81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
import pickle
|
||
|
||
from sqlalchemy import text
|
||
from orm import engine, connection
|
||
import pandas as pd
|
||
import networkx as nx
|
||
import json
|
||
import matplotlib.pyplot as plt
|
||
|
||
# Prepare data
|
||
Firm = pd.read_csv("../../input_data/input_firm_data/firm_amended.csv")
|
||
Firm['Code'] = Firm['Code'].astype('string')
|
||
Firm.fillna(0, inplace=True)
|
||
BomNodes = pd.read_csv('../../input_data/input_product_data/BomNodes.csv', index_col=0)
|
||
|
||
# SQL query
|
||
with open('../../SQL_analysis_risk.sql', 'r') as f:
|
||
str_sql = text(f.read())
|
||
|
||
result = pd.read_sql(sql=str_sql, con=connection)
|
||
result.to_csv('count.csv', index=False, encoding='utf-8-sig')
|
||
print(result)
|
||
|
||
# Count firm product
|
||
count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
|
||
count_firm_prod.name = 'count'
|
||
count_firm_prod = count_firm_prod.to_frame().reset_index()
|
||
count_firm_prod.to_csv('count_firm_prod.csv', index=False, encoding='utf-8-sig')
|
||
print(count_firm_prod)
|
||
|
||
# Count firm
|
||
count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
|
||
count_firm = count_firm.to_frame().reset_index()
|
||
count_firm.sort_values('count', inplace=True, ascending=False)
|
||
count_firm.to_csv('count_firm.csv', index=False, encoding='utf-8-sig')
|
||
print(count_firm)
|
||
|
||
# Count product
|
||
count_prod = count_firm_prod.groupby('id_product')['count'].sum()
|
||
count_prod = count_prod.to_frame().reset_index()
|
||
count_prod.sort_values('count', inplace=True, ascending=False)
|
||
count_prod.to_csv('count_prod.csv', index=False, encoding='utf-8-sig')
|
||
print(count_prod)
|
||
|
||
# DCP disruption causing probability
|
||
result_disrupt_ts_above_0 = result[result['ts'] > 0]
|
||
print(result_disrupt_ts_above_0)
|
||
result_dcp = pd.DataFrame(columns=[
|
||
's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
|
||
])
|
||
|
||
result_dcp_list = [] # 用列表收集数据，避免DataFrame逐行增长的问题
|
||
for sid, group in result.groupby('s_id'):
|
||
ts_start = max(group['ts'])
|
||
while ts_start >= 1:
|
||
ts_end = ts_start - 1
|
||
while ts_end >= 0:
|
||
up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
|
||
down = group.loc[group['ts'] == ts_start, ['id_firm', 'id_product']]
|
||
for _, up_row in up.iterrows():
|
||
for _, down_row in down.iterrows():
|
||
result_dcp_list.append([sid] + up_row.tolist() + down_row.tolist())
|
||
ts_end -= 1
|
||
ts_start -= 1
|
||
|
||
# 转换为DataFrame
|
||
result_dcp = pd.DataFrame(result_dcp_list, columns=[
|
||
's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
|
||
])
|
||
|
||
# 统计
|
||
count_dcp = result_dcp.value_counts(
|
||
subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product']
|
||
).reset_index(name='count')
|
||
|
||
# 保存文件
|
||
count_dcp.to_csv('count_dcp.csv', index=False, encoding='utf-8-sig')
|
||
|
||
# 输出结果
|
||
print(count_dcp)
|