# Integration tests for bpm_valsamp() (R/valsamp.R): solve for the sample size
# that meets precision / assurance targets via pre-posterior simulation and
# Robbins-Monro stochastic root finding. Stochastic and slow, so n_sim is kept
# small and the heavier net-benefit optimisation is exercised separately.

test_that("bpm_valsamp() returns an estimated sample size for a precision target", {
  skip_on_cran()
  set.seed(42)
  res <- quietly(bpm_valsamp(
    evidence = example_evidence(),
    targets = list(eciw.cstat = 0.1),
    n_sim = 40
  ))

  expect_type(res, "list")
  expect_true(all(c("results", "sample", "evidence", "targets") %in% names(res)))
  expect_true("eciw.cstat" %in% names(res$results))

  n <- res$results[["eciw.cstat"]]
  expect_true(is.finite(n) && n > 0)
  expect_s3_class(res$evidence, "bpm_evidence")
})

test_that("bpm_valsamp() carries through the supplied targets and a sample", {
  skip_on_cran()
  set.seed(7)
  targets <- list(eciw.cal_slp = 0.3)
  res <- quietly(bpm_valsamp(
    evidence = example_evidence(),
    targets = targets,
    n_sim = 40
  ))

  expect_identical(res$targets, targets)
  expect_s3_class(res$sample, "data.frame")
  expect_true(nrow(res$sample) == 40)
  # The simulated c-statistic draws are kept inside the valid (0.51, 0.99) band.
  expect_true(all(res$sample$cstat >= 0.51 & res$sample$cstat <= 0.99))
})

test_that("bpm_valsamp() accepts a previously simulated sample as evidence", {
  skip_on_cran()
  set.seed(11)
  first <- quietly(bpm_valsamp(
    evidence = example_evidence(),
    targets = list(eciw.cstat = 0.1),
    n_sim = 40
  ))
  # Re-using the returned $sample data frame should not re-process evidence.
  res <- quietly(bpm_valsamp(
    evidence = first$sample,
    targets = list(eciw.cstat = 0.1)
  ))
  expect_true("eciw.cstat" %in% names(res$results))
})

# NB targets. StoSOO searches over log(N), so small and large sample sizes are
# resolved alike. These tests check the answers against bpm_valprec()
# evaluated on the same pre-posterior sample.

test_that("bpm_valsamp() returns NA for an oa.nb target already met under current information", {
  skip_on_cran()
  set.seed(2026)
  # With this evidence, assurance with no new data is about 0.82 at threshold 0.2.
  expect_message(
    res <- suppressWarnings(bpm_valsamp(
      evidence = example_evidence(),
      targets = list(oa.nb = 0.75),
      n_sim = 1000,
      threshold = 0.2,
      ex_args = no_progress
    )),
    "already meets the target"
  )
  expect_true(is.na(res$results[["oa.nb"]]))
  expect_gte(res$nb$oa.nb$value0, 0.75)
  expect_true(is.na(res$nb$oa.nb$N))

  # value0 is the no-data assurance that bpm_valprec() reports as oa.nb0.
  prec <- quietly(bpm_valprec(
    N = 100, evidence = res$sample,
    targets = list(oa.nb = TRUE), threshold = 0.2
  ))
  expect_equal(unname(prec$results[1, "oa.nb0"]), res$nb$oa.nb$value0)
})

test_that("bpm_valsamp() resolves small oa.nb and voi.nb sample sizes, matching bpm_valprec()", {
  skip_on_cran()
  set.seed(2)
  res <- quietly(bpm_valsamp(
    evidence = example_evidence(),
    targets = list(oa.nb = 0.87, voi.nb = 0.8),
    n_sim = 2000,
    threshold = 0.2
  ))
  n_oa <- res$results[["oa.nb"]]
  n_voi <- res$results[["voi.nb"]]
  expect_true(res$nb$oa.nb$value0 < 0.87)
  # This target is met in the low hundreds.
  expect_true(is.finite(n_oa) && n_oa < 306)
  expect_true(is.finite(n_voi))

  prec <- quietly(bpm_valprec(
    N = c(round(n_oa / 2), n_oa, 2 * n_oa, n_voi),
    evidence = res$sample,
    targets = list(oa.nb = TRUE, voi.nb = TRUE),
    threshold = 0.2
  ))
  m <- prec$results
  expect_true(m[1, "oa.nb"] < 0.87)
  expect_true(abs(m[2, "oa.nb"] - 0.87) < 0.025)
  expect_true(m[3, "oa.nb"] > 0.87)
  expect_true(abs(m[4, "voi.nb"] - 0.8) < 0.05)

  # A target not reached within the search range gives NA and a warning.
  expect_warning(
    capped <- suppressMessages(bpm_valsamp(
      evidence = res$sample,
      targets = list(oa.nb = 0.99),
      threshold = 0.2,
      ex_args = c(no_progress, list(nb_n_range = c(1, 100)))
    )),
    "not reached"
  )
  expect_true(is.na(capped$results[["oa.nb"]]))
})

test_that("bpm_valsamp() returns NA for both NB targets when there is no decision uncertainty", {
  skip_on_cran()
  set.seed(3)
  # A precise, well-calibrated model: it has the highest NB in every draw.
  evidence <- list(
    prev     ~ beta(3000, 7000),
    cstat    ~ beta(9000, 1000),
    cal_mean ~ norm(0, 0.001),
    cal_slp  ~ norm(1, 0.001)
  )
  msgs <- character(0)
  res <- withCallingHandlers(
    suppressWarnings(bpm_valsamp(
      evidence = evidence,
      targets = list(oa.nb = 0.9, voi.nb = 0.8),
      n_sim = 200,
      threshold = 0.3,
      impute_cor = FALSE,
      ex_args = no_progress
    )),
    message = function(m) {
      msgs <<- c(msgs, conditionMessage(m))
      invokeRestart("muffleMessage")
    }
  )
  expect_equal(res$nb$oa.nb$value0, 1)
  expect_true(is.na(res$results[["oa.nb"]]))
  expect_true(is.na(res$results[["voi.nb"]]))
  expect_true(any(grepl("already meets the target", msgs)))
  expect_true(any(grepl("EVPI = 0", msgs)))
})

test_that("bpm_valsamp() rejects NB targets outside (0, 1)", {
  expect_error(
    bpm_valsamp(evidence = example_evidence(), targets = list(oa.nb = 1.2), threshold = 0.2),
    "strictly between 0 and 1"
  )
  expect_error(
    bpm_valsamp(evidence = example_evidence(), targets = list(voi.nb = c(0.5, 0.8)), threshold = 0.2),
    "strictly between 0 and 1"
  )
})