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prepare.py
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prepare.py
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#!/usr/bin/env python
# _*_ coding:utf-8 _*_
# ============================================
# @Time : 2020/01/15 22:43
# @Author : WanDaoYi
# @FileName : prepare.py
# ============================================
import numpy as np
import random
from config import cfg
class DataPrepare(object):
def __init__(self):
self.data_path = cfg.TRAIN.DATA_PATH
self.test_path = cfg.TEST.DATA_PATH
self.train_data_info_path = cfg.TRAIN.TRAIN_DATA_INFO_PATH
self.val_data_info_path = cfg.TRAIN.VAL_DATA_INFO_PATH
self.test_data_info_path = cfg.TEST.TEST_DATA_INFO_PATH
self.train_percent = cfg.COMMON.TRAIN_PERCENT
pass
# 读取数据
def read_data(self, data_path):
"""
:param data_path: 读取文件的路径
:return:
"""
time_info = []
concate_info = []
# 去掉数据中的 空格符 制表符 . + 等符合。
with open(data_path, "r") as file:
txt_info = file.readlines()
for data in txt_info:
data_info = data.strip()
data_num = data_info.split("\t")
time_num = data_num[0]
time_info.append(time_num)
num_info = data_num[-1]
num_list = num_info.split(".")
last_num = num_list[-1]
split_num = last_num.split("+")
concate_num = num_list[: -1] + split_num
concate_info.append(concate_num)
pass
# 判断彩票周期是否倒序,如果倒序则要处理为顺序。后面设置 label 需要用到
time_first = int(time_info[0])
time_last = int(time_info[-1])
if time_first > time_last:
time_info.reverse()
concate_info.reverse()
pass
# 将分开的数据整合
data_info = np.c_[time_info, concate_info]
return data_info
# 数据划分方法
def data_split(self, data_list, split_percent):
"""
:param data_list: 要划分的list
:param split_percent: 划分的百分比
:return:
"""
# 数据的长度
data_len = len(data_list)
# 划分的长度
n_split = int(split_percent * data_len)
i = 0
n_split_index_list = []
while True:
random_num = random.randint(0, data_len)
if random_num in n_split_index_list:
continue
n_split_index_list.append(random_num)
i += 1
if i == n_split:
break
pass
n_split_data_list = []
leave_data_list = []
for index_number, value_info in enumerate(data_list):
if index_number in n_split_index_list:
n_split_data_list.append(value_info)
else:
leave_data_list.append(value_info)
pass
pass
return n_split_data_list, leave_data_list
def add_label(self, data_list):
# 设置 label 值。
# 将下一期开的特别码 的 单双,设置为 这期的label 值。双为 0,单为 1
label_list = []
for number in data_list:
obj_number = int(number[-1])
if obj_number % 2 == 0:
label_list.append("0")
else:
label_list.append("1")
pass
obj_data = np.c_[data_list[: -1], label_list[1:]]
return obj_data
pass
# 保存数据
def data_save(self, data_path, data_list):
"""
:param data_path: 保存路径
:param data_list: 需要保存的 python 原生 list
:return:
"""
# 将目标数据进行保存,每个数值,以 . 号 隔开
data_file = open(data_path, "w")
for data in data_list:
data_file.write(".".join([info for info in data]))
data_file.write("\n")
pass
data_file.close()
pass
# 数据拼接保存
def generate_data(self):
train_info = self.read_data(self.data_path)
test_info = self.read_data(self.test_path)
# 下面的方法,可以将 list 中内容的类型之间转为 int 类型
# data_info = np.array(data_info, dtype=int)
train_val_data = self.add_label(train_info)
# 转为python 原生的list,下面 write 方法需要原生的 list,numpy 的list无法保存
train_val_data = train_val_data.tolist()
test_data_list = test_info.tolist()
train_data_list, val_data_list = self.data_split(train_val_data, self.train_percent)
self.data_save(self.train_data_info_path, train_data_list)
self.data_save(self.val_data_info_path, val_data_list)
self.data_save(self.test_data_info_path, test_data_list)
print("data prepare already!")
pass
if __name__ == "__main__":
demo = DataPrepare()
demo.generate_data()
pass