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"""Build stratified validation datasets for cognate detection ML training.
Reads lexicon TSVs and cognate-pair TSVs from data/training/, builds a
phylogenetic tree of language relationships, and generates stratified
validation sets split by phylogenetic distance, timespan, family, and
concept domain (religious terms).
Output goes to data/training/validation/.
"""
from __future__ import annotations
import csv
import json
import random
import sys
from collections import defaultdict
from pathlib import Path
from typing import Any
# Ensure cognate_pipeline package is importable
sys.path.insert(
0,
str(Path(__file__).resolve().parent.parent / "cognate_pipeline" / "src"),
)
from cognate_pipeline.normalise.sound_class import ipa_to_sound_class
# ---------------------------------------------------------------------------
# Paths
# ---------------------------------------------------------------------------
REPO_ROOT = Path(__file__).resolve().parent.parent
TRAINING_DIR = REPO_ROOT / "data" / "training"
LEXICONS_DIR = TRAINING_DIR / "lexicons"
COGNATE_DIR = TRAINING_DIR / "cognate_pairs"
OUTPUT_DIR = TRAINING_DIR / "validation"
FAMILY_MAP_PATH = (
REPO_ROOT
/ "cognate_pipeline"
/ "src"
/ "cognate_pipeline"
/ "cognate"
/ "family_map.json"
)
PAIR_CAP = 50_000
SEED = 42
MAX_PAIRS_PER_CONCEPT_PER_LEVEL = 100
MAX_CROSS_FAMILY_PAIRS_PER_CONCEPT = 50
TRUE_NEG_SAMPLE_ATTEMPTS = 2_000_000
# ---------------------------------------------------------------------------
# TSV field names for output
# ---------------------------------------------------------------------------
OUTPUT_FIELDS = [
"Lang_A",
"Word_A",
"IPA_A",
"SCA_A",
"Lang_B",
"Word_B",
"IPA_B",
"SCA_B",
"Concept_ID",
"Label",
"Phylo_Dist",
"Timespan",
"Score",
"Source",
]
# ---------------------------------------------------------------------------
# Era classification
# ---------------------------------------------------------------------------
ANCIENT: set[str] = {
"grc", "lat", "san", "ave", "got", "akk", "egy", "phn", "uga",
"sux", "hit", "osc", "xum", "gmy", "sga", "chu", "och", "obr",
"cop", "arc", "syc", "ett",
}
MEDIEVAL: set[str] = {
"ang", "enm", "fro", "osp", "non", "goh", "dum", "mga", "wlm",
"orv", "otk", "ota", "okm", "kaw", "mnc", "bod",
}
def classify_era(iso: str) -> str:
"""Return 'ancient', 'medieval', or 'modern'."""
if iso in ANCIENT:
return "ancient"
if iso in MEDIEVAL:
return "medieval"
return "modern"
def get_timespan(iso_a: str, iso_b: str) -> str:
"""Return one of the four canonical timespan buckets."""
era_a = classify_era(iso_a)
era_b = classify_era(iso_b)
eras = frozenset((era_a, era_b))
if eras == {"ancient"}:
return "ancient_ancient"
if eras == {"modern"}:
return "modern_modern"
if eras == {"medieval"}:
return "medieval_modern"
if "ancient" in eras and "medieval" in eras:
return "ancient_modern"
if "ancient" in eras and "modern" in eras:
return "ancient_modern"
# medieval + modern
return "medieval_modern"
# ---------------------------------------------------------------------------
# Religious concepts — organised by sub-domain (generous classification)
# ---------------------------------------------------------------------------
RELIGIOUS_CORE: set[str] = {
"DEITY", "DEITY/GOD", "GOD", "SPIRIT", "TEMPLE", "ALTAR", "SACRIFICE",
"WORSHIP", "PRAY", "PRIEST", "HOLY", "PREACH", "BLESS", "CHURCH",
"MOSQUE", "SOUL", "RELIGION", "IDOL", "MINISTER",
}
RELIGIOUS_SUPERNATURAL: set[str] = {
"GHOST", "DEMON", "MAGIC", "SORCERER", "MAGICIAN", "OMEN",
"ELF OR FAIRY", "FAIRY TALE", "DREAM (SOMETHING)", "DREAM",
}
RELIGIOUS_MORAL: set[str] = {
"SIN", "BAD OR EVIL", "EVIL", "BELIEVE", "TRUTH", "SHAME",
"GUILTY", "CRIME", "ADULTERY", "PITY", "FORGIVE", "INNOCENT",
"ACCUSE", "CONDEMN", "JUDGE", "JUDGMENT", "LAW", "PUNISHMENT",
"WITNESS", "POOR", "RICH", "FAITHFUL", "GOOD",
}
RELIGIOUS_RITUAL: set[str] = {
"CURSE", "FAST", "CIRCUMCISION", "INITIATION CEREMONY", "WEDDING",
"OATH", "SWEAR", "CUSTOM", "BURY", "GRAVE", "CORPSE", "DANCE",
"DRUM", "SONG",
}
RELIGIOUS_VERBS: set[str] = {
"GIVE", "DONATE", "KNEEL", "BURN (SOMETHING)", "KILL", "POUR",
"FEED", "SHARE", "INVITE", "COMMAND", "PROMISE", "OBEY",
"HELP", "PROTECT", "DEFEND", "RESCUE", "HOPE (SOMETHING)",
"FEAR (BE AFRAID)", "FEAR (FRIGHT)", "LOVE",
}
RELIGIOUS_COSMIC: set[str] = {
"HEAVEN", "HELL", "LIGHTNING", "THUNDER", "FIRE", "SUN", "MOON",
"STAR", "SKY", "RAINBOW", "EARTHQUAKE", "WORLD", "LIFE",
"DEATH", "BE DEAD OR DIE", "BE ALIVE", "BE BORN",
"ANCESTORS", "DESCENDANTS",
}
RELIGIOUS_PLACES: set[str] = {
"TEMPLE", "CHURCH", "MOSQUE", "CAVE", "MOUNTAIN", "MOUNTAIN OR HILL",
"SPRING OR WELL", "GARDEN", "FOREST", "STONE", "STONE OR ROCK",
"VILLAGE", "TOWN", "COUNTRY", "ISLAND", "RIVER", "SEA",
"NATIVE COUNTRY",
}
# Additional numeric IDs from cross-lingual datasets that map to religious concepts
_RELIGIOUS_NUMERIC_IDS: set[str] = {
"3231", "53", "911", "853", "1103", "257", "24", "852", "1702", "304",
"391", "8", "303", "1565", "878", "1973", "1945", "392", "2137", "1175",
"107", "1349", "1603", "811", "2971", "661", "1944",
}
# Union of all sub-domains — the generous set
RELIGIOUS_ALL: set[str] = (
RELIGIOUS_CORE
| RELIGIOUS_SUPERNATURAL
| RELIGIOUS_MORAL
| RELIGIOUS_RITUAL
| RELIGIOUS_VERBS
| RELIGIOUS_COSMIC
| RELIGIOUS_PLACES
| _RELIGIOUS_NUMERIC_IDS
)
# Sub-domain name → concept set mapping (for generating sub-domain files)
RELIGIOUS_SUBDOMAINS: dict[str, set[str]] = {
"core_religious": RELIGIOUS_CORE,
"supernatural": RELIGIOUS_SUPERNATURAL,
"moral_ethical": RELIGIOUS_MORAL,
"ritual_ceremony": RELIGIOUS_RITUAL,
"religious_verbs": RELIGIOUS_VERBS,
"cosmic_spiritual": RELIGIOUS_COSMIC,
"sacred_places": RELIGIOUS_PLACES,
}
# Pre-compute uppercase set for fast case-insensitive matching
_RELIGIOUS_ALL_UPPER: set[str] = {c.upper() for c in RELIGIOUS_ALL}
def is_religious(concept_id: str) -> bool:
"""Return True if *concept_id* refers to a religious concept."""
if concept_id in RELIGIOUS_ALL:
return True
return concept_id.upper() in _RELIGIOUS_ALL_UPPER
def _in_subdomain(concept_id: str, subdomain_set: set[str]) -> bool:
"""Return True if *concept_id* belongs to the given sub-domain set."""
if concept_id in subdomain_set:
return True
return concept_id.upper() in {c.upper() for c in subdomain_set}
# ---------------------------------------------------------------------------
# Top families
# ---------------------------------------------------------------------------
TOP_FAMILIES = [
"germanic", "italic", "balto_slavic", "indo_iranian", "hellenic",
"celtic", "uralic", "turkic", "sino_tibetan", "austronesian",
"semitic", "dravidian", "japonic", "koreanic", "kartvelian",
]
# Map family_map values that differ from the tree's branch names
FAMILY_ALIAS = {
"slavic": "balto_slavic",
"baltic": "balto_slavic",
}
# ---------------------------------------------------------------------------
# SCA similarity (standalone, mirrors baseline_levenshtein.py)
# ---------------------------------------------------------------------------
_VOWELS = set("AEIOU")
_LABIALS = {"P", "B", "M"}
_CORONALS = {"T", "D", "N", "S", "L", "R"}
_VELARS = {"K", "G"}
_LARYNGEALS = {"H"}
_GLIDES = {"W", "Y"}
_NATURAL_CLASSES = [_VOWELS, _LABIALS, _CORONALS, _VELARS, _LARYNGEALS, _GLIDES]
def _substitution_cost(a: str, b: str) -> float:
if a == b:
return 0.0
for cls in _NATURAL_CLASSES:
if a in cls and b in cls:
return 0.3
return 1.0
def weighted_levenshtein(s1: str, s2: str) -> float:
n, m = len(s1), len(s2)
if n == 0:
return m * 0.5
if m == 0:
return n * 0.5
dp = [[0.0] * (m + 1) for _ in range(n + 1)]
for i in range(n + 1):
dp[i][0] = i * 0.5
for j in range(m + 1):
dp[0][j] = j * 0.5
for i in range(1, n + 1):
for j in range(1, m + 1):
sub = _substitution_cost(s1[i - 1], s2[j - 1])
dp[i][j] = min(
dp[i - 1][j] + 0.5,
dp[i][j - 1] + 0.5,
dp[i - 1][j - 1] + sub,
)
return dp[n][m]
def normalised_similarity(s1: str, s2: str) -> float:
if not s1 and not s2:
return 1.0
max_len = max(len(s1), len(s2))
dist = weighted_levenshtein(s1, s2)
return 1.0 - (dist / max_len) if max_len > 0 else 1.0
# ---------------------------------------------------------------------------
# Phylogenetic tree definition
# ---------------------------------------------------------------------------
def build_raw_tree() -> dict[str, Any]:
"""Return the hard-coded phylogenetic tree.
Leaf-group values are either lists of ISO codes or the sentinel
``"__from_family_map__"`` which is resolved later.
"""
return {
"indo_european": {
"germanic": {
"west_germanic": {
"anglo_frisian": ["eng", "ang", "enm", "fry", "frr", "ofs"],
"franconian": ["nld", "dum", "lim", "afr"],
"high_german": ["deu", "goh", "gsw", "bar", "ltz", "yid"],
},
"north_germanic": [
"swe", "dan", "nor", "nno", "nob", "isl", "fao", "non",
],
"east_germanic": ["got"],
},
"italic": {
"romance": {
"ibero_romance": ["spa", "por", "cat", "glg", "osp"],
"gallo_romance": ["fra", "oci", "fro"],
"italo_dalmatian": ["ita", "nap", "scn", "dlm", "cos"],
"eastern_romance": ["ron", "rup"],
},
"latino_faliscan": ["lat", "osc", "xum"],
},
"celtic": {
"goidelic": ["gle", "gla", "sga", "mga"],
"brythonic": ["cym", "bre", "cor", "wlm"],
},
"balto_slavic": {
"baltic": ["lit", "lav", "ltg"],
"east_slavic": ["rus", "ukr", "bel", "orv"],
"west_slavic": ["pol", "ces", "slk", "dsb", "hsb", "csb", "pox"],
"south_slavic": ["bul", "mkd", "hrv", "slv", "hbs", "chu"],
},
"hellenic": ["ell", "grc", "gmy"],
"indo_iranian": {
"iranian": [
"fas", "pes", "oss", "kmr", "ckb", "pbu", "tgk", "ave",
"zza",
],
"indic": [
"hin", "ben", "san", "guj", "mar", "pan", "sin", "urd",
"asm", "nep", "rom", "rmn",
],
},
"armenian": ["hye"],
"albanian": ["sqi"],
"anatolian": ["hit"],
},
"uralic": {
"finnic": [
"fin", "est", "ekk", "krl", "olo", "vep", "vot", "izh", "liv",
],
"ugric": ["hun", "mns", "kca"],
"samic": ["sme", "sma", "smj", "smn", "sms", "sjd"],
"mordvinic": ["myv", "mdf"],
"permic": ["kpv", "koi", "udm"],
"mari": ["mhr", "mrj"],
"samoyedic": ["yrk", "enf", "sel", "nio"],
},
"turkic": {
"oghuz": ["tur", "aze", "azj", "ota", "otk"],
"kipchak": ["kaz", "kir", "tat", "bak"],
"siberian": ["sah", "tyv"],
"karluk": ["uzb", "uzn"],
"oghur": ["chv"],
},
"sino_tibetan": {
"sinitic": ["zho", "cmn", "yue", "och"],
"tibeto_burman": ["bod", "mya", "obr", "new", "lif"],
},
"austronesian": {
"malayo_polynesian": "__from_family_map__",
},
"semitic": [
"heb", "arb", "ara", "amh", "mlt", "syc", "arc", "akk", "phn",
"uga",
],
"dravidian": ["tam", "tel", "kan", "mal"],
"japonic": ["jpn"],
"koreanic": ["kor", "jje", "okm"],
"kartvelian": ["kat", "lzz"],
}
# ---------------------------------------------------------------------------
# Tree resolution helpers
# ---------------------------------------------------------------------------
def _collect_isos_from_tree(node: Any) -> set[str]:
"""Recursively collect all ISO codes that already appear in *node*."""
if isinstance(node, list):
return set(node)
if isinstance(node, str):
if node == "__from_family_map__":
return set()
return {node}
isos: set[str] = set()
for v in node.values():
isos |= _collect_isos_from_tree(v)
return isos
def resolve_tree(tree: dict[str, Any], family_map: dict[str, str]) -> dict[str, Any]:
"""Replace ``"__from_family_map__"`` sentinels and add catch-all groups.
Returns a new tree (original is not mutated).
"""
tree = _deep_copy_tree(tree)
# Phase 1: resolve sentinels -----------------------------------------
_resolve_sentinels(tree, family_map)
# Phase 2: add catch-all for languages in family_map but not in tree --
present = _collect_isos_from_tree(tree)
extras: dict[str, list[str]] = defaultdict(list)
for iso, fam in family_map.items():
if iso in present:
continue
canonical = FAMILY_ALIAS.get(fam, fam)
extras[canonical].append(iso)
for fam, isos in extras.items():
if fam not in tree:
tree[fam] = sorted(isos)
else:
# Family exists as a top-level node — add under an
# "other_{fam}" subgroup so we don't clobber existing structure.
node = tree[fam]
if isinstance(node, dict):
existing = _collect_isos_from_tree(node)
new_isos = [i for i in isos if i not in existing]
if new_isos:
node[f"other_{fam}"] = sorted(new_isos)
elif isinstance(node, list):
existing = set(node)
for iso in isos:
if iso not in existing:
node.append(iso)
# If the node is a single string, wrap it
elif isinstance(node, str) and node != "__from_family_map__":
tree[fam] = [node] + sorted(isos)
return tree
def _deep_copy_tree(node: Any) -> Any:
if isinstance(node, dict):
return {k: _deep_copy_tree(v) for k, v in node.items()}
if isinstance(node, list):
return list(node)
return node
def _resolve_sentinels(node: Any, family_map: dict[str, str]) -> None:
"""In-place replacement of ``"__from_family_map__"`` values."""
if not isinstance(node, dict):
return
for key, val in list(node.items()):
if val == "__from_family_map__":
# key is the family name that should match family_map values
# For "malayo_polynesian" under "austronesian", pull all
# family_map entries mapped to "austronesian".
parent_family = _find_parent_family(node, key)
if parent_family is None:
parent_family = key
isos = sorted(
iso for iso, fam in family_map.items() if fam == parent_family
)
node[key] = isos if isos else []
elif isinstance(val, dict):
_resolve_sentinels(val, family_map)
def _find_parent_family(node: dict, child_key: str) -> str | None: # noqa: ARG001
"""Heuristic: the sentinel is typically placed one level below the
actual family name. Walk the raw tree keys for a match. For our
tree, ``malayo_polynesian`` is under ``austronesian``, so we return
``austronesian``."""
# We rely on the caller context; this is called from _resolve_sentinels
# which walks the tree recursively. At the point we find the sentinel
# the *node* dict is ``{"malayo_polynesian": "__from_family_map__"}``,
# and we need the grandparent key. Since we don't track the parent key
# inside the recursive walk, we use a simpler approach: just look up in
# a mapping.
_SENTINEL_PARENT: dict[str, str] = {
"malayo_polynesian": "austronesian",
}
return _SENTINEL_PARENT.get(child_key)
# ---------------------------------------------------------------------------
# Language path index & phylo distance
# ---------------------------------------------------------------------------
def build_lang_paths(
tree: dict[str, Any],
) -> dict[str, list[str]]:
"""Map each ISO code to its full path from root to its leaf group.
For ``eng`` inside ``indo_european > germanic > west_germanic >
anglo_frisian`` the path is
``["indo_european", "germanic", "west_germanic", "anglo_frisian"]``.
"""
paths: dict[str, list[str]] = {}
def _walk(node: Any, prefix: list[str]) -> None:
if isinstance(node, list):
for iso in node:
paths[iso] = list(prefix)
elif isinstance(node, dict):
for key, child in node.items():
_walk(child, prefix + [key])
elif isinstance(node, str):
# Single ISO as a leaf
paths[node] = list(prefix)
_walk(tree, [])
return paths
def compute_distance(
lang_a: str,
lang_b: str,
lang_paths: dict[str, list[str]],
) -> tuple[int, str]:
"""Return (edge_count, level_label) between two languages.
Level mapping:
L1 — same leaf group (same last path element)
L2 — LCA is two levels above leaf
L3 — LCA is deeper within same top-level family
L4 — different top-level families or not in tree
"""
pa = lang_paths.get(lang_a)
pb = lang_paths.get(lang_b)
if pa is None or pb is None:
return (99, "L4")
if not pa or not pb:
return (99, "L4")
# Find LCA depth
lca_depth = 0
for i, (a, b) in enumerate(zip(pa, pb)):
if a != b:
break
lca_depth = i + 1
else:
# Exhausted the shorter path without mismatch
lca_depth = min(len(pa), len(pb))
if lca_depth == 0:
# Different top-level families
return (len(pa) + len(pb), "L4")
depth_a = len(pa)
depth_b = len(pb)
edge_count = (depth_a - lca_depth) + (depth_b - lca_depth)
# Determine level from the relationship between paths.
#
# L1 — same leaf group: paths are identical (both languages listed under
# the same terminal node, e.g. both in "anglo_frisian").
# L2 — same branch, different leaf group: LCA is the *parent* of the
# leaf groups (e.g. eng in anglo_frisian, deu in high_german; LCA
# is west_germanic) OR LCA is one more level up within the same
# major branch. Concretely: the paths diverge at the last or
# second-to-last position relative to the *shorter* path.
# L3 — same top-level family, deeper divergence (e.g. eng vs fra both in
# indo_european but in different major branches).
# L4 — different top-level families (already handled above).
if pa == pb:
# Same leaf group
return (edge_count, "L1")
# Classification based on how deep the shared ancestry is:
# L2 — same branch: they share at least 2 path elements
# e.g. both under [indo_european, germanic, ...]
# or both under [uralic, finnic, ...]
# L3 — same top-level family but different branches:
# they share only 1 path element (the super-family)
# e.g. [indo_european, germanic, ...] vs [indo_european, italic, ...]
if lca_depth >= 2:
return (edge_count, "L2")
# lca_depth == 1: same top-level family, different branches
return (edge_count, "L3")
def get_top_family(iso: str, lang_paths: dict[str, list[str]], family_map: dict[str, str]) -> str:
"""Return the top-level family name for *iso*.
Tries the tree path first; falls back to family_map.
"""
path = lang_paths.get(iso)
if path:
# The first element is the top-level family node; but we want the
# *branch* name that corresponds to TOP_FAMILIES. The branch is
# typically the second element (e.g. "germanic" under "indo_european").
# For top-level families like "uralic", the first element IS the family.
if len(path) >= 2 and path[1] in _TOP_FAMILY_SET:
return path[1]
if path[0] in _TOP_FAMILY_SET:
return path[0]
# Search path for any matching family name
for segment in path:
if segment in _TOP_FAMILY_SET:
return segment
# Return the first path element as family
return path[0] if path else "unknown"
# Fallback: family_map
fam = family_map.get(iso, "unknown")
return FAMILY_ALIAS.get(fam, fam)
_TOP_FAMILY_SET = set(TOP_FAMILIES)
# ---------------------------------------------------------------------------
# Data loading
# ---------------------------------------------------------------------------
LexEntry = tuple[str, str, str] # (word, ipa, sca)
def load_lexicons(
lexicons_dir: Path,
) -> dict[tuple[str, str], list[LexEntry]]:
"""Load all lexicon TSVs into ``{(iso, concept_id): [(word, ipa, sca), ...]}``.
Entries with ``Concept_ID`` of ``"-"`` or empty are skipped.
"""
lexicon: dict[tuple[str, str], list[LexEntry]] = defaultdict(list)
files = sorted(lexicons_dir.glob("*.tsv"))
total_entries = 0
skipped_no_concept = 0
for i, fp in enumerate(files, 1):
iso = fp.stem
if i % 100 == 0 or i == len(files):
print(f" Loading lexicon {i}/{len(files)} ({iso}) ...")
try:
with fp.open(encoding="utf-8") as fh:
reader = csv.DictReader(fh, delimiter="\t")
for row in reader:
cid = row.get("Concept_ID", "").strip()
if cid in ("", "-"):
skipped_no_concept += 1
continue
word = row.get("Word", "").strip()
ipa = row.get("IPA", "").strip()
sca = row.get("SCA", "").strip()
if not word and not ipa and not sca:
continue
lexicon[(iso, cid)].append((word, ipa, sca))
total_entries += 1
except Exception as exc:
print(f" WARNING: failed to read {fp.name}: {exc}", file=sys.stderr)
print(f" Loaded {total_entries:,} entries across {len(files)} lexicons "
f"(skipped {skipped_no_concept:,} without concept).")
return lexicon
def build_concept_index(
lexicon: dict[tuple[str, str], list[LexEntry]],
) -> dict[str, list[str]]:
"""Map each concept_id to the list of ISO codes that have entries for it."""
concept_langs: dict[str, set[str]] = defaultdict(set)
for (iso, cid) in lexicon:
concept_langs[cid].add(iso)
return {cid: sorted(isos) for cid, isos in concept_langs.items()}
def load_cognate_pairs(path: Path) -> list[dict[str, str]]:
"""Load a cognate-pairs TSV file into a list of dicts."""
rows: list[dict[str, str]] = []
if not path.exists():
print(f" WARNING: {path} not found, skipping.")
return rows
with path.open(encoding="utf-8") as fh:
reader = csv.DictReader(fh, delimiter="\t")
for row in reader:
rows.append(dict(row))
print(f" Loaded {len(rows):,} pairs from {path.name}")
return rows
# ---------------------------------------------------------------------------
# Pair record builder
# ---------------------------------------------------------------------------
def make_pair_record(
lang_a: str,
word_a: str,
ipa_a: str,
sca_a: str,
lang_b: str,
word_b: str,
ipa_b: str,
sca_b: str,
concept_id: str,
label: str,
lang_paths: dict[str, list[str]],
source: str = "lexicon",
score_override: float | None = None,
) -> dict[str, str]:
"""Build a single output-row dict."""
_, level = compute_distance(lang_a, lang_b, lang_paths)
ts = get_timespan(lang_a, lang_b)
if score_override is not None:
score = score_override
else:
score = normalised_similarity(sca_a, sca_b) if sca_a and sca_b else 0.0
return {
"Lang_A": lang_a,
"Word_A": word_a,
"IPA_A": ipa_a,
"SCA_A": sca_a,
"Lang_B": lang_b,
"Word_B": word_b,
"IPA_B": ipa_b,
"SCA_B": sca_b,
"Concept_ID": concept_id,
"Label": label,
"Phylo_Dist": level,
"Timespan": ts,
"Score": f"{score:.4f}",
"Source": source,
}
# ---------------------------------------------------------------------------
# TSV writing helper
# ---------------------------------------------------------------------------
def write_pairs_tsv(path: Path, pairs: list[dict[str, str]]) -> None:
"""Write a list of pair dicts as a TSV file."""
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("w", encoding="utf-8", newline="") as fh:
writer = csv.DictWriter(fh, fieldnames=OUTPUT_FIELDS, delimiter="\t",
extrasaction="ignore")
writer.writeheader()
for row in pairs:
writer.writerow(row)
print(f" Wrote {len(pairs):,} pairs to {path.relative_to(REPO_ROOT)}")
# ---------------------------------------------------------------------------
# Step 3: True cognate pair generation
# ---------------------------------------------------------------------------
def generate_true_cognates(
lexicon: dict[tuple[str, str], list[LexEntry]],
concept_langs: dict[str, list[str]],
lang_paths: dict[str, list[str]],
family_map: dict[str, str],
inherited_pairs: list[dict[str, str]],
) -> tuple[list[dict[str, str]], list[dict[str, str]], list[dict[str, str]]]:
"""Generate true cognate pairs at L1, L2, L3 levels.
Returns (l1_pairs, l2_pairs, l3_pairs).
"""
l1: list[dict[str, str]] = []
l2: list[dict[str, str]] = []
l3: list[dict[str, str]] = []
buckets = {"L1": l1, "L2": l2, "L3": l3}
thresholds = {"L1": 0.5, "L2": 0.5, "L3": 0.4}
# Step 3a: incorporate expert pairs FIRST (they have priority)
print("Step 3a: Adding expert cognate pairs ...")
expert_added = 0
for row in inherited_pairs:
if all(len(b) >= PAIR_CAP for b in buckets.values()):
break
lang_a = row.get("Lang_A", "")
lang_b = row.get("Lang_B", "")
if not lang_a or not lang_b:
continue
_, level = compute_distance(lang_a, lang_b, lang_paths)
if level not in buckets or len(buckets[level]) >= PAIR_CAP:
continue
cid = row.get("Concept_ID", "")
ipa_a = row.get("IPA_A", "")
ipa_b = row.get("IPA_B", "")
word_a = row.get("Word_A", "")
word_b = row.get("Word_B", "")
sca_a = _lookup_sca(lexicon, lang_a, cid, word_a, ipa_a)
sca_b = _lookup_sca(lexicon, lang_b, cid, word_b, ipa_b)
score_str = row.get("Score", "0")
try:
score = float(score_str)
except (ValueError, TypeError):
score = 0.0
if sca_a and sca_b:
sca_sim = normalised_similarity(sca_a, sca_b)
else:
sca_sim = score
rec = make_pair_record(
lang_a, word_a, ipa_a, sca_a,
lang_b, word_b, ipa_b, sca_b,
cid, "true_cognate", lang_paths,
source=row.get("Source", "expert"),
score_override=sca_sim,
)
rec["Phylo_Dist"] = level
buckets[level].append(rec)
expert_added += 1
print(f" Added {expert_added:,} expert pairs "
f"(L1={len(l1):,} L2={len(l2):,} L3={len(l3):,})")
# Step 3b: Fill remaining slots from lexicon data
print("Step 3b: Generating true cognates from lexicon data ...")
concepts_processed = 0
total_concepts = len(concept_langs)
for cid, langs in concept_langs.items():
concepts_processed += 1
if concepts_processed % 500 == 0:
print(f" Concept {concepts_processed}/{total_concepts} "
f"(L1={len(l1):,} L2={len(l2):,} L3={len(l3):,})")
# Early termination
if all(len(b) >= PAIR_CAP for b in buckets.values()):
break
# Group languages by top-level family
family_groups: dict[str, list[str]] = defaultdict(list)
for iso in langs:
fam = get_top_family(iso, lang_paths, family_map)
family_groups[fam].append(iso)
# Within each family, generate pairs
for fam, fam_langs in family_groups.items():
if len(fam_langs) < 2:
continue
# Sample pairs if too many languages
if len(fam_langs) > 50:
sampled = random.sample(fam_langs, 50)
else:
sampled = fam_langs
pair_count_this_concept: dict[str, int] = {"L1": 0, "L2": 0, "L3": 0}
for i in range(len(sampled)):
for j in range(i + 1, len(sampled)):
iso_a = sampled[i]
iso_b = sampled[j]
if iso_a == iso_b:
continue
_, level = compute_distance(iso_a, iso_b, lang_paths)
if level not in buckets or len(buckets[level]) >= PAIR_CAP:
continue
if pair_count_this_concept.get(level, 0) >= MAX_PAIRS_PER_CONCEPT_PER_LEVEL:
continue
thresh = thresholds[level]
entries_a = lexicon.get((iso_a, cid), [])
entries_b = lexicon.get((iso_b, cid), [])
if not entries_a or not entries_b:
continue
# Pick one entry from each language (first with SCA)
ea = _pick_best_entry(entries_a)
eb = _pick_best_entry(entries_b)
if ea is None or eb is None:
continue
sca_sim = normalised_similarity(ea[2], eb[2]) if ea[2] and eb[2] else 0.0
if sca_sim < thresh:
continue
rec = make_pair_record(
iso_a, ea[0], ea[1], ea[2],
iso_b, eb[0], eb[1], eb[2],
cid, "true_cognate", lang_paths,
source="lexicon",
score_override=sca_sim,
)
rec["Phylo_Dist"] = level # ensure correct level
buckets[level].append(rec)
pair_count_this_concept[level] = pair_count_this_concept.get(level, 0) + 1
print(f" From lexicon: L1={len(l1):,} L2={len(l2):,} L3={len(l3):,}")
# Cap each bucket
for level_name, bucket in buckets.items():
if len(bucket) > PAIR_CAP:
random.shuffle(bucket)
buckets[level_name] = bucket[:PAIR_CAP]
if level_name == "L1":
l1[:] = buckets[level_name]
elif level_name == "L2":
l2[:] = buckets[level_name]
else:
l3[:] = buckets[level_name]
print(f" Final: L1={len(l1):,} L2={len(l2):,} L3={len(l3):,}")
return l1, l2, l3
def _pick_best_entry(entries: list[LexEntry]) -> LexEntry | None:
"""Pick an entry preferring ones with non-empty SCA."""
for e in entries:
if e[2]: # has SCA
return e
return entries[0] if entries else None
def _lookup_sca(
lexicon: dict[tuple[str, str], list[LexEntry]],
iso: str,
concept_id: str,
word: str,
ipa: str = "",
) -> str:
"""Try to find the SCA encoding for a given word from the lexicon.
Falls back to computing SCA on-the-fly from IPA if all lookups fail.
"""
entries = lexicon.get((iso, concept_id), [])
# Exact word match first
for e in entries:
if e[0] == word and e[2]:
return e[2]
# Any entry with SCA for this (iso, concept)
for e in entries:
if e[2]:
return e[2]
# Use the word index for cross-concept fallback (fast)
sca = _word_sca_index.get((iso, word), "")
if sca:
return sca
# Last resort: compute from IPA on-the-fly
if ipa:
return ipa_to_sound_class(ipa)
return ""
# ---------------------------------------------------------------------------
# Step 4: False positives
# ---------------------------------------------------------------------------
def generate_false_positives(
lexicon: dict[tuple[str, str], list[LexEntry]],
concept_langs: dict[str, list[str]],
lang_paths: dict[str, list[str]],
family_map: dict[str, str],
similarity_pairs: list[dict[str, str]],
) -> list[dict[str, str]]:
"""Cross-family pairs with same concept and SCA similarity >= 0.5."""
print("Step 4: Generating false positives ...")
fps: list[dict[str, str]] = []
# 4a: From lexicon — cross-family same-concept pairs
concepts_processed = 0
for cid, langs in concept_langs.items():
if len(fps) >= PAIR_CAP:
break
concepts_processed += 1
if concepts_processed % 500 == 0:
print(f" Concept {concepts_processed}/{len(concept_langs)} "
f"(fps={len(fps):,})")
# Group by family
family_groups: dict[str, list[str]] = defaultdict(list)
for iso in langs:
fam = get_top_family(iso, lang_paths, family_map)
family_groups[fam].append(iso)
families = list(family_groups.keys())
if len(families) < 2:
continue
# Cross-family pairs
pair_count = 0
for fi in range(len(families)):
for fj in range(fi + 1, len(families)):
if len(fps) >= PAIR_CAP:
break
if pair_count >= MAX_CROSS_FAMILY_PAIRS_PER_CONCEPT:
break
fam_a_langs = family_groups[families[fi]]
fam_b_langs = family_groups[families[fj]]
# Sample one from each
iso_a = random.choice(fam_a_langs)
iso_b = random.choice(fam_b_langs)
entries_a = lexicon.get((iso_a, cid), [])
entries_b = lexicon.get((iso_b, cid), [])
if not entries_a or not entries_b:
continue
ea = _pick_best_entry(entries_a)
eb = _pick_best_entry(entries_b)
if ea is None or eb is None:
continue
sca_sim = normalised_similarity(ea[2], eb[2]) if ea[2] and eb[2] else 0.0
if sca_sim < 0.5:
continue
rec = make_pair_record(
iso_a, ea[0], ea[1], ea[2],
iso_b, eb[0], eb[1], eb[2],
cid, "false_positive", lang_paths,
source="lexicon",
score_override=sca_sim,
)
fps.append(rec)
pair_count += 1
print(f" From lexicon: {len(fps):,} false positives")
# 4b: From similarity_pairs file
sim_added = 0
for row in similarity_pairs:
if len(fps) >= PAIR_CAP:
break
lang_a = row.get("Lang_A", "")
lang_b = row.get("Lang_B", "")
if not lang_a or not lang_b:
continue
fam_a = get_top_family(lang_a, lang_paths, family_map)
fam_b = get_top_family(lang_b, lang_paths, family_map)
if fam_a == fam_b:
continue # only want cross-family
cid = row.get("Concept_ID", "")
sca_a = _lookup_sca(lexicon, lang_a, cid, row.get("Word_A", ""),
row.get("IPA_A", ""))
sca_b = _lookup_sca(lexicon, lang_b, cid, row.get("Word_B", ""),
row.get("IPA_B", ""))
score_str = row.get("Score", "0")
try:
score = float(score_str)
except (ValueError, TypeError):
score = 0.0
if sca_a and sca_b:
sca_sim = normalised_similarity(sca_a, sca_b)
else:
sca_sim = score
if sca_sim < 0.5:
continue
rec = make_pair_record(
lang_a, row.get("Word_A", ""), row.get("IPA_A", ""), sca_a,
lang_b, row.get("Word_B", ""), row.get("IPA_B", ""), sca_b,
cid, "false_positive", lang_paths,
source=row.get("Source", "similarity"),
score_override=sca_sim,
)
fps.append(rec)
sim_added += 1
print(f" Added {sim_added:,} from similarity pairs")
if len(fps) > PAIR_CAP:
random.shuffle(fps)
fps = fps[:PAIR_CAP]
print(f" Final false positives: {len(fps):,}")
return fps
# ---------------------------------------------------------------------------
# Step 5: True negatives
# ---------------------------------------------------------------------------
def generate_true_negatives(
lexicon: dict[tuple[str, str], list[LexEntry]],
lang_paths: dict[str, list[str]],
family_map: dict[str, str],
) -> list[dict[str, str]]:
"""Random cross-family pairs with different concepts and SCA sim < 0.3."""
print("Step 5: Generating true negatives ...")
negs: list[dict[str, str]] = []
# Build index of all (iso, concept_id) keys for sampling
all_keys = list(lexicon.keys())
if len(all_keys) < 2:
print(" Not enough lexicon entries for true negatives.")
return negs
attempts = 0
while len(negs) < PAIR_CAP and attempts < TRUE_NEG_SAMPLE_ATTEMPTS:
attempts += 1
if attempts % 50_000 == 0:
print(f" Attempt {attempts:,}, negatives so far: {len(negs):,}")
# Pick two random entries
key_a = random.choice(all_keys)
key_b = random.choice(all_keys)
iso_a, cid_a = key_a
iso_b, cid_b = key_b
# Must be different concepts
if cid_a == cid_b:
continue
# Must be different families
fam_a = get_top_family(iso_a, lang_paths, family_map)
fam_b = get_top_family(iso_b, lang_paths, family_map)
if fam_a == fam_b:
continue
# Must be different languages
if iso_a == iso_b:
continue
entries_a = lexicon[key_a]
entries_b = lexicon[key_b]
ea = _pick_best_entry(entries_a)
eb = _pick_best_entry(entries_b)
if ea is None or eb is None:
continue
if not ea[2] or not eb[2]:
continue
sca_sim = normalised_similarity(ea[2], eb[2])
if sca_sim >= 0.4:
continue
# Use concept_a for the record (arbitrary; both concepts are different)
rec = make_pair_record(
iso_a, ea[0], ea[1], ea[2],
iso_b, eb[0], eb[1], eb[2],
f"{cid_a} / {cid_b}", "true_negative", lang_paths,
source="random_sample",
score_override=sca_sim,
)
negs.append(rec)
print(f" Generated {len(negs):,} true negatives in {attempts:,} attempts")
return negs
# ---------------------------------------------------------------------------
# Step 6: Borrowings
# ---------------------------------------------------------------------------
def generate_borrowings(
borrowing_pairs: list[dict[str, str]],
lexicon: dict[tuple[str, str], list[LexEntry]],
lang_paths: dict[str, list[str]],
) -> list[dict[str, str]]:
"""Process borrowing pairs from WOLD."""
print("Step 6: Processing borrowing pairs ...")
rows: list[dict[str, str]] = []
for row in borrowing_pairs:
lang_a = row.get("Lang_A", "")
lang_b = row.get("Lang_B", "")
if not lang_a or not lang_b:
continue
cid = row.get("Concept_ID", "")
word_a = row.get("Word_A", "")
word_b = row.get("Word_B", "")
ipa_a = row.get("IPA_A", "")
ipa_b = row.get("IPA_B", "")
sca_a = _lookup_sca(lexicon, lang_a, cid, word_a, ipa_a)
sca_b = _lookup_sca(lexicon, lang_b, cid, word_b, ipa_b)
score_str = row.get("Score", "0")
try:
score = float(score_str)
except (ValueError, TypeError):
score = 0.0
if sca_a and sca_b:
sca_sim = normalised_similarity(sca_a, sca_b)
else:
sca_sim = score
rec = make_pair_record(
lang_a, word_a, ipa_a, sca_a,
lang_b, word_b, ipa_b, sca_b,
cid, "borrowing", lang_paths,
source=row.get("Source", "wold"),
score_override=sca_sim,
)
rows.append(rec)
print(f" Processed {len(rows):,} borrowing pairs")
return rows
# ---------------------------------------------------------------------------
# Step 7: Religious subsets
# ---------------------------------------------------------------------------
def generate_religious_pairs(
all_pairs: list[dict[str, str]],
lexicon: dict[tuple[str, str], list[LexEntry]],
concept_langs: dict[str, list[str]],
lang_paths: dict[str, list[str]],
family_map: dict[str, str],
) -> tuple[
list[dict[str, str]],
dict[str, list[dict[str, str]]],
dict[str, list[dict[str, str]]],
]:
"""Filter existing pairs for religious concepts and generate additional ones.
Returns (all_religious_pairs, per_family_dict, subdomain_dict).
"""
print("Step 7: Building religious concept subsets ...")
# 7a: Filter all existing pairs — SKIP compound concept IDs (true negatives)
religious: list[dict[str, str]] = []
skipped_compound = 0
for rec in all_pairs:
cid = rec.get("Concept_ID", "")
# Skip compound concept IDs (true negatives use "cid_a / cid_b")
if "/" in cid:
skipped_compound += 1
continue
if is_religious(cid):
religious.append(rec)
print(f" Filtered {len(religious):,} religious pairs from existing data "
f"(skipped {skipped_compound:,} compound IDs)")
# 7b: Generate additional within-family and cross-family pairs for
# religious concepts not already covered
religious_concepts = [c for c in concept_langs if is_religious(c)]
print(f" Found {len(religious_concepts)} religious concepts in lexicon")
additional = 0
for cid in religious_concepts:
langs = concept_langs[cid]
if len(langs) < 2:
continue
# Group by family
family_groups: dict[str, list[str]] = defaultdict(list)
for iso in langs:
fam = get_top_family(iso, lang_paths, family_map)
family_groups[fam].append(iso)
# Within-family pairs
for fam, fam_langs in family_groups.items():
if len(fam_langs) < 2:
continue
sample_size = min(len(fam_langs), 20)
sampled = random.sample(fam_langs, sample_size) if len(fam_langs) > sample_size else fam_langs
for i in range(len(sampled)):
for j in range(i + 1, len(sampled)):
iso_a, iso_b = sampled[i], sampled[j]
entries_a = lexicon.get((iso_a, cid), [])
entries_b = lexicon.get((iso_b, cid), [])
if not entries_a or not entries_b:
continue
ea = _pick_best_entry(entries_a)
eb = _pick_best_entry(entries_b)
if ea is None or eb is None:
continue
sca_sim = normalised_similarity(ea[2], eb[2]) if ea[2] and eb[2] else 0.0
_, level = compute_distance(iso_a, iso_b, lang_paths)
label = "true_cognate" if level in ("L1", "L2", "L3") and sca_sim >= 0.4 else "false_positive"
rec = make_pair_record(
iso_a, ea[0], ea[1], ea[2],
iso_b, eb[0], eb[1], eb[2],
cid, label, lang_paths,
source="religious_gen",
score_override=sca_sim,
)
religious.append(rec)
additional += 1
# Cross-family pairs (limited)
families = list(family_groups.keys())
if len(families) >= 2:
cross_count = 0
for fi in range(len(families)):
for fj in range(fi + 1, len(families)):
if cross_count >= 10:
break
iso_a = random.choice(family_groups[families[fi]])
iso_b = random.choice(family_groups[families[fj]])
entries_a = lexicon.get((iso_a, cid), [])
entries_b = lexicon.get((iso_b, cid), [])
if not entries_a or not entries_b:
continue
ea = _pick_best_entry(entries_a)
eb = _pick_best_entry(entries_b)
if ea is None or eb is None:
continue
sca_sim = normalised_similarity(ea[2], eb[2]) if ea[2] and eb[2] else 0.0
rec = make_pair_record(
iso_a, ea[0], ea[1], ea[2],
iso_b, eb[0], eb[1], eb[2],
cid, "false_positive", lang_paths,
source="religious_gen",
score_override=sca_sim,
)
religious.append(rec)
additional += 1
cross_count += 1
print(f" Generated {additional:,} additional religious pairs")
print(f" Total religious pairs: {len(religious):,}")
# Build per-family subsets
per_family: dict[str, list[dict[str, str]]] = defaultdict(list)
for rec in religious:
fam_a = get_top_family(rec["Lang_A"], lang_paths, family_map)
fam_b = get_top_family(rec["Lang_B"], lang_paths, family_map)
if fam_a in _TOP_FAMILY_SET:
per_family[fam_a].append(rec)
if fam_b in _TOP_FAMILY_SET and fam_b != fam_a:
per_family[fam_b].append(rec)
# Build sub-domain subsets
subdomain_pairs: dict[str, list[dict[str, str]]] = {}
for sd_name, sd_concepts in RELIGIOUS_SUBDOMAINS.items():
sd_list: list[dict[str, str]] = []
for rec in religious:
cid = rec.get("Concept_ID", "")
if _in_subdomain(cid, sd_concepts):
sd_list.append(rec)
subdomain_pairs[sd_name] = sd_list
print(f" Sub-domain {sd_name}: {len(sd_list):,} pairs")
return religious, dict(per_family), subdomain_pairs
# ---------------------------------------------------------------------------
# Step 8: Timespan stratification
# ---------------------------------------------------------------------------
def stratify_by_timespan(
all_pairs: list[dict[str, str]],
) -> dict[str, list[dict[str, str]]]:
"""Split all pairs into timespan buckets."""
print("Step 8: Stratifying by timespan ...")
buckets: dict[str, list[dict[str, str]]] = defaultdict(list)
for rec in all_pairs:
ts = rec.get("Timespan", "modern_modern")
buckets[ts].append(rec)
for ts, pairs in sorted(buckets.items()):
print(f" {ts}: {len(pairs):,}")
return dict(buckets)
# ---------------------------------------------------------------------------
# Step 9: Per-family sets
# ---------------------------------------------------------------------------
def stratify_by_family(
all_pairs: list[dict[str, str]],
lang_paths: dict[str, list[str]],
family_map: dict[str, str],
) -> dict[str, list[dict[str, str]]]:
"""Split pairs into per-family buckets for TOP_FAMILIES."""
print("Step 9: Stratifying by family ...")
buckets: dict[str, list[dict[str, str]]] = defaultdict(list)
for rec in all_pairs:
fam_a = get_top_family(rec["Lang_A"], lang_paths, family_map)
fam_b = get_top_family(rec["Lang_B"], lang_paths, family_map)
# Include pair if either language belongs to a top family
if fam_a == fam_b and fam_a in _TOP_FAMILY_SET:
buckets[fam_a].append(rec)
else:
# Cross-family pair: include in both relevant families
if fam_a in _TOP_FAMILY_SET:
buckets[fam_a].append(rec)
if fam_b in _TOP_FAMILY_SET:
buckets[fam_b].append(rec)
for fam in TOP_FAMILIES:
count = len(buckets.get(fam, []))
print(f" {fam}: {count:,}")
return dict(buckets)
# ---------------------------------------------------------------------------
# Step 10: Statistics
# ---------------------------------------------------------------------------
def write_stats(
output_dir: Path,
l1: list, l2: list, l3: list,
fps: list, negs: list, borrows: list,
religious: list,
timespan_buckets: dict[str, list],
family_buckets: dict[str, list],
religious_by_family: dict[str, list],
) -> None:
"""Write validation_stats.tsv with summary counts."""
print("Step 10: Writing statistics ...")
stats_path = output_dir / "validation_stats.tsv"
rows: list[tuple[str, str, str]] = []
rows.append(("Category", "Subset", "Count"))
rows.append(("true_cognates", "L1", str(len(l1))))
rows.append(("true_cognates", "L2", str(len(l2))))
rows.append(("true_cognates", "L3", str(len(l3))))
rows.append(("true_cognates", "total", str(len(l1) + len(l2) + len(l3))))
rows.append(("false_positives", "all", str(len(fps))))
rows.append(("true_negatives", "all", str(len(negs))))
rows.append(("borrowings", "all", str(len(borrows))))
rows.append(("religious", "all", str(len(religious))))
for ts_name in ["ancient_ancient", "ancient_modern", "medieval_modern", "modern_modern"]:
count = len(timespan_buckets.get(ts_name, []))
rows.append(("timespan", ts_name, str(count)))
for fam in TOP_FAMILIES:
count = len(family_buckets.get(fam, []))
rows.append(("per_family", fam, str(count)))
for fam in sorted(religious_by_family.keys()):
count = len(religious_by_family[fam])
rows.append(("religious_by_family", fam, str(count)))
total_all = len(l1) + len(l2) + len(l3) + len(fps) + len(negs) + len(borrows)
rows.append(("total", "all_pairs", str(total_all)))
# Compute label distribution
label_counts: dict[str, int] = defaultdict(int)
for pairs_list in [l1, l2, l3, fps, negs, borrows]:
for rec in pairs_list:
label_counts[rec.get("Label", "unknown")] += 1
for label, count in sorted(label_counts.items()):
rows.append(("label_distribution", label, str(count)))
# Compute era distribution
era_counts: dict[str, int] = defaultdict(int)
for pairs_list in [l1, l2, l3, fps, negs, borrows]:
for rec in pairs_list:
ts = rec.get("Timespan", "unknown")
era_counts[ts] += 1
for era, count in sorted(era_counts.items()):
rows.append(("era_distribution", era, str(count)))
# Unique languages
all_langs: set[str] = set()
for pairs_list in [l1, l2, l3, fps, negs, borrows]:
for rec in pairs_list:
all_langs.add(rec.get("Lang_A", ""))
all_langs.add(rec.get("Lang_B", ""))
all_langs.discard("")
rows.append(("coverage", "unique_languages", str(len(all_langs))))
# Unique concepts
all_concepts: set[str] = set()
for pairs_list in [l1, l2, l3, fps, negs, borrows]:
for rec in pairs_list:
cid = rec.get("Concept_ID", "")
if cid:
all_concepts.add(cid)
rows.append(("coverage", "unique_concepts", str(len(all_concepts))))
# Unique families
family_set: set[str] = set()
for lang in all_langs:
fam = get_top_family(lang, _global_lang_paths, _global_family_map)
family_set.add(fam)
rows.append(("coverage", "unique_families", str(len(family_set))))
with stats_path.open("w", encoding="utf-8", newline="") as fh:
writer = csv.writer(fh, delimiter="\t")
for row in rows:
writer.writerow(row)
print(f" Wrote stats to {stats_path.relative_to(REPO_ROOT)}")
print(f" Total pairs across main sets: {total_all:,}")
print(f" Unique languages: {len(all_langs):,}")
print(f" Unique concepts: {len(all_concepts):,}")
# Global references set in main() for use in stats helper
_global_lang_paths: dict[str, list[str]] = {}
_global_family_map: dict[str, str] = {}
# Fast word→SCA index: (iso, word) → sca, built in main()
_word_sca_index: dict[tuple[str, str], str] = {}
def build_word_sca_index(
lexicon: dict[tuple[str, str], list[LexEntry]],
) -> dict[tuple[str, str], str]:
"""Build (iso, word) → SCA index for fast cross-concept lookups."""
idx: dict[tuple[str, str], str] = {}
for (iso, _cid), entries in lexicon.items():
for word, _ipa, sca in entries:
if sca and (iso, word) not in idx:
idx[(iso, word)] = sca
return idx
# ---------------------------------------------------------------------------
# Main
# ---------------------------------------------------------------------------
def main() -> None:
global _global_lang_paths, _global_family_map
random.seed(SEED)
print("=" * 70)
print("build_validation_sets.py — Stratified ML Validation Datasets")
print("=" * 70)
# ----- Step 1: Build and resolve phylogenetic tree --------------------
print("\nStep 1: Building phylogenetic tree ...")
if not FAMILY_MAP_PATH.exists():
print(f"ERROR: family_map.json not found at {FAMILY_MAP_PATH}", file=sys.stderr)
sys.exit(1)
with FAMILY_MAP_PATH.open(encoding="utf-8") as fh:
family_map: dict[str, str] = json.load(fh)
print(f" Loaded family_map.json: {len(family_map):,} languages")
raw_tree = build_raw_tree()
tree = resolve_tree(raw_tree, family_map)
lang_paths = build_lang_paths(tree)
print(f" Tree covers {len(lang_paths):,} languages")
# Store globals for stats
_global_lang_paths = lang_paths
_global_family_map = family_map
# Write phylo_tree.json
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
tree_path = OUTPUT_DIR / "phylo_tree.json"
with tree_path.open("w", encoding="utf-8") as fh:
json.dump(tree, fh, indent=2, ensure_ascii=False)
print(f" Wrote {tree_path.relative_to(REPO_ROOT)}")
# Quick distance sanity checks
for pair, expected in [
(("eng", "ang"), "L1"),
(("eng", "deu"), "L2"),
(("eng", "fra"), "L3"),
(("eng", "fin"), "L4"),
]:
_, level = compute_distance(pair[0], pair[1], lang_paths)
status = "OK" if level == expected else f"MISMATCH (got {level})"
print(f" Distance {pair[0]}-{pair[1]}: {level} ({status})")
# ----- Step 2: Load data ----------------------------------------------
print("\nStep 2: Loading data ...")
if not LEXICONS_DIR.exists():
print(f"ERROR: Lexicons directory not found: {LEXICONS_DIR}", file=sys.stderr)
sys.exit(1)
lexicon = load_lexicons(LEXICONS_DIR)
concept_langs = build_concept_index(lexicon)
print(f" {len(concept_langs):,} unique concepts across all lexicons")
# Build fast word→SCA index for cross-concept lookups
global _word_sca_index
_word_sca_index = build_word_sca_index(lexicon)
print(f" Built word-SCA index: {len(_word_sca_index):,} entries")
inherited_path = COGNATE_DIR / "cognate_pairs_inherited.tsv"
borrowing_path = COGNATE_DIR / "cognate_pairs_borrowing.tsv"
similarity_path = COGNATE_DIR / "cognate_pairs_similarity.tsv"
# For very large files, stream-sample instead of loading everything
print(" Loading cognate pair files (may take a moment for large files) ...")
inherited_pairs = _load_cognate_pairs_sampled(inherited_path, max_rows=200_000)
borrowing_pairs = load_cognate_pairs(borrowing_path)
similarity_pairs = load_cognate_pairs(similarity_path)
# ----- Step 3: True cognates ------------------------------------------
print("\nStep 3: Generating true cognate pairs ...")
l1, l2, l3 = generate_true_cognates(
lexicon, concept_langs, lang_paths, family_map, inherited_pairs,
)
write_pairs_tsv(OUTPUT_DIR / "true_cognates_L1.tsv", l1)
write_pairs_tsv(OUTPUT_DIR / "true_cognates_L2.tsv", l2)
write_pairs_tsv(OUTPUT_DIR / "true_cognates_L3.tsv", l3)
# ----- Step 4: False positives ----------------------------------------
print("\nStep 4: Generating false positives ...")
fps = generate_false_positives(
lexicon, concept_langs, lang_paths, family_map, similarity_pairs,
)
write_pairs_tsv(OUTPUT_DIR / "false_positives.tsv", fps)
# ----- Step 5: True negatives -----------------------------------------
print("\nStep 5: Generating true negatives ...")
negs = generate_true_negatives(lexicon, lang_paths, family_map)
write_pairs_tsv(OUTPUT_DIR / "true_negatives.tsv", negs)
# ----- Step 6: Borrowings --------------------------------------------
print("\nStep 6: Processing borrowings ...")
borrows = generate_borrowings(borrowing_pairs, lexicon, lang_paths)
write_pairs_tsv(OUTPUT_DIR / "borrowings.tsv", borrows)
# ----- Collect all pairs for downstream splits ------------------------
all_pairs: list[dict[str, str]] = l1 + l2 + l3 + fps + negs + borrows
# ----- Step 7: Religious subsets --------------------------------------
print("\nStep 7: Building religious subsets ...")
religious, religious_by_family, subdomain_pairs = generate_religious_pairs(
all_pairs, lexicon, concept_langs, lang_paths, family_map,
)
# Write to religious/ subdirectory
rel_dir = OUTPUT_DIR / "religious"
rel_dir.mkdir(parents=True, exist_ok=True)
write_pairs_tsv(rel_dir / "all_pairs.tsv", religious)
# Split by label
rel_true_cog = [r for r in religious if r.get("Label") == "true_cognate"]
rel_false_pos = [r for r in religious if r.get("Label") == "false_positive"]
rel_borrowings = [r for r in religious if r.get("Label") == "borrowing"]
write_pairs_tsv(rel_dir / "true_cognates.tsv", rel_true_cog)
write_pairs_tsv(rel_dir / "false_positives.tsv", rel_false_pos)
write_pairs_tsv(rel_dir / "borrowings.tsv", rel_borrowings)
# Sub-domain files
for sd_name, sd_pairs in sorted(subdomain_pairs.items()):
write_pairs_tsv(rel_dir / f"{sd_name}.tsv", sd_pairs)
# Per-family under religious/by_family/
rel_family_dir = rel_dir / "by_family"
rel_family_dir.mkdir(parents=True, exist_ok=True)
for fam, fam_pairs in sorted(religious_by_family.items()):
write_pairs_tsv(rel_family_dir / f"{fam}.tsv", fam_pairs)
# Clean up old flat files if they exist
old_religious = OUTPUT_DIR / "religious_pairs.tsv"
if old_religious.exists():
old_religious.unlink()
print(f" Removed old {old_religious.relative_to(REPO_ROOT)}")
old_rel_family = OUTPUT_DIR / "religious_by_family"
if old_rel_family.exists() and old_rel_family.is_dir():
import shutil
shutil.rmtree(old_rel_family)
print(f" Removed old {old_rel_family.relative_to(REPO_ROOT)}")
# ----- Step 8: Timespan stratification --------------------------------
print("\nStep 8: Stratifying by timespan ...")
timespan_buckets = stratify_by_timespan(all_pairs)
for ts_name in ["ancient_ancient", "ancient_modern", "medieval_modern", "modern_modern"]:
ts_pairs = timespan_buckets.get(ts_name, [])
write_pairs_tsv(OUTPUT_DIR / f"timespan_{ts_name}.tsv", ts_pairs)
# ----- Step 9: Per-family sets ----------------------------------------
print("\nStep 9: Writing per-family sets ...")
family_buckets = stratify_by_family(all_pairs, lang_paths, family_map)
per_family_dir = OUTPUT_DIR / "per_family"
per_family_dir.mkdir(parents=True, exist_ok=True)
for fam in TOP_FAMILIES:
fam_pairs = family_buckets.get(fam, [])
write_pairs_tsv(per_family_dir / f"{fam}.tsv", fam_pairs)
# ----- Step 10: Statistics --------------------------------------------
print("\nStep 10: Writing statistics ...")
write_stats(
OUTPUT_DIR,
l1, l2, l3,
fps, negs, borrows,
religious,
timespan_buckets,
family_buckets,
religious_by_family,
)
print("\n" + "=" * 70)
print("Done! Output written to:")
print(f" {OUTPUT_DIR.relative_to(REPO_ROOT)}/")
print("=" * 70)
def _load_cognate_pairs_sampled(
path: Path,
max_rows: int = 200_000,
) -> list[dict[str, str]]:
"""Load cognate pairs with reservoir sampling for very large files.
For files with millions of rows, we use reservoir sampling to get a
representative sample of at most *max_rows* entries.
"""
if not path.exists():
print(f" WARNING: {path} not found, skipping.")
return []
# First pass: count rows to decide whether to sample
total = 0
with path.open(encoding="utf-8") as fh:
header = fh.readline() # skip header
if not header:
return []
for _ in fh:
total += 1
print(f" {path.name}: {total:,} rows total", end="")
if total <= max_rows:
# Small enough — load everything
print(" (loading all)")
return load_cognate_pairs(path)
# Reservoir sampling
print(f" (sampling {max_rows:,})")
reservoir: list[dict[str, str]] = []
with path.open(encoding="utf-8") as fh:
reader = csv.DictReader(fh, delimiter="\t")
for i, row in enumerate(reader):
if i < max_rows:
reservoir.append(dict(row))
else:
j = random.randint(0, i)
if j < max_rows:
reservoir[j] = dict(row)
print(f" Sampled {len(reservoir):,} pairs from {path.name}")
return reservoir
if __name__ == "__main__":
main()
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