catboost代码模板

之前讲原理的时候就说到算法受随机参数影响比价大且所占内存和时间非常大，因此我们需要用到网格搜索法，且需要时间以及耐心等待

对于里面参数不了解的话，可以去网站：https://catboost.ai/docs/concepts/python-reference_catboostclassifier_fit.html查看，下面放一个使用catboost算法的例子

# -*- coding: utf-8 -*-
"""
Created on Tue Feb  9 10:04:26 2021

@author: Administrator
"""

#%%
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

#pip3 install --user  catboost -i https://pypi.tuna.tsinghua.edu.cn/simple/

#%%
train = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/train.csv')
testA = pd.read_csv('D:/python_home/阿里云金融风控-贷款违约预测/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)

 

 具体的话自己去学习，总之，你构建的特征中有很多事类别特征，可以尝试这个算法，且类别特征不需要我们做前期处理，直接喂给算法就可以了，我们只需要耐心地等待