1 // Core Part about Movie Recommendations |
1 // Core Part about Movie Recommendations |
2 // at Danube.co.uk |
2 // at Danube.co.uk |
3 //=========================================== |
3 //======================================== |
4 |
4 |
5 object CW7b { |
5 |
|
6 object M2 { // for purposes of generating a jar |
6 |
7 |
7 import io.Source |
8 import io.Source |
8 import scala.util._ |
9 import scala.util._ |
9 |
10 |
10 |
11 |
11 // (1) Implement the function get_csv_url which takes an url-string |
12 // (1) Implement the function get_csv_url which takes an url-string |
12 // as argument and requests the corresponding file. The two urls |
13 // as argument and requests the corresponding file. The two urls |
13 // of interest are ratings_url and movies_url, which correspond |
14 // of interest are ratings_url and movies_url, which correspond |
14 // to CSV-files. |
15 // to CSV-files. |
15 // |
16 // The function should return the CSV file appropriately broken |
16 // The function should ReTurn the CSV-file appropriately broken |
|
17 // up into lines, and the first line should be dropped (that is without |
17 // up into lines, and the first line should be dropped (that is without |
18 // the header of the CSV-file). The result is a list of strings (lines |
18 // the header of the CSV file). The result is a list of strings (lines |
19 // in the file). |
19 // in the file). |
20 |
20 |
21 def get_csv_url(url: String) : List[String] = { |
21 def get_csv_url(url: String) : List[String] = { |
22 val site = Source.fromURL(url, "ISO-8859-1") |
22 val csv = Source.fromURL(url)("ISO-8859-1") |
23 val site_string = site.mkString |
23 csv.mkString.split("\n").toList.drop(1) |
24 val output = (site_string.split("\n")).toList |
|
25 output.tail |
|
26 } |
24 } |
27 |
25 |
28 // get_csv_url("https://nms.kcl.ac.uk/christian.urban/ratings.csv") |
26 val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv""" |
|
27 val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv""" |
29 |
28 |
30 //val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv""" |
29 // test cases |
31 //val movies_url = """https://nms.kcl.ac.uk/christian.urban/movies.csv""" |
|
32 |
30 |
33 // testcases |
31 //val ratings = get_csv_url(ratings_url) |
34 //----------- |
|
35 //: |
|
36 //val movies = get_csv_url(movies_url) |
32 //val movies = get_csv_url(movies_url) |
37 // val ratings = get_csv_url(ratings_url) |
|
38 |
33 |
39 //ratings.length // 87313 |
34 //ratings.length // 87313 |
40 //movies.length // 9742 |
35 //movies.length // 9742 |
41 |
36 |
42 |
37 // (2) Implement two functions that process the CSV files. The ratings |
43 // (2) Implement two functions that process the CSV-files from (1). The ratings |
38 // function filters out all ratings below 4 and returns a list of |
44 // function filters out all ratings below 4 and ReTurns a list of |
39 // (userID, movieID) pairs. The movies function just returns a list |
45 // (userID, movieID) pairs. The movies function just ReTurns a list |
40 // of (movieId, title) pairs. |
46 // of (movieID, title) pairs. Note the input to these functions, that is |
|
47 // the argument lines, will be the output of the function get_csv_url. |
|
48 |
41 |
49 |
42 |
50 def process_ratings(lines: List[String]) : List[(String, String)] = { |
43 def process_ratings(lines: List[String]) : List[(String, String)] = { |
51 val filter = lines.filter(_.last.asDigit >=4) |
44 for (cols <- lines.map(_.split(",").toList); |
52 val output = (for(i <- 0 until filter.length) yield ((filter(i).split(",").toList)(0), (filter(i).split(",").toList)(1))).toList |
45 if (cols(2).toInt >= 4)) yield (cols(0), cols(1)) |
53 output |
|
54 } |
46 } |
55 |
47 |
56 def process_movies(lines: List[String]) : List[(String, String)] = { |
48 def process_movies(lines: List[String]) : List[(String, String)] = { |
57 val output = (for(i <- 0 until lines.length) yield ((lines(i).split(",").toList)(0), (lines(i).split(",").toList)(1))).toList |
|
58 output |
|
59 } |
|
60 |
|
61 |
|
62 |
|
63 def process_ratings2(lines: List[String]) : List[(String, String)] = { |
|
64 for (cols <- lines.map(_.split(",").toList); |
|
65 if (cols(2).toFloat >= 4)) yield (cols(0), cols(1)) |
|
66 } |
|
67 |
|
68 def process_movies2(lines: List[String]) : List[(String, String)] = { |
|
69 for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1)) |
49 for (cols <- lines.map(_.split(",").toList)) yield (cols(0), cols(1)) |
70 } |
50 } |
71 |
51 |
72 // testcases |
52 // test cases |
73 //----------- |
53 |
74 //val good_ratings = process_ratings(ratings) |
54 //val good_ratings = process_ratings(ratings) |
75 //val movie_names = process_movies(movies) |
55 //val movie_names = process_movies(movies) |
76 |
56 |
77 //good_ratings.length //48580 |
57 //good_ratings.length //48580 |
78 //movie_names.length // 9742 |
58 //movie_names.length // 9742 |
79 |
59 |
80 |
60 |
81 |
61 // (3) Implement a grouping function that calulates a map |
82 |
62 // containing the userIds and all the corresponding recommendations |
83 // (3) Implement a grouping function that calculates a Map |
63 // (list of movieIds). This should be implemented in a tail |
84 // containing the userIDs and all the corresponding recommendations |
64 // recursive fashion, using a map m as accumulator. This map |
85 // (list of movieIDs). This should be implemented in a tail |
65 // is set to Map() at the beginning of the claculation. |
86 // recursive fashion, using a Map m as accumulator. This Map m |
|
87 // is set to Map() at the beginning of the calculation. |
|
88 |
|
89 val ratings_url = """https://nms.kcl.ac.uk/christian.urban/ratings.csv""" |
|
90 val ratings = get_csv_url(ratings_url) |
|
91 val good_ratings = process_ratings(ratings) |
|
92 val v515 = good_ratings.filter(_._1 == "515") |
|
93 val v515_2 = v515.map(_._2) |
|
94 |
66 |
95 def groupById(ratings: List[(String, String)], |
67 def groupById(ratings: List[(String, String)], |
96 m: Map[String, List[String]]) : Map[String, List[String]] = { |
|
97 val users = (for((k,v) <- ratings) yield k).distinct |
|
98 val movie_ids = (for(i <- 1 to users.length) yield |
|
99 (for ((k,v) <- ratings if(i.toString == k)) yield v).toList).toList |
|
100 val out_map = (users zip movie_ids).toMap |
|
101 out_map |
|
102 } |
|
103 |
|
104 def groupById2(ratings: List[(String, String)], |
|
105 m: Map[String, List[String]]) : Map[String, List[String]] = ratings match { |
68 m: Map[String, List[String]]) : Map[String, List[String]] = ratings match { |
106 case Nil => m |
69 case Nil => m |
107 case (id, mov) :: rest => { |
70 case (id, mov) :: rest => { |
108 val old_ratings = m.getOrElse (id, Nil) |
71 val old_ratings = m.getOrElse (id, Nil) |
109 val new_ratings = m + (id -> (mov :: old_ratings)) |
72 val new_ratings = m + (id -> (mov :: old_ratings)) |
110 groupById2(rest, new_ratings) |
73 groupById(rest, new_ratings) |
111 } |
74 } |
112 } |
75 } |
113 |
76 |
114 val ls0_urban = |
77 // test cases |
115 List(("1", "a"), ("1", "c"), ("1", "c")) |
|
116 |
|
117 groupById(ls0_urban, Map()) |
|
118 groupById2(ls0_urban, Map()) |
|
119 |
|
120 val ls00_urban = |
|
121 List(("3", "a"), ("3", "c"), ("3", "c")) |
|
122 |
|
123 groupById(ls00_urban, Map()) |
|
124 groupById2(ls00_urban, Map()) |
|
125 |
|
126 groupById(good_ratings, Map()).getOrElse("515", Nil) |
|
127 groupById2(good_ratings, Map()).getOrElse("515", Nil) |
|
128 |
|
129 val ls1_urban = |
|
130 List(("1", "a"), ("2", "a"), |
|
131 ("1", "c"), ("2", "a"), ("1", "c")) |
|
132 |
|
133 groupById(ls1_urban, Map()) |
|
134 groupById2(ls1_urban, Map()) |
|
135 |
|
136 val ls2_urban = |
|
137 List(("1", "a"), ("1", "b"), ("2", "x"), |
|
138 ("3", "a"), ("2", "y"), ("3", "c")) |
|
139 |
|
140 groupById(ls2_urban, Map()) |
|
141 groupById2(ls2_urban, Map()) |
|
142 |
|
143 val ls3_urban = (1 to 1000 by 10).map(_.toString).toList |
|
144 val ls4_urban = ls3_urban zip ls3_urban.tail |
|
145 val ls5_urban = ls4_urban ::: ls4_urban.reverse |
|
146 |
|
147 groupById(ls5_urban, Map()) == groupById2(ls5_urban, Map()) |
|
148 |
|
149 groupById(ls5_urban, Map()) |
|
150 groupById2(ls5_urban, Map()) |
|
151 |
|
152 groupById(v515, Map()) |
|
153 groupById2(v515, Map()) |
|
154 |
|
155 groupById(v515.take(1), Map()) |
|
156 groupById2(v515.take(2), Map()) |
|
157 |
|
158 // testcases |
|
159 //----------- |
|
160 //val ratings_map = groupById(good_ratings, Map()) |
78 //val ratings_map = groupById(good_ratings, Map()) |
161 //val movies_map = movie_names.toMap |
79 //val movies_map = movie_names.toMap |
162 |
80 |
163 //ratings_map.get("414").get.map(movies_map.get(_)).length |
81 //ratings_map.get("414").get.map(movies_map.get(_)) // most prolific recommender with 1227 positive ratings |
164 // => most prolific recommender with 1227 positive ratings |
82 //ratings_map.get("474").get.map(movies_map.get(_)) // second-most prolific recommender with 787 positive ratings |
165 |
83 //ratings_map.get("214").get.map(movies_map.get(_)) // least prolific recommender with only 1 positive rating |
166 //ratings_map.get("475").get.map(movies_map.get(_)).length |
|
167 // => second-most prolific recommender with 787 positive ratings |
|
168 |
|
169 //ratings_map.get("214").get.map(movies_map.get(_)).length |
|
170 // => least prolific recommender with only 1 positive rating |
|
171 |
84 |
172 |
85 |
173 // (4) Implement a function that takes a ratings map and a movie_name as argument. |
|
174 // The function calculates all suggestions containing |
|
175 // the movie in its recommendations. It ReTurns a list of all these |
|
176 // recommendations (each of them is a list and needs to have the movie deleted, |
|
177 // otherwise it might happen we recommend the same movie). |
|
178 |
86 |
|
87 //(4) Implement a function that takes a ratings map and a movie_name as argument. |
|
88 // The function calculates all suggestions containing |
|
89 // the movie mov in its recommendations. It returns a list of all these |
|
90 // recommendations (each of them is a list and needs to have mov deleted, |
|
91 // otherwise it might happen we recommend the same movie). |
179 |
92 |
180 def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = { |
93 def favourites(m: Map[String, List[String]], mov: String) : List[List[String]] = |
181 (for((k,v) <- m if (v.contains(mov))) yield v.filter(_!=mov).toList).toList |
|
182 } |
|
183 |
|
184 def favourites2(m: Map[String, List[String]], mov: String) : List[List[String]] = |
|
185 (for (id <- m.keys.toList; |
94 (for (id <- m.keys.toList; |
186 if m(id).contains(mov)) yield m(id).filter(_ != mov)) |
95 if m(id).contains(mov)) yield m(id).filter(_ != mov)) |
187 |
96 |
188 |
97 |
189 // testcases |
98 |
190 //----------- |
99 // test cases |
191 // movie ID "912" -> Casablanca (1942) |
100 // movie ID "912" -> Casablanca (1942) |
192 // "858" -> Godfather |
101 // "858" -> Godfather |
193 // "260" -> Star Wars: Episode IV - A New Hope (1977) |
102 // "260" -> Star Wars: Episode IV - A New Hope (1977) |
194 |
103 |
195 //favourites(ratings_map, "912").length // => 80 |
104 //favourites(ratings_map, "912").length // => 80 |
197 // That means there are 80 users that recommend the movie with ID 912. |
106 // That means there are 80 users that recommend the movie with ID 912. |
198 // Of these 80 users, 55 gave a good rating to movie 858 and |
107 // Of these 80 users, 55 gave a good rating to movie 858 and |
199 // 52 a good rating to movies 260, 318, 593. |
108 // 52 a good rating to movies 260, 318, 593. |
200 |
109 |
201 |
110 |
202 |
|
203 // (5) Implement a suggestions function which takes a rating |
111 // (5) Implement a suggestions function which takes a rating |
204 // map and a movie_name as arguments. It calculates all the recommended |
112 // map and a movie_name as arguments. It calculates all the recommended |
205 // movies sorted according to the most frequently suggested movie(s) first. |
113 // movies sorted according to the most frequently suggested movie(s) first. |
206 |
|
207 def suggestions(recs: Map[String, List[String]], |
114 def suggestions(recs: Map[String, List[String]], |
208 mov_name: String) : List[String] = { |
|
209 val flat = favourites(recs, mov_name).flatten.groupMapReduce(identity)(_ => 1)(_ + _) |
|
210 val sorted = flat.toList.sortWith(_._2 > _._2).map(_._1) |
|
211 sorted |
|
212 } |
|
213 |
|
214 |
|
215 def mapValues[S, T, R](m: Map[S, T], f: T => R) = |
|
216 m.map { case (x, y) => (x, f(y)) } |
|
217 |
|
218 def suggestions2(recs: Map[String, List[String]], |
|
219 mov_name: String) : List[String] = { |
115 mov_name: String) : List[String] = { |
220 val favs = favourites(recs, mov_name).flatten |
116 val favs = favourites(recs, mov_name).flatten |
221 val favs_counted = mapValues(favs.groupBy(identity), (v:List[String]) => v.size).toList |
117 val favs_counted = favs.groupBy(identity).view.mapValues(_.size).toList |
222 val favs_sorted = favs_counted.sortBy(_._2).reverse |
118 val favs_sorted = favs_counted.sortBy(_._2).reverse |
223 favs_sorted.map(_._1) |
119 favs_sorted.map(_._1) |
224 } |
120 } |
225 |
121 |
226 // testcases |
122 // test cases |
227 //----------- |
|
228 |
123 |
229 //suggestions(ratings_map, "912") |
124 //suggestions(ratings_map, "912") |
230 //suggestions(ratings_map, "912").length |
125 //suggestions(ratings_map, "912").length |
231 // => 4110 suggestions with List(858, 260, 318, 593, ...) |
126 // => 4110 suggestions with List(858, 260, 318, 593, ...) |
232 // being the most frequently suggested movies |
127 // being the most frequently suggested movies |
233 |
128 |
234 |
129 // (6) Implement recommendations functions which generates at most |
235 |
130 // *two* of the most frequently suggested movies. It Returns the |
236 // (6) Implement a recommendations function which generates at most |
131 // actual movie names, not the movieIDs. |
237 // *two* of the most frequently suggested movies. It ReTurns the |
|
238 // actual movie names, not the movieIDs. |
|
239 |
|
240 |
132 |
241 def recommendations(recs: Map[String, List[String]], |
133 def recommendations(recs: Map[String, List[String]], |
242 movs: Map[String, String], |
134 movs: Map[String, String], |
243 mov_name: String) : List[String] = { |
135 mov_name: String) : List[String] = |
244 val sugg = suggestions(recs, mov_name) |
136 suggestions(recs, mov_name).take(2).map(movs.get(_).get) |
245 val movies = (for (i <- 0 until 2 if (i < sugg.length)) yield movs(sugg(i))).toList |
|
246 movies |
|
247 } |
|
248 |
|
249 |
137 |
250 |
138 |
251 // testcases |
139 // testcases |
252 //----------- |
140 |
253 // recommendations(ratings_map, movies_map, "912") |
141 // recommendations(ratings_map, movies_map, "912") |
254 // => List(Godfather, Star Wars: Episode IV - A NewHope (1977)) |
142 // => List(Godfather, Star Wars: Episode IV - A NewHope (1977)) |
255 |
143 |
256 //recommendations(ratings_map, movies_map, "260") |
144 //recommendations(ratings_map, movies_map, "260") |
257 // => List(Star Wars: Episode V - The Empire Strikes Back (1980), |
145 // => List(Star Wars: Episode V - The Empire Strikes Back (1980), |