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
!pip install lightgbm
!pip install xgboost
Collecting lightgbm
Downloading lightgbm-3.2.1-py3-none-win_amd64.whl (1.0 MB)
Requirement already satisfied: numpy in c:\programdata\anaconda3\lib\site-packages (from lightgbm) (1.18.1)
Requirement already satisfied: wheel in c:\programdata\anaconda3\lib\site-packages (from lightgbm) (0.34.2)
Requirement already satisfied: scipy in c:\programdata\anaconda3\lib\site-packages (from lightgbm) (1.4.1)
Requirement already satisfied: scikit-learn!=0.22.0 in c:\programdata\anaconda3\lib\site-packages (from lightgbm) (0.22.1)
Requirement already satisfied: joblib>=0.11 in c:\programdata\anaconda3\lib\site-packages (from scikit-learn!=0.22.0->lightgbm) (0.14.1)
Installing collected packages: lightgbm
Successfully installed lightgbm-3.2.1
Collecting xgboost
Downloading xgboost-1.4.2-py3-none-win_amd64.whl (97.8 MB)
Requirement already satisfied: scipy in c:\programdata\anaconda3\lib\site-packages (from xgboost) (1.4.1)
Requirement already satisfied: numpy in c:\programdata\anaconda3\lib\site-packages (from xgboost) (1.18.1)
Installing collected packages: xgboost
Successfully installed xgboost-1.4.2
WARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/xgboost/
WARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ProtocolError('Connection aborted.', OSError(0, 'Error'))': /simple/xgboost/
WARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError("HTTPSConnectionPool(host='pypi.org', port=443): Read timed out. (read timeout=15)")': /simple/xgboost/
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') #latin-1向下兼容ASCII
train = train[train["happiness"]!=-8].reset_index(drop=True)
train_data_copy = train.copy() #删去"happiness" 为-8的行
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)
train.happiness.describe()
count 7988.000000
mean 3.867927
std 0.818717
min 1.000000
25% 4.000000
50% 4.000000
75% 4.000000
max 5.000000
Name: happiness, dtype: float64
#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)
data['neg1']
0 5
1 0
2 3
3 2
4 2
..
10951 0
10952 2
10953 8
10954 4
10955 11
Name: neg1, Length: 10956, dtype: int64
#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
data['leisure_4'].mode()
0 5.0
dtype: float64
#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
#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'])
print('shape',data.shape)
data.head()
shape (10956, 272)
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>id</th>
<th>survey_type</th>
<th>province</th>
<th>city</th>
<th>county</th>
<th>survey_time</th>
<th>gender</th>
<th>birth</th>
<th>nationality</th>
<th>religion</th>
<th>...</th>
<th>depression/age</th>
<th>floor_area/age</th>
<th>health/age</th>
<th>class_10_diff/age</th>
<th>class/age</th>
<th>health_problem/age</th>
<th>family_status/age</th>
<th>leisure_sum/age</th>
<th>public_service_sum/age</th>
<th>trust_sum/age</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>1</td>
<td>12</td>
<td>32</td>
<td>59</td>
<td>2015</td>
<td>1</td>
<td>1959</td>
<td>1</td>
<td>1</td>
<td>...</td>
<td>1.285211</td>
<td>0.410351</td>
<td>0.848837</td>
<td>0.000000</td>
<td>0.683307</td>
<td>0.521429</td>
<td>0.733668</td>
<td>0.724620</td>
<td>0.666638</td>
<td>0.925941</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>2</td>
<td>18</td>
<td>52</td>
<td>85</td>
<td>2015</td>
<td>1</td>
<td>1992</td>
<td>1</td>
<td>1</td>
<td>...</td>
<td>0.733333</td>
<td>0.952824</td>
<td>1.179337</td>
<td>1.012552</td>
<td>1.344444</td>
<td>0.891344</td>
<td>1.359551</td>
<td>1.011792</td>
<td>1.130778</td>
<td>1.188442</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>2</td>
<td>29</td>
<td>83</td>
<td>126</td>
<td>2015</td>
<td>2</td>
<td>1967</td>
<td>1</td>
<td>0</td>
<td>...</td>
<td>1.343537</td>
<td>0.972328</td>
<td>1.150485</td>
<td>1.190955</td>
<td>1.195762</td>
<td>1.055679</td>
<td>1.190955</td>
<td>0.966470</td>
<td>1.193204</td>
<td>0.803693</td>
</tr>
<tr>
<th>3</th>
<td>4</td>
<td>2</td>
<td>10</td>
<td>28</td>
<td>51</td>
<td>2015</td>
<td>2</td>
<td>1943</td>
<td>1</td>
<td>1</td>
<td>...</td>
<td>1.111663</td>
<td>0.642329</td>
<td>1.276353</td>
<td>4.977778</td>
<td>1.199143</td>
<td>1.188329</td>
<td>1.162630</td>
<td>0.899346</td>
<td>1.153810</td>
<td>1.300950</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>1</td>
<td>7</td>
<td>18</td>
<td>36</td>
<td>2015</td>
<td>2</td>
<td>1994</td>
<td>1</td>
<td>1</td>
<td>...</td>
<td>0.750000</td>
<td>0.587284</td>
<td>1.177106</td>
<td>0.000000</td>
<td>0.236957</td>
<td>1.116803</td>
<td>1.093645</td>
<td>1.045313</td>
<td>0.728161</td>
<td>1.117428</td>
</tr>
</tbody>
</table>
<p>5 rows × 272 columns</p>
</div>
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)
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
(7988, 49)
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)
##### 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)))
fold n°1
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.499759 valid_1's l2: 0.532511
[1000] training's l2: 0.451529 valid_1's l2: 0.499127
[1500] training's l2: 0.425443 valid_1's l2: 0.485366
[2000] training's l2: 0.407395 valid_1's l2: 0.479303
[2500] training's l2: 0.393001 valid_1's l2: 0.475556
[3000] training's l2: 0.380761 valid_1's l2: 0.473666
[3500] training's l2: 0.370005 valid_1's l2: 0.472563
[4000] training's l2: 0.360215 valid_1's l2: 0.471631
[4500] training's l2: 0.351235 valid_1's l2: 0.470938
[5000] training's l2: 0.342828 valid_1's l2: 0.470683
[5500] training's l2: 0.334901 valid_1's l2: 0.470155
[6000] training's l2: 0.3274 valid_1's l2: 0.470072
[6500] training's l2: 0.320153 valid_1's l2: 0.47005
[7000] training's l2: 0.313311 valid_1's l2: 0.469912
[7500] training's l2: 0.306748 valid_1's l2: 0.46995
[8000] training's l2: 0.300336 valid_1's l2: 0.470026
Early stopping, best iteration is:
[7447] training's l2: 0.307448 valid_1's l2: 0.469826
fold n°2
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.504322 valid_1's l2: 0.513628
[1000] training's l2: 0.454889 valid_1's l2: 0.47926
[1500] training's l2: 0.428783 valid_1's l2: 0.465975
[2000] training's l2: 0.410928 valid_1's l2: 0.459213
[2500] training's l2: 0.39726 valid_1's l2: 0.455058
[3000] training's l2: 0.385428 valid_1's l2: 0.45243
[3500] training's l2: 0.374844 valid_1's l2: 0.45074
[4000] training's l2: 0.365252 valid_1's l2: 0.449328
[4500] training's l2: 0.356333 valid_1's l2: 0.448423
[5000] training's l2: 0.348003 valid_1's l2: 0.447458
[5500] training's l2: 0.339994 valid_1's l2: 0.446682
[6000] training's l2: 0.332355 valid_1's l2: 0.446153
[6500] training's l2: 0.325106 valid_1's l2: 0.445942
[7000] training's l2: 0.318209 valid_1's l2: 0.445729
[7500] training's l2: 0.311488 valid_1's l2: 0.445203
[8000] training's l2: 0.305185 valid_1's l2: 0.444862
[8500] training's l2: 0.299025 valid_1's l2: 0.444872
[9000] training's l2: 0.293039 valid_1's l2: 0.444637
[9500] training's l2: 0.287237 valid_1's l2: 0.444412
[10000] training's l2: 0.281718 valid_1's l2: 0.444121
Did not meet early stopping. Best iteration is:
[10000] training's l2: 0.281718 valid_1's l2: 0.444121
fold n°3
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.50317 valid_1's l2: 0.518027
[1000] training's l2: 0.455064 valid_1's l2: 0.480542
[1500] training's l2: 0.429866 valid_1's l2: 0.464074
[2000] training's l2: 0.412419 valid_1's l2: 0.455414
[2500] training's l2: 0.39819 valid_1's l2: 0.449861
[3000] training's l2: 0.386279 valid_1's l2: 0.446579
[3500] training's l2: 0.375505 valid_1's l2: 0.444652
[4000] training's l2: 0.365722 valid_1's l2: 0.442958
[4500] training's l2: 0.356747 valid_1's l2: 0.442154
[5000] training's l2: 0.348329 valid_1's l2: 0.441595
[5500] training's l2: 0.340206 valid_1's l2: 0.440923
[6000] training's l2: 0.332514 valid_1's l2: 0.440634
[6500] training's l2: 0.325136 valid_1's l2: 0.440318
[7000] training's l2: 0.318154 valid_1's l2: 0.440439
Early stopping, best iteration is:
[6645] training's l2: 0.323052 valid_1's l2: 0.440181
fold n°4
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.504279 valid_1's l2: 0.512194
[1000] training's l2: 0.455536 valid_1's l2: 0.477492
[1500] training's l2: 0.429192 valid_1's l2: 0.465315
[2000] training's l2: 0.411059 valid_1's l2: 0.459402
[2500] training's l2: 0.396766 valid_1's l2: 0.455937
[3000] training's l2: 0.384721 valid_1's l2: 0.453696
[3500] training's l2: 0.3741 valid_1's l2: 0.452256
[4000] training's l2: 0.364289 valid_1's l2: 0.45118
[4500] training's l2: 0.355254 valid_1's l2: 0.450291
[5000] training's l2: 0.346816 valid_1's l2: 0.44973
[5500] training's l2: 0.338933 valid_1's l2: 0.449181
[6000] training's l2: 0.331387 valid_1's l2: 0.448914
[6500] training's l2: 0.32404 valid_1's l2: 0.448702
[7000] training's l2: 0.317208 valid_1's l2: 0.448468
[7500] training's l2: 0.310593 valid_1's l2: 0.448375
[8000] training's l2: 0.304157 valid_1's l2: 0.448561
Early stopping, best iteration is:
[7381] training's l2: 0.312154 valid_1's l2: 0.448282
fold n°5
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.503075 valid_1's l2: 0.519874
[1000] training's l2: 0.454635 valid_1's l2: 0.484867
[1500] training's l2: 0.42871 valid_1's l2: 0.471137
[2000] training's l2: 0.410716 valid_1's l2: 0.464987
[2500] training's l2: 0.396241 valid_1's l2: 0.46153
[3000] training's l2: 0.383972 valid_1's l2: 0.459225
[3500] training's l2: 0.372947 valid_1's l2: 0.458011
[4000] training's l2: 0.362992 valid_1's l2: 0.457356
[4500] training's l2: 0.353769 valid_1's l2: 0.457291
[5000] training's l2: 0.345122 valid_1's l2: 0.457313
[5500] training's l2: 0.33702 valid_1's l2: 0.45702
[6000] training's l2: 0.329492 valid_1's l2: 0.456985
[6500] training's l2: 0.322039 valid_1's l2: 0.457153
Early stopping, best iteration is:
[5850] training's l2: 0.33172 valid_1's l2: 0.45687
CV score: 0.45185567
#---------------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()
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