Nothing
test_that("minimum set objective (numeric, compile, single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# create problems
p1 <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(3, c(terra::values(sim_features[[1]])) * 8)
# solve problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- 3 * sim_features[[1]][pu_idx][[1]] * 8
# tests
expect_equal(o2$obj(), o1$obj() + pen)
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (matrix, compile, single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# create penalty data
pd <- matrix(terra::values(sim_features[[1]]) * 8, ncol = 1)
# create problems
p1 <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(3, pd)
p3 <-
p1 %>%
add_linear_penalties(3, as_Matrix(pd, "dgCMatrix"))
# solve problems
o1 <- compile(p1)
o2 <- compile(p2)
o3 <- compile(p3)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
# tests
expect_equal(o2$obj(), o1$obj() + c(3 * pd[pu_idx]))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
expect_equal(as.list(o2), as.list(o3))
})
test_that("minimum set objective (raster, compile, single zone)", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
# create problems
p1 <-
problem(sim_pu_raster, sim_features) %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(3, sim_features[[1]] * 8)
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- 3 * sim_features[[1]][pu_idx][[1]] * 8
# tests
expect_equal(o2$obj(), o1$obj() + pen)
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (character/Spatial, compile, single zone)", {
# import data
sim_pu_polygons <- sf::as_Spatial(get_sim_pu_polygons())
sim_features <- raster::stack(get_sim_features())
# create penalty data
sim_pu_polygons$penalty_data <- runif(nrow(sim_pu_polygons)) * 3
# create problems
expect_warning(
expect_warning(
p1 <-
problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions(),
"deprecated"
),
"deprecated"
)
p2 <-
p1 %>%
add_linear_penalties(5, "penalty_data")
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- 5 * sim_pu_polygons$penalty_data[pu_idx]
# tests
expect_equal(o2$obj(), o1$obj() + pen)
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (character/sf, compile, single zone)", {
# import data
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()
# create penalty data
sim_pu_polygons$penalty_data <- runif(nrow(sim_pu_polygons)) * 5
# create problems
p1 <-
problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(3, "penalty_data")
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- 3 * sim_pu_polygons$penalty_data[pu_idx]
# tests
expect_equal(o2$obj(), o1$obj() + pen)
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that(
"minimum set objective (character/data.frame, compile, single zone)", {
# create data
pu <- data.frame(
id = seq_len(10), cost = c(runif(1), NA, runif(8)),
spp1 = runif(10), spp2 = c(rpois(9, 4), NA),
penalty_data = runif(10) * 5
)
# create problems
p1 <-
problem(pu, c("spp1", "spp2"), cost_column = "cost") %>%
add_min_set_objective() %>%
add_relative_targets(0.1) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(3, "penalty_data")
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- 3 * pu$penalty_data[pu_idx]
# tests
expect_equal(o2$obj(), o1$obj() + pen)
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (solve, single zone)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
cost <- terra::rast(matrix(c(3, 2, 2, NA), ncol = 4))
locked_in <- 2
locked_out <- 1
features <- c(
terra::rast(matrix(c(2, 1, 1, 0), ncol = 4)),
terra::rast(matrix(c(10, 10, 10, 10), ncol = 4))
)
names(features) <- make.unique(names(features))
# create problem
p <-
problem(cost, features) %>%
add_min_set_objective() %>%
add_linear_penalties(10, c(0, 1, 1, 0)) %>%
add_absolute_targets(c(2, 10)) %>%
add_locked_in_constraints(locked_in) %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p)
# test for correct solution
expect_equal(c(terra::values(s)), c(1, 1, 0, NA))
})
test_that("minimum set objective (matrix, compile, multiple zones)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# create penalty data
pd <- terra::values(sim_zones_features[[1]][[seq_len(3)]])
# create targets data
targ <- matrix(
runif(
number_of_features(sim_zones_features) *
number_of_zones(sim_zones_features)
) * 10,
nrow = number_of_features(sim_zones_features),
ncol = number_of_zones(sim_zones_features)
)
# create problems
p1 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(c(3, 5, 7), pd)
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- pd[pu_idx, , drop = FALSE]
for (i in seq_len(ncol(pen))) pen[, i ] <- pen[, i] * c(3, 5, 7)[i]
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (SpatRaster, compile, multiple zones)", {
# import data
sim_zones_pu_raster <- get_sim_zones_pu_raster()
sim_zones_features <- get_sim_zones_features()
# create penalty data
pd <- sim_zones_features[[1]][[seq_len(3)]]
# create targets data
targ <- matrix(
runif(
number_of_features(sim_zones_features) *
number_of_zones(sim_zones_features)
) * 10,
nrow = number_of_features(sim_zones_features),
ncol = number_of_zones(sim_zones_features)
)
# create problems
p1 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(c(3, 5, 7), pd)
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- as.matrix(pd[pu_idx])
for (i in seq_len(ncol(pen))) pen[, i] <- pen[, i] * c(3, 5, 7)[i]
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (Raster, compile, multiple zones)", {
# import data
sim_zones_pu_raster <- raster::stack(get_sim_zones_pu_raster())
sim_zones_features <- as.ZonesRaster(get_sim_zones_features())
# create targets data
targ <- matrix(
runif(
number_of_features(sim_zones_features) *
number_of_zones(sim_zones_features)
) * 10,
nrow = number_of_features(sim_zones_features),
ncol = number_of_zones(sim_zones_features)
)
# create problems
expect_warning(
p1 <-
problem(sim_zones_pu_raster, sim_zones_features) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions(),
"deprecated"
)
expect_warning(
p2 <-
p1 %>%
add_linear_penalties(c(3, 5, 7), sim_zones_features[[1]][[seq_len(3)]]),
"deprecated"
)
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pd <- raster::values(sim_zones_features[[1]][[seq_len(3)]])
pen <- pd[pu_idx, , drop = FALSE]
for (i in seq_len(ncol(pen))) pen[, i ] <- pen[, i ] * c(3, 5, 7)[i]
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (character/Spatial, compile, multiple zones)",
{
# create data
sim_zones_pu_polygons <- sf::as_Spatial(get_sim_zones_pu_polygons())
sim_zones_features <- as.ZonesRaster(get_sim_zones_features())
# create penalty data
sim_zones_pu_polygons$p1 <- runif(nrow(sim_zones_pu_polygons)) * 5
sim_zones_pu_polygons$p2 <- runif(nrow(sim_zones_pu_polygons)) * 6
sim_zones_pu_polygons$p3 <- runif(nrow(sim_zones_pu_polygons)) * 9
# create zones data
targ <- matrix(
runif(
number_of_features(sim_zones_features) *
number_of_zones(sim_zones_features)
) * 10,
nrow = number_of_features(sim_zones_features),
ncol = number_of_zones(sim_zones_features)
)
# create problems
expect_warning(
expect_warning(
p1 <-
problem(
sim_zones_pu_polygons, sim_zones_features,
cost_column = c("cost_1", "cost_2", "cost_3")
) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions(),
"deprecated"
),
"deprecated"
)
p2 <-
p1 %>%
add_linear_penalties(c(2, 9, 4), c("p1", "p2", "p3"))
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- sim_zones_pu_polygons@data[pu_idx, c("p1", "p2", "p3"), drop = FALSE]
pen <- as.matrix(pen)
for (i in seq_len(ncol(pen))) pen[, i ] <- pen[, i] * c(2, 9, 4)[i]
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (character/sf, compile, multiple zones)", {
# import data
sim_zones_pu_polygons <- get_sim_zones_pu_polygons()
sim_zones_features <- get_sim_zones_features()
# create penalty data
sim_zones_pu_polygons$p1 <- runif(nrow(sim_zones_pu_polygons)) * 5
sim_zones_pu_polygons$p2 <- runif(nrow(sim_zones_pu_polygons)) * 6
sim_zones_pu_polygons$p3 <- runif(nrow(sim_zones_pu_polygons)) * 9
# calculate targets data
targ <- matrix(
runif(
number_of_features(sim_zones_features) *
number_of_zones(sim_zones_features)
) * 10,
nrow = number_of_features(sim_zones_features),
ncol = number_of_zones(sim_zones_features)
)
# create problems
p1 <-
problem(
sim_zones_pu_polygons, sim_zones_features,
cost_column = c("cost_1", "cost_2", "cost_3")
) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(c(2, 9, 4), c("p1", "p2", "p3"))
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- sf::st_drop_geometry(sim_zones_pu_polygons)
pen <- pen[pu_idx, c("p1", "p2", "p3"), drop = FALSE]
pen <- as.matrix(pen)
for (i in seq_len(ncol(pen))) pen[, i ] <- pen[, i ] * c(2, 9, 4)[i]
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that(
paste(
"minimum set objective (character/data.frame, compile,",
"multiple zones)"
), {
# create data
pu <- data.frame(
id = seq_len(10),
cost_1 = c(NA, NA, runif(8)), cost_2 = c(0.3, NA, runif(8)),
spp1_1 = runif(10), spp2_1 = c(rpois(9, 4), NA),
spp1_2 = runif(10), spp2_2 = runif(10),
p1 = runif(10) * 2, p2 = runif(10) * 4
)
targ <- matrix(runif(4) * 3, 2, 2)
# create problems
p1 <-
problem(
pu, zones(c("spp1_1", "spp2_1"), c("spp1_2", "spp2_2")),
cost_column = c("cost_1", "cost_2")
) %>%
add_min_set_objective() %>%
add_absolute_targets(targ) %>%
add_binary_decisions()
p2 <-
p1 %>%
add_linear_penalties(c(3, 8), c("p1", "p2"))
# compile problems
o1 <- compile(p1)
o2 <- compile(p2)
# calculations for tests
pu_idx <- p1$planning_unit_indices()
pen <- as.matrix(pu[pu_idx, c("p1", "p2"), drop = FALSE])
pen[, 1] <- pen[, 1] * 3
pen[, 2] <- pen[, 2] * 8
pen <- unname(c(pen))
# tests
expect_equal(o2$obj(), o1$obj() + c(pen))
expect_equal(o2$A(), o1$A())
expect_equal(o2$ub(), o1$ub())
expect_equal(o2$lb(), o1$lb())
expect_equal(o2$rhs(), o1$rhs())
expect_equal(o2$sense(), o1$sense())
expect_equal(o2$modelsense(), o1$modelsense())
})
test_that("minimum set objective (solve, multiple zones)", {
skip_on_cran()
skip_if_no_fast_solvers_installed()
# create data
costs <- c(
terra::rast(matrix(c(1, 2, NA, 3, 100, 100, NA), ncol = 7)),
terra::rast(matrix(c(10, 10, 10, 10, 4, 1, NA), ncol = 7))
)
spp <- c(
terra::rast(matrix(c(1, 2, 0, 0, 0, 0, 0), ncol = 7)),
terra::rast(matrix(c(NA, 0, 1, 1, 0, 0, 0), ncol = 7)),
terra::rast(matrix(c(1, 0, 0, 0, 1, 0, 0), ncol = 7)),
terra::rast(matrix(c(0, 0, 0, 0, 0, 10, 0), ncol = 7))
)
# create problem
p <-
problem(costs, zones(spp[[1:2]], spp[[3:4]])) %>%
add_min_set_objective() %>%
add_linear_penalties(
c(100, 150),
matrix(
c(
10, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 10, 0, 0
),
ncol = 2
)
) %>%
add_absolute_targets(matrix(c(1, 1, 1, 0), nrow = 2, ncol = 2)) %>%
add_binary_decisions() %>%
add_default_solver(gap = 0, verbose = FALSE)
# solve problem
s <- solve(p)
# tests
expect_inherits(s, "SpatRaster")
expect_equal(c(terra::values(s[[1]])), c(0, 1, NA, 1, 0, 0, NA))
expect_equal(c(terra::values(s[[2]])), c(1, 0, 0, 0, 0, 0, NA))
})
test_that("invalid inputs", {
# import data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()
sim_pu_polygons <- get_sim_pu_polygons()
# tests
expect_tidy_error({
problem(sim_pu_raster, sim_features) %>%
add_linear_penalties(NA_real_, sim_features[[1]])
})
expect_tidy_error({
problem(sim_pu_raster, sim_features) %>%
add_linear_penalties(c(3, 3), sim_features[[1]])
})
expect_tidy_error({
problem(sim_pu_raster, sim_features) %>%
add_linear_penalties(c(3, 3), sim_features[[1:2]])
})
expect_tidy_error({
problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
add_linear_penalties(3, c("cost", "cost"))
})
expect_tidy_error({
problem(sim_pu_raster, sim_features) %>%
add_linear_penalties(3, "a")
})
expect_tidy_error({
problem(sim_pu_raster, sim_features) %>%
add_linear_penalties(
3, terra::crop(sim_features[[1]], terra::ext(c(0, 0.5, 0, 0.5)))
)
})
})
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