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import `polars as pl#`和pandas类似,但是处理大型数据集有更好的性能.
#necessary
import pandas as pd#导入csv文件的库
import numpy as np#进行矩阵运算的库
#metric
from sklearn.metrics import roc_auc_score#导入roc_auc曲线
#KFold是直接分成k折,StratifiedKFold还要考虑每种类别的占比
from sklearn.model_selection import StratifiedKFold
from sklearn.decomposition import TruncatedSVD#截断奇异值分解,是一种数据降维的方法
import dill#对对象进行序列化和反序列化(例如保存和加载树模型)
import gc#垃圾回收模块
import time#标准库的时间模块
#为了方便后期调用训练的模型时不会调用错版本,提供模型训练的时间
#time.strftime()函数用于将时间对象格式化为字符串，time.localtime()函数返回表示当前本地时间的time.s`truct_time对象
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print("this notebook training time is ", current_time)#config
class Config():seed=2024num_folds=10TARGET_NAME ='target'batch_size=1000#由于不知道测试数据的大小,所以分批次放入模型.import random#提供了一些用于生成随机数的函数
#设置随机种子,保证模型可以复现
def seed_everything(seed):np.random.seed(seed)#numpy的随机种子random.seed(seed)#python内置的随机种子
seed_everything(Config.seed)#读取训练数据中每个特征的dtype
colname2dtype=pd.read_csv("/kaggle/input/home-credit-inconsistent-data-types/colname2dtype.csv")
colname=colname2dtype['Column'].values
dtype=colname2dtype['DataType'].valuesdtype2pl={}
dtype2pl['Int64']=pl.Int64
dtype2pl['Float64']=pl.Float64
dtype2pl['String']=pl.String
dtype2pl['Boolean']=pl.Stringcolname2dtype={}
for idx in range(len(colname)):colname2dtype[colname[idx]]=dtype2pl[dtype[idx]]#找出表格df里缺失值占比大于margin的列,pandas
def find_df_null_col(df,margin=0.975):cols=[]for col in df.columns:if df[col].isna().mean()>margin:cols.append(col)return cols
#对于某个文件有很多个相同的case_id保留最后一个.
#有些文件我们就需要某个用户最新的某些信息,这时候就可以用这个函数.
def find_last_case_id(df,id='case_id'):#假设传入的df已经按照'case_id'排序好了.df_copy=df.clone()df_tail=df.tail(1)#最后的一个'case_id'单独取出#找出除了最后一个的其他的case_id,shift没用了,也要drop掉df_copy=df_copy.with_columns(pl.col(id).shift(-1).alias(f"{id}_shift_-1"))df_last=df_copy.filter(pl.col(id)-pl.col(f'{id}_shift_-1')!=0).drop(f'{id}_shift_-1')#每个case_id只保留最新的信息.df_last=pl.concat([df_last,df_tail])#这个比赛有很多文件,为了节省内存一定要及时清理.del df_copy,df_tailgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存return df_last
#对表格df的某列col用method进行填充
def df_fillna(df,col,method=None):if method ==None:#我不打算填充这列的缺失值passif method == "forward":#使用前一个值填充缺失值df = df.select([pl.col(col).fill_null('forward')])else:#method=['NaN',0].如果把缺失本身当作一种信息可以填充为"NaN",二分类0和1中0占大多数的列可能会用0填充.df=df.with_columns(pl.col(col).fill_null(method).alias(col))return df#返回填充后的表格#对表格df的某列col进行独热编码,为了保证训练集和测试集增加同样多的列,这里直接给出独热编码的类别unique.
def one_hot_encoder(df,col,unique):#如果类别为2的话,直接选择其中一个=if len(unique)==2:df=df.with_columns((pl.col(col)==unique[0]).cast(pl.Int8).alias(f"{col}_{unique[0]}"))else:#类别为多的时候才一个一个类别考虑过去.for idx in range(len(unique)):df=df.with_columns((pl.col(col)==unique[idx]).cast(pl.Int8).alias(f"{col}_{unique[idx]}"))return df.drop(col)#drop掉col这列,因为有独热编码了.
#由于last_features是每个case_id最新的信息,所以case_id不会有重复的,所以直接按case_id merge到原来表格里就行了.
#last_df是每个case_id保留最新信息的表格,last_features是哪些特征要统计最新信息,feats是总特征表格.
def last_features_merge(feats,last_df,last_features=[]):#从last_df中选出要统计最新信息的几列last_df=last_df.select(['case_id']+[last[0] for last in last_features])#对last_df的那几列填充缺失值for last in last_features:col,fill=lastlast_df=df_fillna(last_df,col,method=fill)#填充好缺失值之后就merge进feats表格.feats填充列还有缺失值是因为那些列有些case_id没有数据在last_df中.feats=feats.join(last_df,on='case_id',how='left')return feats#feats是总特征,group_df是有多个相同case_id的表格,group_features是要用来group的特征,name是csv文件名.
#fillna+one-hot,groupby
def group_features_merge(feats,group_df,group_features=[],group_name='applprev2'):#挑选出group_features这些列group_df=group_df.select(['case_id']+[g[0] for g in group_features])#先把字符串列单独处理.for group in group_features:if group_df[group[0]].dtype==pl.String:#如果是字符串类型是one-hotcol,fill,one_hot=groupgroup_df=df_fillna(group_df,col,method=fill)#填充是第一步if one_hot==None:#如果不要one-hot直接drop colgroup_df=group_df.drop(col) else:#或者one-hot-encodinggroup_df=one_hot_encoder(group_df,col,one_hot)for value in one_hot:new_col=f"{col}_{value}"feat=feat=group_df.group_by('case_id').agg( pl.mean(new_col).alias(f"mean_{group_name}_{new_col}"),pl.std(new_col).alias(f"std_{group_name}_{new_col}"),pl.count(new_col).alias(f"count_{group_name}_{new_col}"),)feats=feats.join(feat,on='case_id',how='left')else:#如果不是字符串,是数值列,对col填充为fillcol,fill=groupgroup_df=df_fillna(group_df,col,method=fill)#填充是第一步feat=group_df.group_by('case_id').agg( pl.max(col).alias(f"max_{group_name}_{col}"),pl.mean(col).alias(f"mean_{group_name}_{col}"),pl.median(col).alias(f"median_{group_name}_{col}"),pl.std(col).alias(f"std_{group_name}_{col}"),pl.min(col).alias(f"min_{group_name}_{col}"),pl.count(col).alias(f"count_{group_name}_{col}"),pl.sum(col).alias(f"sum_{group_name}_{col}"),pl.n_unique(col).alias(f"n_unique_{group_name}_{col}"),pl.first(col).alias(f"first_{group_name}_{col}"),pl.last(col).alias(f"last_{group_name}_{col}"))feats=feats.join(feat,on='case_id',how='left')return featsdef set_table_dtypes(df):for col in df.columns:df=df.with_columns(pl.col(col).cast(colname2dtype[col]).alias(col))return df#after break 就是仔细研究过文件每个特征含义的意思.
def preprocessor(mode='train'):#mode='train'|'test'print(f"{mode} base file after break.number 1")feats=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_base.csv").pipe(set_table_dtypes)feats=feats.drop(['date_decision','MONTH','WEEK_NUM'])print("-"*30)print(f"{mode} applprev_2 file after break. number:1")applprev2=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_applprev_2.csv").pipe(set_table_dtypes)applprev2=applprev2.with_columns(#账户没有被冻结,所以没有冻结的原因, 以前没有申请过信用卡,也没有留下联系方式( (pl.col('cacccardblochreas_147M')!=pl.col('cacccardblochreas_147M'))&(pl.col('conts_type_509L')!=pl.col('conts_type_509L')) )\.alias("no_credit")#.cast(pl.Int8))applprev2=applprev2.with_columns(#账户没有被冻结,所以没有冻结的原因,但是申请过信用卡( (pl.col('cacccardblochreas_147M')!=pl.col('cacccardblochreas_147M'))&(pl.col('conts_type_509L')==pl.col('conts_type_509L'))) \.alias("no_frozen_credit").cast(pl.Int8))applprev2=applprev2.with_columns(#有冻结的原因,所以账户被冻结过,也自然有信用卡(pl.col('cacccardblochreas_147M')==pl.col('cacccardblochreas_147M'))\.alias("frozen_credit").cast(pl.Int8))applprev2_last=find_last_case_id(applprev2)"""这些列有些是要取最新的特征,有些是需要groupby.联系方式要最新的看一个人最新状态是不是还没有信用卡有没有信用卡冻结也考虑一下最新状态吧,反正就一个特征.信用卡冻结列特征可以从冻结原因那列构造"""#这里只需要把缺失值填充就可以merge了,后续训练数据和测试数据字符串一起one-hot.last_features=[['conts_type_509L','WHATSAPP'],#WHATSAPP只有1个,那就把NaN当成WHATSAPP吧.['no_credit',0],['no_frozen_credit',0],['frozen_credit',0]]feats=last_features_merge(feats,applprev2_last,last_features)#groupby需要考虑fillna,onehot(对于字符串如果是None就是不要one-hot,直接drop掉,如果要one-hot,搞出个类别的列表),然后groupby,mergegroup_features=[['cacccardblochreas_147M','a55475b1',\["P19_60_110","P17_56_144","a55475b1","P201_63_60","P127_74_114","P133_119_56","P41_107_150","P23_105_103""P33_145_161"]],['credacc_cards_status_52L','UNCONFIRMED',\['BLOCKED','UNCONFIRMED','RENEWED', 'CANCELLED', 'INACTIVE', 'ACTIVE']],['num_group1',0],#'num_group1', 'num_group2',暂时不考虑.['num_group2',0],#'num_group1', 'num_group2',暂时不考虑.]feats=group_features_merge(feats,applprev2,group_features,group_name='applprev2')del applprev2,applprev2_lastgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("-"*30)print("credit bureau b num 2")bureau_b_1=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_credit_bureau_b_1.csv").pipe(set_table_dtypes)bureau_b_2=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_credit_bureau_b_2.csv").pipe(set_table_dtypes)bureau_b_1_last=find_last_case_id(bureau_b_1,id='case_id')bureau_b_2_last=find_last_case_id(bureau_b_2,id='case_id')feats=feats.join(bureau_b_1_last,on='case_id',how='left')feats=feats.join(bureau_b_2_last,on='case_id',how='left')del bureau_b_1,bureau_b_1_last,bureau_b_2,bureau_b_2_lastgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"{mode} debitcard file after break num 1")#'openingdate_857D':借记卡开户日期.暂时不处理.debitcard=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_debitcard_1.csv").pipe(set_table_dtypes)debitcard_last=find_last_case_id(debitcard,id='case_id')last_features=[['last180dayaveragebalance_704A',0],#过去180天内借记卡平均余额,用众数0来填充.['last180dayturnover_1134A',30000],#借记卡过去180天营业额,这里没有特别明显的众数,中位数数填充.['last30dayturnover_651A',0]#用众数0来填充.]feats=last_features_merge(feats,debitcard_last,last_features)group_features=[['num_group1',0]#用众数来填充.]feats=group_features_merge(feats,debitcard,group_features,group_name='debitcard')del debitcard,debitcard_lastgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"{mode} deposit file num 1")deposit=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_deposit_1.csv").pipe(set_table_dtypes)#数值列的特征工程  从1开始是为了把'case_id'去掉    for idx in range(1,len(deposit.columns)):col=deposit.columns[idx]column_type = deposit[col].dtypeis_numeric = (column_type == pl.datatypes.Int64) or (column_type == pl.datatypes.Float64) if is_numeric:#数值列构造特征feat=deposit.group_by('case_id').agg( pl.max(col).alias(f"max_deposit_{col}"),pl.mean(col).alias(f"mean_deposit_{col}"),pl.median(col).alias(f"median_deposit_{col}"),pl.std(col).alias(f"std_deposit_{col}"),pl.min(col).alias(f"min_deposit_{col}"),pl.count(col).alias(f"count_deposit_{col}"),pl.sum(col).alias(f"sum_deposit_{col}"),pl.n_unique(col).alias(f"n_unique_deposit_{col}"),pl.first(col).alias(f"first_deposit_{col}"),pl.last(col).alias(f"last_deposit_{col}"))feats=feats.join(feat,on='case_id',how='left')del depositgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"{mode} other file after break number 1")other=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_other_1.csv").pipe(set_table_dtypes)other_last=find_last_case_id(other)#这里只需要把缺失值填充就可以merge了,后续训练数据和测试数据字符串一起one-hot.last_features=[['amtdepositbalance_4809441A',0]#amtdepositbalance_4809441A:客户存款余额.用众数0来填充.]feats=last_features_merge(feats,other_last,last_features)group_features=[['amtdebitincoming_4809443A',0],#amtdebitincoming_4809443A,0传入借记卡交易金额,用众数0来填充.['amtdebitoutgoing_4809440A',0],#amtdebitoutgoing_4809440A传出借记卡交易金额,用众数0来填充.['amtdepositincoming_4809444A',0], #amtdepositincoming_4809444A客户账户入金金额.众数为0.['amtdepositoutgoing_4809442A',0]#amtdepositoutgoing_4809442A:客户账户出金金额.众数为0.]feats=group_features_merge(feats,other,group_features,group_name='other')del other,other_lastgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("person 1 num 1")person1=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_person_1.csv").pipe(set_table_dtypes)#缺失值>=0.99的列直接drop掉.person1=person1.drop(['birthdate_87D','childnum_185L','gender_992L','housingtype_772L','isreference_387L','maritalst_703L','role_993L'])                   person1=person1.select(['case_id','contaddr_matchlist_1032L','contaddr_smempladdr_334L','empl_employedtotal_800L','language1_981M','persontype_1072L','persontype_792L','remitter_829L','role_1084L','safeguarantyflag_411L','sex_738L'])person1_last=find_last_case_id(person1)feats=feats.join(person1_last,on='case_id',how='left')del person1,person1_lastgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"{mode} person2 file after break number 1")#经过检查person2训练集和测试集对应的列dtype都对应的上person2=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_person_2.csv").pipe(set_table_dtypes)#这些特征缺失值占比>=0.96,不用填充,直接drop吧.person2=person2.drop(['addres_role_871L','empls_employedfrom_796D','relatedpersons_role_762T'])#个人地址,地址邮政编码,雇主名字算私人信息,不拿来训练.person2=person2.drop(['addres_district_368M','addres_zip_823M','empls_employer_name_740M'])group_features=[['conts_role_79M','a55475b1',#人员的联系人角色类型.['a55475b1', 'P38_92_157', 'P7_147_157', 'P177_137_98', 'P125_14_176', 'P125_105_50', 'P115_147_77', 'P58_79_51','P124_137_181', 'P206_38_166', 'P42_134_91']],['empls_economicalst_849M','a55475b1',['a55475b1', 'P164_110_33', 'P22_131_138', 'P28_32_178','P148_57_109', 'P7_47_145', 'P164_122_65', 'P112_86_147','P82_144_169', 'P191_80_124']],['num_group1',0],#用众数0填充.['num_group2',0],#用众数0填充.]del person2gc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"static_0 file num 2(3)")#pipe用于在DataFrame上自定义自己的函数static_0_0=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_static_0_0.csv").pipe(set_table_dtypes)static_0_1=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_static_0_1.csv").pipe(set_table_dtypes)static=pl.concat([static_0_0,static_0_1],how="vertical_relaxed")#垂直合并,并且放宽了数据类型匹配的限制if mode=='test':#如果是测试数据的话还有一个文件static_0_2=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_static_0_2.csv").pipe(set_table_dtypes)static=pl.concat([static,static_0_2],how="vertical_relaxed")feats=feats.join(static,on='case_id',how='left')del static,static_0_0,static_0_1gc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print(f"{mode} static_cb_file after break num 1")static_cb=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_static_cb_0.csv").pipe(set_table_dtypes)#缺失值占比>=0.95的直接drop掉.static_cb=static_cb.drop(['assignmentdate_4955616D', 'dateofbirth_342D','for3years_128L','for3years_504L','for3years_584L','formonth_118L','formonth_206L','formonth_535L','forquarter_1017L', 'forquarter_462L','forquarter_634L','fortoday_1092L','forweek_1077L','forweek_528L','forweek_601L','foryear_618L','foryear_818L','foryear_850L','pmtaverage_4955615A','pmtcount_4955617L','riskassesment_302T','riskassesment_940T'])static_cb=static_cb.drop(['birthdate_574D','dateofbirth_337D',#两个都是客户的出生日期,暂时不用这个数据.'assignmentdate_238D','assignmentdate_4527235D',#税务机关数据:分配日期和转让日期.'responsedate_1012D','responsedate_4527233D','responsedate_4917613D',#税务机关回复日期有3个特征.])#static_cb中每个case_id都是1个数据,所以需要填充缺失值,然后merge即可.last_features=[ ['contractssum_5085716L',0],#从外部信贷机构检索到的合同价值总额['days120_123L',0],#过去120天信用局查询数,0是众数但是不突出.['days180_256L',0],#过去180天的信用局查询数,0是众数但是不突出.['days30_165L',0],#过去30天的信用局查询数,这里0突出一点.['days360_512L',1],#1略比0多一点.['days90_310L',0],#0稍微多一点.['description_5085714M','a55475b1'],#按信贷局对客户进行分类.10:1的二分类.#['education_1103M','a55475b1'],#外部来源的客户受教育水平,5个类别,['education_88M','a55475b1'],#客户受教育水平.['firstquarter_103L',0],#第一季度从信贷局获得的业绩数量['secondquarter_766L',0],#第二季度的业绩数.['thirdquarter_1082L',0],#第3季度的业绩数量.['fourthquarter_440L',0],#第4季度的业绩数.['maritalst_385M','a55475b1'],#客户的婚姻状况.#['maritalst_893M', 'a55475b1'],#客户的婚姻状况.['numberofqueries_373L',1],#向征信机构查询的数量.['pmtaverage_3A',0],#'税收减免的平均值#['pmtaverage_4527227A',7222.2],#'税收减免的平均值.#['pmtcount_4527229L', 6],#税收减免数量['pmtcount_693L', 6],#'税收减免数量'['pmtscount_423L',6.0],#'税款扣减付款的数量.['pmtssum_45A',0],#客户的税收减免总额.['requesttype_4525192L','DEDUCTION_6'],#税务机关请求类型]feats=last_features_merge(feats,static_cb,last_features)#60天的信用局查询数.feats=feats.with_columns( (pl.col('days180_256L')-pl.col('days120_123L')).alias("daysgap60"))feats=feats.with_columns( (pl.col('days180_256L')-pl.col('days30_165L')).alias("daysgap150"))feats=feats.with_columns( (pl.col('days120_123L')-pl.col('days30_165L')).alias("daysgap90"))#一年的业绩数.feats=feats.with_columns( (pl.col('firstquarter_103L')+pl.col('secondquarter_766L')+pl.col('thirdquarter_1082L')+pl.col('fourthquarter_440L')).alias("totalyear_result"))del static_cbgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("-"*30)print(f"{mode} tax_a file after break num 1")tax_a=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_tax_registry_a_1.csv").pipe(set_table_dtypes)#雇主名字属于私人信息,表格中的数据很可能是加密过的,所以没什么用.recorddate_4527225D暂时不使用.group_features=[['amount_4527230A',850],#政府登记的税收减免金额,如果有缺失值用众数850填充['num_group1',0]]feats=group_features_merge(feats,tax_a,group_features,group_name='tax_a')del tax_agc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("-"*30)print(f"{mode} tax_b file after break num 1")tax_b=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_tax_registry_b_1.csv").pipe(set_table_dtypes)#雇主名字是私人信息,不能用来训练模型.num_group1,'deductiondate_4917603D'暂时不使用.group_features=[['amount_4917619A',6885],#政府登记处跟踪的税收减免金额,如果有缺失值用众数填充['num_group1',0]]feats=group_features_merge(feats,tax_b,group_features,group_name='tax_b')del tax_bgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("-"*30)print(f"{mode} tax_c file after break num 1")tax_c=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/{mode}/{mode}_tax_registry_c_1.csv").pipe(set_table_dtypes)if len(tax_c)==0:tax_c=pl.read_csv(f"/kaggle/input/home-credit-credit-risk-model-stability/csv_files/train/train_tax_registry_c_1.csv").pipe(set_table_dtypes)#employername_160M:雇主的名字,隐私信息不使用.processingdate_168D:处理税款扣减的日期.tax_c=tax_c.drop(['employername_160M','processingdate_168D'])group_features=[['pmtamount_36A',850],#pmtamount_36A:信贷局付款的税收减免额,用众数850填充['num_group1',0]#0是众数但是并不是特别突出.]feats=group_features_merge(feats,tax_c,group_features,group_name='tax_c')del tax_cgc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存print("-"*30)return feats
train_feats=preprocessor(mode='train')
test_feats=preprocessor(mode='test')train_feats=train_feats.to_pandas()
test_feats=test_feats.to_pandas()# 计算每列的众数，忽略含有缺失值的列
mode_values = train_feats.mode().iloc[0]
# 使用众数填充训练集中的缺失值
train_feats = train_feats.fillna(mode_values)
# 使用众数填充测试集中的缺失值
test_feats = test_feats.fillna(mode_values)#对字符串特征列进行独热编码的转换
print("----------string one hot encoder ****")
for col in test_feats.columns:n_unique=train_feats[col].nunique()#如果是类别型变量的话,独热编码转换#如果类别是2类,像性别一样,如果是(0,1)了,或者说数值类型的话,没必要转换.如果是字符串类型的话,转换成数值if n_unique==2 and train_feats[col].dtype=='object':print(f"one_hot_2:{col}")unique=train_feats[col].unique()#随便选择一个类别进行转换,比如gender='Female'train_feats[col]=(train_feats[col]==unique[0]).astype(int)test_feats[col]=(test_feats[col]==unique[0]).astype(int)elif (n_unique<10) and train_feats[col].dtype=='object':#由于内存有限 类别型变量的n_unique设置为10print(f"one_hot_10:{col}")unique=train_feats[col].unique()for idx in range(len(unique)):if unique[idx]==unique[idx]:#这里是为了避免字符串中存在nan值的情况train_feats[col+"_"+str(idx)]=(train_feats[col]==unique[idx]).astype(int)test_feats[col+"_"+str(idx)]=(test_feats[col]==unique[idx]).astype(int)train_feats.drop([col],axis=1,inplace=True)test_feats.drop([col],axis=1,inplace=True)
print("----------drop other string or unique value or full null value ****")
drop_cols=[]
for col in test_feats.columns:if (train_feats[col].dtype=='object') or (test_feats[col].dtype=='object') \or (train_feats[col].nunique()==1) or train_feats[col].isna().mean()>0.99:drop_cols+=[col]
#'case_id'没什么用.
drop_cols+=['case_id']
train_feats.drop(drop_cols,axis=1,inplace=True)
test_feats.drop(drop_cols,axis=1,inplace=True)
print(f"len(train_feats):{len(train_feats)},total_features_counts:{len(test_feats.columns)}")
train_feats.head()#遍历表格df的所有列修改数据类型减少内存使用
def reduce_mem_usage(df, float16_as32=True):#memory_usage()是df每列的内存使用量,sum是对它们求和, B->KB->MBstart_mem = df.memory_usage().sum() / 1024**2print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))for col in df.columns:#遍历每列的列名col_type = df[col].dtype#列名的typeif col_type != object:#不是object也就是说这里处理的是数值类型的变量c_min,c_max = df[col].min(),df[col].max() #求出这列的最大值和最小值if str(col_type)[:3] == 'int':#如果是int类型的变量,不管是int8,int16,int32还是int64#如果这列的取值范围是在int8的取值范围内,那就对类型进行转换 (-128 到 127)if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:df[col] = df[col].astype(np.int8)#如果这列的取值范围是在int16的取值范围内,那就对类型进行转换(-32,768 到 32,767)elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:df[col] = df[col].astype(np.int16)#如果这列的取值范围是在int32的取值范围内,那就对类型进行转换(-2,147,483,648到2,147,483,647)elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:df[col] = df[col].astype(np.int32)#如果这列的取值范围是在int64的取值范围内,那就对类型进行转换(-9,223,372,036,854,775,808到9,223,372,036,854,775,807)elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:df[col] = df[col].astype(np.int64)  else:#如果是浮点数类型.#如果数值在float16的取值范围内,如果觉得需要更高精度可以考虑float32if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:if float16_as32:#如果数据需要更高的精度可以选择float32df[col] = df[col].astype(np.float32)else:df[col] = df[col].astype(np.float16)  #如果数值在float32的取值范围内，对它进行类型转换elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:df[col] = df[col].astype(np.float32)#如果数值在float64的取值范围内，对它进行类型转换else:df[col] = df[col].astype(np.float64)#计算一下结束后的内存end_mem = df.memory_usage().sum() / 1024**2print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))#相比一开始的内存减少了百分之多少print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))return df
train_feats = reduce_mem_usage(train_feats)
test_feats = reduce_mem_usage(test_feats)def pearson_corr(x1,x2):"""x1,x2:np.array"""mean_x1=np.mean(x1)mean_x2=np.mean(x2)std_x1=np.std(x1)std_x2=np.std(x2)pearson=np.mean((x1-mean_x1)*(x2-mean_x2))/(std_x1*std_x2)return pearson
#有没有和target相关性特别高的特征,拿来做逻辑回归
choose_cols=[]
for col in train_feats.columns:if col!='target':pearson=pearson_corr(train_feats[col].values,train_feats['target'].values) if abs(pearson)>0.0025:choose_cols.append(col)
print(f"len(choose_cols):{len(choose_cols)},choose_cols:{choose_cols}")#mean_gini:0.5428968427934477
from sklearn.linear_model import LinearRegressionX=train_feats[choose_cols].copy()
y=train_feats[Config.TARGET_NAME].copy()
test_X=test_feats[choose_cols].copy()
oof_pred_pro=np.zeros((len(X)))
test_pred_pro=np.zeros((Config.num_folds,len(test_X)))
del train_feats,test_feats
gc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存#10折交叉验证
skf = StratifiedKFold(n_splits=Config.num_folds,random_state=Config.seed, shuffle=True)for fold, (train_index, valid_index) in (enumerate(skf.split(X, y.astype(str)))):print(f"fold:{fold}")X_train, X_valid = X.iloc[train_index], X.iloc[valid_index]y_train, y_valid = y.iloc[train_index], y.iloc[valid_index]# 创建线性回归模型model = LinearRegression()model.fit(X_train,y_train)oof_pred_pro[valid_index]=model.predict(X_valid)#将数据分批次进行预测.for idx in range(0,len(test_X),Config.batch_size):test_pred_pro[fold][idx:idx+Config.batch_size]=model.predict(test_X[idx:idx+Config.batch_size]) del model,X_train, X_valid,y_train, y_valid#模型用完直接删掉gc.collect()#手动触发垃圾回收,强制回收由垃圾回收器标记为未使用的内存
gini=2*roc_auc_score(y.values,oof_pred_pro)-1
print(f"mean_gini:{gini}")test_preds=test_pred_pro.mean(axis=0)
submission=pd.read_csv("/kaggle/input/home-credit-credit-risk-model-stability/sample_submission.csv")
submission['score']=np.clip(np.nan_to_num(test_preds,nan=0.3),0,1)
submission.to_csv("submission.csv",index=None)
submission.head()