Baseline_data[f + '_rank'] = data.groupby([f])['id'].rank(a

import pandas as pd
import os
import gc
import lightgbm as lgb
import xgboost as xgb
from catboost import CatBoostRegressor
from sklearn.linear_model import SGDRegressor,LinearRegression,Ridge
from sklearn.preprocessing import MinMaxScaler
import math
import numpy as np
from tqdm import tqdm
from sklearn.model_selection import StratifiedKFold,KFold
from sklearn.metrics import accuracy_score,f1_score,roc_auc_score,log_loss
import matplotlib.pyplot as plt
import time
import warnings
warnings.filterwarnings('ignore')
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train=pd.read_csv('train.csv')
testA=pd.read_csv('testA.csv')
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train.head()
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5 rows × 47 columns

list(train.select_dtypes('object'))
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['grade', 'subGrade', 'employmentLength', 'issueDate', 'earliesCreditLine']
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data=pd.concat([train,testA],axis=0,ignore_index=True)
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• concat函数：https://www.jianshu.com/p/421f040dfe2f

数据预处理

可以看到很多变量不能直接训练,比如’grade’, ‘subGrade’,‘employmentLength’, ‘issueDate’, ‘earliesCreditLine’，需要进行预处理

print(sorted(data.grade.unique()))
print(sorted(data.subGrade.unique()))
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['A', 'B', 'C', 'D', 'E', 'F', 'G']
['A1', 'A2', 'A3', 'A4', 'A5', 'B1', 'B2', 'B3', 'B4', 'B5', 'C1', 'C2', 'C3', 'C4', 'C5', 'D1', 'D2', 'D3', 'D4', 'D5', 'E1', 'E2', 'E3', 'E4', 'E5', 'F1', 'F2', 'F3', 'F4', 'F5', 'G1', 'G2', 'G3', 'G4', 'G5']
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data['employmentLength'].value_counts(dropna=False).sort_index()
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1 year        65671
10+ years    328525
2 years       90565
3 years       80163
4 years       59818
5 years       62645
6 years       46582
7 years       44230
8 years       45168
9 years       37866
< 1 year      80226
NaN           58541
Name: employmentLength, dtype: int64
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• 首先对employmentLength进行转换到数值

data['employmentLength'].replace('10+ years','10 years',inplace=True)
data['employmentLength'].replace('< 1 year','0 years',inplace=True)
def employmentLength_to_int(s):
    if pd.isnull(s):
        return s
    else:
        return np.int8(s.split()[0])
data['employmentLength']=data['employmentLength'].apply(employmentLength_to_int)       
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• https://blog.csdn.net/weixin_41712499/article/details/82719987
• https://blog.csdn.net/kai123wen/article/details/99321824

data['employmentLength'].value_counts(dropna=False).sort_index()
#dropna=False 表示不删除NaN
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0.0      80226
1.0      65671
2.0      90565
3.0      80163
4.0      59818
5.0      62645
6.0      46582
7.0      44230
8.0      45168
9.0      37866
10.0    328525
NaN      58541
Name: employmentLength, dtype: int64
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• 对earliesCreditLine进行预处理

data.earliesCreditLine.sample(5)
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618907    Nov-2004
145773    Oct-2001
21633     Mar-2005
697120    Sep-1990
815318    Feb-2004
Name: earliesCreditLine, dtype: object
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data.earliesCreditLine=data.earliesCreditLine.apply(lambda s:int(s[-4:]))
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data.earliesCreditLine.describe()
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count    1000000.000000
mean        1998.688632
std            7.606231
min         1944.000000
25%         1995.000000
50%         2000.000000
75%         2004.000000
max         2015.000000
Name: earliesCreditLine, dtype: float64
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data.info()
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<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 47 columns):
 #   Column              Non-Null Count    Dtype  
---  ------              --------------    -----  
 0   id                  1000000 non-null  int64  
 1   loanAmnt            1000000 non-null  float64
 2   term                1000000 non-null  int64  
 3   interestRate        1000000 non-null  float64
 4   installment         1000000 non-null  float64
 5   grade               1000000 non-null  object 
 6   subGrade            1000000 non-null  object 
 7   employmentTitle     999999 non-null   float64
 8   employmentLength    941459 non-null   float64
 9   homeOwnership       1000000 non-null  int64  
 10  annualIncome        1000000 non-null  float64
 11  verificationStatus  1000000 non-null  int64  
 12  issueDate           1000000 non-null  object 
 13  isDefault           800000 non-null   float64
 14  purpose             1000000 non-null  int64  
 15  postCode            999999 non-null   float64
 16  regionCode          1000000 non-null  int64  
 17  dti                 999700 non-null   float64
 18  delinquency_2years  1000000 non-null  float64
 19  ficoRangeLow        1000000 non-null  float64
 20  ficoRangeHigh       1000000 non-null  float64
 21  openAcc             1000000 non-null  float64
 22  pubRec              1000000 non-null  float64
 23  pubRecBankruptcies  999479 non-null   float64
 24  revolBal            1000000 non-null  float64
 25  revolUtil           999342 non-null   float64
 26  totalAcc            1000000 non-null  float64
 27  initialListStatus   1000000 non-null  int64  
 28  applicationType     1000000 non-null  int64  
 29  earliesCreditLine   1000000 non-null  int64  
 30  title               999999 non-null   float64
 31  policyCode          1000000 non-null  float64
 32  n0                  949619 non-null   float64
 33  n1                  949619 non-null   float64
 34  n2                  949619 non-null   float64
 35  n3                  949619 non-null   float64
 36  n4                  958367 non-null   float64
 37  n5                  949619 non-null   float64
 38  n6                  949619 non-null   float64
 39  n7                  949619 non-null   float64
 40  n8                  949618 non-null   float64
 41  n9                  949619 non-null   float64
 42  n10                 958367 non-null   float64
 43  n11                 912673 non-null   float64
 44  n12                 949619 non-null   float64
 45  n13                 949619 non-null   float64
 46  n14                 949619 non-null   float64
dtypes: float64(35), int64(9), object(3)
memory usage: 358.6+ MB
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• 类别特征处理

#部分类别特征
cate_features=['grade', 'subGrade', 'employmentTitle', 'homeOwnership', 'verificationStatus', 'purpose', 'postCode', 'regionCode', \
                 'applicationType', 'initialListStatus', 'title', 'policyCode']
for f in cate_features:
    print(f,'类型数：',data[f].nunique())
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grade 类型数： 7
subGrade 类型数： 35
employmentTitle 类型数： 298101
homeOwnership 类型数： 6
verificationStatus 类型数： 3
purpose 类型数： 14
postCode 类型数： 935
regionCode 类型数： 51
applicationType 类型数： 2
initialListStatus 类型数： 2
title 类型数： 47903
policyCode 类型数： 1
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#类型数在2之上，又不是高维稀疏的
data=pd.get_dummies(data,columns=['grade','subGrade','homeOwnership', 'verificationStatus', 'purpose', 'regionCode'], drop_first=True)
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• 稀疏矩阵定义：https://www.cnblogs.com/chenzhijuan-324/p/10637028.html

#高维类别特征需要进行转换
for f in ['employmentTitle','postCode','title']:
    data[f+'_cnts']=data.groupby([f])['id'].transform('count')
    data[f+'_rank']=data.groupby([f])['id'].rank(ascending=False).astype(int)
    del data[f]
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训练数据/测试数据准备

features=[f for f in data.columns if f not in ['id','issueDate','isDefault']]
train=data[data.isDefault.notnull()].reset_index(drop=True)
test=data[data.isDefault.isnull()].reset_index(drop=True)
x_train=train[features]
x_test=test[features]
y_train=train['isDefault']
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• reset_index(drop=True):https://www.cnblogs.com/keye/p/11229863.html
• x_train, x_test, y_train,y_test的含义：https://blog.csdn.net/lichunxia516/article/details/107707336

模型训练

• 直接构建了一个函数，可以调用三种树模型，方便快捷

def cv_model(clf,train_x,train_y,test_x,clf_name):
    folds=5
    seed=2020
    kf=KFold(n_splits=folds,shuffle=True,random_state=seed)
    train=np.zeros(train_x.shape[0])# shape[0]：表示矩阵的行数
    test=np.zeros(test_x.shape[0])
    cv_scores=[]
    for i,(train_index,valid_index) in enumerate(kf.split(train_x,train_y)):
        print('******{}******'.format(str(i+1)))
        trn_x,trn_y,val_x,val_y=train_x.iloc[train_index],train_y[train_index],train_x.iloc[valid_index],train_y[valid_index]
        
        if clf_name=='lgb':
            train_matrix=clf.Dataset(trn_x,label=trn_y)
            valid_matrix=clf.Dataset(val_x,label=val_y)
            
            params={'boosting_type': 'gbdt',
                'objective': 'binary',
                'metric': 'auc',
                'min_child_weight': 5,
                'num_leaves': 2 ** 5,
                'lambda_l2': 10,
                'feature_fraction': 0.8,
                'bagging_fraction': 0.8,
                'bagging_freq': 4,
                'learning_rate': 0.1,
                'seed': 2020,
                'nthread': 28,
                'n_jobs':24,
                'silent': True,
                'verbose': -1,}
        
            model=clf.train(params,train_matrix,50000,valid_sets=[train_matrix,valid_matrix],verbose_eval=200,early_stopping_rounds=200)
            val_pred=model.predict(val_x,num_iteration=model.best_iteration)
            test_pred=model.predict(test_x,num_iteration=model.best_iteration)
        
            # print(list(sorted(zip(features, model.feature_importance("gain")), key=lambda x: x[1], reverse=True))[:20])
        
        if clf_name=='xgb':
            train_matrix=clf.DMatrix(trn_x,label=trn_y)
            valid_matrix=clf.DMatrix(val_x,label=val_y)
            test_matrix=clf.DMatrix(test_x)
            
            params={'booster':'gbtree',
                    'objective':'binary:logietic',
                    'eval_metric':'auc',
                    'gamma':1,
                    'min_child_weight':1.5,
                    'max_depth':5,
                    'lambda':10,
                    'subsample':0.7,
                    'colsample_bytree':0.7,
                    'colsample_bylevel':0.7,
                    'eta':0.04,
                    'tree_method':'exact',
                    'seed':2020,
                    'nthread':36,
                    'silent':True,
                   }
            
            watchlist=[(train_matrix,'train'),(valid_matrix,'eval')]
            
            model=clf.train(params,train_matrix,num_boost_round=50000,evals=watchlist,verbose_eval=200,early_stopping_rounds=200)
            val_pred=model.predict(valid_matrix,ntree_limit=model.best_ntree_limit)
            test_pred=model.predict(test_matrix,ntree_limit=model.best_ntree_limit)
        
        if clf_name=='cat':
            params={'learning_rate':0.05,'depth':5,'l2_leaf_reg':10,'bootstrap_type':'Bernoulli',
                   'od_type':'Iter','od_wait':50,'random_seed':11,'allow_writing_files':False}
           
            model=clf(iterations=20000,**params)
            model.fit(trn_x,trn_y,eval_set=(val_x,val_y),
                     cat_features=[],use_best_model=True,verbose=500)
            
            val_pred=model.predict(val_x)
            test_pred=model.predict(test_x)
            
        train[valid_index]=val_pred
        test=test_pred/kf.n_splits
        cv_scores.append(roc_auc_score(val_y,val_pred))
        
        print(cv_scores)
        
    print('%s_scotrainre_list:'%clf_name,cv_scores)
    print('%s_score_mean:'%clf_name,np.mean(cv_scores))
    print('%s_score_std:'%clf_name,np.std(cv_scores))
    return train,test

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K折交叉验证：

• https://blog.csdn.net/weixin_39183369/article/details/78953653
• https://blog.csdn.net/qq_41076797/article/details/101123197

调参：

• https://blog.csdn.net/u012735708/article/details/83749703
• https://blog.csdn.net/iyuanshuo/article/details/80142730

def lgb_model(x_train,y_train,x_test):
    lgb_train,lgb_test=cv_model(lgb,x_train,y_train,x_test,'lgb')
    return lgb_train,lgb_test

def xgb_model(x_train,y_train,x_test):
    xgb_train,xgb_test=cv_model(xgb,x_train,y_train,x_test,'xgb')
    return xgb_train,xgb_test

def cat_model(x_train,y_train,x_test):
    cat_train,cat_test=cv_model(CatBoostRegressor,x_train,y_train,x_test,'cat')
    return cat_train,cat_test
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lgb_train,lgb_test=lgb_model(x_train,y_train,x_test)
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******1******
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
[LightGBM] [Warning] Unknown parameter: silent
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.742898	valid_1's auc: 0.730406
[400]	training's auc: 0.755553	valid_1's auc: 0.731185
[600]	training's auc: 0.766567	valid_1's auc: 0.731421
[800]	training's auc: 0.77656	valid_1's auc: 0.731297
Early stopping, best iteration is:
[658]	training's auc: 0.769561	valid_1's auc: 0.731571
[0.7315707699391983]
******2******
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
[LightGBM] [Warning] Unknown parameter: silent
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.743889	valid_1's auc: 0.726598
[400]	training's auc: 0.756346	valid_1's auc: 0.727829
[600]	training's auc: 0.767237	valid_1's auc: 0.728122
[800]	training's auc: 0.777257	valid_1's auc: 0.728164
Early stopping, best iteration is:
[700]	training's auc: 0.772432	valid_1's auc: 0.728318
[0.7315707699391983, 0.7283181812019169]
******3******
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
[LightGBM] [Warning] Unknown parameter: silent
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.743204	valid_1's auc: 0.731376
[400]	training's auc: 0.7554	valid_1's auc: 0.732444
[600]	training's auc: 0.766372	valid_1's auc: 0.732822
[800]	training's auc: 0.776228	valid_1's auc: 0.732611
Early stopping, best iteration is:
[620]	training's auc: 0.767377	valid_1's auc: 0.732834
[0.7315707699391983, 0.7283181812019169, 0.732833858510838]
******4******
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
[LightGBM] [Warning] Unknown parameter: silent
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.742844	valid_1's auc: 0.730001
[400]	training's auc: 0.755185	valid_1's auc: 0.731181
[600]	training's auc: 0.766741	valid_1's auc: 0.731697
[800]	training's auc: 0.776848	valid_1's auc: 0.731685
Early stopping, best iteration is:
[722]	training's auc: 0.773097	valid_1's auc: 0.731733
[0.7315707699391983, 0.7283181812019169, 0.732833858510838, 0.7317333003550207]
******5******
[LightGBM] [Warning] num_threads is set with nthread=28, will be overridden by n_jobs=24. Current value: num_threads=24
[LightGBM] [Warning] Unknown parameter: silent
Training until validation scores don't improve for 200 rounds
[200]	training's auc: 0.743219	valid_1's auc: 0.729179
[400]	training's auc: 0.755904	valid_1's auc: 0.730599
[600]	training's auc: 0.766513	valid_1's auc: 0.731059
[800]	training's auc: 0.776506	valid_1's auc: 0.730971
Early stopping, best iteration is:
[735]	training's auc: 0.773511	valid_1's auc: 0.731143
[0.7315707699391983, 0.7283181812019169, 0.732833858510838, 0.7317333003550207, 0.7311427854544066]
lgb_scotrainre_list: [0.7315707699391983, 0.7283181812019169, 0.732833858510838, 0.7317333003550207, 0.7311427854544066]
lgb_score_mean: 0.7311197790922761
lgb_score_std: 0.001507802995682687
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#听说时间很久，那我就不跑了hh
#xgb_train, xgb_test = xgb_model(x_train, y_train, x_test)
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cat_train,cat_test=cat_model(x_train,y_train,x_test)
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******1******
0:	learn: 0.3985252	test: 0.3966187	best: 0.3966187 (0)	total: 178ms	remaining: 59m 12s
500:	learn: 0.3771946	test: 0.3759285	best: 0.3759285 (500)	total: 30.8s	remaining: 19m 59s
1000:	learn: 0.3756449	test: 0.3751634	best: 0.3751634 (1000)	total: 1m 4s	remaining: 20m 32s
1500:	learn: 0.3745709	test: 0.3748276	best: 0.3748276 (1500)	total: 1m 39s	remaining: 20m 28s
2000:	learn: 0.3736588	test: 0.3746263	best: 0.3746258 (1998)	total: 2m 14s	remaining: 20m 10s
2500:	learn: 0.3728292	test: 0.3744849	best: 0.3744849 (2500)	total: 2m 48s	remaining: 19m 41s
Stopped by overfitting detector  (50 iterations wait)

bestTest = 0.3744018679
bestIteration = 2905

Shrink model to first 2906 iterations.
[0.7327200609336475]
******2******
0:	learn: 0.3979537	test: 0.3988945	best: 0.3988945 (0)	total: 126ms	remaining: 42m 3s
500:	learn: 0.3764995	test: 0.3787237	best: 0.3787237 (500)	total: 41.2s	remaining: 26m 42s
1000:	learn: 0.3749374	test: 0.3779174	best: 0.3779174 (1000)	total: 1m 19s	remaining: 24m 59s
1500:	learn: 0.3738552	test: 0.3775812	best: 0.3775812 (1500)	total: 1m 54s	remaining: 23m 34s
2000:	learn: 0.3729340	test: 0.3773443	best: 0.3773436 (1998)	total: 2m 31s	remaining: 22m 46s
Stopped by overfitting detector  (50 iterations wait)

bestTest = 0.3773239679
bestIteration = 2052

Shrink model to first 2053 iterations.
[0.7327200609336475, 0.7282917118426803]
******3******
0:	learn: 0.3980280	test: 0.3987527	best: 0.3987527 (0)	total: 153ms	remaining: 51m 8s
500:	learn: 0.3767797	test: 0.3776461	best: 0.3776461 (500)	total: 37.8s	remaining: 24m 32s
1000:	learn: 0.3752307	test: 0.3768433	best: 0.3768433 (1000)	total: 1m 12s	remaining: 22m 58s
1500:	learn: 0.3741403	test: 0.3764607	best: 0.3764605 (1499)	total: 1m 47s	remaining: 22m 3s
2000:	learn: 0.3732161	test: 0.3762495	best: 0.3762493 (1997)	total: 2m 21s	remaining: 21m 9s
2500:	learn: 0.3723968	test: 0.3761103	best: 0.3761098 (2495)	total: 2m 55s	remaining: 20m 27s
3000:	learn: 0.3716474	test: 0.3760186	best: 0.3760186 (3000)	total: 3m 29s	remaining: 19m 45s
Stopped by overfitting detector  (50 iterations wait)

bestTest = 0.3759560948
bestIteration = 3410

Shrink model to first 3411 iterations.
[0.7327200609336475, 0.7282917118426803, 0.7338399687776773]
******4******
0:	learn: 0.3980748	test: 0.3983970	best: 0.3983970 (0)	total: 129ms	remaining: 42m 51s
500:	learn: 0.3767830	test: 0.3777709	best: 0.3777709 (500)	total: 36.3s	remaining: 23m 33s
1000:	learn: 0.3752528	test: 0.3769020	best: 0.3769020 (1000)	total: 1m 25s	remaining: 26m 53s
1500:	learn: 0.3741987	test: 0.3765448	best: 0.3765448 (1500)	total: 2m 20s	remaining: 28m 54s
2000:	learn: 0.3732910	test: 0.3763156	best: 0.3763156 (2000)	total: 3m 16s	remaining: 29m 23s
2500:	learn: 0.3724645	test: 0.3761445	best: 0.3761435 (2498)	total: 4m 10s	remaining: 29m 15s
3000:	learn: 0.3716982	test: 0.3760409	best: 0.3760409 (3000)	total: 5m 6s	remaining: 28m 58s
3500:	learn: 0.3709615	test: 0.3759851	best: 0.3759842 (3495)	total: 6m 2s	remaining: 28m 28s
Stopped by overfitting detector  (50 iterations wait)

bestTest = 0.3759786172
bestIteration = 3597

Shrink model to first 3598 iterations.
[0.7327200609336475, 0.7282917118426803, 0.7338399687776773, 0.7325672923232748]
******5******
0:	learn: 0.3981448	test: 0.3980859	best: 0.3980859 (0)	total: 144ms	remaining: 47m 51s
500:	learn: 0.3767559	test: 0.3775909	best: 0.3775909 (500)	total: 58.4s	remaining: 37m 54s
1000:	learn: 0.3752239	test: 0.3768122	best: 0.3768122 (1000)	total: 1m 51s	remaining: 35m 7s
1500:	learn: 0.3741592	test: 0.3764654	best: 0.3764654 (1500)	total: 2m 21s	remaining: 28m 59s
2000:	learn: 0.3732513	test: 0.3762308	best: 0.3762294 (1997)	total: 2m 58s	remaining: 26m 42s
2500:	learn: 0.3724325	test: 0.3760785	best: 0.3760785 (2500)	total: 3m 37s	remaining: 25m 23s
3000:	learn: 0.3716690	test: 0.3759789	best: 0.3759789 (3000)	total: 4m 15s	remaining: 24m 8s
3500:	learn: 0.3709385	test: 0.3759029	best: 0.3759014 (3491)	total: 4m 55s	remaining: 23m 13s
4000:	learn: 0.3702519	test: 0.3758301	best: 0.3758288 (3970)	total: 5m 35s	remaining: 22m 20s
Stopped by overfitting detector  (50 iterations wait)

bestTest = 0.3758115042
bestIteration = 4164

Shrink model to first 4165 iterations.
[0.7327200609336475, 0.7282917118426803, 0.7338399687776773, 0.7325672923232748, 0.7317952826099017]
cat_scotrainre_list: [0.7327200609336475, 0.7282917118426803, 0.7338399687776773, 0.7325672923232748, 0.7317952826099017]
cat_score_mean: 0.7318428632974363
cat_score_std: 0.0018918585561348224
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rh_test=lgb_test*0.5+xgb_test*0.5
testA['isDefault']=rh_test
testA[['id','isDefault']].to_csv
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---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-49-81ca03ed5f9f> in <module>
----> 1 rh_test=lgb_test*0.5+xgb_test*0.5
      2 testA['isDefault']=rh_test
      3 testA[['id','isDefault']].to_csv


NameError: name 'xgb_test' is not defined
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