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f8392aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | #!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "sentence-transformers[train]>=5.5.0",
# "datasets>=2.19.0",
# "numpy",
# ]
# ///
"""Side-by-side comparison of all chess static-embedding variants on the same
held-out compositional eval. Produces the final table for NOTES.md.
"""
from __future__ import annotations
import os
import sys
from collections import defaultdict
import numpy as np
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
sys.stdout.reconfigure(line_buffering=True)
VARIANTS = [
("v3 baseline", "models/static-embedding-chess/final"),
("v4-A hard-neg only", "models/static-embedding-chess-triplet/final"),
("v4-B theme distill", "models/static-embedding-chess-theme-only/final"),
("v4-C multitask 500x", "models/static-embedding-chess-multitask-500x/final"),
("v4-C2 multitask 5000x", "models/static-embedding-chess-multitask-5000x/final"),
]
HELDOUT_FREQ_MIN = 3
HELDOUT_FREQ_MAX = 30
EVAL_QUERIES = 200
def _join_tags(tags):
return " ".join(t.replace("_", " ") for t in tags) if tags else ""
def _bigram_token_str(moves):
toks = moves.split()
if len(toks) < 2:
return moves
return moves + " " + " ".join(f"{a}+{b}" for a, b in zip(toks, toks[1:]))
def build_puzzle_pairs(batch):
anchors, positives = [], []
for themes, op, moves in zip(batch["Themes"], batch["OpeningTags"], batch["Moves"]):
themes_txt = _join_tags(themes)
op_txt = _join_tags(op)
if not themes_txt:
continue
anchor = themes_txt + (f" {op_txt}" if op_txt else "")
positive = f"themes {themes_txt}"
if op_txt:
positive += f" opening {op_txt}"
positive += f" moves {_bigram_token_str(moves)}"
anchors.append(anchor)
positives.append(positive)
return {"anchor": anchors, "positive": positives}
def strip_theme_echo(p):
i = p.find(" moves ")
return p[i + 1 :] if i != -1 else p
def ndcg_at_k(scores, rel, k=10):
ranked = sorted(scores, key=lambda kv: -kv[1])[:k]
dcg = sum((1.0 if d in rel else 0.0) / np.log2(r + 2) for r, (d, _) in enumerate(ranked))
idcg = sum(1.0 / np.log2(r + 2) for r in range(min(len(rel), k)))
return dcg / idcg if idcg > 0 else 0.0
def main():
print("Loading + held-out selection...")
puzzles = load_dataset("Lichess/chess-puzzles", split="train")
pair_puzzles = puzzles.map(
build_puzzle_pairs,
batched=True, batch_size=20_000,
remove_columns=puzzles.column_names,
num_proc=4,
)
anchors = pair_puzzles["anchor"]
freq = defaultdict(int)
for a in anchors:
freq[a] += 1
rare_pool = sorted(
((a, c) for a, c in freq.items() if HELDOUT_FREQ_MIN <= c <= HELDOUT_FREQ_MAX),
key=lambda kv: kv[1],
)
heldout = {a for a, _ in rare_pool[:EVAL_QUERIES]}
held_idx = [i for i, h in enumerate([a in heldout for a in anchors]) if h]
held_anchors = [anchors[i] for i in held_idx]
corpus_texts = [strip_theme_echo(pair_puzzles["positive"][i]) for i in held_idx]
corpus_ids = [f"d{i}" for i in range(len(corpus_texts))]
by_anchor = defaultdict(list)
for i, a in enumerate(held_anchors):
by_anchor[a].append(corpus_ids[i])
queries = list(by_anchor.keys())
print(f" {len(queries)} queries, {len(corpus_texts)} corpus")
results = []
for name, path in VARIANTS:
if not os.path.exists(path):
print(f"\nSKIPPING {name}: {path} not found")
continue
print(f"\n=== {name} ({path}) ===")
m = SentenceTransformer(path)
c = m.encode(corpus_texts, batch_size=128, convert_to_numpy=True, show_progress_bar=False)
c = c / np.linalg.norm(c, axis=1, keepdims=True)
q = m.encode(queries, batch_size=128, convert_to_numpy=True, show_progress_bar=False)
q = q / np.linalg.norm(q, axis=1, keepdims=True)
sims = q @ c.T
ndcgs = []
for qi, query in enumerate(queries):
score_pairs = [(corpus_ids[ci], float(sims[qi, ci])) for ci in range(len(corpus_ids))]
rel = set(by_anchor[query])
ndcgs.append(ndcg_at_k(score_pairs, rel, k=10))
ndcg = np.mean(ndcgs)
median = np.median(ndcgs)
zero = sum(1 for n in ndcgs if n == 0)
results.append((name, ndcg, median, zero, len(ndcgs)))
print(f" NDCG@10 = {ndcg:.4f} median = {median:.4f} zero = {zero}/{len(ndcgs)}")
print("\n" + "=" * 70)
print(f"{'Variant':<30} {'NDCG@10':>10} {'Median':>10} {'Zero/All':>15}")
print("=" * 70)
for name, ndcg, median, zero, total in results:
print(f"{name:<30} {ndcg:>10.4f} {median:>10.4f} {zero:>7}/{total:<7}")
print("=" * 70)
# === Token-similarity probe ===
# Measures the orthogonal-tokens problem from Phase 1: do related themes
# cluster in embedding space? Higher = more semantic structure.
print("\n=== Theme-token similarity (higher = more semantic clustering) ===")
PROBES = [
("fork", "skewer"), # tactical motifs (should be close)
("fork", "pin"),
("backRankMate", "smotheredMate"), # mate patterns
("kingsideAttack", "queensideAttack"),
("endgame", "middlegame"), # phases
("fork", "promotion"), # unrelated (control)
]
print(f"{'Pair':<40}", end="")
for name, _ in VARIANTS:
if os.path.exists([p for n, p in VARIANTS if n == name][0]):
print(f" {name[:14]:>16}", end="")
print()
print("-" * 70)
for a, b in PROBES:
line = f"{a} <-> {b}".ljust(40)
for name, path in VARIANTS:
if not os.path.exists(path):
continue
m = SentenceTransformer(path)
ea = m.encode([a], convert_to_numpy=True)[0]
eb = m.encode([b], convert_to_numpy=True)[0]
ea = ea / max(np.linalg.norm(ea), 1e-9)
eb = eb / max(np.linalg.norm(eb), 1e-9)
sim = float(np.dot(ea, eb))
line += f" {sim:>+16.3f}"
print(line)
if __name__ == "__main__":
main()
if __name__ == "__main__":
main()
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