def testDataFrame(self):
ampl = self.ampl
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame('NUTR', ('n_min', 'n_max'))
df1.addRow(('A', 700, 20000))
df1.addRow(('B1', 700, 20000))
df1.addRow(('B2', 700, 20000))
df1.addRow(('C', 700, 20000))
df1.addRow(('CAL', 16000, 24000))
df1.addRow(('NA', 0.0, 50000))
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame('FOOD')
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
df2.setColumn('FOOD', foods)
contents = [2] * 8
df2.addColumn('f_min', contents)
contents = [10] * 8
df2.addColumn('f_max', contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn('cost', costs)
print(df2.getColumn('FOOD'))
for index in df2.getColumn('FOOD'):
print(df2.getRow(index))
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(('NUTR', 'FOOD'))
# Populate the set columns
nutrWithMultiplicity = [''] * 48
foodWithMultiplicity = [''] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn('NUTR', nutrWithMultiplicity)
df3.setColumn('FOOD', foodWithMultiplicity)
# Populate with all these values
values = [
60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15, 35, 15, 15, 25, 15, 15,
20, 10, 10, 15, 15, 15, 10, 20, 0, 10, 40, 35, 30, 50, 20, 295,
770, 440, 430, 315, 400, 370, 450, 968, 2180, 945, 278, 1182, 896,
1329, 1397
]
df3.addColumn('amt', values)
def testDataFrame(self):
ampl = self.ampl
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame("NUTR", ("n_min", "n_max"))
df1.addRow(("A", 700, 20000))
df1.addRow(("B1", 700, 20000))
df1.addRow(("B2", 700, 20000))
df1.addRow(("C", 700, 20000))
df1.addRow(("CAL", 16000, 24000))
df1.addRow(("NA", 0.0, 50000))
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame("FOOD")
foods = ["BEEF", "CHK", "FISH", "HAM", "MCH", "MTL", "SPG", "TUR"]
df2.setColumn("FOOD", foods)
self.assertEqual(list(df2.getColumn("FOOD")), foods)
contents = [2] * 8
df2.addColumn("f_min", contents)
self.assertEqual(list(df2.getColumn("f_min")), contents)
contents = [10] * 8
df2.addColumn("f_max", contents)
self.assertEqual(list(df2.getColumn("f_max")), contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn("cost", costs)
self.assertEqual(list(df2.getColumn("cost")), costs)
labels = [random.choice(string.ascii_letters)] * 8
df2.addColumn("labels", labels)
self.assertEqual(list(df2.getColumn("labels")), labels)
df2.addColumn("empty", [])
self.assertEqual(list(df2.getColumn("empty")), [None] * 8)
print(df2.getColumn("FOOD"))
for index in df2.getColumn("FOOD"):
print(df2.getRow(index))
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(("NUTR", "FOOD"))
# Populate the set columns
nutrWithMultiplicity = [""] * 48
foodWithMultiplicity = [""] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn("NUTR", nutrWithMultiplicity)
df3.setColumn("FOOD", foodWithMultiplicity)
# Populate with all these values
values = [
60,
8,
8,
40,
15,
70,
25,
60,
10,
20,
15,
35,
15,
15,
25,
15,
15,
20,
10,
10,
15,
15,
15,
10,
20,
0,
10,
40,
35,
30,
50,
20,
295,
770,
440,
430,
315,
400,
370,
450,
968,
2180,
945,
278,
1182,
896,
1329,
1397,
]
df3.addColumn("amt", values)
def clearing_algorithm(id, v_e, li, c, ref_entities):
"""Stress testing algorithm : Solve the clearing problem.
type : number of infeasible solutions
num of non maximal solutions
recovery rate vector
equity
vector of error in the update function
Parameters
----------
id : string
v_ea : 2d array ( post externall assets; vector of assets after shock)
li : 2d array (liability matrix, debt contract)
c : 3d array (cds contracts)
ref_entities : list (set of reference entities)
"""
id_file_nonmax = open('id_file_nonmax.txt', 'a')
id_file_infeasible = open('id_file_infeasible.txt', 'a')
nBanks = len(v_e[0])
fixed_vec = list()
alpha, beta = 0.6, 0.6
externalAssets, liability, CDS_contract, reference = v_e[
0], li, c, ref_entities
## call AMPL
ampl = AMPL()
# set default cost parameters
ampl.read('models/clearing_optimization.mod') #c2.mod
ampl.readData(
'data/clearing_optimization.dat'
) # change this to irrational.dat if you don't have default costs
#Set ampl options
ampl.setOption('display_precision', 0)
ampl.setOption('solution_precision', 0)
# we will use Knitro as the solver, other options: lgo, loqo, conopt, ...
ampl.setOption('solver', 'knitro')
ampl.setOption('presolve_eps', 1.0e-6)
ampl.setOption('outlev', 0)
ampl.setOption('show_stats', 0)
# set knitro options
ampl.setOption(
'knitro_options',
'par_msnumthreads =32 ms_maxsolves=1 outlev=0 ma_terminate = 2 feastol = 1.0e-9 infeastol= 1.0e-1 feastol_abs= 1.0e-9 ms_deterministic=0 ma_terminate = 1 ma_outsub =1 bar_refinement=1 bar_slackboundpush=1.0 delta=1.0e-1 gradopt=1 hessopt=1 honorbnds=0 linesearch=0 linsolver_ooc=0 ms_enable=1 tuner=0 soc=0 initpenalty=3.0e10 bar_penaltyrule=1'
) # derivcheck=3')
# another set of options to use, the above options has been tested and compared to other optio sets,
# it obtained the most accurate results
# one can use his/her options : see
# ampl.setOption('knitro_options', 'par_msnumthreads =16 ms_maxsolves=10 ma_terminate = 2 feastol = 1.0e-9 infeastol= 1.0e-1 feastol_abs= 1.0e-9 ms_deterministic=0 ma_terminate = 1 ma_outsub =1 bar_refinement=1 bar_slackboundpush=1.0 delta=1.0e-1 gradopt=1 hessopt=1 honorbnds=0 linesearch=0 linsolver_ooc=0 ms_enable=1 tuner=0 soc=0 initpenalty=3.0e10 bar_penaltyrule=1 derivcheck=3')# out_csvinfo=1 restarts=10 ms_maxsolves=0 soc=1 tuner=1 #bar_relaxcons=2 #bar_watchdog=1
solver_status_maximal = ""
### prepare ampl, and initialize it by setting the data
ampl_alpha = ampl.getParameter('alpha')
ampl_beta = ampl.getParameter('betta')
ampl_alpha.setValues(alpha)
ampl_beta.setValues(beta)
banks = [i for i in xrange(nBanks)] #vector of indices
ampl.getSet('Banks').setValues(banks)
ampl.getSet('reference').setValues(d)
ampl_liab = array_to_ampl_dataframe(nBanks, liability)
ampl.setData(ampl_liab)
ampl_external_assets = DataFrame('Banks', 'externalAssets')
ampl_external_assets.setColumn('Banks', banks)
ampl_external_assets.setColumn('externalAssets', externalAssets)
ampl.setData(ampl_external_assets)
ampl_CDSs = DataFrame(('i', 'j', 'k'), 'CDS_contract')
for i in xrange(nBanks):
for j in xrange(nBanks):
for k in d:
ampl_CDSs.addRow(i, j, k, c[i][j][k])
ampl.setData(ampl_CDSs)
maximal_out = []
infeasible_out = []
total_recovery_rates = []
total_equity = []
f_vec = []
"""
set the objective, named recov
if we have this objective funtion
then the problem become Clearing Feasibility Problem
as the value of recovery_rate_no_debts is constant
"""
ampl.eval('maximize recov : sum{i in Banks} recovery_rate_no_debts[i];')
'''
for each element of the post external asset we need to solve the clearing problem
remark: post_externall_assets are defined in the cl_btie_main.py or cl_uni_main.py;
they contain external assets after applying shocks
since we need to run the clearing algorithm for various array of external assets (see shock_gen.py)
and we don't want to have AMPL's loading and initializing overhead at every time.
we will just update the externall assets parameter at each round.'''
for m in range(len(v_e)):
# if external asset is zero then, obviously, we don't do the clearing:
# shock on a bank without externall assets does not change the vector of externall assets
if v_e[0][m] != 0:
equity = [1000000000000 for i in range(nBanks)]
# set value of previous equity to infinity as we want this constraint be redundant in solving
# the Clearing Optimization Problem and checking if the problem is infeasible
prev_eq = ampl.getParameter('preveq') #.getValues()
prev_eq.setValues(equity)
# drop the Clearing Optimization objective
ampl.obj['recov'].drop()
# restore new objective which is constant: to use if for the Clearing Feasibility Problem, and checking maximality
#Solve the clearing optimization problem,
# we solve the model given our data, but this time the objective function is maximizing 'total_equity'
# as defined in the clearing_optimization.mod .
ampl.obj['Tot_recov'].restore()
ea = ampl.getParameter('externalAssets')
ea.setValues(v_e[m])
# set ampl options again
## Clearing Optimization Problem, checking feasibility
ampl.setOption(
'knitro_options',
'par_msnumthreads =32 ms_maxsolves=10 outlev=0 ma_terminate = 2 feastol = 1.0e-9 infeastol= 1.0e-1 feastol_abs= 1.0e-9 ms_deterministic=0 ma_terminate = 1 ma_outsub =1 bar_refinement=1 bar_slackboundpush=1.0 delta=1.0e-1 gradopt=1 hessopt=1 honorbnds=0 linesearch=0 linsolver_ooc=0 ms_enable=1 tuner=0 soc=0 initpenalty=3.0e10 bar_penaltyrule=1'
) # derivcheck=3')
ampl.solve()
tot_payment_1 = ampl.obj['Tot_recov']
solver_status = tot_payment_1.result()
tot_payment_1 = tot_payment_1.drop()
ampl.obj['Tot_recov'].drop()
ampl.obj['recov'].restore()
recoveryRate = ampl.getVariable('result').getValues().toList()
equity = (ampl.getVariable('equity').getValues()).toList()
## update recovery results by rounding those near to 1, to 1
for x in xrange(len(recoveryRate)):
if recoveryRate[x][1] > 1 or recoveryRate[x][1] > 0.99999999:
recoveryRate[x] = 1
else:
recoveryRate[x] = (recoveryRate[x])[1]
for x in range(len(equity)):
equity[x] = equity[x][1]
'''
#retrieve alpha and beta
alpha = ampl.getParameter('alpha').getValues()
alpha = ((alpha.toList())[0][0])
beta = ampl.getParameter('betta').getValues()
beta = ((beta.toList())[0][0])
'''
CDSs = d
# s is the result of update function, i.e., the difference of approximate and actual value
s = abs(
uf.update_f(alpha, beta, nBanks, CDSs, v_e[m], b, c,
recoveryRate))
if solver_status == 'solved':
## CLearing Feasibility Problem (maximality check)
maximality = 'maximal'
prev_eq.setValues(equity)
ampl.setOption(
'knitro_options',
'par_msnumthreads =32 ms_maxsolves=1 outlev=0 ma_terminate = 2 feastol = 1.0e-9 infeastol= 1.0e-1 feastol_abs= 1.0e-9 ms_deterministic=0 ma_terminate = 1 ma_outsub =1 bar_refinement=1 bar_slackboundpush=1.0 delta=1.0e-1 gradopt=1 hessopt=1 honorbnds=0 linesearch=0 linsolver_ooc=0 ms_enable=1 tuner=0 soc=0 initpenalty=3.0e10 bar_penaltyrule=1'
) # derivcheck=3')
## solve the clearing feasibility problem, this time to check if the previous solution is maximal
# if it returns infeasible, then the solution of Clearing Optimization program is not maximal, other wise
# we have found a maximal solution
ampl.solve()
tot_payment_2 = ampl.getObjective('recov')
solver_status_maximal = tot_payment_2.result()
tot_payment_2 = tot_payment_2.drop()
if solver_status_maximal == 'solved':
maximality = 'non_maximal'
else:
maximality = 'maximal'
else:
maximality = 'none'
total_equity.append(sum(equity))
total_recovery_rates.append(
np.count_nonzero(np.array(recoveryRate) - 1))
if solver_status != 'infeasible':
f_vec.append(s)
if maximality == 'non_maximal':
maximal_out.append(m)
status = 'nonmax'
id_file_nonmax.write(id)
generate_polynomials(status, id, v_e[m], li, c, ref_entities,
alpha, beta)
if solver_status == 'infeasible':
infeasible_out.append(m)
status = 'infeasible'
id_file_infeasible.write(id)
generate_polynomials(status, id, v_e[m], li, c, ref_entities,
alpha, beta)
ampl.reset()
return f_vec, total_recovery_rates, total_equity, infeasible_out, maximal_out
def modelo():
# Variáveis globais
global otimo, m, n, s, capacidade_dep, capacidade_vei, SOC_max, ampl
global velocidad_carga, demanda, servico, inicio, ultimo, custo
global distancia, tempo, nodos, arcos
global titulo, dados_1, graf, indices, indices_2
# Chamar função cluster
cluster()
# Carregar parãmetros e variáveis
ampl = AMPL() # Carregar objeto AMPL
ampl.reset() # Reiniciar as variáveis e parâmetros
ampl.read('modelo_PRFVE.mod') # Carregar modelo
m = ampl.getParameter('m') # Número de nós
n = ampl.getParameter('n') # Número de veículos
s = ampl.getParameter('s') # Número de estações de carregamento
M = ampl.getParameter('M') # Minutos na estação de carregamento
alpha = ampl.getParameter('alpha') # Limite superior do estado de carga
beta = ampl.getParameter('beta') # Limite inferior do estado de carga
consumo = ampl.getParameter(
'consumo') # Consumo de energia por quilômetro percorrido
capacidade_dep = ampl.getParameter(
'capacidade_dep') # Capacidade do depósito
capacidade_vei = ampl.getParameter('capacidade_vei') # Capacidade veículos
SOC_max = ampl.getParameter('SOC_max') # SOC max bateria
velocidad_carga = ampl.getParameter(
'velocidad_carga') # Velocidade de carga
demanda = ampl.getParameter(
'demanda') # Demanda de mercadorias dos clientes
servico = ampl.getParameter('servico') # Tempo de serviço no cliente
inicio = ampl.getParameter(
'inicio') # Inicio da janela de tempo de atendimento
ultimo = ampl.getParameter(
'ultimo') # Fim da janela de tempo de atendimento
custo = ampl.getParameter('custo') # Custo de transporte do nó i ao nó j
distancia = ampl.getParameter('distancia') # Distancia do nó i ao nó j
tempo = ampl.getParameter('tempo') # Tempo de percorrer o nó i ao nó j
nodos = ampl.getSet('NODOS') # Conjunto de nós
arcos = ampl.getSet('ARCOS') # Conjunto de arcos
#ar = ampl.getSet('AR') # Conjunto de arcos
# Atribuir valores
m.setValues([int(e_0.get()) + int(e_3.get())]) # Valor do número de n
n.setValues([int(e_2.get())]) # Valor de veículos
s.setValues([int(e_3.get())]) # Valor de estações
M.setValues([1246]) # Valor de minutos de carga na estação
alpha.setValues([0.8]) # Valor do limite superior do estado de carga
beta.setValues([0.2]) # Valor do limite inferior do estado de carga
capacidade_dep.setValues([int(e_4.get())]) # Valor de capacidade
# Adicionar as estações de carregamento
for i in range(1, int(s.value()) + 1):
titulo.loc[i, 'X_coord'] = round(centroides[i - 1, 0], 1)
titulo.loc[i, 'Y_coord'] = round(centroides[i - 1, 1], 1)
# Valores das capacidades de mercadorias dos veículos
for i in range(1, capacidade_vei.numInstances() + 1):
capacidade_vei[i] = int(e_5.get())
# Valores do SOC máximo das baterias dos veículos
for i in range(1, SOC_max.numInstances() + 1):
SOC_max[i] = int(e_6.get())
# Valores da velocidade da carga das baterias dos veículos
for i in range(1, velocidad_carga.numInstances() + 1):
velocidad_carga[i] = int(e_7.get())
# Consumo de energia por quilômetro percorrido dos veículos
for i in range(1, consumo.numInstances() + 1):
consumo[i] = 1
# Valores de demanda de mercadorias dos clientes
for i in range(1, demanda.numInstances() + 1):
if i >= demanda.numInstances() + 1 - s.value():
demanda[i] = 0
else:
demanda[i] = int(titulo.iloc[i - 1, 3])
# Valores de tempo de serviço nos clientes
for i in range(1, servico.numInstances() + 1):
if i >= servico.numInstances() + 1 - s.value():
servico[i] = 0
else:
servico[i] = int(titulo.iloc[i - 1, 6])
# Valores do inicio da janela de tempo de atendimento
for i in range(1, inicio.numInstances() + 1):
if i >= inicio.numInstances() + 1 - s.value():
inicio[i] = 0
else:
inicio[i] = int(titulo.iloc[i - 1, 4])
# Valores do fim da janela de tempo de atendimento
for i in range(1, ultimo.numInstances() + 1):
if i >= ultimo.numInstances() + 1 - s.value():
ultimo[i] = int(titulo.iloc[0, 5])
else:
ultimo[i] = int(titulo.iloc[i - 1, 5])
# Criar indices para os conjuntos de dados
indices = DataFrame(index=('Index0', 'Index1'))
for i in range(1, int(m.value()) + 1):
for j in range(1, int(m.value()) + 1):
if i != j:
indices.addRow(i, j)
# Valores dos arcos
arcos.setValues(indices)
#ar.setValues(indices_2)
# Valores do custo, distância e tempo
for i in range(1, int(m.value()) + 1):
for j in range(1, int(m.value()) + 1):
if i != j:
x = float(titulo.iloc[i - 1, 1]) - float(titulo.iloc[j - 1, 1])
y = float(titulo.iloc[i - 1, 2]) - float(titulo.iloc[j - 1, 2])
dist = round(math.sqrt(pow(x, 2) + pow(y, 2)), 1)
distancia[i, j] = dist
custo[i, j] = dist * 3
tempo[i, j] = dist
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
# Create first dataframe (for data indexed over NUTR)
# Add data row by row
df1 = DataFrame('NUTR', ('n_min', 'n_max'))
df1.addRow('A', 700, 20000)
df1.addRow('B1', 700, 20000)
df1.addRow('B2', 700, 20000)
df1.addRow('C', 700, 20000)
df1.addRow('CAL', 16000, 24000)
df1.addRow('NA', 0.0, 50000)
# Create second dataframe (for data indexed over FOOD)
# Add column by column
df2 = DataFrame('FOOD')
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
df2.setColumn('FOOD', foods)
contents = [2] * 8
df2.addColumn('f_min', contents)
contents = [10] * 8
df2.addColumn('f_max', contents)
costs = [3.19, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
df2.addColumn('cost', costs)
# Create third dataframe, to assign data to the AMPL entity
# param amt{NUTR, FOOD};
df3 = DataFrame(index=('NUTR', 'FOOD'))
# Populate the set columns
nutrWithMultiplicity = [''] * 48
foodWithMultiplicity = [''] * 48
i = 0
for n in range(6):
for f in range(8):
print(df1.getRowByIndex(n)[0])
nutrWithMultiplicity[i] = df1.getRowByIndex(n)[0]
foodWithMultiplicity[i] = foods[f]
i += 1
df3.setColumn('NUTR', nutrWithMultiplicity)
df3.setColumn('FOOD', foodWithMultiplicity)
# Populate with all these values
values = [
60, 8, 8, 40, 15, 70, 25, 60, 10, 20, 15, 35, 15, 15, 25, 15, 15,
20, 10, 10, 15, 15, 15, 10, 20, 0, 10, 40, 35, 30, 50, 20, 295,
770, 440, 430, 315, 400, 370, 450, 968, 2180, 945, 278, 1182, 896,
1329, 1397
]
df3.addColumn('amt', values)
# Create AMPL object
ampl = AMPL()
if argc > 1:
ampl.setOption('solver', argv[1])
# Read the model file
modelDirectory = argv[2] if argc == 3 else os.path.join('..', 'models')
ampl.read(os.path.join(modelDirectory, 'diet/diet.mod'))
# Assign data to NUTR, n_min and n_max
ampl.setData(df1, 'NUTR')
# Assign data to FOOD, f_min, f_max and cost
ampl.setData(df2, 'FOOD')
# Assign data to amt
ampl.setData(df3)
# Solve the model
ampl.solve()
# Print out the result
print("Objective function value: {}".format(
ampl.getObjective('total_cost').value()))
# Get the values of the variable Buy in a dataframe
results = ampl.getVariable('Buy').getValues()
# Print
print(results)
except Exception as e:
print(e)
raise