formatting
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model.py
123
model.py
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@ -50,10 +50,7 @@ class Model(ap.Model):
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# init graph firm prod
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Firm_Prod = pd.read_csv("Firm_amended.csv")
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# Firm['Code'] = Firm['Code'].astype('string')
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Firm_Prod.fillna(0, inplace=True)
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# Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
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# Firm_attr.set_index('Code', inplace=True)
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firm_prod = pd.DataFrame({'bool': Firm_Prod.loc[:, '1':].stack()})
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firm_prod = firm_prod[firm_prod['bool'] == 1].reset_index()
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firm_prod.drop('bool', axis=1, inplace=True)
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@ -216,9 +213,6 @@ class Model(ap.Model):
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self.a_lst_total_firms.select(
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[firm.code == pro_firm_prod_node['Firm_Code']
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for firm in self.a_lst_total_firms])[0]
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# print(list(self.firm_prod_network.predecessors(firm_prod_node)))
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lst_shortest_path = \
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list(nx.all_shortest_paths(self.firm_prod_network,
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source=init_node,
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@ -246,16 +240,11 @@ class Model(ap.Model):
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sum([True if di_supp_code in path else False
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for path in lst_shortest_path]) \
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/ len(lst_shortest_path)
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# print(lst_shortest_path)
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# print(sum([True if di_supp_code in path else False
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# for path in lst_shortest_path]), len(lst_shortest_path))
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drs = n2n_betweenness / \
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(len(lst_cand) * di_supp_firm.revenue_log)
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# print(drs)
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dct_drs[di_supp_code] = drs
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dct_drs = dict(sorted(
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dct_drs.items(), key=lambda kv: kv[1], reverse=True))
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print(dct_drs)
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for di_supp_code in dct_drs.keys():
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di_supp_node = \
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self.firm_prod_network.nodes[di_supp_code]
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@ -278,42 +267,12 @@ class Model(ap.Model):
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'Product': di_supp_prod.code
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})
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])
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# change capacity
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select_cand.dct_prod_capacity[di_supp_prod] -= 1
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# draw network
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self.draw_network()
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print(self.a_lst_total_firms)
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print(self.a_lst_total_firms[133])
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print(self.a_lst_total_firms[133].code)
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print(self.firm_network.positions[self.a_lst_total_firms[133]])
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print(
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self.firm_network.graph[self.firm_network.positions[self.a_lst_total_firms[133]]])
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iter_g = iter(
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self.firm_network.graph[self.firm_network.positions[self.a_lst_total_firms[133]]])
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f_t = next(iter_g)
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print(f_t)
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iter_g = iter(self.firm_network.graph[f_t])
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print(next(iter_g))
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print(self.a_lst_total_firms[133].get_firm_network_node())
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print(list(self.dfs_edges_prod(self.firm_network.graph,
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self.a_lst_total_firms[133],
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self.a_lst_total_firms[133].a_lst_product[0])))
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# print(self.betweenness_centrality_firm_prod(self.firm_network.graph))
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t = nx.dfs_successors(self.firm_prod_network, [n for n, v in self.firm_prod_network.nodes(data=True)
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if v['Firm_Code'] == self.a_lst_total_firms[133].code and
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v['Product_Code'] == self.a_lst_total_firms[133].a_lst_product[0].code][0])
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print(t)
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for key, values in t.items():
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pred_node = self.firm_prod_network.nodes[key]
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for value in values:
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succ_node = self.firm_prod_network.nodes[value]
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print(pred_node['Firm_Code'], pred_node['Product_Code'],
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succ_node['Firm_Code'], succ_node['Product_Code'])
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print()
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def update(self):
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self.a_lst_total_firms.clean_before_time_step()
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# output
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@ -500,83 +459,3 @@ class Model(ap.Model):
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edge_label,
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font_size=4)
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plt.savefig("network.png")
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@ staticmethod
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def dfs_edges_prod(G, start_firm, start_prod, depth_limit=None):
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assert start_prod in start_firm.a_lst_product, \
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f"product {start_prod.code} not in firm {start_firm.code}"
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visited = set()
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if depth_limit is None:
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depth_limit = len(G)
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start_node = start_firm.get_firm_network_node()
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visited.add(start_firm)
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stack = [(start_firm, start_prod, depth_limit, iter(G[start_node]))]
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while stack:
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parent_firm, parent_prod, depth_now, children_node = stack[-1]
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try:
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child_node = next(children_node)
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child_firm = \
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ap.AgentIter(start_firm.model, child_node).to_list()[0]
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if child_firm not in visited:
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for child_prod in child_firm.a_lst_product:
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if child_prod not in parent_prod.a_successors():
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continue
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yield parent_firm.code, parent_prod.code, child_firm.code, child_prod.code
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visited.add(child_firm)
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if depth_now > 1:
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stack.append((child_firm,
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child_prod,
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depth_now - 1,
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iter(G[child_node])))
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except StopIteration:
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stack.pop()
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@ staticmethod
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def betweenness_centrality_firm_prod(G):
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def _single_source_shortest_path_basic(G, s):
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S = []
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P = {}
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for v in G:
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P[v] = []
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sigma = dict.fromkeys(G, 0.0) # sigma[v]=0 for v in G
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D = {}
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sigma[s] = 1.0
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D[s] = 0
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Q = [s]
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while Q: # use BFS to find shortest paths
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v = Q.pop(0)
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S.append(v)
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Dv = D[v]
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sigmav = sigma[v]
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for w in G[v]:
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if w not in D:
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Q.append(w)
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D[w] = Dv + 1
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if D[w] == Dv + 1: # this is a shortest path, count paths
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sigma[w] += sigmav
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P[w].append(v) # predecessors
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return S, P, sigma
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def _accumulate_basic(betweenness, S, P, sigma, s):
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delta = dict.fromkeys(S, 0)
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while S:
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w = S.pop()
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coeff = (1 + delta[w]) / sigma[w]
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for v in P[w]:
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delta[v] += sigma[v] * coeff
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if w != s:
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betweenness[w] += delta[w]
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return betweenness
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betweenness = dict.fromkeys(G, 0.0) # b[v]=0 for v in G
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nodes = G
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for s in nodes:
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# single source shortest paths
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# use BFS for unweighted g
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S, P, sigma = _single_source_shortest_path_basic(G, s)
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# accumulation without endpoint
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betweenness = _accumulate_basic(betweenness, S, P, sigma, s)
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return betweenness
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