集成学习案例一 （幸福感预测）
背景介绍
幸福感是一个古老而深刻的话题，是人类世代追求的方向。与幸福感相关的因素成千上万、因人而异，大如国计民生，小如路边烤红薯，都会对幸福感产生影响。这些错综复杂的因素中，我们能找到其中的共性，一窥幸福感的要义吗？
另外，在社会科学领域，幸福感的研究占有重要的位置。这个涉及了哲学、心理学、社会学、经济学等多方学科的话题复杂而有趣；同时与大家生活息息相关，每个人对幸福感都有自己的衡量标准。如果能发现影响幸福感的共性，生活中是不是将多一些乐趣；如果能找到影响幸福感的政策因素，便能优化资源配置来提升国民的幸福感。目前社会科学研究注重变量的可解释性和未来政策的落地，主要采用了线性回归和逻辑回归的方法，在收入、健康、职业、社交关系、休闲方式等经济人口因素；以及*公共服务、宏观经济环境、税负等宏观因素上有了一系列的推测和发现。
该案例为幸福感预测这一经典课题，希望在现有社会科学研究外有其他维度的算法尝试，结合多学科各自优势，挖掘潜在的影响因素，发现更多可解释、可理解的相关关系。
具体来说，该案例就是一个数据挖掘类型的比赛——幸福感预测的baseline。具体来说，我们需要使用包括个体变量（性别、年龄、地域、职业、健康、婚姻与政治面貌等等）、家庭变量（父母、配偶、子女、家庭资本等等）、社会态度（公平、信用、公共服务等等）等139维度的信息来预测其对幸福感的影响。
我们的数据来源于国家官方的《中国综合社会调查（CGSS）》文件中的调查结果中的数据，数据来源可靠可依赖:)
数据信息
赛题要求使用以上 139 维的特征，使用 8000 余组数据进行对于个人幸福感的预测（预测值为1，2，3，4，5，其中1代表幸福感最低，5代表幸福感最高）。
因为考虑到变量个数较多，部分变量间关系复杂，数据分为完整版和精简版两类。可从精简版入手熟悉赛题后，使用完整版挖掘更多信息。在这里我直接使用了完整版的数据。赛题也给出了index文件中包含每个变量对应的问卷题目，以及变量取值的含义；survey文件中为原版问卷，作为补充以方便理解问题背景。
评价指标
最终的评价指标为均方误差MSE，即：
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Score = \frac{1}{n} \sum_1 ^n (y_i - y ^*)^2
Score=n11∑n(yi−y∗)2
导入package
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)
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)
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
首先需要对于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)
填充缺失值，在这里我采取的方式是将缺失值补全，使用fillna(value)，其中value的数值根据具体的情况来确定。例如将大部分缺失信息认为是零，将家庭成员数认为是1，将家庭收入这个特征认为是66365，即所有家庭的收入平均值。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
除此之外，还有特殊格式的信息需要另外处理，比如与时间有关的信息，这里主要分为两部分进行处理：首先是将“连续”的年龄，进行分层处理，即划分年龄段，具体地在这里我们将年龄分为了6个区间。其次是计算具体的年龄，在Excel表格中，只有出生年月以及调查时间等信息，我们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，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。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，这也是我在这一步骤中使用最多的一种方式。就像我自己填表一样，这里我全部都使用了我自己的想法进行缺省值的补全。
#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
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-748b6a512a9e> in <module>
44 #部分特征处理，取众数
45 for i in range(1,9+1):
---> 46 data.loc[data['public_service_'+str(i)]<0,'public_service_'+str(i)] = data['public_service_'+str(i)].dropna().mode().values
47 for i in range(1,13+1):
48 data.loc[data['trust_'+str(i)]<0,'trust_'+str(i)] = data['trust_'+str(i)].dropna().mode().values
E:\My_ruan_jian\anaconda3\lib\site-packages\pandas\core\indexing.py in __setitem__(self, key, value)
690
691 iloc = self if self.name == "iloc" else self.obj.iloc
--> 692 iloc._setitem_with_indexer(indexer, value, self.name)
693
694 def _validate_key(self, key, axis: int):
E:\My_ruan_jian\anaconda3\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer(self, indexer, value, name)
1633 if take_split_path:
1634 # We have to operate column-wise
-> 1635 self._setitem_with_indexer_split_path(indexer, value, name)
1636 else:
1637 self._setitem_single_block(indexer, value, name)
E:\My_ruan_jian\anaconda3\lib\site-packages\pandas\core\indexing.py in _setitem_with_indexer_split_path(self, indexer, value, name)
1687
1688 raise ValueError(
-> 1689 "Must have equal len keys and value "
1690 "when setting with an iterable"
1691 )
ValueError: Must have equal len keys and value when setting with an iterable
数据增广
这一步，我们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，这里我添加了如下的特征：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，我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以及一个个体相对于同省、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 | 0.690649 | 0.337071 |
| 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 | 1.166536 | 1.466460 |
| 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 | 1.234718 | 1.085312 |
| 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 | 1.254347 | 2.223827 |
| 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 | 0.756717 | 1.298322 |
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: 0.500331 valid_1's l2: 0.533427
[1000] training's l2: 0.4525 valid_1's l2: 0.500777
[1500] training's l2: 0.426508 valid_1's l2: 0.487597
[2000] training's l2: 0.4084 valid_1's l2: 0.481467
[2500] training's l2: 0.394027 valid_1's l2: 0.477529
[3000] training's l2: 0.381774 valid_1's l2: 0.475263
[3500] training's l2: 0.370932 valid_1's l2: 0.474145
[4000] training's l2: 0.361149 valid_1's l2: 0.47294
[4500] training's l2: 0.352063 valid_1's l2: 0.472231
[5000] training's l2: 0.343702 valid_1's l2: 0.471765
[5500] training's l2: 0.335758 valid_1's l2: 0.47144
[6000] training's l2: 0.328102 valid_1's l2: 0.471331
[6500] training's l2: 0.320743 valid_1's l2: 0.471134
[7000] training's l2: 0.313889 valid_1's l2: 0.470992
[7500] training's l2: 0.307232 valid_1's l2: 0.470861
[8000] training's l2: 0.300824 valid_1's l2: 0.470847
[8500] training's l2: 0.294612 valid_1's l2: 0.470747
[9000] training's l2: 0.288702 valid_1's l2: 0.470792
Early stopping, best iteration is:
[8525] training's l2: 0.294291 valid_1's l2: 0.470632
fold n°2
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.504939 valid_1's l2: 0.514236
[1000] training's l2: 0.455927 valid_1's l2: 0.480216
[1500] training's l2: 0.429853 valid_1's l2: 0.466773
[2000] training's l2: 0.411906 valid_1's l2: 0.459944
[2500] training's l2: 0.397753 valid_1's l2: 0.456118
[3000] training's l2: 0.385615 valid_1's l2: 0.453513
[3500] training's l2: 0.37487 valid_1's l2: 0.451986
[4000] training's l2: 0.365165 valid_1's l2: 0.450867
[4500] training's l2: 0.356014 valid_1's l2: 0.449942
[5000] training's l2: 0.347677 valid_1's l2: 0.449112
[5500] training's l2: 0.339698 valid_1's l2: 0.448458
[6000] training's l2: 0.332008 valid_1's l2: 0.448121
[6500] training's l2: 0.324805 valid_1's l2: 0.44822
Early stopping, best iteration is:
[6067] training's l2: 0.331041 valid_1's l2: 0.447987
fold n°3
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.503954 valid_1's l2: 0.51823
[1000] training's l2: 0.45601 valid_1's l2: 0.481778
[1500] training's l2: 0.430579 valid_1's l2: 0.465715
[2000] training's l2: 0.413078 valid_1's l2: 0.456785
[2500] training's l2: 0.398959 valid_1's l2: 0.451336
[3000] training's l2: 0.386889 valid_1's l2: 0.448179
[3500] training's l2: 0.375929 valid_1's l2: 0.44658
[4000] training's l2: 0.366107 valid_1's l2: 0.444923
[4500] training's l2: 0.357063 valid_1's l2: 0.444236
[5000] training's l2: 0.348507 valid_1's l2: 0.443648
[5500] training's l2: 0.340358 valid_1's l2: 0.443224
[6000] training's l2: 0.332738 valid_1's l2: 0.442732
[6500] training's l2: 0.325277 valid_1's l2: 0.442314
[7000] training's l2: 0.318207 valid_1's l2: 0.442253
[7500] training's l2: 0.311511 valid_1's l2: 0.442414
Early stopping, best iteration is:
[6952] training's l2: 0.31887 valid_1's l2: 0.442143
fold n°4
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.505084 valid_1's l2: 0.512556
[1000] training's l2: 0.456559 valid_1's l2: 0.477796
[1500] training's l2: 0.429929 valid_1's l2: 0.465724
[2000] training's l2: 0.411692 valid_1's l2: 0.459847
[2500] training's l2: 0.397526 valid_1's l2: 0.45647
[3000] training's l2: 0.385483 valid_1's l2: 0.454713
[3500] training's l2: 0.37476 valid_1's l2: 0.453502
[4000] training's l2: 0.364943 valid_1's l2: 0.452811
[4500] training's l2: 0.355902 valid_1's l2: 0.451961
[5000] training's l2: 0.347386 valid_1's l2: 0.45147
[5500] training's l2: 0.339364 valid_1's l2: 0.451099
[6000] training's l2: 0.331798 valid_1's l2: 0.450918
[6500] training's l2: 0.324397 valid_1's l2: 0.450487
[7000] training's l2: 0.3175 valid_1's l2: 0.450161
[7500] training's l2: 0.310766 valid_1's l2: 0.450245
Early stopping, best iteration is:
[7169] training's l2: 0.315228 valid_1's l2: 0.45005
fold n°5
Training until validation scores don't improve for 800 rounds
[500] training's l2: 0.503623 valid_1's l2: 0.520436
[1000] training's l2: 0.455596 valid_1's l2: 0.485716
[1500] training's l2: 0.429871 valid_1's l2: 0.472261
[2000] training's l2: 0.411812 valid_1's l2: 0.465697
[2500] training's l2: 0.397307 valid_1's l2: 0.4619
[3000] training's l2: 0.385007 valid_1's l2: 0.459606
[3500] training's l2: 0.373939 valid_1's l2: 0.458301
[4000] training's l2: 0.363967 valid_1's l2: 0.457405
[4500] training's l2: 0.3547 valid_1's l2: 0.457031
[5000] training's l2: 0.345991 valid_1's l2: 0.456911
[5500] training's l2: 0.33777 valid_1's l2: 0.456732
[6000] training's l2: 0.33005 valid_1's l2: 0.456461
[6500] training's l2: 0.322639 valid_1's l2: 0.456643
Early stopping, best iteration is:
[5849] training's l2: 0.332304 valid_1's l2: 0.456373
CV score: 0.45343717
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()
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-MkZJ7d0E-1621347179784)(output_33_0.png)]
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
[20:06:58] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
[20:06:58] WARNING: C:\Users\Administrator\workspace\xgboost-win64_release_1.2.0\src\learner.cc:516:
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:3.40428 valid_data-rmse:3.38320
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:0.40344 valid_data-rmse:0.70623
Stopping. Best iteration:
[336] train-rmse:0.46330 valid_data-rmse:0.70529
[20:08:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
fold n°2
[20:08:40] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
[20:08:40] WARNING: C:\Users\Administrator\workspace\xgboost-win64_release_1.2.0\src\learner.cc:516:
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:3.39809 valid_data-rmse:3.40800
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:0.40458 valid_data-rmse:0.69290
[1000] train-rmse:0.26903 valid_data-rmse:0.69440
Stopping. Best iteration:
[569] train-rmse:0.38229 valid_data-rmse:0.69234
[20:11:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
fold n°3
[20:11:01] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
[20:11:01] WARNING: C:\Users\Administrator\workspace\xgboost-win64_release_1.2.0\src\learner.cc:516:
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:3.40186 valid_data-rmse:3.39314
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:0.41135 valid_data-rmse:0.66300
[1000] train-rmse:0.27377 valid_data-rmse:0.66309
Stopping. Best iteration:
[616] train-rmse:0.37410 valid_data-rmse:0.66237
[20:13:13] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
fold n°4
[20:13:14] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
[20:13:14] WARNING: C:\Users\Administrator\workspace\xgboost-win64_release_1.2.0\src\learner.cc:516:
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:3.40239 valid_data-rmse:3.39019
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:0.40864 valid_data-rmse:0.66595
[1000] train-rmse:0.27049 valid_data-rmse:0.66780
Stopping. Best iteration:
[457] train-rmse:0.42267 valid_data-rmse:0.66566
[20:15:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
fold n°5
[20:15:07] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
[20:15:07] WARNING: C:\Users\Administrator\workspace\xgboost-win64_release_1.2.0\src\learner.cc:516:
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:3.39342 valid_data-rmse:3.42636
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:0.41371 valid_data-rmse:0.65543
[1000] train-rmse:0.27373 valid_data-rmse:0.65529
Stopping. Best iteration:
[839] train-rmse:0.31295 valid_data-rmse:0.65465
[20:17:49] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.
CV score: 0.45744189
- 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 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 5.2s
[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 22.4s
[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 51.6s
[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 1.5min
[Parallel(n_jobs=-1)]: Done 1242 tasks | elapsed: 2.3min
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 2.9min finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.3s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.6s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 0.9s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.2s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.5s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 0.8s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.1s finished
fold n°2
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 5.1s
[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 20.8s
[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 47.2s
[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 1.4min
[Parallel(n_jobs=-1)]: Done 1242 tasks | elapsed: 2.2min
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 2.8min finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.4s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.6s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 0.9s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.2s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.3s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.6s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 1.0s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.3s finished
fold n°3
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 4.9s
[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 21.1s
[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 48.0s
[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 1.4min
[Parallel(n_jobs=-1)]: Done 1242 tasks | elapsed: 2.2min
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 2.8min finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.3s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.5s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.8s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 1.1s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.3s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.4s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.7s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 1.0s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.3s finished
fold n°4
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 4.3s
[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 19.8s
[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 47.3s
[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 1.4min
[Parallel(n_jobs=-1)]: Done 1242 tasks | elapsed: 2.2min
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 2.8min finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.3s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.6s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 0.9s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.2s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.3s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.5s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.7s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 1.0s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 1.3s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.7s finished
fold n°5
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 4.4s
[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 20.1s
[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 46.4s
[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 1.4min
[Parallel(n_jobs=-1)]: Done 1242 tasks | elapsed: 2.2min
[Parallel(n_jobs=-1)]: Done 1600 out of 1600 | elapsed: 2.8min finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.0s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.5s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 0.8s
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.0s finished
[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.
[Parallel(n_jobs=4)]: Done 42 tasks | elapsed: 0.1s
[Parallel(n_jobs=4)]: Done 192 tasks | elapsed: 0.2s
[Parallel(n_jobs=4)]: Done 442 tasks | elapsed: 0.4s
[Parallel(n_jobs=4)]: Done 792 tasks | elapsed: 0.6s
[Parallel(n_jobs=4)]: Done 1242 tasks | elapsed: 1.0s
CV score: 0.47924236
[Parallel(n_jobs=4)]: Done 1600 out of 1600 | elapsed: 1.3s 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 0.6583 0.0031 1.61m
2 0.6656 0.0031 1.35m
3 0.6663 0.0033 1.30m
4 0.6349 0.0034 1.24m
5 0.6675 0.0027 1.24m
6 0.6409 0.0026 1.23m
7 0.6309 0.0031 1.25m
8 0.6528 0.0028 1.24m
9 0.6276 0.0027 1.22m
10 0.6371 0.0027 1.24m
20 0.5957 0.0023 1.17m
30 0.5698 0.0018 1.12m
40 0.5345 0.0017 1.07m
50 0.5004 0.0014 1.03m
60 0.4857 0.0009 1.03m
70 0.4955 0.0008 1.01m
80 0.4649 0.0007 58.68s
90 0.4372 0.0009 56.63s
100 0.4375 0.0004 54.70s
200 0.3390 0.0001 36.03s
300 0.3101 -0.0001 18.22s
400 0.2749 0.0000 0.00s
fold n°2
Iter Train Loss OOB Improve Remaining Time
1 0.6612 0.0034 1.29m
2 0.6568 0.0034 1.24m
3 0.6565 0.0033 1.29m
4 0.6617 0.0030 1.26m
5 0.6358 0.0029 1.25m
6 0.6339 0.0034 1.22m
7 0.6325 0.0030 1.23m
8 0.6246 0.0031 1.21m
9 0.6283 0.0027 1.20m
10 0.6133 0.0028 1.22m
20 0.6028 0.0026 1.24m
30 0.5407 0.0021 1.20m
40 0.5357 0.0015 1.17m
50 0.5213 0.0011 1.13m
60 0.5030 0.0012 1.13m
70 0.5000 0.0007 1.07m
80 0.4545 0.0007 1.03m
90 0.4307 0.0008 59.42s
100 0.4397 0.0005 56.96s
200 0.3538 0.0001 36.79s
300 0.3048 -0.0001 18.71s
400 0.2749 -0.0000 0.00s
fold n°3
Iter Train Loss OOB Improve Remaining Time
1 0.6562 0.0031 1.52m
2 0.6468 0.0036 1.58m
3 0.6495 0.0036 1.54m
4 0.6504 0.0030 1.50m
5 0.6629 0.0031 1.48m
6 0.6473 0.0029 1.46m
7 0.6513 0.0029 1.45m
8 0.6398 0.0032 1.45m
9 0.6301 0.0029 1.43m
10 0.6478 0.0027 1.44m
20 0.5981 0.0023 1.30m
30 0.5671 0.0019 1.20m
40 0.5369 0.0018 1.13m
50 0.5104 0.0014 1.08m
60 0.4754 0.0012 1.04m
70 0.4749 0.0009 1.01m
80 0.4484 0.0007 58.13s
90 0.4386 0.0008 55.98s
100 0.4353 0.0005 54.19s
200 0.3388 0.0001 36.73s
300 0.2974 0.0000 18.01s
400 0.2632 -0.0000 0.00s
fold n°4
Iter Train Loss OOB Improve Remaining Time
1 0.6657 0.0029 1.53m
2 0.6623 0.0034 1.31m
3 0.6551 0.0030 1.27m
4 0.6599 0.0029 1.22m
5 0.6502 0.0034 1.25m
6 0.6392 0.0032 1.23m
7 0.6348 0.0031 1.24m
8 0.6360 0.0029 1.22m
9 0.6566 0.0026 1.21m
10 0.6149 0.0028 1.20m
20 0.5905 0.0025 1.12m
30 0.5666 0.0023 1.09m
40 0.5455 0.0017 1.07m
50 0.5141 0.0012 1.05m
60 0.4845 0.0013 1.02m
70 0.4729 0.0010 59.10s
80 0.4513 0.0008 57.20s
90 0.4423 0.0006 55.55s
100 0.4226 0.0004 53.67s
200 0.3391 0.0001 36.29s
300 0.2916 0.0000 18.17s
400 0.2650 -0.0000 0.00s
fold n°5
Iter Train Loss OOB Improve Remaining Time
1 0.6697 0.0034 1.46m
2 0.6548 0.0033 1.27m
3 0.6771 0.0028 1.31m
4 0.6392 0.0033 1.24m
5 0.6352 0.0035 1.27m
6 0.6395 0.0031 1.26m
7 0.6341 0.0032 1.24m
8 0.6443 0.0029 1.22m
9 0.6133 0.0031 1.22m
10 0.6246 0.0027 1.20m
20 0.5877 0.0024 1.12m
30 0.5756 0.0019 1.10m
40 0.5345 0.0018 1.08m
50 0.4980 0.0014 1.05m
60 0.4877 0.0010 1.01m
70 0.4684 0.0009 58.74s
80 0.4428 0.0006 57.71s
90 0.4408 0.0008 56.30s
100 0.4332 0.0006 55.48s
200 0.3418 0.0002 37.91s
300 0.3023 0.0000 18.70s
400 0.2721 0.0000 0.00s
CV score: 0.45697153
- 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