// Core Part about Movie Recommendations 
// at Danube.co.uk
//========================================


object CW7b { // for purposes of generating a jar

import io.Source
import scala.util._


// (1) Implement the function get_csv_url which takes an url-string
//     as argument and requests the corresponding file. The two urls
//     of interest are ratings_url and movies_url, which correspond 
//     to CSV-files.
//     The function should return the CSV file appropriately broken
//     up into lines, and the first line should be dropped (that is without
//     the header of the CSV file). The result is a list of strings (lines
//     in the file).

def get_csv_url(url: String) : List[String] = {
  val csv = Source.fromURL(url)("ISO-8859-1")
  csv.mkString.split("\n").toList.drop(1)
}

val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""
val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv"""

// test cases

//val ratings = get_csv_url(ratings_url)
//val movies = get_csv_url(movies_url)

//ratings.length  // 87313
//movies.length   // 9742

// (2) Implement two functions that process the CSV files. The ratings
//     function filters out all ratings below 4 and returns a list of 
//     (userID, movieID) pairs. The movies function just returns a list 
//     of (movieId, title) pairs.


def process_ratings(lines: List[String]) : List[(String, String)] = {
  for (cols <- lines.map(_.split(",").toList); 
       if (cols(2).toInt >= 4)) yield (cols(0), cols(1))  
}

def process_movies(lines: List[String]) : List[(String, String)] = {
  for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1))  
}

// test cases

//val good_ratings = process_ratings(ratings)
//val movie_names = process_movies(movies)

//good_ratings.length   //48580
//movie_names.length    // 9742

//==============================================
// Do not change anything below, unless you want 
// to submit the file for the advanced part 3!
//==============================================


// (3) Implement a grouping function that calulates a map
//     containing the userIds and all the corresponding recommendations 
//     (list of movieIds). This  should be implemented in a tail
//     recursive fashion, using a map m as accumulator. This map
//     is set to Map() at the beginning of the claculation.

def groupById(ratings: List[(String, String)], 
              m: Map[String, List[String]]) : Map[String, List[String]] = ratings match {
  case Nil => m
  case (id, mov) :: rest => {
    val old_ratings = m.getOrElse (id, Nil)
    val new_ratings = m + (id -> (mov :: old_ratings))
    groupById(rest, new_ratings)
  }
}

// test cases
//val ratings_map = groupById(good_ratings, Map())
//val movies_map = movie_names.toMap

//ratings_map.get("414").get.map(movies_map.get(_)) // most prolific recommender with 1227 positive ratings
//ratings_map.get("474").get.map(movies_map.get(_)) // second-most prolific recommender with 787 positive ratings
//ratings_map.get("214").get.map(movies_map.get(_)) // least prolific recommender with only 1 positive rating



//(4) Implement a function that takes a ratings map and a movie_name as argument.
// The function calculates all suggestions containing
// the movie mov in its recommendations. It returns a list of all these
// recommendations (each of them is a list and needs to have mov deleted, 
// otherwise it might happen we recommend the same movie).

def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = 
  (for (id <- m.keys.toList;
        if m(id).contains(mov)) yield m(id).filter(_ != mov))



// test cases
// movie ID "912" -> Casablanca (1942)
//          "858" -> Godfather
//          "260" -> Star Wars: Episode IV - A New Hope (1977)

//favourites(ratings_map, "912").length  // => 80

// That means there are 80 users that recommend the movie with ID 912.
// Of these 80  users, 55 gave a good rating to movie 858 and
// 52 a good rating to movies 260, 318, 593.


// (5) Implement a suggestions function which takes a rating
// map and a movie_name as arguments. It calculates all the recommended
// movies sorted according to the most frequently suggested movie(s) first.
def suggestions(recs: Map[String, List[String]], 
                    mov_name: String) : List[String] = {
  val favs = favourites(recs, mov_name).flatten
  val favs_counted = favs.groupBy(identity).view.mapValues(_.size).toList
  val favs_sorted = favs_counted.sortBy(_._2).reverse
  favs_sorted.map(_._1)
}

// test cases

//suggestions(ratings_map, "912")
//suggestions(ratings_map, "912").length  
// => 4110 suggestions with List(858, 260, 318, 593, ...)
//    being the most frequently suggested movies

// (6) Implement recommendations functions which generates at most
// *two* of the most frequently suggested movies. It Returns the 
// actual movie names, not the movieIDs.

def recommendations(recs: Map[String, List[String]],
                   movs: Map[String, String],
                   mov_name: String) : List[String] =
  suggestions(recs, mov_name).take(2).map(movs.get(_).get)                 


// testcases

// recommendations(ratings_map, movies_map, "912")
//   => List(Godfather, Star Wars: Episode IV - A NewHope (1977))

//recommendations(ratings_map, movies_map, "260")
//   => List(Star Wars: Episode V - The Empire Strikes Back (1980), 
//           Star Wars: Episode VI - Return of the Jedi (1983))

// recommendations(ratings_map, movies_map, "2")
//   => List(Lion King, Jurassic Park (1993))

// recommendations(ratings_map, movies_map, "0")
//   => Nil

// recommendations(ratings_map, movies_map, "1")
//   => List(Shawshank Redemption, Forrest Gump (1994))

// recommendations(ratings_map, movies_map, "4")
//   => Nil  (there are three ratings for this movie in ratings.csv but they are not positive)     

// (7) Calculate the recommendations for all movies according to
// what the recommendations function in (6) produces (this
// can take a few seconds). Put all recommendations into a list 
// (of strings) and count how often the strings occur in
// this list. This produces a list of string-int pairs,
// where the first component is the movie name and the second
// is the number of how many times they were recommended. 
// Sort all the pairs according to the number
// of times they were recommended (most recommended movie name 
// first).

def occurrences(xs: List[String]): List[(String, Int)] =
  for (x <- xs.distinct) yield (x, xs.count(_ == x))

def most_recommended(recs: Map[String, List[String]],
                     movs: Map[String, String]) : List[(String, Int)] = {
   val all =  (for (name <- movs.toList.map(_._1)) yield {
     recommendations(recs, movs, name)                     
   }).flatten
   val occs = occurrences(all)
   occs.sortBy(_._2).reverse
}


//most_recommended(ratings_map, movies_map).take(3)
// =>
// List((Matrix,698), 
//      (Star Wars: Episode IV - A New Hope (1977),402), 
//      (Jerry Maguire (1996),382))


}