此部分为零基础入门金融风控的 Task3 特征工程部分,带你来了解各种特征工程以及分析方法,欢迎大家后续多多交流。
赛题:零基础入门数据挖掘 - 零基础入门金融风控之贷款违约
项目地址:https://github.com/datawhalechina/team-learning-data-mining/tree/master/FinancialRiskControl
比赛地址:https://tianchi.aliyun.com/competition/entrance/531830/introduction
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
from tqdm import tqdm
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgb
import lightgbm as lgb
from catboost import CatBoostRegressor
import warnings
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.metrics import accuracy_score, f1_score, roc_auc_score, log_loss
warnings.filterwarnings('ignore')
data_train =pd.read_csv('../train.csv')
data_test_a = pd.read_csv('../testA.csv')
首先我们查找出数据中的对象特征和数值特征
numerical_fea = list(data_train.select_dtypes(exclude=['object']).columns)
category_fea = list(filter(lambda x: x not in numerical_fea,list(data_train.columns)))
label = 'isDefault'
numerical_fea.remove(label)
在比赛中数据预处理是必不可少的一部分,对于缺失值的填充往往会影响比赛的结果,在比赛中不妨尝试多种填充然后比较结果选择结果最优的一种; 比赛数据相比真实场景的数据相对要“干净”一些,但是还是会有一定的“脏”数据存在,清洗一些异常值往往会获得意想不到的效果。
#查看缺失值情况
data_train.isnull().sum()
id 0
loanAmnt 0
term 0
interestRate 0
installment 0
grade 0
subGrade 0
employmentTitle 1
employmentLength 46799
homeOwnership 0
annualIncome 0
verificationStatus 0
issueDate 0
isDefault 0
purpose 0
postCode 1
regionCode 0
dti 239
delinquency_2years 0
ficoRangeLow 0
ficoRangeHigh 0
openAcc 0
pubRec 0
pubRecBankruptcies 405
revolBal 0
revolUtil 531
totalAcc 0
initialListStatus 0
applicationType 0
earliesCreditLine 0
title 1
policyCode 0
n0 40270
n1 40270
n2 40270
n2.1 40270
n4 33239
n5 40270
n6 40270
n7 40270
n8 40271
n9 40270
n10 33239
n11 69752
n12 40270
n13 40270
n14 40270
dtype: int64
#按照平均数填充数值型特征
data_train[numerical_fea] = data_train[numerical_fea].fillna(data_train[numerical_fea].median())
data_test_a[numerical_fea] = data_test_a[numerical_fea].fillna(data_train[numerical_fea].median())
#按照众数填充类别型特征
data_train[category_fea] = data_train[category_fea].fillna(data_train[category_fea].mode())
data_test_a[category_fea] = data_test_a[category_fea].fillna(data_train[category_fea].mode())
data_train.isnull().sum()
id 0
loanAmnt 0
term 0
interestRate 0
installment 0
grade 0
subGrade 0
employmentTitle 0
employmentLength 46799
homeOwnership 0
annualIncome 0
verificationStatus 0
issueDate 0
isDefault 0
purpose 0
postCode 0
regionCode 0
dti 0
delinquency_2years 0
ficoRangeLow 0
ficoRangeHigh 0
openAcc 0
pubRec 0
pubRecBankruptcies 0
revolBal 0
revolUtil 0
totalAcc 0
initialListStatus 0
applicationType 0
earliesCreditLine 0
title 0
policyCode 0
n0 0
n1 0
n2 0
n2.1 0
n4 0
n5 0
n6 0
n7 0
n8 0
n9 0
n10 0
n11 0
n12 0
n13 0
n14 0
dtype: int64
#查看类别特征
category_fea
['grade', 'subGrade', 'employmentLength', 'issueDate', 'earliesCreditLine']
#转化成时间格式
for data in [data_train, data_test_a]:
data['issueDate'] = pd.to_datetime(data['issueDate'],format='%Y-%m-%d')
startdate = datetime.datetime.strptime('2007-06-01', '%Y-%m-%d')
#构造时间特征
data['issueDateDT'] = data['issueDate'].apply(lambda x: x-startdate).dt.days
data_train['employmentLength'].value_counts(dropna=False).sort_index()
1 year 52489
10+ years 262753
2 years 72358
3 years 64152
4 years 47985
5 years 50102
6 years 37254
7 years 35407
8 years 36192
9 years 30272
< 1 year 64237
NaN 46799
Name: employmentLength, dtype: int64
def employmentLength_to_int(s):
if pd.isnull(s):
return s
else:
return np.int8(s.split()[0])
for data in [data_train, data_test_a]:
data['employmentLength'].replace(to_replace='10+ years', value='10 years', inplace=True)
data['employmentLength'].replace('< 1 year', '0 years', inplace=True)
data['employmentLength'] = data['employmentLength'].apply(employmentLength_to_int)
data['employmentLength'].value_counts(dropna=False).sort_index()
0.0 15989
1.0 13182
2.0 18207
3.0 16011
4.0 11833
5.0 12543
6.0 9328
7.0 8823
8.0 8976
9.0 7594
10.0 65772
NaN 11742
Name: employmentLength, dtype: int64
data_train['earliesCreditLine'].sample(5)
519915 Sep-2002
564368 Dec-1996
768209 May-2004
453092 Nov-1995
763866 Sep-2000
Name: earliesCreditLine, dtype: object
for data in [data_train, data_test_a]:
data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
# 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())
grade 类型数: 7
subGrade 类型数: 35
employmentTitle 类型数: 79282
homeOwnership 类型数: 6
verificationStatus 类型数: 3
purpose 类型数: 14
postCode 类型数: 889
regionCode 类型数: 51
applicationType 类型数: 2
initialListStatus 类型数: 2
title 类型数: 12058
policyCode 类型数: 1
像等级这种类别特征,是有优先级的可以labelencode或者自映射
for data in [data_train, data_test_a]:
data['grade'] = data['grade'].map({'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7})
# 类型数在2之上,又不是高维稀疏的,且纯分类特征
for data in [data_train, data_test_a]:
data = pd.get_dummies(data, columns=['subGrade', 'homeOwnership', 'verificationStatus', 'purpose', 'regionCode'], drop_first=True)
在统计学中,如果一个数据分布近似正态,那么大约 68% 的数据值会在均值的一个标准差范围内,大约 95% 会在两个标准差范围内,大约 99.7% 会在三个标准差范围内。
def find_outliers_by_3segama(data,fea):
data_std = np.std(data[fea])
data_mean = np.mean(data[fea])
outliers_cut_off = data_std * 3
lower_rule = data_mean - outliers_cut_off
upper_rule = data_mean + outliers_cut_off
data[fea+'_outliers'] = data[fea].apply(lambda x:str('异常值') if x > upper_rule or x < lower_rule else '正常值')
return data
data_train = data_train.copy()
for fea in numerical_fea:
data_train = find_outliers_by_3segama(data_train,fea)
print(data_train[fea+'_outliers'].value_counts())
print(data_train.groupby(fea+'_outliers')['isDefault'].sum())
print('*'*10)
正常值 800000
Name: id_outliers, dtype: int64
id_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 800000
Name: loanAmnt_outliers, dtype: int64
loanAmnt_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 800000
Name: term_outliers, dtype: int64
term_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 794259
异常值 5741
Name: interestRate_outliers, dtype: int64
interestRate_outliers
异常值 2916
正常值 156694
Name: isDefault, dtype: int64
**********
正常值 792046
异常值 7954
Name: installment_outliers, dtype: int64
installment_outliers
异常值 2152
正常值 157458
Name: isDefault, dtype: int64
**********
正常值 800000
Name: employmentTitle_outliers, dtype: int64
employmentTitle_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 799701
异常值 299
Name: homeOwnership_outliers, dtype: int64
homeOwnership_outliers
异常值 62
正常值 159548
Name: isDefault, dtype: int64
**********
正常值 793973
异常值 6027
Name: annualIncome_outliers, dtype: int64
annualIncome_outliers
异常值 756
正常值 158854
Name: isDefault, dtype: int64
**********
正常值 800000
Name: verificationStatus_outliers, dtype: int64
verificationStatus_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 783003
异常值 16997
Name: purpose_outliers, dtype: int64
purpose_outliers
异常值 3635
正常值 155975
Name: isDefault, dtype: int64
**********
正常值 798931
异常值 1069
Name: postCode_outliers, dtype: int64
postCode_outliers
异常值 221
正常值 159389
Name: isDefault, dtype: int64
**********
正常值 799994
异常值 6
Name: regionCode_outliers, dtype: int64
regionCode_outliers
异常值 1
正常值 159609
Name: isDefault, dtype: int64
**********
正常值 798440
异常值 1560
Name: dti_outliers, dtype: int64
dti_outliers
异常值 466
正常值 159144
Name: isDefault, dtype: int64
**********
正常值 778245
异常值 21755
Name: delinquency_2years_outliers, dtype: int64
delinquency_2years_outliers
异常值 5089
正常值 154521
Name: isDefault, dtype: int64
**********
正常值 788261
异常值 11739
Name: ficoRangeLow_outliers, dtype: int64
ficoRangeLow_outliers
异常值 778
正常值 158832
Name: isDefault, dtype: int64
**********
正常值 788261
异常值 11739
Name: ficoRangeHigh_outliers, dtype: int64
ficoRangeHigh_outliers
异常值 778
正常值 158832
Name: isDefault, dtype: int64
**********
正常值 790889
异常值 9111
Name: openAcc_outliers, dtype: int64
openAcc_outliers
异常值 2195
正常值 157415
Name: isDefault, dtype: int64
**********
正常值 792471
异常值 7529
Name: pubRec_outliers, dtype: int64
pubRec_outliers
异常值 1701
正常值 157909
Name: isDefault, dtype: int64
**********
正常值 794120
异常值 5880
Name: pubRecBankruptcies_outliers, dtype: int64
pubRecBankruptcies_outliers
异常值 1423
正常值 158187
Name: isDefault, dtype: int64
**********
正常值 790001
异常值 9999
Name: revolBal_outliers, dtype: int64
revolBal_outliers
异常值 1359
正常值 158251
Name: isDefault, dtype: int64
**********
正常值 799948
异常值 52
Name: revolUtil_outliers, dtype: int64
revolUtil_outliers
异常值 23
正常值 159587
Name: isDefault, dtype: int64
**********
正常值 791663
异常值 8337
Name: totalAcc_outliers, dtype: int64
totalAcc_outliers
异常值 1668
正常值 157942
Name: isDefault, dtype: int64
**********
正常值 800000
Name: initialListStatus_outliers, dtype: int64
initialListStatus_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 784586
异常值 15414
Name: applicationType_outliers, dtype: int64
applicationType_outliers
异常值 3875
正常值 155735
Name: isDefault, dtype: int64
**********
正常值 775134
异常值 24866
Name: title_outliers, dtype: int64
title_outliers
异常值 3900
正常值 155710
Name: isDefault, dtype: int64
**********
正常值 800000
Name: policyCode_outliers, dtype: int64
policyCode_outliers
正常值 159610
Name: isDefault, dtype: int64
**********
正常值 782773
异常值 17227
Name: n0_outliers, dtype: int64
n0_outliers
异常值 3485
正常值 156125
Name: isDefault, dtype: int64
**********
正常值 790500
异常值 9500
Name: n1_outliers, dtype: int64
n1_outliers
异常值 2491
正常值 157119
Name: isDefault, dtype: int64
**********
正常值 789067
异常值 10933
Name: n2_outliers, dtype: int64
n2_outliers
异常值 3205
正常值 156405
Name: isDefault, dtype: int64
**********
正常值 789067
异常值 10933
Name: n2.1_outliers, dtype: int64
n2.1_outliers
异常值 3205
正常值 156405
Name: isDefault, dtype: int64
**********
正常值 788660
异常值 11340
Name: n4_outliers, dtype: int64
n4_outliers
异常值 2476
正常值 157134
Name: isDefault, dtype: int64
**********
正常值 790355
异常值 9645
Name: n5_outliers, dtype: int64
n5_outliers
异常值 1858
正常值 157752
Name: isDefault, dtype: int64
**********
正常值 786006
异常值 13994
Name: n6_outliers, dtype: int64
n6_outliers
异常值 3182
正常值 156428
Name: isDefault, dtype: int64
**********
正常值 788430
异常值 11570
Name: n7_outliers, dtype: int64
n7_outliers
异常值 2746
正常值 156864
Name: isDefault, dtype: int64
**********
正常值 789625
异常值 10375
Name: n8_outliers, dtype: int64
n8_outliers
异常值 2131
正常值 157479
Name: isDefault, dtype: int64
**********
正常值 786384
异常值 13616
Name: n9_outliers, dtype: int64
n9_outliers
异常值 3953
正常值 155657
Name: isDefault, dtype: int64
**********
正常值 788979
异常值 11021
Name: n10_outliers, dtype: int64
n10_outliers
异常值 2639
正常值 156971
Name: isDefault, dtype: int64
**********
正常值 799434
异常值 566
Name: n11_outliers, dtype: int64
n11_outliers
异常值 112
正常值 159498
Name: isDefault, dtype: int64
**********
正常值 797585
异常值 2415
Name: n12_outliers, dtype: int64
n12_outliers
异常值 545
正常值 159065
Name: isDefault, dtype: int64
**********
正常值 788907
异常值 11093
Name: n13_outliers, dtype: int64
n13_outliers
异常值 2482
正常值 157128
Name: isDefault, dtype: int64
**********
正常值 788884
异常值 11116
Name: n14_outliers, dtype: int64
n14_outliers
异常值 3364
正常值 156246
Name: isDefault, dtype: int64
**********
#删除异常值
for fea in numerical_fea:
data_train = data_train[data_train[fea+'_outliers']=='正常值']
data_train = data_train.reset_index(drop=True)
当数值横跨多个数量级时,最好按照 10 的幂(或任何常数的幂)来进行分组:09、1099、100999、10009999,等等。固定宽度分箱非常容易计算,但如果计数值中有比较大的缺口,就会产生很多没有任何数据的空箱子。
# 通过除法映射到间隔均匀的分箱中,每个分箱的取值范围都是loanAmnt/1000
data['loanAmnt_bin1'] = np.floor_divide(data['loanAmnt'], 1000)
## 通过对数函数映射到指数宽度分箱
data['loanAmnt_bin2'] = np.floor(np.log10(data['loanAmnt']))
data['loanAmnt_bin3'] = pd.qcut(data['loanAmnt'], 10, labels=False)
for col in ['grade', 'subGrade']:
temp_dict = data_train.groupby([col])['isDefault'].agg(['mean']).reset_index().rename(columns={'mean': col + '_target_mean'})
temp_dict.index = temp_dict[col].values
temp_dict = temp_dict[col + '_target_mean'].to_dict()
data_train[col + '_target_mean'] = data_train[col].map(temp_dict)
data_test_a[col + '_target_mean'] = data_test_a[col].map(temp_dict)
# 其他衍生变量 mean 和 std
for df in [data_train, data_test_a]:
for item in ['n0','n1','n2','n2.1','n4','n5','n6','n7','n8','n9','n10','n11','n12','n13','n14']:
df['grade_to_mean_' + item] = df['grade'] / df.groupby([item])['grade'].transform('mean')
df['grade_to_std_' + item] = df['grade'] / df.groupby([item])['grade'].transform('std')
这里给出一些特征交互的思路,但特征和特征间的交互衍生出新的特征还远远不止于此,抛砖引玉,希望大家多多探索。请学习者尝试其他的特征交互方法。
#label-encode:subGrade,postCode,title
# 高维类别特征需要进行转换
for col in tqdm(['employmentTitle', 'postCode', 'title','subGrade']):
le = LabelEncoder()
le.fit(list(data_train[col].astype(str).values) + list(data_test_a[col].astype(str).values))
data_train[col] = le.transform(list(data_train[col].astype(str).values))
data_test_a[col] = le.transform(list(data_test_a[col].astype(str).values))
print('Label Encoding 完成')
100%|██████████| 4/4 [00:08<00:00, 2.04s/it]
Label Encoding 完成
# 举例归一化过程
#伪代码
for fea in [要归一化的特征列表]:
data[fea] = ((data[fea] - np.min(data[fea])) / (np.max(data[fea]) - np.min(data[fea])))
特征选择的方法:
方差选择法
from sklearn.feature_selection import VarianceThreshold
#其中参数threshold为方差的阈值
VarianceThreshold(threshold=3).fit_transform(train,target_train)
相关系数法
from sklearn.feature_selection import SelectKBest
from scipy.stats import pearsonr
#选择K个最好的特征,返回选择特征后的数据
#第一个参数为计算评估特征是否好的函数,该函数输入特征矩阵和目标向量,
#输出二元组(评分,P值)的数组,数组第i项为第i个特征的评分和P值。在此定义为计算相关系数
#参数k为选择的特征个数
SelectKBest(k=5).fit_transform(train,target_train)
卡方检验
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2
#参数k为选择的特征个数
SelectKBest(chi2, k=5).fit_transform(train,target_train)
互信息法
from sklearn.feature_selection import SelectKBest
from minepy import MINE
#由于MINE的设计不是函数式的,定义mic方法将其为函数式的,
#返回一个二元组,二元组的第2项设置成固定的P值0.5
def mic(x, y):
m = MINE()
m.compute_score(x, y)
return (m.mic(), 0.5)
#参数k为选择的特征个数
SelectKBest(lambda X, Y: array(map(lambda x:mic(x, Y), X.T)).T, k=2).fit_transform(train,target_train)
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
#递归特征消除法,返回特征选择后的数据
#参数estimator为基模型
#参数n_features_to_select为选择的特征个数
RFE(estimator=LogisticRegression(), n_features_to_select=2).fit_transform(train,target_train)
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LogisticRegression
#带L1惩罚项的逻辑回归作为基模型的特征选择
SelectFromModel(LogisticRegression(penalty="l1", C=0.1)).fit_transform(train,target_train)
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import GradientBoostingClassifier
#GBDT作为基模型的特征选择
SelectFromModel(GradientBoostingClassifier()).fit_transform(train,target_train)
本数据集中我们删除非入模特征后,并对缺失值填充,然后用计算协方差的方式看一下特征间相关性,然后进行模型训练
# 删除不需要的数据
for data in [data_train, data_test_a]:
data.drop(['issueDate','id'], axis=1,inplace=True)
"纵向用缺失值上面的值替换缺失值"
data_train = data_train.fillna(axis=0,method='ffill')
x_train = data_train.drop(['isDefault','id'], axis=1)
#计算协方差
data_corr = x_train.corrwith(data_train.isDefault) #计算相关性
result = pd.DataFrame(columns=['features', 'corr'])
result['features'] = data_corr.index
result['corr'] = data_corr.values
# 当然也可以直接看图
data_numeric = data_train[numerical_fea]
correlation = data_numeric.corr()
f , ax = plt.subplots(figsize = (7, 7))
plt.title('Correlation of Numeric Features with Price',y=1,size=16)
sns.heatmap(correlation,square = True, vmax=0.8)
<matplotlib.axes._subplots.AxesSubplot at 0x12d88ad10>
output_81_1.png
features = [f for f in data_train.columns if f not in ['id','issueDate','isDefault'] and '_outliers' not in f]
x_train = data_train[features]
x_test = data_test_a[features]
y_train = data_train['isDefault']
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])
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)
params = {'booster': 'gbtree',
'objective': 'binary:logistic',
'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_x , 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
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")
lgb_train, lgb_test = lgb_model(x_train, y_train, x_test)
************************************ 1 ************************************
Training until validation scores don't improve for 200 rounds
[200] training's auc: 0.749225 valid_1's auc: 0.729679
[400] training's auc: 0.765075 valid_1's auc: 0.730496
[600] training's auc: 0.778745 valid_1's auc: 0.730435
Early stopping, best iteration is:
[455] training's auc: 0.769202 valid_1's auc: 0.730686
[0.7306859913754798]
************************************ 2 ************************************
Training until validation scores don't improve for 200 rounds
[200] training's auc: 0.749221 valid_1's auc: 0.731315
[400] training's auc: 0.765117 valid_1's auc: 0.731658
[600] training's auc: 0.778542 valid_1's auc: 0.731333
Early stopping, best iteration is:
[407] training's auc: 0.765671 valid_1's auc: 0.73173
[0.7306859913754798, 0.7317304414673989]
************************************ 3 ************************************
Training until validation scores don't improve for 200 rounds
[200] training's auc: 0.748436 valid_1's auc: 0.732775
[400] training's auc: 0.764216 valid_1's auc: 0.733173
Early stopping, best iteration is:
[386] training's auc: 0.763261 valid_1's auc: 0.733261
[0.7306859913754798, 0.7317304414673989, 0.7332610441015461]
************************************ 4 ************************************
Training until validation scores don't improve for 200 rounds
[200] training's auc: 0.749631 valid_1's auc: 0.728327
[400] training's auc: 0.765139 valid_1's auc: 0.728845
Early stopping, best iteration is:
[286] training's auc: 0.756978 valid_1's auc: 0.728976
[0.7306859913754798, 0.7317304414673989, 0.7332610441015461, 0.7289759386807912]
************************************ 5 ************************************
Training until validation scores don't improve for 200 rounds
[200] training's auc: 0.748414 valid_1's auc: 0.732727
[400] training's auc: 0.763727 valid_1's auc: 0.733531
[600] training's auc: 0.777489 valid_1's auc: 0.733566
Early stopping, best iteration is:
[524] training's auc: 0.772372 valid_1's auc: 0.733772
[0.7306859913754798, 0.7317304414673989, 0.7332610441015461, 0.7289759386807912, 0.7337723979789789]
lgb_scotrainre_list: [0.7306859913754798, 0.7317304414673989, 0.7332610441015461, 0.7289759386807912, 0.7337723979789789]
lgb_score_mean: 0.7316851627208389
lgb_score_std: 0.0017424259863954693
testA_result = pd.read_csv('../testA_result.csv')
roc_auc_score(testA_result['isDefault'].values, lgb_test)
0.7290917729487896
特征工程是机器学习,甚至是深度学习中最为重要的一部分,在实际应用中往往也是所花费时间最多的一步。各种算法书中对特征工程部分的讲解往往少得可怜,因为特征工程和具体的数据结合的太紧密,很难系统地覆盖所有场景。本章主要是通过一些常用的方法来做介绍,例如缺失值异常值的处理方法详细对任何数据集来说都是适用的。但对于分箱等操作本章给出了具体的几种思路,需要读者自己探索。在特征工程中比赛和具体的应用还是有所不同的,在实际的金融风控评分卡制作过程中,由于强调特征的可解释性,特征分箱尤其重要。