context("evdsi")

test_that("single species", {
  skip_on_cran()
  skip_on_os("windows")
  # data
  ## set seeds
  set.seed(123)
  ## set constants
  n_f <- 1
  n_sites <- 20
  n_folds <- 2
  n_vars <- 3
  seed <- 123
  ## simulate data
  site_data <- simulate_site_data(n_sites, n_f, 0.1, n_vars)
  feature_data <- simulate_feature_data(n_f)
  feature_data$target <- rep(7, n_f)
  total_budget <- sum(site_data$management_cost * 0.8)
  site_data$survey <- FALSE
  site_data$survey[which(site_data$f1 < 0.5)[1:2]] <- TRUE
  ## create matrices for data
  pij <- prior_probability_matrix(
    site_data, feature_data,
    paste0("f", seq_len(n_f)),
    paste0("n", seq_len(n_f)), paste0("p", seq_len(n_f)),
    "survey_sensitivity", "survey_specificity",
    "model_sensitivity", "model_specificity")
  # calculations
  r1 <- evdsi(
    site_data = site_data,
    feature_data = feature_data,
    site_detection_columns = paste0("f", seq_len(n_f)),
    site_n_surveys_columns = paste0("n", seq_len(n_f)),
    site_probability_columns = paste0("p", seq_len(n_f)),
    site_survey_scheme_column = "survey",
    site_management_cost_column = "management_cost",
    site_survey_cost_column = "survey_cost",
    feature_survey_column = "survey",
    feature_survey_sensitivity_column = "survey_sensitivity",
    feature_survey_specificity_column = "survey_specificity",
    feature_model_sensitivity_column = "model_sensitivity",
    feature_model_specificity_column = "model_specificity",
    feature_target_column = "target",
    total_budget = total_budget)
  r2 <- r_expected_value_of_decision_given_survey_scheme(
    pij = pij,
    survey_features = feature_data$survey,
    survey_sensitivity = feature_data$survey_sensitivity,
    survey_specificity = feature_data$survey_specificity,
    pu_survey_solution = site_data$survey,
    pu_survey_costs  = site_data$survey_cost,
    pu_purchase_costs = site_data$management_cost,
    pu_purchase_locked_in = rep(FALSE, nrow(site_data)),
    pu_purchase_locked_out = rep(FALSE, nrow(site_data)),
    obj_fun_target = feature_data$target,
    total_budget = total_budget)
  # tests
  expect_equal(r1, r2)
})

test_that("multiple species", {
  skip_on_cran()
  skip_on_os("windows")
  # data
  ## set seeds
  set.seed(123)
  ## set constants
  n_f <- 3
  n_sites <- 20
  n_folds <- 2
  n_vars <- 3
  seed <- 123
  ## simulate data
  site_data <- simulate_site_data(n_sites, n_f, 0.1, n_vars)
  feature_data <- simulate_feature_data(n_f)
  feature_data$target <- rep(7, n_f)
  feature_data$survey <- c(TRUE, FALSE, TRUE)
  total_budget <- sum(site_data$management_cost * 0.8)
  site_data$survey <- FALSE
  site_data$survey[which(site_data$f1 < 0.5)[1:2]] <- TRUE
  ## create matrices for data
  pij <- prior_probability_matrix(
    site_data, feature_data,
    paste0("f", seq_len(n_f)),
    paste0("n", seq_len(n_f)), paste0("p", seq_len(n_f)),
    "survey_sensitivity", "survey_specificity",
    "model_sensitivity", "model_specificity")
  # calculations
  r1 <- evdsi(
    site_data = site_data,
    feature_data = feature_data,
    site_detection_columns = paste0("f", seq_len(n_f)),
    site_n_surveys_columns = paste0("n", seq_len(n_f)),
    site_probability_columns = paste0("p", seq_len(n_f)),
    site_survey_scheme_column = "survey",
    site_management_cost_column = "management_cost",
    site_survey_cost_column = "survey_cost",
    feature_survey_column = "survey",
    feature_survey_sensitivity_column = "survey_sensitivity",
    feature_survey_specificity_column = "survey_specificity",
    feature_model_sensitivity_column = "model_sensitivity",
    feature_model_specificity_column = "model_specificity",
    feature_target_column = "target",
    total_budget = total_budget)
  r2 <- r_expected_value_of_decision_given_survey_scheme(
    pij = pij,
    survey_features = feature_data$survey,
    survey_sensitivity = feature_data$survey_sensitivity,
    survey_specificity = feature_data$survey_specificity,
    pu_survey_solution = site_data$survey,
    pu_survey_costs = site_data$survey_cost,
    pu_purchase_costs = site_data$management_cost,
    pu_purchase_locked_in = rep(FALSE, nrow(site_data)),
    pu_purchase_locked_out = rep(FALSE, nrow(site_data)),
    obj_fun_target = feature_data$target,
    total_budget = total_budget)
  # tests
  expect_equal(r1, r2)
})