kdudzic/fuzzy-game-recommender
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add random

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s444417 2023-02-02 01:12:00 +01:00
parent 9ce43cafad
commit 8f3a3f7bb1
3 changed files with 536 additions and 21 deletions
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540
Fuzzy_presentation.ipynb
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4
README.md
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@ -10,6 +10,10 @@
python main.py --pres python main.py --pres
it will generate .json file which can be presented by running all cells of `Fuzzy_presentation.ipynb` 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: Processed dataset files are already provided, but can be created from the base ``games.csv`` file by running:
python process_dataset.py python process_dataset.py

13
main.py
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@ -4,11 +4,11 @@ from numpy import dot
from numpy.linalg import norm from numpy.linalg import norm
import json import json
import multiprocessing import multiprocessing
import tqdm from tqdm.auto import tqdm
from sys import argv from sys import argv
import sys, getopt import sys, getopt
import argparse import argparse
import random
def find_games_categorical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float: def find_games_categorical_similarity(game_1: pd.DataFrame, game_2: pd.DataFrame) -> float:
game_1_categorical = set(game_1['all_categorical'].tolist()[0]) 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 = [] similarities = []
# call the function for each item in parallel with multiprocessing # call the function for each item in parallel with multiprocessing
with multiprocessing.Pool() as pool: 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) similarities.append(result)
all_games = [] all_games = []
@ -99,12 +99,17 @@ def main(argv):
title_list = df["name"].values.tolist() title_list = df["name"].values.tolist()
test_mode = False test_mode = False
opts, args = getopt.getopt(argv, "", ["pres"]) random_mode = False
opts, args = getopt.getopt(argv, "r:", ["pres"])
for opt, arg in opts: for opt, arg in opts:
if "--pres" == opt: if "--pres" == opt:
test_mode = True test_mode = True
if "-r" == opt:
random_mode = arg
if (True == test_mode): if (True == test_mode):
game_list = ["Call of Duty®: Modern Warfare® 2", "Project CARS", "DayZ", "STAR WARS™ Jedi Knight - Mysteries of the Sith™", "Overcooked"] 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": []} result_dict = {"results": []}
for item in game_list: for item in game_list:
titles_results = calculate_similarities(game_title=item, title_list=title_list, df=df, test=test_mode) titles_results = calculate_similarities(game_title=item, title_list=title_list, df=df, test=test_mode)