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model_monitoring_monthly
Commit cb1c5b5d authored by 舒皓月's avatar 舒皓月
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添加新功能 07 27

parent f0404627
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.gitignore 0 → 100644
View file @ cb1c5b5d
tmp.py
test.py
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.idea/misc.xml 0 → 100644
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.idea/model_monitor.iml 0 → 100644
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.idea/modules.xml 0 → 100644
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README.md
View file @ cb1c5b5d
......@@ -15,11 +15,21 @@
- Lift Chart
# 版本信息 - 新代码
# 版本信息
- V 0.0.1
基本完成对PSI和Lift Chart关于模型分在MongoDB的重写.
- V 0.0.2
- 删除last_month参数, 设定运行当天的前一天为最新日期, 往前的(num_month - 1)月的1号为起始日期, 最新日期往前45天(若设定passdue_day=15)那天为有响应的最新日期.
- 对PSI的计算, 时间跨度为(起始日期 --> 最新日期)
- 对AUC的计算, 时间跨度为(起始日期 --> 有响应最新日期)
- 添加对PSI和AUCR(后面月份相对基准月的AUC比率)的异常检测.
# VLM
......@@ -29,49 +39,44 @@
- 因为这两个指标的统计都需要用到模型分, 所以放到一起.
## 代码流程
## 计算流程
- 首先对需要计算的模型分, 在指定的统一时间跨度内进行数据抽取(在MySQL和MongoDB中). 包括如下一些主要字段:
- 模型分1, 模型分2, ...
- 订单号, 申请时间, 申请类型, 渠道类型, 逾期天数.
- 根据预定义逾期阈值, 得到是否逾期标签.
- 根据该模型分有记录的第一个月, 计算分箱规则(由模型分等频分箱区间, PSI在申请集上计算, AUC在放款集上计算).
- 分别对全样本, 首申/复申/复贷, 以及各达标客群(客群数量大于预设阈值), 计算每月统计信息:
- PSI:
- 样本量.
- 各分箱样本量
- 各分箱样本量占比
- 以该客群模型分有记录的第一个月为基准的PSI.
- Lift Chart:
- 样本量
- 各分箱样本量
- 各分箱逾期率
- AUC
- 以该客群模型分有记录的第一个月为基准的AUC ratio.
- 统计表格信息, 方便筛选排序查看.
- 包含以下字段:
- 模型分名称.
- a月样本量, b月样本量...
- a月PSI, b月PSI...
- a月AUC, b月AUC...
- NOTE:
- 当某月样本量很小, 或者没有样本时, 标记为NaN. 对应的PSI, AUC也为NaN.
- 当某月样本量比较小, 导致PSI, AUC计算异常(如某些分箱没有样本, 全为非逾期样本), 则标记为-999.
- 基准月的PSI为0.
- 示例:
![PSI](doc/image/C6640ABE-9017-42b5-A92A-2DE5601A15D8.png)
![](doc/image/31EA97A8-19B7-45c6-8302-4148D19BAABA.png)
## 代码使用方法
- 分别对全样本, 首申/复申/复贷, 以及各达标客群(客群数量大于预设阈值), 计算每月统计信息.
- PSI统计信息:
- 样本量.
- 各分箱样本量
- 各分箱样本量占比
- 以该客群模型分有记录的第一个月为基准的PSI.
- Lift Chart统计信息:
- 样本量
- 各分箱样本量
- 各分箱逾期率
- AUC
- 以该客群模型分有记录的第一个月为基准的AUC ratio.
- 统计表格信息, 方便筛选排序查看, 包含以下字段:
- 模型分名称.
- a月样本量, b月样本量...
- a月PSI, b月PSI...
- a月AUC, b月AUC...
- 某个客群是否异常(AUC明显下降, PSI较大).
- NOTE:
- 当某月样本量很小, 或者没有样本时, 标记为NaN. 对应的PSI, AUC也为NaN.
- 当某月样本量比较小, 导致PSI, AUC计算异常(如某些分箱没有样本, 全为非逾期样本), 则标记为-999.
- 基准月的PSI为0.
## 使用方法
- 准备一个Excel表格, 其中放置模型分名称, 以及对应的在数据库中的字段名.
- 创建一个模型监控对象(这样你就有对象了).
- 创建一个模型监控对象(这样我们就有对象了^v^).
```python
mm = ModelMonitor(excel_path='./model_score.xlsx', save_path='./image/', last_month=7, num_month=4, min_user_group=200)
mm = ModelMonitor(excel_path='./model_score.xlsx', save_path='./image/', num_month=4, min_user_group=500, max_psi=0.1, min_aucr=0.8)
```
- excel_path: Excel文件路径.
......@@ -80,19 +85,17 @@
不用自己再另外手动创建文件夹, 代码会判断文件夹是否存在并创建.
- last_month: 想要统计的最后一个月.
- num_month: 想要统计几个月.
如last_month=7, num_month=3, 表示统计4, 5, 6三个月的信息.
NOTE: AUC的计算逻辑为了保证样本有响应, 在此基础上还要往前推一个月, 会统计4, 5月的信息.
- num_month: 想要统计近期几个月(包含运行程序时所在的日期前一天, 如运行时为7.1, 则不包含7月数据, 如运行时为7.10, 则将7月9天算作7月数据).
- min_user_group: 最小客群数量.
这个参数越大, 颗粒越粗, 最后的统计图(客群数量)会越少.
反之颗粒越小, 最后统计图会越多.
- max_psi: 最大PSI, 大于则视为该客群异常.
- min_aucr: 最小AUCR, 小于则视为该客群异常.
- 执行run函数.
......@@ -100,19 +103,20 @@
mm.run()
```
- 输出
- 输出:
- 图片保存在./image中.
- PSI: ./image/PSI
- Lift Chart: ./image/AUC
- 统计信息.
- PSI统计信息: ./psi_info.csv
- AUC统计信息: ./auc_info.csv
- 图片保存在./image中.
- PSI: ./image/PSI
- Lift Chart: ./image/AUC
- 统计信息.
- PSI统计信息: ./psi_info.csv
- AUC统计信息: ./auc_info.csv
# TODO
- 添加对存在MySQL中模型分计算PSI, AUC的代码.
- 完成对VLM的重写.
- 部分(量信分, app模型)模型分报错, 进一步与模型维护者交流, 看是否字段名或者其它地方有问题.
# 贡献
......
model_monitor_PSI_AUC.py
View file @ cb1c5b5d
......@@ -19,8 +19,8 @@ from collections import OrderedDict
class ModelMonitor:
def __init__(self, excel_path='../model_score.xlsx', sheet_name='mongo_model',
passdue_day=15, save_path='../image/',
def __init__(self, excel_path='./model_score.xlsx', sheet_name='mongo_model',
passdue_day=15, save_path='./image/',
last_month=7, num_month=4, min_user_group=500):
# 考虑到数据库配置基本不变, 所以不设置创建对象时对应输入变量.
......@@ -426,28 +426,27 @@ class ModelMonitor:
def run(self):
# 获取MySQL数据, 取last_month往前num_month个月数据.
# self.mysql_df = self.sql_query('''SELECT order_no, applied_at,
# applied_type, applied_from, applied_channel, transacted, passdue_day
# FROM risk_analysis
# WHERE applied_at > "2019-%s-01 00:00:00"
# AND applied_at < "2019-%s-01 00:00:00"'''
# % (self.int2str(self.last_month - self.num_month), self.int2str(self.last_month)))
# print('MySQL数据获取成功.')
self.mysql_df = self.sql_query('''SELECT order_no, applied_at,
applied_type, applied_from, applied_channel, transacted, passdue_day
FROM risk_analysis
WHERE applied_at > "2019-%s-01 00:00:00"
AND applied_at < "2019-%s-01 00:00:00"'''
% (self.int2str(self.last_month - self.num_month), self.int2str(self.last_month)))
print('MySQL数据获取成功.')
# self.mysql_df.to_csv('./mysql_data.csv', index=False)
self.mysql_df = pd.read_csv('./mysql_data.csv')
# self.mysql_df = pd.read_csv('./mysql_data.csv')
# 获取MongoDB数据, 取last_month往前num_month个月数据.
# condition = {'wf_created_at': {'$gte': '2019-%s-01 00:00:00' % self.int2str(self.last_month - self.num_month),
# '$lte': '2019-%s-01 00:00:00' % self.int2str(self.last_month)}}
# fields = {'wf_biz_no': 1, 'wf_created_at': 1}
# for f in self.model_feild_list: # 加入Excel中预置的模型分名称
# fields[f] = 1
# self.mongo_df = self.mongo_query(condition, fields)
# print('MongoDB数据获取成功.')
# self.mongo_df.to_csv('./mongo_data.csv', index=False)
condition = {'wf_created_at': {'$gte': '2019-%s-01 00:00:00' % self.int2str(self.last_month - self.num_month),
'$lte': '2019-%s-01 00:00:00' % self.int2str(self.last_month)}}
fields = {'wf_biz_no': 1, 'wf_created_at': 1}
for f in self.model_feild_list: # 加入Excel中预置的模型分名称
fields[f] = 1
self.mongo_df = self.mongo_query(condition, fields)
print('MongoDB数据获取成功.')
self.mongo_df = pd.read_csv('./mongo_data.csv')
# self.mongo_df.to_csv('./mongo_data.csv', index=False)
# self.mongo_df = pd.read_csv('./mongo_data.csv')
# MySQL数据去重.
self.mysql_df = self.mysql_df.sort_values('passdue_day')
......@@ -459,8 +458,27 @@ class ModelMonitor:
left_on='order_no', right_on='wf_biz_no', how='left')
## 定义逾期用户.
self.merge_data['overdue'] = self.merge_data['passdue_day'] > self.passdue_day
# 清洗数据.
def clean_data(data):
try:
return float(data)
except:
return np.nan
na_field_list = []
for field in self.model_feild_list:
self.merge_data[field] = self.merge_data[field].astype('float')
if field in self.merge_data.columns.tolist():
print('正在清洗%s' % self.model_feild_name_dict[field])
self.merge_data[field] = self.merge_data[field].apply(clean_data)
else:
na_field_list.append(field)
## 去除因为一些原因未抽取到的字段.
print('不包含以下字段:')
for field in na_field_list:
self.model_feild_list.remove(field)
self.model_name_list.remove(self.model_feild_name_dict[field])
del self.model_feild_name_dict[field]
print(self.model_feild_name_dict[field])
print('数据拼接完成.')
# 数据按月划分.
......@@ -489,6 +507,6 @@ class ModelMonitor:
print('统计信息保存成功.')
if __name__ == '__main__':
pass
mm = ModelMonitor(excel_path='./model_score.xlsx', save_path='./image/', last_month=7, num_month=2)
# if __name__ == '__main__':
# pass
# mm = ModelMonitor(excel_path='./model_score.xlsx', save_path='./image/', last_month=7, num_month=2)
test.py deleted 100644 → 0
View file @ f0404627
class Solution:
def __init__(self):
pass
def find_max_length(self, array, k):
if not array:
return 0
sum_subarray = array[0]
left, right = 0, 1
max_length = 0
while right < len(array):
if sum_subarray == k:
max_length = max(max_length, right - left)
sum_subarray += array[right]
right += 1
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