Derive growth percentile records

1library(yamaa)23yamaa_project("project.yaml")$spec("ADVS")$run()$save()4yamaa_spec("spec_norm_cdf.yaml")$run()$output

1import yamaa23yamaa.project("project.yaml").spec("ADVS").run().save()4yamaa.spec("spec_norm_cdf.yaml").run().output

1# Reference solution for the yamaa benchmark adam-advs-percentiles (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)910raw <- read_csv(11  "/app/input/advs_raw.csv",12  col_types = cols(AVAL = col_double(), AGE = col_integer(), .default = col_character())13)14ref <- read_csv(15  "/app/input/lms_ref.csv",16  col_types = cols(17    AGE = col_integer(), L = col_double(), M = col_double(), S = col_double(),18    .default = col_character()19  )20)2122# The growth percentile against the matched L, M, and S coefficients.23advs <- raw |>24  left_join(ref, by = c("PARAMCD", "SEX", "AGE")) |>25  mutate(26    Z = case_when(27      is.na(AVAL) | is.na(L) | is.na(M) | is.na(S) | M == 0 | S == 0 ~ NA_real_,28      L == 0 ~ log(AVAL / M) / S,29      .default = ((AVAL / M) ^ L - 1) / (L * S)30    ),31    AVAL = 100 * pnorm(Z),32    PARAMCD = if_else(PARAMCD == "BMI", "BMIPCTL", "WGTPCTL"),33    PARAM = if_else(PARAMCD == "BMIPCTL", "BMI-for-Age Percentile", "Weight-for-Age Percentile")34  ) |>35  arrange(USUBJID, AVISIT, PARAMCD) |>36  select(STUDYID, USUBJID, AVISIT, PARAMCD, PARAM, AVAL)3738dir.create("/app/output", showWarnings = FALSE)39write_csv(advs, "/app/output/advs.csv", na = "")

1# Reference solution for the yamaa benchmark adam-advs-percentiles (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 ADVS growth percentiles from ADVS_RAW and LMS_REF.89For each collected BMI or WEIGHT measurement, compute the LMS z-score10using the reference coefficients matched on measurement code, sex, and11age in days, then convert to a percentile via the standard normal CDF.12"""1314import csv15import math16import os1718INPUT_DIR = "/app/input"19OUTPUT_DIR = "/app/output"2021PARAM_MAP = {22    "BMI": ("BMIPCTL", "BMI-for-Age Percentile"),23    "WEIGHT": ("WGTPCTL", "Weight-for-Age Percentile"),24}252627def norm_cdf(z):28    return 0.5 * (1.0 + math.erf(z / math.sqrt(2.0)))293031def main():32    with open(os.path.join(INPUT_DIR, "lms_ref.csv"), newline="") as f:33        ref = {}34        for row in csv.DictReader(f):35            key = (row["PARAMCD"], row["SEX"], int(row["AGE"]))36            ref[key] = (float(row["L"]), float(row["M"]), float(row["S"]))3738    with open(os.path.join(INPUT_DIR, "advs_raw.csv"), newline="") as f:39        raw_rows = list(csv.DictReader(f))4041    out_rows = []42    for row in raw_rows:43        code = row["PARAMCD"]44        paramcd, param = PARAM_MAP[code]45        aval = ""46        value_text = (row["AVAL"] or "").strip()47        coeffs = ref.get((code, row["SEX"], int(row["AGE"])))48        if value_text != "" and coeffs is not None:49            x = float(value_text)50            L, M, S = coeffs51            z = ((x / M) ** L - 1.0) / (L * S)52            aval = 100.0 * norm_cdf(z)53        out_rows.append(54            {55                "STUDYID": row["STUDYID"],56                "USUBJID": row["USUBJID"],57                "AVISIT": row["AVISIT"],58                "PARAMCD": paramcd,59                "PARAM": param,60                "AVAL": aval,61            }62        )6364    os.makedirs(OUTPUT_DIR, exist_ok=True)65    with open(os.path.join(OUTPUT_DIR, "advs.csv"), "w", newline="") as f:66        writer = csv.DictWriter(67            f, fieldnames=["STUDYID", "USUBJID", "AVISIT", "PARAMCD", "PARAM", "AVAL"]68        )69        writer.writeheader()70        writer.writerows(out_rows)717273if __name__ == "__main__":74    main()

1/* opensas reference solution for the yamaa benchmark adam-advs-percentiles.2   Reads the benchmark inputs and derives the requested output.3   Execute with the existing opensas runtime: opensas /app/output/result.sas. */45data advs_raw;6  length STUDYID USUBJID AVISIT PARAMCD SEX $1024;7  infile "/app/input/advs_raw.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;8  input STUDYID : $char1024. USUBJID : $char1024. AVISIT : $char1024. PARAMCD : $char1024. AVAL AGE SEX : $char1024.;9run;1011data lms_ref;12  length PARAMCD SEX $1024;13  infile "/app/input/lms_ref.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;14  input PARAMCD : $char1024. SEX : $char1024. AGE L M S;15run;1617proc sql;18  create table joined as select a.*,b.L,b.M,b.S from advs_raw as a left join lms_ref as b on a.PARAMCD=b.PARAMCD and a.SEX=b.SEX and a.AGE=b.AGE;19quit;20data result;21  length PARAM $40;22  set joined;23  value=AVAL; AVAL=.;24  if not missing(value) and not missing(M) and not missing(L) and not missing(S) then do;25    if L=0 then z=log(value/M)/S; else z=((value/M)**L-1)/(L*S);26    AVAL=100*probnorm(z);27  end;28  if PARAMCD='BMI' then do; PARAMCD='BMIPCTL'; PARAM='BMI-for-Age Percentile'; end;29  else if PARAMCD='WEIGHT' then do; PARAMCD='WGTPCTL'; PARAM='Weight-for-Age Percentile'; end;30run;3132proc sql;33  create table final as select STUDYID, USUBJID, AVISIT, PARAMCD, PARAM, AVAL from result;34quit;35proc export data=final outfile="/app/output/advs.csv" dbms=csv replace; run;