diff --git a/.idea/csv-editor.xml b/.idea/csv-editor.xml
index 9b71347..dba7d86 100644
--- a/.idea/csv-editor.xml
+++ b/.idea/csv-editor.xml
@@ -136,6 +136,13 @@
+
+
+
+
+
+
+
diff --git a/__pycache__/computation.cpython-38.pyc b/__pycache__/computation.cpython-38.pyc
index 409e8fa..cd356de 100644
Binary files a/__pycache__/computation.cpython-38.pyc and b/__pycache__/computation.cpython-38.pyc differ
diff --git a/__pycache__/firm.cpython-38.pyc b/__pycache__/firm.cpython-38.pyc
index d5d075f..b6388e5 100644
Binary files a/__pycache__/firm.cpython-38.pyc and b/__pycache__/firm.cpython-38.pyc differ
diff --git a/__pycache__/my_model.cpython-38.pyc b/__pycache__/my_model.cpython-38.pyc
index 2ce3b7a..d4057b2 100644
Binary files a/__pycache__/my_model.cpython-38.pyc and b/__pycache__/my_model.cpython-38.pyc differ
diff --git a/__pycache__/product.cpython-38.pyc b/__pycache__/product.cpython-38.pyc
index acdf26f..a858e0d 100644
Binary files a/__pycache__/product.cpython-38.pyc and b/__pycache__/product.cpython-38.pyc differ
diff --git a/computation.py b/computation.py
index 4561f76..6cfa875 100644
--- a/computation.py
+++ b/computation.py
@@ -148,7 +148,7 @@ class Computation:
# 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 np.isclose(sum(lst_prob), 1.0):
+ 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
@@ -234,7 +234,7 @@ class Computation:
# 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 np.isclose(sum(lst_prob), 1.0):
+ 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]
diff --git a/firm.py b/firm.py
index 81f48cf..e66be0f 100644
--- a/firm.py
+++ b/firm.py
@@ -45,7 +45,7 @@ class FirmAgent(Agent):
self.c_consumption = demand_quantity
# 设备c购买价格（初始值）
# self.c_price = c_price
- # 资源r补货库存阈值
+ # 资源r补货库存阈值 很重要设置
self.s_r = 40
self.S_r = 120
# 设备补货阙值 可选
diff --git a/input_data/产品消耗制造比例.csv b/input_data/产品消耗制造比例.csv
new file mode 100644
index 0000000..0c42e76
--- /dev/null
+++ b/input_data/产品消耗制造比例.csv
@@ -0,0 +1,238 @@
+IndustryID,MaterialID,Quantity
+38,47,1.0
+39,49,1.0
+40,44,1.0
+41,15,0.02
+41,18,0.1
+41,20,0.4
+41,22,0.15
+41,23,0.04
+41,25,0.02
+41,31,0.25
+41,36,1.0
+42,15,0.02
+42,18,0.1
+42,20,0.4
+42,22,0.15
+42,23,0.04
+42,25,0.02
+42,31,0.25
+43,46,1.0
+44,7,1.4
+44,15,0.02
+44,18,0.1
+44,20,0.4
+44,22,0.15
+44,23,0.04
+44,25,0.02
+44,27,0.25
+45,7,1.4
+45,15,0.02
+45,18,0.1
+45,20,0.4
+45,22,0.15
+45,23,0.04
+45,25,0.02
+45,27,0.25
+46,15,0.02
+46,18,0.1
+46,20,0.4
+46,22,0.15
+46,23,0.04
+46,25,0.02
+46,27,0.25
+46,32,1.3
+47,15,0.02
+47,18,0.1
+47,20,0.4
+47,22,0.15
+47,23,0.04
+47,25,0.02
+47,27,0.25
+47,33,1.0
+48,15,0.02
+48,18,0.1
+48,20,0.4
+48,22,0.15
+48,23,0.04
+48,25,0.02
+48,27,0.25
+48,34,1.3
+49,15,0.02
+49,18,0.1
+49,20,0.4
+49,22,0.15
+49,23,0.04
+49,25,0.02
+49,27,0.25
+49,35,1.3
+50,19,0.005
+50,20,0.4
+50,22,0.15
+50,23,0.04
+50,25,0.02
+50,28,0.25
+50,29,0.03
+50,27,0.01
+50,40,1.0
+51,19,0.005
+51,20,0.4
+51,22,0.15
+51,23,0.04
+51,25,0.02
+51,28,0.25
+51,29,0.03
+51,27,0.01
+51,38,1.0
+52,19,0.005
+52,20,0.4
+52,22,0.15
+52,23,0.04
+52,25,0.02
+52,28,0.25
+52,29,0.03
+52,27,0.01
+52,41,1.0
+53,19,0.005
+53,20,0.4
+53,22,0.15
+53,23,0.04
+53,25,0.02
+53,28,0.25
+53,29,0.03
+53,27,0.01
+53,39,1.0
+54,19,0.005
+54,20,0.4
+54,22,0.15
+54,23,0.04
+54,25,0.02
+54,28,0.25
+54,29,0.03
+54,27,0.01
+54,43,1.0
+55,19,0.005
+55,20,0.4
+55,22,0.15
+55,23,0.04
+55,25,0.02
+55,28,0.25
+55,29,0.03
+55,27,0.01
+55,42,1.0
+90,7,1.7
+90,8,0.07
+90,9,0.07
+90,10,0.02
+90,11,0.04
+90,17,0.1
+90,19,0.005
+90,20,0.4
+90,21,0.04
+90,23,0.04
+90,24,0.07
+90,25,0.02
+90,28,0.03
+90,31,0.25
+90,44,0.2
+90,45,0.1
+91,8,0.07
+91,9,0.07
+91,10,0.02
+91,11,0.04
+91,17,0.1
+91,18,0.1
+91,19,0.005
+91,20,0.4
+91,23,0.04
+91,24,0.07
+91,28,0.03
+91,31,0.25
+92,8,0.07
+92,9,0.07
+92,10,0.02
+92,11,0.04
+92,17,0.1
+92,18,0.1
+92,19,0.005
+92,20,0.4
+92,23,0.04
+92,24,0.07
+92,28,0.03
+92,31,0.26
+93,8,0.07
+93,9,0.07
+93,10,0.02
+93,11,0.04
+93,17,0.1
+93,18,0.1
+93,19,0.005
+93,20,0.4
+93,23,0.04
+93,24,0.07
+93,28,0.03
+93,31,0.27
+94,8,0.07
+94,9,0.07
+94,10,0.02
+94,11,0.04
+94,17,0.1
+94,18,0.1
+94,19,0.005
+94,20,0.4
+94,23,0.04
+94,24,0.07
+94,28,0.03
+94,31,0.28
+95,8,0.1
+95,9,0.0
+95,10,7.0
+95,11,0.02
+95,12,0.03
+95,13,0.01
+95,14,0.01
+95,15,0.07
+95,16,0.05
+95,17,0.003
+95,18,0.1
+95,19,0.04
+95,20,0.01
+95,21,0.04
+95,22,0.03
+95,23,0.02
+95,24,0.01
+95,25,0.05
+95,26,0.01
+95,27,0.003
+95,28,0.1
+95,29,0.05
+95,30,0.04
+95,31,0.01
+95,37,0.1
+95,44,0.04
+95,45,0.7
+95,46,0.07
+95,47,0.01
+95,48,0.03
+95,49,0.01
+95,50,0.01
+95,51,0.01
+95,52,0.2
+95,53,0.03
+95,54,0.01
+95,55,0.01
+95,90,0.01
+95,91,0.01
+95,92,1.0
+95,93,1.0
+95,94,1.0
+96,95,1.0
+96,101,0.01
+96,102,0.01
+96,103,0.02
+96,104,0.01
+96,105,0.2
+96,106,0.1
+96,107,0.01
+96,108,0.03
+96,109,0.02
diff --git a/main.py b/main.py
index 93d7407..4880349 100644
--- a/main.py
+++ b/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='1')
+ parser.add_argument('--job', type=int, default='3')
parser.add_argument('--reset_sample', type=int, default='0')
parser.add_argument('--reset_db', type=bool, default=False)
diff --git a/my_model.py b/my_model.py
index c78cfc1..255b10f 100644
--- a/my_model.py
+++ b/my_model.py
@@ -316,21 +316,23 @@ class MyModel(Model):
for ag_node, attr in self.product_network.nodes(data=True):
# 产业种类
+ production_ratio = self.data_production_ratio[self.data_production_ratio['IndustryID'] == ag_node]['Production_ratio']
+ # 转换为字典
+ if not production_ratio.empty: # 检查 Series 是否为空
+ production_ratio_dict = production_ratio.iloc[0] # 提取第一个值
+ else:
+ # 如果 Series 是空的，返回一个空字典
+ production_ratio_dict = {}
- # type2 = self.type2[self.type2["Index"] == ag_node]["产业种类"]
- # depreciation ratio 折旧比值
- # product_id = product_id.iloc[0]
- product = ProductAgent(ag_node, self, name=attr['Name'], type2=0)
+ product = ProductAgent(ag_node, self, name=attr['Name'], type2=0, production_ratio=production_ratio_dict)
self.add_agent(product)
- # self.grid.place_agent(product, ag_node)
- ##print(f"Product agent created: {product.name}, ID: {product.unique_id}")
for ag_node, attr in self.firm_network.nodes(data=True):
a_lst_product = [agent for agent in self.product_agents if agent.unique_id in attr['Product_Code']]
- demand_quantity = self.data_consumed[self.data_consumed['Firm_Code'] == ag_node]
+ demand_quantity = self.data_materials[self.data_materials['Firm_Code'] == ag_node]
- production_output = self.data_produced[self.data_consumed['Firm_Code'] == ag_node]
+ production_output = self.data_produced[self.data_materials['Firm_Code'] == ag_node]
# c购买价格？ 数据预处理
# c_price = self.Firm.loc[self.Firm['Code'] == ag_node, 'c_price'].values[0]
@@ -418,24 +420,26 @@ class MyModel(Model):
def j_comp_consumed_produced(self):
# 着重修改这 然后考虑逻辑 如何传递值
- data_consumed = pd.read_csv('input_data/input_firm_data/firms_materials.csv')
+ data_materials = pd.read_csv('input_data/input_firm_data/firms_materials.csv')
data_produced = pd.read_csv('input_data/input_firm_data/firms_products.csv')
- data_not_consumed = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
+ data_production_ratio = pd.read_csv('input_data/产品消耗制造比例.csv')
+
+ data_not_consumed = data_materials.groupby('Firm_Code')[['材料id', '材料数量']] \
.apply(lambda x: dict(zip(x['材料id'], x['材料数量']))) \
.reset_index(name='Material_not_Consumed')
- # 这里简单设置为折半 考虑 企业的设备量
- # 可以引入 换算率 也就是 材料——计算产品比例 通过上游产业 现在假设为 2:1 的比例
- data_consumed0 = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
- .apply(lambda x: dict(zip(x['材料id'], x['材料数量'] / 2))) \
- .reset_index(name='Material_not_Consumed')
- data_produced = data_consumed.groupby('Firm_Code')[['材料id', '材料数量']] \
- .apply(lambda x: dict(zip(x['材料id'], x['材料数量'] / 4))) \
+ data_not_produced = data_produced.groupby('Firm_Code')[['产品id', '产品数量']] \
+ .apply(lambda x: dict(zip(x['产品id'], x['产品数量']))) \
.reset_index(name='Material_not_Consumed')
- self.data_consumed = data_consumed0
- self.data_produced = data_produced
+ data_production_ratio = data_production_ratio.groupby('IndustryID')[['MaterialID', 'Quantity']] \
+ .apply(lambda x: dict(zip(x['MaterialID'], x['Quantity']))) \
+ .reset_index(name='Production_ratio')
+
+ self.data_materials = data_not_consumed
+ self.data_produced = data_not_produced
+ self.data_production_ratio = data_production_ratio
def step(self):
# 1. Remove edge to customer and disrupt customer up product
@@ -538,16 +542,25 @@ class MyModel(Model):
C_list[2] = C0_list[2]
# 消耗资源过程
# 这里需要修改
+ k = 0.6
+ # 特定的产业
+ firm_consumed_nums_dct = {}
+ for indus_i in firm.indus_i:
+ print("这是 公司产业 编码：" + indus_i)
+ print(type(indus_i))
+ for p_id, p_nums in firm.P.items():
+ print("这是 产品 编码：" + p_id)
+ print(type(p_id))
+ if p_id == indus_i.unique_id:
+ consumed_nums = p_nums * k
+ firm_consumed_nums_dct[indus_i] = consumed_nums
for r_id, r_nums in firm.R.items():
- for consumed_id, consumed_nums in firm.c_consumption:
- if consumed_id == r_id:
- r_nums = r_nums - consumed_nums
-
+ for i, value in firm_consumed_nums_dct.items():
+ if r_id in i:
+ r_nums = r_nums - i[r_id] * value
# 生产产品过程
for p_id, p_nums in firm.P.items():
- for product_id, product_nums in firm.c_consumption:
- if product_id == p_id:
- p_nums = p_nums + product_nums
+ p_nums = p_nums * 1.6
firm.refresh_R()
# 刷新 C状态
diff --git a/product.py b/product.py
index 24ef514..62c8659 100644
--- a/product.py
+++ b/product.py
@@ -2,13 +2,14 @@ from mesa import Agent
class ProductAgent(Agent):
- def __init__(self, unique_id, model, name, type2):
+ def __init__(self, unique_id, model, name, type2, production_ratio):
# 调用超类的 __init__ 方法
super().__init__(unique_id, model)
# 初始化代理属性
self.name = name
self.product_network = self.model.product_network
+ self.production_ratio = production_ratio
if type2 == 0:
self.is_equip = True
else: