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| """Extract Sino-Tibetan cognate pairs from sinotibetan_dump.tsv.
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| Fixes: filters out entries with BORROWING flag, uses IPA column (not concept).
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| Output: staging/cognate_pairs/sinotibetan_cognate_pairs.tsv (14-column schema)
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| """
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|
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| from __future__ import annotations
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|
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| import csv
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| import io
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| import sys
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| from collections import defaultdict
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| from itertools import combinations
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| from pathlib import Path
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| sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8")
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| sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8")
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|
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| ROOT = Path(__file__).resolve().parent.parent
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| sys.path.insert(0, str(ROOT / "cognate_pipeline" / "src"))
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| sys.path.insert(0, str(ROOT / "scripts"))
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|
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| from cognate_pipeline.normalise.sound_class import ipa_to_sound_class
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|
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| SOURCE_FILE = ROOT / "sources" / "sinotibetan" / "sinotibetan_dump.tsv"
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| STAGING_DIR = ROOT / "staging" / "cognate_pairs"
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| STAGING_DIR.mkdir(parents=True, exist_ok=True)
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|
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| HEADER = (
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| "Lang_A\tWord_A\tIPA_A\tLang_B\tWord_B\tIPA_B\tConcept_ID\t"
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| "Relationship\tScore\tSource\tRelation_Detail\tDonor_Language\t"
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| "Confidence\tSource_Record_ID\n"
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| )
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| DOCULECT_MAP = {
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| "Old_Chinese": "och",
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| "Japhug": "jya",
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| "Tibetan_Written": "bod",
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| "Old_Burmese": "obr",
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| "Jingpho": "kac",
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| "Lisu": "lis",
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| "Naxi": "nxq",
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| "Khaling": "klr",
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| "Limbu": "lif",
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| "Pumi_Lanping": "pmi",
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| "Qiang_Mawo": "qxs",
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| "Tujia": "tji",
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| "Dulong": "duu",
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| "Hakha": "cnh",
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| "Bai_Jianchuan": "bca",
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| }
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| def sca_similarity(ipa_a: str, ipa_b: str) -> float:
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| """Compute normalised Levenshtein similarity on SCA strings."""
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| try:
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| sca_a = ipa_to_sound_class(ipa_a)
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| sca_b = ipa_to_sound_class(ipa_b)
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| except Exception:
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| return 0.0
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| if not sca_a or not sca_b:
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| return 0.0
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| m, n = len(sca_a), len(sca_b)
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| if m == 0 or n == 0:
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| return 0.0
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| dp = list(range(n + 1))
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| for i in range(1, m + 1):
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| prev = dp[0]
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| dp[0] = i
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| for j in range(1, n + 1):
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| temp = dp[j]
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| if sca_a[i - 1] == sca_b[j - 1]:
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| dp[j] = prev
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| else:
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| dp[j] = 1 + min(prev, dp[j], dp[j - 1])
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| prev = temp
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| dist = dp[n]
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| return round(1.0 - dist / max(m, n), 4)
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| def main():
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| print("=" * 60)
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| print("Sino-Tibetan Cognate Extraction v2")
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| print("=" * 60)
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| if not SOURCE_FILE.exists():
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| print(f"ERROR: Source file not found: {SOURCE_FILE}")
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| sys.exit(1)
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| cogsets: dict[str, list[dict]] = defaultdict(list)
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| total_rows = 0
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| skipped_borrowing = 0
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| skipped_no_cogid = 0
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| skipped_unknown_doculect = 0
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| with open(SOURCE_FILE, "r", encoding="utf-8") as f:
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| reader = csv.DictReader(f, delimiter="\t")
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| for row in reader:
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| total_rows += 1
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| doculect = row.get("DOCULECT", "").strip()
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| iso = DOCULECT_MAP.get(doculect, "")
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| if not iso:
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| skipped_unknown_doculect += 1
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| continue
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| borrowing = row.get("BORROWING", "").strip()
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| if borrowing:
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| skipped_borrowing += 1
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| continue
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| cogid = row.get("COGID", "").strip()
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| if not cogid:
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| skipped_no_cogid += 1
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| continue
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| ipa = row.get("IPA", "").strip()
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| concept = row.get("CONCEPT", "").strip()
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| if not ipa:
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| continue
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| cogsets[f"st_{cogid}"].append({
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| "iso": iso,
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| "word": ipa,
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| "ipa": ipa,
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| "concept": concept,
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| })
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| print(f" Total rows: {total_rows}")
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| print(f" Skipped (borrowing): {skipped_borrowing}")
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| print(f" Skipped (no COGID): {skipped_no_cogid}")
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| print(f" Skipped (unknown doculect): {skipped_unknown_doculect}")
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| print(f" Cognate sets: {len(cogsets)}")
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| output_path = STAGING_DIR / "sinotibetan_cognate_pairs.tsv"
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| pair_count = 0
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| with open(output_path, "w", encoding="utf-8") as out:
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| out.write(HEADER)
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| for cogset_id, members in cogsets.items():
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| for a, b in combinations(members, 2):
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| if a["iso"] == b["iso"]:
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| continue
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| score = sca_similarity(a["ipa"], b["ipa"])
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| out.write(
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| f"{a['iso']}\t{a['word']}\t{a['ipa']}\t"
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| f"{b['iso']}\t{b['word']}\t{b['ipa']}\t"
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| f"{a['concept']}\texpert_cognate\t{score}\tsinotibetan\t"
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| f"inherited\t-\tcertain\t{cogset_id}\n"
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| )
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| pair_count += 1
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| print(f"\n Total pairs: {pair_count:,}")
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| print(f" Output: {output_path}")
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| print("=" * 60)
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| if __name__ == "__main__":
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| main()
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|
