#!/usr/bin/env python3 """Extract IE-CoR (Indo-European Cognate Relationships) cognate pairs. Reads sources/iecor/cldf/cognates.csv + forms.csv + languages.csv. Standard CLDF CognateTable format. Output: staging/cognate_pairs/iecor_cognate_pairs.tsv (14-column schema) """ from __future__ import annotations import csv import io import sys from collections import defaultdict from itertools import combinations from pathlib import Path sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8") sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8") ROOT = Path(__file__).resolve().parent.parent sys.path.insert(0, str(ROOT / "cognate_pipeline" / "src")) sys.path.insert(0, str(ROOT / "scripts")) from cognate_pipeline.normalise.sound_class import ipa_to_sound_class # noqa: E402 SOURCES_DIR = ROOT / "sources" / "iecor" / "cldf" STAGING_DIR = ROOT / "staging" / "cognate_pairs" STAGING_DIR.mkdir(parents=True, exist_ok=True) HEADER = ( "Lang_A\tWord_A\tIPA_A\tLang_B\tWord_B\tIPA_B\tConcept_ID\t" "Relationship\tScore\tSource\tRelation_Detail\tDonor_Language\t" "Confidence\tSource_Record_ID\n" ) def sca_similarity(ipa_a: str, ipa_b: str) -> float: """Compute normalised Levenshtein similarity on SCA strings.""" try: sca_a = ipa_to_sound_class(ipa_a) sca_b = ipa_to_sound_class(ipa_b) except Exception: return 0.0 if not sca_a or not sca_b: return 0.0 m, n = len(sca_a), len(sca_b) if m == 0 or n == 0: return 0.0 dp = list(range(n + 1)) for i in range(1, m + 1): prev = dp[0] dp[0] = i for j in range(1, n + 1): temp = dp[j] if sca_a[i - 1] == sca_b[j - 1]: dp[j] = prev else: dp[j] = 1 + min(prev, dp[j], dp[j - 1]) prev = temp dist = dp[n] return round(1.0 - dist / max(m, n), 4) def main(): print("=" * 60) print("IE-CoR Cognate Extraction") print("=" * 60) # Step 1: Read languages.csv → Language_ID → ISO code lang_path = SOURCES_DIR / "languages.csv" lang_iso = {} with open(lang_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: lid = row["ID"] iso = row.get("ISO639P3code", "").strip() if iso: lang_iso[lid] = iso print(f" Languages with ISO codes: {len(lang_iso)}") # Step 2: Read parameters.csv for concept glosses params_path = SOURCES_DIR / "parameters.csv" param_concept = {} if params_path.exists(): with open(params_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: pid = row["ID"] concept = row.get("Concepticon_Gloss", row.get("Name", pid)).strip() if concept: param_concept[pid] = concept # Step 3: Read forms.csv → Form_ID → {iso, word, ipa, concept} forms_path = SOURCES_DIR / "forms.csv" forms = {} with open(forms_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: fid = row["ID"] lid = str(row["Language_ID"]) iso = lang_iso.get(lid, "") if not iso: continue form = row.get("Form", row.get("Value", "")).strip() if not form: continue # IE-CoR has phon_form and Phonemic columns for IPA ipa = row.get("phon_form", "").strip() if not ipa: ipa = row.get("Phonemic", "").strip() if not ipa: ipa = form.lower() # fallback to orthographic param_id = row.get("Parameter_ID", "").strip() concept = param_concept.get(param_id, param_id) forms[fid] = { "iso": iso, "word": form, "ipa": ipa, "concept": concept, } print(f" Forms loaded: {len(forms)}") # Step 4a: Read loans.csv → collect Cognateset_IDs involved in loans loans_path = SOURCES_DIR / "loans.csv" loan_cogset_ids: set[str] = set() if loans_path.exists(): with open(loans_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: cid = row.get("Cognateset_ID", "").strip() if cid: loan_cogset_ids.add(cid) print(f" Loan-involved cognate sets: {len(loan_cogset_ids)}") # Step 4b: Read cognates.csv → group by Cognateset_ID cognates_path = SOURCES_DIR / "cognates.csv" cogsets: dict[str, list[dict]] = defaultdict(list) doubt_count = 0 total_rows = 0 with open(cognates_path, "r", encoding="utf-8") as f: reader = csv.DictReader(f) for row in reader: total_rows += 1 form_id = row["Form_ID"] cogset_id = row["Cognateset_ID"] doubt = row.get("Doubt", "false").strip().lower() == "true" if doubt: doubt_count += 1 form_data = forms.get(form_id) if form_data is None: continue cogsets[cogset_id].append({ **form_data, "doubt": doubt, "cogset_id": cogset_id, }) print(f" Cognate rows: {total_rows}") print(f" Doubtful: {doubt_count}") print(f" Cognate sets: {len(cogsets)}") # Step 5: Generate cross-language pairs output_path = STAGING_DIR / "iecor_cognate_pairs.tsv" pair_count = 0 loan_pair_count = 0 inherited_pair_count = 0 with open(output_path, "w", encoding="utf-8") as out: out.write(HEADER) for cogset_id, members in cogsets.items(): is_loan = cogset_id in loan_cogset_ids relation_detail = "loan_involved" if is_loan else "inherited" for a, b in combinations(members, 2): if a["iso"] == b["iso"]: continue score = sca_similarity(a["ipa"], b["ipa"]) confidence = "doubtful" if (a["doubt"] or b["doubt"]) else "certain" out.write( f"{a['iso']}\t{a['word']}\t{a['ipa']}\t" f"{b['iso']}\t{b['word']}\t{b['ipa']}\t" f"{a['concept']}\texpert_cognate\t{score}\tiecor\t" f"{relation_detail}\t-\t{confidence}\t{cogset_id}\n" ) pair_count += 1 if is_loan: loan_pair_count += 1 else: inherited_pair_count += 1 if pair_count % 100000 == 0: print(f" ... {pair_count:,} pairs written") print(f"\n Total pairs: {pair_count:,}") print(f" Loan-involved pairs: {loan_pair_count:,}") print(f" Purely inherited pairs: {inherited_pair_count:,}") print(f" Output: {output_path}") print("=" * 60) if __name__ == "__main__": main()