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Migrate parallel computing in as.sir() from parallel:: to future/future.apply (#280)
* Migrate parallel computing in as.sir() from parallel:: to future/future.apply Replace parallel::mclapply() and parallel::parLapply() with future.apply::future_lapply(), enabling transparent support for any future backend (multisession, multicore, mirai_multisession, cluster) on all platforms including Windows. When parallel = TRUE the function now: (1) respects an active future::plan() set by the user without overriding it on exit, or (2) sets a temporary multisession plan with parallelly::availableCores() and tears it down on exit. The max_cores argument controls worker count only when no user plan is active. future and future.apply are added to Suggests in DESCRIPTION. https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8 * Require user plan() for parallel=TRUE; fix as_wt_nwt false-positive warnings - parallel = TRUE now errors with a cli-styled message if no non-sequential future::plan() is active; users must call e.g. future::plan(future::multisession) before using parallel = TRUE (breaking change) - Removed auto-setup/teardown of multisession plan inside as.sir(), which was slow and caused version-mismatch issues with load_all() workflows - Added as_wt_nwt to the exclusion list in as_sir_method() to suppress false-positive "no longer used" warnings during parallel runs - Fixed pieces_per_col row-batch calculation to use n_workers (total available workers from the active plan) instead of n_cores (workers clipped to n_cols), so row-batch mode activates correctly when n_cols < n_workers - Updated @param parallel and @param max_cores roxygen docs; regenerated man/as.sir.Rd - Updated sequential-mode hint to instruct users to set plan() first https://claude.ai/code/session_01M1Jvf2Miu6JL4TQrEh1wS8 * fix parallel * fix parallel * unit tests * unit tedts --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
GitHub
@@ -408,13 +408,13 @@ test_that("test-sir.R", {
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# Issue #278: re-running as.sir() on already-<sir> data must preserve columns
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df_already_sir <- data.frame(
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mo = "B_ESCHR_COLI",
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mo = "B_ESCHR_COLI",
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AMC = as.mic(c("1", "2", "4")),
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GEN = sample(c("S", "I", "R"), 3, replace = TRUE),
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stringsAsFactors = FALSE
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)
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first_pass <- suppressMessages(as.sir(df_already_sir, col_mo = "mo", info = FALSE))
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second_pass <- suppressMessages(as.sir(first_pass, col_mo = "mo", info = FALSE))
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first_pass <- suppressMessages(as.sir(df_already_sir, col_mo = "mo", info = FALSE))
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second_pass <- suppressMessages(as.sir(first_pass, col_mo = "mo", info = FALSE))
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expect_equal(ncol(first_pass), ncol(second_pass))
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expect_true(is.sir(second_pass[["AMC"]]))
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expect_true(is.sir(second_pass[["GEN"]]))
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@@ -424,15 +424,15 @@ test_that("test-sir.R", {
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# Issue #278: metadata columns whose names coincidentally match antibiotic
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# codes (e.g. 'patient' -> OXY, 'ward' -> PRU) must not be processed
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df_meta <- data.frame(
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mo = "B_ESCHR_COLI",
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mo = "B_ESCHR_COLI",
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patient = paste0("Pt_", 1:20),
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ward = rep(c("ICU", "Surgery", "Outpatient", "ED"), 5),
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AMC = as.mic(rep(c("1", "2", "4", "8"), 5)),
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ward = rep(c("ICU", "Surgery", "Outpatient", "ED"), 5),
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AMC = as.mic(rep(c("1", "2", "4", "8"), 5)),
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stringsAsFactors = FALSE
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)
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df_meta_sir <- suppressMessages(as.sir(df_meta, col_mo = "mo", info = FALSE))
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expect_true("patient" %in% colnames(df_meta_sir))
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expect_true("ward" %in% colnames(df_meta_sir))
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expect_true("ward" %in% colnames(df_meta_sir))
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expect_false(is.sir(df_meta_sir[["patient"]]))
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expect_false(is.sir(df_meta_sir[["ward"]]))
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expect_true(is.sir(df_meta_sir[["AMC"]]))
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@@ -441,92 +441,111 @@ test_that("test-sir.R", {
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# Tests must pass even when only 1 core is available; parallel = TRUE then
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# silently falls back to sequential, but results must still be identical.
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set.seed(42)
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n_par <- 200
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df_par <- data.frame(
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mo = "B_ESCHR_COLI",
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AMC = as.mic(sample(c("0.25", "0.5", "1", "2", "4", "8", "16", "32"), n_par, TRUE)),
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GEN = as.mic(sample(c("0.5", "1", "2", "4", "8", "16", "32", "64"), n_par, TRUE)),
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CIP = as.mic(sample(c("0.001", "0.002", "0.004", "0.008", "0.016", "0.032"), n_par, TRUE)),
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PEN = sample(c("S", "I", "R", NA_character_), n_par, TRUE),
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stringsAsFactors = FALSE
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)
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if (AMR:::pkg_is_available("future.apply")) {
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set.seed(42)
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n_par <- 200
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df_par <- data.frame(
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mo = "B_ESCHR_COLI",
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AMC = as.mic(sample(c("0.25", "0.5", "1", "2", "4", "8", "16", "32"), n_par, TRUE)),
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GEN = as.mic(sample(c("0.5", "1", "2", "4", "8", "16", "32", "64"), n_par, TRUE)),
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CIP = as.mic(sample(c("0.001", "0.002", "0.004", "0.008", "0.016", "0.032"), n_par, TRUE)),
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PEN = sample(c("S", "I", "R", NA_character_), n_par, TRUE),
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stringsAsFactors = FALSE
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)
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# clear any existing history before comparing
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sir_interpretation_history(clean = TRUE)
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sir_seq <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE))
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log_seq <- sir_interpretation_history(clean = TRUE)
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# clear any existing history before comparing
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sir_interpretation_history(clean = TRUE)
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sir_seq <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE))
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log_seq <- sir_interpretation_history(clean = TRUE)
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sir_par <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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log_par <- sir_interpretation_history(clean = TRUE)
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future::plan(future::multicore)
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n_max_workers <- future::nbrOfWorkers()
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# 1. parallel = TRUE gives identical SIR results to sequential
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expect_identical(sir_seq[["AMC"]], sir_par[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_par[["GEN"]])
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expect_identical(sir_seq[["CIP"]], sir_par[["CIP"]])
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expect_identical(sir_seq[["PEN"]], sir_par[["PEN"]])
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sir_par <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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log_par <- sir_interpretation_history(clean = TRUE)
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# 2. same number of log rows as sequential
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expect_equal(nrow(log_seq), nrow(log_par))
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# 1. parallel = TRUE gives identical SIR results to sequential
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expect_identical(sir_seq[["AMC"]], sir_par[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_par[["GEN"]])
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expect_identical(sir_seq[["CIP"]], sir_par[["CIP"]])
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expect_identical(sir_seq[["PEN"]], sir_par[["PEN"]])
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# 3. pre-existing log entries must not be duplicated
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# run sequential once to populate the history, then run parallel and
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# verify the new parallel run adds exactly as many rows as sequential
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sir_interpretation_history(clean = TRUE)
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suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE)) # populate history
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pre_n <- nrow(sir_interpretation_history())
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suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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post_n <- nrow(sir_interpretation_history())
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expect_equal(post_n - pre_n, nrow(log_seq)) # exactly one run's worth of new rows
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sir_interpretation_history(clean = TRUE)
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# 2. same number of log rows as sequential
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expect_equal(nrow(log_seq), nrow(log_par))
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# 4. two sequential runs and two parallel runs yield identical results
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sir_par2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_par[["AMC"]], sir_par2[["AMC"]])
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expect_identical(sir_par[["GEN"]], sir_par2[["GEN"]])
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# 3. pre-existing log entries must not be duplicated
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# run sequential once to populate the history, then run parallel and
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# verify the new parallel run adds exactly as many rows as sequential
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sir_interpretation_history(clean = TRUE)
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future::plan(future::sequential)
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suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE)) # populate history
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pre_n <- nrow(sir_interpretation_history())
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future::plan(future::multicore)
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suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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post_n <- nrow(sir_interpretation_history())
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expect_equal(post_n - pre_n, nrow(log_seq)) # exactly one run's worth of new rows
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sir_interpretation_history(clean = TRUE)
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# 5. max_cores = 1 gives same results as default sequential
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sir_mc1 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 1L))
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expect_identical(sir_seq[["AMC"]], sir_mc1[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_mc1[["GEN"]])
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# 4. two sequential runs and two parallel runs yield identical results
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sir_par2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_par[["AMC"]], sir_par2[["AMC"]])
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expect_identical(sir_par[["GEN"]], sir_par2[["GEN"]])
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# 6. max_cores = 2 and max_cores = 3 give same results as sequential
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sir_mc2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 2L))
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sir_mc3 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 3L))
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expect_identical(sir_seq[["AMC"]], sir_mc2[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_mc3[["GEN"]])
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# 5. used cores = 1 gives same results as default sequential
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future::plan(future::multicore, workers = 1)
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sir_mc1 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_seq[["AMC"]], sir_mc1[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_mc1[["GEN"]])
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# 7. single-column data frame falls back silently to sequential
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df_single <- df_par[, c("mo", "AMC")]
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sir_single_seq <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE))
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sir_single_par <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_single_seq[["AMC"]], sir_single_par[["AMC"]])
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# 6. used cores = 2 and used cores = 3 give same results as sequential
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if (n_max_workers >= 3) {
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future::plan(future::multicore, workers = 2)
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sir_mc2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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future::plan(future::multicore, workers = 3)
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sir_mc3 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_seq[["AMC"]], sir_mc2[["AMC"]])
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expect_identical(sir_seq[["GEN"]], sir_mc3[["GEN"]])
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}
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# 9. row-batch mode (n_cols < n_cores): force row splitting via max_cores and
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# verify identical output to sequential for a dataset with 2 AB columns so
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# pieces_per_col = ceiling(max_cores / 2) >= 2 and row batching activates
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df_wide <- data.frame(
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mo = "B_ESCHR_COLI",
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AMC = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
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GEN = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
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stringsAsFactors = FALSE
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)
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sir_wide_seq <- suppressMessages(as.sir(df_wide, col_mo = "mo", info = FALSE))
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sir_wide_par <- suppressMessages(as.sir(df_wide, col_mo = "mo", info = FALSE,
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parallel = TRUE, max_cores = 8L))
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expect_identical(sir_wide_seq[["AMC"]], sir_wide_par[["AMC"]])
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expect_identical(sir_wide_seq[["GEN"]], sir_wide_par[["GEN"]])
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# 7. single-column data frame falls back silently to sequential
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df_single <- df_par[, c("mo", "AMC")]
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future::plan(future::sequential)
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sir_single_seq <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE))
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future::plan(future::multicore)
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sir_single_par <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE, parallel = TRUE))
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expect_identical(sir_single_seq[["AMC"]], sir_single_par[["AMC"]])
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# 8. info = TRUE with parallel does not produce per-column worker messages
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# (messages should only appear in the main process, not duplicated from workers)
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msgs <- capture.output(
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suppressWarnings(as.sir(df_par, col_mo = "mo", info = TRUE, parallel = TRUE)),
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type = "message"
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)
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# each AB column name should appear at most once in all messages combined
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for (ab_nm in c("AMC", "GEN", "CIP", "PEN")) {
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n_mentions <- sum(grepl(ab_nm, msgs, fixed = TRUE))
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expect_lte(n_mentions, 1L)
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# 8. row-batch mode (n_cols < n_cores): force row splitting via used cores and
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# verify identical output to sequential for a dataset with 2 AB columns so
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# pieces_per_col = ceiling(used cores / 2) >= 2 and row batching activates
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df_wide <- data.frame(
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mo = "B_ESCHR_COLI",
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AMC = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
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GEN = as.mic(sample(c("1", "2", "4", "8"), n_par, TRUE)),
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stringsAsFactors = FALSE
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)
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future::plan(future::sequential)
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sir_wide_seq <- suppressMessages(as.sir(df_wide, col_mo = "mo", info = FALSE))
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future::plan(future::multicore)
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sir_wide_par <- suppressMessages(as.sir(df_wide,
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col_mo = "mo", info = FALSE,
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parallel = TRUE
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))
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expect_identical(sir_wide_seq[["AMC"]], sir_wide_par[["AMC"]])
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expect_identical(sir_wide_seq[["GEN"]], sir_wide_par[["GEN"]])
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# 8. info = TRUE with parallel does not produce per-column worker messages
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# (messages should only appear in the main process, not duplicated from workers)
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msgs <- capture.output(
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suppressWarnings(as.sir(df_par, col_mo = "mo", info = TRUE, parallel = TRUE)),
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type = "message"
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)
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# each AB column name should appear at most once in all messages combined
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for (ab_nm in c("AMC", "GEN", "CIP", "PEN")) {
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n_mentions <- sum(grepl(ab_nm, msgs, fixed = TRUE))
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expect_lte(n_mentions, 1L)
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}
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future::plan(future::sequential)
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}
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})
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@@ -536,9 +555,9 @@ test_that("custom reference_data: non-EUCAST/CLSI guideline produces R", {
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# coerce_reference_data_columns() will coerce mo/ab to the right class.
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my_bp <- clinical_breakpoints[clinical_breakpoints$method == "MIC" &
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clinical_breakpoints$type == "human", ][1, ]
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my_bp$guideline <- "MyLab 2025"
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my_bp$mo <- "B_ACHRMB_XYLS" # plain character — coerced to <mo>
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my_bp$ab <- "MEM" # plain character — coerced to <ab>
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my_bp$guideline <- "MyLab 2025"
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my_bp$mo <- "B_ACHRMB_XYLS" # plain character — coerced to <mo>
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my_bp$ab <- "MEM" # plain character — coerced to <ab>
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my_bp$breakpoint_S <- 8
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my_bp$breakpoint_R <- 32
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@@ -556,26 +575,30 @@ test_that("custom reference_data: non-EUCAST/CLSI guideline produces R", {
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# guideline explicitly set: same result when it matches the data
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expect_equal(as.character(suppressMessages(
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as.sir(as.mic(64), mo = "B_ACHRMB_XYLS", ab = "MEM",
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guideline = "MyLab 2025", reference_data = my_bp)
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as.sir(as.mic(64),
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mo = "B_ACHRMB_XYLS", ab = "MEM",
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guideline = "MyLab 2025", reference_data = my_bp
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)
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)), "R")
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})
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test_that("custom reference_data: host = NA acts as host-agnostic fallback", {
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my_bp <- clinical_breakpoints[clinical_breakpoints$method == "MIC" &
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clinical_breakpoints$type == "human", ][1, ]
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my_bp$guideline <- "MyLab 2025"
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my_bp$mo <- "B_ACHRMB_XYLS"
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my_bp$ab <- "MEM"
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my_bp$type <- "animal"
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my_bp$host <- NA # logical NA — coerced to character by coerce_reference_data_columns()
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my_bp$guideline <- "MyLab 2025"
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my_bp$mo <- "B_ACHRMB_XYLS"
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my_bp$ab <- "MEM"
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my_bp$type <- "animal"
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my_bp$host <- NA # logical NA — coerced to character by coerce_reference_data_columns()
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my_bp$breakpoint_S <- 8
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my_bp$breakpoint_R <- 32
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# NA host should match when no species-specific row exists
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result <- suppressMessages(
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as.sir(as.mic(64), mo = "B_ACHRMB_XYLS", ab = "MEM",
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host = "dogs", breakpoint_type = "animal", reference_data = my_bp)
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as.sir(as.mic(64),
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mo = "B_ACHRMB_XYLS", ab = "MEM",
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host = "dogs", breakpoint_type = "animal", reference_data = my_bp
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)
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)
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expect_equal(as.character(result), "R")
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})
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