├── result.jpg
├── Life.py
├── README.md
├── LICENSE
├── Genetic_algorithm.py
├── Feature_selection_genetic_algorithm.py
└── dataSet
    ├── validate_feature.csv
    └── train_feature.csv


/result.jpg:
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https://raw.githubusercontent.com/rogeroyer/feature_selection_GAAlgorithm/HEAD/result.jpg


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/Life.py:
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 1 | # -*- encoding: utf-8 -*-
 2 | 
 3 | SCORE_NONE = -1
 4 | 
 5 | class Life(object):
 6 |       """个体类"""
 7 |       def __init__(self, aGene=None):
 8 |             self.gene = aGene
 9 |             self.score = SCORE_NONE  # 初始化生命值 #
10 | 


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/README.md:
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 1 | <h2 align="center">基于遗传算法做特征选择</h2>
 2 | 
 3 | ### 运行环境
 4 | - python3.5+
 5 | - numpy
 6 | - pandas
 7 | - matplotlib
 8 | - sklearn
 9 | - LightGBM
10 | 
11 | ### 使用手册
12 | 
13 | - 更换dataSet目录里面的数据集（数据集过大我只上传了一小部分）
14 |     - train_feature.csv : 训练集
15 |     - validate_feature.csv ： 验证集
16 |     - 注：两个数据集内的维度要一致。且包含一个标签列，并命名为“target”
17 |     
18 | - 更换self.columns为train_feature.csv内的属性名，且第一个元素必须为标签名“target”,其它全为特征名称
19 | 
20 | - Genetic_algorithm.py众初始化种群initPopulation那里也要做相应修改
21 | 
22 | - 修改self.ga类参数(可选)
23 | 
24 | - 修改主函数里的群体个数和迭代次数(可选)
25 | ***
26 | 
27 | ### 模型及评价指标
28 | - 评价指标：auc     (可修改)
29 | - 模型：LightGBM    (可修改)
30 | ***
31 | 
32 | ### 结果
33 | 程序运行过程会打印出中间过程，最终会绘出迭代次数与最优个体适应图的[折线图](https://github.com/rogeroyer/feature_selection_GAAlgorithm/blob/master/result.jpg)以及打印出最有个体及其适应度。
34 | 
35 | ***
36 | 
37 | ### Attention
38 | 
39 | `经过两个不同比赛的尝试，评价指标分别是f1_score和auc，线上线下同增减的使用本算法才能收获一个好的结果。`
40 | 


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 roger
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/Genetic_algorithm.py:
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  1 | # -*- coding: utf-8 -*-
  2 | 
  3 | import copy
  4 | import random
  5 | from Life import Life
  6 | import numpy as np
  7 | 
  8 | class GA(object):
  9 |     """遗传算法类"""
 10 | 
 11 |     def __init__(self, aCrossRate, aMutationRage, aLifeCount, aGeneLenght, aMatchFun=lambda life: 1):
 12 |         self.croessRate = aCrossRate  # 交叉概率 #
 13 |         self.mutationRate = aMutationRage  # 突变概率 #
 14 |         self.lifeCount = aLifeCount   # 个体数 #
 15 |         self.geneLenght = aGeneLenght  # 基因长度 #
 16 |         self.matchFun = aMatchFun  # 适配函数
 17 |         self.lives = []  # 种群
 18 |         # self.best = None  # 保存这一代中最好的个体
 19 |         self.best = Life(np.random.randint(0, 2, self.geneLenght))  # 保存这一代中最好的个体
 20 | 
 21 |         self.gene = np.random.randint(0, 2, self.geneLenght)  # 保存全局最好的个体 #
 22 |         self.score = -1   # 保存全局最高的适应度 #
 23 | 
 24 |         self.generation = 0  # 第几代 #
 25 |         self.crossCount = 0  # 交叉数量 #
 26 |         self.mutationCount = 0  # 突变个数 #
 27 |         self.bounds = 0.0  # 适配值之和，用于选择时计算概率
 28 |         self.initPopulation()  # 初始化种群 #
 29 | 
 30 |     def initPopulation(self):
 31 |         """初始化种群"""
 32 |         self.lives = []
 33 |         count = 0
 34 |         while count < self.lifeCount:
 35 |             gene = np.random.randint(0, 2, self.geneLenght)
 36 |             life = Life(gene)
 37 |             random.shuffle(gene)  # 随机洗牌 #
 38 |             self.lives.append(life)
 39 |             count += 1
 40 | 
 41 |     def judge(self):
 42 |         """评估，计算每一个个体的适配值"""
 43 |         self.bounds = 0.0
 44 |         # self.best = self.lives[0]
 45 |         self.best.score = copy.deepcopy(self.score)  ####
 46 |         self.best.gene = copy.deepcopy(self.gene)  ####
 47 |         for life in self.lives:
 48 |             life.score = self.matchFun(life)
 49 |             self.bounds += life.score
 50 |             if self.best.score < life.score:     # score为auc 越大越好 #
 51 |                 self.best = life
 52 | 
 53 |         if self.score < self.best.score:                          ####
 54 |             self.score = copy.deepcopy(self.best.score)           ####
 55 |             self.gene = copy.deepcopy(self.best.gene)             ####
 56 | 
 57 |         # self.best.score = copy.deepcopy(self.score)               ####
 58 |         # self.best.gene = copy.deepcopy(self.gene)                 ####
 59 | 
 60 |     def cross(self, parent1, parent2):
 61 |         """
 62 |         函数功能：交叉
 63 |         """
 64 |         index1 = random.randint(0, self.geneLenght - 1)  # 随机生成突变起始位置 #
 65 |         index2 = random.randint(index1, self.geneLenght - 1)  # 随机生成突变终止位置 #
 66 | 
 67 |         for index in range(len(parent1.gene)):
 68 |             if (index >= index1) and (index <= index2):
 69 |                 parent1.gene[index], parent2.gene[index] = parent2.gene[index], parent1.gene[index]
 70 | 
 71 |         self.crossCount += 1
 72 |         return parent1.gene
 73 | 
 74 |     def mutation(self, gene):
 75 |         """突变"""
 76 |         index1 = random.randint(0, self.geneLenght - 1)
 77 |         index2 = random.randint(0, self.geneLenght - 1)
 78 |         # 随机选择两个位置的基因交换--变异 #
 79 |         newGene = gene[:]  # 产生一个新的基因序列，以免变异的时候影响父种群
 80 |         newGene[index1], newGene[index2] = newGene[index2], newGene[index1]
 81 |         self.mutationCount += 1
 82 |         return newGene
 83 | 
 84 |     def getOne(self):
 85 |         """选择一个个体"""
 86 |         r = random.uniform(0, self.bounds)
 87 |         for life in self.lives:
 88 |             r -= life.score
 89 |             if r <= 0:
 90 |                 return life
 91 | 
 92 |         raise Exception("选择错误", self.bounds)
 93 | 
 94 |     def newChild(self):
 95 |         """产生新的后代"""
 96 |         parent1 = self.getOne()
 97 |         rate = random.random()
 98 | 
 99 |         # 按概率交叉 #
100 |         if rate < self.croessRate:
101 |             # 交叉 #
102 |             parent2 = self.getOne()
103 |             gene = self.cross(parent1, parent2)
104 |         else:
105 |             gene = parent1.gene
106 | 
107 |         # 按概率突变 #
108 |         rate = random.random()
109 |         if rate < self.mutationRate:
110 |             gene = self.mutation(gene)
111 | 
112 |         return Life(gene)
113 | 
114 |     def next(self):
115 |         """产生下一代"""
116 |         self.judge()
117 |         newLives = []
118 |         newLives.append(self.best)  # 把最好的个体加入下一代 #
119 |         newLives[0].gene = copy.deepcopy(self.gene)
120 |         newLives[0].score = copy.deepcopy(self.score)
121 |         while len(newLives) < self.lifeCount:
122 |             newLives.append(self.newChild())
123 |         self.lives = newLives
124 |         self.generation += 1
125 | 
126 | 


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/Feature_selection_genetic_algorithm.py:
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  1 | # -*- encoding: utf-8 -*-
  2 | 
  3 | import random
  4 | import math
  5 | import numpy as np
  6 | import lightgbm as lgb
  7 | import pandas as pd
  8 | from Genetic_algorithm import GA
  9 | import matplotlib.pyplot as plt
 10 | from sklearn.metrics import roc_auc_score
 11 | 
 12 | 
 13 | class FeatureSelection(object):
 14 |     def __init__(self, aLifeCount=10):
 15 |         # self.columns = ['target', 'credit_score', 'overdraft', 'quota', 'quota_is_zero', 'quota_surplus', 'quota_rate', 'credit_score_rank', 'all_is_null_x', 'all_is_zero', 'credit_score_is_null', 'quota_surplus_is_null', 'unit_price_mean', 'unit_price_max', 'unit_price_min', 'unit_price_std', 'record_is_unique', 'auth_id_card_is_null', 'auth_time_is_null', 'phone_is_null', 'all_is_null_y', 'all_not_null', 'card_time_is_null', 'time_phone_is_null', 'feature_1', 'register_days', 'day_mean', 'day_max', 'day_min', 'order_record_count', 'order_record_unique']
 16 |         self.columns = ['target', 'credit_score', 'overdraft', 'quota', 'quota_is_zero', 'quota_surplus', 'quota_rate', 'credit_score_rank', 'all_is_null_x', 'all_is_zero', 'credit_score_is_null', 'quota_surplus_is_null', 'unit_price_mean', 'unit_price_max', 'unit_price_min', 'unit_price_std', 'order_all_is_null', 'amt_order_mean', 'amt_order_max', 'amt_order_min', 'amt_order_std', 'type_pay_count', 'sts_order_count', 'order_phone_count', 'name_rec_md5_count', '货到付款', '在线+京券支付', '上门自提', '余额+限品东券', '在线+余额', '在线+全品东券', 'null_x', '定向京券支付', '高校代理-自己支付', '京豆混合支付', '在线+全品京券', '在线支付', '在线', '京豆支付', '在线+京豆', '前台自付', '白条支付', '在线+东券', '混合支付', '在线+限品东券', '余额', '积分支付', '京券全额支付', '定向京券', '分期付款', '在线支付 ', '邮局汇款', '高校代理-代理支付', '在线预付', '分期付款(招行)', '定向东券', '京豆东券混合支付', '东券混合支付', '在线+东券支付', '京豆', '在线+余额+限品东券', '在线+定向东券', '全品京券', '限品京券', '在线+限品京券', '公司转账', '京券混合支付', 'type_pay_len', '已晒单', '已完成', '等待审核', '已收货', '预订结束', '抢票已取消', 'null_y', '订单已取消', '正在处理', '配送退货', '预约完成', '未抢中', '充值成功', '未入住', '请上门自提', '等待付款', '已退款', '等待付款确认', '已取消', '下单失败', '失败退款', '商品出库', '缴费成功', '已取消订单', '出票成功', '购买成功', '付款成功', '等待收货', '等待处理', '等待退款', '充值失败;退款成功', '退款成功', '完成', '退款完成', '出票失败', '订单取消', '充值失败', '正在出库', 'sts_order_len', 'birthday_is_zero', 'sex_not_male', 'female', 'male', 'sex_secret', 'merriage1', 'merriage2', 'merriage3', 'merriage_is_null', 'account_grade1', 'account_grade2', 'account_grade3', 'account_grade4', 'account_grade5', 'account_grade_is_null', 'qq_bound_is_null', 'wechat_bound_is_null', 'degree', 'id_card_is_null', 'income1', 'income2', 'income3', 'income4', 'income5', 'age_one', 'age_two', 'age_three', 'age_four', 'age_five', 'all_null', 'record_count', '湖南', '河北', '甘肃', '北京', '香港', '陕西', '安徽', '山东', '天津', 'null', '青海', '吉林', '江西', '海南', '重庆', '台湾', '新疆', '辽宁', '广东', '上海', '河南', '广西', '贵州', '山西', '四川', '内蒙', '浙江', '黑龙', '湖北', '宁夏', '福建', '江苏', '云南', '西藏', 'province_len', 'phone_count', 'card_record_count', 'store_card_count', 'have_credit_card', 'card_category_count', 'credit_count', 'card_count_one', 'record_is_unique', 'auth_id_card_is_null', 'auth_time_is_null', 'phone_is_null', 'all_is_null_y', 'all_not_null', 'card_time_is_null', 'time_phone_is_null', 'feature_1', 'register_days', 'day_mean', 'day_max', 'day_min', 'order_record_count', 'order_record_unique']
 17 |         self.train_data = pd.read_csv(r'dataSet/train_feature.csv', low_memory=False, usecols=self.columns)
 18 |         self.validate_data = pd.read_csv(r'dataSet/validate_feature.csv', low_memory=False, usecols=self.columns)                  # 由于特征数量较多，这里只读取了上面的部分特征 #
 19 |         self.lifeCount = aLifeCount
 20 |         self.ga = GA(aCrossRate=0.7,
 21 |                      aMutationRage=0.1,
 22 |                      aLifeCount=self.lifeCount,
 23 |                      aGeneLenght=len(self.columns) - 1,
 24 |                      aMatchFun=self.matchFun())
 25 | 
 26 |     def auc_score(self, order):
 27 |         print(order)
 28 |         features = self.columns[1:]
 29 |         features_name = []
 30 |         for index in range(len(order)):
 31 |             if order[index] == 1:
 32 |                 features_name.append(features[index])
 33 | 
 34 |         labels = np.array(self.train_data['target'], dtype=np.int8)
 35 |         d_train = lgb.Dataset(self.train_data[features_name], label=labels)
 36 |         params = {
 37 |             'boosting': 'gbdt',
 38 |             'objective': 'binary',
 39 |             'metric': 'auc',
 40 |             'train_metric': False,
 41 |             'subsample': 0.8,
 42 |             'learning_rate': 0.05,
 43 |             'num_leaves': 96,
 44 |             'num_threads': 4,
 45 |             'max_depth': 5,
 46 |             'colsample_bytree': 0.8,
 47 |             'lambda_l2': 0.01,
 48 |             'verbose': -1,     # inhibit print info #
 49 |         }
 50 |         rounds = 100
 51 |         watchlist = [d_train]
 52 |         bst = lgb.train(params=params, train_set=d_train, num_boost_round=rounds, valid_sets=watchlist, verbose_eval=10)
 53 |         predict = bst.predict(self.validate_data[features_name])
 54 |         print(features_name)
 55 |         score = roc_auc_score(self.validate_data['target'], predict)
 56 |         print('validate score:', score)
 57 |         return score
 58 | 
 59 |     def matchFun(self):
 60 |         return lambda life: self.auc_score(life.gene)
 61 | 
 62 |     def run(self, n=0):
 63 |         distance_list = []
 64 |         generate = [index for index in range(1, n + 1)]
 65 |         while n > 0:
 66 |             self.ga.next()
 67 |             # distance = self.auc_score(self.ga.best.gene)
 68 |             distance = self.ga.score                      ####
 69 |             distance_list.append(distance)
 70 |             print(("第%d代 : 当前最好特征组合的线下验证结果为：%f") % (self.ga.generation, distance))
 71 |             n -= 1
 72 | 
 73 |         print('当前最好特征组合:')
 74 |         string = []
 75 |         flag = 0
 76 |         features = self.columns[1:]
 77 |         for index in self.ga.gene:                                  ####
 78 |             if index == 1:
 79 |                 string.append(features[flag])
 80 |             flag += 1
 81 |         print(string)
 82 |         print('线下最高为auc：', self.ga.score)                      ####
 83 | 
 84 |         '''画图函数'''
 85 |         plt.plot(generate, distance_list)
 86 |         plt.xlabel('generation')
 87 |         plt.ylabel('distance')
 88 |         plt.title('generation--auc-score')
 89 |         plt.show()
 90 | 
 91 | 
 92 | def main():
 93 |     fs = FeatureSelection(aLifeCount=20)
 94 |     rounds = 100    # 算法迭代次数 #
 95 |     fs.run(rounds)
 96 | 
 97 | 
 98 | if __name__ == '__main__':
 99 |     main()
100 | 
101 | 
102 | 


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/dataSet/validate_feature.csv:
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 1 | target,id,credit_score,overdraft,quota,quota_is_zero,quota_surplus,quota_rate,credit_score_rank,all_is_null_x,all_is_zero,credit_score_is_null,quota_surplus_is_null,unit_price_mean,unit_price_max,unit_price_min,unit_price_std,order_all_is_null,amt_order_mean,amt_order_max,amt_order_min,amt_order_std,type_pay_count,sts_order_count,order_phone_count,name_rec_md5_count,货到付款,在线+京券支付,上门自提,余额+限品东券,在线+余额,在线+全品东券,null_x,定向京券支付,高校代理-自己支付,京豆混合支付,在线+全品京券,在线支付,在线,京豆支付,在线+京豆,前台自付,白条支付,在线+东券,混合支付,在线+限品东券,余额,积分支付,京券全额支付,定向京券,分期付款,在线支付 ,邮局汇款,高校代理-代理支付,在线预付,分期付款(招行),定向东券,京豆东券混合支付,东券混合支付,在线+东券支付,京豆,在线+余额+限品东券,在线+定向东券,全品京券,限品京券,在线+限品京券,公司转账,京券混合支付,type_pay_len,已晒单,已完成,等待审核,已收货,预订结束,抢票已取消,null_y,订单已取消,正在处理,配送退货,预约完成,未抢中,充值成功,未入住,请上门自提,等待付款,已退款,等待付款确认,已取消,下单失败,失败退款,商品出库,缴费成功,已取消订单,出票成功,购买成功,付款成功,等待收货,等待处理,等待退款,充值失败;退款成功,退款成功,完成,退款完成,出票失败,订单取消,充值失败,正在出库,sts_order_len,birthday_is_zero,sex_not_male,female,male,sex_secret,merriage1,merriage2,merriage3,merriage_is_null,account_grade1,account_grade2,account_grade3,account_grade4,account_grade5,account_grade_is_null,qq_bound_is_null,wechat_bound_is_null,degree,id_card_is_null,income1,income2,income3,income4,income5,age_one,age_two,age_three,age_four,age_five,all_null,record_count,湖南,河北,甘肃,北京,香港,陕西,安徽,山东,天津,null,青海,吉林,江西,海南,重庆,台湾,新疆,辽宁,广东,上海,河南,广西,贵州,山西,四川,内蒙,浙江,黑龙,湖北,宁夏,福建,江苏,云南,西藏,province_len,phone_count,card_record_count,store_card_count,have_credit_card,card_category_count,credit_count,card_count_one,record_is_unique,auth_id_card_is_null,auth_time_is_null,phone_is_null,all_is_null_y,all_not_null,card_time_is_null,time_phone_is_null,feature_1,register_days,day_mean,day_max,day_min,order_record_count,order_record_unique
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29 | 


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/dataSet/train_feature.csv:
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