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* Generalise interpretive rules for multi-guideline support (#268) - Rename data-raw/eucast_rules.tsv → interpretive_rules.tsv; add rule.provider column (value: "EUCAST") to distinguish future CLSI rows - Rename EUCAST_RULES_DF → INTERPRETIVE_RULES_DF in _pre_commit_checks.R; filter by rule.provider == guideline when applying rules in interpretive_rules() - Rename custom_eucast_rules() → custom_interpretive_rules() with new S3 class "custom_interpretive_rules"; old function becomes a deprecated wrapper in zz_deprecated.R; backward-compat S3 dispatch shims added for old class - Remove stop_if(guideline == "CLSI", ...) so clsi_rules() no longer errors - Add .onLoad shim in zzz.R to create INTERPRETIVE_RULES_DF from EUCAST_RULES_DF for transitional compatibility until sysdata.rda is regenerated https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP * Fix namespace load failure: remove assignInNamespace from .onLoad (#268) assignInNamespace cannot add NEW bindings to a locked package namespace (R locks namespace bindings before .onLoad runs). Replace the .onLoad shim with a runtime fallback inside interpretive_rules(): if INTERPRETIVE_RULES_DF is absent (pre-regeneration sysdata.rda), derive it from EUCAST_RULES_DF by adding the rule.provider column. This also fixes the screening_abx line to reuse the already-resolved interpretive_rules_df_total instead of a bare INTERPRETIVE_RULES_DF reference. https://claude.ai/code/session_01D46BTsfJSPo3HnLWp3PRkP * fixes * fixes --------- Co-authored-by: Claude <noreply@anthropic.com>
This commit is contained in:
Matthijs Berends
GitHub
250
tests/testthat/test-interpretive_rules.R
Executable file
250
tests/testthat/test-interpretive_rules.R
Executable file
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# ==================================================================== #
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# TITLE: #
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# AMR: An R Package for Working with Antimicrobial Resistance Data #
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# #
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# SOURCE CODE: #
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# https://github.com/msberends/AMR #
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# #
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# PLEASE CITE THIS SOFTWARE AS: #
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# Berends MS, Luz CF, Friedrich AW, et al. (2022). #
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# AMR: An R Package for Working with Antimicrobial Resistance Data. #
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# Journal of Statistical Software, 104(3), 1-31. #
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# https://doi.org/10.18637/jss.v104.i03 #
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# #
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# Developed at the University of Groningen and the University Medical #
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# Center Groningen in The Netherlands, in collaboration with many #
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# colleagues from around the world, see our website. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://amr-for-r.org #
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# ==================================================================== #
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test_that("test-interpretive_rules.R", {
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skip_on_cran()
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# thoroughly check input table
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expect_equal(
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sort(colnames(AMR:::INTERPRETIVE_RULES_DF)),
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sort(c(
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"rule.provider",
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"if_mo_property", "like.is.one_of", "this_value",
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"and_these_antibiotics", "have_these_values",
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"then_change_these_antibiotics", "to_value",
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"reference.rule", "reference.rule_group",
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"reference.version",
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"note"
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))
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)
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MOs_mentioned <- unique(AMR:::INTERPRETIVE_RULES_DF$this_value)
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MOs_mentioned <- sort(trimws(unlist(strsplit(MOs_mentioned[!AMR:::is_valid_regex(MOs_mentioned)], ",", fixed = TRUE))))
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MOs_test <- suppressWarnings(
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trimws(paste(
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mo_genus(MOs_mentioned, keep_synonyms = TRUE, language = NULL),
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mo_species(MOs_mentioned, keep_synonyms = TRUE, language = NULL),
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mo_subspecies(MOs_mentioned, keep_synonyms = TRUE, language = NULL)
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))
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)
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MOs_test[MOs_test == ""] <- mo_fullname(MOs_mentioned[MOs_test == ""], keep_synonyms = TRUE, language = NULL)
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expect_equal(MOs_mentioned, MOs_test)
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expect_error(suppressWarnings(interpretive_rules(example_isolates, col_mo = "Non-existing")))
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expect_error(interpretive_rules(x = "text"))
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expect_error(interpretive_rules(data.frame(a = "test")))
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expect_error(interpretive_rules(data.frame(mo = "test"), rules = "invalid rules set"))
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# expect_warning(interpretive_rules(data.frame(mo = "Escherichia coli", vancomycin = "S", stringsAsFactors = TRUE)))
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expect_identical(
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colnames(example_isolates),
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colnames(suppressWarnings(interpretive_rules(example_isolates, info = FALSE)))
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)
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expect_output(suppressMessages(interpretive_rules(example_isolates, info = TRUE)))
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a <- data.frame(
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mo = c(
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"Klebsiella pneumoniae",
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"Pseudomonas aeruginosa",
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"Enterobacter cloacae"
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),
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amox = "-", # Amoxicillin
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stringsAsFactors = FALSE
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)
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b <- data.frame(
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mo = c(
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"Klebsiella pneumoniae",
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"Pseudomonas aeruginosa",
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"Enterobacter cloacae"
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),
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amox = "R", # Amoxicillin
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stringsAsFactors = FALSE
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)
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expect_identical(suppressWarnings(interpretive_rules(a, "mo", info = FALSE)), b)
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expect_output(suppressMessages(suppressWarnings(interpretive_rules(a, "mo", info = TRUE))))
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a <- data.frame(
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mo = c(
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"Staphylococcus aureus",
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"Streptococcus group A"
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),
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COL = "-", # Colistin
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stringsAsFactors = FALSE
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)
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b <- data.frame(
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mo = c(
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"Staphylococcus aureus",
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"Streptococcus group A"
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),
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COL = "R", # Colistin
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stringsAsFactors = FALSE
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)
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expect_equal(suppressWarnings(interpretive_rules(a, "mo", info = FALSE)), b)
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# piperacillin must be R in Enterobacteriaceae when tica is R
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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expect_equal(
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suppressWarnings(
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example_isolates %>%
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filter(mo_family(mo) == "Enterobacteriaceae") %>%
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mutate(
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TIC = as.sir("R"),
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PIP = as.sir("S")
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) %>%
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interpretive_rules(col_mo = "mo", version_expertrules = 3.1, rules = "expert", info = FALSE, overwrite = TRUE) %>%
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pull(PIP) %>%
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unique() %>%
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as.character()
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),
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"R"
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)
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}
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# azithromycin and clarythromycin must be equal to Erythromycin
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a <- suppressWarnings(as.sir(interpretive_rules(
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data.frame(
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mo = example_isolates$mo,
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ERY = example_isolates$ERY,
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AZM = as.sir("R"),
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CLR = factor("R"),
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stringsAsFactors = FALSE
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),
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version_expertrules = 3.1,
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rules = "expert",
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overwrite = TRUE,
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only_sir_columns = FALSE
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)$CLR))
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b <- example_isolates$ERY
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expect_identical(
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a[!is.na(b)],
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b[!is.na(b)]
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)
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# amox is inferred by benzylpenicillin in Kingella kingae
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expect_equal(
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suppressWarnings(
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as.list(interpretive_rules(
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data.frame(
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mo = as.mo("Kingella kingae"),
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PEN = "S",
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AMX = "-",
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stringsAsFactors = FALSE
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),
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info = FALSE
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))$AMX
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),
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"S"
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)
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# also test norf
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if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
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expect_output(suppressWarnings(interpretive_rules(example_isolates %>% mutate(NOR = "S", NAL = "S"), info = TRUE)))
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}
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# check verbose output
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expect_output(suppressWarnings(interpretive_rules(example_isolates, verbose = TRUE, rules = "all", info = TRUE)))
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# AmpC de-repressed cephalo mutants
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expect_identical(
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interpretive_rules(
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data.frame(
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mo = c("Escherichia coli", "Enterobacter cloacae"),
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cefotax = as.sir(c("S", "S"))
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),
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ampc_cephalosporin_resistance = TRUE,
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rules = "expert",
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overwrite = TRUE,
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info = FALSE
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)$cefotax,
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as.sir(c("S", "R"))
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)
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expect_identical(
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interpretive_rules(
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data.frame(
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mo = c("Escherichia coli", "Enterobacter cloacae"),
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cefotax = as.sir(c("S", "S"))
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),
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ampc_cephalosporin_resistance = NA,
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rules = "expert",
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overwrite = TRUE,
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info = FALSE
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)$cefotax,
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as.sir(c("S", NA))
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)
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expect_identical(
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interpretive_rules(
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data.frame(
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mo = c("Escherichia coli", "Enterobacter cloacae"),
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cefotax = as.sir(c("S", "S"))
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),
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rules = "expert",
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ampc_cephalosporin_resistance = NULL,
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overwrite = TRUE,
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info = FALSE
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)$cefotax,
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as.sir(c("S", "S"))
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)
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# EUCAST dosage -----------------------------------------------------------
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expect_equal(nrow(eucast_dosage(c("tobra", "genta", "cipro"))), 3)
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expect_inherits(eucast_dosage(c("tobra", "genta", "cipro")), "data.frame")
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x <- custom_interpretive_rules(
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AMC == "R" & genus == "Klebsiella" ~ aminopenicillins == "R",
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AMC == "I" & genus == "Klebsiella" ~ aminopenicillins == "I",
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AMX == "S" ~ AMC == "S"
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)
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expect_output(print(x))
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expect_output(print(c(x, x)))
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expect_output(print(as.list(x, x)))
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# this custom rules makes 8 changes
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expect_equal(
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nrow(interpretive_rules(example_isolates,
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rules = "custom",
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custom_rules = x,
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info = FALSE,
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overwrite = TRUE,
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verbose = TRUE
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)),
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8,
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tolerance = 0.5
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)
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# clsi_rules() no longer errors (returns data unchanged until CLSI rows are added)
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expect_identical(
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suppressWarnings(clsi_rules(example_isolates, info = FALSE)),
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example_isolates
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)
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})
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