def test_diet_amplpy(self):
dat = _diet_input_tdf.copy_to_ampl(
_diet_dat,
field_renamings={
("foods", "Cost"): "cost",
("categories", "Min Nutrition"): "n_min",
("categories", "Max Nutrition"): "n_max",
("nutrition_quantities", "Quantity"): "amt",
("nutrition_quantities", "Other Quantity"): "other_amt"
})
self.assertTrue({"n_min",
"n_max"}.issubset(dat.categories.toPandas().columns))
ampl = amplpy.AMPL()
ampl.setOption('solver', 'gurobi')
ampl.eval(_diet_mod)
_diet_input_tdf.set_ampl_data(dat, ampl, {
"categories": "CAT",
"foods": "FOOD"
})
ampl.solve()
sln = _diet_sln_tdf.copy_from_ampl_variables({
("buy_food", "Quantity"):
ampl.getVariable("Buy"),
("consume_nutrition", "Quantity"):
ampl.getVariable("Consume")
})
sln.parameters['Total Cost'] = ampl.getObjective('Total_Cost').value()
diet_dat_two = _diet_input_tdf.copy_tic_dat(_diet_dat)
for r in diet_dat_two.nutrition_quantities.values():
r["Quantity"], r["Other Quantity"] = [0.5 * r["Quantity"]] * 2
dat = _diet_input_tdf.copy_to_ampl(
diet_dat_two,
field_renamings={
("foods", "Cost"): "cost",
("categories", "Min Nutrition"): "n_min",
("categories", "Max Nutrition"): "n_max",
("nutrition_quantities", "Quantity"): "amt",
("nutrition_quantities", "Other Quantity"): "other_amt"
})
ampl = amplpy.AMPL()
ampl.setOption('solver', 'gurobi')
ampl.eval(_diet_mod)
_diet_input_tdf.set_ampl_data(dat, ampl, {
"categories": "CAT",
"foods": "FOOD"
})
ampl.solve()
self.assertTrue("solved" == ampl.getValue("solve_result"))
sln = _diet_sln_tdf.copy_from_ampl_variables({
("buy_food", "Quantity"):
ampl.getVariable("Buy"),
("consume_nutrition", "Quantity"):
ampl.getVariable("Consume")
})
sln.parameters['Total Cost'] = ampl.getObjective('Total_Cost').value()
self.assertTrue(_nearly_same_dat(_diet_sln_tdf, sln, _diet_sln_ticdat))
dat = _diet_input_tdf.copy_to_ampl(
_diet_dat, {
("foods", "Cost"): "cost",
("categories", "Min Nutrition"): "",
("categories", "Max Nutrition"): "n_max"
}, ["nutrition_quantities"])
self.assertFalse(hasattr(dat, "nutrition_quantities"))
self.assertTrue({"n_min", "n_max"}.intersection(
dat.categories.toPandas().columns) == {"n_max"})
sln_tdf_2 = TicDatFactory(buy_food=[["Food"], ["Quantity"]],
consume_nutrition=[["Category"], []])
sln_tdf_2.set_default_value("buy_food", "Quantity", 1)
sln_2 = sln_tdf_2.copy_from_ampl_variables({
("buy_food", False):
ampl.getVariable("Buy"),
("consume_nutrition", False):
(ampl.getVariable("Consume"), lambda x: x < 100)
})
self.assertTrue(
set(sln_2.buy_food) == set(sln.buy_food)
and all(v["Quantity"] == 1 for v in sln_2.buy_food.values()))
self.assertTrue(sln_2.consume_nutrition
and set(sln_2.consume_nutrition) == {
k
for k, v in sln.consume_nutrition.items()
if v["Quantity"] < 100
})
diet_dat_two = _diet_input_tdf.copy_tic_dat(_diet_dat)
diet_dat_two.categories["calories"] = [0, 200]
dat = _diet_input_tdf.copy_to_ampl(
diet_dat_two,
field_renamings={
("foods", "Cost"): "cost",
("categories", "Min Nutrition"): "n_min",
("categories", "Max Nutrition"): "n_max",
("nutrition_quantities", "Quantity"): "amt",
("nutrition_quantities", "Other Quantity"): "other_amt"
})
ampl = amplpy.AMPL()
ampl.setOption('solver', 'gurobi')
ampl.eval(_diet_mod)
_diet_input_tdf.set_ampl_data(dat, ampl, {
"categories": "CAT",
"foods": "FOOD"
})
ampl.solve()
self.assertTrue("infeasible" == ampl.getValue("solve_result"))
diet_dat_two = _diet_input_tdf.copy_tic_dat(_diet_dat)
for v in diet_dat_two.categories.values():
v["Max Nutrition"] = float("inf")
diet_dat_two.foods["hamburger"] = -1
dat = _diet_input_tdf.copy_to_ampl(
diet_dat_two,
field_renamings={
("foods", "Cost"): "cost",
("categories", "Min Nutrition"): "n_min",
("categories", "Max Nutrition"): "n_max",
("nutrition_quantities", "Quantity"): "amt",
("nutrition_quantities", "Other Quantity"): "other_amt"
})
ampl = amplpy.AMPL()
ampl.setOption('solver', 'gurobi')
ampl.eval(_diet_mod)
_diet_input_tdf.set_ampl_data(dat, ampl, {
"categories": "CAT",
"foods": "FOOD"
})
ampl.solve()
self.assertTrue("unbounded" == ampl.getValue("solve_result"))
def _metro_solve(dat,
sln_read_method="amplToDict",
excluded_tables=frozenset(
_metro_input_tdf.all_tables).difference({"load_amounts"
})):
assert sln_read_method in ["amplToDict", "ticdat"]
input_schema = _metro_input_tdf
ampl_format = utils.ampl_format
AMPL = amplpy.AMPL
default_parameters = {
"One Way Price": 2.25,
"Amount Leftover Constraint": "Upper Bound"
}
assert input_schema.good_tic_dat_object(dat)
full_parameters = dict(
default_parameters,
**{k: v["Value"]
for k, v in dat.parameters.items()})
sln = _metro_solution_tdf.TicDat() # create an empty solution
ampl_dat = input_schema.copy_to_ampl(dat, excluded_tables=excluded_tables)
# solve a distinct MIP for each pair of (# of one-way-trips, amount leftover)
for number_trips, amount_leftover in product(dat.number_of_one_way_trips,
dat.amount_leftover):
ampl = AMPL()
ampl.setOption('solver', 'gurobi')
# use the ampl_format function for AMPL friendly key-named text substitutions
ampl.eval(
ampl_format("""
set LOAD_AMTS;
var Num_Visits {LOAD_AMTS} integer >= 0;
var Amt_Leftover >= {{amount_leftover_lb}}, <= {{amount_leftover_ub}};
minimize Total_Visits:
sum {la in LOAD_AMTS} Num_Visits[la];
subj to Set_Amt_Leftover:
Amt_Leftover = sum {la in LOAD_AMTS} la * Num_Visits[la] - {{one_way_price}} * {{number_trips}};""",
number_trips=number_trips,
one_way_price=full_parameters["One Way Price"],
amount_leftover_lb=amount_leftover
if full_parameters["Amount Leftover Constraint"]
== "Equality" else 0,
amount_leftover_ub=amount_leftover))
input_schema.set_ampl_data(ampl_dat, ampl,
{"load_amounts": "LOAD_AMTS"})
ampl.solve()
if ampl.getValue("solve_result") != "infeasible":
# store the results if and only if the model is feasible
if sln_read_method == "ticdat":
temp_tdf = TicDatFactory(
num_visits=[["Load Amount"], ["Number Of Visits"]])
temp_sln = temp_tdf.copy_from_ampl_variables({
("num_visits", "Number Of Visits"):
(ampl.getVariable("Num_Visits"), lambda _: round(_) > 0)
})
for la, r in temp_sln.num_visits.items():
x = round(r["Number Of Visits"])
sln.load_amount_details[number_trips, amount_leftover,
la] = round(x)
sln.load_amount_summary[
number_trips, amount_leftover]["Number Of Visits"] += x
else:
for la, x in ampl.getVariable(
"Num_Visits").getValues().toDict().items():
if round(x) > 0:
sln.load_amount_details[number_trips, amount_leftover,
la] = round(x)
sln.load_amount_summary[number_trips, amount_leftover]["Number Of Visits"]\
+= round(x)
return sln
