--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/solutions2/danube.scala Sat Nov 17 15:35:50 2018 +0000
@@ -0,0 +1,177 @@
+// Part 2 and 3 about Movie Recommendations
+// at Danube.co.uk
+//===========================================
+
+import io.Source
+import scala.util._
+
+object CW7b { // for purposes of generating a jar
+
+// (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).toFloat >= 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).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 fro this movie in ratings.csv but they are not positive)
+
+
+// If you want to calculate the recomendations for all movies.
+// 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)
+
+}