dsyoon
546
practice_3.py
Normal file
546
practice_3.py
Normal file
@@ -0,0 +1,546 @@
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# 웹 호출 라이브러리를 호출합니다.
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import time
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import requests
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from DataCrawler import DataCrawler
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import json
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import os
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import copy
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import pandas as pd
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import itertools
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from datetime import datetime, timedelta
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from TelegramBot import TelegramBot
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from filter_model_3 import BallFilter
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class Practice:
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bot = None
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preprocessor = None
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predictor = None
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extract_count = None
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TARGET_MIN_SURVIVORS = 30
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TARGET_MAX_SURVIVORS = 150
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PREDICT_TIMEOUT_SECONDS = 180
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def __init__(self, resources_path):
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self.bot = TelegramBot()
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self.resources_path = resources_path
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return
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# 로또 당첨 데이터를 수집해서 파일로 저장합니다.
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# lottoHistoryFile: 로또 당첨 데이터를 저장할 파일
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def craw(self, lottoHistoryFile, drwNo=None):
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ball = None
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if drwNo != None:
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# 로또 데이터를 저장할 파일을 선언합니다.
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jsonFp = open(lottoHistoryFile + ".json", 'a', encoding="utf-8")
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textFp = open(lottoHistoryFile + ".txt", 'a', encoding="utf-8")
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url = 'https://dhlottery.co.kr/common.do?method=getLottoNumber&drwNo=' + str(drwNo)
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# URL을 호출합니다.
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res = requests.post(url)
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# 호출한 결과에 대해서 Json 포맷을 가져옵니다.
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result = res.json()
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||||
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||||
if result['returnValue'] != 'success':
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return None
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||||
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# 가져온 Json 포맷을 파일로 저장합니다.
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jsonFp.write(json.dumps(result, ensure_ascii=False) + "\n")
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textFp.write("%d,%d,%d,%d,%d,%d,%d,%d\n" % (drwNo, result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']))
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print("%d,%d,%d,%d,%d,%d,%d,%d" % (drwNo, result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']))
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ball = [result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']]
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else:
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# 로또 데이터를 저장할 파일을 선언합니다.
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jsonFp = open(lottoHistoryFile + ".json", 'w', encoding="utf-8")
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textFp = open(lottoHistoryFile + ".txt", 'w', encoding="utf-8")
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# 1회차부터 지정된 회차까지 로또 당첨 번호를 수집합니다.
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idx = 1
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while True:
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# 1회차부터 지정된 회차까지의 URL을 생성합니다.
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url = 'https://dhlottery.co.kr/common.do?method=getLottoNumber&drwNo=' + str(idx)
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# URL을 호출합니다.
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res = requests.post(url)
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# 호출한 결과에 대해서 Json 포맷을 가져옵니다.
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result = res.json()
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if result['returnValue'] != 'success':
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break
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# 가져온 Json 포맷을 파일로 저장합니다.
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jsonFp.write(json.dumps(result, ensure_ascii=False) + "\n")
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textFp.write("%d,%d,%d,%d,%d,%d,%d,%d\n" % (idx, result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']))
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print("%d,%d,%d,%d,%d,%d,%d,%d" % (idx, result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']))
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ball = [result['drwtNo1'], result['drwtNo2'], result['drwtNo3'], result['drwtNo4'], result['drwtNo5'], result['drwtNo6'], result['bnusNo']]
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idx += 1
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time.sleep(0.5)
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# 저장한 파일을 종료합니다.
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jsonFp.close()
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textFp.close()
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return ball
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def predict1(self, result_json):
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result_json.append([6, 7, 10, 11, 20, 45])
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result_json.append([2, 7, 17, 28, 35, 39])
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result_json.append([6, 10, 19, 25, 33, 35])
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result_json.append([3, 17, 20, 24, 35, 45])
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result_json.append([5, 15, 18, 29, 36, 41])
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result_json.append([6, 15, 20, 23, 37, 43])
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result_json.append([8, 15, 19, 23, 38, 41])
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result_json.append([5, 11, 19, 24, 40, 45])
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result_json.append([9, 16, 18, 23, 35, 43])
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result_json.append([7, 13, 19, 28, 33, 44])
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result_json.append([7, 11, 18, 29, 37, 42])
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print("회차(predict1)")
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return
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def predict2(self, resources_path, ymd, result_json):
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candidates = [i for i in range(1, 46)]
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lottoHistoryFileName = os.path.join(resources_path, 'lotto_history.json')
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ballFilter = BallFilter(lottoHistoryFileName)
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no = ballFilter.getNextNo(ymd)
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print("회차(predict2): {}".format(no))
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lottoHistoryFileName = os.path.join(resources_path, 'lotto_history.txt')
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df_ball = pd.read_csv(lottoHistoryFileName, header=None)
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df_ball.columns = ['no', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'bn']
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#filter_ball=[1,2,4,6,10,11,11,17,18,20,21,22,23,24,26,27,28,30,31,32,33,34,37,38,39,40,42,44]
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nCr = list(itertools.combinations(candidates, 6))
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for idx, ball in enumerate(nCr):
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if idx % 1000000 == 0:
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print(" - {} processed...".format(idx))
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ball = list(ball)
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filter_type = ballFilter.filter(ball=ball, no=no, until_end=False, df=df_ball)
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filter_size = len(filter_type)
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if 0 < filter_size:
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continue
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result_json.append(ball)
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p_ball = df_ball[df_ball['no'] == no - 1].values.tolist()[0]
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p_no = p_ball[0]
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p_ball = p_ball[1:7]
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return p_no, p_ball
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def predict3(self, resources_path, ymd, result_json):
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candidates = [i for i in range(1, 46)]
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lottoHistoryFileName = os.path.join(resources_path, 'lotto_history.json')
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no = BallFilter(lottoHistoryFileName).getNextNo(ymd)
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print("회차(predict3): {}".format(no))
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predict_start_ts = time.time()
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deadline_ts = predict_start_ts + self.PREDICT_TIMEOUT_SECONDS
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lottoHistoryFileName = os.path.join(resources_path, 'lotto_history.txt')
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df_ball = pd.read_csv(lottoHistoryFileName, header=None)
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df_ball.columns = ['no', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'bn']
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p_ball = df_ball[df_ball['no'] == no - 1].values.tolist()[0]
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p_no = p_ball[0]
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p_ball = sorted(p_ball[1:7])
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base_ruleset = self._get_base_ruleset()
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tighten_rulesets = [
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self._build_ruleset(
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base_ruleset=base_ruleset,
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enabled_overrides={
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"paper_patterns": True,
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"ban_triples_legacy": True,
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"all_in_previous7": True,
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"previous_neighbors": True,
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},
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allowed_overrides={
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"ac_value": [8, 9],
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"uniq_last_digit_count": [4, 5],
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"even_count": [2, 3, 4],
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},
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),
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self._build_ruleset(
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base_ruleset=base_ruleset,
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enabled_overrides={
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"paper_patterns": True,
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"ban_triples_legacy": True,
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"all_in_previous7": True,
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"previous_neighbors": True,
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},
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allowed_overrides={
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"ac_value": [8, 9],
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"uniq_last_digit_count": [4, 5],
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"even_count": [2, 3, 4],
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"sum": [112, 114, 121, 123, 126, 127, 131, 132, 138, 146, 148],
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"sum_prev_diff": [13, 14, 17, 18, 26, 28, 29, 30, 32, 39, 40],
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},
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),
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]
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relax_rulesets = [
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self._build_ruleset(
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base_ruleset=base_ruleset,
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enabled_overrides={
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"paper_patterns": False,
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"ban_triples_legacy": False,
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},
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),
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self._build_ruleset(
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base_ruleset=base_ruleset,
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enabled_overrides={
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"paper_patterns": False,
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"ban_triples_legacy": False,
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"previous_neighbors": False,
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"all_in_previous7": False,
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},
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),
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self._build_ruleset(
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base_ruleset=base_ruleset,
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enabled_overrides={
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"paper_patterns": False,
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"ban_triples_legacy": False,
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"previous_neighbors": False,
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"all_in_previous7": False,
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"weeks_8_count": False,
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"weeks_12_count": False,
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"weeks_16_count": False,
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"weeks_20_count": False,
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},
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),
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]
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min_survivors = self.TARGET_MIN_SURVIVORS
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max_survivors = self.TARGET_MAX_SURVIVORS
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chosen = []
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stage_name = "base"
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current_info = self._collect_candidates(
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candidates=candidates,
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no=no,
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df_ball=df_ball,
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ruleset=base_ruleset,
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stop_when_gt=max_survivors,
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stage_name="base",
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predict_start_ts=predict_start_ts,
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deadline_ts=deadline_ts,
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)
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current = current_info["candidates"]
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if current_info["timed_out"]:
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chosen = self._finalize_on_timeout(current, p_ball, min_survivors, max_survivors)
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stage_name = "base_timeout_fallback"
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print("predict3 stage: {}, survivors: {}".format(stage_name, len(chosen)))
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for ball in chosen:
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result_json.append(ball)
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return p_no, p_ball
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if min_survivors <= len(current) <= max_survivors:
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chosen = current
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elif len(current) > max_survivors:
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chosen = current
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stage_name = "base_overflow"
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for idx, rs in enumerate(tighten_rulesets, start=1):
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t_info = self._collect_candidates(
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candidates=candidates,
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no=no,
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df_ball=df_ball,
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ruleset=rs,
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stop_when_gt=max_survivors,
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stage_name="tighten_{}".format(idx),
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predict_start_ts=predict_start_ts,
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deadline_ts=deadline_ts,
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)
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t = t_info["candidates"]
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if t_info["timed_out"]:
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chosen = self._finalize_on_timeout(t, p_ball, min_survivors, max_survivors)
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stage_name = "tighten_{}_timeout_fallback".format(idx)
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break
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if min_survivors <= len(t) <= max_survivors:
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chosen = t
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stage_name = "tighten_{}".format(idx)
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break
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||||
if len(t) <= max_survivors:
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chosen = t
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stage_name = "tighten_{}".format(idx)
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if len(chosen) > max_survivors:
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full_info = self._collect_candidates(
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candidates=candidates,
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no=no,
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df_ball=df_ball,
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ruleset=tighten_rulesets[-1],
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||||
stop_when_gt=None,
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stage_name="tighten_full_rank",
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predict_start_ts=predict_start_ts,
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deadline_ts=deadline_ts,
|
||||
)
|
||||
full_for_ranking = full_info["candidates"]
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||||
if full_info["timed_out"]:
|
||||
chosen = self._finalize_on_timeout(full_for_ranking, p_ball, min_survivors, max_survivors)
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stage_name = "tighten_rank_timeout_fallback"
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||||
else:
|
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chosen = self._rank_and_trim(full_for_ranking, p_ball, max_survivors)
|
||||
stage_name = "tighten_rank_trim"
|
||||
else:
|
||||
chosen = current
|
||||
stage_name = "base_underflow"
|
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for idx, rs in enumerate(relax_rulesets, start=1):
|
||||
r_info = self._collect_candidates(
|
||||
candidates=candidates,
|
||||
no=no,
|
||||
df_ball=df_ball,
|
||||
ruleset=rs,
|
||||
stop_when_gt=None,
|
||||
stop_when_gte=min_survivors,
|
||||
stage_name="relax_{}".format(idx),
|
||||
predict_start_ts=predict_start_ts,
|
||||
deadline_ts=deadline_ts,
|
||||
)
|
||||
r = r_info["candidates"]
|
||||
chosen = r
|
||||
stage_name = "relax_{}".format(idx)
|
||||
if r_info["timed_out"]:
|
||||
chosen = self._finalize_on_timeout(r, p_ball, min_survivors, max_survivors)
|
||||
stage_name = "relax_{}_timeout_fallback".format(idx)
|
||||
break
|
||||
if len(r) >= min_survivors:
|
||||
break
|
||||
|
||||
if len(chosen) == 0:
|
||||
stage_name = "relax_zero_fallback"
|
||||
chosen = self._fallback_candidates_from_prev(p_ball, min_survivors)
|
||||
elif len(chosen) < min_survivors:
|
||||
stage_name = "{}_fill".format(stage_name)
|
||||
fill = self._fallback_candidates_from_prev(
|
||||
p_ball,
|
||||
min_survivors - len(chosen),
|
||||
exclude=set(tuple(x) for x in chosen),
|
||||
)
|
||||
chosen.extend(fill)
|
||||
|
||||
print("predict3 stage: {}, survivors: {}".format(stage_name, len(chosen)))
|
||||
for ball in chosen:
|
||||
result_json.append(ball)
|
||||
return p_no, p_ball
|
||||
|
||||
def _get_base_ruleset(self):
|
||||
history_json = os.path.join(self.resources_path, "lotto_history.json")
|
||||
base_filter = BallFilter(history_json)
|
||||
return copy.deepcopy(base_filter.m1.ruleset)
|
||||
|
||||
def _build_ruleset(self, base_ruleset, enabled_overrides=None, allowed_overrides=None):
|
||||
ruleset = copy.deepcopy(base_ruleset)
|
||||
ruleset.setdefault("filters", {})
|
||||
enabled_overrides = enabled_overrides or {}
|
||||
allowed_overrides = allowed_overrides or {}
|
||||
for key, value in enabled_overrides.items():
|
||||
ruleset["filters"].setdefault(key, {})
|
||||
ruleset["filters"][key]["enabled"] = bool(value)
|
||||
for key, values in allowed_overrides.items():
|
||||
ruleset["filters"].setdefault(key, {})
|
||||
ruleset["filters"][key]["enabled"] = True
|
||||
ruleset["filters"][key]["allowed"] = list(values)
|
||||
return ruleset
|
||||
|
||||
def _collect_candidates(
|
||||
self,
|
||||
candidates,
|
||||
no,
|
||||
df_ball,
|
||||
ruleset,
|
||||
stop_when_gt=None,
|
||||
stop_when_gte=None,
|
||||
stage_name="base",
|
||||
predict_start_ts=None,
|
||||
deadline_ts=None,
|
||||
):
|
||||
lottoHistoryFileName = os.path.join(self.resources_path, "lotto_history.json")
|
||||
ballFilter = BallFilter(lottoHistoryFileName, ruleset=ruleset)
|
||||
result = []
|
||||
last_idx = 0
|
||||
for idx, ball in enumerate(itertools.combinations(candidates, 6), start=1):
|
||||
last_idx = idx
|
||||
if deadline_ts is not None and deadline_ts <= time.time():
|
||||
elapsed = (time.time() - predict_start_ts) if predict_start_ts is not None else 0.0
|
||||
print(" - [{}] timeout after {:,} processed (elapsed: {:.1f}s, survivors: {:,})".format(stage_name, idx, elapsed, len(result)))
|
||||
return {"candidates": result, "timed_out": True, "processed": idx}
|
||||
if idx % 1000000 == 0:
|
||||
elapsed = (time.time() - predict_start_ts) if predict_start_ts is not None else 0.0
|
||||
print(" - [{}] {:,} processed... (elapsed: {:.1f}s, survivors: {:,})".format(stage_name, idx, elapsed, len(result)))
|
||||
b = list(ball)
|
||||
if len(ballFilter.filter(ball=b, no=no, until_end=False, df=df_ball)) == 0:
|
||||
result.append(b)
|
||||
if stop_when_gt is not None and len(result) > stop_when_gt:
|
||||
return {"candidates": result, "timed_out": False, "processed": idx}
|
||||
if stop_when_gte is not None and len(result) >= stop_when_gte:
|
||||
return {"candidates": result, "timed_out": False, "processed": idx}
|
||||
return {"candidates": result, "timed_out": False, "processed": last_idx}
|
||||
|
||||
def _finalize_on_timeout(self, partial_candidates, prev_ball, min_survivors, max_survivors):
|
||||
chosen = list(partial_candidates)
|
||||
if len(chosen) > max_survivors:
|
||||
chosen = self._rank_and_trim(chosen, prev_ball, max_survivors)
|
||||
elif len(chosen) < min_survivors:
|
||||
fill = self._fallback_candidates_from_prev(
|
||||
prev_ball,
|
||||
min_survivors - len(chosen),
|
||||
exclude=set(tuple(x) for x in chosen),
|
||||
)
|
||||
chosen.extend(fill)
|
||||
return chosen
|
||||
|
||||
def _rank_and_trim(self, candidates, prev_ball, limit):
|
||||
scored = [(self._score_candidate(ball, prev_ball), ball) for ball in candidates]
|
||||
scored.sort(key=lambda x: x[0])
|
||||
return [ball for _, ball in scored[:limit]]
|
||||
|
||||
def _score_candidate(self, ball, prev_ball):
|
||||
sum_diff = abs(sum(ball) - sum(prev_ball))
|
||||
even_cnt = len([x for x in ball if x % 2 == 0])
|
||||
uniq_last = len(set([x % 10 for x in ball]))
|
||||
contiguous_penalty = 0
|
||||
s = sorted(ball)
|
||||
for i in range(1, len(s)):
|
||||
if s[i] - s[i - 1] == 1:
|
||||
contiguous_penalty += 1
|
||||
score = 0
|
||||
score += sum_diff
|
||||
score += abs(even_cnt - 3) * 2
|
||||
score += abs(uniq_last - 5) * 2
|
||||
score += contiguous_penalty
|
||||
return score
|
||||
|
||||
def _fallback_candidates_from_prev(self, prev_ball, need_count, exclude=None):
|
||||
exclude = exclude or set()
|
||||
seed = sorted(prev_ball)
|
||||
out = []
|
||||
delta_patterns = [
|
||||
(0, 0, 0, 0, 0, 0),
|
||||
(-1, 0, 0, 0, 0, 1),
|
||||
(0, -1, 0, 0, 1, 0),
|
||||
(0, 0, -1, 1, 0, 0),
|
||||
(-2, 0, 0, 0, 0, 2),
|
||||
(0, -2, 0, 0, 2, 0),
|
||||
(0, 0, -2, 2, 0, 0),
|
||||
(-1, -1, 0, 0, 1, 1),
|
||||
(1, 0, -1, 0, 0, 0),
|
||||
(0, 1, 0, -1, 0, 0),
|
||||
(1, -1, 1, -1, 1, -1),
|
||||
(-1, 1, -1, 1, -1, 1),
|
||||
]
|
||||
shift = 0
|
||||
while len(out) < need_count and shift <= 8:
|
||||
for delta in delta_patterns:
|
||||
cand = [seed[i] + delta[i] for i in range(6)]
|
||||
cand = [min(45, max(1, v + shift)) for v in cand]
|
||||
cand = sorted(cand)
|
||||
if len(set(cand)) != 6:
|
||||
continue
|
||||
t = tuple(cand)
|
||||
if t in exclude:
|
||||
continue
|
||||
exclude.add(t)
|
||||
out.append(cand)
|
||||
if len(out) >= need_count:
|
||||
break
|
||||
shift += 1
|
||||
return out
|
||||
|
||||
def _merge_unique_balls(self, base_balls, extra_balls):
|
||||
seen = set(tuple(sorted(x)) for x in base_balls)
|
||||
for ball in extra_balls:
|
||||
key = tuple(sorted(ball))
|
||||
if key not in seen:
|
||||
base_balls.append(list(ball))
|
||||
seen.add(key)
|
||||
return base_balls
|
||||
|
||||
def _sorted_unique_balls(self, balls):
|
||||
"""
|
||||
Normalize (sort within ball), de-duplicate, then sort lexicographically.
|
||||
Returns List[List[int]].
|
||||
"""
|
||||
uniq = {}
|
||||
for b in balls:
|
||||
key = tuple(sorted(b))
|
||||
uniq[key] = list(key)
|
||||
return [list(t) for t in sorted(uniq.keys())]
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
PROJECT_HOME = '.'
|
||||
resources_path = os.path.join(PROJECT_HOME, 'resources')
|
||||
|
||||
today = datetime.today()
|
||||
if today.weekday() == 5:
|
||||
if today.hour > 20:
|
||||
this_weekend = today + timedelta(days=(12 - today.weekday()))
|
||||
else:
|
||||
this_weekend = today + timedelta(days=(5 - today.weekday()))
|
||||
elif today.weekday() == 6:
|
||||
this_weekend = today + timedelta(days=(12 - today.weekday()))
|
||||
else:
|
||||
this_weekend = today + timedelta(days=(5 - today.weekday()))
|
||||
|
||||
last_weekend = (this_weekend - timedelta(days=7)).strftime('%Y%m%d')
|
||||
ymd = this_weekend.strftime('%Y%m%d')
|
||||
|
||||
print("ymd: {}".format(ymd))
|
||||
|
||||
# 로또 예측
|
||||
practice = Practice(resources_path)
|
||||
|
||||
# 데이터 수집
|
||||
lottoHistoryFile = PROJECT_HOME + '/resources/lotto_history'
|
||||
lottoHistoryFileName = lottoHistoryFile + '.json'
|
||||
with open(lottoHistoryFileName, "r", encoding='utf-8') as f:
|
||||
for line in f:
|
||||
if line != '\n':
|
||||
last_json = json.loads(line)
|
||||
|
||||
#ball = practice.craw(lottoHistoryFile, drwNo=last_json['drwNo'] + 1)
|
||||
|
||||
result_json = {ymd: []}
|
||||
|
||||
# 매주 고정
|
||||
practice.predict1(result_json[ymd])
|
||||
# 필터 기반 예측(기존/신규): 결과는 합친 후 정렬해서 predict1 결과 뒤에 붙인다.
|
||||
predict2_json = []
|
||||
p_no, p_ball = practice.predict2(resources_path, ymd, predict2_json)
|
||||
|
||||
predict3_json = []
|
||||
p_no3, p_ball3 = practice.predict3(resources_path, ymd, predict3_json)
|
||||
|
||||
merged_predict = []
|
||||
practice._merge_unique_balls(merged_predict, predict2_json)
|
||||
practice._merge_unique_balls(merged_predict, predict3_json)
|
||||
merged_predict = practice._sorted_unique_balls(merged_predict)
|
||||
|
||||
# predict1 결과에 merged_predict를 정렬된 순서로 append(중복 제거)
|
||||
practice._merge_unique_balls(result_json[ymd], merged_predict)
|
||||
if p_no3 == p_no:
|
||||
p_ball = p_ball3
|
||||
|
||||
p_str = "[지난주] {}\n - {} 회차, {}\n[금주] {}\n - {} 회차\n[모델#25]\n".format(last_weekend, p_no, str(p_ball), ymd, (p_no + 1))
|
||||
for i, ball in enumerate(result_json[ymd]):
|
||||
p_str += " {}. {}\n".format((i+1), str(ball))
|
||||
if (i+1) % 100 == 0:
|
||||
practice.bot.sendMsg("{}".format(p_str))
|
||||
p_str = ''
|
||||
|
||||
if len(result_json[ymd]) % 100 != 0:
|
||||
practice.bot.sendMsg("{}".format(p_str))
|
||||
|
||||
size = len(result_json[ymd])
|
||||
print("size: {}".format(size))
|
||||
|
||||
# https://youtu.be/QjBsui8Ob14?si=4dC3q8p0Yu5ZWK1K
|
||||
# https://www.youtube.com/watch?v=YwiHaa1KNwA
|
||||
|
||||
print("done...")
|
||||
Reference in New Issue
Block a user