Added docs

core/src/main/scala/com/salesforce/op/OpWorkflow.scala

@@ -503,8 +503,9 @@ class OpWorkflow(val uid: String = UID[OpWorkflow]) extends OpWorkflowCore {
  * @param protectedFeatures list of features that should never be removed (features that are used to create them will
  * also be protected)
  * @param textBinsFormula formula to compute the text features bin size.
- * Input arguments are [[Summary]] and number of bins to use in computing feature distributions
- * (histograms for numerics, hashes for strings). Output is the bins for the text features.
+ * Input arguments are [[Summary]] and number of bins to use in computing
+ * feature distributions (histograms for numerics, hashes for strings).
+ * Output is the bins for the text features.
  * @tparam T Type of the data read in
  */
  @Experimental

core/src/main/scala/com/salesforce/op/filters/FeatureDistribution.scala

@@ -154,7 +154,10 @@ private[op] object FeatureDistribution {
  * @param summary feature summary
  * @param value optional processed sequence
  * @param bins number of histogram bins
- * @param textBinsFormula formula to compute the text features bin size
+ * @param textBinsFormula formula to compute the text features bin size.
+ * Input arguments are [[Summary]] and number of bins to use in computing feature distributions
+ * (histograms for numerics, hashes for strings). Output is the bins for the text features.
+ * @return a pair consisting of response and predictor feature distributions (in this order)
  * @return feature distribution given the provided information
  */
  def apply(
@@ -183,7 +186,10 @@ private[op] object FeatureDistribution {
  * @param values values to bin
  * @param summary summary info for feature (max, min, etc)
  * @param bins number of bins to produce
- * @param textBinsFormula formula to compute the text features bin size
+ * @param textBinsFormula formula to compute the text features bin size.
+ * Input arguments are [[Summary]] and number of bins to use in computing feature distributions
+ * (histograms for numerics, hashes for strings). Output is the bins for the text features.
+ * @return a pair consisting of response and predictor feature distributions (in this order)
  * @return the bin information and the binned counts
  */
  private def histValues(

core/src/main/scala/com/salesforce/op/filters/RawFeatureFilter.scala

@@ -75,8 +75,9 @@ import scala.util.Failure
  * @param jsDivergenceProtectedFeatures features that are protected from removal by JS divergence check
  * @param protectedFeatures features that are protected from removal
  * @param textBinsFormula formula to compute the text features bin size.
- * Input arguments are [[Summary]] and number of bins to use in computing feature distributions
- * (histograms for numerics, hashes for strings). Output is the bins for the text features.
+ * Input arguments are [[Summary]] and number of bins to use in computing feature
+ * distributions (histograms for numerics, hashes for strings).
+ * Output is the bins for the text features.
  * @tparam T datatype of the reader
  */
 class RawFeatureFilter[T]