Carry one observed record to each planned visit

1library(yamaa)23yamaa_spec("spec.yaml")$run()$output

1import yamaa23yamaa.spec("spec.yaml").run().output

1# Reference solution for the yamaa benchmark adam-advs-locf-record (R track).2#3# Written from the benchmark's full prompt and its inputs alone, as an agent4# would write it. Harbor's oracle agent runs it to check that the prompt can5# be solved and that the grader scores a correct answer 1.67library(dplyr, warn.conflicts = FALSE)8library(purrr)9library(readr)10library(tidyr)1112plan <- read_csv(13  "/app/input/plan.csv",14  col_types = cols(AVISITN = col_integer(), .default = col_character())15)16vs <- read_csv(17  "/app/input/vs.csv",18  col_types = cols(19    AVISITN = col_integer(), AVAL = col_double(), ADT = col_date(),20    QSSEQ = col_integer(), .default = col_character()21  )22)2324# Only results selected for analysis can be carried.25candidates <- vs |>26  filter(ANL01FL %in% "Y", !is.na(AVAL))2728# The latest non-missing selected result at or before the planned visit:29# the highest visit number, then the highest sequence number.30pick_donor <- function(usubjid, paramcd, avisitn) {31  donor <- candidates |>32    filter(USUBJID == usubjid, PARAMCD == paramcd, AVISITN <= avisitn) |>33    arrange(AVISITN, QSSEQ) |>34    slice_tail(n = 1)35  if (nrow(donor) == 0) {36    tibble::tibble(AVAL = NA_real_, ADT = as.Date(NA), QSSEQ = NA_integer_)37  } else {38    donor |> select(AVAL, ADT, QSSEQ)39  }40}4142advs <- plan |>43  arrange(USUBJID, PARAMCD, AVISITN) |>44  mutate(donor = pmap(list(USUBJID, PARAMCD, AVISITN), pick_donor)) |>45  unnest(donor) |>46  select(USUBJID, PARAMCD, AVISITN, AVAL, ADT, QSSEQ)4748dir.create("/app/output", showWarnings = FALSE)49write_csv(advs, "/app/output/advs.csv", na = "")

1# Reference solution for the yamaa benchmark adam-advs-locf-record (Python track).2#3# Written from the benchmark's full prompt and its inputs alone, as an agent4# would write it. Harbor's oracle agent runs it to check that the prompt can5# be solved and that the grader scores a correct answer 1.67"""Build the ADVS LOCF dataset from PLAN and VS.89One output record per planned visit (per subject and parameter). For each10planned visit, AVAL is the latest non-missing result selected for analysis11(ANL01FL = "Y") at or before the planned visit within the same subject and12parameter; the latest observation is the one with the highest visit number,13then the highest sequence number within that visit. ADT and QSSEQ are the14date and source sequence number of that same observation. A result of zero15counts as a collected result. With no such observation, AVAL, ADT, and16QSSEQ are blank.17"""1819import csv20import os2122INPUT_DIR = "/app/input"23OUTPUT_DIR = "/app/output"242526def read_rows(path):27    with open(path, newline="", encoding="utf-8") as handle:28        return list(csv.DictReader(handle))293031def main():32    plan = read_rows(os.path.join(INPUT_DIR, "plan.csv"))33    vs = read_rows(os.path.join(INPUT_DIR, "vs.csv"))3435    # Candidate observations: selected for analysis with a collected result.36    candidates = {}37    for row in vs:38        if row["ANL01FL"] != "Y":39            continue40        if row["AVAL"] is None or row["AVAL"].strip() == "":41            continue42        key = (row["USUBJID"], row["PARAMCD"])43        candidates.setdefault(key, []).append(row)4445    out_rows = []46    for planned in plan:47        key = (planned["USUBJID"], planned["PARAMCD"])48        planned_visit = int(planned["AVISITN"])49        eligible = [50            row51            for row in candidates.get(key, [])52            if int(row["AVISITN"]) <= planned_visit53        ]54        aval = adt = qsseq = ""55        if eligible:56            latest = max(57                eligible,58                key=lambda row: (int(row["AVISITN"]), int(row["QSSEQ"])),59            )60            aval = latest["AVAL"]61            adt = latest["ADT"]62            qsseq = latest["QSSEQ"]63        out_rows.append(64            {65                "USUBJID": planned["USUBJID"],66                "PARAMCD": planned["PARAMCD"],67                "AVISITN": planned["AVISITN"],68                "AVAL": aval,69                "ADT": adt,70                "QSSEQ": qsseq,71            }72        )7374    os.makedirs(OUTPUT_DIR, exist_ok=True)75    fieldnames = ["USUBJID", "PARAMCD", "AVISITN", "AVAL", "ADT", "QSSEQ"]76    with open(77        os.path.join(OUTPUT_DIR, "advs.csv"), "w", newline="", encoding="utf-8"78    ) as handle:79        writer = csv.DictWriter(handle, fieldnames=fieldnames)80        writer.writeheader()81        writer.writerows(out_rows)828384if __name__ == "__main__":85    main()

1/* opensas reference solution for the yamaa benchmark adam-advs-locf-record.2   Reads the benchmark inputs and derives the requested output.3   Execute with the existing opensas runtime: opensas /app/output/result.sas. */45data plan;6  length USUBJID PARAMCD $1024;7  infile "/app/input/plan.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;8  input USUBJID : $char1024. PARAMCD : $char1024. AVISITN;9run;1011data vs;12  length USUBJID PARAMCD ADT ANL01FL $1024;13  infile "/app/input/vs.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;14  input USUBJID : $char1024. PARAMCD : $char1024. AVISITN AVAL ADT : $char1024. QSSEQ ANL01FL : $char1024.;15run;1617proc sql;18  create table candidates as select a.USUBJID,a.PARAMCD,a.AVISITN,b.AVAL,b.ADT,b.QSSEQ,b.AVISITN as observedvisit from plan as a left join vs as b19    on a.USUBJID=b.USUBJID and a.PARAMCD=b.PARAMCD and b.AVISITN<=a.AVISITN and b.ANL01FL='Y' and not missing(b.AVAL);20quit;21proc sort data=candidates; by USUBJID PARAMCD AVISITN descending observedvisit descending QSSEQ; run;22data result; set candidates; by USUBJID PARAMCD AVISITN; if first.AVISITN; run;2324proc export data=result(keep=USUBJID PARAMCD AVISITN AVAL ADT QSSEQ) outfile="/app/output/advs.csv" dbms=csv replace; run;