188 lines
7.3 KiB
Python
188 lines
7.3 KiB
Python
from orm import engine
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import pandas as pd
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import networkx as nx
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import json
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import matplotlib.pyplot as plt
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# prep data
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Firm = pd.read_csv("Firm_amended.csv")
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Firm['Code'] = Firm['Code'].astype('string')
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Firm.fillna(0, inplace=True)
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BomNodes = pd.read_csv('BomNodes.csv', index_col=0)
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result = pd.read_sql(sql='select * from iiabmdb.not_test_result where ts > 0;',
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con=engine)
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lst_s_id = list(set(result['s_id'].to_list()))
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for s_id in lst_s_id:
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query = pd.read_sql(
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sql=
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f'select * from iiabmdb.not_test_result where ts = 0 and s_id = {s_id};',
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con=engine)
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result = pd.concat([result, query])
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result.set_index('id', inplace=True)
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result.sort_index(inplace=True)
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result['id_firm'] = result['id_firm'].astype('string')
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# G bom
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plt.rcParams['font.sans-serif'] = 'SimHei'
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exp_id = 1
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G_bom_str = pd.read_sql(
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sql=f'select g_bom from iiabmdb.not_test_experiment where id = {exp_id};',
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con=engine)['g_bom'].tolist()[0]
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G_bom = nx.adjacency_graph(json.loads(G_bom_str))
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pos = nx.nx_agraph.graphviz_layout(G_bom, prog="twopi", args="")
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node_labels = nx.get_node_attributes(G_bom, 'Name')
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plt.figure(figsize=(12, 12), dpi=300)
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nx.draw_networkx_nodes(G_bom, pos)
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nx.draw_networkx_edges(G_bom, pos)
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nx.draw_networkx_labels(G_bom, pos, labels=node_labels, font_size=6)
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plt.savefig(f"analysis\\g_bom_exp_id_{exp_id}.png")
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plt.close()
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# G firm
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plt.rcParams['font.sans-serif'] = 'SimHei'
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sample_id = 1
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G_firm_str = pd.read_sql(
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sql=f'select g_firm from iiabmdb.not_test_sample where id = {exp_id};',
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con=engine)['g_firm'].tolist()[0]
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G_firm = nx.adjacency_graph(json.loads(G_firm_str))
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pos = nx.nx_agraph.graphviz_layout(G_firm, prog="twopi", args="")
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node_label = nx.get_node_attributes(G_firm, 'Name')
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node_degree = dict(G_firm.out_degree())
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node_label = {
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key: f"{node_label[key]} {node_degree[key]}"
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for key in node_label.keys()
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}
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node_size = list(nx.get_node_attributes(G_firm, 'Revenue_Log').values())
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node_size = list(map(lambda x: x**2, node_size))
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edge_label = nx.get_edge_attributes(G_firm, "Product")
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edge_label = {(n1, n2): label for (n1, n2, _), label in edge_label.items()}
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plt.figure(figsize=(12, 12), dpi=300)
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nx.draw(G_firm, pos, node_size=node_size, labels=node_label, font_size=6)
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nx.draw_networkx_edge_labels(G_firm, pos, edge_label, font_size=4)
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plt.savefig(f"analysis\\g_firm_sample_id_{exp_id}.png")
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plt.close()
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# count firm product
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count_firm_prod = result.value_counts(subset=['id_firm', 'id_product'])
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count_firm_prod.name = 'count'
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count_firm_prod = count_firm_prod.to_frame().reset_index()
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count_firm_prod = pd.merge(count_firm_prod,
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Firm[['Code', 'Name']],
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how='left',
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left_on='id_firm',
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right_on='Code')
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count_firm_prod.drop('Code', axis=1, inplace=True)
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count_firm_prod.rename(columns={'Name': 'name_firm'}, inplace=True)
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count_firm_prod = pd.merge(count_firm_prod,
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BomNodes[['Code', 'Name']],
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how='left',
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left_on='id_product',
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right_on='Code')
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count_firm_prod.drop('Code', axis=1, inplace=True)
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count_firm_prod.rename(columns={'Name': 'name_product'}, inplace=True)
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count_firm_prod = count_firm_prod[[
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'id_firm', 'name_firm', 'id_product', 'name_product', 'count'
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]]
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count_firm_prod.to_csv('analysis\\count_firm_prod.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_firm_prod)
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# count firm
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count_firm = count_firm_prod.groupby('id_firm')['count'].sum()
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count_firm = count_firm.to_frame().reset_index()
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count_firm = pd.merge(count_firm,
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Firm[['Code', 'Name']],
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how='left',
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left_on='id_firm',
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right_on='Code')
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count_firm.drop('Code', axis=1, inplace=True)
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count_firm.sort_values('count', inplace=True, ascending=False)
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count_firm = count_firm[['id_firm', 'Name', 'count']]
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count_firm.to_csv('analysis\\count_firm.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_firm)
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# count product
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count_prod = count_firm_prod.groupby('id_product')['count'].sum()
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count_prod = count_prod.to_frame().reset_index()
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count_prod = pd.merge(count_prod,
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BomNodes[['Code', 'Name']],
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how='left',
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left_on='id_product',
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right_on='Code')
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count_prod.drop('Code', axis=1, inplace=True)
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count_prod.sort_values('count', inplace=True, ascending=False)
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count_prod = count_prod[['id_product', 'Name', 'count']]
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count_prod.to_csv('analysis\\count_prod.csv',
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index=False,
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encoding='utf-8-sig')
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print(count_prod)
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# DCP disruption causing probability
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result_disrupt_ts_above_0 = result[(result['ts'] > 0)
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& (result['is_disrupted'] == 1)]
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print(result_disrupt_ts_above_0)
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result_dcp = pd.DataFrame(columns=[
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's_id', 'up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'
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])
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for sid, group in result.groupby('s_id'):
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ts_start = max(group['ts'])
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while ts_start >= 1:
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ts_end = ts_start - 1
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while ts_end >= 0:
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up = group.loc[group['ts'] == ts_end, ['id_firm', 'id_product']]
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down = group.loc[group['ts'] == ts_start,
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['id_firm', 'id_product']]
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for _, up_row in up.iterrows():
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for _, down_row in down.iterrows():
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row = [sid]
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row += up_row.tolist()
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row += down_row.tolist()
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result_dcp.loc[len(result_dcp.index)] = row
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ts_end -= 1
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ts_start -= 1
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count_dcp = result_dcp.value_counts(
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subset=['up_id_firm', 'up_id_product', 'down_id_firm', 'down_id_product'])
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count_dcp.name = 'count'
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count_dcp = count_dcp.to_frame().reset_index()
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count_dcp = pd.merge(count_dcp,
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Firm[['Code', 'Name']],
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how='left',
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left_on='up_id_firm',
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right_on='Code')
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count_dcp.drop('Code', axis=1, inplace=True)
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count_dcp.rename(columns={'Name': 'up_name_firm'}, inplace=True)
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count_dcp = pd.merge(count_dcp,
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BomNodes[['Code', 'Name']],
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how='left',
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left_on='up_id_product',
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right_on='Code')
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count_dcp.drop('Code', axis=1, inplace=True)
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count_dcp.rename(columns={'Name': 'up_name_product'}, inplace=True)
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count_dcp = pd.merge(count_dcp,
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Firm[['Code', 'Name']],
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how='left',
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left_on='down_id_firm',
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right_on='Code')
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count_dcp.drop('Code', axis=1, inplace=True)
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count_dcp.rename(columns={'Name': 'down_name_firm'}, inplace=True)
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count_dcp = pd.merge(count_dcp,
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BomNodes[['Code', 'Name']],
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how='left',
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left_on='down_id_product',
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right_on='Code')
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count_dcp.drop('Code', axis=1, inplace=True)
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count_dcp.rename(columns={'Name': 'down_name_product'}, inplace=True)
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count_dcp = count_dcp[[
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'up_id_firm', 'up_name_firm', 'up_id_product', 'up_name_product',
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'down_id_firm', 'down_name_firm', 'down_id_product', 'down_name_product',
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'count'
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]]
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count_dcp.to_csv('analysis\\count_dcp.csv', index=False, encoding='utf-8-sig')
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print(count_dcp)
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