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)
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