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HaoYizhi 2023-03-13 19:47:25 +08:00
parent 2f162b970b
commit 09b59d8778
10 changed files with 113 additions and 210 deletions
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2
.vscode/launch.json vendored
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@ -8,7 +8,7 @@
"name": "Python: Current File", "name": "Python: Current File",
"type": "python", "type": "python",
"request": "launch", "request": "launch",
"program": "C:\\Users\\ASUS\\OneDrive\\Project\\ScrAbm\\Dissertation\\IIabm\\model.py", "program": "C:\\Users\\ASUS\\OneDrive\\Project\\ScrAbm\\Dissertation\\IIabm\\main.py",
"console": "integratedTerminal", "console": "integratedTerminal",
"justMyCode": true "justMyCode": true
} }

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__pycache__/model.cpython-38.pyc
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conf_experiment.yaml
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@ -3,36 +3,12 @@
# run settings # run settings
meta_seed: 0 meta_seed: 0
fixed: # unchanged all the time
int_n_country: 2
max_int_n_supplier: 3 # make firms heterogeneous
flt_bm_price_ratio: 20.0
flt_beta_developing: 0.5
test: # only for test scenarios test: # only for test scenarios
int_n_product: 12 n_sample: 1
int_n_firm_per_product_per_country: 2
flt_demand_total: 1000.0
n_sample: 5
n_iter: 100 n_iter: 100
n_max_trial: 3
not_test: # normal scenarios not_test: # normal scenarios
int_n_product: 50
int_n_firm_per_product_per_country: 10
flt_demand_total: 10000.0
n_sample: 50 n_sample: 50
n_iter: 10000 n_iter: 100
n_max_trial: 3
default:
is_eliminated: 0 # add when all positive profits and keep max n supplier; remove the worst when all negative wealth
flt_beta_developed: 0.5 # benchmarks flt_beta_developed
tariff_percentage: 0
experiment:
1:
range_lambda_tier: 0, 1, 0.1 # describe the network. 0: chain 1: one iter
2:
is_eliminated: 1
3:
range_flt_beta_developed: 0.5, 0.9, 0.1
range_tariff_percentage: 0, 1, 0.1

186
controller_db.py
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@ -1,10 +1,11 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
from orm import db_session, engine, Base, ins from orm import db_session, engine, Base, ins
from orm import Experiment, Sample, Product, Firm from orm import Experiment, Sample, Result
from sqlalchemy.exc import OperationalError from sqlalchemy.exc import OperationalError
from sqlalchemy import text from sqlalchemy import text
import yaml import yaml
import random import random
import pandas as pd
import numpy as np import numpy as np
import platform import platform
@ -17,6 +18,7 @@ class ControllerDB:
lst_saved_s_id_3: list lst_saved_s_id_3: list
lst_saved_s_id_1_2: list lst_saved_s_id_1_2: list
# n_sample_1_2: int # n_sample_1_2: int
def __init__(self, prefix, reset_flag=0): def __init__(self, prefix, reset_flag=0):
@ -24,88 +26,59 @@ class ControllerDB:
dct_conf_experiment = yaml.full_load(yaml_file) dct_conf_experiment = yaml.full_load(yaml_file)
self.is_test = prefix == 'test' self.is_test = prefix == 'test'
self.db_name_prefix = prefix self.db_name_prefix = prefix
dct_para_in_test = dct_conf_experiment['test'] if self.is_test else dct_conf_experiment['not_test'] dct_para_in_test = dct_conf_experiment[
self.dct_parameter = {'meta_seed': dct_conf_experiment['meta_seed'], 'test'] if self.is_test else dct_conf_experiment['not_test']
'experiment': dct_conf_experiment['experiment'], self.dct_parameter = {
**dct_conf_experiment['fixed'], **dct_conf_experiment['default'], **dct_para_in_test} 'meta_seed': dct_conf_experiment['meta_seed'],
**dct_para_in_test
}
print(self.dct_parameter)
self.reset_flag = reset_flag # 0, not reset; 1, reset self; 2, reset all self.reset_flag = reset_flag # 0, not reset; 1, reset self; 2, reset all
self.lst_saved_s_id_1_2, self.lst_saved_s_id_3 = [], [] self.lst_saved_s_id = []
def init_tables(self): def init_tables(self):
self.fill_experiment_table() self.fill_experiment_table()
self.fill_sample_table() self.fill_sample_table()
@staticmethod
def get_lst_of_range(str_range: str):
s1, s2, s3 = tuple(str_range.split(','))
return list(np.linspace(float(s1), float(s2), num=int((float(s2) - float(s1)) / float(s3)) + 1))
def fill_experiment_table(self): def fill_experiment_table(self):
# prepare the list of lambda tier Firm = pd.read_csv("Firm_amended.csv")
lst_lambda = self.get_lst_of_range(self.dct_parameter['experiment'][1]['range_lambda_tier']) Firm['Code'] = Firm['Code'].astype('string')
# prepare the list of alpha_2nd_country Firm.fillna(0, inplace=True)
lst_beta_developed = self.get_lst_of_range(self.dct_parameter['experiment'][3]['range_flt_beta_developed']) list_dct = []
# prepare the list of tariff_percentage for _, row in Firm.iterrows():
lst_tariff = self.get_lst_of_range(self.dct_parameter['experiment'][3]['range_tariff_percentage']) code = row['Code']
# prepare the default values row = row['1':]
is_eliminated = int(self.dct_parameter['is_eliminated']) for product_code in row.index[row == 1].to_list():
beta_developed = float(self.dct_parameter['flt_beta_developed']) dct = {code: [product_code]}
tariff_percentage_1 = tariff_percentage_2 = float(self.dct_parameter['tariff_percentage']) list_dct.append(dct)
for idx_scenario in self.dct_parameter['experiment'].keys(): for idx_exp, dct in enumerate(list_dct):
n_exp = 0 self.add_experiment_1(idx_exp, self.dct_parameter['n_max_trial'],
# if idx_scenario == 1: # add S1 experiments dct)
# n_exp = self.add_experiment_1(idx_scenario, lst_lambda, is_eliminated, print(f'Inserted experiment for exp {idx_exp}!')
# beta_developed, tariff_percentage_1, tariff_percentage_2)
# if idx_scenario == 2: # add S2 experiments
# n_exp = self.add_experiment_1(idx_scenario, lst_lambda,
# int(self.dct_parameter['experiment'][idx_scenario]['is_eliminated']),
# beta_developed, tariff_percentage_1, tariff_percentage_2)
if idx_scenario == 3:
# int_eliminated = int(self.dct_parameter['experiment'][idx_scenario-1]['is_eliminated'])
int_eliminated = is_eliminated # is_eliminated is 0 at default, so stop eliminating under S3
for beta_developed in lst_beta_developed:
# for beta_developed in [0.5]: # fix beta as 0.5
for tariff_percentage_1 in lst_tariff:
for tariff_percentage_2 in lst_tariff:
# fix lambda as 0.5
n_exp += self.add_experiment_1(idx_scenario, [0.5], int_eliminated,
beta_developed, tariff_percentage_1, tariff_percentage_2)
print(f'Inserted {n_exp} experiments for exp {idx_scenario}!')
def add_experiment_1(self, idx_exp, lst_lambda, is_eliminated, flt_beta_developed, def add_experiment_1(self, idx_exp, n_max_trial,
tariff_percentage_1, tariff_percentage_2): dct_list_init_remove_firm_prod):
lst_exp = [] e = Experiment(
for lambda_tier in lst_lambda: idx_exp=idx_exp,
e = Experiment(idx_exp=idx_exp, n_sample=int(self.dct_parameter['n_sample']),
int_n_country=int(self.dct_parameter['int_n_country']), n_iter=int(self.dct_parameter['n_iter']),
max_int_n_supplier=int(self.dct_parameter['max_int_n_supplier']), n_max_trial=n_max_trial,
int_n_product=int(self.dct_parameter['int_n_product']), dct_list_init_remove_firm_prod=dct_list_init_remove_firm_prod)
int_n_firm_per_product_per_country=int( db_session.add(e)
self.dct_parameter['int_n_firm_per_product_per_country']),
flt_demand_total=float(self.dct_parameter['flt_demand_total']),
flt_bm_price_ratio=float(self.dct_parameter['flt_bm_price_ratio']),
flt_beta_developing=float(self.dct_parameter['flt_beta_developing']),
n_sample=int(self.dct_parameter['n_sample']),
n_iter=int(self.dct_parameter['n_iter']),
is_eliminated=is_eliminated,
flt_beta_developed=flt_beta_developed,
tariff_percentage_1=tariff_percentage_1,
tariff_percentage_2=tariff_percentage_2,
lambda_tier=float(lambda_tier))
lst_exp.append(e)
db_session.bulk_save_objects(lst_exp)
db_session.commit() db_session.commit()
return len(lst_exp)
def fill_sample_table(self): def fill_sample_table(self):
rng = random.Random(self.dct_parameter['meta_seed']) rng = random.Random(self.dct_parameter['meta_seed'])
lst_seed = [rng.getrandbits(32) for _ in range(int(self.dct_parameter['n_sample']))] lst_seed = [
rng.getrandbits(32)
for _ in range(int(self.dct_parameter['n_sample']))
]
lst_exp = db_session.query(Experiment).all() lst_exp = db_session.query(Experiment).all()
lst_sample = [] lst_sample = []
for experiment in lst_exp: for experiment in lst_exp:
for idx_sample in range(int(experiment.n_sample)): for idx_sample in range(int(experiment.n_sample)):
s = Sample(e_id=experiment.id, s = Sample(e_id=experiment.id,
idx_sample=idx_sample+1, idx_sample=idx_sample + 1,
seed=lst_seed[idx_sample], seed=lst_seed[idx_sample],
is_done_flag=-1) is_done_flag=-1)
lst_sample.append(s) lst_sample.append(s)
@ -115,8 +88,11 @@ class ControllerDB:
def reset_db(self, force_drop=False): def reset_db(self, force_drop=False):
# first, check if tables exist # first, check if tables exist
lst_table_obj = [Base.metadata.tables[str_table] for str_table in ins.get_table_names() lst_table_obj = [
if str_table.startswith(self.db_name_prefix)] Base.metadata.tables[str_table]
for str_table in ins.get_table_names()
if str_table.startswith(self.db_name_prefix)
]
is_exist = len(lst_table_obj) > 0 is_exist = len(lst_table_obj) > 0
if force_drop: if force_drop:
while is_exist: while is_exist:
@ -129,51 +105,58 @@ class ControllerDB:
pass pass
else: else:
lst_table_obj.remove(a_table) lst_table_obj.remove(a_table)
print(f"Table {a_table.name} is dropped for exp: {self.db_name_prefix}!!!") print(
f"Table {a_table.name} is dropped for exp: {self.db_name_prefix}!!!"
)
finally: finally:
is_exist = len(lst_table_obj) > 0 is_exist = len(lst_table_obj) > 0
if is_exist: if is_exist:
print(f"All tables exist. No need to reset for exp: {self.db_name_prefix}.") print(
f"All tables exist. No need to reset for exp: {self.db_name_prefix}."
)
# change the is_done_flag from 0 to -1, to rerun the in-finished tasks # change the is_done_flag from 0 to -1, to rerun the in-finished tasks
if self.reset_flag > 0: if self.reset_flag > 0:
if self.reset_flag == 2: if self.reset_flag == 2:
result = db_session.query(Sample).filter(Sample.is_done_flag == 0) sample = db_session.query(Sample).filter(
Sample.is_done_flag == 0)
elif self.reset_flag == 1: elif self.reset_flag == 1:
result = db_session.query(Sample).filter(Sample.is_done_flag == 0, sample = db_session.query(Sample).filter(
Sample.computer_name == platform.node()) Sample.is_done_flag == 0,
Sample.computer_name == platform.node())
else: else:
raise ValueError('Wrong reset flag') raise ValueError('Wrong reset flag')
if result.count() > 0: if sample.count() > 0:
for res in result: for s in sample:
qry_product = db_session.query(Product).filter_by(s_id=res.id) qry_result = db_session.query(Result).filter_by(
if qry_product.count() > 0: s_id=s.id)
for p in qry_product: if qry_result.count() > 0:
db_session.query(Firm).filter(Firm.p_id == p.id).delete() db_session.query(Result).filter(s_id = s.id).delete()
db_session.commit() db_session.commit()
db_session.query(Product).filter(Product.id == p.id).delete() s.is_done_flag = -1
db_session.commit()
res.is_done_flag = -1
db_session.commit() db_session.commit()
print(f"Reset the task id {res.id} flag from 0 to -1") print(f"Reset the sample id {s.id} flag from 0 to -1")
else: else:
Base.metadata.create_all(bind=engine) Base.metadata.create_all(bind=engine)
self.init_tables() self.init_tables()
print(f"All tables are just created and initialized for exp: {self.db_name_prefix}.") print(
f"All tables are just created and initialized for exp: {self.db_name_prefix}."
)
def prepare_list_sample(self): def prepare_list_sample(self):
res = db_session.execute(text(f'''SELECT count(*) FROM {self.db_name_prefix}_sample s, res = db_session.execute(
{self.db_name_prefix}_experiment e WHERE s.e_id=e.id and e.idx_exp < 3''')).scalar() text(f'''SELECT count(*) FROM {self.db_name_prefix}_sample s,
n_sample_1_2 = 0 if res is None else res {self.db_name_prefix}_experiment e WHERE s.e_id=e.id '''
print(f'There are {n_sample_1_2} sample for exp 1 and 2.') )).scalar()
res = db_session.execute(text(f'SELECT id FROM {self.db_name_prefix}_sample WHERE is_done_flag = -1')) n_sample = 0 if res is None else res
print(f'There are a total of {n_sample} samples.')
res = db_session.execute(
text(
f'SELECT id FROM {self.db_name_prefix}_sample WHERE is_done_flag = -1'
))
for row in res: for row in res:
s_id = row[0] s_id = row[0]
if s_id <= n_sample_1_2: self.lst_saved_s_id.append(s_id)
self.lst_saved_s_id_1_2.append(s_id)
else:
self.lst_saved_s_id_3.append(s_id)
print(f'Left: {len(self.lst_saved_s_id_1_2)} for exp 1 and 2; {len(self.lst_saved_s_id_3)} for exp 3')
@staticmethod @staticmethod
def select_random_sample(lst_s_id): def select_random_sample(lst_s_id):
@ -182,7 +165,8 @@ class ControllerDB:
return None return None
s_id = random.choice(lst_s_id) s_id = random.choice(lst_s_id)
lst_s_id.remove(s_id) lst_s_id.remove(s_id)
res = db_session.query(Sample).filter(Sample.id == int(s_id), Sample.is_done_flag == -1) res = db_session.query(Sample).filter(Sample.id == int(s_id),
Sample.is_done_flag == -1)
if res.count() == 1: if res.count() == 1:
return res[0] return res[0]
@ -194,11 +178,7 @@ class ControllerDB:
else: else:
return res[0] return res[0]
sample = self.select_random_sample(self.lst_saved_s_id_1_2) sample = self.select_random_sample(self.lst_saved_s_id)
if sample is not None:
return sample
sample = self.select_random_sample(self.lst_saved_s_id_3)
if sample is not None: if sample is not None:
return sample return sample
@ -214,7 +194,9 @@ if __name__ == '__main__':
# pprint.pprint(dct_exp_config) # pprint.pprint(dct_exp_config)
# pprint.pprint(dct_conf_problem) # pprint.pprint(dct_conf_problem)
db = ControllerDB('first') db = ControllerDB('first')
ratio = db_session.execute('SELECT COUNT(*) / 332750 FROM first_sample s WHERE s.is_done_flag = 1').scalar() ratio = db_session.execute(
'SELECT COUNT(*) / 332750 FROM first_sample s WHERE s.is_done_flag = 1'
).scalar()
print(ratio) print(ratio)
# db.fill_experiment_table() # db.fill_experiment_table()
# print(db.dct_parameter) # print(db.dct_parameter)

3
main.py
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@ -36,7 +36,8 @@ if __name__ == '__main__':
from controller_db import ControllerDB from controller_db import ControllerDB
controller_db = ControllerDB(args.exp, reset_flag=args.reset) controller_db = ControllerDB(args.exp, reset_flag=args.reset)
controller_db.reset_db() # controller_db.reset_db()
controller_db.reset_db(force_drop=True)
controller_db.prepare_list_sample() controller_db.prepare_list_sample()

19
model.py
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@ -16,10 +16,10 @@ n_iter = 10
# 2: ['1.1.3'] # 2: ['1.1.3']
# } # }
dct_list_init_remove_firm_prod = { dct_list_init_remove_firm_prod = {
140: ['1.4.5.1'], '140': ['1.4.5.1'],
135: ['1.3.2.1'], '135': ['1.3.2.1'],
133: ['1.4.4.1'], '133': ['1.4.4.1'],
2: ['1.1.3'] '2': ['1.1.3']
} }
n_max_trial = 5 n_max_trial = 5
dct_sample_para = { dct_sample_para = {
@ -56,13 +56,14 @@ class Model(ap.Model):
# init graph firm # init graph firm
Firm = pd.read_csv("Firm_amended.csv") Firm = pd.read_csv("Firm_amended.csv")
Firm['Code'] = Firm['Code'].astype('string')
Firm.fillna(0, inplace=True) Firm.fillna(0, inplace=True)
Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]] Firm_attr = Firm.loc[:, ["Code", "Name", "Type_Region", "Revenue_Log"]]
firm_product = [] firm_product = []
for _, row in Firm.loc[:, '1':].iterrows(): for _, row in Firm.loc[:, '1':].iterrows():
firm_product.append(row[row == 1].index.to_list()) firm_product.append(row[row == 1].index.to_list())
Firm_attr.loc[:, 'Product_Code'] = firm_product Firm_attr.loc[:, 'Product_Code'] = firm_product
Firm_attr.set_index('Code') Firm_attr.set_index('Code', inplace=True)
G_Firm = nx.MultiDiGraph() G_Firm = nx.MultiDiGraph()
G_Firm.add_nodes_from(Firm["Code"]) G_Firm.add_nodes_from(Firm["Code"])
@ -78,7 +79,7 @@ class Model(ap.Model):
# print(product_code) # print(product_code)
for succ_product_code in list(G_bom.successors(product_code)): for succ_product_code in list(G_bom.successors(product_code)):
# print(succ_product_code) # print(succ_product_code)
list_succ_firms = Firm.index[Firm[succ_product_code] == list_succ_firms = Firm['Code'][Firm[succ_product_code] ==
1].to_list() 1].to_list()
list_revenue_log = [ list_revenue_log = [
G_Firm.nodes[succ_firm]['Revenue_Log'] G_Firm.nodes[succ_firm]['Revenue_Log']
@ -132,7 +133,7 @@ class Model(ap.Model):
for ag_node, attr in self.firm_network.graph.nodes(data=True): for ag_node, attr in self.firm_network.graph.nodes(data=True):
firm_agent = FirmAgent( firm_agent = FirmAgent(
self, self,
code=attr['Code'], code=ag_node.label,
name=attr['Name'], name=attr['Name'],
type_region=attr['Type_Region'], type_region=attr['Type_Region'],
revenue_log=attr['Revenue_Log'], revenue_log=attr['Revenue_Log'],
@ -352,5 +353,5 @@ class Model(ap.Model):
plt.savefig("network.png") plt.savefig("network.png")
model = Model(dct_sample_para) # model = Model(dct_sample_para)
model.run() # model.run()

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81
orm.py
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@ -1,7 +1,7 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
from sqlalchemy import create_engine, inspect from sqlalchemy import create_engine, inspect
from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import Column, Integer, String, ForeignKey, BigInteger, DECIMAL, DateTime, Text from sqlalchemy import Column, Integer, String, ForeignKey, BigInteger, DateTime, PickleType, Boolean
from sqlalchemy.sql import func from sqlalchemy.sql import func
from sqlalchemy.orm import relationship, Session from sqlalchemy.orm import relationship, Session
from sqlalchemy.pool import NullPool from sqlalchemy.pool import NullPool
@ -40,22 +40,12 @@ class Experiment(Base):
idx_exp = Column(Integer, nullable=False) idx_exp = Column(Integer, nullable=False)
# fixed parameters # fixed parameters
int_n_country = Column(Integer, nullable=False)
max_int_n_supplier = Column(Integer, nullable=False) # uni(1, max), random parameter 1 of firm
int_n_product = Column(Integer, nullable=False)
int_n_firm_per_product_per_country = Column(Integer, nullable=False)
flt_demand_total = Column(DECIMAL(10, 2), nullable=False) # tri(0, total_demand, mean), to compute random para a
flt_bm_price_ratio = Column(DECIMAL(10, 2), nullable=False) # benchmark value of b, same for both countries
flt_beta_developing = Column(DECIMAL(10, 2), nullable=False) # benchmark value of c(beta), for developing countries
n_sample = Column(Integer, nullable=False) n_sample = Column(Integer, nullable=False)
n_iter = Column(Integer, nullable=False) n_iter = Column(Integer, nullable=False)
# variables # variables
is_eliminated = Column(Integer, nullable=False) n_max_trial = Column(Integer, nullable=False)
flt_beta_developed = Column(DECIMAL(10, 2), nullable=False) # larger, for developed countries dct_list_init_remove_firm_prod = Column(PickleType, nullable=False)
lambda_tier = Column(DECIMAL(10, 2), nullable=False)
tariff_percentage_1 = Column(DECIMAL(10, 2), nullable=False)
tariff_percentage_2 = Column(DECIMAL(10, 2), nullable=False)
sample = relationship('Sample', back_populates='experiment', lazy='dynamic') sample = relationship('Sample', back_populates='experiment', lazy='dynamic')
@ -75,77 +65,30 @@ class Sample(Base):
ts_done = Column(DateTime(timezone=True), onupdate=func.now()) ts_done = Column(DateTime(timezone=True), onupdate=func.now())
stop_t = Column(Integer, nullable=True) stop_t = Column(Integer, nullable=True)
c1_wealth = Column(DECIMAL(20, 2), nullable=True) # country 1, developing countries
c2_wealth = Column(DECIMAL(20, 2), nullable=True) # country 2, developed countries
c1_wealth_dgt = Column(Integer, nullable=True)
c2_wealth_dgt = Column(Integer, nullable=True)
c1_tariff = Column(DECIMAL(20, 2), nullable=True) # country 1, developing countries
c2_tariff = Column(DECIMAL(20, 2), nullable=True) # country 2, developed countries
c1_tariff_dgt = Column(Integer, nullable=True)
c2_tariff_dgt = Column(Integer, nullable=True)
c1_n_firms = Column(Integer, nullable=True)
c2_n_firms = Column(Integer, nullable=True)
c1_n_positive_firms = Column(Integer, nullable=True)
c2_n_positive_firms = Column(Integer, nullable=True)
network = Column(Text(4294000000), nullable=True)
network_order = Column(Text(4294000000), nullable=True)
network_country = Column(Text(4294000000), nullable=True)
experiment = relationship('Experiment', back_populates='sample', uselist=False) experiment = relationship('Experiment', back_populates='sample', uselist=False)
product = relationship('Product', back_populates='sample', lazy='dynamic') result = relationship('Result', back_populates='sample', lazy='dynamic')
def __repr__(self): def __repr__(self):
return f'<Sample id: {self.id}>' return f'<Sample id: {self.id}>'
class Product(Base): class Result(Base):
__tablename__ = f"{db_name_prefix}_product" __tablename__ = f"{db_name_prefix}_result"
id = Column(Integer, primary_key=True, autoincrement=True) id = Column(Integer, primary_key=True, autoincrement=True)
s_id = Column(Integer, ForeignKey('{}.id'.format(f"{db_name_prefix}_sample")), nullable=False) s_id = Column(Integer, ForeignKey('{}.id'.format(f"{db_name_prefix}_sample")), nullable=False)
int_name = Column(Integer, nullable=False) id_firm = Column(Integer, nullable=False)
int_tier = Column(Integer, nullable=False) id_product = Column(Integer, nullable=False)
n_up_products = Column(Integer, nullable=False) ts = Column(Integer, nullable=False)
n_peer_products = Column(Integer, nullable=False) is_disrupted = Column(Boolean, nullable=True)
n_positive_firms = Column(Integer, nullable=False) is_removed = Column(Boolean, nullable=True)
n_all_firms = Column(Integer, nullable=False)
gini_acc_demand_per_age = Column(DECIMAL(10, 2), nullable=False)
gini_acc_wealth_per_age = Column(DECIMAL(10, 2), nullable=False)
gini_acc_demand_per_age_all = Column(DECIMAL(10, 2), nullable=False)
gini_acc_wealth_per_age_all = Column(DECIMAL(10, 2), nullable=False)
# lst_n_positive_firms = Column(Text(4294000000), nullable=False)
# lst_n_all_firms = Column(Text(4294000000), nullable=False)
# lst_gini_acc_demand_per_age = Column(Text(4294000000), nullable=False)
# lst_gini_acc_wealth_per_age = Column(Text(4294000000), nullable=False)
# lst_gini_acc_demand_per_age_all = Column(Text(4294000000), nullable=False)
# lst_gini_acc_wealth_per_age_all = Column(Text(4294000000), nullable=False)
sample = relationship('Sample', back_populates='product', uselist=False) sample = relationship('Sample', back_populates='result', uselist=False)
firm = relationship('Firm', back_populates='product', lazy='dynamic')
def __repr__(self): def __repr__(self):
return f'<Product id: {self.id}>' return f'<Product id: {self.id}>'
class Firm(Base):
__tablename__ = f"{db_name_prefix}_firm"
id = Column(Integer, primary_key=True, autoincrement=True)
p_id = Column(Integer, ForeignKey('{}.id'.format(f"{db_name_prefix}_product")), nullable=False)
idx_firm = Column(Integer, nullable=False)
int_n_supplier = Column(Integer, nullable=False)
flt_fix_cost = Column(DECIMAL(20, 2), nullable=False)
flt_q_star = Column(DECIMAL(20, 2), nullable=False)
acc_demand_per_age = Column(DECIMAL(20, 2), nullable=False)
acc_wealth_per_age = Column(DECIMAL(20, 2), nullable=False)
std_demand_per_age = Column(DECIMAL(20, 2), nullable=False)
product = relationship('Product', back_populates='firm', uselist=False)
def __repr__(self):
return f'<Firm id: {self.id}>'
if __name__ == '__main__': if __name__ == '__main__':
Base.metadata.drop_all() Base.metadata.drop_all()
Base.metadata.create_all() Base.metadata.create_all()