Nothing
context("MILP: solution")
test_that("export single var to numeric", {
model <- MILPModel() %>%
add_variable(x, ub = 1) %>%
add_variable(y, ub = 1) %>%
add_constraint(x + y <= 1) %>%
set_objective(x + y)
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 2,
solution = setNames(c(1, 1), c("x", "y"))
)
result <- get_solution(solution, x)
expect_equivalent(result, 1)
})
test_that("export solutions to data.frame if var is indexed", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(1, 1, 1),
c("x[1]", "x[3]", "x[3]")
)
)
expect_error(get_solution(solution, x))
})
test_that("export solutions to data.frame with index", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(c(1, 1, 1), c("x[1]", "x[2]", "x[3]"))
)
result <- get_solution(solution, x[i])
expect_s3_class(result, "data.frame")
expect_equivalent(as.numeric(result$i), c(1, 2, 3))
})
test_that("export infeasible solutions to data.frame", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "infeasible",
model = model,
objective_value = 3,
solution = setNames(c(1, 1, 1), c("x[1]", "x[3]", "x[3]"))
)
result <- get_solution(solution, x[i])
expect_s3_class(result, "data.frame")
expect_equal(nrow(result), 3)
})
test_that("export solutions to single value if all indexes bound", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
add_variable(y[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"y[1]", "y[3]", "y[3]",
"x[1]", "x[3]", "x[3]"
)
)
)
result <- get_solution(solution, y[1])
expect_equivalent(result, 2)
})
test_that("export solutions to df in a model with more than one variable", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
add_variable(y[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"y[1]", "y[3]", "y[3]",
"x[1]", "x[3]", "x[3]"
)
)
)
result <- get_solution(solution, y[i])
expect_equivalent(result$value, c(2, 2, 2))
})
test_that("solution has a nice default output", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
add_variable(y[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"y[1]", "y[3]", "y[3]",
"x[1]", "x[3]", "x[3]"
)
)
)
expect_output(show(solution), "Status: optimal\n")
expect_output(show(solution), "Objective value: 3")
})
test_that("solution indexes should not be factors", {
model <- MILPModel() %>%
add_variable(x[i], i = 1:3, ub = 1) %>%
add_variable(y[i], i = 1:3, ub = 1) %>%
set_objective(sum_expr(x[i], i = 1:3))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"y[1]", "y[2]", "y[3]",
"x[1]", "x[2]", "x[3]"
)
)
)
expect_equal(class(get_solution(solution, y[i])$i), "integer")
})
test_that("objective_value gets the obj. value", {
model <- MILPModel() %>%
add_variable(x[i, j], i = 10:11, j = 10:12, ub = 1) %>%
set_objective(sum_expr(x[10, i], i = 10:12))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"x[10,10]", "x[10,11]", "x[10,12]",
"x[11,10]", "x[11,11]", "x[11,12]"
)
)
)
expect_equal(3, objective_value(solution))
})
test_that("solver_status gets the solver_status", {
model <- MILPModel() %>%
add_variable(x[i, j], i = 10:11, j = 10:12, ub = 1) %>%
set_objective(sum_expr(x[10, i], i = 10:12))
solution <- new_solution(
status = "optimal",
model = model,
objective_value = 3,
solution = setNames(
c(2, 2, 2, 1, 1, 1),
c(
"x[10,10]", "x[10,11]", "x[10,12]",
"x[11,10]", "x[11,11]", "x[11,12]"
)
)
)
expect_equal("optimal", solver_status(solution))
})
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