Sum the Target Lesion Diameters

1library(yamaa)23adtr <- yamaa_domain("spec.yaml")$output4adtr

1import yamaa23adtr = yamaa.yamaa_domain("spec.yaml").output4adtr

1# Reference solution for the yamaa benchmark adam-adtr-sum (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)910trvisit <- read_csv(11  "/app/input/trvisit.csv",12  col_types = cols(AVISITN = col_integer(), ADT = col_date(), .default = col_character())13)14tr <- read_csv(15  "/app/input/tr.csv",16  col_types = cols(TRSTRESN = col_double(), .default = col_character())17)18tu <- read_csv(19  "/app/input/tu.csv",20  col_types = cols(.default = col_character())21)2223# Lesions selected as target at study entry: the same count at every24# assessment of the subject.25ntarget <- tu |>26  filter(TUGRPID == "TARGET") |>27  count(STUDYID, USUBJID, name = "NTARGET")2829# Target longest diameters at each assessment. A record with no result30# contributes nothing, while a zero counts as measured.31agg <- tr |>32  filter(TRGRPID == "TARGET", TRTESTCD == "LDIAM") |>33  group_by(STUDYID, USUBJID, AVISIT) |>34  summarise(35    AVAL_sum = sum(TRSTRESN, na.rm = TRUE),36    NMEAS = sum(!is.na(TRSTRESN)),37    .groups = "drop"38  ) |>39  mutate(AVAL_sum = if_else(NMEAS == 0L, NA_real_, AVAL_sum))4041adtr <- trvisit |>42  left_join(agg, by = c("STUDYID", "USUBJID", "AVISIT")) |>43  left_join(ntarget, by = c("STUDYID", "USUBJID")) |>44  mutate(45    AVAL = AVAL_sum,46    PARAMCD = "SDIAM",47    PARAM = "Sum of Target Lesion Diameters (mm)",48    ANL01FL = if_else(!is.na(NMEAS) & !is.na(NTARGET) & NMEAS == NTARGET, "Y", NA_character_)49  ) |>50  select(51    STUDYID, USUBJID, AVISIT, AVISITN, ADT, PARAMCD, PARAM,52    AVAL, NMEAS, NTARGET, ANL01FL53  ) |>54  arrange(USUBJID, AVISITN)5556dir.create("/app/output", showWarnings = FALSE)57write_csv(adtr, "/app/output/adtr.csv", na = "")

1# Reference solution for the yamaa benchmark adam-adtr-sum (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 pl1011trvisit = pl.read_csv("/app/input/trvisit.csv", infer_schema=False, null_values="").with_columns(12    pl.col("AVISITN").cast(pl.Int64, strict=False),13    pl.col("ADT").str.to_date(strict=False),14)15tr = pl.read_csv("/app/input/tr.csv", infer_schema=False, null_values="").with_columns(16    pl.col("TRSTRESN").cast(pl.Float64, strict=False),17)18tu = pl.read_csv("/app/input/tu.csv", infer_schema=False, null_values="")1920# Lesions selected as target at study entry: the same count at every21# assessment of the subject.22ntarget = (23    tu.filter(pl.col("TUGRPID") == "TARGET")24    .group_by(["STUDYID", "USUBJID"])25    .agg(NTARGET=pl.len())26)2728# Target longest diameters at each assessment. A record with no result29# contributes nothing, while a zero counts as measured.30agg = (31    tr.filter((pl.col("TRGRPID") == "TARGET") & (pl.col("TRTESTCD") == "LDIAM"))32    .group_by(["STUDYID", "USUBJID", "AVISIT"])33    .agg(34        __sum=pl.col("TRSTRESN").sum(),35        __cnt=pl.col("TRSTRESN").count(),36    )37)3839adtr = (40    trvisit.join(agg, on=["STUDYID", "USUBJID", "AVISIT"], how="left", maintain_order="left")41    .join(ntarget, on=["STUDYID", "USUBJID"], how="left")42    .with_columns(43        AVAL=pl.when(pl.col("__cnt") == 0)44        .then(None)45        .otherwise(pl.col("__sum")),46        NMEAS=pl.col("__cnt"),47        PARAMCD=pl.lit("SDIAM"),48        PARAM=pl.lit("Sum of Target Lesion Diameters (mm)"),49    )50    .with_columns(51        ANL01FL=pl.when(52            pl.col("NMEAS").is_not_null()53            & pl.col("NTARGET").is_not_null()54            & (pl.col("NMEAS") == pl.col("NTARGET"))55        )56        .then(pl.lit("Y"))57        .otherwise(None),58    )59    .select(60        "STUDYID",61        "USUBJID",62        "AVISIT",63        "AVISITN",64        "ADT",65        "PARAMCD",66        "PARAM",67        "AVAL",68        "NMEAS",69        "NTARGET",70        "ANL01FL",71    )72    .sort(["USUBJID", "AVISITN"])73)7475Path("/app/output").mkdir(parents=True, exist_ok=True)76adtr.write_csv("/app/output/adtr.csv", null_value="")

1/* opensas reference solution for the yamaa benchmark adam-adtr-sum.2   Reads the benchmark inputs and derives the requested output.3   Execute with the existing opensas runtime: opensas /app/output/result.sas. */45data tr;6  length STUDYID USUBJID AVISIT TRLNKID TRGRPID TRTESTCD TRSTAT $1024;7  infile "/app/input/tr.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;8  input STUDYID : $char1024. USUBJID : $char1024. AVISIT : $char1024. TRSEQ TRLNKID : $char1024. TRGRPID : $char1024. TRTESTCD : $char1024. TRSTRESN TRSTAT : $char1024.;9run;1011data trvisit;12  length STUDYID USUBJID AVISIT ADT $1024;13  infile "/app/input/trvisit.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;14  input STUDYID : $char1024. USUBJID : $char1024. AVISIT : $char1024. AVISITN ADT : $char1024.;15run;1617data tu;18  length STUDYID USUBJID TULNKID TUGRPID $1024;19  infile "/app/input/tu.csv" dsd dlm="," firstobs=2 truncover lrecl=32767;20  input STUDYID : $char1024. USUBJID : $char1024. TULNKID : $char1024. TUGRPID : $char1024.;21run;2223proc sql;24  create table inventory as select STUDYID,USUBJID,count(*) as NTARGET from tu where TUGRPID='TARGET' group by STUDYID,USUBJID;25  create table measures as select a.* from tr as a inner join tu as b on a.STUDYID=b.STUDYID and a.USUBJID=b.USUBJID and a.TRLNKID=b.TULNKID where a.TRGRPID='TARGET' and a.TRTESTCD='LDIAM' and b.TUGRPID='TARGET';26  create table totals as select STUDYID,USUBJID,AVISIT,sum(TRSTRESN) as AVAL,count(TRSTRESN) as NMEAS from measures group by STUDYID,USUBJID,AVISIT;27  create table combined as select a.*,b.AVAL,b.NMEAS,c.NTARGET from trvisit as a left join totals as b on a.STUDYID=b.STUDYID and a.USUBJID=b.USUBJID and a.AVISIT=b.AVISIT left join inventory as c on a.STUDYID=c.STUDYID and a.USUBJID=c.USUBJID;28quit;29data result;30  length PARAMCD $8 PARAM $40 ANL01FL $1;31  set combined; PARAMCD='SDIAM'; PARAM='Sum of Target Lesion Diameters (mm)';32  if not missing(NMEAS) and not missing(NTARGET) and NMEAS=NTARGET then ANL01FL='Y';33run;3435proc sql;36  create table final as select STUDYID, USUBJID, AVISIT, AVISITN, ADT, PARAMCD, PARAM, AVAL, NMEAS, NTARGET, ANL01FL from result;37quit;38proc export data=final outfile="/app/output/adtr.csv" dbms=csv replace; run;