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1fed70a | 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 | #!/usr/bin/env python3
"""
Evaluate ByT5 + XLM-RoBERTa reranker on internal_test_set_500.csv.
CSV columns: id, category, input, code_mixed_reference
Usage:
python misc/eval_internal_500.py # full 500
python misc/eval_internal_500.py --max 10 # dry run
python misc/eval_internal_500.py --max 10 --cat colloquial
"""
import sys
import os
import argparse
import csv
import time
from pathlib import Path
from collections import defaultdict
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from core.decoder import BeamSearchDecoder
# ── Metrics ─────────────────────────────────────────────────────────────────
def levenshtein(a, b):
if not a: return len(b)
if not b: return len(a)
prev = list(range(len(b) + 1))
for i, ca in enumerate(a, 1):
curr = [i] + [0] * len(b)
for j, cb in enumerate(b, 1):
curr[j] = min(prev[j] + 1, curr[j-1] + 1, prev[j-1] + (0 if ca == cb else 1))
prev = curr
return prev[-1]
def cer(pred, ref):
if not ref: return 0.0 if not pred else 1.0
return levenshtein(pred, ref) / len(ref)
def wer(pred, ref):
pw, rw = pred.split(), ref.split()
if not rw: return 0.0 if not pw else 1.0
return levenshtein(pw, rw) / len(rw)
def bleu1(pred, ref):
pt, rt = pred.split(), ref.split()
if not rt: return 1.0 if not pt else 0.0
matches = sum(1 for t in pt if t in rt)
return matches / len(rt)
def exact_match(pred, ref):
return pred.strip() == ref.strip()
# ── Main ─────────────────────────────────────────────────────────────────────
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--max", type=int, default=None, help="Max samples to evaluate")
parser.add_argument("--cat", type=str, default=None, help="Filter to one category")
parser.add_argument("--out", type=str, default="misc/internal_500_results.csv", help="Output CSV path")
args = parser.parse_args()
csv_path = project_root / "misc" / "internal_test_set_500.csv"
# Load samples
samples = []
with open(csv_path, newline="", encoding="utf-8-sig") as f:
reader = csv.DictReader(f)
for row in reader:
if args.cat and row["category"] != args.cat:
continue
samples.append(row)
if args.max and len(samples) >= args.max:
break
print(f"Loaded {len(samples)} samples" + (f" (category={args.cat})" if args.cat else ""))
# Load decoder
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Loading BeamSearchDecoder on {device}...")
decoder = BeamSearchDecoder(device=device)
print("Ready.\n")
results = []
category_stats = defaultdict(list)
for i, row in enumerate(samples):
inp = row["input"].strip()
ref = row["code_mixed_reference"].strip()
cat = row["category"]
sid = row["id"]
t0 = time.time()
try:
pred, _, _ = decoder.decode(inp)
except Exception as e:
print(f" ERROR id={sid}: {e}")
pred = "[ERROR]"
elapsed = time.time() - t0
c = cer(pred, ref)
w = wer(pred, ref)
b = bleu1(pred, ref)
em = exact_match(pred, ref)
result = {
"id": sid,
"category": cat,
"input": inp,
"reference": ref,
"prediction": pred,
"cer": round(c, 4),
"wer": round(w, 4),
"bleu": round(b, 4),
"exact_match": em,
"time_s": round(elapsed, 3),
}
results.append(result)
category_stats[cat].append(result)
status = "PASS" if em else "FAIL"
print(f"[{i+1:>4}/{len(samples)}] {status} id={sid:>4} cat={cat:<15} CER={c:.3f} WER={w:.3f} BLEU={b:.3f}")
if not em:
print(f" IN: {inp}")
print(f" REF: {ref}")
print(f" GOT: {pred}")
# Write results CSV
out_path = project_root / args.out
with open(out_path, "w", newline="", encoding="utf-8") as f:
writer = csv.DictWriter(f, fieldnames=results[0].keys())
writer.writeheader()
writer.writerows(results)
print(f"\nResults saved to: {out_path}")
# Summary by category
print(f"\n{'='*70}")
print(f"{'CATEGORY':<18} {'n':>4} {'CER':>7} {'WER':>7} {'BLEU':>7} {'EM%':>7}")
print(f"{'='*70}")
all_results = results
for cat in sorted(category_stats):
rows = category_stats[cat]
n = len(rows)
avg_cer = sum(r["cer"] for r in rows) / n
avg_wer = sum(r["wer"] for r in rows) / n
avg_bleu = sum(r["bleu"] for r in rows) / n
em_pct = sum(1 for r in rows if r["exact_match"]) / n * 100
print(f"{cat:<18} {n:>4} {avg_cer:>7.4f} {avg_wer:>7.4f} {avg_bleu:>7.4f} {em_pct:>6.1f}%")
n = len(all_results)
print(f"{'─'*70}")
print(f"{'OVERALL':<18} {n:>4} "
f"{sum(r['cer'] for r in all_results)/n:>7.4f} "
f"{sum(r['wer'] for r in all_results)/n:>7.4f} "
f"{sum(r['bleu'] for r in all_results)/n:>7.4f} "
f"{sum(1 for r in all_results if r['exact_match'])/n*100:>6.1f}%")
print(f"{'='*70}")
if __name__ == "__main__":
main()
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