unit = {
+    super.validate(paramMap)
   }
 }
 

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

@@ -43,7 +43,7 @@ class StandardScaler extends Estimator[StandardScalerModel] with HasInputCol {
     val scaler = new feature.StandardScaler().fit(input)
     val model = new StandardScalerModel(scaler)
     Params.copyValues(modelParams, model)
-    if (!model.paramMap.contains(model.inputCol)) {
+    if (!model.isSet(model.inputCol)) {
       model.setInputCol(inputCol)
     }
     model