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experiments in model and firm py

This commit is contained in:
HaoYizhi 2023-05-15 18:18:41 +08:00
parent a5f278c4cb
commit 839e47cf0b
10 changed files with 90 additions and 41 deletions
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__pycache__/controller_db.cpython-38.pyc
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__pycache__/firm.cpython-38.pyc
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__pycache__/model.cpython-38.pyc
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19
controller_db.py
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@ -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()

46
firm.py
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@ -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]):
@ -70,13 +77,13 @@ class FirmAgent(ap.Agent):
if not self.dct_cand_alt_supply_up_prod_removed[product]:
continue
# select based on size
lst_prob = [
size /
sum(self.dct_cand_alt_supply_up_prod_removed[
product].revenue_log)
for size in self.dct_cand_alt_supply_up_prod_removed[
product].revenue_log
]
lst_size_damp = \
[size ** self.flt_firm_pref_request for size in
self.dct_cand_alt_supply_up_prod_removed[
product].revenue_log]
lst_prob = [size_damp / sum(lst_size_damp)
for size_damp in lst_size_damp
]
select_alt_supply = self.model.nprandom.choice(
self.dct_cand_alt_supply_up_prod_removed[product],
p=lst_prob)
@ -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_prob = [
size / sum(lst_firm_size) for size in lst_firm_size
]
lst_firm_size_damp = \
[firm.revenue_log ** self.flt_firm_pref_accept
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 = [
firm.revenue_log for firm in lst_firm_connect]
lst_prob = [
size / sum(lst_firm_size) for size in lst_firm_size
]
lst_firm_size_damp = \
[firm.revenue_log ** self.flt_firm_pref_accept
for firm in lst_firm_connect]
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([

43
model.py
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@ -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 = [
G_Firm.nodes[succ_firm]['Revenue_Log']
for succ_firm in lst_succ_firm
]
lst_prob = [
size / sum(lst_succ_firm_size)
for size in lst_succ_firm_size
]
# select multiple successors
lst_succ_firm_size_damp = \
[G_Firm.nodes[succ_firm]['Revenue_Log'] **
self.flt_netw_pref_cust_size
for succ_firm in lst_succ_firm
]
lst_prob = \
[size_damp / sum(lst_succ_firm_size_damp)
for size_damp in lst_succ_firm_size_damp
]
# 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.revenue_log / 5, firm_agent.revenue_log / 5 + 2)
firm_agent.dct_prod_capacity[product] = \
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]):

5
oa.csv
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@ -1,3 +1,2 @@
X1
0
1
X1,X2,X3,X4,X5,X6,X7,X8,X9
1,1,1,1,1,1,1,1,1

1 X1 X2 X3 X4 X5 X6 X7 X8 X9
2 0 1 1 1 1 1 1 1 1 1
1

11
orm.py
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@ -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')

7
xv.csv
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@ -1,3 +1,4 @@
n_max_trial
5
10
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
12,2,2,2,0.5,2,4,0.5,0.5
8,1,1,1,1,1,2,1,1
4,0.5,0.5,0.5,2,0.5,0,2,2

1 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
2 5 12 2 2 2 0.5 2 4 0.5 0.5
3 10 8 1 1 1 1 1 2 1 1
4 4 0.5 0.5 0.5 2 0.5 0 2 2