add random

README.md

@@ -10,6 +10,10 @@
     python main.py --pres
 it will generate .json file which can be presented by running all cells of `Fuzzy_presentation.ipynb`
 
+#### Random mode 
+
+    python main.py --pres -r True
+
 Processed dataset files are already provided, but can be created from the base ``games.csv`` file by running:
 
     python process_dataset.py

main.py

@@ -4,11 +4,11 @@ from numpy import dot
 from numpy.linalg import norm
 import json
 import multiprocessing
-import tqdm
+from tqdm.auto import tqdm
 from sys import argv
 import sys, getopt
 import argparse
-
+import random
 
 def find_games_categorical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
     game_1_categorical = set(game_1['all_categorical'].tolist()[0])
@@ -39,7 +39,7 @@ def calculate_similarities(game_title, title_list, df, test=False):
     similarities = []
     # call the function for each item in parallel with multiprocessing
     with multiprocessing.Pool() as pool:
-        for result in pool.starmap(compare_games, tqdm.tqdm(args_list, total=len(args_list), desc='Searching')):
+        for result in pool.starmap(compare_games, tqdm(args_list, total=len(args_list), desc='Searching')):
             similarities.append(result)
 
     all_games = []
@@ -99,12 +99,17 @@ def main(argv):
     title_list = df["name"].values.tolist()
 
     test_mode = False
-    opts, args = getopt.getopt(argv, "", ["pres"])
+    random_mode = False
+
+    opts, args = getopt.getopt(argv, "r:", ["pres"])
     for opt, arg in opts:
         if "--pres" == opt: 
             test_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)]
         result_dict = {"results": []}
         for item in game_list:
             titles_results = calculate_similarities(game_title=item, title_list=title_list, df=df, test=test_mode)