def test_create_table(self):
"""
测试创建表
:return:
"""
# 创建表之前确定表不存在
self.assertEqual(False, query_table(self.db, self.table_name))
# 创建表操作
create_table(self.db, self.np_array[0], self.np_array[1, 0])
# 创建表之后确定表已经存在
self.assertEqual(True, query_table(self.db, self.table_name))
def test_drop_table(self):
"""
测试删除表
:return:
"""
# 创建表操作
create_table(self.db, self.np_array[0], self.np_array[1, 0])
# 创建表后能查到表的存在
self.assertEqual(True, query_table(self.db, self.table_name))
# 删除表
drop_table(self.db, self.table_name)
# 删除表后查不到表
self.assertEqual(False, query_table(self.db, self.table_name))
def save_datas_with_labels(file_name, abnormal_rate):
"""
存储已经由孤立森林学习过的带有标签的数据
:return:True or False
"""
cases = load_csv(file_name)
# file_name是文件路径名
print("file name", file_name)
# 文件名
title = file_name.split("/")[-1]
print(type(title), title)
isolate1 = Isolate('isolate', cases, rate=abnormal_rate)
np_array = isolate1.merge_arrays()
table_name = np_array[1, 0]
db = connectdb()
if not query_table(db, table_name):
create_table(db, np_array[0], table_name)
# 插入数据，表名为uuid
if insert_train_datas(db, table_name, np_array[1:]):
# 数据集列表存储表名（redis存储），断电就清空
redis_conn = get_redis_connection("default")
redis_conn.sadd('data_set_name', title)
# sv.data_set.append(title)
# 存储数据集表名（磁盘存储），断电可恢复
save_dataset_name_to_file(title)
# 存储文件与UUID对应关系到file2uuid表中
insert_file2uuid(title, table_name)
return True
return False
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time : 2019/1/11 17:52
# @Author : zsj
# @File : xgb_func_test.py
# @Description:
from db.mysql_operation import insert_train_datas, connectdb, query_table, create_table
from isolate_model.base_function import load_csv, translate_to_xgboost_datas, save_xgboost_class, load_xgboost_class
from isolate_model.isolate_class import Isolate
from xgboost_model.xgboost_class import XGBoost
cases = load_csv("../file/customs_cpu_test.csv")
isolate1 = Isolate('2_7', cases)
np_array = isolate1.merge_arrays()
table_name = np_array[1, 0]
db = connectdb()
if not query_table(db, table_name):
create_table(db, np_array[0], table_name)
insert_train_datas(db, table_name, np_array[1:])
xgb1 = XGBoost(table_name)
print(xgb1.name)
save_xgboost_class(xgb1)
pre = load_xgboost_class("982c78b5-435a-40b3-9a31-9fb5fbf8b16")
print(pre.precision, pre.recall, pre.lasted_update)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time : 2019/1/5 17:45
# @Author : zsj
# @File : db_test.py
# @Description:
from db.mysql_operation import connectdb, create_table, query_table, query_datas, update_datas, delete_datas, \
drop_table, insert_train_datas, closedb
from isolate_model.base_function import load_csv
from isolate_model.isolate_class import Isolate
cases = load_csv("../file/customs_msmq_test.csv")
isolate1 = Isolate('2_7', cases)
np_array = isolate1.merge_arrays()
db = connectdb()
table_name = np_array[1, 0]
# 判断是否存在该表
is_exists = query_table(db, table_name)
# 如果不存在则创建该表
if not is_exists:
create_table(db, np_array[0], table_name)
# 插入数据
insert_train_datas(db, table_name, np_array[1:])
result = query_datas(db, table_name)
print(len(result))