improve test cov

src/rcpp_expected_value_of_action.cpp

@@ -45,6 +45,11 @@ double expected_value_of_action(
   return spp_prob.sum();
 }
 
+// this function is used for internal debugging when we need to calculate
+// the expected value of actions using methods that produce correct
+// (not approximation) results
+
+// nocov start
 double exact_expected_value_of_action(
   Eigen::MatrixXd &pij,
   Rcpp::IntegerVector &target_values) {
@@ -70,6 +75,7 @@ double exact_expected_value_of_action(
   }
   return out;
 }
+// nocov end
 
 double approx_expected_value_of_action(
   std::vector<std::vector<double>> &pij,
@@ -130,15 +136,6 @@ double approx_expected_value_of_action(
   return out;
 }
 
-double log_proxy_expected_value_of_action(
-  Eigen::MatrixXd &log_1m_pij) {
-  Eigen::VectorXd out = log_1m_pij.rowwise().sum();
-  // note we that we use log_substract(0, ..) because 0 is log(1.0)
-  for (auto itr = out.data(); itr != out.data() + out.size(); ++itr)
-    *itr = log_subtract(0.0, *itr);
-  return log_sum(out);
-}
-
 // [[Rcpp::export]]
 double rcpp_expected_value_of_action(
   std::vector<bool> solution,

tests/testthat/test_greedy_heuristic_prioritization.R

@@ -38,3 +38,40 @@ test_that("expected result", {
     (1 - ((1 - 0.8) * (1 - 0.8))) * 2
   )
 })
+
+test_that("expected result (default locked in and locked out)", {
+  # data
+  site_data <- sf::st_as_sf(
+    tibble::tibble(
+      x = seq_len(3),
+      y = x,
+      management_cost = c(100, 100, 100),
+      locked_in = c(FALSE, FALSE, FALSE),
+      locked_out = c(FALSE, FALSE, FALSE),
+      p1 = c(0.8, 0.8, 0.8),
+      p2 = c(0.05, 0.8, 0.8)
+    ),
+    coords = c("x", "y")
+  )
+  feature_data <- tibble::tibble(
+    name = letters[1:2],
+    target = c(1, 1)
+  )
+  budget = 300
+  # results
+  x <- greedy_heuristic_prioritization(
+    site_data,
+    feature_data,
+    site_probability_columns = c("p1", "p2"),
+    site_management_cost_column = "management_cost",
+    feature_target_column = "target",
+    total_budget = budget
+  )
+  # tests
+  expect_equal(x$x, c(FALSE, TRUE, TRUE))
+  expect_equal(
+    x$objval,
+    # sum of probability values that each species meets its target
+    (1 - ((1 - 0.8) * (1 - 0.8))) * 2
+  )
+})