Nothing
tests/testthat/test_add_max_phylo_div_objective.R
In oppr: Optimal Project Prioritization
context("add_max_phylo_div_objective")
test_that("compile", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0),
locked_in = FALSE,
locked_out = FALSE)
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problem
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
# create optimization problem
o1 <- compile(p)
o2 <- r_phylo_div_mip_formulation(projects, actions, tree, 0.16, 1000)
# run tests
expect_equal(o1$obj(), o2$obj)
expect_equal(o1$vtype(), o2$vtype)
expect_equal(o1$lb(), o2$lb)
expect_equal(o1$ub(), o2$ub)
expect_equal(o1$sense(), o2$sense)
expect_equal(o1$rhs(), o2$rhs)
expect_true(all(o1$A() == o2$A))
})
test_that("exact solver (simple problem, single solution", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
tree2 <- tree
tree2$edge.length <- c(5, 100, 5, 5)
# make problems
p1 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
p2 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.21, tree) %>%
add_binary_decisions()
p3 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.11, tree2) %>%
add_binary_decisions()
# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)
# tests
## s1
expect_is(s1, "tbl_df")
expect_equal(nrow(s1), 1)
expect_equal(s1$solution, 1L)
expect_equal(s1$status, "OPTIMAL")
expect_equal(s1$cost, 0.15)
expect_equal(s1$obj, (5 * s1$F1) +
(5 * s1$F2) +
(5 * s1$F3) +
(100 * (1 - ((1 - (s1$F1)) * (1 - (s1$F2))))))
expect_equal(s1$A1, 0)
expect_equal(s1$A2, 0)
expect_equal(s1$A3, 1)
expect_equal(s1$A4, 1)
expect_equal(s1$F1, 0.94 * 0.8)
expect_equal(s1$F2, 0.94 * 0.8)
expect_equal(s1$F3, 0.1 * 1)
## s2
expect_is(s2, "tbl_df")
expect_equal(nrow(s2), 1)
expect_equal(s2$solution, 1L)
expect_equal(s2$status, "OPTIMAL")
expect_equal(s2$obj, (5 * s2$F1) +
(5 * s2$F2) +
(5 * s2$F3) +
(100 * (1 - ((1 - (s2$F1)) * (1 - (s2$F2))))))
expect_equal(s2$cost, 0.2)
expect_equal(s2$A1, 1)
expect_equal(s2$A2, 1)
expect_equal(s2$A3, 0)
expect_equal(s2$A4, 1)
expect_equal(s2$F1, 0.95 * 0.91)
expect_equal(s2$F2, 0.96 * 0.92)
expect_equal(s2$F3, 0.1 * 1)
## s3
expect_is(s3, "tbl_df")
expect_equal(nrow(s3), 1)
expect_equal(s3$solution, 1L)
expect_equal(s3$status, "OPTIMAL")
expect_equal(s3$obj, (100 * s3$F1) +
(5 * s3$F2) +
(5 * s3$F3) +
(5 * (1 - ((1 - (s3$F1)) * (1 - (s3$F2))))))
expect_equal(s3$cost, 0.1)
expect_equal(s3$A1, 1)
expect_equal(s3$A2, 0)
expect_equal(s3$A3, 0)
expect_equal(s3$A4, 1)
expect_equal(s3$F1, 0.95 * 0.91)
expect_equal(s3$F2, 0.1 * 1)
expect_equal(s3$F3, 0.1 * 1)
})
test_that("exact solver (random order, no weights, single solution)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_gurobi_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj, (5 * 0.95 * 0.91) +
(5 * 1 * 0.80) +
(5 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (random order, weights, single solution)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.00, 0.10),
F2 = c(0.00, 0.00, 0.05, 0.01),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3", "F4"))
tree <- ape::read.tree(text = "(((F3, F4), F1), F2);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_feature_weights(c(4, 1000, 2, 4)) %>%
add_binary_decisions() %>%
add_gurobi_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj,
(5 * 0.1 * 1) + (4 * 0.1 * 1) +
(5 * 0.05 * 0.94) + (1000 * 0.05 * 0.94) +
(5 * 1 * 0.80) + (2 * 0.8 * 1) +
(5 * 1 * 0.80) + (4 * 0.8 * 1) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8)) * (1 - (0.1 * 1))))))
expect_equal(s$cost, 0.15)
expect_equal(s$A1, 0)
expect_equal(s$A2, 0)
expect_equal(s$A3, 1)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.1 * 1)
expect_equal(s$F2, 0.05 * 0.94)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (simple problem, multiple solutions", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_binary_decisions() %>%
add_gurobi_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.2))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, ifelse(abs(s$obj - max(s$obj)) < 1e-10,
"OPTIMAL", "SUBOPTIMAL"))
expect_is(s$A1, "numeric")
expect_is(s$A2, "numeric")
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("exact solver (constant branch probabilities, single solution", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 1.0),
F4 = c(0.00, 0.00, 0.00, 1.0),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3", "F4"))
tree <- ape::read.tree(text = "((F1,F2),(F3,F4));")
tree$edge.length <- c(5, 5, 5, 5, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj, (0.752 * 5) +
(0.752 * 5) +
(1 * 5) +
(1 * 5) +
((1 - ((1 - 0.752) * (1 - 0.752))) * 5) +
((1 - ((1 - 1) * (1 - 1))) * 5))
expect_equal(s$cost, 0.15)
expect_equal(s$F1, 0.94 * 0.8)
expect_equal(s$F2, 0.94 * 0.8)
expect_equal(s$F3, 1.0)
expect_equal(s$F4, 1.0)
expect_equal(s$A1, 0)
expect_equal(s$A2, 0)
expect_equal(s$A3, 1)
expect_equal(s$A4, 1)
})
test_that("exact solver (locked constraints, multiple solutions)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(100, tree) %>%
add_locked_in_constraints(1) %>%
add_locked_out_constraints(2) %>%
add_binary_decisions() %>%
add_gurobi_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.25))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, ifelse(abs(s$obj - max(s$obj)) < 1e-10,
"OPTIMAL", "SUBOPTIMAL"))
expect_is(s$A1, "numeric")
expect_true(all(s$A1 > 0.5))
expect_is(s$A2, "numeric")
expect_true(all(s$A2 < 0.5))
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("heuristic solver (simple problem, single solution", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver()
# solve problems
s <- solve(p)
# tests
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$cost, 0.1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_equal(s$A1, 0)
expect_equal(s$A2, 1)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.1 * 1)
expect_equal(s$F2, 0.96 * 0.92)
expect_equal(s$F3, 0.1 * 1)
})
test_that("exact solver (random order, weights, single solution)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_feature_weights(c(4, 8, 90)) %>%
add_heuristic_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$obj, (5 * 0.95 * 0.91) + ((4 * 0.95 * 0.91)) +
(5 * 1 * 0.80) + (8 * 1 * 0.80) +
(5 * 1 * 0.80) + (90 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (random order, no weights, single solution)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$obj, (5 * 0.95 * 0.91) +
(5 * 1 * 0.80) +
(5 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("heuristic solver (simple problem, multiple solutions", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.2))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, rep(NA_character_, nrow(s)))
expect_is(s$A1, "numeric")
expect_is(s$A2, "numeric")
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("heuristic solver (locked constraints, multiple solutions)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_locked_in_constraints(1) %>%
add_locked_out_constraints(2) %>%
add_binary_decisions() %>%
add_heuristic_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$cost, 0.1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F2, 0.1 * 1)
expect_equal(s$F3, 0.1 * 1)
})
test_that("invalid arguments", {
data(sim_projects, sim_actions, sim_features, sim_tree)
p <- problem(sim_projects, sim_actions, sim_features,
"name", "success", "name", "cost", "name", FALSE)
## budgets
expect_error({
add_max_phylo_div_objective(p, NA_real_, sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, c(1, 1), sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, "a", sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, TRUE, sim_tree)
})
## tree
expect_error({
add_max_phylo_div_objective(p, 1e+5, 1)
})
expect_error({
sim_tree2 <- sim_tree
sim_tree2$Nnode <- 1
add_max_phylo_div_objective(p, 1e+5, sim_tree2)
})
expect_error({
sim_tree2 <- ape::drop.tip(sim_tree, "F1")
add_max_phylo_div_objective(p, 1e+5, sim_tree2)
})
expect_error({
p %>%
add_max_phylo_div_objective(1e+5, sim_tree) %>%
add_rsymphony_solver() %>%
solve()
})
skip_if_not_installed("gurobi", "8.0.0")
expect_warning({
p %>%
add_max_phylo_div_objective(1e+5, replace(sim_tree, "edge.length",
NULL)) %>%
solve()
})
})
test_that("solution_statistics", {
# create data
projects <- tibble::tibble(name = c("P1", "P2", "P3", "P4"),
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"),
weight = c(100, 4, 9))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# create problem
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(budget = 0.16, tree) %>%
add_feature_weights("weight") %>%
add_binary_decisions()
# create solutions
s <- data.frame(A1 = c(1, 0, 0, 1),
A2 = c(1, 1, 0, 1),
A3 = c(0, 0, 1, 1),
A4 = c(1, 1, 1, 1))
# evaluate solutions
ss <- solution_statistics(p, s)
# tests
expect_equal(ss$cost, c(0.1 + 0.1 + 0,
0.1 + 0,
0.15 + 0,
0.1 + 0.1 + 0.15 + 0))
expect_equal(ss$obj,
(ss$F1 * 100) + (ss$F2 * 4) + (ss$F3[1] * 9) +
(ss$F1 * 5) + (ss$F2 * 5) + (ss$F3 * 5) +
((1 - (1 - ss$F1) * (1 - ss$F2)) * 100))
expect_equal(ss$F1, c(0.95 * 0.91,
0.1 * 1,
0.94 * 0.8,
0.95 * 0.91))
expect_equal(ss$F2, c(0.96 * 0.92,
0.96 * 0.92,
0.94 * 0.8,
0.96 * 0.92))
expect_equal(ss$F3, c(0.1 * 1,
0.1 * 1,
0.1 * 1,
0.1 * 1))
})
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context("add_max_phylo_div_objective")
test_that("compile", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0),
locked_in = FALSE,
locked_out = FALSE)
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problem
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
# create optimization problem
o1 <- compile(p)
o2 <- r_phylo_div_mip_formulation(projects, actions, tree, 0.16, 1000)
# run tests
expect_equal(o1$obj(), o2$obj)
expect_equal(o1$vtype(), o2$vtype)
expect_equal(o1$lb(), o2$lb)
expect_equal(o1$ub(), o2$ub)
expect_equal(o1$sense(), o2$sense)
expect_equal(o1$rhs(), o2$rhs)
expect_true(all(o1$A() == o2$A))
})
test_that("exact solver (simple problem, single solution", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
tree2 <- tree
tree2$edge.length <- c(5, 100, 5, 5)
# make problems
p1 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
p2 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.21, tree) %>%
add_binary_decisions()
p3 <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.11, tree2) %>%
add_binary_decisions()
# solve problems
s1 <- solve(p1)
s2 <- solve(p2)
s3 <- solve(p3)
# tests
## s1
expect_is(s1, "tbl_df")
expect_equal(nrow(s1), 1)
expect_equal(s1$solution, 1L)
expect_equal(s1$status, "OPTIMAL")
expect_equal(s1$cost, 0.15)
expect_equal(s1$obj, (5 * s1$F1) +
(5 * s1$F2) +
(5 * s1$F3) +
(100 * (1 - ((1 - (s1$F1)) * (1 - (s1$F2))))))
expect_equal(s1$A1, 0)
expect_equal(s1$A2, 0)
expect_equal(s1$A3, 1)
expect_equal(s1$A4, 1)
expect_equal(s1$F1, 0.94 * 0.8)
expect_equal(s1$F2, 0.94 * 0.8)
expect_equal(s1$F3, 0.1 * 1)
## s2
expect_is(s2, "tbl_df")
expect_equal(nrow(s2), 1)
expect_equal(s2$solution, 1L)
expect_equal(s2$status, "OPTIMAL")
expect_equal(s2$obj, (5 * s2$F1) +
(5 * s2$F2) +
(5 * s2$F3) +
(100 * (1 - ((1 - (s2$F1)) * (1 - (s2$F2))))))
expect_equal(s2$cost, 0.2)
expect_equal(s2$A1, 1)
expect_equal(s2$A2, 1)
expect_equal(s2$A3, 0)
expect_equal(s2$A4, 1)
expect_equal(s2$F1, 0.95 * 0.91)
expect_equal(s2$F2, 0.96 * 0.92)
expect_equal(s2$F3, 0.1 * 1)
## s3
expect_is(s3, "tbl_df")
expect_equal(nrow(s3), 1)
expect_equal(s3$solution, 1L)
expect_equal(s3$status, "OPTIMAL")
expect_equal(s3$obj, (100 * s3$F1) +
(5 * s3$F2) +
(5 * s3$F3) +
(5 * (1 - ((1 - (s3$F1)) * (1 - (s3$F2))))))
expect_equal(s3$cost, 0.1)
expect_equal(s3$A1, 1)
expect_equal(s3$A2, 0)
expect_equal(s3$A3, 0)
expect_equal(s3$A4, 1)
expect_equal(s3$F1, 0.95 * 0.91)
expect_equal(s3$F2, 0.1 * 1)
expect_equal(s3$F3, 0.1 * 1)
})
test_that("exact solver (random order, no weights, single solution)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_gurobi_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj, (5 * 0.95 * 0.91) +
(5 * 1 * 0.80) +
(5 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (random order, weights, single solution)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.00, 0.10),
F2 = c(0.00, 0.00, 0.05, 0.01),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3", "F4"))
tree <- ape::read.tree(text = "(((F3, F4), F1), F2);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_feature_weights(c(4, 1000, 2, 4)) %>%
add_binary_decisions() %>%
add_gurobi_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj,
(5 * 0.1 * 1) + (4 * 0.1 * 1) +
(5 * 0.05 * 0.94) + (1000 * 0.05 * 0.94) +
(5 * 1 * 0.80) + (2 * 0.8 * 1) +
(5 * 1 * 0.80) + (4 * 0.8 * 1) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8)) * (1 - (0.1 * 1))))))
expect_equal(s$cost, 0.15)
expect_equal(s$A1, 0)
expect_equal(s$A2, 0)
expect_equal(s$A3, 1)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.1 * 1)
expect_equal(s$F2, 0.05 * 0.94)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (simple problem, multiple solutions", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_binary_decisions() %>%
add_gurobi_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.2))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, ifelse(abs(s$obj - max(s$obj)) < 1e-10,
"OPTIMAL", "SUBOPTIMAL"))
expect_is(s$A1, "numeric")
expect_is(s$A2, "numeric")
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("exact solver (constant branch probabilities, single solution", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 1.0),
F4 = c(0.00, 0.00, 0.00, 1.0),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3", "F4"))
tree <- ape::read.tree(text = "((F1,F2),(F3,F4));")
tree$edge.length <- c(5, 5, 5, 5, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions()
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, "OPTIMAL")
expect_equal(s$obj, (0.752 * 5) +
(0.752 * 5) +
(1 * 5) +
(1 * 5) +
((1 - ((1 - 0.752) * (1 - 0.752))) * 5) +
((1 - ((1 - 1) * (1 - 1))) * 5))
expect_equal(s$cost, 0.15)
expect_equal(s$F1, 0.94 * 0.8)
expect_equal(s$F2, 0.94 * 0.8)
expect_equal(s$F3, 1.0)
expect_equal(s$F4, 1.0)
expect_equal(s$A1, 0)
expect_equal(s$A2, 0)
expect_equal(s$A3, 1)
expect_equal(s$A4, 1)
})
test_that("exact solver (locked constraints, multiple solutions)", {
skip_on_cran()
skip_if_not_installed("gurobi", "8.0.0")
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(100, tree) %>%
add_locked_in_constraints(1) %>%
add_locked_out_constraints(2) %>%
add_binary_decisions() %>%
add_gurobi_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.25))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, ifelse(abs(s$obj - max(s$obj)) < 1e-10,
"OPTIMAL", "SUBOPTIMAL"))
expect_is(s$A1, "numeric")
expect_true(all(s$A1 > 0.5))
expect_is(s$A2, "numeric")
expect_true(all(s$A2 < 0.5))
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("heuristic solver (simple problem, single solution", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver()
# solve problems
s <- solve(p)
# tests
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$cost, 0.1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_equal(s$A1, 0)
expect_equal(s$A2, 1)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.1 * 1)
expect_equal(s$F2, 0.96 * 0.92)
expect_equal(s$F3, 0.1 * 1)
})
test_that("exact solver (random order, weights, single solution)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_feature_weights(c(4, 8, 90)) %>%
add_heuristic_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$obj, (5 * 0.95 * 0.91) + ((4 * 0.95 * 0.91)) +
(5 * 1 * 0.80) + (8 * 1 * 0.80) +
(5 * 1 * 0.80) + (90 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("exact solver (random order, no weights, single solution)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.80),
F4 = c(0.00, 0.00, 0.00, 0.80),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F3", "F4"))
tree <- ape::read.tree(text = "((F3, F4), F1);")
tree$edge.length <- rep(5, nrow(tree$edge))
# make problems
s <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.16, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver(verbose = FALSE) %>%
solve()
# solve problem
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$obj, (5 * 0.95 * 0.91) +
(5 * 1 * 0.80) +
(5 * 1 * 0.80) +
(5 * (1 - ((1 - (0.8)) * (1 - (0.8))))))
expect_equal(s$cost, 0.1)
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F3, 0.8 * 1)
expect_equal(s$F4, 0.8 * 1)
})
test_that("heuristic solver (simple problem, multiple solutions", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_binary_decisions() %>%
add_heuristic_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_gt(nrow(s), 1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_true(all(s$cost <= 0.2))
expect_equal(s$cost, (s$A1 * actions$cost[1]) +
(s$A2 * actions$cost[2]) +
(s$A3 * actions$cost[3]) +
(s$A4 * actions$cost[4]))
expect_equal(s$status, rep(NA_character_, nrow(s)))
expect_is(s$A1, "numeric")
expect_is(s$A2, "numeric")
expect_is(s$A3, "numeric")
expect_is(s$A4, "numeric")
expect_is(s$F1, "numeric")
expect_is(s$F2, "numeric")
expect_is(s$F3, "numeric")
expect_true(all(rowSums(as.matrix(s[, actions$name])) >= 1))
})
test_that("heuristic solver (locked constraints, multiple solutions)", {
# create data
projects <- tibble::tibble(name = letters[1:4],
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# make problems
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(0.2, tree) %>%
add_locked_in_constraints(1) %>%
add_locked_out_constraints(2) %>%
add_binary_decisions() %>%
add_heuristic_solver(number_solutions = 100)
# solve problem
s <- solve(p)
# tests
expect_is(s, "tbl_df")
expect_equal(nrow(s), 1)
expect_equal(s$solution, 1L)
expect_equal(s$status, NA_character_)
expect_equal(s$cost, 0.1)
expect_equal(s$obj, (5 * s$F1) +
(5 * s$F2) +
(5 * s$F3) +
(100 * (1 - ((1 - (s$F1)) * (1 - (s$F2))))))
expect_equal(s$A1, 1)
expect_equal(s$A2, 0)
expect_equal(s$A3, 0)
expect_equal(s$A4, 1)
expect_equal(s$F1, 0.95 * 0.91)
expect_equal(s$F2, 0.1 * 1)
expect_equal(s$F3, 0.1 * 1)
})
test_that("invalid arguments", {
data(sim_projects, sim_actions, sim_features, sim_tree)
p <- problem(sim_projects, sim_actions, sim_features,
"name", "success", "name", "cost", "name", FALSE)
## budgets
expect_error({
add_max_phylo_div_objective(p, NA_real_, sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, c(1, 1), sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, "a", sim_tree)
})
expect_error({
add_max_phylo_div_objective(p, TRUE, sim_tree)
})
## tree
expect_error({
add_max_phylo_div_objective(p, 1e+5, 1)
})
expect_error({
sim_tree2 <- sim_tree
sim_tree2$Nnode <- 1
add_max_phylo_div_objective(p, 1e+5, sim_tree2)
})
expect_error({
sim_tree2 <- ape::drop.tip(sim_tree, "F1")
add_max_phylo_div_objective(p, 1e+5, sim_tree2)
})
expect_error({
p %>%
add_max_phylo_div_objective(1e+5, sim_tree) %>%
add_rsymphony_solver() %>%
solve()
})
skip_if_not_installed("gurobi", "8.0.0")
expect_warning({
p %>%
add_max_phylo_div_objective(1e+5, replace(sim_tree, "edge.length",
NULL)) %>%
solve()
})
})
test_that("solution_statistics", {
# create data
projects <- tibble::tibble(name = c("P1", "P2", "P3", "P4"),
success = c(0.95, 0.96, 0.94, 1.00),
F1 = c(0.91, 0.00, 0.80, 0.10),
F2 = c(0.00, 0.92, 0.80, 0.10),
F3 = c(0.00, 0.00, 0.00, 0.10),
A1 = c(TRUE, FALSE, FALSE, FALSE),
A2 = c(FALSE, TRUE, FALSE, FALSE),
A3 = c(FALSE, FALSE, TRUE, FALSE),
A4 = c(FALSE, FALSE, FALSE, TRUE))
actions <- tibble::tibble(name = c("A1", "A2", "A3", "A4"),
cost = c(0.10, 0.10, 0.15, 0))
features <- tibble::tibble(name = c("F1", "F2", "F3"),
weight = c(100, 4, 9))
tree <- ape::read.tree(text = "((F1,F2),F3);")
tree$edge.length <- c(100, 5, 5, 5)
# create problem
p <- problem(projects, actions, features, "name", "success", "name", "cost",
"name", FALSE) %>%
add_max_phylo_div_objective(budget = 0.16, tree) %>%
add_feature_weights("weight") %>%
add_binary_decisions()
# create solutions
s <- data.frame(A1 = c(1, 0, 0, 1),
A2 = c(1, 1, 0, 1),
A3 = c(0, 0, 1, 1),
A4 = c(1, 1, 1, 1))
# evaluate solutions
ss <- solution_statistics(p, s)
# tests
expect_equal(ss$cost, c(0.1 + 0.1 + 0,
0.1 + 0,
0.15 + 0,
0.1 + 0.1 + 0.15 + 0))
expect_equal(ss$obj,
(ss$F1 * 100) + (ss$F2 * 4) + (ss$F3[1] * 9) +
(ss$F1 * 5) + (ss$F2 * 5) + (ss$F3 * 5) +
((1 - (1 - ss$F1) * (1 - ss$F2)) * 100))
expect_equal(ss$F1, c(0.95 * 0.91,
0.1 * 1,
0.94 * 0.8,
0.95 * 0.91))
expect_equal(ss$F2, c(0.96 * 0.92,
0.96 * 0.92,
0.94 * 0.8,
0.96 * 0.92))
expect_equal(ss$F3, c(0.1 * 1,
0.1 * 1,
0.1 * 1,
0.1 * 1))
})
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