README.md

@@ -14,6 +14,11 @@ it will generate .json file which can be presented by running all cells of `Fuzz
 
     python main.py --pres -r True
 
+#### Evaluation mode
+
+    python main.py --pres --eval 
+generates result.json file with 10 random games and 10 recomendations for each game, results can be evaluated in `Fuzzy_presentation.ipynb` file, with Jaccard Similiarity
+
 Processed dataset files are already provided, but can be created from the base ``games.csv`` file by running:
 
     python process_dataset.py

main.py

@@ -100,19 +100,29 @@ def main(argv):
 
     test_mode = False
     random_mode = False
+    eval_mode = False
+    eval_random_mode = False
 
-    opts, args = getopt.getopt(argv, "r:", ["pres"])
+    opts, args = getopt.getopt(argv, "r:", ["pres", "eval", "evalrandom"])
     for opt, arg in opts:
         if "--pres" == opt: 
             test_mode = True
+        if "--eval" == opt: 
+            eval_mode = True
+        if "--evalrandom" == opt: 
+            eval_random_mode = True
         if "-r" == opt: 
             random_mode = arg
     if (True == test_mode):
         game_list = ["Call of Duty®: Modern Warfare® 2", "Project CARS", "DayZ", "STAR WARS™ Jedi Knight - Mysteries of the Sith™", "Overcooked"]
         if (random_mode): game_list = [random.choice(title_list)]
+        if (eval_mode or eval_random_mode): game_list = [random.choice(title_list) for i in range(10)]
         result_dict = {"results": []}
         for item in game_list:
+            if not eval_random_mode:
                 titles_results = calculate_similarities(game_title=item, title_list=title_list, df=df, test=test_mode)
+            if eval_random_mode:
+                titles_results = [{"title": random.choice(title_list)} for i in range(10)]
             game_result = get_game_info_from_df(df, item)
             game_result["fuzzy_similiar"] = [get_game_info_from_df(df, title_item["title"]) for title_item in titles_results[:10]]
             result_dict["results"].append(game_result)