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MathTok Benchmark Runner
Evaluates the MathTok pipeline against baseline tokenizers on a curated
dataset of mathematical expressions and mixed text+math problems.
Usage
─────
python -m evaluation.benchmark # run full benchmark
python -m evaluation.benchmark --quick # 20 examples only
python -m evaluation.benchmark --json # JSON output
python -m evaluation.benchmark --baselines # include GPT-2 baseline
"""
from __future__ import annotations
import argparse
import json
import logging
import time
from pathlib import Path
from typing import Callable
from mathtok.pipeline import MathTokPipeline
from .metrics import (
EvaluationReport, MetricResult,
structural_compression_ratio,
canonical_consistency_score,
operator_preservation_score,
token_stability,
tree_depth_fidelity,
make_gpt2_tokenizer,
tokenize_character_level,
)
logger = logging.getLogger(__name__)
_DATASET_PATH = Path(__file__).parent / "datasets" / "sample_problems.json"
# ── Dataset loading ───────────────────────────────────────────────────────
def load_dataset(path: Path = _DATASET_PATH) -> dict:
"""Load the benchmark dataset JSON."""
with open(path, "r", encoding="utf-8") as f:
return json.load(f)
# ── Benchmark runner ──────────────────────────────────────────────────────
class MathTokBenchmark:
"""
Run all five evaluation metrics on the benchmark dataset.
Parameters
----------
pipeline : MathTokPipeline to evaluate
dataset : loaded benchmark dict (from load_dataset())
max_n : maximum number of examples to evaluate (None = all)
"""
def __init__(
self,
pipeline: MathTokPipeline,
dataset: dict,
max_n: int | None = None,
) -> None:
self.pipeline = pipeline
self.dataset = dataset
self.max_n = max_n
def run(self) -> EvaluationReport:
"""Run all five metrics and return an EvaluationReport."""
ds = self.dataset
# Slice if max_n is set
exprs = ds.get("expressions", [])[:self.max_n]
eq_pairs = ds.get("equivalent_pairs", [])[:self.max_n]
expr_groups = ds.get("rewriting_groups", [])[:self.max_n]
mixed = ds.get("mixed_text_math", [])[:self.max_n]
# Build the primary tokenizer function
def tokenize(text: str) -> list[str]:
return self.pipeline.encode(text).tokens
def tokenize_math(expr: str) -> list[str]:
return self.pipeline.encode_math_only(expr).tokens
print(f"Running MathTok benchmark on {len(exprs)} expressions...")
t0 = time.time()
# ── SCR ──────────────────────────────────────────────────────────
print(" Computing SCR...")
tok_lengths = []
for expr in exprs:
try:
out = self.pipeline.encode_math_only(expr)
tok_lengths.append(len(out.tokens))
except Exception:
tok_lengths.append(0)
scr = structural_compression_ratio(exprs, tok_lengths)
# ── CCS ──────────────────────────────────────────────────────────
print(" Computing CCS...")
ccs = canonical_consistency_score(eq_pairs, tokenize_math)
# ── OPS ──────────────────────────────────────────────────────────
print(" Computing OPS...")
ops = operator_preservation_score(exprs, tokenize_math)
# ── TS ───────────────────────────────────────────────────────────
print(" Computing TS...")
ts = token_stability(expr_groups, tokenize_math)
# ── TDF ──────────────────────────────────────────────────────────
print(" Computing TDF...")
tdf = tree_depth_fidelity(exprs, self.pipeline.encode_math_only)
elapsed = time.time() - t0
print(f" Done in {elapsed:.1f}s")
return EvaluationReport(
scr=scr, ccs=ccs, ops=ops, ts=ts, tdf=tdf,
num_examples=len(exprs),
)
def run_baseline_comparison(self, baseline_name: str = "gpt2") -> dict:
"""
Compare MathTok against a baseline tokenizer on SCR and CCS.
Returns a dict with 'mathtok' and 'baseline' results.
"""
ds = self.dataset
exprs = ds.get("expressions", [])[:self.max_n]
eq_pairs = ds.get("equivalent_pairs", [])[:self.max_n]
if baseline_name == "gpt2":
baseline_fn = make_gpt2_tokenizer()
elif baseline_name == "char":
baseline_fn = tokenize_character_level
else:
raise ValueError(f"Unknown baseline: {baseline_name}")
def mathtok_fn(expr: str) -> list[str]:
return self.pipeline.encode_math_only(expr).tokens
# MathTok metrics
mt_tok_lengths = [len(mathtok_fn(e)) for e in exprs]
mt_scr = structural_compression_ratio(exprs, mt_tok_lengths)
mt_ccs = canonical_consistency_score(eq_pairs, mathtok_fn)
# Baseline metrics
bl_tok_lengths = []
for e in exprs:
try:
bl_tok_lengths.append(len(baseline_fn(e)))
except Exception:
bl_tok_lengths.append(0)
bl_scr = structural_compression_ratio(exprs, bl_tok_lengths)
bl_ccs = canonical_consistency_score(eq_pairs, baseline_fn)
return {
"mathtok": {"SCR": mt_scr.value, "CCS": mt_ccs.value},
"baseline": {"name": baseline_name, "SCR": bl_scr.value, "CCS": bl_ccs.value},
}
# ── CLI ───────────────────────────────────────────────────────────────────
def main() -> None:
logging.basicConfig(level=logging.WARNING)
parser = argparse.ArgumentParser(description="MathTok Benchmark Runner")
parser.add_argument("--quick", action="store_true", help="Run on first 20 examples only")
parser.add_argument("--json", action="store_true", help="Output JSON")
parser.add_argument("--baselines", action="store_true", help="Include GPT-2 baseline comparison")
parser.add_argument("--dataset", default=str(_DATASET_PATH), help="Dataset JSON path")
args = parser.parse_args()
dataset = load_dataset(Path(args.dataset))
pipeline = MathTokPipeline()
max_n = 20 if args.quick else None
bench = MathTokBenchmark(pipeline, dataset, max_n=max_n)
report = bench.run()
if args.json:
result = report.to_dict()
if args.baselines:
result["baseline_comparison"] = bench.run_baseline_comparison("char")
print(json.dumps(result, indent=2))
else:
print(report.summary())
if args.baselines:
comp = bench.run_baseline_comparison("char")
print("\nBaseline comparison (char-level):")
print(f" MathTok SCR={comp['mathtok']['SCR']:.4f} CCS={comp['mathtok']['CCS']:.4f}")
print(f" CharLvl SCR={comp['baseline']['SCR']:.4f} CCS={comp['baseline']['CCS']:.4f}")
if __name__ == "__main__":
main()
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