集成学习实战 - 幸福感预测

一、赛题来源

  该案例为幸福感预测的经典课题，希望在现有科学研究之外有其它维度的算法尝试。具体来说，这个案例是数据挖掘类型的比赛 – 幸福感预测的baseline。我们需要使用个体变量（性别、年龄、地域、职业、健康、婚姻与政治面貌等）、家庭变量（父母、配偶、子女、家庭资本等等）、社会态度（公平、信用、公共服务等等）等139维度的信息来预测其对幸福感的影响。
这个比赛是来自天池的训练赛，地址为：https://tianchi.aliyun.com/competition/entrance/231702/introduction

二、处理思路

• 数据预处理
• 数据增广
• 特征工程
• 特征建模（LightGBM、XGBoost、随机森林、梯度提升决策树、极端随机森林回归）
• 佛系调参

三、代表实践

3.1 导入库文件

import os
import time 
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from datetime import datetime
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score, roc_curve, mean_squared_error,mean_absolute_error, f1_score
import lightgbm as lgb
import xgboost as xgb
from sklearn.ensemble import RandomForestRegressor as rfr
from sklearn.ensemble import ExtraTreesRegressor as etr
from sklearn.linear_model import BayesianRidge as br
from sklearn.ensemble import GradientBoostingRegressor as gbr
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from sklearn.linear_model import LinearRegression as lr
from sklearn.linear_model import ElasticNet as en
from sklearn.kernel_ridge import KernelRidge as kr
from sklearn.model_selection import  KFold, StratifiedKFold,GroupKFold, RepeatedKFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn import preprocessing
import logging
import warnings

warnings.filterwarnings('ignore') 

3.2 读取数据

train = pd.read_csv("train.csv", index_col='id', parse_dates=['survey_time'],encoding='latin-1') 
test = pd.read_csv("test.csv", index_col='id', parse_dates=['survey_time'],encoding='latin-1') #latin-1向下兼容ASCII

#存在happiness为-8的样本，且数量为12，占比不多，当作噪声点直接剔除
train = train[train["happiness"]!=-8].reset_index(drop=True)
train_data_copy = train.copy()
target_col = "happiness" #目标列
target = train_data_copy[target_col]
del train_data_copy[target_col] #去除目标列

data = pd.concat([train_data_copy,test],axis=0,ignore_index=True)

部分数据标签展示：

3.3 填充缺失值

  填充缺失值，在这里我采取的方式是将缺失值补全，使用fillna(value)，其中value的数值根据具体的情况来确定。例如将大部分缺失信息认为是零，将家庭成员数认为是1，将家庭收入这个特征认为是66365，即所有家庭的收入平均值

#填充缺失值 共25列 去掉4列 填充21列
#以下的列都是缺省的，视情况填补
data['work_status'] = data['work_status'].fillna(0)
data['work_yr'] = data['work_yr'].fillna(0)
data['work_manage'] = data['work_manage'].fillna(0)
data['work_type'] = data['work_type'].fillna(0)

data['edu_yr'] = data['edu_yr'].fillna(0)
data['edu_status'] = data['edu_status'].fillna(0)

data['s_work_type'] = data['s_work_type'].fillna(0)
data['s_work_status'] = data['s_work_status'].fillna(0)
data['s_political'] = data['s_political'].fillna(0)
data['s_hukou'] = data['s_hukou'].fillna(0)
data['s_income'] = data['s_income'].fillna(0)
data['s_birth'] = data['s_birth'].fillna(0)
data['s_edu'] = data['s_edu'].fillna(0)
data['s_work_exper'] = data['s_work_exper'].fillna(0)

data['minor_child'] = data['minor_child'].fillna(0)
data['marital_now'] = data['marital_now'].fillna(0)
data['marital_1st'] = data['marital_1st'].fillna(0)
data['social_neighbor']=data['social_neighbor'].fillna(0)
data['social_friend']=data['social_friend'].fillna(0)
data['hukou_loc']=data['hukou_loc'].fillna(1) #最少为1，表示户口
data['family_income']=data['family_income'].fillna(66365) #删除问题值后的平均值

我们还要处理一些特殊格式的信息，比如时间。我们首先通过划分年龄段将连续的年龄进行分层处理，这里分成6个区间。然后去计算具体年龄。
因为表格中给的数据只有到年，我们还要进行一个简单的转换：

#144+1 =145
#继续进行特殊的列进行数据处理
#读happiness_index.xlsx
data['survey_time'] = pd.to_datetime(data['survey_time'], format='%Y-%m-%d',errors='coerce')#防止时间格式不同的报错errors='coerce‘
data['survey_time'] = data['survey_time'].dt.year #仅仅是year，方便计算年龄
data['age'] = data['survey_time']-data['birth']
# print(data['age'],data['survey_time'],data['birth'])
#年龄分层 145+1=146
bins = [0,17,26,34,50,63,100]
data['age_bin'] = pd.cut(data['age'], bins, labels=[0,1,2,3,4,5]) 

以下是根据个人想法对缺失值的补全，补全方式不一，根据自己想法补填就行。

#对‘宗教’处理
data.loc[data['religion']<0,'religion'] = 1 #1为不信仰宗教
data.loc[data['religion_freq']<0,'religion_freq'] = 1 #1为从来没有参加过
#对‘教育程度’处理
data.loc[data['edu']<0,'edu'] = 4 #初中
data.loc[data['edu_status']<0,'edu_status'] = 0
data.loc[data['edu_yr']<0,'edu_yr'] = 0
#对‘个人收入’处理
data.loc[data['income']<0,'income'] = 0 #认为无收入
#对‘政治面貌’处理
data.loc[data['political']<0,'political'] = 1 #认为是群众
#对体重处理
data.loc[(data['weight_jin']<=80)&(data['height_cm']>=160),'weight_jin']= data['weight_jin']*2
data.loc[data['weight_jin']<=60,'weight_jin']= data['weight_jin']*2  #个人的想法，哈哈哈，没有60斤的成年人吧
#对身高处理
data.loc[data['height_cm']<150,'height_cm'] = 150 #成年人的实际情况
#对‘健康’处理
data.loc[data['health']<0,'health'] = 4 #认为是比较健康
data.loc[data['health_problem']<0,'health_problem'] = 4
#对‘沮丧’处理
data.loc[data['depression']<0,'depression'] = 4 #一般人都是很少吧
#对‘媒体’处理
data.loc[data['media_1']<0,'media_1'] = 1 #都是从不
data.loc[data['media_2']<0,'media_2'] = 1
data.loc[data['media_3']<0,'media_3'] = 1
data.loc[data['media_4']<0,'media_4'] = 1
data.loc[data['media_5']<0,'media_5'] = 1
data.loc[data['media_6']<0,'media_6'] = 1
#对‘空闲活动’处理
data.loc[data['leisure_1']<0,'leisure_1'] = 1 #都是根据自己的想法
data.loc[data['leisure_2']<0,'leisure_2'] = 5
data.loc[data['leisure_3']<0,'leisure_3'] = 3

我们还可以去用众数（代码中使用mode()来实现异常值的修正）。也是考虑其特征是空闲活动，所以选择去采用众数来对缺失值进行处理。

data.loc[data['leisure_4']<0,'leisure_4'] = data['leisure_4'].mode() #取众数
data.loc[data['leisure_5']<0,'leisure_5'] = data['leisure_5'].mode()
data.loc[data['leisure_6']<0,'leisure_6'] = data['leisure_6'].mode()
data.loc[data['leisure_7']<0,'leisure_7'] = data['leisure_7'].mode()
data.loc[data['leisure_8']<0,'leisure_8'] = data['leisure_8'].mode()
data.loc[data['leisure_9']<0,'leisure_9'] = data['leisure_9'].mode()
data.loc[data['leisure_10']<0,'leisure_10'] = data['leisure_10'].mode()
data.loc[data['leisure_11']<0,'leisure_11'] = data['leisure_11'].mode()
data.loc[data['leisure_12']<0,'leisure_12'] = data['leisure_12'].mode()
data.loc[data['socialize']<0,'socialize'] = 2 #很少
data.loc[data['relax']<0,'relax'] = 4 #经常
data.loc[data['learn']<0,'learn'] = 1 #从不，哈哈哈哈
#对‘社交’处理
data.loc[data['social_neighbor']<0,'social_neighbor'] = 0
data.loc[data['social_friend']<0,'social_friend'] = 0
data.loc[data['socia_outing']<0,'socia_outing'] = 1
data.loc[data['neighbor_familiarity']<0,'social_neighbor']= 4
#对‘社会公平性’处理
data.loc[data['equity']<0,'equity'] = 4
#对‘社会等级’处理
data.loc[data['class_10_before']<0,'class_10_before'] = 3
data.loc[data['class']<0,'class'] = 5
data.loc[data['class_10_after']<0,'class_10_after'] = 5
data.loc[data['class_14']<0,'class_14'] = 2
#对‘工作情况’处理
data.loc[data['work_status']<0,'work_status'] = 0
data.loc[data['work_yr']<0,'work_yr'] = 0
data.loc[data['work_manage']<0,'work_manage'] = 0
data.loc[data['work_type']<0,'work_type'] = 0
#对‘社会保障’处理
data.loc[data['insur_1']<0,'insur_1'] = 1
data.loc[data['insur_2']<0,'insur_2'] = 1
data.loc[data['insur_3']<0,'insur_3'] = 1
data.loc[data['insur_4']<0,'insur_4'] = 1
data.loc[data['insur_1']==0,'insur_1'] = 0
data.loc[data['insur_2']==0,'insur_2'] = 0
data.loc[data['insur_3']==0,'insur_3'] = 0
data.loc[data['insur_4']==0,'insur_4'] = 0

这里是通过取均值来对缺失值进行补全(代码为means())，在这里因为家庭的输入是连续值，不能去使用取众数的方法来处理，所以直接使用均值进行缺失值补全。

#对家庭情况处理
family_income_mean = data['family_income'].mean()
data.loc[data['family_income']<0,'family_income'] = family_income_mean
data.loc[data['family_m']<0,'family_m'] = 2
data.loc[data['family_status']<0,'family_status'] = 3
data.loc[data['house']<0,'house'] = 1
data.loc[data['car']<0,'car'] = 0
data.loc[data['car']==2,'car'] = 0
data.loc[data['son']<0,'son'] = 1
data.loc[data['daughter']<0,'daughter'] = 0
data.loc[data['minor_child']<0,'minor_child'] = 0
#对‘婚姻’处理
data.loc[data['marital_1st']<0,'marital_1st'] = 0
data.loc[data['marital_now']<0,'marital_now'] = 0
#对‘配偶’处理
data.loc[data['s_birth']<0,'s_birth'] = 0
data.loc[data['s_edu']<0,'s_edu'] = 0
data.loc[data['s_political']<0,'s_political'] = 0
data.loc[data['s_hukou']<0,'s_hukou'] = 0
data.loc[data['s_income']<0,'s_income'] = 0
data.loc[data['s_work_type']<0,'s_work_type'] = 0
data.loc[data['s_work_status']<0,'s_work_status'] = 0
data.loc[data['s_work_exper']<0,'s_work_exper'] = 0
#对‘父母情况’处理
data.loc[data['f_birth']<0,'f_birth'] = 1945
data.loc[data['f_edu']<0,'f_edu'] = 1
data.loc[data['f_political']<0,'f_political'] = 1
data.loc[data['f_work_14']<0,'f_work_14'] = 2
data.loc[data['m_birth']<0,'m_birth'] = 1940
data.loc[data['m_edu']<0,'m_edu'] = 1
data.loc[data['m_political']<0,'m_political'] = 1
data.loc[data['m_work_14']<0,'m_work_14'] = 2
#和同龄人相比社会经济地位
data.loc[data['status_peer']<0,'status_peer'] = 2
#和3年前比社会经济地位
data.loc[data['status_3_before']<0,'status_3_before'] = 2
#对‘观点’处理
data.loc[data['view']<0,'view'] = 4
#对期望年收入处理
data.loc[data['inc_ability']<=0,'inc_ability']= 2
inc_exp_mean = data['inc_exp'].mean()
data.loc[data['inc_exp']<=0,'inc_exp']= inc_exp_mean #取均值

#部分特征处理，取众数
for i in range(1,9+1):
    data.loc[data['public_service_'+str(i)]<0,'public_service_'+str(i)] = data['public_service_'+str(i)].dropna().mode().values
for i in range(1,13+1):
    data.loc[data['trust_'+str(i)]<0,'trust_'+str(i)] = data['trust_'+str(i)].dropna().mode().values

3.4 数据增广及特征工程

3.4.1 主要从以下方面进行数据增广

• 结婚年龄，配偶结婚年龄，配偶年龄差
• 各种收入比，和配偶，和自己的前10年、后10年，收入和住房面积比
• 各种小类的累计加和，做成指数，悠闲指数、满意指数、信任指数
• 同一省、市、县的个人收入、家人收入、住房面积及其他感觉比较
• 个人收入、家人收入、住房面积及其他感觉和同年龄比较
• 131个特征增加到263个特征
• 将离散变量进行one-hot转换，得到383维

3.4.2 代码实战

#第一次结婚年龄 147
data['marital_1stbir'] = data['marital_1st'] - data['birth'] 
#最近结婚年龄 148
data['marital_nowtbir'] = data['marital_now'] - data['birth'] 
#是否再婚 149
data['mar'] = data['marital_nowtbir'] - data['marital_1stbir']
#配偶年龄 150
data['marital_sbir'] = data['marital_now']-data['s_birth']
#配偶年龄差 151
data['age_'] = data['marital_nowtbir'] - data['marital_sbir'] 

#收入比 151+7 =158
data['income/s_income'] = data['income']/(data['s_income']+1)
data['income+s_income'] = data['income']+(data['s_income']+1)
data['income/family_income'] = data['income']/(data['family_income']+1)
data['all_income/family_income'] = (data['income']+data['s_income'])/(data['family_income']+1)
data['income/inc_exp'] = data['income']/(data['inc_exp']+1)
data['family_income/m'] = data['family_income']/(data['family_m']+0.01)
data['income/m'] = data['income']/(data['family_m']+0.01)

#收入/面积比 158+4=162
data['income/floor_area'] = data['income']/(data['floor_area']+0.01)
data['all_income/floor_area'] = (data['income']+data['s_income'])/(data['floor_area']+0.01)
data['family_income/floor_area'] = data['family_income']/(data['floor_area']+0.01)
data['floor_area/m'] = data['floor_area']/(data['family_m']+0.01)

#class 162+3=165
data['class_10_diff'] = (data['class_10_after'] - data['class'])
data['class_diff'] = data['class'] - data['class_10_before']
data['class_14_diff'] = data['class'] - data['class_14']
#悠闲指数 166
leisure_fea_lis = ['leisure_'+str(i) for i in range(1,13)]
data['leisure_sum'] = data[leisure_fea_lis].sum(axis=1) #skew
#满意指数 167
public_service_fea_lis = ['public_service_'+str(i) for i in range(1,10)]
data['public_service_sum'] = data[public_service_fea_lis].sum(axis=1) #skew

#信任指数 168
trust_fea_lis = ['trust_'+str(i) for i in range(1,14)]
data['trust_sum'] = data[trust_fea_lis].sum(axis=1) #skew

#province mean 168+13=181
data['province_income_mean'] = data.groupby(['province'])['income'].transform('mean').values
data['province_family_income_mean'] = data.groupby(['province'])['family_income'].transform('mean').values
data['province_equity_mean'] = data.groupby(['province'])['equity'].transform('mean').values
data['province_depression_mean'] = data.groupby(['province'])['depression'].transform('mean').values
data['province_floor_area_mean'] = data.groupby(['province'])['floor_area'].transform('mean').values
data['province_health_mean'] = data.groupby(['province'])['health'].transform('mean').values
data['province_class_10_diff_mean'] = data.groupby(['province'])['class_10_diff'].transform('mean').values
data['province_class_mean'] = data.groupby(['province'])['class'].transform('mean').values
data['province_health_problem_mean'] = data.groupby(['province'])['health_problem'].transform('mean').values
data['province_family_status_mean'] = data.groupby(['province'])['family_status'].transform('mean').values
data['province_leisure_sum_mean'] = data.groupby(['province'])['leisure_sum'].transform('mean').values
data['province_public_service_sum_mean'] = data.groupby(['province'])['public_service_sum'].transform('mean').values
data['province_trust_sum_mean'] = data.groupby(['province'])['trust_sum'].transform('mean').values

#city   mean 181+13=194
data['city_income_mean'] = data.groupby(['city'])['income'].transform('mean').values
data['city_family_income_mean'] = data.groupby(['city'])['family_income'].transform('mean').values
data['city_equity_mean'] = data.groupby(['city'])['equity'].transform('mean').values
data['city_depression_mean'] = data.groupby(['city'])['depression'].transform('mean').values
data['city_floor_area_mean'] = data.groupby(['city'])['floor_area'].transform('mean').values
data['city_health_mean'] = data.groupby(['city'])['health'].transform('mean').values
data['city_class_10_diff_mean'] = data.groupby(['city'])['class_10_diff'].transform('mean').values
data['city_class_mean'] = data.groupby(['city'])['class'].transform('mean').values
data['city_health_problem_mean'] = data.groupby(['city'])['health_problem'].transform('mean').values
data['city_family_status_mean'] = data.groupby(['city'])['family_status'].transform('mean').values
data['city_leisure_sum_mean'] = data.groupby(['city'])['leisure_sum'].transform('mean').values
data['city_public_service_sum_mean'] = data.groupby(['city'])['public_service_sum'].transform('mean').values
data['city_trust_sum_mean'] = data.groupby(['city'])['trust_sum'].transform('mean').values

#county  mean 194 + 13 = 207
data['county_income_mean'] = data.groupby(['county'])['income'].transform('mean').values
data['county_family_income_mean'] = data.groupby(['county'])['family_income'].transform('mean').values
data['county_equity_mean'] = data.groupby(['county'])['equity'].transform('mean').values
data['county_depression_mean'] = data.groupby(['county'])['depression'].transform('mean').values
data['county_floor_area_mean'] = data.groupby(['county'])['floor_area'].transform('mean').values
data['county_health_mean'] = data.groupby(['county'])['health'].transform('mean').values
data['county_class_10_diff_mean'] = data.groupby(['county'])['class_10_diff'].transform('mean').values
data['county_class_mean'] = data.groupby(['county'])['class'].transform('mean').values
data['county_health_problem_mean'] = data.groupby(['county'])['health_problem'].transform('mean').values
data['county_family_status_mean'] = data.groupby(['county'])['family_status'].transform('mean').values
data['county_leisure_sum_mean'] = data.groupby(['county'])['leisure_sum'].transform('mean').values
data['county_public_service_sum_mean'] = data.groupby(['county'])['public_service_sum'].transform('mean').values
data['county_trust_sum_mean'] = data.groupby(['county'])['trust_sum'].transform('mean').values

#ratio 相比同省 207 + 13 =220
data['income/province'] = data['income']/(data['province_income_mean'])                                      
data['family_income/province'] = data['family_income']/(data['province_family_income_mean'])   
data['equity/province'] = data['equity']/(data['province_equity_mean'])       
data['depression/province'] = data['depression']/(data['province_depression_mean'])                                                
data['floor_area/province'] = data['floor_area']/(data['province_floor_area_mean'])
data['health/province'] = data['health']/(data['province_health_mean'])
data['class_10_diff/province'] = data['class_10_diff']/(data['province_class_10_diff_mean'])
data['class/province'] = data['class']/(data['province_class_mean'])
data['health_problem/province'] = data['health_problem']/(data['province_health_problem_mean'])
data['family_status/province'] = data['family_status']/(data['province_family_status_mean'])
data['leisure_sum/province'] = data['leisure_sum']/(data['province_leisure_sum_mean'])
data['public_service_sum/province'] = data['public_service_sum']/(data['province_public_service_sum_mean'])
data['trust_sum/province'] = data['trust_sum']/(data['province_trust_sum_mean']+1)

#ratio 相比同市 220 + 13 =233
data['income/city'] = data['income']/(data['city_income_mean'])                                      
data['family_income/city'] = data['family_income']/(data['city_family_income_mean'])   
data['equity/city'] = data['equity']/(data['city_equity_mean'])       
data['depression/city'] = data['depression']/(data['city_depression_mean'])                                                
data['floor_area/city'] = data['floor_area']/(data['city_floor_area_mean'])
data['health/city'] = data['health']/(data['city_health_mean'])
data['class_10_diff/city'] = data['class_10_diff']/(data['city_class_10_diff_mean'])
data['class/city'] = data['class']/(data['city_class_mean'])
data['health_problem/city'] = data['health_problem']/(data['city_health_problem_mean'])
data['family_status/city'] = data['family_status']/(data['city_family_status_mean'])
data['leisure_sum/city'] = data['leisure_sum']/(data['city_leisure_sum_mean'])
data['public_service_sum/city'] = data['public_service_sum']/(data['city_public_service_sum_mean'])
data['trust_sum/city'] = data['trust_sum']/(data['city_trust_sum_mean'])

#ratio 相比同个地区 233 + 13 =246
data['income/county'] = data['income']/(data['county_income_mean'])                                      
data['family_income/county'] = data['family_income']/(data['county_family_income_mean'])   
data['equity/county'] = data['equity']/(data['county_equity_mean'])       
data['depression/county'] = data['depression']/(data['county_depression_mean'])                                                
data['floor_area/county'] = data['floor_area']/(data['county_floor_area_mean'])
data['health/county'] = data['health']/(data['county_health_mean'])
data['class_10_diff/county'] = data['class_10_diff']/(data['county_class_10_diff_mean'])
data['class/county'] = data['class']/(data['county_class_mean'])
data['health_problem/county'] = data['health_problem']/(data['county_health_problem_mean'])
data['family_status/county'] = data['family_status']/(data['county_family_status_mean'])
data['leisure_sum/county'] = data['leisure_sum']/(data['county_leisure_sum_mean'])
data['public_service_sum/county'] = data['public_service_sum']/(data['county_public_service_sum_mean'])
data['trust_sum/county'] = data['trust_sum']/(data['county_trust_sum_mean'])

#age   mean 246+ 13 =259
data['age_income_mean'] = data.groupby(['age'])['income'].transform('mean').values
data['age_family_income_mean'] = data.groupby(['age'])['family_income'].transform('mean').values
data['age_equity_mean'] = data.groupby(['age'])['equity'].transform('mean').values
data['age_depression_mean'] = data.groupby(['age'])['depression'].transform('mean').values
data['age_floor_area_mean'] = data.groupby(['age'])['floor_area'].transform('mean').values
data['age_health_mean'] = data.groupby(['age'])['health'].transform('mean').values
data['age_class_10_diff_mean'] = data.groupby(['age'])['class_10_diff'].transform('mean').values
data['age_class_mean'] = data.groupby(['age'])['class'].transform('mean').values
data['age_health_problem_mean'] = data.groupby(['age'])['health_problem'].transform('mean').values
data['age_family_status_mean'] = data.groupby(['age'])['family_status'].transform('mean').values
data['age_leisure_sum_mean'] = data.groupby(['age'])['leisure_sum'].transform('mean').values
data['age_public_service_sum_mean'] = data.groupby(['age'])['public_service_sum'].transform('mean').values
data['age_trust_sum_mean'] = data.groupby(['age'])['trust_sum'].transform('mean').values

# 和同龄人相比259 + 13 =272
data['income/age'] = data['income']/(data['age_income_mean'])                                      
data['family_income/age'] = data['family_income']/(data['age_family_income_mean'])   
data['equity/age'] = data['equity']/(data['age_equity_mean'])       
data['depression/age'] = data['depression']/(data['age_depression_mean'])                                                
data['floor_area/age'] = data['floor_area']/(data['age_floor_area_mean'])
data['health/age'] = data['health']/(data['age_health_mean'])
data['class_10_diff/age'] = data['class_10_diff']/(data['age_class_10_diff_mean'])
data['class/age'] = data['class']/(data['age_class_mean'])
data['health_problem/age'] = data['health_problem']/(data['age_health_problem_mean'])
data['family_status/age'] = data['family_status']/(data['age_family_status_mean'])
data['leisure_sum/age'] = data['leisure_sum']/(data['age_leisure_sum_mean'])
data['public_service_sum/age'] = data['public_service_sum']/(data['age_public_service_sum_mean'])
data['trust_sum/age'] = data['trust_sum']/(data['age_trust_sum_mean'])

此时，我们将特征从一开始的131维，扩充为了272维的特征。再去删除一些样本数很少的特征，比如负数太多的特征或是缺失值太多的特征。

#272-9=263
#删除数值特别少的和之前用过的特征
del_list=['id','survey_time','edu_other','invest_other','property_other','join_party','province','city','county']
use_feature = [clo for clo in data.columns if clo not in del_list]
data.fillna(0,inplace=True) #还是补0
train_shape = train.shape[0] #一共的数据量，训练集
features = data[use_feature].columns #删除后所有的特征
X_train_263 = data[:train_shape][use_feature].values
y_train = target
X_test_263 = data[train_shape:][use_feature].values
X_train_263.shape #最终一种263个特征```

我们得到了263维度特征，这里再去选择49个特征，去作为除了上面的263维度特征之外的另一组特征。

```python
imp_fea_49 = ['equity','depression','health','class','family_status','health_problem','class_10_after',
           'equity/province','equity/city','equity/county',
           'depression/province','depression/city','depression/county',
           'health/province','health/city','health/county',
           'class/province','class/city','class/county',
           'family_status/province','family_status/city','family_status/county',
           'family_income/province','family_income/city','family_income/county',
           'floor_area/province','floor_area/city','floor_area/county',
           'leisure_sum/province','leisure_sum/city','leisure_sum/county',
           'public_service_sum/province','public_service_sum/city','public_service_sum/county',
           'trust_sum/province','trust_sum/city','trust_sum/county',
           'income/m','public_service_sum','class_diff','status_3_before','age_income_mean','age_floor_area_mean',
           'weight_jin','height_cm',
           'health/age','depression/age','equity/age','leisure_sum/age'
          ]
train_shape = train.shape[0]
X_train_49 = data[:train_shape][imp_fea_49].values
X_test_49 = data[train_shape:][imp_fea_49].values
X_train_49.shape #最重要的49个特征

此时，对离散变量进行one-hot编码。再合成第三类特征，共383维度

imp_fea_49 = ['equity','depression','health','class','family_status','health_problem','class_10_after',
           'equity/province','equity/city','equity/county',
           'depression/province','depression/city','depression/county',
           'health/province','health/city','health/county',
           'class/province','class/city','class/county',
           'family_status/province','family_status/city','family_status/county',
           'family_income/province','family_income/city','family_income/county',
           'floor_area/province','floor_area/city','floor_area/county',
           'leisure_sum/province','leisure_sum/city','leisure_sum/county',
           'public_service_sum/province','public_service_sum/city','public_service_sum/county',
           'trust_sum/province','trust_sum/city','trust_sum/county',
           'income/m','public_service_sum','class_diff','status_3_before','age_income_mean','age_floor_area_mean',
           'weight_jin','height_cm',
           'health/age','depression/age','equity/age','leisure_sum/age'
          ]
train_shape = train.shape[0]
X_train_49 = data[:train_shape][imp_fea_49].values
X_test_49 = data[train_shape:][imp_fea_49].values
X_train_49.shape #最重要的49个特征

4、特征建模

• 运行速度：LightGBM > GBDT > XGBoost > RandomForest

lgb_263_param = {
'num_leaves': 7, 
'min_data_in_leaf': 20, #叶子可能具有的最小记录数
'objective':'regression',
'max_depth': -1,
'learning_rate': 0.003,
"boosting": "gbdt", #用gbdt算法
"feature_fraction": 0.18, #例如 0.18时，意味着在每次迭代中随机选择18％的参数来建树
"bagging_freq": 1,
"bagging_fraction": 0.55, #每次迭代时用的数据比例
"bagging_seed": 14,
"metric": 'mse',
"lambda_l1": 0.1005,
"lambda_l2": 0.1996, 
"verbosity": -1}
folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=4)   #交叉切分：5
oof_lgb_263 = np.zeros(len(X_train_263))
predictions_lgb_263 = np.zeros(len(X_test_263))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_263, y_train)):

    print("fold n°{}".format(fold_+1))
    trn_data = lgb.Dataset(X_train_263[trn_idx], y_train[trn_idx])
    val_data = lgb.Dataset(X_train_263[val_idx], y_train[val_idx])#train:val=4:1

    num_round = 10000
    lgb_263 = lgb.train(lgb_263_param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=500, early_stopping_rounds = 800)
    oof_lgb_263[val_idx] = lgb_263.predict(X_train_263[val_idx], num_iteration=lgb_263.best_iteration)
    predictions_lgb_263 += lgb_263.predict(X_test_263, num_iteration=lgb_263.best_iteration) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_lgb_263, target)))

CV score: 0.45145995

#---------------特征重要性
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
#设置value的显示长度为100，默认为50
pd.set_option('max_colwidth',100)
df = pd.DataFrame(data[use_feature].columns.tolist(), columns=['feature'])
df['importance']=list(lgb_263.feature_importance())
df = df.sort_values(by='importance',ascending=False)
plt.figure(figsize=(14,28))
sns.barplot(x="importance", y="feature", data=df.head(50))
plt.title('Features importance (averaged/folds)')
plt.tight_layout()

• XGBoost

#xgboost
xgb_263_params = {'eta': 0.02,  #lr
              'max_depth': 6,  
              'min_child_weight':3,#最小叶子节点样本权重和
              'gamma':0, #指定节点分裂所需的最小损失函数下降值。
              'subsample': 0.7,  #控制对于每棵树，随机采样的比例
              'colsample_bytree': 0.3,  #用来控制每棵随机采样的列数的占比 (每一列是一个特征)。
              'lambda':2,
              'objective': 'reg:linear', 
              'eval_metric': 'rmse', 
              'silent': True, 
              'nthread': -1}


folds = KFold(n_splits=5, shuffle=True, random_state=2019)
oof_xgb_263 = np.zeros(len(X_train_263))
predictions_xgb_263 = np.zeros(len(X_test_263))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_263, y_train)):
    print("fold n°{}".format(fold_+1))
    trn_data = xgb.DMatrix(X_train_263[trn_idx], y_train[trn_idx])
    val_data = xgb.DMatrix(X_train_263[val_idx], y_train[val_idx])

    watchlist = [(trn_data, 'train'), (val_data, 'valid_data')]
    xgb_263 = xgb.train(dtrain=trn_data, num_boost_round=3000, evals=watchlist, early_stopping_rounds=600, verbose_eval=500, params=xgb_263_params)
    oof_xgb_263[val_idx] = xgb_263.predict(xgb.DMatrix(X_train_263[val_idx]), ntree_limit=xgb_263.best_ntree_limit)
    predictions_xgb_263 += xgb_263.predict(xgb.DMatrix(X_test_263), ntree_limit=xgb_263.best_ntree_limit) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_xgb_263, target)))

CV score: 0.45170888

• RandomForest

#RandomForestRegressor随机森林
folds = KFold(n_splits=5, shuffle=True, random_state=2019)
oof_rfr_263 = np.zeros(len(X_train_263))
predictions_rfr_263 = np.zeros(len(X_test_263))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_263, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_263[trn_idx]
    tr_y = y_train[trn_idx]
    rfr_263 = rfr(n_estimators=1600,max_depth=9, min_samples_leaf=9, min_weight_fraction_leaf=0.0,
            max_features=0.25,verbose=1,n_jobs=-1)
    #verbose = 0 为不在标准输出流输出日志信息
#verbose = 1 为输出进度条记录
#verbose = 2 为每个epoch输出一行记录
    rfr_263.fit(tr_x,tr_y)
    oof_rfr_263[val_idx] = rfr_263.predict(X_train_263[val_idx])
    
    predictions_rfr_263 += rfr_263.predict(X_test_263) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_rfr_263, target)))

CV score: 0.47891165

• GBDT

#GradientBoostingRegressor梯度提升决策树
folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=2018)
oof_gbr_263 = np.zeros(train_shape)
predictions_gbr_263 = np.zeros(len(X_test_263))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_263, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_263[trn_idx]
    tr_y = y_train[trn_idx]
    gbr_263 = gbr(n_estimators=400, learning_rate=0.01,subsample=0.65,max_depth=7, min_samples_leaf=20,
            max_features=0.22,verbose=1)
    gbr_263.fit(tr_x,tr_y)
    oof_gbr_263[val_idx] = gbr_263.predict(X_train_263[val_idx])
    
    predictions_gbr_263 += gbr_263.predict(X_test_263) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_gbr_263, target)))

CV score: 0.45740604

5、Stacking集成

train_stack2 = np.vstack([oof_lgb_263,oof_xgb_263,oof_gbr_263,oof_rfr_263]).transpose()
# transpose()函数的作用就是调换x,y,z的位置,也就是数组的索引值
test_stack2 = np.vstack([predictions_lgb_263, predictions_xgb_263,predictions_gbr_263,predictions_rfr_263]).transpose()

#交叉验证:5折，重复2次
folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=7)
oof_stack2 = np.zeros(train_stack2.shape[0])
predictions_lr2 = np.zeros(test_stack2.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack2,target)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack2[trn_idx], target.iloc[trn_idx].values
    val_data, val_y = train_stack2[val_idx], target.iloc[val_idx].values
    #Kernel Ridge Regression
    lr2 = kr()
    lr2.fit(trn_data, trn_y)
    
    oof_stack2[val_idx] = lr2.predict(val_data)
    predictions_lr2 += lr2.predict(test_stack2) / 10
    
mean_squared_error(target.values, oof_stack2) 

CV Score:0.44679440309869306