[人工智能]day7-案例（幸福感预测）详解

思路理解

准备工作：首先进行数据分析，将几张数据表对应的信息内容加以对比，理解数据讲述的内容；导入各种包；导入数据集，可选用‘latin-1’的编码方式（向下兼容多），删去一些统计错误的数据（按行删除），删去‘happiness’目标列，合并训练集与测试集；查看数据的基本信息。

数据预处理：填补缺失值，利用所给的数据集信息查看变量对应的意思，根据常识对缺失值做出相应判断（主观想法）进行填充，其他缺失值用正常数据的平均值或者众数等合理值填充；对特殊格式的数据进行处理，将数据与数据联系起来（例如：用调查时间的年份减去出生年份得到年龄，进行年龄分层，将连续值转换为离散值）；对错误值进行处理，参考所给数据集对数据进行合理性判断；

数据增广：进一步分析特征之间的关系，添加新特征（例如：收入比、悠闲指数）。这里将原来131维的特征扩充到了272维。然后删去数值特别少和之前使用过的特征，最后得到263个特征。

特征工程：构建完成三组特征工程。第一组即为刚刚构建的原始数据集；第二组为用LightGBM选出263个特征中最重要的49个特征，计算每个特征的重要性，并做重要性排序，选择图像跳变前的49个最重要特征；第三组为对有需要的离散型变量进行one-hot编码后合成的数据集，共383维。

金字塔型建模：利用lightgbm、xgboost等多种模型对原始263维数据进行建模，得到预测结果，然后对得到的结果进行模型融合集成学习（stacking），得到融合后的预测结果；如法炮制对最重要的49维数据集、one-hot编码后的383维数据集进行处理，得到两者的预测结果；最后对三种维度数据集的预测结果进行模型融合，得到最终结果（整个过程其实就是套娃，举个例子：假设我有三组数据A、B、C，然后A组数据用4个模型去跑，得到A1、A2、A3、A4的预测结果，将四组预测结果模型融合得到AS预测结果，同理对B、C组数据进行处理，得到BS、CS预测结果，最后，将AS、BS、CS三组结果进行融合，得到最终结果S）

模型保存：最后保存为csv等格式，这里我们预测出的值为1-5的连续值，但是我们的ground truth是整数值，所以为了进一步优化我们的结果，我们对结果进行整数解的近似处理。（例如：值大于1.96，小于2.04的直接置为整数2）

一、案例资料

1、背景介绍

幸福感是一个古老而深刻的话题，是人类世代追求的方向。与幸福感相关的因素成千上万、因人而异，大如国计民生，小如路边烤红薯，都会对幸福感产生影响。这些错综复杂的因素中，我们能找到其中的共性，一窥幸福感的要义吗？

另外，在社会科学领域，幸福感的研究占有重要的位置。这个涉及了哲学、心理学、社会学、经济学等多方学科的话题复杂而有趣；同时与大家生活息息相关，每个人对幸福感都有自己的衡量标准。如果能发现影响幸福感的共性，生活中是不是将多一些乐趣；如果能找到影响幸福感的政策因素，便能优化资源配置来提升国民的幸福感。目前社会科学研究注重变量的可解释性和未来政策的落地，主要采用了线性回归和逻辑回归的方法，在收入、健康、职业、社交关系、休闲方式等经济人口因素；以及政府公共服务、宏观经济环境、税负等宏观因素上有了一系列的推测和发现。

该案例为幸福感预测这一经典课题，希望在现有社会科学研究外有其他维度的算法尝试，结合多学科各自优势，挖掘潜在的影响因素，发现更多可解释、可理解的相关关系。

具体来说，该案例就是一个数据挖掘类型的比赛——幸福感预测的baseline。具体来说，我们需要使用包括个体变量（性别、年龄、地域、职业、健康、婚姻与政治面貌等等）、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。

我们的数据来源于国家官方的《中国综合社会调查（CGSS）》文件中的调查结果中的数据，数据来源可靠可依赖。

2、数据信息

赛题要求使用以上 139 维的特征，使用 8000 余组数据进行对于个人幸福感的预测（预测值为1，2，3，4，5，其中1代表幸福感最低，5代表幸福感最高）。 因为考虑到变量个数较多，部分变量间关系复杂，数据分为完整版和精简版两类。可从精简版入手熟悉赛题后，使用完整版挖掘更多信息。在这里我直接使用了完整版的数据。赛题也给出了index文件中包含每个变量对应的问卷题目，以及变量取值的含义；survey文件中为原版问卷，作为补充以方便理解问题背景。

3、评价指标

最终的评价指标为均方误差MSE，即：

二、第三方包准备

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') #消除warning

注意这里安装lightgbm时，要指定版本号为2.0.3

pip install lightgbm==2.0.3

三、数据导入及预处理

1、数据导入

#加载数据集
#latin-1向下兼容ASCII
train = pd.read_csv("train.csv", parse_dates=['survey_time'],encoding='latin-1') 
test = pd.read_csv("test.csv", parse_dates=['survey_time'],encoding='latin-1') 
#删去异常数据 删去"happiness" 为-8的行
train = train[train["happiness"]!=-8].reset_index(drop=True)
train_data_copy = train.copy()
target_col = "happiness" #目标列
#这里的目的是什么？
#答：目的是避免重复操作，所以把train和test数据先纵向合并
target = train_data_copy[target_col]
del train_data_copy[target_col] 
# 纵向合并
data = pd.concat([train_data_copy,test],axis=0,ignore_index=True)
data.shape

(10956, 139)

train.happiness.describe()

2、数据预处理

2.1、异常值处理

#make feature +5
#csv中有复数值：-1、-2、-3、-8，将他们视为有问题的特征，但是不删去
def getres1(row):
    return len([x for x in row.values if type(x)==int and x<0])

def getres2(row):
    return len([x for x in row.values if type(x)==int and x==-8])

def getres3(row):
    return len([x for x in row.values if type(x)==int and x==-1])

def getres4(row):
    return len([x for x in row.values if type(x)==int and x==-2])

def getres5(row):
    return len([x for x in row.values if type(x)==int and x==-3])

#检查数据
data['neg1'] = data[data.columns].apply(lambda row:getres1(row),axis=1)
data.loc[data['neg1']>20,'neg1'] = 20  #平滑处理,最多出现20次
data['neg2'] = data[data.columns].apply(lambda row:getres2(row),axis=1)
data['neg3'] = data[data.columns].apply(lambda row:getres3(row),axis=1)
data['neg4'] = data[data.columns].apply(lambda row:getres4(row),axis=1)
data['neg5'] = data[data.columns].apply(lambda row:getres5(row),axis=1)

结果如下：

#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

#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]) 

#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

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

#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[0]
for i in range(1,13+1):
    data.loc[data['trust_'+str(i)]<0,'trust_'+str(i)] = data['trust_'+str(i)].dropna().mode().values[0]

查看此时数据为146维

data.shape
(10956, 146)

2.2、数据増广

#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'])

此处可用构造一个函数来简化重复性代码，节省空间方便阅读

查看数据为272维

data.shape
shape (10956, 272)

data.head()

得到263维特征：

#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

(7988, 263)

选择最重要的49个特征：

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

(7988, 49)

选择需要进行onehot编码的离散变量进行one-hot编码，再合成为第三类特征，共383维：

cat_fea = ['survey_type','gender','nationality','edu_status','political','hukou','hukou_loc','work_exper','work_status','work_type',
           'work_manage','marital','s_political','s_hukou','s_work_exper','s_work_status','s_work_type','f_political','f_work_14',
           'm_political','m_work_14']
noc_fea = [clo for clo in use_feature if clo not in cat_fea]

onehot_data = data[cat_fea].values
enc = preprocessing.OneHotEncoder(categories = 'auto')
oh_data=enc.fit_transform(onehot_data).toarray()
oh_data.shape #变为onehot编码格式

X_train_oh = oh_data[:train_shape,:]
X_test_oh = oh_data[train_shape:,:]
X_train_oh.shape #其中的训练集

X_train_383 = np.column_stack([data[:train_shape][noc_fea].values,X_train_oh])#先是noc，再是cat_fea
X_test_383 = np.column_stack([data[train_shape:][noc_fea].values,X_test_oh])
X_train_383.shape

(7988, 383)

四、特征建模

1、选择263维原始特征进行建模

1.1、lightGBM

##### lgb_263 #
#lightGBM决策树
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)))

#---------------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()

1.2、xgboost

##### xgb_263
#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)))

1.3、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)))

1.4、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)))

1.5、ExtraTreesRegressor 极端随机森林回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_etr_263 = np.zeros(train_shape)
predictions_etr_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]
    etr_263 = etr(n_estimators=1000,max_depth=8, min_samples_leaf=12, min_weight_fraction_leaf=0.0,
            max_features=0.4,verbose=1,n_jobs=-1)
    etr_263.fit(tr_x,tr_y)
    oof_etr_263[val_idx] = etr_263.predict(X_train_263[val_idx])
    
    predictions_etr_263 += etr_263.predict(X_test_263) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_etr_263, target)))

1.6、集成建模-stack2

train_stack2 = np.vstack([oof_lgb_263,oof_xgb_263,oof_gbr_263,oof_rfr_263,oof_etr_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,predictions_etr_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) 

2、选择49维重要特征进行建模

2.1、lightGBM

lgb_49_param = {
'num_leaves': 9,
'min_data_in_leaf': 23,
'objective':'regression',
'max_depth': -1,
'learning_rate': 0.002,
"boosting": "gbdt",
"feature_fraction": 0.45,
"bagging_freq": 1,
"bagging_fraction": 0.65,
"bagging_seed": 15,
"metric": 'mse',
"lambda_l2": 0.2, 
"verbosity": -1}
folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=9)   
oof_lgb_49 = np.zeros(len(X_train_49))
predictions_lgb_49 = np.zeros(len(X_test_49))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    trn_data = lgb.Dataset(X_train_49[trn_idx], y_train[trn_idx])
    val_data = lgb.Dataset(X_train_49[val_idx], y_train[val_idx])

    num_round = 12000
    lgb_49 = lgb.train(lgb_49_param, trn_data, num_round, valid_sets = [trn_data, val_data], verbose_eval=1000, early_stopping_rounds = 1000)
    oof_lgb_49[val_idx] = lgb_49.predict(X_train_49[val_idx], num_iteration=lgb_49.best_iteration)
    predictions_lgb_49 += lgb_49.predict(X_test_49, num_iteration=lgb_49.best_iteration) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_lgb_49, target)))

2.2、xgboost

xgb_49_params = {'eta': 0.02, 
              'max_depth': 5, 
              'min_child_weight':3,
              'gamma':0,
              'subsample': 0.7, 
              'colsample_bytree': 0.35, 
              'lambda':2,
              'objective': 'reg:linear', 
              'eval_metric': 'rmse', 
              'silent': True, 
              'nthread': -1}


folds = KFold(n_splits=5, shuffle=True, random_state=2019)
oof_xgb_49 = np.zeros(len(X_train_49))
predictions_xgb_49 = np.zeros(len(X_test_49))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    trn_data = xgb.DMatrix(X_train_49[trn_idx], y_train[trn_idx])
    val_data = xgb.DMatrix(X_train_49[val_idx], y_train[val_idx])

    watchlist = [(trn_data, 'train'), (val_data, 'valid_data')]
    xgb_49 = xgb.train(dtrain=trn_data, num_boost_round=3000, evals=watchlist, early_stopping_rounds=600, verbose_eval=500, params=xgb_49_params)
    oof_xgb_49[val_idx] = xgb_49.predict(xgb.DMatrix(X_train_49[val_idx]), ntree_limit=xgb_49.best_ntree_limit)
    predictions_xgb_49 += xgb_49.predict(xgb.DMatrix(X_test_49), ntree_limit=xgb_49.best_ntree_limit) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_xgb_49, target)))

2.3、GradientBoostingRegressor梯度提升决策树

folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=2018)
oof_gbr_49 = np.zeros(train_shape)
predictions_gbr_49 = np.zeros(len(X_test_49))
for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_49[trn_idx]
    tr_y = y_train[trn_idx]
    gbr_49 = gbr(n_estimators=600, learning_rate=0.01,subsample=0.65,max_depth=6, min_samples_leaf=20,
            max_features=0.35,verbose=1)
    gbr_49.fit(tr_x,tr_y)
    oof_gbr_49[val_idx] = gbr_49.predict(X_train_49[val_idx])
    
    predictions_gbr_49 += gbr_49.predict(X_test_49) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_gbr_49, target)))

2.3、集成建模-stack3

train_stack3 = np.vstack([oof_lgb_49,oof_xgb_49,oof_gbr_49]).transpose()
test_stack3 = np.vstack([predictions_lgb_49, predictions_xgb_49,predictions_gbr_49]).transpose()
#
folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=7)
oof_stack3 = np.zeros(train_stack3.shape[0])
predictions_lr3 = np.zeros(test_stack3.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack3,target)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack3[trn_idx], target.iloc[trn_idx].values
    val_data, val_y = train_stack3[val_idx], target.iloc[val_idx].values
        #Kernel Ridge Regression
    lr3 = kr()
    lr3.fit(trn_data, trn_y)
    
    oof_stack3[val_idx] = lr3.predict(val_data)
    predictions_lr3 += lr3.predict(test_stack3) / 10
    
mean_squared_error(target.values, oof_stack3) 

3.选择383维one-hot扩展特征进行建模

3.1、Kernel Ridge Regression 基于核的岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_kr_383 = np.zeros(train_shape)
predictions_kr_383 = np.zeros(len(X_test_383))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_383, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_383[trn_idx]
    tr_y = y_train[trn_idx]
    #Kernel Ridge Regression 岭回归
    kr_383 = kr()
    kr_383.fit(tr_x,tr_y)
    oof_kr_383[val_idx] = kr_383.predict(X_train_383[val_idx])
    
    predictions_kr_383 += kr_383.predict(X_test_383) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_kr_383, target)))

3.2、使用普通岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_ridge_383 = np.zeros(train_shape)
predictions_ridge_383 = np.zeros(len(X_test_383))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_383, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_383[trn_idx]
    tr_y = y_train[trn_idx]
    #使用岭回归
    ridge_383 = Ridge(alpha=1200)
    ridge_383.fit(tr_x,tr_y)
    oof_ridge_383[val_idx] = ridge_383.predict(X_train_383[val_idx])
    
    predictions_ridge_383 += ridge_383.predict(X_test_383) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_ridge_383, target)))

3.3、使用ElasticNet 弹性网络

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_en_383 = np.zeros(train_shape)
predictions_en_383 = np.zeros(len(X_test_383))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_383, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_383[trn_idx]
    tr_y = y_train[trn_idx]
    #ElasticNet 弹性网络
    en_383 = en(alpha=1.0,l1_ratio=0.06)
    en_383.fit(tr_x,tr_y)
    oof_en_383[val_idx] = en_383.predict(X_train_383[val_idx])
    
    predictions_en_383 += en_383.predict(X_test_383) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_en_383, target)))

3.4、使用BayesianRidge 贝叶斯岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_br_383 = np.zeros(train_shape)
predictions_br_383 = np.zeros(len(X_test_383))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_383, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_383[trn_idx]
    tr_y = y_train[trn_idx]
    #BayesianRidge 贝叶斯回归
    br_383 = br()
    br_383.fit(tr_x,tr_y)
    oof_br_383[val_idx] = br_383.predict(X_train_383[val_idx])
    
    predictions_br_383 += br_383.predict(X_test_383) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_br_383, target)))

3.5、集成建模-stack1

train_stack1 = np.vstack([oof_br_383,oof_kr_383,oof_en_383,oof_ridge_383]).transpose()
test_stack1 = np.vstack([predictions_br_383, predictions_kr_383,predictions_en_383,predictions_ridge_383]).transpose()

folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=7)
oof_stack1 = np.zeros(train_stack1.shape[0])
predictions_lr1 = np.zeros(test_stack1.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack1,target)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack1[trn_idx], target.iloc[trn_idx].values
    val_data, val_y = train_stack1[val_idx], target.iloc[val_idx].values
    # LinearRegression简单的线性回归
    lr1 = lr()
    lr1.fit(trn_data, trn_y)
    
    oof_stack1[val_idx] = lr1.predict(val_data)
    predictions_lr1 += lr1.predict(test_stack1) / 10
    
mean_squared_error(target.values, oof_stack1) 

4、再次选择49维重要特征进行额外建模

4.1、KernelRidge 核岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_kr_49 = np.zeros(train_shape)
predictions_kr_49 = np.zeros(len(X_test_49))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_49[trn_idx]
    tr_y = y_train[trn_idx]
    kr_49 = kr()
    kr_49.fit(tr_x,tr_y)
    oof_kr_49[val_idx] = kr_49.predict(X_train_49[val_idx])
    
    predictions_kr_49 += kr_49.predict(X_test_49) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_kr_49, target)))

4.2、Ridge 岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_ridge_49 = np.zeros(train_shape)
predictions_ridge_49 = np.zeros(len(X_test_49))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_49[trn_idx]
    tr_y = y_train[trn_idx]
    ridge_49 = Ridge(alpha=6)
    ridge_49.fit(tr_x,tr_y)
    oof_ridge_49[val_idx] = ridge_49.predict(X_train_49[val_idx])
    
    predictions_ridge_49 += ridge_49.predict(X_test_49) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_ridge_49, target)))

4.3、BayesianRidge 贝叶斯岭回归

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_br_49 = np.zeros(train_shape)
predictions_br_49 = np.zeros(len(X_test_49))

for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_49[trn_idx]
    tr_y = y_train[trn_idx]
    br_49 = br()
    br_49.fit(tr_x,tr_y)
    oof_br_49[val_idx] = br_49.predict(X_train_49[val_idx])
    
    predictions_br_49 += br_49.predict(X_test_49) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_br_49, target)))

4.4、ElasticNet 弹性网络

folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_en_49 = np.zeros(train_shape)
predictions_en_49 = np.zeros(len(X_test_49))
#
for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_49, y_train)):
    print("fold n°{}".format(fold_+1))
    tr_x = X_train_49[trn_idx]
    tr_y = y_train[trn_idx]
    en_49 = en(alpha=1.0,l1_ratio=0.05)
    en_49.fit(tr_x,tr_y)
    oof_en_49[val_idx] = en_49.predict(X_train_49[val_idx])
    
    predictions_en_49 += en_49.predict(X_test_49) / folds.n_splits

print("CV score: {:<8.8f}".format(mean_squared_error(oof_en_49, target)))

4.5、集成建模-stack4

train_stack4 = np.vstack([oof_br_49,oof_kr_49,oof_en_49,oof_ridge_49]).transpose()
test_stack4 = np.vstack([predictions_br_49, predictions_kr_49,predictions_en_49,predictions_ridge_49]).transpose()

folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=7)
oof_stack4 = np.zeros(train_stack4.shape[0])
predictions_lr4 = np.zeros(test_stack4.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack4,target)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack4[trn_idx], target.iloc[trn_idx].values
    val_data, val_y = train_stack4[val_idx], target.iloc[val_idx].values
    #LinearRegression
    lr4 = lr()
    lr4.fit(trn_data, trn_y)
    
    oof_stack4[val_idx] = lr4.predict(val_data)
    predictions_lr4 += lr4.predict(test_stack1) / 10
    
mean_squared_error(target.values, oof_stack4) 

五、模型融合

5.1、线性加权

mean_squared_error(target.values, 0.7*(0.6*oof_stack2 + 0.4*oof_stack3)+0.3*(0.55*oof_stack1+0.45*oof_stack4))
0.4527515432292745

5.2、将集成学习模型再次进行集成学习训练

train_stack5 = np.vstack([oof_stack1,oof_stack2,oof_stack3,oof_stack4]).transpose()
test_stack5 = np.vstack([predictions_lr1, predictions_lr2,predictions_lr3,predictions_lr4]).transpose()

folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=7)
oof_stack5 = np.zeros(train_stack5.shape[0])
predictions_lr5= np.zeros(test_stack5.shape[0])

for fold_, (trn_idx, val_idx) in enumerate(folds_stack.split(train_stack5,target)):
    print("fold {}".format(fold_))
    trn_data, trn_y = train_stack5[trn_idx], target.iloc[trn_idx].values
    val_data, val_y = train_stack5[val_idx], target.iloc[val_idx].values
    #LinearRegression
    lr5 = lr()
    lr5.fit(trn_data, trn_y)
    
    oof_stack5[val_idx] = lr5.predict(val_data)
    predictions_lr5 += lr5.predict(test_stack5) / 10
    
mean_squared_error(target.values, oof_stack5) 

六、保存预测结果

submit_example = pd.read_csv('submit_example.csv',sep=',',encoding='latin-1')
submit_example['happiness'] = predictions_lr5
submit_example.happiness.describe()

count    2968.000000
mean        3.879776
std         0.463997
min         1.644653
25%         3.662051
50%         3.953215
75%         4.187563
max         5.058825
Name: happiness, dtype: float64

进行结果保存，这里我们预测出的值是1-5的连续值，但是我们的ground truth是整数值，所以为了进一步优化我们的结果，我们对于结果进行了整数解的近似，并保存到了csv文件中：

submit_example.loc[submit_example['happiness']<=1.04,'happiness']= 1
submit_example.loc[(submit_example['happiness']>1.96)&(submit_example['happiness']<2.04),'happiness']= 2
submit_example.loc[(submit_example['happiness']>2.96)&(submit_example['happiness']<3.04),'happiness']= 3
submit_example.loc[(submit_example['happiness']>3.96)&(submit_example['happiness']<4.04),'happiness']= 3
submit_example.loc[submit_example['happiness']>4.96,'happiness']= 5

submit_example.to_csv("submision.csv",index=False)
submit_example.happiness.describe()

count    2968.000000
mean        3.797191
std         0.520707
min         1.644653
25%         3.512802
50%         3.874427
75%         4.187563
max         5.000000
Name: happiness, dtype: float64

参考：
【1】教案：https://github.com/datawhalechina/ensemble-learning
【2】datawhale开源学习社区：http://datawhale.club/