def __init__(self):
self.url_base = 'https://kr.api.riotgames.com'
path_key = folder.get_data_path('lol_api_key.txt')
f = open(path_key, 'r')
self.api_key = f.readline()
f.close()
def save_data(self, data, file_name: str):
"""
크롤링한 데이터를 data 폴더에 저장하는 함수
:param data: 크롤링한 데이터를 담은 Dataframe
:param file_name: 저장할 csv 파일명
"""
path_data = folder.get_data_path(file_name)
data.to_csv(path_data)
def get_data(self):
"""
data를 가져옴
:return: loaded data
"""
path = folder.get_data_path('challenger_0521.csv')
data = pd.read_csv(path, index_col=0)
return data
def evaluate_model(self):
path = folder.get_data_path('grandmaster_0521.csv')
data = pd.read_csv(path, index_col=0)
data = data.iloc[:500]
# 20분 이상, mid 포지션만 남김
data = formatter.longer_playtime(data, '20:00')
data = data[data['position'] == self.position]
data = data.drop(['position'], axis=1)
LR = self.modeling
x, y = formatter.split_input_output(data)
x = LR.scale_input_data(x)
print("evaluate ", end='')
LR.get_score(x, y)
def get_data(self):
"""
data를 가져옴
:return: loaded data
"""
path = folder.get_data_path('challenger_0521.csv')
data = pd.read_csv(path, index_col=0)
data = formatter.longer_playtime(data, '20:00')
# get only one position
data = data[data['position'] == self.position]
data = data.drop(['position'], axis=1)
self.data = data
print('columns : ', data.columns)
# coding=utf-8
"""
-----------------------------------------------------------------------------------
메인 함수
# Description:
1.
2.
3.
4.
5.
-----------------------------------------------------------------------------------
"""
from MiniProject1.src.config import folder
# from MiniProject1.src.data_process import summoners
# from MiniProject1.src.data_process import crawling
# from MiniProject1.src.data_process import formatter
# from MiniProject1.src.model import modeling
import pandas as pd
from MiniProject1.src.config import folder
path = folder.get_data_path('challenger_0521.csv')
data = pd.read_csv(path, index_col=0)
data = formatter.longer_playtime(data, '20:00')
# get only one position
data = data[data['position'] == self.position]
data = data.drop(['position'], axis=1)
data = formatter.longer_playtime(data, '20:00')
data = data[data['position'] == self.position]
data = data.drop(['position'], axis=1)
LR = self.modeling
x, y = formatter.split_input_output(data)
x = LR.scale_input_data(x)
print("evaluate ", end='')
LR.get_score(x, y)
if __name__ == '__main__':
import sys
path_log = folder.get_log_path('test1_log.txt')
sys.stdout = open(path_log, 'w', encoding="UTF-8")
positions = ['top', 'jug', 'mid', 'adc', 'sup']
for position in positions:
print("============= Position :", position)
func = MainFunction(position)
func.get_data()
func.train_test_model()
func.evaluate_model()
path = folder.get_data_path(f'coef_{position}.csv')
func.coef.to_csv(path)
print()
sys.stdout.close()