# -*- coding: utf-8 -*-

# 信用卡违约率分析

import pandas as pd

from sklearn.model_selection import learning_curve, train_test_split,GridSearchCV

from sklearn.preprocessing import StandardScaler

from sklearn.pipeline import Pipeline

from sklearn.metrics import accuracy_score

from sklearn.svm import SVC

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.neighbors import KNeighborsClassifier

from matplotlib import pyplot as plt

import seaborn as sns

# 数据加载

data = data = pd.read_csv('./UCI_Credit_Card.csv')

# 数据探索

print(data.shape) # 查看数据集大小

print(data.describe()) # 数据集概览

# 查看下一个月违约率的情况

next_month = data['default.payment.next.month'].value_counts()

print(next_month)

df = pd.DataFrame({'default.payment.next.month': next_month.index,'values': next_month.values})

plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签

plt.figure(figsize = (6,6))

plt.title('信用卡违约率客户n (违约：1，守约：0)')

sns.set_color_codes("pastel")

sns.barplot(x = 'default.payment.next.month', y="values", data=df)

locs, labels = plt.xticks()

plt.show()

# 特征选择，去掉 ID 字段、最后一个结果字段即可

data.drop(['ID'], inplace=True, axis =1) #ID 这个字段没有用

target = data['default.payment.next.month'].values

columns = data.columns.tolist()

columns.remove('default.payment.next.month')

features = data[columns].values

# 30% 作为测试集，其余作为训练集

train_x, test_x, train_y, test_y = train_test_split(features, target, test_size=0.30, stratify = target, random_state = 1)

# 构造各种分类器

classifiers = [

SVC(random_state = 1, kernel = 'rbf'),

DecisionTreeClassifier(random_state = 1, criterion = 'gini'),

RandomForestClassifier(random_state = 1, criterion = 'gini'),

KNeighborsClassifier(metric = 'minkowski'),

]

# 分类器名称

classifier_names = [

'svc',

'decisiontreeclassifier',

'randomforestclassifier',

'kneighborsclassifier',

]

# 分类器参数

classifier_param_grid = [

{'svc__C':[1], 'svc__gamma':[0.01]},

{'decisiontreeclassifier__max_depth':[6,9,11]},

{'randomforestclassifier__n_estimators':[3,5,6]} ,

{'kneighborsclassifier__n_neighbors':[4,6,8]},

]

# 对具体的分类器进行 GridSearchCV 参数调优

def GridSearchCV_work(pipeline, train_x, train_y, test_x, test_y, param_grid, score = 'accuracy'):

response = {}

gridsearch = GridSearchCV(estimator = pipeline, param_grid = param_grid, scoring = score)

# 寻找最优的参数 和最优的准确率分数

search = gridsearch.fit(train_x, train_y)

print("GridSearch 最优参数：", search.best_params_)

print("GridSearch 最优分数： %0.4lf" %search.best_score_)

predict_y = gridsearch.predict(test_x)

print(" 准确率 %0.4lf" %accuracy_score(test_y, predict_y))

response['predict_y'] = predict_y

response['accuracy_score'] = accuracy_score(test_y,predict_y)

return response

for model, model_name, model_param_grid in zip(classifiers, classifier_names, classifier_param_grid):

pipeline = Pipeline([

('scaler', StandardScaler()),

(model_name, model)

])

result = GridSearchCV_work(pipeline, train_x, train_y, test_x, test_y, model_param_grid , score = 'accuracy')