Nothing
test_that("compile (compressed formulation, single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# calculate budget
b <- floor(
terra::global(sim_pu_raster, "sum", na.rm = TRUE)[[1]] * 0.25
)
# calculate targets data
targ <- floor(
terra::global(sim_features, "sum", na.rm = TRUE)[[1]] * 0.25
)
targ[2] <- 0
# create problem
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_largest_shortfall_objective(budget = b) %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
o <- compile(p)
# calculations for tests
n_pu <- length(sim_pu_raster[[1]][!is.na(sim_pu_raster)])
n_f <- terra::nlyr(sim_features)
# tests
expect_equal(o$modelsense(), "min")
expect_equal(o$obj(), c(rep(0, n_pu), rep(0, n_f), 1))
expect_equal(o$sense(), c(rep(">=", n_f), rep(">=", n_f), "<="))
expect_equal(o$rhs(), c(targ, rep(0, n_f), b))
expect_equal(
o$col_ids(),
c(rep("pu", n_pu), rep("spp_met", n_f), "max_shortfall")
)
expect_equal(
o$row_ids(),
c(rep("spp_target", n_f), rep("max_shortfall", n_f), "budget")
)
expect_true(
all(o$A()[seq_len(n_f), seq_len(n_pu)] == p$data$rij_matrix[[1]])
)
expect_equal(
o$A()[n_f + n_f + 1, ],
c(p$planning_unit_costs(), rep(0, n_f), 0)
)
expect_true(
all(
o$A()[seq_len(n_f), n_pu + seq_len(n_f)] ==
triplet_sparse_matrix(i = seq_len(n_f), j = seq_len(n_f), x = 1)
)
)
expect_true(
all(
o$A()[n_f + seq_len(n_f), n_pu + seq_len(n_f + 1)] ==
cbind(
triplet_sparse_matrix(
i = seq_len(n_f), j = seq_len(n_f), x = replace(-1 / targ, 2, 0)
),
1
)
)
)
expect_true(all(o$lb() == 0))
expect_equal(o$ub(), c(rep(1, n_pu), rep(Inf, n_f), Inf))
})
test_that("solution (compressed formulation, single zone)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
budget <- 4.23
cost <- terra::rast(matrix(c(1, 2, 2, NA), ncol = 4))
locked_in <- 2
locked_out <- 1
features <- c(
terra::rast(matrix(c(2, 1, 1, 0), ncol = 4)),
terra::rast(matrix(c(10, 10, 10, 10), ncol = 4))
)
names(features) <- make.unique(names(features))
# create problem
p <-
problem(cost, features) %>%
add_min_largest_shortfall_objective(budget = budget) %>%
add_locked_in_constraints(locked_in) %>%
add_locked_out_constraints(locked_out) %>%
add_absolute_targets(c(2, 21)) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s1 <- solve(p)
s2 <- solve(p)
# tests
expect_equal(c(terra::values(s1)), c(0, 1, 1, NA))
expect_equal(terra::values(s1), terra::values(s2))
})
test_that("compile (expanded formulation, single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# calculate budget
b <- floor(terra::global(sim_pu_raster, "sum", na.rm = TRUE)[[1]] * 0.2)
# calculate targets data
targ <- floor(terra::global(sim_features, "sum", na.rm = TRUE)[[1]] * 0.25)
targ[2] <- 0
# create problem
p <-
problem(sim_pu_raster, sim_features) %>%
add_min_largest_shortfall_objective(budget = b) %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
o <- compile(p, compressed_formulation = FALSE)
# calculations for tests
n_pu <- length(sim_pu_raster[[1]][!is.na(sim_pu_raster)])
n_f <- terra::nlyr(sim_features)
rij <- rij_matrix(sim_pu_raster, sim_features)
# tests
expect_equal(o$modelsense(), "min")
expect_equal(
o$obj(),
c(rep(0, n_pu), rep(0, n_pu * n_f), rep(0, n_f), 1)
)
expect_equal(
o$sense(),
c(rep("<=", n_pu * n_f), rep(">=", n_f), rep(">=", n_f), "<=")
)
expect_equal(
o$rhs(),
c(rep(0, n_pu * n_f), targ, rep(0, n_f), b)
)
expect_equal(
o$col_ids(),
c(
rep("pu", n_pu), rep("pu_ijz", n_pu * n_f), rep("spp_met", n_f),
"max_shortfall"
)
)
expect_equal(
o$row_ids(),
c(
rep("pu_ijz", n_pu * n_f), rep("spp_target", n_f),
rep("max_shortfall", n_f), "budget"
)
)
expect_equal(o$lb(), rep(0, n_pu + (n_pu * n_f) + n_f + 1))
expect_equal(o$ub(), c(rep(1, n_pu + (n_pu * n_f)), rep(Inf, n_f), Inf))
# test that model matrix has been constructed correctly
row <- 0
for (i in seq_len(n_f)) {
for (j in seq_len(n_pu)) {
row <- row + 1
curr_row <- rep(0, n_pu + (n_pu * n_f) + n_f + 1)
curr_row[j] <- -1
curr_row[n_pu + ( (i - 1) * n_pu) + j] <- 1
expect_equal(o$A()[row, ], curr_row)
}
}
for (i in seq_len(n_f)) {
curr_row <- rep(0, n_pu + (n_pu * n_f) + n_f + 1)
curr_row[(i * n_pu) + seq_len(n_pu)] <- rij[i, ]
curr_row[n_pu + (n_f * n_pu) + i] <- 1
expect_equal(o$A()[(n_f * n_pu) + i, ], curr_row)
}
for (i in seq_len(n_f)) {
curr_row <- rep(0, n_pu + (n_pu * n_f) + n_f + 1)
curr_row[n_pu + (n_pu * n_f) + i] <-
ifelse(targ[i] < 1e-5, 0, -1 / targ[i])
curr_row[n_pu + (n_pu * n_f) + n_f + 1] <- 1
expect_equal(o$A()[(n_f * n_pu) + n_f + i, ], curr_row)
}
expect_equal(
o$A()[(n_pu * n_f) + n_f + n_f + 1, ],
c(p$planning_unit_costs(), rep(0, n_f * n_pu), rep(0, n_f), 0)
)
})
test_that("solution (expanded formulation, single zone)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
budget <- 4.23
cost <- terra::rast(matrix(c(1, 2, 2, NA), ncol = 4))
locked_in <- 2
locked_out <- 1
features <- c(
terra::rast(matrix(c(2, 1, 1, 0), ncol = 4)),
terra::rast(matrix(c(10, 10, 10, 10), ncol = 4))
)
names(features) <- make.unique(names(features))
# create problem
p <-
problem(cost, features) %>%
add_min_largest_shortfall_objective(budget = budget) %>%
add_locked_in_constraints(locked_in) %>%
add_locked_out_constraints(locked_out) %>%
add_absolute_targets(c(2, 21)) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p, compressed_formulation = FALSE)
# tests
expect_equal(c(terra::values(s)), c(0, 1, 1, NA))
})
test_that("invalid inputs (single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# tests
expect_tidy_error(
problem(sim_pu_raster, sim_features) %>%
add_min_largest_shortfall_objective(budget = -5)
)
expect_tidy_error(
problem(sim_pu_raster, sim_features) %>%
add_min_largest_shortfall_objective(budget = NA)
)
expect_tidy_error(
problem(sim_pu_raster, sim_features) %>%
add_min_largest_shortfall_objective(budget = Inf)
)
expect_tidy_error(
problem(sim_pu_raster, sim_pu_raster) %>%
add_min_largest_shortfall_objective(budget = 5) %>%
compile()
)
})
test_that("compile (compressed formulation, multiple zones, scalar budget)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# calculate budget
b <- min(floor(
terra::global(sim_zones_pu_raster, "sum", na.rm = TRUE)[[1]] * 0.25
))
# calculate targets data
targs <- tibble::tibble(
feature = feature_names(sim_zones_features)[1:3],
zone = list("zone_1", "zone_2", c("zone_1", "zone_3")),
sense = c(">=", "<=", ">="),
target = c(5, 300, 10),
type = c("absolute", "absolute", "absolute")
)
# create problem
p <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = b) %>%
add_manual_targets(targs) %>%
add_binary_decisions()
o <- compile(p)
# calculations for tests
n_pu <- p$number_of_planning_units()
n_f <- p$number_of_features()
n_z <- p$number_of_zones()
n_t <- nrow(targs)
# tests
expect_equal(o$modelsense(), "min")
expect_equal(
o$obj(),
c(rep(0, length(p$planning_unit_costs())), rep(0, n_t), 1)
)
expect_equal(
o$sense(),
c(targs$sense, rep(">=", n_t), rep("<=", length(b)), rep("<=", n_pu))
)
expect_equal(
o$rhs(),
c(targs$target, rep(0, n_t), b, rep(1, n_pu))
)
expect_equal(
o$col_ids(),
c(rep("pu", n_pu * n_z), rep("spp_met", n_t), "max_shortfall")
)
expect_equal(
o$row_ids(),
c(
rep("spp_target", n_t), rep("max_shortfall", n_t),
rep("budget", length(b)), rep("pu_zone", n_pu)
)
)
expect_equal(o$lb(), rep(0, (n_pu * n_z) + n_t + 1))
expect_equal(o$ub(), c(rep(1, (n_pu * n_z)), rep(Inf, n_t), Inf))
# test that problem matrix is correct
m <- matrix(
0, nrow = n_t + n_t + length(b) + n_pu,
ncol = (n_pu * n_z) + n_t + 1
)
counter <- 0
for (i in seq_len(n_t)) {
zs <- match(targs$zone[[i]], zone_names(sim_zones_features))
f <- match(targs$feature[i], feature_names(sim_zones_features))
counter <- counter + 1
for (z in zs)
m[counter, ((z - 1) * n_pu) + seq_len(n_pu)] <-
p$data$rij_matrix[[z]][f, ]
m[counter, (n_z * n_pu) + i] <- 1
}
for (i in seq_len(n_t)) {
counter <- counter + 1
m[counter, (n_pu * n_z) + i] <- -1 / targs$target[i]
m[counter, (n_pu * n_z) + n_t + 1] <- 1
}
counter <- counter + 1
m[counter, seq_len(n_pu * n_z)] <- c(p$planning_unit_costs())
for (i in seq_len(n_pu)) {
m[i + counter, i] <- 1
m[i + counter, n_pu + i] <- 1
m[i + counter, (2 * n_pu) + i] <- 1
}
m <- as(m, "Matrix")
expect_true(all(o$A() == m))
})
test_that("solve (compressed formulation, multiple zones, scalar budget)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# make and solve problem
budget <- 7
locked_out <- matrix(FALSE, ncol = 2, nrow = 5)
locked_out[1, 1] <- TRUE
targs <- tibble::tibble(
feature = c("f1", "f2", "f2"),
zone = list("zone_1", "zone_2", c("zone_1", "zone_2")),
sense = c(">=", ">=", "<="),
target = c(5, 1, 300),
type = c("absolute", "absolute", "absolute"))
cost <- c(
terra::rast(matrix(c(1, 2, 4, NA, NA), nrow = 1)),
terra::rast(matrix(c(0.5, 1, 1, 1, NA), nrow = 1))
)
features <- c(
terra::rast(matrix(c(5, 2, 3, 0, 4), nrow = 1)),
terra::rast(matrix(c(5, 5, 5, 5, 5), nrow = 1)),
terra::rast(matrix(c(5, 0, 1, 10, 4), nrow = 1)),
terra::rast(matrix(c(500, 5, 5, 5, 5), nrow = 1))
)
# create problem
p <-
problem(
cost,
zones(
features[[1:2]], features[[3:4]],
zone_names = c("zone_1", "zone_2"), feature_names = c("f1", "f2")
)
) %>%
add_min_largest_shortfall_objective(budget = budget) %>%
add_manual_targets(targs) %>%
add_locked_out_constraints(locked_out) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p)
# tests
expect_equal(c(terra::values(s[[1]])), c(0, 1, 1, NA, NA))
expect_equal(c(terra::values(s[[2]])), c(0, 0, 0, 1, NA))
})
test_that("compile (compressed formulation, multiple zones, vector budget)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# calculate budget
b <- floor(
terra::global(sim_zones_pu_raster, "sum", na.rm = TRUE)[[1]] * 0.25
)
# calculate targets data
targs <- tibble::tibble(
feature = feature_names(sim_zones_features)[1:3],
zone = list("zone_1", "zone_2", c("zone_1", "zone_3")),
sense = c(">=", "<=", ">="),
target = c(5, 300, 10),
type = c("absolute", "absolute", "absolute")
)
# create problem
p <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = b) %>%
add_manual_targets(targs) %>%
add_binary_decisions()
o <- compile(p)
# calculations for tests
n_pu <- p$number_of_planning_units()
n_f <- p$number_of_features()
n_z <- p$number_of_zones()
n_t <- nrow(targs)
# tests
expect_equal(o$modelsense(), "min")
expect_equal(
o$obj(),
c(rep(0, length(p$planning_unit_costs())), rep(0, n_t), 1)
)
expect_equal(
o$sense(),
c(targs$sense, rep(">=", n_t), rep("<=", length(b)), rep("<=", n_pu))
)
expect_equal(
o$rhs(),
c(targs$target, rep(0, n_t), b, rep(1, n_pu))
)
expect_equal(
o$col_ids(),
c(rep("pu", n_pu * n_z), rep("spp_met", n_t), "max_shortfall")
)
expect_equal(
o$row_ids(),
c(
rep("spp_target", n_t),
rep("max_shortfall", n_t),
rep("budget", length(b)),
rep("pu_zone", n_pu)
)
)
expect_equal(o$lb(), rep(0, (n_pu * n_z) + n_t + 1))
expect_equal(o$ub(), c(rep(1, (n_pu * n_z)), rep(Inf, n_t), Inf))
# test that problem matrix is correct
m <- matrix(
0, nrow = n_t + n_t + length(b) + n_pu,
ncol = (n_pu * n_z) + n_t + 1
)
counter <- 0
for (i in seq_len(n_t)) {
zs <- match(targs$zone[[i]], zone_names(sim_zones_features))
f <- match(targs$feature[i], feature_names(sim_zones_features))
counter <- counter + 1
for (z in zs)
m[counter, ((z - 1) * n_pu) + seq_len(n_pu)] <-
p$data$rij_matrix[[z]][f, ]
m[counter, (n_z * n_pu) + i] <- 1
}
for (i in seq_len(n_t)) {
counter <- counter + 1
m[counter, (n_pu * n_z) + i] <- -1 / targs$target[i]
m[counter, (n_pu * n_z) + n_t + 1] <- 1
}
counter <- counter + 1
m[counter, seq_len(n_pu)] <- p$planning_unit_costs()[, 1]
counter <- counter + 1
m[counter, n_pu + seq_len(n_pu)] <- p$planning_unit_costs()[, 2]
counter <- counter + 1
m[counter, (2 * n_pu) + seq_len(n_pu)] <- p$planning_unit_costs()[, 3]
for (i in seq_len(n_pu)) {
m[i + counter, i] <- 1
m[i + counter, n_pu + i] <- 1
m[i + counter, (2 * n_pu) + i] <- 1
}
m <- as(m, "Matrix")
expect_true(all(o$A() == m))
})
test_that("solve (compressed formulation, multiple zones, vector budget)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
budget <- c(6, 1)
locked_out <- matrix(FALSE, ncol = 2, nrow = 5)
locked_out[1, 1] <- TRUE
targs <- tibble::tibble(
feature = c("f1", "f2", "f2"),
zone = list("zone_1", "zone_2", c("zone_1", "zone_2")),
sense = c(">=", ">=", "<="),
target = c(5, 1, 300),
type = c("absolute", "absolute", "absolute")
)
cost <- c(
terra::rast(matrix(c(1, 2, 4, NA, NA), nrow = 1)),
terra::rast(matrix(c(0.5, 1, 1, 1, NA), nrow = 1))
)
features <- c(
terra::rast(matrix(c(5, 2, 3, 0, 4), nrow = 1)),
terra::rast(matrix(c(5, 5, 5, 5, 5), nrow = 1)),
terra::rast(matrix(c(5, 0, 1, 10, 4), nrow = 1)),
terra::rast(matrix(c(500, 5, 5, 5, 5), nrow = 1))
)
# create problem
p <-
problem(
cost,
zones(
features[[1:2]], features[[3:4]],
zone_names = c("zone_1", "zone_2"), feature_names = c("f1", "f2")
)
) %>%
add_min_largest_shortfall_objective(budget = budget) %>%
add_manual_targets(targs) %>%
add_locked_out_constraints(locked_out) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p)
# tests
expect_equal(c(terra::values(s[[1]])), c(0, 1, 1, NA, NA))
expect_equal(c(terra::values(s[[2]])), c(0, 0, 0, 1, NA))
})
test_that("compile (expanded formulation, multiple zones, vector budget)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# calculate budget
b <- floor(
terra::global(sim_zones_pu_raster, "sum", na.rm = TRUE)[[1]] * 0.25
)
# calculate targets data
targs <- tibble::tibble(
feature = feature_names(sim_zones_features)[1:3],
zone = list("zone_1", "zone_2", c("zone_1", "zone_3")),
sense = c(">=", "<=", ">="),
target = c(5, 300, 10),
type = c("absolute", "absolute", "absolute")
)
# create problem
p <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = b) %>%
add_manual_targets(targs) %>%
add_binary_decisions()
o <- compile(p, compressed_formulation = FALSE)
# calculations for tests
n_pu <- p$number_of_planning_units()
n_f <- p$number_of_features()
n_z <- p$number_of_zones()
n_t <- nrow(targs)
# tests
expect_equal(o$modelsense(), "min")
expect_equal(
o$obj(),
c(rep(0, n_pu * n_z), rep(0, n_pu * n_f * n_z), rep(0, n_t), 1)
)
expect_equal(
o$sense(),
c(
rep("<=", n_f * n_z * n_pu), targs$sense, rep(">=", n_t),
rep("<=", length(b)), rep("<=", n_pu)
)
)
expect_equal(
o$rhs(),
c(rep(0, n_pu * n_z * n_f), targs$target, rep(0, n_t), b, rep(1, n_pu))
)
expect_equal(
o$col_ids(),
c(
rep("pu", n_pu * n_z), rep("pu_ijz", n_pu * n_z * n_f),
rep("spp_met", n_t), "max_shortfall"
)
)
expect_equal(
o$row_ids(),
c(rep("pu_ijz", n_pu * n_z * n_f), rep("spp_target", n_t),
rep("max_shortfall", n_t), rep("budget", length(b)),
rep("pu_zone", n_pu)
)
)
expect_equal(
o$lb(),
rep(0, (n_pu * n_z) + (n_pu * n_z * n_f) + n_t + 1)
)
expect_equal(
o$ub(),
c(rep(1, (n_pu * n_z) + (n_pu * n_z * n_f)), rep(Inf, n_t + 1))
)
# test that problem matrix is correct
m <- matrix(
0, nrow = (n_pu * n_z * n_f) + n_t + n_t + length(b) + n_pu,
ncol = (n_pu * n_z) + (n_pu * n_z * n_f) + n_t + 1
)
counter <- 0
for (z in seq_len(n_z)) {
for (i in seq_len(n_f)) {
for (j in seq_len(n_pu)) {
counter <- counter + 1
m[counter, ((z - 1) * n_pu) + j] <- -1
idx <- (n_pu * n_z) + ((z - 1) * n_pu * n_f) + ((i - 1) * n_pu) + j
m[counter, idx] <- 1
}
}
}
for (i in seq_len(nrow(targs))) {
zs <- match(targs$zone[[i]], zone_names(sim_zones_features))
f <- match(targs$feature[i], feature_names(sim_zones_features))
counter <- counter + 1
for (z in zs) {
for (pu in seq_len(n_pu)) {
col <- (n_pu * n_z) + ((z - 1) * n_f * n_pu) + ((f - 1) * n_pu) + pu
m[counter, col] <- p$data$rij_matrix[[z]][f, pu]
}
col <- (n_pu * n_z) + (n_pu * n_f * n_z) + i
m[counter, col] <- 1
}
}
for (i in seq_len(n_t)) {
counter <- counter + 1
m[counter, (n_pu * n_z) + (n_pu * n_f * n_z) + i] <- -1 / targs$target[i]
m[counter, (n_pu * n_z) + (n_pu * n_f * n_z) + n_t + 1] <- 1
}
counter <- counter + 1
m[counter, seq_len(n_pu)] <- p$planning_unit_costs()[, 1]
counter <- counter + 1
m[counter, n_pu + seq_len(n_pu)] <- p$planning_unit_costs()[, 2]
counter <- counter + 1
m[counter, (2 *n_pu) + seq_len(n_pu)] <- p$planning_unit_costs()[, 3]
for (i in seq_len(n_pu)) {
m[i + counter, i] <- 1
m[i + counter, n_pu + i] <- 1
m[i + counter, (2 * n_pu) + i] <- 1
}
m <- as(m, "Matrix")
expect_true(all(o$A() == m))
})
test_that("solve (expanded formulation, multiple zones, vector budget)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
budget <- c(6, 1)
locked_out <- matrix(FALSE, ncol = 2, nrow = 5)
locked_out[1, 1] <- TRUE
targs <- tibble::tibble(
feature = c("f1", "f2", "f2"),
zone = list("zone_1", "zone_2", c("zone_1", "zone_2")),
sense = c(">=", ">=", "<="),
target = c(5, 1, 300),
type = c("absolute", "absolute", "absolute")
)
cost <- c(
terra::rast(matrix(c(1, 2, 4, NA, NA), nrow = 1)),
terra::rast(matrix(c(0.5, 1, 1, 1, NA), nrow = 1))
)
features <- c(
terra::rast(matrix(c(5, 2, 3, 0, 4), nrow = 1)),
terra::rast(matrix(c(5, 5, 5, 5, 5), nrow = 1)),
terra::rast(matrix(c(5, 0, 1, 10, 4), nrow = 1)),
terra::rast(matrix(c(500, 5, 5, 5, 5), nrow = 1))
)
# create problem
p <-
problem(
cost,
zones(
features[[1:2]], features[[3:4]],
zone_names = c("zone_1", "zone_2"), feature_names = c("f1", "f2")
)
) %>%
add_min_largest_shortfall_objective(budget = budget) %>%
add_manual_targets(targs) %>%
add_locked_out_constraints(locked_out) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p, compressed_formulation = FALSE)
# tests
expect_equal(c(terra::values(s[[1]])), c(0, 1, 1, NA, NA))
expect_equal(c(terra::values(s[[2]])), c(0, 0, 0, 1, NA))
})
test_that("invalid inputs (multiple zones)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# tests
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(1, -5, 1))
)
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(1, NA, 1))
)
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(NA, NA, NA))
)
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(1, Inf, 9))
)
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(1, Inf, 9))
)
expect_tidy_error(
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_largest_shortfall_objective(budget = c(5, 5, 5)) %>%
compile()
)
})
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