极大优化模型运行速度

computation.py

@@ -44,227 +44,8 @@ class Computation:
         product_network_test = nx.adjacency_graph(json.loads(dct_sample_para['g_bom']))
 
         model = MyModel(dct_sample_para)
-        for i in range(1):
-            model.step()
-            print(i, datetime.datetime.now())
-        model.end()
+
+        model.step()  # 运行仿真一步
+
         return False
 
-    def initialize_firm_network(self):
-        # Read the firm data
-
-        firm = pd.read_csv("input_data/input_firm_data/Firm_amended.csv")
-
-        firm['Code'] = firm['Code'].astype(str)
-
-        firm.fillna(0, inplace=True)
-
-        firm_attr = firm.loc[:, ["Code", "Type_Region", "Revenue_Log"]]
-
-        firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
-        firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
-
-        firm_product = []
-
-        grouped = firm_industry_relation.groupby('Firm_Code')['Product_Code'].apply(list)
-        firm_product.append(grouped)
-
-        firm_attr['Product_Code'] = firm_attr['Code'].map(grouped)
-        firm_attr.set_index('Code', inplace=True)
-
-        grouped = firm_industry_relation.groupby('Firm_Code')
-        self.firm_prod_labels_dict = {code: group['Product_Code'].tolist() for code, group in grouped}
-
-        # 遍历'Product_Code' 与 index 交换
-        for index, row in firm_attr.iterrows():
-            id_index_list = []
-            for i in row['Product_Code']:
-                for key_values in self.id_code.items():
-                    if int(key_values[0]) == i:
-                        for id in key_values[1]:
-                            id_index_list.append(id)
-                        firm_attr.at[index, 'Product_Code'] = id_index_list
-
-        self.G_Firm.add_nodes_from(firm["Code"])
-        # Assign attributes to the firm nodes
-        firm_labels_dict = {code: firm_attr.loc[code].to_dict() for code in self.G_Firm.nodes}
-        nx.set_node_attributes(self.G_Firm, firm_labels_dict)
-
-        self.Firm = firm
-
-    def initialize_firm_product_network(self):
-
-        firm_industry_relation = pd.read_csv("input_data/firm_industry_relation.csv")
-        firm_industry_relation['Firm_Code'] = firm_industry_relation['Firm_Code'].astype('string')
-        firm_industry_relation['Product_Code'] = firm_industry_relation['Product_Code'].apply(lambda x: [x])
-        # 将 'firm_prod' 表中的每一行作为图中的节点
-        self.G_FirmProd.add_nodes_from(firm_industry_relation.index)
-        # 为每个节点分配属性
-
-        # 遍历'Product_Code' 与 index 交换
-        for index, row in firm_industry_relation.iterrows():
-            id_index_list = []
-            for i in row['Product_Code']:
-                for key_values in self.id_code.items():
-                    if int(key_values[0]) == i:
-                        for id in key_values[1]:
-                            id_index_list.append(id)
-                        firm_industry_relation.at[index, 'Product_Code'] = id_index_list
-
-        firm_prod_labels_dict = {code: firm_industry_relation.loc[code].to_dict() for code in
-                                 firm_industry_relation.index}
-        nx.set_node_attributes(self.G_FirmProd, firm_prod_labels_dict)
-
-    def add_edges_to_firm_network(self):
-        """ Add edges between firms based on the product BOM relationships """
-        # Add edges to G_Firm according to G_bom
-        for node in nx.nodes(self.G_Firm):
-            lst_pred_product_code = []
-            for product_code in self.G_Firm.nodes[node]['Product_Code']:
-                lst_pred_product_code += list(self.G_bom.predecessors(product_code))
-            lst_pred_product_code = list(set(lst_pred_product_code))
-            lst_pred_product_code = list(sorted(lst_pred_product_code))  # Ensure consistency
-
-            for pred_product_code in lst_pred_product_code:
-                # Get a list of firms producing the component (pred_product_code)
-                lst_pred_firm = [firm_code for firm_code, product in self.firm_prod_labels_dict.items() if
-                                 pred_product_code in product]
-
-                # Select multiple suppliers (multi-sourcing)
-                n_pred_firm = self.int_netw_prf_n
-                if n_pred_firm > len(lst_pred_firm):
-                    n_pred_firm = len(lst_pred_firm)
-
-                if self.is_prf_size:
-                    # 获取 firm 的 size 列表
-                    lst_pred_firm_size = [self.G_Firm.nodes[pred_firm]['Revenue_Log'] for pred_firm in lst_pred_firm]
-                    # 检查 lst_pred_firm_size 是否为空或总和为 0
-                    if len(lst_pred_firm_size) == 0 or sum(lst_pred_firm_size) == 0:
-                        # print("警告: lst_pred_firm_size 为空或总和为 0，无法生成概率分布")
-                        lst_choose_firm = []  # 返回空结果，或根据需要处理
-                    else:
-                        # 计算总和
-                        sum_pred_firm_size = sum(lst_pred_firm_size)
-                        # 归一化生成 lst_prob
-                        lst_prob = [size / sum_pred_firm_size for size in lst_pred_firm_size]
-                        # 使用 np.isclose() 确保概率总和接近 1
-                        if not pd.np.isclose(sum(lst_prob), 1.0):
-                            # print(f"警告: 概率总和为 {sum(lst_prob)}，现在进行修正")
-                            lst_prob = [prob / sum(lst_prob) for prob in lst_prob]
-                        # 确保没有负值或 0
-                        lst_prob = [max(0, prob) for prob in lst_prob]
-                        # 根据修正后的概率选择 firm
-                        lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False, p=lst_prob)
-                else:
-                    # 直接进行随机选择
-                    lst_choose_firm = self.nprandom.choice(lst_pred_firm, n_pred_firm, replace=False)
-
-                # Add edges from predecessor firms to current node (firm)
-                lst_add_edge = [(pred_firm, node, {'Product': pred_product_code}) for pred_firm in lst_choose_firm]
-                self.G_Firm.add_edges_from(lst_add_edge)
-
-                # Add edges to firm-product network
-                self.add_edges_to_firm_product_network(node, pred_product_code, lst_choose_firm)
-
-    def add_edges_to_firm_product_network(self, node, pred_product_code, lst_choose_firm):
-        """ Helper function to add edges to the firm-product network """
-        set_node_prod_code = set(self.G_Firm.nodes[node]['Product_Code'])
-        set_pred_succ_code = set(self.G_bom.successors(pred_product_code))
-        lst_use_pred_prod_code = list(set_node_prod_code & set_pred_succ_code)
-
-        if len(lst_use_pred_prod_code) == 0:
-            print("错误")
-
-        pred_node_list = []
-        for pred_firm in lst_choose_firm:
-            for n, v in self.G_FirmProd.nodes(data=True):
-                for v1 in v['Product_Code']:
-                    if v1 == pred_product_code and v['Firm_Code'] == pred_firm:
-                        pred_node_list.append(n)
-            if len(pred_node_list) != 0:
-                pred_node = pred_node_list[0]
-            else:
-                pred_node = -1
-            current_node_list = []
-            for use_pred_prod_code in lst_use_pred_prod_code:
-                for n, v in self.G_FirmProd.nodes(data=True):
-                    for v1 in v['Product_Code']:
-                        if v1 == use_pred_prod_code and v['Firm_Code'] == node:
-                            current_node_list.append(n)
-            if len(current_node_list) != 0:
-                current_node = current_node_list[0]
-            else:
-                current_node = -1
-            if current_node != -1 and pred_node != -1:
-                self.G_FirmProd.add_edge(pred_node, current_node)
-
-    def connect_unconnected_nodes(self):
-        """ Connect unconnected nodes in the firm network """
-        for node in nx.nodes(self.G_Firm):
-            if self.G_Firm.degree(node) == 0:
-                current_node_list = []
-                for product_code in self.G_Firm.nodes[node]['Product_Code']:
-                    for n, v in self.G_FirmProd.nodes(data=True):
-                        for v1 in v['Product_Code']:
-                            if v['Firm_Code'] == node and v1 == product_code:
-                                current_node_list.append(n)
-                    if len(current_node_list) != 0:
-                        current_node = current_node_list[0]
-                    else:
-                        current_node = -1
-                    lst_succ_product_code = list(self.G_bom.successors(product_code))
-
-                    for succ_product_code in lst_succ_product_code:
-                        lst_succ_firm = [firm_code for firm_code, product in self.firm_prod_labels_dict.items() if
-                                         succ_product_code in product]
-
-                        n_succ_firm = self.int_netw_prf_n
-                        if n_succ_firm > len(lst_succ_firm):
-                            n_succ_firm = len(lst_succ_firm)
-
-                        if self.is_prf_size:
-                            lst_succ_firm_size = [self.G_Firm.nodes[succ_firm]['Revenue_Log'] for succ_firm in
-                                                  lst_succ_firm]
-                            if len(lst_succ_firm_size) == 0 or sum(lst_succ_firm_size) == 0:
-                                # print("警告: lst_pred_firm_size 为空或总和为 0，无法生成概率分布")
-                                lst_choose_firm = []  # 返回空结果，或根据需要处理
-                            else:
-                                # 计算总和
-                                sum_pred_firm_size = sum(lst_succ_firm_size)
-                                # 归一化生成 lst_prob
-                                lst_prob = [size / sum_pred_firm_size for size in lst_succ_firm_size]
-                                # 使用 np.isclose() 确保概率总和接近 1
-                                if not pd.np.isclose(sum(lst_prob), 1.0):
-                                    # print(f"警告: 概率总和为 {sum(lst_prob)}，现在进行修正")
-                                    lst_prob = [prob / sum(lst_prob) for prob in lst_prob]
-
-                                # 确保没有负值或 0
-                                lst_prob = [max(0, prob) for prob in lst_prob]
-
-                                lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False,
-                                                                       p=lst_prob)
-                        else:
-                            lst_choose_firm = self.nprandom.choice(lst_succ_firm, n_succ_firm, replace=False)
-
-                        lst_add_edge = [(node, succ_firm, {'Product': product_code}) for succ_firm in
-                                        lst_choose_firm]
-                        self.G_Firm.add_edges_from(lst_add_edge)
-
-                        # Add edges to firm-product network
-                        succ_node_list = []
-                        for succ_firm in lst_choose_firm:
-                            for n, v in self.G_FirmProd.nodes(data=True):
-                                for v1 in v['Product_Code']:
-                                    if v1 == succ_product_code and v['Firm_Code'] == succ_firm:
-                                        succ_node_list.append(n)
-                            if len(succ_node_list) != 0:
-                                succ_node = succ_node_list[0]
-                            else:
-                                succ_node = -1
-
-                            if current_node != -1 and succ_node != -1:
-                                self.G_FirmProd.add_edge(current_node, succ_node)
-
-        self.sample.g_firm = json.dumps(nx.adjacency_data(self.G_Firm))
-        self.firm_network = self.G_Firm  # 直接使用 networkx 图对象
-        self.firm_prod_network = self.G_FirmProd  # 直接使用 networkx 图对象

input_data/input_firm_data/firm_amended.csv

@@ -11142,4 +11142,4 @@
 11221883697,245.891363896894,142.090621109369,353.107586951086,22.31839,吉林省新胜工程材料有限公司,吉林省,2.5,0
 11222997596,245.891363896894,142.090621109369,353.107586951086,22.31839,临泽众城国发新能源有限公司,甘肃省,2.5,0
 11223028855,245.891363896894,142.090621109369,353.107586951086,22.31839,兰州海锐普技术有限公司,甘肃省,2.5,0
-11223297226,245.891363896894,142.090621109369,353.107586951086,22.31839,吉林省纪明科技有限公司,吉林省,2.5,0
+11223297226,245.891363896894,142.090621109369,353.107586951086,22.31839,吉林省纪明科技有限公司,吉林省,2.5,0

input_data/input_firm_data/firms_materials.csv

@@ -4965,7 +4965,6 @@
 2313177432,7,142.0
 2313179005,91,3.0
 2313208830,91,3.0
-2313209417,32,44.0
 2313251141,12,719.0
 2313307136,7,71.0
 2313316346,95,93.0

input_data/input_firm_data/firms_products.csv

@@ -3715,7 +3715,6 @@
 716918375,15,74.3946333333333
 718074552,12,4011.65997468354
 718492116,91,205.648022142857
-718610477,32,37.4214719971428
 719663677,25,1115.9195
 720139706,95,8.927356
 720139706,84,2.231839
@@ -5758,7 +5757,6 @@
 2313177432,7,22.31839
 2313179005,91,0.398542678571429
 2313208830,91,0.478251214285714
-2313209417,32,6.862904925
 2313251141,12,113.004506329114
 2313262775,85,0.607556172222222
 2313262775,84,0.309977638888889

main.py

@@ -44,7 +44,7 @@ if __name__ == '__main__':
     # 输入参数
     parser = argparse.ArgumentParser(description='setting')
     parser.add_argument('--exp', type=str, default='without_exp')
-    parser.add_argument('--job', type=int, default='3')
+    parser.add_argument('--job', type=int, default='4')
     parser.add_argument('--reset_sample', type=int, default='0')
     parser.add_argument('--reset_db', type=bool, default=False)