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05b9702 | 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 | from __future__ import annotations
import argparse
import json
from collections import Counter, defaultdict
from dataclasses import dataclass
from pathlib import Path
from typing import Any, DefaultDict, Dict, Iterable, List, Mapping, Tuple
from dialect_analysis.pipeline import classify_text
@dataclass(frozen=True)
class Sample:
id: str
label: str
text: str
strip_diacritics: bool = True
synthetic: bool = False
def load_samples(path: Path) -> List[Sample]:
samples: List[Sample] = []
for i, line in enumerate(path.read_text(encoding="utf-8").splitlines(), start=1):
line = line.strip()
if not line or line.startswith("#"):
continue
obj = json.loads(line)
samples.append(
Sample(
id=str(obj.get("id") or f"sample_{i}"),
label=str(obj["label"]),
text=str(obj["text"]),
strip_diacritics=bool(obj.get("strip_diacritics", True)),
synthetic=bool(obj.get("synthetic", False)),
)
)
return samples
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser(description="Evaluate dialect classifier against a JSONL sample set.")
p.add_argument(
"--samples",
type=Path,
default=Path(__file__).with_name("samples.jsonl"),
help="Path to JSONL file with {id,label,text,strip_diacritics[,synthetic]}",
)
return p.parse_args()
def confusion_matrix(rows: Iterable[Tuple[str, str]]) -> Tuple[List[str], List[List[int]]]:
labels = sorted({t for t, _ in rows} | {p for _, p in rows})
idx = {l: i for i, l in enumerate(labels)}
mat = [[0 for _ in labels] for _ in labels]
for true_label, pred_label in rows:
mat[idx[true_label]][idx[pred_label]] += 1
return labels, mat
def main() -> int:
args = parse_args()
path = Path(args.samples)
if not path.exists():
print(f"Missing samples file: {path}")
return 2
samples = load_samples(path)
if not samples:
print("No samples found.")
return 2
pairs: List[Tuple[str, str]] = []
correct = 0
confidences: List[float] = []
pairs_real: List[Tuple[str, str]] = []
pairs_synth: List[Tuple[str, str]] = []
correct_real = 0
correct_synth = 0
by_label: DefaultDict[str, Counter[str]] = defaultdict(Counter)
for s in samples:
result: Mapping[str, Any] = classify_text(s.text, strip_diacritics=s.strip_diacritics)
pred = str(result.get("dialect", ""))
conf = float(result.get("confidence", 0.0) or 0.0)
confidences.append(conf)
pairs.append((s.label, pred))
if s.synthetic:
pairs_synth.append((s.label, pred))
if pred == s.label:
correct_synth += 1
else:
pairs_real.append((s.label, pred))
if pred == s.label:
correct_real += 1
by_label[s.label][pred] += 1
if pred == s.label:
correct += 1
else:
scores: Mapping[str, float] = result.get("scores", {}) or {}
top2 = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:2]
top2_str = ", ".join(f"{d}={pct:.1f}%" for d, pct in top2)
print(f"MISS {s.id}: true={s.label} pred={pred} conf={conf*100:.1f}% top2=({top2_str})")
acc = correct / max(1, len(samples))
avg_conf = sum(confidences) / max(1, len(confidences))
print("\nSummary")
print(f" File: {path.name}")
print(f" Samples: {len(samples)}")
print(f" Accuracy: {acc*100:.1f}%")
print(f" Avg confidence: {avg_conf*100:.1f}%")
if pairs_real and pairs_synth:
acc_real = correct_real / max(1, len(pairs_real))
acc_synth = correct_synth / max(1, len(pairs_synth))
print(f" Accuracy (real): {acc_real*100:.1f}% (n={len(pairs_real)})")
print(f" Accuracy (synthetic): {acc_synth*100:.1f}% (n={len(pairs_synth)})")
labels, mat = confusion_matrix(pairs)
print("\nConfusion matrix (rows=true, cols=pred)")
header = "".ljust(14) + " ".join(l[:10].ljust(10) for l in labels)
print(header)
for i, true_label in enumerate(labels):
row = " ".join(str(mat[i][j]).ljust(10) for j in range(len(labels)))
print(true_label[:12].ljust(14) + row)
print("\nPer-label predictions")
for true_label in sorted(by_label.keys()):
counts = by_label[true_label]
total = sum(counts.values())
ordered = sorted(counts.items(), key=lambda kv: kv[1], reverse=True)
dist = ", ".join(f"{p}:{c}" for p, c in ordered)
print(f" {true_label} (n={total}): {dist}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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