Normal Limit Check

1library(yamaa)23adlb <- yamaa_domain("spec.yaml")$output4adlb

1import yamaa23adlb = yamaa.yamaa_domain("spec.yaml").output4adlb

1# Reference solution for the yamaa benchmark adam-adlb-reported-precision (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(readr)910lb <- read_csv(11  "/app/input/lb.csv",12  col_types = cols(13    AVAL = col_double(),14    ANRLO = col_double(),15    .default = col_character()16  )17)1819# The result as a multiple of the lower limit, rounded once to four places20# with an exact half away from zero.21adlb <- lb |>22  mutate(23    ratio = AVAL / ANRLO,24    R2ANRLO = case_when(25      is.na(AVAL) | is.na(ANRLO) | ANRLO == 0 ~ NA_real_,26      ratio >= 0 ~ floor(ratio * 10000 + 0.5) / 10000,27      .default = ceiling(ratio * 10000 - 0.5) / 1000028    )29  ) |>30  arrange(USUBJID) |>31  select(STUDYID, USUBJID, PARAMCD, AVAL, ANRLO, R2ANRLO)3233dir.create("/app/output", showWarnings = FALSE)34write_csv(adlb, "/app/output/adlb.csv", na = "")

1# Reference solution for the yamaa benchmark adam-adlb-reported-precision (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 1.67from pathlib import Path89import polars as pl1011lb = pl.read_csv("/app/input/lb.csv", infer_schema=False).with_columns(12    pl.col("AVAL").cast(pl.Float64, strict=False),13    pl.col("ANRLO").cast(pl.Float64, strict=False).alias("ANRLO_NUM"),14)1516# Keep the collected AVAL/ANRLO as numbers; the ratio is rounded once to17# four places with half away from zero.18ratio = pl.col("AVAL") / pl.col("ANRLO_NUM")19rounded = (20    pl.when(ratio >= 0)21    .then(((ratio * 10000) + 0.5).floor() / 10000)22    .otherwise(((ratio * 10000) - 0.5).ceil() / 10000)23)2425adlb = (26    lb.with_columns(27        ANRLO=pl.col("ANRLO_NUM"),28        R2ANRLO=pl.when(29            pl.col("AVAL").is_null()30            | pl.col("ANRLO_NUM").is_null()31            | (pl.col("ANRLO_NUM") == 0)32        )33        .then(None)34        .otherwise(rounded),35    )36    .select("STUDYID", "USUBJID", "PARAMCD", "AVAL", "ANRLO", "R2ANRLO")37    .sort("USUBJID")38)3940Path("/app/output").mkdir(exist_ok=True)41adlb.write_csv("/app/output/adlb.csv")

1/* opensas reference solution for the yamaa benchmark adam-adlb-reported-precision.2   Reads the benchmark inputs and derives the requested output.3   Execute with the existing opensas runtime: opensas /app/output/result.sas. */45data lb;6  length STUDYID USUBJID PARAMCD $1024;7  infile "/app/input/lb.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;8  input STUDYID : $char1024. USUBJID : $char1024. PARAMCD : $char1024. AVAL ANRLO;9run;1011data result;12  set lb;13  if not missing(AVAL) and not missing(ANRLO) and ANRLO ne 0 then R2ANRLO=round(AVAL/ANRLO,0.0001);14run;1516proc sql;17  create table final as select STUDYID, USUBJID, PARAMCD, AVAL, ANRLO, R2ANRLO from result;18quit;19proc export data=final outfile="/app/output/adlb.csv" dbms=csv replace; run;