diff -r de59aa20a1dc -r ffce7b61b446 main_testing2/danube.scala --- a/main_testing2/danube.scala Mon Nov 08 01:16:13 2021 +0000 +++ b/main_testing2/danube.scala Mon Nov 08 01:39:00 2021 +0000 @@ -1,8 +1,9 @@ -// Core Part about Movie Recommendations +// Core Part about Movie Recommendations // at Danube.co.uk -//=========================================== +//======================================== -object CW7b { + +object M2 { // for purposes of generating a jar import io.Source import scala.util._ @@ -12,65 +13,44 @@ // 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 +// 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 +// 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 site = Source.fromURL(url, "ISO-8859-1") - val site_string = site.mkString - val output = (site_string.split("\n")).toList - output.tail + val csv = Source.fromURL(url)("ISO-8859-1") + csv.mkString.split("\n").toList.drop(1) } - // get_csv_url("https://nms.kcl.ac.uk/christian.urban/ratings.csv") - -//val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv""" -//val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv""" +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 -//----------- -//: +// test cases + +//val ratings = get_csv_url(ratings_url) //val movies = get_csv_url(movies_url) - // val ratings = get_csv_url(ratings_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. +// (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)] = { - val filter = lines.filter(_.last.asDigit >=4) - val output = (for(i <- 0 until filter.length) yield ((filter(i).split(",").toList)(0), (filter(i).split(",").toList)(1))).toList - output + 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)] = { - val output = (for(i <- 0 until lines.length) yield ((lines(i).split(",").toList)(0), (lines(i).split(",").toList)(1))).toList - output -} - - - -def process_ratings2(lines: List[String]) : List[(String, String)] = { - for (cols <- lines.map(_.split(",").toList); - if (cols(2).toFloat >= 4)) yield (cols(0), cols(1)) -} - -def process_movies2(lines: List[String]) : List[(String, String)] = { for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1)) } -// testcases -//----------- +// test cases + //val good_ratings = process_ratings(ratings) //val movie_names = process_movies(movies) @@ -78,116 +58,45 @@ //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. - -val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv""" -val ratings = get_csv_url(ratings_url) -val good_ratings = process_ratings(ratings) -val v515 = good_ratings.filter(_._1 == "515") -val v515_2 = v515.map(_._2) +// (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]] = { -val users = (for((k,v) <- ratings) yield k).distinct -val movie_ids = (for(i <- 1 to users.length) yield - (for ((k,v) <- ratings if(i.toString == k)) yield v).toList).toList - val out_map = (users zip movie_ids).toMap -out_map -} - -def groupById2(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)) - groupById2(rest, new_ratings) + groupById(rest, new_ratings) } } -val ls0_urban = - List(("1", "a"), ("1", "c"), ("1", "c")) - -groupById(ls0_urban, Map()) -groupById2(ls0_urban, Map()) - -val ls00_urban = - List(("3", "a"), ("3", "c"), ("3", "c")) - -groupById(ls00_urban, Map()) -groupById2(ls00_urban, Map()) - -groupById(good_ratings, Map()).getOrElse("515", Nil) -groupById2(good_ratings, Map()).getOrElse("515", Nil) - -val ls1_urban = - List(("1", "a"), ("2", "a"), - ("1", "c"), ("2", "a"), ("1", "c")) - -groupById(ls1_urban, Map()) -groupById2(ls1_urban, Map()) - -val ls2_urban = - List(("1", "a"), ("1", "b"), ("2", "x"), - ("3", "a"), ("2", "y"), ("3", "c")) - -groupById(ls2_urban, Map()) -groupById2(ls2_urban, Map()) - -val ls3_urban = (1 to 1000 by 10).map(_.toString).toList -val ls4_urban = ls3_urban zip ls3_urban.tail -val ls5_urban = ls4_urban ::: ls4_urban.reverse - -groupById(ls5_urban, Map()) == groupById2(ls5_urban, Map()) - -groupById(ls5_urban, Map()) -groupById2(ls5_urban, Map()) - -groupById(v515, Map()) -groupById2(v515, Map()) - -groupById(v515.take(1), Map()) -groupById2(v515.take(2), Map()) - -// testcases -//----------- +// test cases //val ratings_map = groupById(good_ratings, Map()) //val movies_map = movie_names.toMap -//ratings_map.get("414").get.map(movies_map.get(_)).length -// => most prolific recommender with 1227 positive ratings +//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 -//ratings_map.get("475").get.map(movies_map.get(_)).length -// => second-most prolific recommender with 787 positive ratings - -//ratings_map.get("214").get.map(movies_map.get(_)).length -// => 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). +//(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((k,v) <- m if (v.contains(mov))) yield v.filter(_!=mov).toList).toList -} - -def favourites2(m: Map[String, List[String]], mov: String) : List[List[String]] = +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)) -// testcases -//----------- + +// test cases // movie ID "912" -> Casablanca (1942) // "858" -> Godfather // "260" -> Star Wars: Episode IV - A New Hope (1977) @@ -199,57 +108,36 @@ // 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. - +// 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 flat = favourites(recs, mov_name).flatten.groupMapReduce(identity)(_ => 1)(_ + _) - val sorted = flat.toList.sortWith(_._2 > _._2).map(_._1) - sorted -} - - -def mapValues[S, T, R](m: Map[S, T], f: T => R) = - m.map { case (x, y) => (x, f(y)) } - -def suggestions2(recs: Map[String, List[String]], mov_name: String) : List[String] = { val favs = favourites(recs, mov_name).flatten - val favs_counted = mapValues(favs.groupBy(identity), (v:List[String]) => v.size).toList + val favs_counted = favs.groupBy(identity).view.mapValues(_.size).toList val favs_sorted = favs_counted.sortBy(_._2).reverse favs_sorted.map(_._1) } -// testcases -//----------- +// 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 a recommendations function which generates at most -// *two* of the most frequently suggested movies. It ReTurns the -// actual movie names, not the movieIDs. - +// (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] = { - val sugg = suggestions(recs, mov_name) - val movies = (for (i <- 0 until 2 if (i < sugg.length)) yield movs(sugg(i))).toList - movies -} - + 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)) @@ -270,33 +158,4 @@ // => 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 the movie was recommended. -// Sort all the pairs according to the number -// of times they were recommended (most recommended movie name -// first). - -def most_recommended(recs: Map[String, List[String]], - movs: Map[String, String]) : List[(String, Int)] = { - val movies = (((for((k,v) <- movs) yield recommendations(recs, movs, k)).toList).flatten).groupMapReduce(identity)(_ => 1)(_ + _) - val sorted = movies.toList.sortWith(_._2 > _._2) - sorted } - -// testcase -// -//most_recommended(ratings_map, movies_map).take(3) -// => -// List((Matrix,698), -// (Star Wars: Episode IV - A New Hope (1977),402), -// (Jerry Maguire (1996),382)) - - - -}