--- a/testing2/danube.scala Tue Dec 03 11:07:09 2019 +0000
+++ b/testing2/danube.scala Mon Jan 27 10:18:13 2020 +0000
@@ -2,118 +2,114 @@
// at Danube.co.uk
//===========================================
-object CW7b {
-
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
+// 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] = {
- Try(Source.fromURL(url)("UTF-8").mkString.split("\n").toList.tail).getOrElse(List())
+ 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"""
-// testcases
-//-----------
-// val ratings = get_csv_url(ratings_url)
-// val movies = get_csv_url(movies_url)
+// 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 from (1). The ratings
+// (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.
+// of (movieId, title) pairs.
def process_ratings(lines: List[String]) : List[(String, String)] = {
- val filteredLines = lines.filter(line => line.split(",")(2).toInt >= 4)
- filteredLines.map(line => (line.split(",")(0), line.split(",")(1)))
+ 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)] = {
- lines.map(line => (line.split(",")(0), line.split(",")(1)))
+ for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1))
}
+// test cases
-// testcases
-//-----------
-// val good_ratings = process_ratings(ratings)
-// val movie_names = process_movies(movies)
+//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)
+ }
+}
+
+//
+//val ls = List(("1", "a"), ("2", "a"), ("1", "c"), ("2", "a"), ("1", "c"))
+//
+//val m = groupById(ls, Map())
+//
+//m.getOrElse("1", Nil).count(_ == "c") // => 2
+//m.getOrElse("1", Nil).count(_ == "a") // => 1
+
+// test cases
+//val ratings_map = groupById(good_ratings, Map())
+//groupById(good_ratings, Map()).get("214")
+//groupById(good_ratings, Map()).toList.minBy(_._2.length)
+//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
-// (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.
+//(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 groupById(ratings: List[(String, String)],
- m: Map[String, List[String]]) : Map[String, List[String]] = {
- if (ratings.length == 0) m
- else {
- val firstUser = ratings(0)._1
- val userRatings = ratings.filter(r => r._1 == firstUser)
- val movieIds = userRatings.map(r => r._2)
- val newMap = m + (firstUser -> movieIds)
- groupById(ratings.filter(r => r._1 != firstUser), newMap)
- }
-}
-
-
-// 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
+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))
-// (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]] = {
- val movieLists = m.map(r => r._2).toList.filter(_.contains(mov))
- for (movieList <- movieLists) yield {
- movieList.filter(_!=mov)
- }
-}
-
-
-// testcases
-//-----------
+// test cases
// movie ID "912" -> Casablanca (1942)
// "858" -> Godfather
// "260" -> Star Wars: Episode IV - A New Hope (1977)
@@ -125,53 +121,45 @@
// 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.
-// (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.
-
+// needed in Scala 2.13.
+
+def mapValues[S, T, R](m: Map[S, T], f: T => R) =
+ m.map { case (x, y) => (x, f(y)) }
def suggestions(recs: Map[String, List[String]],
- mov_name: String) : List[String] = {
- val favs = favourites(recs, mov_name).flatten
- favs.map(x => (x, favs.count(_==x)))
- .sortBy(_._1)
- .reverse
- .sortBy(_._2)
- .reverse
- .distinct
- .map(_._1)
+ 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_sorted = favs_counted.sortBy(_._2).reverse
+ favs_sorted.map(_._1)
}
+// check
+// groupMap is equivalent to groupBy(key).mapValues(_.map(f))
-// 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 sug = suggestions(recs, mov_name)
- val toptwo = sug.take(2)
- if (toptwo.length == 0) Nil
- else toptwo.map(movs(_))
-}
-
+ 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))
@@ -189,11 +177,11 @@
// => 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)
+// => Nil (there are three ratings fro 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).
+// 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)
@@ -205,32 +193,4 @@
//List(1,2).take(2)
//List(1,2,3).take(2)
-
-
}
-
-// 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 = CW7b.get_csv_url(ratings_url)
-// val movies = CW7b.get_csv_url(movies_url)
-
-// println(movies.length)
-// val good_ratings = CW7b.process_ratings(ratings)
-// val movie_names = CW7b.process_movies(movies)
-
-// val ratings_map = CW7b.groupById(good_ratings, Map())
-// val movies_map = movie_names.toMap
-
-
-
-//println(CW7b.recommendations(ratings_map, movies_map, "912"))
-/*
-val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv"""
-
-val ratings = CW7b.get_csv_url(ratings_url)
-
-val good_ratings = CW7b.process_ratings(ratings)
-val ratings_map = CW7b.groupById(good_ratings, Map())
-
-println(CW7b.suggestions(ratings_map, "912").length)*/