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model_monitoring_monthly
Commit c1985e9d authored by 舒皓月's avatar 舒皓月
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2019-09-03

parent b4fb47f1
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Showing with 633 additions and 2 deletions
PSI.py → PSI_time_filed_channel.py
View file @ c1985e9d
...@@ -28,6 +28,7 @@ class PSIMonitor: ...@@ -28,6 +28,7 @@ class PSIMonitor:
min_user_group=500, max_psi=0.1, min_user_group=500, max_psi=0.1,
if_save=True, if_read=True, if_save=True, if_read=True,
date_list=('2019-03-01', '2019-03-15', '2019-03-31', '2019-04-15'), date_list=('2019-03-01', '2019-03-15', '2019-03-31', '2019-04-15'),
interval_days=15,
field_query='model_exec_data_source#fst_v6_xy_br_dhb_raw', field_query='model_exec_data_source#fst_v6_xy_br_dhb_raw',
channel='融360'): channel='融360'):
...@@ -60,7 +61,7 @@ class PSIMonitor: ...@@ -60,7 +61,7 @@ class PSIMonitor:
self.na_app_chan = self.field_info_df.na_app_chan.tolist() # 空跑渠道 self.na_app_chan = self.field_info_df.na_app_chan.tolist() # 空跑渠道
# 一些定义的常量 # 一些定义的常量
self.min_user_group = min_user_group # 最小客群数量. self.min_user_group = min_user_group * interval_days / 30 # 最小客群数量.
self.max_psi = max_psi # 最大PSI, 超过视为异常. self.max_psi = max_psi # 最大PSI, 超过视为异常.
# 数据规格 # 数据规格
...@@ -260,7 +261,6 @@ class PSIMonitor: ...@@ -260,7 +261,6 @@ class PSIMonitor:
print('处理完成.') print('处理完成.')
print('=' * 40) print('=' * 40)
def plot_psi(self, field): def plot_psi(self, field):
# 分离数据. # 分离数据.
......
VLM_time.py 0 → 100644
View file @ c1985e9d
# coding=utf-8
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import pickle
import datetime
from dateutil.relativedelta import relativedelta
from collections import OrderedDict
from scipy.stats import norm
import pymysql
import pymongo
import warnings
warnings.filterwarnings('ignore')
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['savefig.dpi'] = 150
# TODO
# 分模型放图片.
# 信息里面添加模型名称, 申请类型, 渠道.
class VLMMonitor:
'''
定时段查看字段的VLM.
'''
def __init__(self, excel_path='./model_score.xlsx',
sheet_name='model',
save_path='./vlm/',
if_read=True, if_save=True,
alpha=0.01, min_user_group=600,
start_date='2019-08-01', end_date='2019-08-14'):
# 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.
self.mysql_engine = pymysql.connect(host='172.20.6.9',
port=9030,
user='fengkong_read_only',
passwd='mT2HFUgI',
db='risk_analysis',
charset='utf8')
self.mongo_client = pymongo.MongoClient(
"mongodb://haoyue.shu:x2egwRHk7WhQ4So1@172.18.3.22:27017/?authSource=rc_mgo_feature_dp")
self.mongo_db = self.mongo_client['rc_mgo_feature_dp']
self.mongo_table = self.mongo_db['wf_audit_log_with_feature']
# 读取整理在Excel中的模型相关信息.
self.field_info_df = pd.read_excel(excel_path, sheet_name=sheet_name)
self.field_name_list = self.field_info_df.field_name.tolist()
self.field_query_list = self.field_info_df.field_query.tolist()
self.field_app_type_list = self.field_info_df.app_type.tolist()
self.field_app_type_list = [str(x) for x in self.field_app_type_list]
self.field_DB_list = self.field_info_df.DB.tolist()
self.field_query_name_dict = dict(zip(self.field_query_list, self.field_name_list))
self.field_query_app_type_dict = dict(zip(self.field_query_list, self.field_app_type_list))
## 空跑信息.
self.na_time = self.field_info_df.na_time.tolist() # 空跑时间段
self.na_app_type = self.field_info_df.na_app_type.tolist() # 空跑申请类型
self.na_app_chan = self.field_info_df.na_app_chan.tolist() # 空跑渠道
# 文件存储路径.
self.fig_save_path = save_path + 'image/'
self.info_save_path = save_path + 'info/'
self.data_save_path = save_path + 'data/'
if not os.path.exists(save_path):
os.mkdir(save_path)
if not os.path.exists(self.fig_save_path):
os.mkdir(self.fig_save_path)
if not os.path.exists(self.fig_save_path + 'image/'):
os.mkdir(self.fig_save_path + 'image/')
if not os.path.exists(self.fig_save_path + 'trend/'):
os.mkdir(self.fig_save_path + 'trend')
if not os.path.exists(self.fig_save_path + 'over3std/'):
os.mkdir(self.fig_save_path + 'over3std/')
if not os.path.exists(self.info_save_path):
os.mkdir(self.info_save_path)
if not os.path.exists(self.data_save_path):
os.mkdir(self.data_save_path)
# MK test显著性水平.
self.alpha = alpha
# 数据.
self.mysql_df = None
self.mongo_df = None
self.merge_data = None
# 数据模式.
self.if_read = if_read
self.if_save = if_save
# 时间范围.
self.start_date = start_date
self.end_date = (pd.datetime.strptime(end_date, '%Y-%m-%d') + relativedelta(days=1)).strftime('%Y-%m-%d')
self.num_day = (pd.datetime.strptime(self.end_date, '%Y-%m-%d') -
pd.datetime.strptime(self.start_date, '%Y-%m-%d')).days
# 统计信息.
# {group_name_0: {time_list: [...],
# value_list: [...],
# count_list: [...],
# miss_rate_list: [...],
# zero_rate_list: [...],
# mean: float,
# std: float,
# trend: str,
# h: boll,
# p: float,
# over_3std: bool}
self.vlm_info_dict = OrderedDict()
self.vlm_info_df = None
self.min_user_group = min_user_group * (self.num_day / 30)
def query_mysql(self, sql):
'''
连接MySQL数据库, 根据SQL返回数据.
:param sql: str.
:return: DataFrame.
'''
try:
return pd.read_sql(sql, self.mysql_engine)
except:
print('SQL查询出现错误.')
return None
def query_mongo(self, condition, fields):
'''
连接MongoDB, 根据查询返回数据.
:param condition: dict
:param fields: dict
:return: DataFrame
'''
try:
return pd.DataFrame(list(self.mongo_table.find(condition, fields)))
except:
print('Mongo查询出现错误.')
pass
def filter_data(self, df, field):
'''
过滤空跑数据.
:param df: df.
:param field: str, 字段名.
:return: df, 过滤后的数据.
'''
df = df[~((df['applied_type'] == 1) & (df['applied_channel'].apply(lambda x: 'Android' in x)))]
field_idx = self.field_query_list.index(field)
na_time = self.na_time[field_idx]
na_type = self.na_app_type[field_idx]
na_chan = self.na_app_chan[field_idx]
if pd.isnull(na_time): # 没有空跑时间, 则不记录.
return df
# 时间.
t_s, t_e = na_time.split('~')
if len(t_e) == 0: # 若还在空跑, 则不记录.
return pd.DataFrame()
else:
na_df = df[
(df['applied_at'].apply(lambda x: x[:10] >= t_s)) & (df['applied_at'].apply(lambda x: x[:10] <= t_e))]
if na_df.shape[0] == 0:
return df
# 申请类型.
if pd.isnull(na_type):
return df[~df.index.isin(na_df.index.values)]
else:
tmp_df = pd.DataFrame()
for i in str(int(na_type)):
tmp_df = tmp_df.append(na_df[na_df['applied_type'] == int(i)])
na_df = tmp_df
if na_df.shape[0] == 0:
return df
# 申请渠道.
if pd.isnull(na_chan):
return df[~df.index.isin(na_df.index.values)]
else:
tmp_df = pd.DataFrame()
for i in na_chan.split(','):
tmp_df = tmp_df.append(na_df[na_df['applied_channel'].apply(lambda x: i in x)])
na_df = tmp_df
if na_df.shape[0] == 0:
return df
return df[~df.index.isin(na_df.index.values)]
def mk_test(self, x, alpha=0.01):
'''
MK test.
用于检测时间序列趋势.
:param x: list[float]
:param alpha: float, 显著性水平.
:return: 趋势, 是否显著, P值, Z统计值.
'''
if type(x) != list:
x = x.values.tolist()
n = len(x)
# calculate S
s = 0
for k in range(n - 1):
for j in range(k + 1, n):
s += np.sign(x[j] - x[k])
# calculate the unique data
unique_x = np.unique(x)
g = len(unique_x)
# calculate the var(s)
if n == g: # there is no tie
var_s = (n * (n - 1) * (2 * n + 5)) / 18
else: # there are some ties in data
tp = np.zeros(unique_x.shape)
for i in range(len(unique_x)):
tp[i] = sum(x == unique_x[i])
var_s = (n * (n - 1) * (2 * n + 5) - np.sum(tp * (tp - 1) * (2 * tp + 5))) / 18
if s > 0:
z = (s - 1) / np.sqrt(var_s)
elif s < 0:
z = (s + 1) / np.sqrt(var_s)
else: # s == 0:
z = 0
# calculate the p_value
p = 2 * (1 - norm.cdf(abs(z))) # two tail test
h = abs(z) > norm.ppf(1 - alpha / 2)
if (z < 0) and h:
trend = 'decreasing'
elif (z > 0) and h:
trend = 'increasing'
else:
trend = 'no trend'
return trend, h, p, z
def process_data_helper(self, group_name=None, df=None, field=None):
print('正在处理%s' % group_name)
# 得到一个连续时间序列.
date_list = pd.date_range(self.start_date, self.end_date).strftime('%Y-%m-%d').values.tolist()[:-1]
# 将特殊值标定为缺失.
def set_na(data):
if pd.isnull(data):
return np.nan
if data < 0 or data > 999999:
return np.nan
return data
# 报错则放弃该变量.
try:
df = df[['applied_at', field]]
except:
print('字段处理发生错误: %s' % self.field_query_name_dict[field])
return None
df[field] = df[field].apply(set_na)
# 计算该字段在每天的均值, 数量, 缺失率, 零率.
def count(data):
return len(data)
def miss_rate(data):
return data.isnull().mean()
def zero_rate(data):
return (data == 0).mean()
df_g = df.groupby('applied_at').agg({field: ['mean', count, miss_rate, zero_rate]})
df_g.columns = ['_'.join(x) for x in df_g.columns.ravel()]
df_g = df_g.reset_index()
# 将没有数据的当天均值补为0.
tmp_df = pd.DataFrame({'applied_at': date_list})
df_g = pd.merge(left=tmp_df, right=df_g, on='applied_at', how='left')
df_g = df_g.fillna(0)
# 计算该字段整体的均值.
mean = df[field].mean()
# 计算该字段整体的标准差.
std = df_g[field + '_mean'].std()
# 计算该字段的趋势信息.
trend, h, p, z = self.mk_test(df_g[field + '_mean'], alpha=self.alpha) # 整体.
# 整合信息.
def model_name(data):
return data.split('-')[0]
def app_type(data):
return data.split('-')[1]
def app_channel(data):
data = data.split('-')[2:]
return '-'.join(data)
self.vlm_info_dict[group_name] = {}
self.vlm_info_dict[group_name]['model_name'] = model_name(group_name)
self.vlm_info_dict[group_name]['app_type'] = app_type(group_name)
self.vlm_info_dict[group_name]['app_channel'] = app_channel(group_name)
self.vlm_info_dict[group_name]['time_list'] = date_list
self.vlm_info_dict[group_name]['value_list'] = df_g[field + '_mean'].values.tolist()
self.vlm_info_dict[group_name]['count_list'] = df_g[field + '_count'].values.tolist()
self.vlm_info_dict[group_name]['miss_rate_list'] = df_g[field + '_miss_rate'].values.tolist()
self.vlm_info_dict[group_name]['zero_rate_list'] = df_g[field + '_zero_rate'].values.tolist()
self.vlm_info_dict[group_name]['mean'] = mean
self.vlm_info_dict[group_name]['std'] = std
self.vlm_info_dict[group_name]['trend'] = trend
self.vlm_info_dict[group_name]['h'] = h
self.vlm_info_dict[group_name]['p'] = p
if any([x > mean + 3 * std or x < mean - 3 * std for x in self.vlm_info_dict[group_name]['value_list']]):
self.vlm_info_dict[group_name]['over_3std'] = True
else:
self.vlm_info_dict[group_name]['over_3std'] = False
def process_data(self, field):
'''
对每个字段对应的不同客群进行数据处理.
:param field: str, 需要处理的字段.
:return: .
'''
# 过滤空跑数据.
df_copy = self.merge_data[['applied_at', 'applied_type', 'applied_channel', field]].copy()
df_copy = self.filter_data(df_copy, field)
if df_copy.shape[0] == 0:
print('%s还在空跑.' % self.field_query_name_dict[field])
return None
# 申请类型过滤.
tmp_df = pd.DataFrame()
for i in self.field_query_app_type_dict[field]:
tmp_df = tmp_df.append(df_copy[df_copy['applied_type'] == int(i)])
df_copy = tmp_df
# 收集覆盖客群.
user_group_dict = {} # user_group_dict = {'首申-融360': (1, 融360)}
main_user_group_dict = {}
app_type_dict = {1: '首申', 2: '复申', 3: '复贷'}
df_copy_g = df_copy.groupby(['applied_type', 'applied_channel'])[field].count().sort_values(ascending=False)
df_copy_g = df_copy_g.reset_index()
df_copy_g = df_copy_g.loc[df_copy_g[field] > self.min_user_group / 6] # 太小的客群直接过滤.
for i in range(df_copy_g.shape[0]):
app_type = df_copy_g.iloc[i]['applied_type']
app_chan = df_copy_g.iloc[i]['applied_channel']
user_group_dict[app_type_dict[app_type] + '-' + app_chan] = (app_type, app_chan)
if df_copy_g.iloc[i][field] > int(self.min_user_group):
main_user_group_dict[app_type_dict[app_type] + '-' + app_chan] = (app_type, app_chan)
del df_copy_g
# 过滤非覆盖数据.
tmp_df = pd.DataFrame()
for user_group_name in user_group_dict:
tmp_df = tmp_df.append(df_copy.loc[
(df_copy['applied_type'] == user_group_dict[user_group_name][0]) & (
df_copy['applied_channel'] == user_group_dict[user_group_name][1])])
df_copy = tmp_df
# 报错: 无applied_type.
try:
df_copy['applied_type']
except:
return None
## 覆盖全样本.
self.process_data_helper(group_name=self.field_query_name_dict[field] + '-全样本', df=df_copy, field=field)
## 首申/复申/复贷 若覆盖的话.
if df_copy.loc[df_copy['applied_type'] == 1].shape[0] > int(self.min_user_group):
self.process_data_helper(group_name=self.field_query_name_dict[field] + '-首申',
df=df_copy.loc[df_copy['applied_type'] == 1],
field=field)
if df_copy.loc[df_copy['applied_type'] == 2].shape[0] > int(self.min_user_group):
self.process_data_helper(group_name=self.field_query_name_dict[field] + '-复申',
df=df_copy.loc[df_copy['applied_type'] == 2],
field=field)
if df_copy.loc[df_copy['applied_type'] == 3].shape[0] > int(self.min_user_group):
self.process_data_helper(group_name=self.field_query_name_dict[field] + '-复贷',
df=df_copy.loc[df_copy['applied_type'] == 3],
field=field)
## 按划分的客群处理数据.
for user_group_name in main_user_group_dict:
self.process_data_helper(group_name=self.field_query_name_dict[field] + '-' + user_group_name,
df=df_copy.loc[
(df_copy['applied_type'] == main_user_group_dict[user_group_name][0]) & (
df_copy['applied_channel'] ==
main_user_group_dict[user_group_name][1])],
field=field)
def plot(self):
'''
根据vlm_info_dict进行绘图.
'''
for user_group_name in self.vlm_info_dict:
print(user_group_name)
plt.figure(figsize=(30, 15))
# 画主线.
plt.plot(range(self.num_day),
self.vlm_info_dict[user_group_name]['value_list'])
# 打印趋势信息.
plt.text(x=0.9, y=0.75, s='整体趋势: %s\n'
% self.vlm_info_dict[user_group_name]['trend'],
fontsize=15, transform=plt.gca().transAxes)
# 画均值, 标准差.
plt.hlines(y=self.vlm_info_dict[user_group_name]['mean'], xmin=0, xmax=self.num_day - 1, colors='k',
linestyles='--')
plt.hlines(y=self.vlm_info_dict[user_group_name]['mean'] + 3 * self.vlm_info_dict[user_group_name]['std'],
xmin=0,
xmax=self.num_day - 1, colors='r',
linestyles='--')
plt.hlines(y=self.vlm_info_dict[user_group_name]['mean'] - 3 * self.vlm_info_dict[user_group_name]['std'],
xmin=0,
xmax=self.num_day - 1, colors='r',
linestyles='--')
# 画表格.
cell_text = [[str(round(x, 3)) for x in self.vlm_info_dict[user_group_name]['value_list']],
[str(int(x)) for x in self.vlm_info_dict[user_group_name]['count_list']],
[str(100 * x)[:4] + '%' for x in self.vlm_info_dict[user_group_name]['miss_rate_list']],
[str(100 * x)[:4] + '%' for x in self.vlm_info_dict[user_group_name]['zero_rate_list']]]
rows = ['value', 'count', 'miss_rate', 'zero_rate']
cols = [x[5:] for x in self.vlm_info_dict[user_group_name]['time_list']]
plt.table(cellText=cell_text,
rowLabels=rows,
colLabels=cols,
colWidths=[0.91 / (self.num_day - 1)] * self.num_day,
loc='bottom')
plt.subplots_adjust(left=.1, bottom=.15)
# 画曲线下阴影线.
plt.vlines(x=range(self.num_day),
ymin=[self.vlm_info_dict[user_group_name]['mean'] - 3.5 * self.vlm_info_dict[user_group_name][
'std']] * self.num_day,
ymax=self.vlm_info_dict[user_group_name]['value_list'],
colors='lightgrey',
linestyles='--')
# 展示.
plt.title(user_group_name + '-mean', fontdict={'fontsize': 40})
plt.grid()
plt.xticks([])
plt.subplots_adjust(left=0.03, right=0.99, top=0.94, bottom=0.08)
# 分开保存.
is_save = False
if self.vlm_info_dict[user_group_name]['h'] == True:
plt.savefig(self.fig_save_path + 'trend/' + user_group_name + '-mean')
is_save = True
if self.vlm_info_dict[user_group_name]['over_3std'] == True:
plt.savefig(self.fig_save_path + 'over3std/' + user_group_name + '-mean')
is_save = True
if not is_save:
plt.savefig(self.fig_save_path + 'image/' + user_group_name + '-mean')
plt.show()
def save_vlm_info(self):
self.vlm_info_df = pd.DataFrame(columns=['model_name',
'app_type',
'app_channel',
'group_name',
'trend',
'h',
'p',
'over_3std'])
for g in self.vlm_info_dict:
tmp_df = pd.DataFrame({'model_name': [self.vlm_info_dict[g]['model_name']],
'app_type': [self.vlm_info_dict[g]['app_type']],
'app_channel': [self.vlm_info_dict[g]['app_channel']],
'group_name': [g],
'trend': [self.vlm_info_dict[g]['trend']],
'h': [self.vlm_info_dict[g]['h']],
'p': [self.vlm_info_dict[g]['p']],
'over_3std': [self.vlm_info_dict[g]['over_3std']]
})
self.vlm_info_df = self.vlm_info_df.append(tmp_df)
self.vlm_info_df.to_csv(self.info_save_path + 'vlm_info.csv', index=False)
with open(self.info_save_path + 'vlm_info.dict', 'wb') as f:
pickle.dump(self.vlm_info_dict, f)
f.close()
def run(self):
# 读取数据.
if self.if_read:
mysql_field = [x for i, x in enumerate(self.field_query_list) if self.field_DB_list[i] == 'MySQL']
real_mysql_field = []
for field in mysql_field:
tmp_df = self.query_mysql('''SELECT %s FROM risk_analysis LIMIT 10''' % field)
if tmp_df is not None:
real_mysql_field.append(field)
print('在MySQL中找到该字段: %s' % self.field_query_name_dict[field])
else:
print('在MySQL中不存在该字段: %s' % self.field_query_name_dict[field])
# 删除该字段.
idx = self.field_query_list.index(field)
self.field_query_list.pop(idx)
self.field_DB_list.pop(idx)
self.field_app_type_list.pop(idx)
self.field_name_list.pop(idx)
del self.field_query_name_dict[field]
del self.field_query_app_type_dict[field]
if real_mysql_field:
self.mysql_df = self.query_mysql('''SELECT order_no, applied_at, \
applied_from, applied_channel, passdue_day, %s \
FROM risk_analysis \
WHERE applied_at >= "%s 00:00:00" \
AND applied_at <= "%s 00:00:00"'''
% (','.join(real_mysql_field), self.start_date, self.end_date))
else:
self.mysql_df = self.query_mysql('''SELECT order_no, applied_at, \
applied_from, applied_channel, passdue_day \
FROM risk_analysis \
WHERE applied_at >= "%s 00:00:00" \
AND applied_at <= "%s 00:00:00"'''
% (self.start_date, self.end_date))
if self.if_save:
self.mysql_df.to_csv(self.data_save_path + 'mysql_data.csv', index=False)
else:
self.mysql_df = pd.read_csv(self.data_save_path + 'mysql_data.csv')
print('MySQL数据获取成功.')
def func_0(data):
try:
return int(int(data) + 1)
except:
return np.nan
if self.if_read:
condition = {'wf_created_at': {'$gte': '%s 00:00:00' % self.start_date,
'$lte': '%s 00:00:00' % self.end_date}}
fields = {'wf_biz_no': 1, 'wf_created_at': 1, 'wf_loan_type': 1}
mongo_field = [x for i, x in enumerate(self.field_query_list) if self.field_DB_list[i] == 'mongoDB']
for f in mongo_field: # 加入Excel中预置的模型分名称
fields[f] = 1
self.mongo_df = self.query_mongo(condition, fields)
self.mongo_df['applied_type'] = self.mongo_df['wf_loan_type'].apply(func_0)
del self.mongo_df['wf_loan_type']
if self.if_save:
self.mongo_df.to_csv(self.data_save_path + 'mongo_data.csv', index=False)
else:
self.mongo_df = pd.read_csv(self.data_save_path + 'mongo_data.csv')
self.mongo_df = self.mongo_df.loc[self.mongo_df['applied_type'].notna()]
def func_1(data):
try:
int(data)
return True
except:
return False
self.mongo_df = self.mongo_df.loc[self.mongo_df['applied_type'].apply(func_1)]
print('Mongo数据获取成功.')
# MySQL数据去重.
self.mysql_df = self.mysql_df.sort_values('passdue_day')
self.mysql_df = self.mysql_df.drop_duplicates('order_no', keep='first')
print('数据去重完成.')
# 拼接数据.
self.merge_data = pd.merge(left=self.mysql_df, right=self.mongo_df,
left_on='order_no', right_on='wf_biz_no',
how='inner')
print('拼接数据成功')
# 清洗数据.
def clean_data(data):
try:
return float(data)
except:
return np.nan
na_field_list = []
for field in self.field_query_list:
if field in self.merge_data.columns.tolist():
print('正在清洗%s' % self.field_query_name_dict[field])
self.merge_data[field] = self.merge_data[field].apply(clean_data)
else:
na_field_list.append(field)
# 清洗时间格式, 使其转换成统一的字符串格式.
if repr(self.merge_data['applied_at'].dtype) == "dtype('O')":
self.merge_data['applied_at'] = self.merge_data['applied_at'].apply(lambda x: x[:10])
else:
self.merge_data['applied_at'] = self.merge_data['applied_at'].apply(lambda x: x.strftime('%Y-%m-%d'))
# 确认数据时间范围.
self.merge_data = self.merge_data.loc[
(self.merge_data['applied_at'] >= self.start_date) & (self.merge_data['applied_at'] <= self.end_date)]
# 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:
print(self.field_query_name_dict[field])
self.field_query_list.remove(field)
self.field_name_list.remove(self.field_query_name_dict[field])
del self.field_query_name_dict[field]
# 处理数据.
print('开始处理数据.')
print('=' * 40)
for field in self.field_query_list:
self.process_data(field)
print('数据处理完毕.')
print('=' * 40)
# 画图.
print('开始画图.')
print('=' * 40)
self.plot()
print('画图完成.')
print('=' * 40)
# 保存信息.
print('开始保存信息.')
print('=' * 40)
self.save_vlm_info()
print('保存信息完成.')
print('=' * 40)
if __name__ == '__main__':
pass
model_score.xlsx
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