REFACTOR: Removed model definition and training

from importAnalysis method of AnalyticsFunctions (#237)

src/main/scala/au/csiro/variantspark/api/AnalyticsFunctions.scala

@@ -4,6 +4,8 @@ import au.csiro.variantspark.input.FeatureSource
 import au.csiro.variantspark.input.LabelSource
 import au.csiro.variantspark.algo.PairwiseOperation
 import au.csiro.variantspark.algo.LowerTriangMatrix
+import au.csiro.variantspark.algo.RandomForestModel
+import org.apache.spark.SparkContext
 
 /**
   * Extends a feature with analytical functions.
@@ -14,22 +16,13 @@ class AnalyticsFunctions(val featureSource: FeatureSource) extends AnyVal {
     * Builds random forest classifier for the provided labels and estimates variable
     * importance using gini importance.
     *
-    * @param labelSource: labels to use for importance analysis.
-    * @param nTrees: the number of trees to build in the forest.
-    * @param mtryFraction: the fraction of variables to try at each split.
-    * @param oob: should OOB (Out of Bag) error estimate be calculated.
-    * @param seed: random seed to use.
-    * @param batchSize: the number of trees to build in one batch.
-    * @param varOrdinalLevels: the number levels in the ordinal features.
+    * @param rfModel: The trained random forest model.
     *
     * @return [[au.csiro.variantspark.api.ImportanceAnalysis.apply]] importance analysis model
     */
-  def importanceAnalysis(labelSource: LabelSource, nTrees: Int = 1000,
-      mtryFraction: Option[Double] = None, oob: Boolean = true, seed: Option[Long] = None,
-      batchSize: Int = 100, varOrdinalLevels: Int = 3)(
+  def importanceAnalysis(rfModel: RandomForestModel)(
       implicit vsContext: SqlContextHolder): ImportanceAnalysis = {
-    ImportanceAnalysis(featureSource, labelSource, nTrees, mtryFraction, oob, seed, batchSize,
-      varOrdinalLevels)
+    ImportanceAnalysis(featureSource, rfModel)
   }
 
   /**