| """Convert a blocking candidates parquet into the challenge `candidate_pairs.tsv`. |
| |
| One row per Source-1 id of the S1 file, in file order; candidate ids comma-joined, |
| deduplicated, S2-/S3- ids only, empty when the entity has no candidates. Only the columns |
| s1_id and cand_id are read, so any later-stage parquet (e.g. a filtered candidate set, which |
| is what the rules require: the set actually fed to the matcher) works as input. |
| |
| python -m src.make_candidate_tsv --candidates cands.parquet \ |
| --s1 dataset/test/test_source1.tsv --out output/candidate_pairs.tsv [--order-by sim_nameaddr] |
| """ |
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
|
|
| import polars as pl |
|
|
|
|
| def main(argv=None) -> None: |
| ap = argparse.ArgumentParser(description=__doc__.split("\n\n")[0]) |
| ap.add_argument("--candidates", required=True, help="parquet with columns s1_id, cand_id") |
| ap.add_argument("--s1", required=True, help="Source-1 TSV (or parquet with entity_id) defining the rows") |
| ap.add_argument("--out", required=True) |
| ap.add_argument("--order-by", default=None, help="optional score column; lists sorted by it descending") |
| a = ap.parse_args(argv) |
|
|
| cols = ["s1_id", "cand_id"] + ([a.order_by] if a.order_by else []) |
| c = ( |
| pl.scan_parquet(a.candidates) |
| .select(cols) |
| .filter(pl.col("cand_id").str.starts_with("S2-") | pl.col("cand_id").str.starts_with("S3-")) |
| ) |
| if a.order_by: |
| c = c.group_by(["s1_id", "cand_id"]).agg(pl.col(a.order_by).max()).sort( |
| ["s1_id", a.order_by, "cand_id"], descending=[False, True, False], nulls_last=True |
| ) |
| else: |
| c = c.unique(["s1_id", "cand_id"]).sort(["s1_id", "cand_id"]) |
| grouped = c.group_by("s1_id", maintain_order=True).agg(pl.col("cand_id")).collect() |
| grouped = grouped.with_columns(pl.col("cand_id").list.unique(maintain_order=True).list.join(",").alias("candidate_entity_ids")) |
|
|
| if a.s1.endswith(".parquet"): |
| s1 = pl.read_parquet(a.s1, columns=["entity_id"]).with_columns(pl.col("entity_id").cast(pl.Utf8)) |
| else: |
| s1 = pl.read_csv(a.s1, separator="\t", quote_char=None, infer_schema_length=0, columns=["entity_id"]) |
| s1 = s1.with_columns(pl.col("entity_id").str.strip_chars()).with_row_index("_r") |
| n_dup = s1.height - s1.get_column("entity_id").n_unique() |
| if n_dup: |
| print(f"WARNING: {n_dup} duplicate entity_id rows in {a.s1}; keeping the first", file=sys.stderr) |
| s1 = s1.unique("entity_id", keep="first", maintain_order=True) |
| out = ( |
| s1.join(grouped.select("s1_id", "candidate_entity_ids"), left_on="entity_id", right_on="s1_id", how="left") |
| .sort("_r") |
| .select(pl.col("entity_id").alias("source1_entity_id"), pl.col("candidate_entity_ids").fill_null("")) |
| ) |
| unknown = grouped.filter(~pl.col("s1_id").is_in(s1.get_column("entity_id"))).height |
| if unknown: |
| print(f"WARNING: {unknown} candidate s1_ids are not in {a.s1}; dropped", file=sys.stderr) |
| out.write_csv(a.out, separator="\t", quote_style="never", include_header=True) |
| n_c = out.get_column("candidate_entity_ids").str.split(",").list.len() |
| has = out.get_column("candidate_entity_ids") != "" |
| print( |
| f"wrote {a.out}: {out.height:,} S1 rows, {int(n_c.filter(has).sum()):,} candidate ids, " |
| f"{int((~has).sum()):,} rows without candidates, mean {float(n_c.filter(has).sum()) / max(out.height, 1):.2f} per S1" |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
