import pandas as pd
from tests.helper import Helper
from pyaugmecon import PyAugmecon
from tests.optimization_models import three_kp_model
model_type = "3kp50"
options = {
"name": model_type,
"grid_points": 847,
"nadir_points": [1124, 1041],
}
py_augmecon = PyAugmecon(three_kp_model(model_type), options)
py_augmecon.solve()
xlsx = pd.ExcelFile(f"tests/input/{model_type}.xlsx", engine="openpyxl")
def test_payoff_table():
payoff = Helper.read_excel(xlsx, "payoff_table").to_numpy()
assert Helper.array_equal(py_augmecon.model.payoff, payoff, 2)
def test_pareto_sols():
pareto_sols = Helper.read_excel(xlsx, "pareto_sols").to_numpy()
assert Helper.array_equal(py_augmecon.unique_pareto_sols, pareto_sols, 2)
import numpy as np
from tests.helper import Helper
from pyaugmecon import PyAugmecon
from tests.optimization_models import three_objective_model
model_type = "three_objective_model"
options = {
"name": model_type,
"grid_points": 10,
}
py_augmecon = PyAugmecon(three_objective_model(), options)
py_augmecon.solve()
def test_payoff_table():
payoff = np.array([[3075000, 62460, 33000], [3855000, 45180, 37000],
[3225000, 55260, 23000]])
assert Helper.array_equal(py_augmecon.model.payoff, payoff, 2)
def test_pareto_sols():
pareto_sols = np.array([
[3075000, 62460, 33000],
[3085000, 61980, 32333.33],
[3108333.33, 60860, 30777.78],
[3115000, 60540, 30333.33],
[3131666.67, 59740, 29222.22],
[3155000, 58620, 27666.67],
[3178333.33, 57500, 26111.11],
model_type = "two_objective_model"
options = {
"name": model_type,
"grid_points": 10,
"solver_name": "glpk",
"solver_io": None,
}
solver_options = {
"MIPGap": None,
"NonConvex": None,
}
py_augmecon = PyAugmecon(two_objective_model(), options, solver_options)
py_augmecon.solve()
def test_payoff_table():
payoff = np.array([[20, 160], [8, 184]])
assert Helper.array_equal(py_augmecon.model.payoff, payoff, 2)
def test_pareto_sols():
pareto_sols = np.array([
[8, 184],
[9.33, 181.33],
[10.67, 178.67],
[12, 176],
[13.33, 173.33],
from pyaugmecon import PyAugmecon
from tests.optimization_models import three_kp_model
# Multiprocessing requires this If statement (on Windows)
if __name__ == "__main__":
model_type = "3kp40"
# AUGMECON related options
opts = {
"name": model_type,
"grid_points": 540,
"nadir_points": [1031, 1069],
}
# Options passed to Gurobi
solver_opts = {}
A = PyAugmecon(
three_kp_model(model_type), opts, solver_opts
) # instantiate PyAugmecon
A.solve() # solve PyAugmecon multi-objective optimization problem
print(A.model.payoff) # this prints the payoff table
print(A.unique_pareto_sols) # this prints the unique Pareto optimal solutions