def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
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'))
foods = ['BEEF', 'CHK', 'FISH', 'HAM', 'MCH', 'MTL', 'SPG', 'TUR']
costs = [3.59, 2.59, 2.29, 2.89, 1.89, 1.99, 1.99, 2.49]
fmin = [2, 2, 2, 2, 2, 2, 2, 2]
fmax = [10, 10, 10, 10, 10, 10, 10, 10]
df = DataFrame('FOOD')
df.setColumn('FOOD', foods)
df.addColumn('cost', costs)
df.addColumn('f_min', fmin)
df.addColumn('f_max', fmax)
ampl.setData(df, 'FOOD')
nutrients = ['A', 'C', 'B1', 'B2', 'NA', 'CAL']
nmin = [700, 700, 700, 700, 0, 16000]
nmax = [20000, 20000, 20000, 20000, 50000, 24000]
df = DataFrame('NUTR')
df.setColumn('NUTR', nutrients)
df.addColumn('n_min', nmin)
df.addColumn('n_max', nmax)
ampl.setData(df, 'NUTR')
amounts = [[60, 8, 8, 40, 15, 70, 25, 60],
[20, 0, 10, 40, 35, 30, 50, 20],
[10, 20, 15, 35, 15, 15, 25, 15],
[15, 20, 10, 10, 15, 15, 15, 10],
[928, 2180, 945, 278, 1182, 896, 1329, 1397],
[295, 770, 440, 430, 315, 400, 379, 450]]
df = DataFrame(('NUTR', 'FOOD'), 'amt')
df.setValues({(nutrient, food): amounts[i][j]
for i, nutrient in enumerate(nutrients)
for j, food in enumerate(foods)})
ampl.setData(df)
ampl.solve()
print('Objective: {}'.format(ampl.getObjective('total_cost').value()))
except Exception as e:
print(e)
raise
def testNoIndex(self):
df = DataFrame([], ["x", "y"])
x = [1, 2, 3]
y = [4, 5, 6]
df.setColumn("x", x)
df.setColumn("y", y)
with self.assertRaises(ValueError):
df.toDict()
pd_df = df.toPandas()
self.assertEqual(list(pd_df["x"]), x)
self.assertEqual(list(pd_df["y"]), y)
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 main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
# Create an AMPL instance
ampl = AMPL()
"""
# If the AMPL installation directory is not in the system search path:
from amplpy import Environment
ampl = AMPL(
Environment('full path to the AMPL installation directory'))
"""
ampl.eval('set CITIES; set LINKS within (CITIES cross CITIES);')
ampl.eval('param cost {LINKS} >= 0; param capacity {LINKS} >= 0;')
ampl.eval('data; set CITIES := PITT NE SE BOS EWR BWI ATL MCO;')
cost = [2.5, 3.5, 1.7, 0.7, 1.3, 1.3, 0.8, 0.2, 2.1]
capacity = [250, 250, 100, 100, 100, 100, 100, 100, 100]
LinksFrom = ['PITT', 'PITT', 'NE', 'NE', 'NE', 'SE', 'SE', 'SE', 'SE']
LinksTo = ['NE', 'SE', 'BOS', 'EWR', 'BWI', 'EWR', 'BWI', 'ATL', 'MCO']
df = DataFrame(('LINKSFrom', 'LINKSTo'), ('cost', 'capacity'))
df.setColumn('LINKSFrom', LinksFrom)
df.setColumn('LINKSTo', LinksTo)
df.setColumn('cost', cost)
df.setColumn('capacity', capacity)
print(df)
ampl.setData(df, 'LINKS')
except Exception as e:
print(e)
raise
def main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
ampl = AMPL()
ampl.eval('set CITIES; set LINKS within (CITIES cross CITIES);')
ampl.eval('param cost {LINKS} >= 0; param capacity {LINKS} >= 0;')
ampl.eval('data; set CITIES := PITT NE SE BOS EWR BWI ATL MCO;')
cost = [2.5, 3.5, 1.7, 0.7, 1.3, 1.3, 0.8, 0.2, 2.1]
capacity = [250, 250, 100, 100, 100, 100, 100, 100, 100]
LinksFrom = ['PITT', 'PITT', 'NE', 'NE', 'NE', 'SE', 'SE', 'SE', 'SE']
LinksTo = ['NE', 'SE', 'BOS', 'EWR', 'BWI', 'EWR', 'BWI', 'ATL', 'MCO']
df = DataFrame(('LINKSFrom', 'LINKSTo'), ('cost', 'capacity'))
df.setColumn('LINKSFrom', LinksFrom)
df.setColumn('LINKSTo', LinksTo)
df.setColumn('cost', cost)
df.setColumn('capacity', capacity)
print(df)
ampl.setData(df, 'LINKS')
except Exception as e:
print(e)
raise
def compute_defense(att_stg,
prod_dist,
num_of_hp=args.fix_honeypots,
rationality=args.fix_rationality):
# production ports and attacker"s strategy
df = DataFrame('P')
ports = getRelPorts(att_stg, prod_dist, num=25)
df.setColumn('P', list(ports))
#ports = getAllPorts(att_stg, prod_dist)
#print(('Considered ports are: ', ports))
att = [att_stg.get(x, 0) for x in ports]
prod = [prod_dist.get(x, 0) for x in ports]
#print(('Attack ports: ', att, len(att)))
#print(('Dist ports: ', prod, len(prod)))
df.addColumn('s', prod)
df.addColumn('p', att)
ampl = AMPL(Environment(args.ampl))
ampl.setOption('solver', args.solver)
# ampl.setOption('verbosity', 'terse')
# Read the model file
ampl.read(args.model)
# Assign data to s
ampl.setData(df, 'P')
ampl.eval('let L := {}; let rat := {};'.format(num_of_hp, rationality))
#print(df)
# Solve the model
with suppress_stdout():
ampl.solve()
reward = ampl.getObjective("reward").value()
hp_stg = ampl.getData("{j in P} h[j]")
output = dict()
stg_json = list()
for k, v in hp_stg.toDict().items():
stg_json.append({"port": int(k), "prob": v})
output.update({"stg": stg_json})
output.update({"reward": reward})
output.update({"rationality": rationality})
output.update({"num_of_hp": num_of_hp})
output.update({"used_hps": ampl.getData("tot").toDict().popitem()[1]})
ampl.close()
return output
def assign_set_data(name,data):
"""Method to assign set data taking set name and a list as arguments"""
df = DataFrame(name)
df.setColumn(name,data)
ampl.setData(df,name)
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 main(argc, argv):
from amplpy import AMPL, DataFrame
os.chdir(os.path.dirname(__file__) or os.curdir)
try:
ampl = AMPL()
ampl.setOption('solver', 'cplexamp')
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, 'Dr/pigskin_updated1.mod'))
period0 = [0]
df = DataFrame('ONLY0')
df.setColumn('ONLY0', period0)
ampl.setData(df, 'ONLY0')
period0toends = [0, 1]
df = DataFrame('PERIOD0_TO_END')
df.setColumn('PERIOD0_TO_END', period0toends)
ampl.setData(df, 'PERIOD0_TO_END')
period1toends = [1]
df = DataFrame('PERIOD1_TO_END')
df.setColumn('PERIOD1_TO_END', period1toends)
ampl.setData(df, 'PERIOD1_TO_END')
products = ['1P', '2P']
df = DataFrame('PRODUCT')
df.setColumn('PRODUCT', products)
ampl.setData(df, 'PRODUCT')
resources = ['1R', '2R']
df = DataFrame('RESOURCE')
df.setColumn('RESOURCE', resources)
ampl.setData(df, 'RESOURCE')
scenarios = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30
]
df = DataFrame('SCENARIO')
df.setColumn('SCENARIO', scenarios)
ampl.setData(df, 'SCENARIO')
inv0prod = [[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0], [0, 0],
[0, 0], [0, 0]]
df = DataFrame(('SCENARIO', 'PRODUCT'), 'Inv0prod')
df.setValues({(scenario, product): inv0prod[s][i]
for s, scenario in enumerate(scenarios)
for i, product in enumerate(products)})
ampl.setData(df)
prodcost = [[12.5], [12.55]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'Prodcost')
df.setValues({(product, period1toend): prodcost[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Resource = [[16, 100], [24, 200]]
df = DataFrame(('PRODUCT', 'RESOURCE'), 'Resource')
df.setValues({(product, resource): Resource[i][r]
for i, product in enumerate(products)
for r, resource in enumerate(resources)})
ampl.setData(df)
avail = [[138516], [278847]]
df = DataFrame(('RESOURCE', 'PERIOD1_TO_END'), 'avail')
df.setValues({(resource, period1toend): avail[r][t]
for r, resource in enumerate(resources)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
perhold = [[0.88], [0.88]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'perhold')
df.setValues({(product, period1toend): perhold[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
prob = [
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333,
0.03333333, 0.03333333, 0.03333333, 0.03333333, 0.03333333
]
df = DataFrame(('SCENARIO'), 'prob')
df.setValues({(scenario): prob[s]
for s, scenario in enumerate(scenarios)})
ampl.setData(df)
MPC = [800]
df = DataFrame(('PERIOD1_TO_END'), 'MPC')
df.setValues({(period1toend): MPC[t]
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
MS = [100]
df = DataFrame(('PERIOD1_TO_END'), 'MS')
df.setValues({(period1toend): MS[t]
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Purchase_cost = [[10], [10]]
df = DataFrame(('PRODUCT', 'PERIOD1_TO_END'), 'Purchase_cost')
df.setValues({(product, period1toend): Purchase_cost[i][t]
for i, product in enumerate(products)
for t, period1toend in enumerate(period1toends)})
ampl.setData(df)
Demand = [[[216], [191]], [[290], [330]], [[224], [194]], [[283],
[329]],
[[159], [164]], [[215], [349]], [[247], [201]], [[201],
[328]],
[[343], [230]], [[326], [278]], [[319], [342]], [[223],
[298]],
[[292], [191]], [[262], [284]], [[285], [200]], [[296],
[346]],
[[210], [318]], [[200], [257]], [[314], [336]], [[205],
[290]],
[[174], [184]], [[258], [290]], [[284], [158]], [[264],
[312]],
[[228], [333]], [[239], [255]], [[190], [322]], [[344],
[219]],
[[280], [241]], [[186], [254]]]
df = DataFrame(('SCENARIO', 'PERIOD1_TO_END', 'PRODUCT'), 'Demand')
df.setValues({(scenario, period1toend, product): Demand[s][t][i - 1]
for s, scenario in enumerate(scenarios)
for t, period1toend in enumerate(period1toends)
for i, product in enumerate(products)})
ampl.setData(df)
ampl.solve()
print('Objective: {}'.format(ampl.getObjective('Total_cost').value()))
produce = ampl.getVariable('Produce')
df = produce.getValues()
# Print them
print(df)
# Get the values of the variable in a dataframe object
inventory = ampl.getVariable('Inventory')
df = inventory.getValues()
# Print them
print(df)
except Exception as e:
print(e)
raise
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 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