STYLE: Format with scalamft (#237)

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

@@ -1,22 +1,24 @@
 package au.csiro.variantspark.api
 
-import au.csiro.variantspark.algo.{
-  RandomForest,
-  RandomForestModel,
-  RandomForestParams
-}
+import au.csiro.variantspark.algo.{RandomForest, RandomForestModel, RandomForestParams}
 import au.csiro.variantspark.input.{FeatureSource, LabelSource}
 
 /** Passes a trained random forest model back to the python wrapper
   */
 object RFModelTrainer {
-  def trainModel(
-      featureSource: FeatureSource,
-      labelSource: LabelSource,
-      params: RandomForestParams,
-      nTrees: Int,
-      rfBatchSize: Int
-  ): RandomForestModel = {
+
+  /** Trains a random forest model with provided data and parameters
+    *
+    * @param featureSource: FeatureSource object containing training X
+    * @param labelSource: LabelSource object containing training y
+    * @param params: Random forest hyperparameters (passed to model on initialisation)
+    * @param nTrees: Number of trees to compute (passed to model during training)
+    * @param rfBatchSize: Number of trees per batch (passed to model during training)
+    *
+    * @return Trained random forest model
+    */
+  def trainModel(featureSource: FeatureSource, labelSource: LabelSource,
+      params: RandomForestParams, nTrees: Int, rfBatchSize: Int): RandomForestModel = {
     val labels = labelSource.getLabels(featureSource.sampleNames)
     lazy val inputData = featureSource.features.zipWithIndex.cache()
     val rf = new RandomForest(params)