def __init__(self, path=f'.\\DB\\CSV\\'):
# 필요 변수 선언
self.name = 'DataPreProcessing'
self.path = path
self.logging = log_recorder.Log().dir_recorder('RL', 'Dataprepro')
# DataPreProcessing 클래스 객체 생성시 필요한 폴더 생성
self.mkdir = self.make_dir(path)
def __init__(self):
self.logging = log_recorder.Log().dir_recorder('RL', 'test')
self.prev = None
self.pre = DataPreProcessing.DataPreProcessing()
def __init__(self):
self.logging = log_recorder.Log().dir_recorder('RL', 'visualize')
self.pre = DataPreProcessing.DataPreProcessing()
import os
import pandas as pd
import numpy as np
import RLEnvTrain, RLAgent
from tqdm import tqdm
from datetime import datetime
import RLtest
import log_recorder
import DataPreProcessing
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
pre = DataPreProcessing.DataPreProcessing()
logging = log_recorder.Log().dir_recorder('RL', 'Main')
Test = RLtest.TestEnv()
code = 'A035720'
logging.info(f'{code} 학습을 시작합니다.')
df = pre.change_csv(code, category='m')
df_train, df_test, df_prev = pre.train_test_split(code, category='m')
logging.info(
f'데이터셋 분리 Train : {len(df_train)} | Test : {len(df_test)} | Set-up : {len(df_prev)}학습을 시작합니다.'
)
step = len(df_train)
logging.info(f'한 학습당 step : {step}')
#step = 10
logging.info(f'학습 환경 구성')
env = RLEnvTrain.RLEnv(df_train)
logging.info(f'학습 에이전트 구성')
agent = RLAgent.Agent(gamma=0.98,
eps_start=0.8,
eps_end=0.01,
def __init__(self):
self.input_num = 31
self.step_num = 1
self.logging = log_recorder.Log().dir_recorder('RL','Buildmodel')