R/add_max_targets_met_objective.R

Defines functions add_max_targets_met_objective

Documented in add_max_targets_met_objective

#' @include internal.R pproto.R Objective-proto.R star_phylogeny.R
NULL

#' Add maximum targets met objective
#'
#' Set the objective of a project prioritization [problem()] to
#' maximize the total number of persistence targets met for the features, whilst
#' ensuring that the cost of the solution is within a pre-specified budget
#' (Chades *et al.* 2015). In some project prioritization exercises,
#' decision makers may have a target level of persistence for each feature
#' (e.g. a 90% persistence target corresponding to a 90% chance for the
#' features persisting into the future). In such exercises, the decision makers
#' do not perceive any benefit when a target is not met (e.g. if a feature
#' has a persistence target of 90% and a solution only secures a 70% chance
#' of persistence then no benefit is accrued for that feature) or when a target
#' is surpassed (e.g. if a feature has a persistence target of 50%, then a
#' solution which
#' secures a 95% chance of persistence will accrue the same benefit as a
#' solution which secures a 50% chance of persistence). Furthermore, weights
#' can also be used to specify the relative importance of meeting targets
#' for specific features (see [add_feature_weights()]).
#'
#' @param x [ProjectProblem-class] object.
#'
#' @param budget `numeric` budget for funding actions.
#'
#' @details A problem objective is used to specify the overall goal of the
#'   project prioritization problem.
#'   Here, the maximum targets met objective seeks to find the set of actions
#'   that maximizes the total number of features (e.g. populations, species,
#'   ecosystems) that have met their persistence targets within a
#'   pre-specified budget. Let \eqn{I} represent the set of conservation
#'   actions (indexed by \eqn{i}). Let \eqn{C_i} denote the cost for funding
#'   action \eqn{i}, and let \eqn{m} denote the maximum expenditure (i.e. the
#'   budget). Also, let \eqn{F} represent each feature (indexed by \eqn{f}),
#'   \eqn{W_f} represent the weight for each feature \eqn{f} (defaults to one
#'   for each feature unless specified otherwise), \eqn{T_f} represent the
#'   persistence target for each feature \eqn{f}, and \eqn{E_f} denote the
#'   probability that each feature will go extinct given the funded
#'   conservation projects.
#'
#'   To guide the prioritization, the conservation actions are organized into
#'   conservation projects. Let \eqn{J} denote the set of conservation projects
#'   (indexed by \eqn{j}), and let \eqn{A_{ij}} denote which actions
#'   \eqn{i \in I}{i in I} comprise each conservation project
#'   \eqn{j \in J}{j in J} using zeros and ones. Next, let \eqn{P_j} represent
#'   the probability of project \eqn{j} being successful if it is funded. Also,
#'   let \eqn{B_{fj}} denote the enhanced probability that each feature
#'   \eqn{f \in F}{f in F} associated with the project \eqn{j \in J}{j in J}
#'   will persist if all of the actions that comprise project \eqn{j} are funded
#'   and that project is allocated to feature \eqn{f}.
#'   For convenience,
#'   let \eqn{Q_{fj}} denote the actual probability that each
#'   \eqn{f \in F}{f in F} associated with the project \eqn{j \in J}{j in J}
#'   is expected to persist if the project is funded. If the argument
#'   to `adjust_for_baseline` in the `problem` function was set to
#'   `TRUE`, and this is the default behavior, then
#'   \eqn{Q_{fj} = (P_{j} \times B_{fj}) + \bigg(\big(1 - (P_{j} B_{fj})\big)
#'   \times (P_{n} \times B_{fn})\bigg)}{Q_{fj} = (P_j B_{fj}) + ((1 - (P_j
#'   B_{fj})) * (P_n \times B_{fn}))}, where `n` corresponds to the
#'   baseline "do nothing" project. This means that the probability
#'   of a feature persisting if a project is allocated to a feature
#'   depends on (i) the probability of the project succeeding, (ii) the
#'   probability of the feature persisting if the project does not fail,
#'   and (iii) the probability of the feature persisting even if the project
#'   fails. Otherwise, if the argument is set to `FALSE`, then
#'   \eqn{Q_{fj} = P_{j} \times B_{fj}}{Q_{fj} = P_{j} * B_{fj}}.
#'
#'   The binary control variables \eqn{X_i} in this problem indicate whether
#'   each project \eqn{i \in I}{i in I} is funded or not. The decision
#'   variables in this problem are the \eqn{Y_{j}}, \eqn{Z_{fj}}, \eqn{E_f},
#'   and \eqn{G_f} variables.
#'   Specifically, the binary \eqn{Y_{j}} variables indicate if project \eqn{j}
#'   is funded or not based on which actions are funded; the binary
#'   \eqn{Z_{fj}} variables indicate if project \eqn{j} is used to manage
#'   feature \eqn{f} or not; the semi-continuous \eqn{E_f} variables
#'   denote the probability that feature \eqn{f} will go extinct; and the
#'   \eqn{G_f} variables indicate if the persistence target for feature
#'   \eqn{f} is met.
#'
#'   Now that we have defined all the data and variables, we can formulate
#'   the problem. For convenience, let the symbol used to denote each set also
#'   represent its cardinality (e.g. if there are ten features, let \eqn{F}
#'   represent the set of ten features and also the number ten).
#'
#' \deqn{
#'   \mathrm{Maximize} \space \sum_{f = 0}^{F} G_f W_f \space
#'   \mathrm{(eqn \space 1a)} \\
#'   \mathrm{Subject \space to}
#'   \sum_{i = 0}^{I} C_i \leq m \space \mathrm{(eqn \space 1b)} \\
#'   G_f (1 - E_f) \geq T_f \space \forall \space f \in F \space
#'   \mathrm{(eqn \space 1c)} \\
#'   E_f = 1 - \sum_{j = 0}^{J} Z_{fj} Q_{fj} \space \forall \space f \in F
#'   \space \mathrm{(eqn \space 1d)} \\
#'   Z_{fj} \leq Y_{j} \space \forall \space j \in J \space \mathrm{(eqn \space
#'   1e)} \\
#'   \sum_{j = 0}^{J} Z_{fj} \times \mathrm{ceil}(Q_{fj}) = 1 \space \forall
#'   \space f \in F \space \mathrm{(eqn \space 1f)} \\
#'   A_{ij} Y_{j} \leq X_{i} \space \forall \space i \in I, j \in J \space
#'   \mathrm{(eqn \space 1g)} \\
#'   E_{f} \geq 0, E_{f} \leq 1 \space \forall \space b \in B \space
#'   \mathrm{(eqn \space 1h)} \\
#'   G_{f}, X_{i}, Y_{j}, Z_{fj} \in [0, 1] \space \forall \space i \in I, j
#'   \in J, f \in F \space \mathrm{(eqn \space 1i)}
#'   }{
#'   Maximize sum_f^F G_f W_f (eqn 1a);
#'   Subject to:
#'   sum_i^I C_i X_i <= m for all f in F (eqn 1b),
#'   G_f (1 - E_f) >= T_f for all f \in F (eqn 1c),
#'   E_f = 1 - sum_j^J Y_{fj} Q_{fj} for all f in F (eqn 1d),
#'   Z_{fj} <= Y_j for all j in J (eqn 1e),
#'   sum_j^J Z_{fj} * ceil(Q_{fj}) = 1 for all f in F (eqn 1f),
#'   A_{ij} Y_{j} <= X_{i} for all i I, j in J (eqn 1g),
#'   E_f >= 0, E_f <= 1 for all f in F (eqn 1h),
#'   G_f, X_i, Y_j, Z_{fj} in [0, 1] for all i in I, j in J, f in F (eqn 1i)
#'   }
#'
#'  The objective (eqn 1a) is to maximize the weighted total number of the
#'  features that have their persistence targets met.
#'  Constraints (eqn 1b) calculate which persistence targets have been met.
#'  Constraint (eqn 1c) limits the maximum expenditure (i.e. ensures
#'  that the cost of the funded actions do not exceed the budget).
#'  Constraints (eqn 1d) calculate the probability that each feature
#'  will go extinct according to their allocated project.
#'  Constraints (eqn 1e) ensure that feature can only be allocated to projects
#'  that have all of their actions funded. Constraints (eqn 1f) state that each
#'  feature can only be allocated to a single project. Constraints (eqn 1g)
#'  ensure that a project cannot be funded unless all of its actions are funded.
#'  Constraints (eqns 1h) ensure that the probability variables
#'  (\eqn{E_f}) are bounded between zero and one. Constraints (eqns 1i) ensure
#'  that the target met (\eqn{G_f}), action funding (\eqn{X_i}), project funding
#'  (\eqn{Y_j}), and project allocation (\eqn{Z_{fj}}) variables are binary.
#'
#' @references
#' Chades I, Nicol S, van Leeuwen S, Walters B, Firn J, Reeson A, Martin TG &
#' Carwardine J (2015) Benefits of integrating complementarity into priority
#' threat management. *Conservation Biology*, **29**, 525--536.
#'
#' @inherit add_max_richness_objective seealso return
#'
#' @examples
#' # load the ggplot2 R package to customize plot
#' library(ggplot2)
#'
#' # load data
#' data(sim_projects, sim_features, sim_actions)
#'
#' # manually adjust feature weights
#' sim_features$weight <- c(8, 2, 6, 3, 1)
#'
#' # build problem with maximum targets met objective, a $200 budget,
#' # targets that require each feature to have a 20% chance of persisting into
#' # the future, and zero cost actions locked in
#' p1 <- problem(sim_projects, sim_actions, sim_features,
#'              "name", "success", "name", "cost", "name") %>%
#'       add_max_targets_met_objective(budget = 200) %>%
#'       add_absolute_targets(0.2) %>%
#'       add_locked_in_constraints(which(sim_actions$cost < 1e-5)) %>%
#'       add_binary_decisions()
#'
#' \dontrun{
#' # solve problem
#' s1 <- solve(p1)
#'
#' # print solution
#' print(s1)
#'
#' # plot solution, and add a dashed line to indicate the feature targets
#' # we can see the three features meet the targets under the baseline
#' # scenario, and the project for F5 was prioritized for funding
#' # so that its probability of persistence meets the target
#' plot(p1, s1) +
#' geom_hline(yintercept = 0.2, linetype = "dashed")
#' }
#'
#' # build another problem that includes feature weights
#' p2 <- p1 %>%
#'       add_feature_weights("weight")
#'
#' \dontrun{
#' # solve problem
#' s2 <- solve(p2)
#'
#' # print solution
#' print(s2)
#'
#' # plot solution, and add a dashed line to indicate the feature targets
#' # we can see that adding weights to the problem has changed the solution
#' # specifically, the projects for the feature F3 is now funded
#' # to enhance its probability of persistence
#' plot(p2, s2) +
#' geom_hline(yintercept = 0.2, linetype = "dashed")
#' }
#' @name add_max_targets_met_objective
NULL

#' @rdname add_max_targets_met_objective
#' @export
add_max_targets_met_objective <- function(x, budget) {
  # assert argument is valid
  assertthat::assert_that(inherits(x, "ProjectProblem"),
                          assertthat::is.number(budget),
                          assertthat::noNA(budget),
                          isTRUE(budget >= 0))
  # add objective to problem
  x$add_objective(pproto(
    "MaximumTargetsMetObjective",
    Objective,
    name = "Maximum targets met objective",
    data = list(feature_names = feature_names(x)),
    parameters = parameters(numeric_parameter("budget", budget,
                                              lower_limit = 0)),
    feature_phylogeny = function(self) {
      star_phylogeny(self$data$feature_names)
    },
    replace_feature_weights = function(self) {
      TRUE
    },
    default_feature_weights = function(self) {
      stats::setNames(rep(1, length(self$data$feature_names)),
                      self$data$feature_names)
    },
    evaluate = function(self, y, solution) {
      assertthat::assert_that(inherits(y, "ProjectProblem"),
                              inherits(solution, "tbl_df"))
      if (is.Waiver(y$targets))
        y <- add_default_targets(y)
      fp <- y$feature_phylogeny()
      bm <- branch_matrix(fp, FALSE)
      bo <- rcpp_branch_order(bm)
      w <- y$feature_weights()[y$feature_phylogeny()$tip.label]
      rcpp_evaluate_max_targets_met_objective(
        y$action_costs(), y$pa_matrix(), y$epf_matrix(),
        bm[, bo, drop = FALSE], fp$edge.length[bo],
        y$targets$output()$value, w,
        as_Matrix(as.matrix(solution), "dgCMatrix"))
    },
    apply = function(self, x, y) {
      assertthat::assert_that(inherits(x, "OptimizationProblem"),
                              inherits(y, "ProjectProblem"))
      invisible(rcpp_apply_max_targets_met_objective(
        x$ptr, y$feature_targets(), y$action_costs(),
        self$parameters$get("budget"), rep(1, y$number_of_features())))
    }))
}

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oppr documentation built on Sept. 8, 2022, 5:07 p.m.