机器学习（下）-案例分析1 ：幸福感预测
案例一 （幸福感预测）
背景介绍
幸福感涉及了哲学、心理学、社会学、经济学等多方学科，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，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。
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。
数据来源：国家官方的《中国综合社会调查（CGSS）》文件
数据信息
案例要求使用 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。
评价指标
最终的评价指标为均方误差MSE，即：
S c o r e = 1 n ∑ 1 n ( y i − y ∗ ) 2 Score = \frac{1}{n} \sum_1 ^n (y_i - y ^*)^2 Score=n11∑n(yi−y∗)2
导包
题外话:此处导包较多，一顿操作猛如虎，请耐心等待，“啊，这该死的甜蜜负担”
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
导入数据集
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) #删去"happiness" 为-8的行
train_data_copy = train.copy() # 复制
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)
查看数据的基本信息
train.happiness.describe() #数据的基本信息
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
数据预处理
对数据中的连续出现的负数值进行处理。由于数据中的负数值只有-1，-2，-3，-8这几种数值，所以它们进行分别的操作，实现代码如下：
#make feature +5
#csv中有负数值：-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)
填充缺失值，采取将缺失值补全方式，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。部分实现代码如下：
#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
时间处理–分两部分进行：
首先，将“连续”的年龄进行分层处理，即划分年龄段，将年龄分为了6个区间。
其次，计算具体年龄，在Excel表格中，只有出生年月以及调查时间等信息，根据此计算出每一位调查者的真实年龄。具体实现代码如下：
#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])
在这里因为家庭的收入是连续值，不能使用众数的方法进行处理，直接使用均值进行缺失值的补全。
第三种方法是使用我们日常生活中的真实情况，例如“宗教信息”特征为负数的认为是“不信仰宗教”，并认为“参加宗教活动的频率”为1，即没有参加过宗教活动，主观的进行补全，这是在这一步骤中使用最多的一种方式。
#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
使用众数（代码中使用mode()来实现异常值的修正），由于这里的特征是空闲活动，所以采用众数对于缺失值进行处理比较合理。具体的代码参考如下：
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
取均值进行缺失值的补全（代码实现为means()），在这里因为家庭的收入是连续值，所以不能再使用取众数的方法进行处理，这里就直接使用了均值进行缺失值的补全。具体的代码参考如下：
#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
数据增广
这一步，需要进一步分析每一个特征之间的关系，从而进行数据增广。经过思考，这里添加了如下的特征：第一次结婚年龄、最近结婚年龄、是否再婚、配偶年龄、配偶年龄差、各种收入比（与配偶之间的收入比、十年后预期收入与现在收入之比等等）、收入与住房面积比（其中也包括10年后期望收入等等各种情况）、社会阶级（10年后的社会阶级、14年后的社会阶级等等）、悠闲指数、满意指数、信任指数等等。
除此之外，还考虑了对于同一省、市、县进行了归一化。
例如：同一省市内的收入的平均值等于个体相对于同省、市、县其他人的各个指标的情况。同时也考虑了对于同龄人之间的相互比较，即在同龄人中的收入情况、健康情况等。具体的实现代码如下：
#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'])
经过如上的操作后，最终我们的特征从一开始的131维，扩充为了272维的特征。接下来考虑特征工程、训练模型以及模型融合的工作。
print('shape',data.shape)
data.head()
shape (10956, 272)
| id | survey_type | province | city | county | survey_time | gender | birth | nationality | religion | ... | depression/age | floor_area/age | health/age | class_10_diff/age | class/age | health_problem/age | family_status/age | leisure_sum/age | public_service_sum/age | trust_sum/age | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 12 | 32 | 59 | 2015 | 1 | 1959 | 1 | 1 | ... | 1.285211 | 0.410351 | 0.848837 | 0.000000 | 0.683307 | 0.521429 | 0.733668 | 0.724620 | 0.666638 | 0.925941 |
| 1 | 2 | 2 | 18 | 52 | 85 | 2015 | 1 | 1992 | 1 | 1 | ... | 0.733333 | 0.952824 | 1.179337 | 1.012552 | 1.344444 | 0.891344 | 1.359551 | 1.011792 | 1.130778 | 1.188442 |
| 2 | 3 | 2 | 29 | 83 | 126 | 2015 | 2 | 1967 | 1 | 0 | ... | 1.343537 | 0.972328 | 1.150485 | 1.190955 | 1.195762 | 1.055679 | 1.190955 | 0.966470 | 1.193204 | 0.803693 |
| 3 | 4 | 2 | 10 | 28 | 51 | 2015 | 2 | 1943 | 1 | 1 | ... | 1.111663 | 0.642329 | 1.276353 | 4.977778 | 1.199143 | 1.188329 | 1.162630 | 0.899346 | 1.153810 | 1.300950 |
| 4 | 5 | 1 | 7 | 18 | 36 | 2015 | 2 | 1994 | 1 | 1 | ... | 0.750000 | 0.587284 | 1.177106 | 0.000000 | 0.236957 | 1.116803 | 1.093645 | 1.045313 | 0.728161 | 1.117428 |
5 rows × 272 columns
我们还应该删去有效样本数很少的特征，例如负值太多的特征或者是缺失值太多的特征，这里我一共删除了包括“目前的最高教育程度”在内的9类特征，得到了最终的263维的特征
#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
(7988, 263)
这里选择了最重要的49个特征，作为除了以上263维特征外的另外一组特征
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 #最重要的49个特征
(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)
基于此，我们构建完成了三种特征工程（训练数据集），其一是上面提取的最重要的49中特征，其中包括健康程度、社会阶级、在同龄人中的收入情况等等特征。其二是扩充后的263维特征（这里可以认为是初始特征）。其三是使用One-hot编码后的特征，这里要使用One-hot进行编码的原因在于，有部分特征为分离值，例如性别中男女，男为1，女为2，我们想使用One-hot将其变为男为0，女为1，来增强机器学习算法的鲁棒性能；再如民族这个特征，原本是1-56这56个数值，如果直接分类会让分类器的鲁棒性变差，所以使用One-hot编码将其变为6个特征进行非零即一的处理。
特征建模
首先我们对于原始的263维的特征，使用lightGBM进行处理，这里我们使用5折交叉验证的方法：
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)))
fold n°1
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.507058 valid_1's l2: 0.50963
[1000] training's l2: 0.458952 valid_1's l2: 0.469564
[1500] training's l2: 0.433197 valid_1's l2: 0.453234
[2000] training's l2: 0.415242 valid_1's l2: 0.444839
[2500] training's l2: 0.400993 valid_1's l2: 0.440086
[3000] training's l2: 0.388937 valid_1's l2: 0.436924
[3500] training's l2: 0.378101 valid_1's l2: 0.434974
[4000] training's l2: 0.368159 valid_1's l2: 0.43406
[4500] training's l2: 0.358827 valid_1's l2: 0.433151
[5000] training's l2: 0.350291 valid_1's l2: 0.432544
[5500] training's l2: 0.342368 valid_1's l2: 0.431821
[6000] training's l2: 0.334675 valid_1's l2: 0.431331
[6500] training's l2: 0.327275 valid_1's l2: 0.431014
[7000] training's l2: 0.320398 valid_1's l2: 0.431087
[7500] training's l2: 0.31352 valid_1's l2: 0.430819
[8000] training's l2: 0.307021 valid_1's l2: 0.430848
[8500] training's l2: 0.300811 valid_1's l2: 0.430688
[9000] training's l2: 0.294787 valid_1's l2: 0.430441
[9500] training's l2: 0.288993 valid_1's l2: 0.430433
Early stopping, best iteration is:
[9119] training's l2: 0.293371 valid_1's l2: 0.430308
fold n°2
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.49895 valid_1's l2: 0.52945
[1000] training's l2: 0.450107 valid_1's l2: 0.496478
[1500] training's l2: 0.424394 valid_1's l2: 0.483286
[2000] training's l2: 0.40666 valid_1's l2: 0.476764
[2500] training's l2: 0.392432 valid_1's l2: 0.472668
[3000] training's l2: 0.380438 valid_1's l2: 0.470481
[3500] training's l2: 0.369872 valid_1's l2: 0.468919
[4000] training's l2: 0.36014 valid_1's l2: 0.467318
[4500] training's l2: 0.351175 valid_1's l2: 0.466438
[5000] training's l2: 0.342705 valid_1's l2: 0.466284
[5500] training's l2: 0.334778 valid_1's l2: 0.466151
[6000] training's l2: 0.3273 valid_1's l2: 0.466016
[6500] training's l2: 0.320121 valid_1's l2: 0.466013
Early stopping, best iteration is:
[5915] training's l2: 0.328534 valid_1's l2: 0.465918
fold n°3
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.499658 valid_1's l2: 0.528985
[1000] training's l2: 0.450356 valid_1's l2: 0.497264
[1500] training's l2: 0.424109 valid_1's l2: 0.485403
[2000] training's l2: 0.405965 valid_1's l2: 0.479513
[2500] training's l2: 0.391747 valid_1's l2: 0.47646
[3000] training's l2: 0.379601 valid_1's l2: 0.474691
[3500] training's l2: 0.368915 valid_1's l2: 0.473648
[4000] training's l2: 0.359218 valid_1's l2: 0.47316
[4500] training's l2: 0.350338 valid_1's l2: 0.473043
[5000] training's l2: 0.341842 valid_1's l2: 0.472719
[5500] training's l2: 0.333851 valid_1's l2: 0.472779
Early stopping, best iteration is:
[4942] training's l2: 0.342828 valid_1's l2: 0.472642
fold n°4
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.505224 valid_1's l2: 0.508238
[1000] training's l2: 0.456198 valid_1's l2: 0.473992
[1500] training's l2: 0.430167 valid_1's l2: 0.461419
[2000] training's l2: 0.412084 valid_1's l2: 0.454843
[2500] training's l2: 0.397714 valid_1's l2: 0.450999
[3000] training's l2: 0.385456 valid_1's l2: 0.448697
[3500] training's l2: 0.374527 valid_1's l2: 0.446993
[4000] training's l2: 0.364711 valid_1's l2: 0.44597
[4500] training's l2: 0.355626 valid_1's l2: 0.445132
[5000] training's l2: 0.347108 valid_1's l2: 0.44466
[5500] training's l2: 0.339146 valid_1's l2: 0.444226
[6000] training's l2: 0.331478 valid_1's l2: 0.443992
[6500] training's l2: 0.324231 valid_1's l2: 0.444014
Early stopping, best iteration is:
[5874] training's l2: 0.333372 valid_1's l2: 0.443868
fold n°5
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.504304 valid_1's l2: 0.515256
[1000] training's l2: 0.456062 valid_1's l2: 0.478544
[1500] training's l2: 0.430298 valid_1's l2: 0.463847
[2000] training's l2: 0.412591 valid_1's l2: 0.456182
[2500] training's l2: 0.398635 valid_1's l2: 0.451783
[3000] training's l2: 0.386609 valid_1's l2: 0.449154
[3500] training's l2: 0.375948 valid_1's l2: 0.447265
[4000] training's l2: 0.366291 valid_1's l2: 0.445796
[4500] training's l2: 0.357236 valid_1's l2: 0.445098
[5000] training's l2: 0.348637 valid_1's l2: 0.444364
[5500] training's l2: 0.340736 valid_1's l2: 0.443998
[6000] training's l2: 0.333154 valid_1's l2: 0.443622
[6500] training's l2: 0.325783 valid_1's l2: 0.443226
[7000] training's l2: 0.318802 valid_1's l2: 0.442986
[7500] training's l2: 0.312164 valid_1's l2: 0.442928
[8000] training's l2: 0.305691 valid_1's l2: 0.442696
[8500] training's l2: 0.29935 valid_1's l2: 0.442521
[9000] training's l2: 0.293242 valid_1's l2: 0.442655
Early stopping, best iteration is:
[8594] training's l2: 0.298201 valid_1's l2: 0.44238
CV score: 0.45102656
接着，我使用已经训练完的lightGBM的模型进行特征重要性的判断以及可视化，从结果我们可以看出，排在重要性第一位的是health/age，就是同龄人中的健康程度，与我们主观的看法基本一致。
#---------------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()
后面，我们使用常见的机器学习方法，对于263维特征进行建模：
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)))
fold n°1
[19:14:55] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:14:55] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40426 valid_data-rmse:3.38329
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.40805 valid_data-rmse:0.70588
[1000] train-rmse:0.27046 valid_data-rmse:0.70760
Stopping. Best iteration:
[663] train-rmse:0.35644 valid_data-rmse:0.70521
[19:15:46] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°2
[19:15:46] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:15:46] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.39811 valid_data-rmse:3.40788
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.40719 valid_data-rmse:0.69456
[1000] train-rmse:0.27402 valid_data-rmse:0.69501
Stopping. Best iteration:
[551] train-rmse:0.39079 valid_data-rmse:0.69403
[19:16:31] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°3
[19:16:31] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:16:31] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40181 valid_data-rmse:3.39295
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.41334 valid_data-rmse:0.66250
Stopping. Best iteration:
[333] train-rmse:0.47284 valid_data-rmse:0.66178
[19:17:07] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°4
[19:17:08] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:17:08] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40240 valid_data-rmse:3.39012
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.41021 valid_data-rmse:0.66575
[1000] train-rmse:0.27491 valid_data-rmse:0.66431
Stopping. Best iteration:
[863] train-rmse:0.30689 valid_data-rmse:0.66358
[19:18:06] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°5
[19:18:07] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:18:07] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.39347 valid_data-rmse:3.42628
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.41704 valid_data-rmse:0.64937
[1000] train-rmse:0.27907 valid_data-rmse:0.64914
Stopping. Best iteration:
[598] train-rmse:0.38625 valid_data-rmse:0.64856
[19:18:55] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
CV score: 0.45559329
- RandomForestRegressor随机森林
#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)))
fold n°1
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.6s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 2.6s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 6.5s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 11.8s
[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 18.9s
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 25.6s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
fold n°2
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.6s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 2.8s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 6.9s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 12.9s
[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 21.0s
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 27.5s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.3s finished
fold n°3
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.6s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 3.4s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 7.6s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 13.7s
[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 21.0s
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 26.9s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
fold n°4
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.8s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 3.5s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 7.9s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 13.3s
[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 20.6s
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 26.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
fold n°5
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.6s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 2.7s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 6.8s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 12.2s
[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 19.2s
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 25.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
CV score: 0.47804209
[Parallel(n_jobs=8)]: Done 1234 tasks | elapsed: 0.2s
[Parallel(n_jobs=8)]: Done 1600 out of 1600 | elapsed: 0.3s finished
- GradientBoostingRegressor梯度提升决策树
#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)))
fold n°1
Iter Train Loss OOB Improve Remaining Time
1 0.6419 0.0036 24.34s
2 0.6564 0.0031 23.18s
3 0.6693 0.0031 22.69s
4 0.6589 0.0031 22.78s
5 0.6522 0.0027 22.58s
6 0.6521 0.0031 22.40s
7 0.6370 0.0029 22.23s
8 0.6343 0.0030 22.06s
9 0.6447 0.0029 21.87s
10 0.6397 0.0028 21.75s
20 0.5955 0.0019 20.93s
30 0.5695 0.0016 20.09s
40 0.5460 0.0015 19.34s
50 0.5121 0.0011 18.65s
60 0.4994 0.0012 18.03s
70 0.4912 0.0010 17.44s
80 0.4719 0.0010 16.76s
90 0.4310 0.0007 16.28s
100 0.4437 0.0006 15.84s
200 0.3424 0.0002 10.15s
300 0.3063 -0.0000 4.94s
400 0.2759 -0.0000 0.00s
fold n°2
Iter Train Loss OOB Improve Remaining Time
1 0.6836 0.0034 24.61s
2 0.6613 0.0030 22.86s
3 0.6500 0.0031 24.11s
4 0.6621 0.0036 23.15s
5 0.6356 0.0031 23.49s
6 0.6460 0.0029 23.13s
7 0.6263 0.0032 22.83s
8 0.6149 0.0029 22.72s
9 0.6350 0.0030 22.83s
10 0.6325 0.0026 22.65s
20 0.6064 0.0025 21.62s
30 0.5812 0.0018 20.59s
40 0.5460 0.0018 19.98s
50 0.5016 0.0014 19.52s
60 0.4991 0.0010 18.84s
70 0.4645 0.0009 18.24s
80 0.4621 0.0007 17.76s
90 0.4497 0.0007 17.20s
100 0.4374 0.0005 16.51s
200 0.3420 0.0001 10.35s
300 0.3032 -0.0000 4.95s
400 0.2710 -0.0000 0.00s
fold n°3
Iter Train Loss OOB Improve Remaining Time
1 0.6692 0.0036 24.95s
2 0.6468 0.0031 23.99s
3 0.6313 0.0034 24.05s
4 0.6499 0.0032 23.70s
5 0.6358 0.0033 23.38s
6 0.6343 0.0029 23.05s
7 0.6312 0.0036 22.71s
8 0.6180 0.0032 22.47s
9 0.6275 0.0035 22.57s
10 0.6168 0.0030 22.24s
20 0.5792 0.0021 20.73s
30 0.5583 0.0023 20.27s
40 0.5521 0.0018 19.70s
50 0.5067 0.0013 18.84s
60 0.4754 0.0010 18.42s
70 0.4811 0.0009 17.84s
80 0.4603 0.0008 17.38s
90 0.4439 0.0006 16.74s
100 0.4323 0.0007 16.25s
200 0.3401 0.0002 10.23s
300 0.2862 -0.0000 4.84s
400 0.2690 -0.0000 0.00s
fold n°4
Iter Train Loss OOB Improve Remaining Time
1 0.6687 0.0032 21.09s
2 0.6517 0.0031 23.29s
3 0.6583 0.0031 23.63s
4 0.6607 0.0033 24.45s
5 0.6583 0.0029 24.78s
6 0.6688 0.0028 24.80s
7 0.6320 0.0030 25.08s
8 0.6502 0.0026 24.94s
9 0.6358 0.0026 24.51s
10 0.6258 0.0027 24.24s
20 0.5910 0.0023 22.41s
30 0.5609 0.0020 21.31s
40 0.5399 0.0017 20.50s
50 0.4963 0.0013 19.67s
60 0.4844 0.0012 18.86s
70 0.4781 0.0008 18.21s
80 0.4484 0.0010 17.63s
90 0.4619 0.0006 16.95s
100 0.4430 0.0005 16.46s
200 0.3377 0.0001 10.50s
300 0.3001 0.0001 4.97s
400 0.2623 -0.0000 0.00s
fold n°5
Iter Train Loss OOB Improve Remaining Time
1 0.6857 0.0031 23.50s
2 0.6320 0.0035 24.26s
3 0.6573 0.0033 23.41s
4 0.6494 0.0033 24.20s
5 0.6311 0.0033 24.32s
6 0.6362 0.0031 24.20s
7 0.6291 0.0032 24.05s
8 0.6354 0.0032 23.56s
9 0.6383 0.0030 23.54s
10 0.6250 0.0029 23.64s
20 0.5989 0.0023 21.45s
30 0.5736 0.0019 20.27s
40 0.5457 0.0016 19.60s
50 0.5045 0.0015 18.76s
60 0.4820 0.0012 18.20s
70 0.4756 0.0010 17.44s
80 0.4484 0.0009 16.91s
90 0.4410 0.0007 16.34s
100 0.4195 0.0004 15.72s
200 0.3348 0.0001 10.05s
300 0.2933 -0.0000 4.76s
400 0.2658 -0.0000 0.00s
CV score: 0.45583290
- ExtraTreesRegressor 极端随机森林回归
#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)))
fold n°1
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.4s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.7s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 4.0s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 7.2s
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 9.0s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
fold n°2
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.3s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.6s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 3.8s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 6.9s
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 8.9s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
fold n°3
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.4s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.7s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 4.1s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 7.6s
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 9.6s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
fold n°4
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.4s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.7s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 4.0s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 7.6s
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 10.6s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.2s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.2s finished
fold n°5
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 0.4s
[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 1.9s
[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 4.4s
[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 8.6s
[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed: 10.7s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
[Parallel(n_jobs=8)]: Using backend ThreadingBackend with 8 concurrent workers.
[Parallel(n_jobs=8)]: Done 34 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 184 tasks | elapsed: 0.0s
[Parallel(n_jobs=8)]: Done 434 tasks | elapsed: 0.1s
CV score: 0.48598792
[Parallel(n_jobs=8)]: Done 784 tasks | elapsed: 0.1s
[Parallel(n_jobs=8)]: Done 1000 out of 1000 | elapsed: 0.1s finished
至此，我们得到了以上5种模型的预测结果以及模型架构及参数。其中在每一种特征工程中，进行5折的交叉验证，并重复两次（Kernel Ridge Regression，核脊回归），取得每一个特征数下的模型的结果。
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)
fold 0
fold 1
fold 2
fold 3
fold 4
fold 5
fold 6
fold 7
fold 8
fold 9
0.44815130114230267
接下来我们对于49维的数据进行与上述263维数据相同的操作
1.lightGBM
##### lgb_49
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)))
fold n°1
Training until validation scores don't improve for 1000 rounds
[1000] training's l2: 0.46958 valid_1's l2: 0.500767
[2000] training's l2: 0.429395 valid_1's l2: 0.482214
[3000] training's l2: 0.406748 valid_1's l2: 0.477959
[4000] training's l2: 0.388735 valid_1's l2: 0.476283
[5000] training's l2: 0.373399 valid_1's l2: 0.475506
[6000] training's l2: 0.359798 valid_1's l2: 0.475435
Early stopping, best iteration is:
[5429] training's l2: 0.367348 valid_1's l2: 0.475325
fold n°2
Training until validation scores don't improve for 1000 rounds
[1000] training's l2: 0.469767 valid_1's l2: 0.496741
[2000] training's l2: 0.428546 valid_1's l2: 0.479198
[3000] training's l2: 0.405733 valid_1's l2: 0.475903
[4000] training's l2: 0.388021 valid_1's l2: 0.474891
[5000] training's l2: 0.372619 valid_1's l2: 0.474262
[6000] training's l2: 0.358826 valid_1's l2: 0.47449
Early stopping, best iteration is:
[5002] training's l2: 0.372597 valid_1's l2: 0.47425
fold n°3
Training until validation scores don't improve for 1000 rounds
[1000] training's l2: 0.47361 valid_1's l2: 0.4839
[2000] training's l2: 0.433064 valid_1's l2: 0.462219
[3000] training's l2: 0.410658 valid_1's l2: 0.457989
[4000] training's l2: 0.392859 valid_1's l2: 0.456091
[5000] training's l2: 0.377706 valid_1's l2: 0.455416
[6000] training's l2: 0.364058 valid_1's l2: 0.455285
Early stopping, best iteration is:
[5815] training's l2: 0.3665 valid_1's l2: 0.455119
fold n°4
Training until validation scores don't improve for 1000 rounds
[1000] training's l2: 0.471715 valid_1's l2: 0.496877
[2000] training's l2: 0.431956 valid_1's l2: 0.472828
[3000] training's l2: 0.409505 valid_1's l2: 0.467016
[4000] training's l2: 0.391659 valid_1's l2: 0.464929
[5000] training's l2: 0.376239 valid_1's l2: 0.464048
[6000] training's l2: 0.36213 valid_1's l2: 0.463628
[7000] training's l2: 0.349338 valid_1's l2: 0.463767
Early stopping, best iteration is:
[6272] training's l2: 0.358584 valid_1's l2: 0.463542
fold n°5
Training until validation scores don't improve for 1000 rounds
[1000] training's l2: 0.466349 valid_1's l2: 0.507696
[2000] training's l2: 0.425606 valid_1's l2: 0.492745
[3000] training's l2: 0.403731 valid_1's l2: 0.488917
[4000] training's l2: 0.386479 valid_1's l2: 0.487113
[5000] training's l2: 0.371358 valid_1's l2: 0.485881
[6000] training's l2: 0.357821 valid_1's l2: 0.485185
[7000] training's l2: 0.345577 valid_1's l2: 0.484535
[8000] training's l2: 0.33415 valid_1's l2: 0.484483
Early stopping, best iteration is:
[7649] training's l2: 0.338078 valid_1's l2: 0.484416
CV score: 0.47052692
- xgboost
##### xgb_49
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)))
fold n°1
[19:25:31] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:25:31] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40431 valid_data-rmse:3.38307
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.52770 valid_data-rmse:0.72110
[1000] train-rmse:0.43563 valid_data-rmse:0.72245
Stopping. Best iteration:
[690] train-rmse:0.49010 valid_data-rmse:0.72044
[19:25:44] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°2
[19:25:44] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:25:44] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.39815 valid_data-rmse:3.40784
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.52871 valid_data-rmse:0.70336
[1000] train-rmse:0.43793 valid_data-rmse:0.70446
Stopping. Best iteration:
[754] train-rmse:0.47982 valid_data-rmse:0.70278
[19:25:57] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°3
[19:25:57] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:25:57] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40183 valid_data-rmse:3.39291
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.53169 valid_data-rmse:0.66896
[1000] train-rmse:0.44129 valid_data-rmse:0.67058
Stopping. Best iteration:
[452] train-rmse:0.54177 valid_data-rmse:0.66871
[19:26:07] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°4
[19:26:07] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:26:07] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.40240 valid_data-rmse:3.39014
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.53218 valid_data-rmse:0.67783
[1000] train-rmse:0.44361 valid_data-rmse:0.67978
Stopping. Best iteration:
[566] train-rmse:0.51924 valid_data-rmse:0.67765
[19:26:18] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
fold n°5
[19:26:19] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
[19:26:19] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:480:
Parameters: { silent } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[0] train-rmse:3.39345 valid_data-rmse:3.42619
Multiple eval metrics have been passed: 'valid_data-rmse' will be used for early stopping.
Will train until valid_data-rmse hasn't improved in 600 rounds.
[500] train-rmse:0.53565 valid_data-rmse:0.66150
[1000] train-rmse:0.44204 valid_data-rmse:0.66241
Stopping. Best iteration:
[747] train-rmse:0.48554 valid_data-rmse:0.66016
[19:26:32] WARNING: /Users/travis/build/dmlc/xgboost/src/objective/regression_obj.cu:170: reg:linear is now deprecated in favor of reg:squarederror.
CV score: 0.47102840
- 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))
#GradientBoostingRegressor梯度提升决策树
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)))
fold n°1
Iter Train Loss OOB Improve Remaining Time
1 0.6529 0.0032 9.69s
2 0.6736 0.0029 9.55s
3 0.6522 0.0029 9.29s
4 0.6393 0.0034 9.49s
5 0.6454 0.0032 9.36s
6 0.6467 0.0031 9.22s
7 0.6650 0.0026 9.23s
8 0.6225 0.0030 9.20s
9 0.6350 0.0028 9.09s
10 0.6311 0.0028 9.25s
20 0.6074 0.0022 8.67s
30 0.5790 0.0017 8.19s
40 0.5443 0.0016 7.89s
50 0.5405 0.0013 7.63s
60 0.5141 0.0010 7.47s
70 0.4991 0.0008 7.28s
80 0.4791 0.0007 7.12s
90 0.4707 0.0006 6.92s
100 0.4632 0.0006 6.74s
200 0.4013 0.0001 5.09s
300 0.3924 -0.0001 3.62s
400 0.3526 -0.0000 2.32s
500 0.3355 -0.0000 1.12s
600 0.3201 -0.0000 0.00s
fold n°2
Iter Train Loss OOB Improve Remaining Time
1 0.6518 0.0034 8.83s
2 0.6618 0.0033 8.42s
3 0.6483 0.0032 8.28s
4 0.6592 0.0029 8.27s
5 0.6386 0.0030 8.18s
6 0.6438 0.0031 8.16s
7 0.6477 0.0033 8.12s
8 0.6593 0.0029 8.15s
9 0.6182 0.0029 8.19s
10 0.6358 0.0028 8.32s
20 0.5810 0.0025 7.91s
30 0.5816 0.0020 7.74s
40 0.5529 0.0013 7.53s
50 0.5402 0.0011 7.38s
60 0.5096 0.0011 7.17s
70 0.4883 0.0010 7.03s
80 0.4980 0.0007 6.84s
90 0.4706 0.0006 6.71s
100 0.4704 0.0004 6.55s
200 0.3867 0.0001 5.01s
300 0.3686 -0.0000 3.60s
400 0.3363 -0.0000 2.32s
500 0.3357 -0.0000 1.13s
600 0.3160 -0.0000 0.00s
fold n°3
Iter Train Loss OOB Improve Remaining Time
1 0.6457 0.0038 8.04s
2 0.6687 0.0033 8.08s
3 0.6462 0.0036 8.04s
4 0.6587 0.0035 8.02s
5 0.6430 0.0031 7.99s
6 0.6540 0.0029 7.95s
7 0.6377 0.0030 7.93s
8 0.6414 0.0030 7.97s
9 0.6399 0.0030 8.07s
10 0.6375 0.0028 8.07s
20 0.5949 0.0025 7.67s
30 0.5854 0.0019 7.72s
40 0.5386 0.0016 7.46s
50 0.5156 0.0013 7.32s
60 0.5080 0.0011 7.17s
70 0.5021 0.0009 7.04s
80 0.4654 0.0008 6.85s
90 0.4712 0.0006 6.72s
100 0.4740 0.0006 6.53s
200 0.3924 0.0000 4.96s
300 0.3568 -0.0000 3.58s
400 0.3400 -0.0001 2.31s
500 0.3283 -0.0001 1.12s
600 0.3044 -0.0000 0.00s
fold n°4
Iter Train Loss OOB Improve Remaining Time
1 0.6606 0.0032 8.27s
2 0.6878 0.0030 8.37s
3 0.6490 0.0031 8.37s
4 0.6564 0.0032 8.29s
5 0.6568 0.0027 8.27s
6 0.6496 0.0030 8.27s
7 0.6451 0.0029 8.22s
8 0.6210 0.0031 8.21s
9 0.6239 0.0028 8.35s
10 0.6535 0.0025 8.35s
20 0.6038 0.0022 7.92s
30 0.6032 0.0019 7.76s
40 0.5492 0.0018 7.55s
50 0.5333 0.0011 7.37s
60 0.4973 0.0010 7.24s
70 0.4942 0.0009 7.09s
80 0.4753 0.0008 6.92s
90 0.4806 0.0005 6.76s
100 0.4659 0.0005 6.58s
200 0.4046 0.0000 4.99s
300 0.3647 -0.0000 3.59s
400 0.3561 -0.0000 2.32s
500 0.3330 -0.0000 1.12s
600 0.3152 -0.0000 0.00s
fold n°5
Iter Train Loss OOB Improve Remaining Time
1 0.6721 0.0036 8.28s
2 0.6822 0.0034 8.41s
3 0.6634 0.0033 8.26s
4 0.6584 0.0032 8.21s
5 0.6574 0.0030 8.40s
6 0.6544 0.0033 8.31s
7 0.6533 0.0028 8.30s
8 0.6196 0.0029 8.27s
9 0.6530 0.0028 8.43s
10 0.6108 0.0032 8.49s
20 0.6107 0.0027 7.91s
30 0.5649 0.0020 7.70s
40 0.5555 0.0016 7.55s
50 0.5156 0.0014 7.40s
60 0.5144 0.0010 7.21s
70 0.5001 0.0009 7.05s
80 0.4908 0.0007 6.88s
90 0.4820 0.0008 6.73s
100 0.4617 0.0007 6.55s
200 0.3993 -0.0000 5.01s
300 0.3678 -0.0000 3.61s
400 0.3399 -0.0000 2.31s
500 0.3182 -0.0000 1.12s
600 0.3238 -0.0000 0.00s
CV score: 0.46724198
至此，我们得到了以上3种模型的基于49个特征的预测结果以及模型架构及参数。其中在每一种特征工程中，进行5折的交叉验证，并重复两次（Kernel Ridge Regression，核脊回归），取得每一个特征数下的模型的结果。
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)
fold 0
fold 1
fold 2
fold 3
fold 4
fold 5
fold 6
fold 7
fold 8
fold 9
0.4662728551415085
接下来我们对于383维的数据进行与上述263以及49维数据相同的操作
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.51412085
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.48687670
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.53296555
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.48717310
至此，我们得到了以上4种模型的基于383个特征的预测结果以及模型架构及参数。其中在每一种特征工程中，进行5折的交叉验证，并重复两次（LinearRegression简单的线性回归），取得每一个特征数下的模型的结果。
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)
fold 0
fold 1
fold 2
fold 3
fold 4
fold 5
fold 6
fold 7
fold 8
fold 9
0.4878202780283125
由于49维的特征是最重要的特征，所以这里考虑增加更多的模型进行49维特征的数据的构建工作。
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.50254410
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.49451286
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.49534595
- 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)))
fold n°1
fold n°2
fold n°3
fold n°4
fold n°5
CV score: 0.53841695
我们得到了以上4种新模型的基于49个特征的预测结果以及模型架构及参数。其中在每一种特征工程中，进行5折的交叉验证，并重复两次（LinearRegression简单的线性回归），取得每一个特征数下的模型的结果。
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)
fold 0
fold 1
fold 2
fold 3
fold 4
fold 5
fold 6
fold 7
fold 8
fold 9
0.49491439094008133
模型融合
这里对于上述四种集成学习的模型的预测结果进行加权的求和，得到最终的结果，当然这种方式是很不准确的。
#和下面作对比
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
更好的方式是将以上的4中集成学习模型再次进行集成学习的训练，这里直接使用LinearRegression简单线性回归的进行集成。
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)
fold 0
fold 1
fold 2
fold 3
fold 4
fold 5
fold 6
fold 7
fold 8
fold 9
0.4480223491250565
结果保存
进行index的读取工作
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 3.879322
std 0.462290
min 1.636433
25% 3.667859
50% 3.954825
75% 4.185277
max 5.051027
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']>4.96,'happiness']= 5
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.to_csv("submision.csv",index=False)
submit_example.happiness.describe()
count 2968.000000
mean 3.879330
std 0.462127
min 1.636433
25% 3.667859
50% 3.954825
75% 4.185277
max 5.000000
Name: happiness, dtype: float64
大家可以对于model的参数进行更进一步的调整，例如使用网格搜索的方法。