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common.py
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# -*- coding:utf-8 -*-
"""
Author: KittenCN
"""
import urllib3
import sys
import requests
import pandas as pd
import torch
import torch.nn.functional as F
import datetime
import numpy as np
import modeling
from torch.utils.data import DataLoader
from bs4 import BeautifulSoup
from loguru import logger
from torch import nn
from config import *
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
ori_data = None
filedata = []
filetitle = []
pred_key_d = {}
mini_args = {}
class FocalLoss(nn.Module):
def __init__(self, gamma=2.0, alpha=0.25):
super(FocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
def forward(self, inputs, targets):
# 输入:inputs (模型预测,shape: [batch_size, num_classes]),
# targets (真实标签,shape: [batch_size, num_classes], 独热编码)
BCE_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction='none')
targets = targets.type(torch.float32)
at = self.alpha * targets + (1 - self.alpha) * (1 - targets) # alpha系数调整
pt = torch.exp(-BCE_loss) # 转换为概率
F_loss = at * (1 - pt) ** self.gamma * BCE_loss
return F_loss.mean()
def create_train_data(name, windows, dataset=0, ball_type="red", cq=0, test_flag=0, test_begin=0, f_data=0, model="Transformer", num_classes=80, test_list=[]):
""" 创建训练数据
:param name: 玩法,双色球/大乐透
:param windows: 训练窗口
:return:
"""
global ori_data
strflag = "训练" if test_flag == 0 else "测试"
strball = "红球" if ball_type == "red" else "蓝球"
if ori_data is None:
if cq == 1 and name == "kl8":
ori_data = pd.read_csv("{}{}".format(name_path[name]["path"], data_cq_file_name))
else:
ori_data = pd.read_csv("{}{}".format(name_path[name]["path"], data_file_name))
data = ori_data.copy()
if test_begin >= 0 and len(test_list) <= 0:
if f_data == 0:
if test_flag in [0, 2]:
data = data[data['期数'] > test_begin]
else:
data = data[data['期数'] <= test_begin]
else:
data = data[data['期数'] <= f_data]
data = data.head(windows + 1)
# elif len(test_list) > 0:
# if test_flag == 0:
# data = data[~data['期数'].isin(test_list)]
# else:
# data = data[data['期数'].isin(test_list)]
if not len(data):
raise logger.error(" 请执行 get_data.py 进行数据下载!")
else:
# 创建模型文件夹
if not os.path.exists(model_path):
os.mkdir(model_path)
# logger.info(strball + strflag + "数据已加载! ")
data = data.iloc[:, :].values
tmp = []
for _data in data:
_tmp = []
for item in _data:
_tmp.append([item])
tmp.append(_tmp)
data = np.array(tmp)
cut_num = model_args[name]["model_args"]["red_sequence_len"]
if dataset == 0:
x_data, y_data = [], []
for i in range(len(data) - windows - 1):
sub_data = data[i:(i+windows+1), :]
x_data.append(sub_data[1:])
y_data.append(sub_data[0])
return {
"red": {
"x_data": np.array(x_data)[:, :, :cut_num], "y_data": np.array(y_data)[:, :cut_num]
},
"blue": {
"x_data": np.array(x_data)[:, :, cut_num:], "y_data": np.array(y_data)[:, cut_num:]
}
}
else:
if ball_type == "red":
dataset = modeling.MyDataset(data, windows, cut_num, model, num_classes, test_flag, test_list, f_data)
else:
dataset = modeling.MyDataset(data, windows, cut_num * -1, model, num_classes, test_flag, test_list, f_data)
logger.info(strball + strflag + "集数据维度: {}".format(dataset.data.shape))
return dataset
def get_data_run(name, cq=0):
"""
:param name: 玩法名称
:return:
"""
current_number = get_current_number(name)
logger.info("【{}】最新一期期号:{}".format(name_path[name]["name"], current_number))
logger.info("正在获取【{}】数据。。。".format(name_path[name]["name"]))
if not os.path.exists(name_path[name]["path"]):
os.makedirs(name_path[name]["path"])
if cq == 1 and name == "kl8":
data = spider_cq(name, 1, current_number, "train")
else:
data = spider(name, 1, current_number, "train")
if "data" in os.listdir(os.getcwd()):
logger.info("【{}】数据准备就绪,共{}期, 下一步可训练模型...".format(name_path[name]["name"], len(data)))
else:
logger.error("数据文件不存在!")
def get_url(name):
"""
:param name: 玩法名称
:return:
"""
url = "https://datachart.500.com/{}/history/".format(name)
path = "newinc/history.php?start={}&end={}&limit={}"
if name == "qxc" or name == "pls":
path = "inc/history.php?start={}&end={}&limit={}"
elif name == "kl8":
url = "https://datachart.500.com/{}/zoushi/".format(name)
path = "newinc/jbzs_redblue.php?from=&to=&shujcount=0&sort=1&expect=-1"
return url, path
def get_current_number(name):
""" 获取最新一期数字
:return: int
"""
url, _ = get_url(name)
if name in ["qxc", "pls"]:
r = requests.get("{}{}".format(url, "inc/history.php"), verify=False)
elif name in ["ssq", "dlt"]:
r = requests.get("{}{}".format(url, "history.shtml"), verify=False)
elif name in ["kl8"]:
r = requests.get("{}{}".format(url, "newinc/jbzs_redblue.php"), verify=False)
r.encoding = "gb2312"
soup = BeautifulSoup(r.text, "lxml")
if name in ["kl8"]:
current_num = soup.find("div", class_="wrap_datachart").find("input", id="to")["value"]
else:
current_num = soup.find("div", class_="wrap_datachart").find("input", id="end")["value"]
return current_num
def spider_cq(name="kl8", start=1, end=999999, mode="train", windows_size=0):
syspath = name_path[name]["path"]
if not os.path.exists(syspath):
os.makedirs(syspath)
if name == "kl8" and mode == "train":
url = "https://data.917500.cn/kl81000_cq_asc.txt"
r = requests.get(url, headers = {'User-agent': 'chrome'})
data = []
lines = sorted(r.text.split('\n'), reverse=True)
for line in lines:
if len(line) < 10:
continue
item = dict()
line = line.split(',')
line = line[0].split(' ')
# item[u"id"] = line[0]
strdate = line[1].split('-')
item[u"日期"] = strdate[0] + strdate[1] + strdate[2]
item[u"期数"] = line[0]
for i in range(1, 21):
item[u"红球_{}".format(i)] = line[i + 1]
data.append(item)
df = pd.DataFrame(data)
df.to_csv("{}{}".format(syspath, data_cq_file_name), encoding="utf-8",index=False)
return pd.DataFrame(data)
elif name == "kl8" and mode == "predict":
ori_data = pd.read_csv("{}{}".format(syspath, data_cq_file_name))
data = []
if windows_size > 0:
ori_data = ori_data[0:windows_size]
for i in range(len(ori_data)):
item = dict()
item[u"期数"] = ori_data.iloc[i, 1]
for j in range(20):
item[u"红球_{}".format(j+1)] = ori_data.iloc[i, j+2]
data.append(item)
return pd.DataFrame(data)
else:
spider(name, start, end, mode)
def spider(name="ssq", start=1, end=999999, mode="train", windows_size=0):
""" 爬取历史数据
:param name 玩法
:param start 开始一期
:param end 最近一期
:param mode 模式,train:训练模式,predict:预测模式(训练模式会保持文件)
:return:
"""
syspath = name_path[name]["path"]
if not os.path.exists(syspath):
os.makedirs(syspath)
if mode == "train":
url, path = get_url(name)
limit = int(end) - int(start) + 1
url = "{}{}".format(url, path.format(int(start), int(end), limit))
r = requests.get(url=url, verify=False)
r.encoding = "gb2312"
soup = BeautifulSoup(r.text, "lxml")
if name in ["ssq", "dlt", "kl8"]:
trs = soup.find("tbody", attrs={"id": "tdata"}).find_all("tr")
elif name in ["qxc", "pls"]:
trs = soup.find("div", class_="wrap_datachart").find("table", id="tablelist").find_all("tr")
data = []
for tr in trs:
item = dict()
if name == "ssq":
item[u"期数"] = tr.find_all("td")[0].get_text().strip()
for i in range(6):
item[u"红球_{}".format(i+1)] = tr.find_all("td")[i+1].get_text().strip()
item[u"蓝球"] = tr.find_all("td")[7].get_text().strip()
data.append(item)
elif name == "dlt":
item[u"期数"] = tr.find_all("td")[0].get_text().strip()
for i in range(5):
item[u"红球_{}".format(i+1)] = tr.find_all("td")[i+1].get_text().strip()
for j in range(2):
item[u"蓝球_{}".format(j+1)] = tr.find_all("td")[6+j].get_text().strip()
data.append(item)
elif name == "pls":
if tr.find_all("td")[0].get_text().strip() == "注数" or tr.find_all("td")[1].get_text().strip() == "中奖号码":
continue
item[u"期数"] = tr.find_all("td")[0].get_text().strip()
numlist = tr.find_all("td")[1].get_text().strip().split(" ")
for i in range(3):
item[u"红球_{}".format(i+1)] = numlist[i]
data.append(item)
elif name == "kl8":
tds = tr.find_all("td")
index = 1
for td in tds:
if td.has_attr('align') and td['align'] == 'center':
item[u"期数"] = td.get_text().strip()
elif td.has_attr('class') and td['class'][0] == 'chartBall01':
item[u"红球_{}".format(index)] = td.get_text().strip()
index += 1
if item:
data.append(item)
else:
logger.warning("抱歉,没有找到数据源!")
df = pd.DataFrame(data)
df.to_csv("{}{}".format(syspath, data_file_name), encoding="utf-8")
return pd.DataFrame(data)
elif mode == "predict":
ori_data = pd.read_csv("{}{}".format(syspath, data_file_name))
data = []
if windows_size > 0:
ori_data = ori_data[0:windows_size]
for i in range(len(ori_data)):
item = dict()
if (ori_data.iloc[i, 1] < int(start) or ori_data.iloc[i, 1] > int(end)) and windows_size == 0:
continue
if name == "ssq":
item[u"期数"] = ori_data.iloc[i, 1]
for j in range(6):
item[u"红球_{}".format(j+1)] = ori_data.iloc[i, j+2]
item[u"蓝球"] = ori_data.iloc[i, 8]
data.append(item)
elif name == "dlt":
item[u"期数"] = ori_data.iloc[i, 1]
for j in range(5):
item[u"红球_{}".format(j+1)] = ori_data.iloc[i, j+2]
for k in range(2):
item[u"蓝球_{}".format(k+1)] = ori_data.iloc[i, 7+k]
data.append(item)
elif name == "pls":
item[u"期数"] = ori_data.iloc[i, 1]
for j in range(3):
item[u"红球_{}".format(j+1)] = ori_data.iloc[i, j+2]
data.append(item)
elif name == "kl8":
item[u"期数"] = ori_data.iloc[i, 1]
for j in range(20):
item[u"红球_{}".format(j+1)] = ori_data.iloc[i, j+2]
data.append(item)
else:
logger.warning("抱歉,没有找到数据源!")
return pd.DataFrame(data)
# current_number = get_current_number(mini_args.name)
def setMiniargs(args):
global mini_args
mini_args = args
def init():
global mini_args,pred_key_d, filedata, filetitle
filedata = []
filetitle = []
pred_key_d = {}
mini_args = {}
def predict_ball_model(name, dataset, sequence_len, sub_name="红球", window_size=1, hidden_size=128, num_layers=8, num_heads=16, input_size=20, output_size=20, model_name="Transformer", args=None, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),embedding_dim=50):
""" 模型训练
:param name: 玩法
:param x_data: 训练样本
:param y_data: 训练标签
:return:
"""
global last_save_time
sub_name_eng = "red" if sub_name == "红球" else "blue"
ball_model_name = red_ball_model_name if sub_name == "红球" else blue_ball_model_name
m_args = model_args[name]
ball_index = 0 if sub_name == "红球" else 1
name_list = [(ball_name[ball_index], i + 1) for i in range(sequence_len)]
syspath = model_path + model_args[mini_args.name]["pathname"]['name'] + str(window_size) + model_args[mini_args.name]["subpath"][sub_name_eng]
if not os.path.exists(syspath):
os.makedirs(syspath)
logger.info("标签数据维度: {}".format(dataset.data.shape))
dataset = [dataset[0]]
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
# 定义模型和优化器
if model_name == "Transformer":
_model = modeling.Transformer_Model
elif model_name == "LSTM":
_model = modeling.LSTM_Model
model = _model(input_size=input_size, output_size=output_size, hidden_size=hidden_size, num_layers=num_layers, num_heads=num_heads, dropout=0.5, num_embeddings=m_args["model_args"]["{}_n_class".format(sub_name_eng)], embedding_dim=embedding_dim, windows_size=window_size).to(device)
if os.path.exists("{}{}_ball_model_pytorch_{}.ckpt".format(syspath, sub_name_eng, model_name)):
# model.load_state_dict(torch.load("{}{}_ball_model_pytorch.ckpt".format(syspath, sub_name_eng)))
checkpoint = torch.load("{}{}_ball_model_pytorch_{}.ckpt".format(syspath, sub_name_eng, model_name))
if 'windows_size' in checkpoint and 'hidden_size' in checkpoint and 'num_layers' in checkpoint and 'num_heads' in checkpoint:
if checkpoint['windows_size'] != args.windows_size or checkpoint['hidden_size'] != args.hidden_size or checkpoint['num_layers'] != args.num_layers or checkpoint['num_heads'] != args.num_heads:
logger.info("当前为预测模式,将自动调整训练参数!")
args.windows_size = checkpoint['windows_size']
args.hidden_size = checkpoint['hidden_size']
args.num_layers = checkpoint['num_layers']
args.num_heads = checkpoint['num_heads']
model = _model(input_size=input_size, output_size=output_size, hidden_size=hidden_size, num_layers=num_layers, num_heads=num_heads, dropout=0.5, num_embeddings=m_args["model_args"]["{}_n_class".format(sub_name_eng)], embedding_dim=embedding_dim, windows_size=window_size).to(device)
else:
logger.info("模型不是最新版本,建议重新训练!")
model.load_state_dict(checkpoint['model_state_dict'])
logger.info("已加载{}模型!".format(sub_name))
model.eval()
for batch in dataloader:
x, y = batch
x = x.float().to(device)
y = y.float().to(device)
y_pred = model(x)
return y_pred, name_list, y
def run_predict(window_size, sequence_len, hidden_size=128, num_layers=8, num_heads=16, input_size=20, output_size=20, f_data=0, model="Transformer", args=None, test_mode=0):
global pred_key_d
balls = ['red', 'blue'] if mini_args.name not in ["pls", "kl8"] else ['red']
for sub_name_eng in balls:
sub_name = "红球" if sub_name_eng == "red" else "蓝球"
if window_size != 0:
model_args[mini_args.name]["model_args"]["windows_size"] = window_size
syspath = model_path + model_args[mini_args.name]["pathname"]['name'] + str(mini_args.windows_size) + model_args[mini_args.name]["subpath"][sub_name_eng]
# redpath = model_path + model_args[mini_args.name]["pathname"]['name'] + str(model_args[mini_args.name]["model_args"]["windows_size"]) + model_args[mini_args.name]["subpath"]['red']
# bluepath = model_path + model_args[mini_args.name]["pathname"]['name'] + str(model_args[mini_args.name]["model_args"]["windows_size"]) + model_args[mini_args.name]["subpath"]['blue']
# model = modeling.TransformerModel(input_size=20, output_size=20).to(device)
if os.path.exists("{}{}_ball_model_pytorch_{}.ckpt".format(syspath, sub_name_eng, model)):
# model.load_state_dict(torch.load("{}{}_ball_model_pytorch.ckpt".format(syspath, sub_name_eng)))
# logger.info("已加载{}模型!窗口大小:{}".format(sub_name, model_args[mini_args.name]["model_args"]["windows_size"]))
current_number = get_current_number(mini_args.name)
logger.info("【{}】最近一期:{}".format(name_path[mini_args.name]["name"], current_number))
logger.info("正在创建【{}】数据集...".format(name_path[mini_args.name]["name"]))
data = create_train_data(mini_args.name, model_args[mini_args.name]["model_args"]["windows_size"], 1, sub_name_eng, mini_args.cq,f_data=f_data, model=model, test_flag=2)
y_pred, name_list, y_target = predict_ball_model(mini_args.name, data, sequence_len, sub_name, window_size,hidden_size=hidden_size, num_layers=num_layers, num_heads=num_heads, input_size=input_size, output_size=output_size, model_name=model, args=args,embedding_dim=50)
if test_mode == 0 or f_data == 0:
logger.info("预测{}结果为: \n".format(sub_name))
else:
logger.info("测试{}结果为: ".format(sub_name))
correct_nums = 0
total_nums = 0
result_strings = []
result_strings.append("------------Predict Datetime: {}------------".format(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
if model == "Transformer":
y_pred_list = modeling.binary_decode_array(y_pred, threshold=0.25, top_k=model_args[mini_args.name]["model_args"]["{}_n_class".format(sub_name_eng)])
for row in y_pred_list:
row_limit = row[0:model_args[mini_args.name]["model_args"]["{}_n_class".format(sub_name_eng)]]
if test_mode == 0 or f_data == 0:
logger.info("超过阈值的数据: {}".format(row))
result_strings.append("超过阈值的数据: {}".format(row))
logger.info("前K位超过阈值的数据: {}".format(row_limit))
result_strings.append("前K位超过阈值的数据: {}".format(row_limit))
logger.info("排序后前K位超过阈值的数据: {}".format(sorted(row_limit)))
result_strings.append("排序后前K位超过阈值的数据: {}".format(sorted(row_limit)))
else:
row_limit = list(dict.fromkeys(row_limit))
row_limit_set = set(row_limit)
y_target_set = set((y_target+1).view(y_target.size(0), -1).tolist()[0])
total_nums += len(list(dict.fromkeys(y_target.tolist()[0][0])))
correct_nums += len(row_limit_set & y_target_set)
elif model == "LSTM":
softmax = nn.Softmax(dim=1)
y_pred_list = modeling.decode_one_hot(softmax(y_pred), sort_by_max_value=True, num_classes=model_args[mini_args.name]["model_args"]["{}_n_class".format(sub_name_eng)])
if test_mode == 0 or f_data == 0:
logger.info("超过阈值的数据: {}".format(list(dict.fromkeys(y_pred_list))))
result_strings.append("超过阈值的数据: {}".format(list(dict.fromkeys(y_pred_list))))
logger.info("排序后超过阈值的数据: {}".format(sorted(list(dict.fromkeys(y_pred_list)))))
result_strings.append("排序后超过阈值的数据: {}".format(sorted(list(dict.fromkeys(y_pred_list)))))
else:
y_target_set = set((y_target+1).view(y_target.size(0), -1).tolist()[0])
y_pred_set = set(list(dict.fromkeys(y_pred_list)))
total_nums += len(list(dict.fromkeys(y_target.tolist()[0][0])))
correct_nums += len(y_pred_set & y_target_set)
if test_mode != 0 and f_data > 0:
logger.info("预测{}结果为: {:.2f}%".format(sub_name, correct_nums / (total_nums if total_nums > 0 else 1) * 100))
result_strings.append("预测{}结果为: {:.2f}%".format(sub_name, correct_nums / (total_nums if total_nums > 0 else 1) * 100))
result_strings.append("------------------------------------------------")
write_strings_to_file(result_path, result_strings)
else:
logger.warning("抱歉,没有找到{}模型!".format(sub_name))
exit(0)
def get_year():
""" 截取年份
eg:2020-->20, 2021-->21
:return:
"""
return int(str(datetime.datetime.now().year)[-2:])
def try_error(name, predict_features, windows_size):
""" 处理异常
"""
if len(predict_features) != windows_size:
logger.warning("期号出现跳期,期号不连续!开始查找最近上一期期号!本期预测时间较久!")
last_current_year = (get_year() - 1) * 1000
max_times = 160
while len(predict_features) != windows_size:
# predict_features = spider(name, last_current_year + max_times, get_current_number(name), "predict")[[x[0] for x in ball_name]]
if mini_args.cq == 0:
predict_features = spider(name, last_current_year + max_times, get_current_number(name), "predict", windows_size)
else:
predict_features = spider_cq(name, last_current_year + max_times, get_current_number(name), "predict", windows_size)
# time.sleep(np.random.random(1).tolist()[0])
max_times -= 1
return predict_features
return predict_features
# def get_final_result(name, mode=0):
# """" 最终预测函数
# """
# m_args = model_args[name]["model_args"]
# windows_size = model_args[name]["model_args"]["windows_size"]
# current_number = get_current_number(mini_args.name)
# logger.info("正在创建【{}】数据集...".format(name_path[name]["name"]))
# red_data = create_train_data(name, windows_size, 1, "red")
# blue_data = create_train_data(name, windows_size, 1, "blue")
# logger.info("【{}】预测期号:{} 窗口大小:{}".format(name_path[name]["name"], int(current_number) + 1, windows_size))
# if name == "ssq":
# red_pred, red_name_list = get_red_ball_predict_result(red_data, m_args["sequence_len"], m_args["windows_size"])
# blue_pred = get_blue_ball_predict_result(name, blue_data, 0, m_args["windows_size"])
# ball_name_list = ["{}_{}".format(name[mode], i) for name, i in red_name_list] + [ball_name[1][mode]]
# pred_result_list = red_pred[0].tolist() + blue_pred.tolist()
# return {
# b_name: int(res) + 1 for b_name, res in zip(ball_name_list, pred_result_list)
# }
# elif name == "dlt":
# red_pred, red_name_list = get_red_ball_predict_result(red_data, m_args["red_sequence_len"], m_args["windows_size"])
# blue_pred, blue_name_list = get_blue_ball_predict_result(name, blue_data, m_args["blue_sequence_len"], m_args["windows_size"])
# ball_name_list = ["{}_{}".format(name[mode], i) for name, i in red_name_list] + ["{}_{}".format(name[mode], i) for name, i in blue_name_list]
# pred_result_list = red_pred[0].tolist() + blue_pred[0].tolist()
# return {
# b_name: int(res) + 1 for b_name, res in zip(ball_name_list, pred_result_list)
# }
# elif name == "pls":
# red_pred, red_name_list = get_red_ball_predict_result(red_data, m_args["red_sequence_len"], m_args["windows_size"])
# ball_name_list = ["{}_{}".format(name[mode], i) for name, i in red_name_list]
# pred_result_list = red_pred[0].tolist()
# return {
# b_name: int(res) for b_name, res in zip(ball_name_list, pred_result_list)
# }
# elif name == "kl8":
# red_pred, red_name_list = get_red_ball_predict_result(red_data, m_args["red_sequence_len"], m_args["windows_size"])
# ball_name_list = ["{}_{}".format(name[mode], i) for name, i in red_name_list]
# pred_result_list = red_pred[0].tolist()
# return {
# b_name: int(res) + 1 for b_name, res in zip(ball_name_list, pred_result_list)
# }
# def predict_run(name):
# global filedata, filetitle
# windows_size = model_args[name]["model_args"]["windows_size"]
# diff_number = windows_size - 1
# # logger.info("预测结果:{}".format(get_final_result(name, predict_features_)))
# predict_dict = get_final_result(name)
# ans = ""
# _data = []
# _title = []
# for item in predict_dict:
# if (item == "红球_1" or item == "红球"):
# ans += "红球:"
# if (item == "蓝球_1" or item == "蓝球"):
# ans += "蓝球:"
# ans += str(predict_dict[item]) + " "
# _data.append(int(predict_dict[item]))
# _title.append(item)
# logger.info("预测结果:{}".format(ans))
# filedata.append(_data.copy())
# filetitle = _title.copy()
# return filedata, filetitle
def write_strings_to_file(folder, strings):
# Get the current date and time
current_datetime = datetime.datetime.now()
# Format the current date and time as a string
datetime_string = current_datetime.strftime('%Y%m%d')
# Create the file path
file_path = os.path.join(folder, datetime_string + '.txt')
# Open the file in append mode and write the strings
with open(file_path, 'a') as file:
for string in strings:
file.write(string + '\n')
file.write('\n')
file.close()
# if __name__ == "__main__":
# spider_cq("kl8", "20180101", "20180110", "train")