experiments in model and firm py

controller_db.py

@@ -74,7 +74,7 @@ class ControllerDB:
         # insert exp
         df_xv = pd.read_csv("xv.csv", index_col=None)
         # read the OA table
-        df_oa = pd.read_fwf("oa.csv", index_col=None)
+        df_oa = pd.read_csv("oa.csv", index_col=None)
         for idx_scenario, row in df_oa.iterrows():
             dct_exp_para = {}
             for idx_col, para_level in enumerate(row):
@@ -91,7 +91,11 @@ class ControllerDB:
                       f"init_removal {idx_init_removal}!")
 
     def add_experiment_1(self, idx_scenario, idx_init_removal,
-                         dct_lst_init_remove_firm_prod, g_bom, n_max_trial):
+                         dct_lst_init_remove_firm_prod, g_bom,
+                         n_max_trial, crit_supplier, firm_pref_request,
+                         firm_pref_accept, netw_pref_cust_n,
+                         netw_pref_cust_size, cap_limit, diff_new_conn,
+                         diff_remove,):
         e = Experiment(
             idx_scenario=idx_scenario,
             idx_init_removal=idx_init_removal,
@@ -99,7 +103,16 @@ class ControllerDB:
             n_iter=int(self.dct_parameter['n_iter']),
             dct_lst_init_remove_firm_prod=dct_lst_init_remove_firm_prod,
             g_bom=g_bom,
-            n_max_trial=n_max_trial)
+            n_max_trial=n_max_trial,
+            crit_supplier=crit_supplier,
+            firm_pref_request=firm_pref_request,
+            firm_pref_accept=firm_pref_accept,
+            netw_pref_cust_n=netw_pref_cust_n,
+            netw_pref_cust_size=netw_pref_cust_size,
+            cap_limit=cap_limit,
+            diff_new_conn=diff_new_conn,
+            diff_remove=diff_remove,
+        )
         db_session.add(e)
         db_session.commit()
 

firm.py

@@ -22,6 +22,12 @@ class FirmAgent(ap.Agent):
         self.dct_cand_alt_supply_up_prod_removed = {}
         self.dct_request_prod_from_firm = {}
 
+        # para
+        self.flt_crit_supplier = float(self.p.crit_supplier)
+        self.flt_firm_pref_request = float(self.p.firm_pref_request)
+        self.flt_firm_pref_accept = float(self.p.firm_pref_accept)
+        self.flt_diff_new_conn = float(self.p.diff_new_conn)
+
     def remove_edge_to_cus_remove_cus_up_prod(self, remove_product):
         lst_out_edge = list(
             self.firm_network.graph.out_edges(
@@ -41,7 +47,8 @@ class FirmAgent(ap.Agent):
                     edge for edge in lst_in_edge
                     if edge[-1] == remove_product.code
                 ]
-                prod_remove = math.exp(-1 * len(lst_select_in_edge))
+                prod_remove = math.exp(-1 * self.flt_crit_supplier *
+                                       len(lst_select_in_edge))
                 if self.model.nprandom.choice([True, False],
                                               p=[prod_remove,
                                                  1 - prod_remove]):
@@ -70,12 +77,12 @@ class FirmAgent(ap.Agent):
                 if not self.dct_cand_alt_supply_up_prod_removed[product]:
                     continue
                 # select based on size
-                lst_prob = [
+                lst_size_damp = \
-                    size /
+                    [size ** self.flt_firm_pref_request for size in
-                    sum(self.dct_cand_alt_supply_up_prod_removed[
+                     self.dct_cand_alt_supply_up_prod_removed[
-                        product].revenue_log)
+                         product].revenue_log]
-                    for size in self.dct_cand_alt_supply_up_prod_removed[
+                lst_prob = [size_damp / sum(lst_size_damp)
-                        product].revenue_log
+                            for size_damp in lst_size_damp
                             ]
                 select_alt_supply = self.model.nprandom.choice(
                     self.dct_cand_alt_supply_up_prod_removed[product],
@@ -132,10 +139,12 @@ class FirmAgent(ap.Agent):
                             lst_firm_connect.append(firm)
                     if len(lst_firm_connect) == 0:
                         # handling based on size
-                        lst_firm_size = [firm.revenue_log for firm in lst_firm]
+                        lst_firm_size_damp = \
-                        lst_prob = [
+                            [firm.revenue_log ** self.flt_firm_pref_accept
-                            size / sum(lst_firm_size) for size in lst_firm_size
+                             for firm in lst_firm]
-                        ]
+                        lst_prob = \
+                            [size_damp / sum(lst_firm_size_damp)
+                             for size_damp in lst_firm_size_damp]
                         select_customer = \
                             self.model.nprandom.choice(lst_firm, p=lst_prob)
                         self.accept_request(select_customer, product)
@@ -143,11 +152,12 @@ class FirmAgent(ap.Agent):
                         self.accept_request(lst_firm_connect[0], product)
                     elif len(lst_firm_connect) > 1:
                         # handling based on size of firm that has connection
-                        lst_firm_size = [
+                        lst_firm_size_damp = \
-                            firm.revenue_log for firm in lst_firm_connect]
+                            [firm.revenue_log ** self.flt_firm_pref_accept
-                        lst_prob = [
+                             for firm in lst_firm_connect]
-                            size / sum(lst_firm_size) for size in lst_firm_size
+                        lst_prob = \
-                        ]
+                            [size_damp / sum(lst_firm_size_damp)
+                             for size_damp in lst_firm_size_damp]
                         select_customer = \
                             self.model.nprandom.choice(lst_firm_connect,
                                                        p=lst_prob)
@@ -160,6 +170,8 @@ class FirmAgent(ap.Agent):
             and product not in firm.a_lst_product_removed
         ]
         prod_accept = self.revenue_log / sum(lst_firm_size)
+        # damp prod
+        prod_accept = prod_accept ** self.flt_diff_new_conn
         if self.model.nprandom.choice([True, False],
                                       p=[prod_accept, 1 - prod_accept]):
             self.firm_network.graph.add_edges_from([

model.py

@@ -17,9 +17,15 @@ class Model(ap.Model):
         self.random = random.Random(self.p.seed)
         self.nprandom = np.random.default_rng(self.p.seed)
         self.int_n_iter = int(self.p.n_iter)
-        self.int_n_max_trial = int(self.p.n_max_trial)
+
         self.dct_lst_remove_firm_prod = self.p.dct_lst_init_remove_firm_prod
 
+        self.int_n_max_trial = int(self.p.n_max_trial)
+        self.flt_netw_pref_cust_n = float(self.p.netw_pref_cust_n)
+        self.flt_netw_pref_cust_size = float(self.p.netw_pref_cust_size)
+        self.flt_cap_limit = int(self.p.cap_limit)
+        self.flt_diff_remove = float(self.p.diff_remove)
+
         # init graph bom
         G_bom = nx.adjacency_graph(json.loads(self.p.g_bom))
         self.product_network = ap.Network(self, G_bom)
@@ -51,15 +57,16 @@ class Model(ap.Model):
                     # get a list of firm producing this successor
                     lst_succ_firm = Firm['Code'][Firm[succ_product_code] ==
                                                  1].to_list()
-                    lst_succ_firm_size = [
+                    lst_succ_firm_size_damp = \
-                        G_Firm.nodes[succ_firm]['Revenue_Log']
+                        [G_Firm.nodes[succ_firm]['Revenue_Log'] **
+                         self.flt_netw_pref_cust_size
                          for succ_firm in lst_succ_firm
                          ]
-                    lst_prob = [
+                    lst_prob = \
-                        size / sum(lst_succ_firm_size)
+                        [size_damp / sum(lst_succ_firm_size_damp)
-                        for size in lst_succ_firm_size
+                         for size_damp in lst_succ_firm_size_damp
                          ]
-                    # select multiple successors
+                    # select multiple customer
                     # based on relative size of this firm
                     lst_same_prod_firm = Firm['Code'][Firm[product_code] ==
                                                       1].to_list()
@@ -69,6 +76,8 @@ class Model(ap.Model):
                     ]
                     share = G_Firm.nodes[node]['Revenue_Log'] / sum(
                         lst_same_prod_firm_size)
+                    # damp share
+                    share = share ** self.flt_netw_pref_cust_n
                     n_succ_firm = round(share * len(lst_succ_firm)) if round(
                         share * len(lst_succ_firm)) > 0 else 1  # at least one
                     lst_choose_firm = self.nprandom.choice(lst_succ_firm,
@@ -106,8 +115,10 @@ class Model(ap.Model):
                 ]))
             # init extra capacity based on discrete uniform distribution
             for product in firm_agent.a_lst_product:
-                firm_agent.dct_prod_capacity[product] = self.nprandom.integers(
+                firm_agent.dct_prod_capacity[product] = \
-                    firm_agent.revenue_log / 5, firm_agent.revenue_log / 5 + 2)
+                    self.nprandom.integers(firm_agent.revenue_log / 5,
+                                           firm_agent.revenue_log / 5 +
+                                           self.flt_cap_limit)
 
             self.firm_network.add_agents([firm_agent], [ag_node])
         self.a_lst_total_firms = ap.AgentList(self, self.firm_network.agents)
@@ -220,13 +231,17 @@ class Model(ap.Model):
                         lost_percent = n_up_product_removed / len(
                             product.a_predecessors())
                         lst_size = self.a_lst_total_firms.revenue_log
-                        lst_size = [firm.revenue_log for firm in self.a_lst_total_firms
+                        lst_size = [firm.revenue_log for firm
+                                    in self.a_lst_total_firms
                                     if product in firm.a_lst_product
-                                    and product not in firm.a_lst_product_removed
+                                    and product
+                                    not in firm.a_lst_product_removed
                                     ]
                         std_size = (firm.revenue_log - min(lst_size) +
                                     1) / (max(lst_size) - min(lst_size) + 1)
                         prod_remove = 1 - std_size * (1 - lost_percent)
+                        # damp prod
+                        prod_remove = prod_remove ** self.flt_diff_remove
                         if self.nprandom.choice([True, False],
                                                 p=[prod_remove,
                                                    1 - prod_remove]):

oa.csv

@@ -1,3 +1,2 @@
-X1
+X1,X2,X3,X4,X5,X6,X7,X8,X9
-0
+1,1,1,1,1,1,1,1,1
-1

orm.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 from sqlalchemy import create_engine, inspect
 from sqlalchemy.ext.declarative import declarative_base
-from sqlalchemy import (Column, Integer, String, ForeignKey, BigInteger,
+from sqlalchemy import (Column, Integer, DECIMAL, String, ForeignKey, BigInteger,
                         DateTime, PickleType, Boolean, Text)
 from sqlalchemy.sql import func
 from sqlalchemy.orm import relationship, Session
@@ -53,7 +53,16 @@ class Experiment(Base):
     # variables
     dct_lst_init_remove_firm_prod = Column(PickleType, nullable=False)
     g_bom = Column(Text(4294000000), nullable=False)
+
     n_max_trial = Column(Integer, nullable=False)
+    crit_supplier = Column(DECIMAL(8, 4), nullable=False)
+    firm_pref_request = Column(DECIMAL(8, 4), nullable=False)
+    firm_pref_accept = Column(DECIMAL(8, 4), nullable=False)
+    netw_pref_cust_n = Column(DECIMAL(8, 4), nullable=False)
+    netw_pref_cust_size = Column(DECIMAL(8, 4), nullable=False)
+    cap_limit = Column(Integer, nullable=False)
+    diff_new_conn = Column(DECIMAL(8, 4), nullable=False)
+    diff_remove = Column(DECIMAL(8, 4), nullable=False)
 
     sample = relationship(
         'Sample', back_populates='experiment', lazy='dynamic')

xv.csv

@@ -1,3 +1,4 @@
-n_max_trial
+n_max_trial,crit_supplier,firm_pref_request,firm_pref_accept,netw_pref_cust_n,netw_pref_cust_size,cap_limit,diff_new_conn,diff_remove
-5
+12,2,2,2,0.5,2,4,0.5,0.5
-10
+8,1,1,1,1,1,2,1,1
+4,0.5,0.5,0.5,2,0.5,0,2,2