modellerAndRecommender.scala

/////////////////////////////////
// Generic 
trait Model[T1,T2] {
  def train(t: List[(T1, T2)]): Unit // or Boolean?
  def predict(t: T1): Set[(T2, Confidence)]
}

trait LinearModel[T1,T2] extends Model[T1,T2] {
  private val m: MLLibLinearModel // -ish
}

trait Recommender[T1, T2, R] {
  def m: Model[T1,T2]
  def getRecommendations(t: T1): Set[(R, Confidence)]
}

/////////////////////////////////
// Specific to ProgramModel 
type Contacts = Double
type Budget = Double

case class ProgramModel(m: MLLibLinearModel) extends LinearModel[(Date, Budget), Contacts] {
  def train(t: List[((Date, Budget), Contacts)]): Unit = {
    m.train { ... } // train a linear model with t
  }
  def predict(t: (Date, Budget)): Set[(Contacts, Confidence)] = {
    // run m to generate Contacts out of the date and the budget 
  }
}

trait ProgramRecommendation
case class AugmentBudget(howMuch: Money) extends ProgramRecommendation
case class LowerBudget(howMuch: Money) extends ProgramRecommendation
case class ProgramRecommender(m: Model[((Date, Budget), Contacts)]) extends Recommender[(Date, Budget), Contacts, ProgramRecommendation] {
  def getRecommendations(t: (Date, Budget)): Set[(ProgramRecommendation, Confidence)] = {
    // run <m>, determine Recommendations
  }
}