IN SCALA PLEASE
COULD YOU COMPLETE THE CODE OF FUNCTIONS: get_csv_url, process_ratings, process_movies, groupById, favourites, suggestions and recommendations
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 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. Note the input to these functions, that is
// the argument lines, will be the output of the function get_csv_url.
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
// (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)