Evaluate objective value of solution

Calculate the objective value of a solution to a conservation planning problem.

Usage

eval_objective_summary(x, solution, include_penalties = TRUE)

# S3 method for class 'ConservationProblem'
eval_objective_summary(x, solution, include_penalties = TRUE)

# S3 method for class 'MultiConservationProblem'
eval_objective_summary(x, solution, include_penalties = TRUE)

Arguments

x

problem() or multi_problem() object.

solution

numeric, matrix, data.frame, terra::rast(), or sf::sf() object. Note that solution must have the same format as the planning unit data in x. See the Solution format section for more information.

include_penalties

logical should penalties be included when calculating objectives values? Defaults to TRUE.

Value

A tibble::tibble() object describing the performance of the solution. It contains the following columns.

problem

character name of problem. Note that this column is only present if x is a multi_problem() object.

value

numeric objective value.

Details

The mathematical objective function of an optimization problem describes the performance metric that is minimized or maximized during optimization. In a conservation planning problem(), objectives specify the primary metric should be maximized or minimized (e.g., add_min_set_objective() specify that costs should be minimized) and penalties can (optionally) be used to specify additional metrics that should be maximized or minimized during optimization (e.g., add_boundary_penalties() specify that spatial fragmentation should be minimized). Given this, the mathematical objective function of a conservation planning problem() is calculated based on a weighted sum of the objectives and penalties (i.e., where the weights are the penalty values specified in the penalties function).

See also

Other functions for summarizing solutions: eval_asym_connectivity_summary(), eval_boundary_summary(), eval_connectivity_summary(), eval_cost_summary(), eval_feature_representation_summary(), eval_n_summary(), eval_target_coverage_summary()

Examples

# set seed for reproducibility
set.seed(500)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_features <- get_sim_features()

# build conservation problem with boundary penalties
p1 <-
  problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective() %>%
  add_relative_targets(0.1) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve the problem
s1 <- solve(p1)

# print solution
print(s1)
#> class       : SpatRaster
#> size        : 10, 10, 1  (nrow, ncol, nlyr)
#> resolution  : 0.1, 0.1  (x, y)
#> extent      : 0, 1, 0, 1  (xmin, xmax, ymin, ymax)
#> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857)
#> source(s)   : memory
#> varname     : sim_pu_raster
#> name        : layer
#> min value   :     0
#> max value   :     1

# calculate objective value including penalties
v1 <- eval_objective_summary(p1, s1, include_penalties = TRUE)
print(v1)
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1 1987.

# calculate objective value excluding penalties
v2 <- eval_objective_summary(p1, s1, include_penalties = FALSE)
print(v2)
#> # A tibble: 1 × 1
#>   value
#>   <dbl>
#> 1 1987.