// Part 2 and 3 about Movie Recommendations 

// at Danube.co.uk

//===========================================

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

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

// testcases

//-----------

//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 from (1). 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)] = ...

//def process_movies(lines: List[String]) : List[(String, String)] = ...

// testcases

//-----------

//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 calculates 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 m

//     is set to Map() at the beginning of the calculation.

//def groupById(ratings: List[(String, String)], 

//              m: Map[String, List[String]]) : Map[String, List[String]] = ...

// testcases

//-----------

//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 in its recommendations. It ReTurns a list of all these

//     recommendations (each of them is a list and needs to have the movie deleted, 

//     otherwise it might happen we recommend the same movie).

//def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = ...

// testcases

//-----------

// 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] = ...

// testcases

//-----------

//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 a recommendations function 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] = ...

// 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)     

// If you want to calculate the recommendations for all movies,

// then use this code (it will take a few seconds calculation time).

//val all = for (name <- movie_names.map(_._1)) yield {

//  recommendations(ratings_map, movies_map, name)

//}

// helper functions

//List().take(2

//List(1).take(2)

//List(1,2).take(2)

//List(1,2,3).take(2)