--- a/main_testing2/danube.scala Sat Nov 28 15:58:36 2020 +0000
+++ b/main_testing2/danube.scala Mon Nov 30 00:06:15 2020 +0000
@@ -1,17 +1,19 @@
// Core Part about Movie Recommendations
// at Danube.co.uk
-//===========================================
+//========================================
+
+
+object CW7b { // for purposes of generating a jar
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
// in the file).
@@ -33,14 +35,19 @@
//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
+// 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_ratings(lines: List[String]) : List[(String, String)] = {
for (cols <- lines.map(_.split(",").toList);
- if (cols(2).toFloat >= 4)) yield (cols(0), cols(1))
+ if (cols(2).toInt >= 4)) yield (cols(0), cols(1))
}
def process_movies(lines: List[String]) : List[(String, String)] = {
@@ -77,18 +84,8 @@
}
}
-//
-//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
@@ -96,10 +93,9 @@
//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
+// 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).
@@ -124,23 +120,14 @@
// (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
- 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)
}
-// check
-// groupMap is equivalent to groupBy(key).mapValues(_.map(f))
-
// test cases
//suggestions(ratings_map, "912")
@@ -163,7 +150,7 @@
// recommendations(ratings_map, movies_map, "912")
// => List(Godfather, Star Wars: Episode IV - A NewHope (1977))
-//recommendations(ratings_map, movies_map, "260")
+// 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))
@@ -177,20 +164,53 @@
// => 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)
+// => 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 they were recommended.
+// Sort all the pairs according to the number
+// of times they were recommended (most recommended movie name
+// first).
+
+def occurrences(xs: List[String]): List[(String, Int)] =
+ for (x <- xs.distinct) yield (x, xs.count(_ == x))
+
+def most_recommended(recs: Map[String, List[String]],
+ movs: Map[String, String]) : List[(String, Int)] = {
+ val all = (for (name <- movs.toList.map(_._1)) yield {
+ recommendations(recs, movs, name)
+ }).flatten
+ val occs = occurrences(all)
+ occs.sortBy(_._2).reverse
+}
-// 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)
+//most_recommended(ratings_map, movies_map).take(3)
+// =>
+// List((Matrix,698),
+// (Star Wars: Episode IV - A New Hope (1977),402),
+// (Jerry Maguire (1996),382))
}
+
+//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 = get_csv_url(ratings_url)
+val movies = get_csv_url(movies_url)
+
+val good_ratings = process_ratings(ratings)
+val movie_names = process_movies(movies)
+
+val ratings_map = groupById(good_ratings, Map())
+val movies_map = movie_names.toMap
+
+
+println(most_recommended(ratings_map, movies_map).take(3))
+*/