前言
由于本次为基础赛，所以分为正式赛和长期赛，正式赛时间由于和另外重量级比赛冲突，所以做的大佬很少，本人有幸通过自己写的baseline冲到了Rank10,其实代码很少，也简单，也没有做特征，只是做的人少了普遍分都比较低，这篇博客的主要目的是给初学者一个参考，后面可以加很多特征提高，由此分享一下baseline及心得
此次天池金融风控赛数据量比较小，训练集提供了80万数据，测试集有20万，数据集也在比赛链接里面可以下载，不用多说，直接放代码，有错误大佬勿喷，本人也是初学者
代码如下：
import pandas as pd
import datetime
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import StratifiedKFold
#warnings.filterwarnings('ignore')
#%matplotlib inline
from sklearn.metrics import roc_auc_score
## 数据降维处理的
from sklearn.model_selection import train_test_split
from catboost import CatBoostClassifier
train=pd.read_csv("train.csv")
testA=pd.read_csv("testA.csv")
numerical_fea = list(train.select_dtypes(exclude=['object']).columns)
numerical_fea.remove('isDefault')
train[numerical_fea] = train[numerical_fea].fillna(train[numerical_fea].median())
testA[numerical_fea] = testA[numerical_fea].fillna(testA[numerical_fea].median())
#issueDate
for data in [train]:
data['issueDate'] = pd.to_datetime(data['issueDate'],format='%Y-%m-%d')
data['grade'] = data['grade'].map({
'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7})
data['employmentLength'] = data['employmentLength'].map({
'1 year':1,'2 years':2,'3 years':3,'4 years':4,'5 years':5,'6 years':6,'7 years':7,'8 years':8,'9 years':9,'10+ years':10,'< 1 year':0})
data['subGrade'] = data['subGrade'].map({
'E2':1,'D2':2,'D3':3,'A4':4,'C2':5,'A5':6,'C3':7,'B4':8,'B5':9,'E5':10,
'D4':11,'B3':12,'B2':13,'D1':14,'E1':15,'C5':16,'C1':17,'A2':18,'A3':19,'B1':20,
'E3':21,'F1':22,'C4':23,'A1':24,'D5':25,'F2':26,'E4':27,'F3':28,'G2':29,'F5':30,
'G3':31,'G1':32,'F4':33,'G4':34,'G5':35})
data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
# data['n15']=data['n8']*data['n10']
for data in [testA]:
data['issueDate'] = pd.to_datetime(data['issueDate'],format='%Y-%m-%d')
data['grade'] = data['grade'].map({
'A':1,'B':2,'C':3,'D':4,'E':5,'F':6,'G':7})
data['employmentLength'] = data['employmentLength'].map({
'1 year':1,'2 years':2,'3 years':3,'4 years':4,'5 years':5,'6 years':6,'7 years':7,'8 years':8,'9 years':9,'10+ years':10,'< 1 year':0})
data['subGrade'] = data['subGrade'].map({
'E2':1,'D2':2,'D3':3,'A4':4,'C2':5,'A5':6,'C3':7,'B4':8,'B5':9,'E5':10,
'D4':11,'B3':12,'B2':13,'D1':14,'E1':15,'C5':16,'C1':17,'A2':18,'A3':19,'B1':20,
'E3':21,'F1':22,'C4':23,'A1':24,'D5':25,'F2':26,'E4':27,'F3':28,'G2':29,'F5':30,
'G3':31,'G1':32,'F4':33,'G4':34,'G5':35})
data['earliesCreditLine'] = data['earliesCreditLine'].apply(lambda s: int(s[-4:]))
print("数据预处理完成!")
sub=testA[['id']].copy()
sub['isDefault']=0
testA=testA.drop(['id','issueDate'],axis=1)
data_x=train.drop(['isDefault','id','issueDate'],axis=1)
data_y=train[['isDefault']].copy()
x, val_x, y, val_y = train_test_split(
data_x,
data_y,
test_size=0.25,
random_state=1,
stratify=data_y
)
col=['grade','subGrade','employmentTitle','homeOwnership','verificationStatus','purpose','postCode','regionCode',
'initialListStatus','applicationType','policyCode']
for i in data_x.columns:
if i in col:
data_x[i] = data_x[i].astype('str')
for i in testA.columns:
if i in col:
testA[i] = testA[i].astype('str')
model=CatBoostClassifier(
loss_function="Logloss",
eval_metric="AUC",
task_type="CPU",
learning_rate=0.1,
iterations=500,
random_seed=2020,
od_type="Iter",
depth=7)
answers = []
mean_score = 0
n_folds = 5
sk = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=2019)
for train, test in sk.split(data_x, data_y):
x_train = data_x.iloc[train]
y_train = data_y.iloc[train]
x_test = data_x.iloc[test]
y_test = data_y.iloc[test]
clf = model.fit(x_train,y_train, eval_set=(x_test,y_test),verbose=500,cat_features=col)
yy_pred_valid=clf.predict(x_test)
print('cat验证的auc:{}'.format(roc_auc_score(y_test, yy_pred_valid)))
mean_score += roc_auc_score(y_test, yy_pred_valid) / n_folds
y_pred_valid = clf.predict(testA,prediction_type='Probability')[:,-1]
answers.append(y_pred_valid)
print('mean valAuc:{}'.format(mean_score))
cat_pre=sum(answers)/n_folds
sub['isDefault']=cat_pre
sub.to_csv('金融预测.csv',index=False)
上面代码是我删减过的，应该能做到0.735左右，其实稍微改改就能到0.74+，主要是面向初学者，所以很多东西需要朋友们自己去探索学习，学习的过程比结果更重要，在你不断搜索的过程中学到的东西积累起来就是你自己的经验了，这里我也是尝试了多个模型，这里选择了Catboost模型作为baseline的主要模型，大家也可以尝试不同模型以及融合，效果都会很不一样，主要还是学习。