diff -r 19b75e899d37 -r 9c03b5e89a2a main_templates2/danube.scala --- a/main_templates2/danube.scala Fri Apr 26 17:29:30 2024 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,161 +0,0 @@ -// Main Part 2 about Movie Recommendations -// at Danube.co.uk -//=========================================== - -object M2 { - -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) - - - -}