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scripts/06_build_eval_sets.py
Build held-out evaluation sets from the synthetic data:
- eval_pictogram_to_sentence.jsonl (500 examples, 100 per persona)
- eval_audio_disambig.jsonl (200 examples with audio paths)
- eval_visual_ground.jsonl (150 examples with image paths)
- eval_adapter_specificity.jsonl (100 fixed prompts, cross-persona)
"""
import json
import logging
import random
from pathlib import Path
import pandas as pd
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
log = logging.getLogger(__name__)
SYNTH_DIR = Path("data/synth")
EVAL_DIR = Path("data/eval")
PROCESSED_DIR = Path("data/processed")
RANDOM_SEED = 42
def load_persona_jsonl(persona_id: str) -> list[dict]:
path = SYNTH_DIR / f"persona_{persona_id}.jsonl"
if not path.exists():
log.warning(f"No synth data for persona: {persona_id}")
return []
with open(path, encoding="utf-8") as f:
return [json.loads(l) for l in f if l.strip()]
def build_pictogram_to_sentence_eval(n_per_persona: int = 100) -> list[dict]:
"""Sample n examples per persona as held-out eval set."""
all_eval = []
personas = ["ananya", "arjun", "priya", "rohan", "zara"]
for pid in personas:
records = load_persona_jsonl(pid)
if not records:
log.warning(f"Skipping {pid} — no data")
continue
random.shuffle(records)
sampled = records[:n_per_persona]
for r in sampled:
all_eval.append({
"persona_id": pid,
"pictogram_sequence": r.get("seq", []),
"context": r.get("context", ""),
"target_sentence": r.get("target", ""),
"alternatives": r.get("alternatives", []),
})
log.info(f" {pid}: sampled {len(sampled)} eval examples")
return all_eval
def build_audio_disambig_eval(n: int = 200) -> list[dict]:
"""
Build audio disambiguation eval set.
Each example: (pictogram_seq + audio_path) → target_sentence.
Audio paths reference files in data/processed/audio/.
"""
audio_manifest = Path("data/processed/audio_manifest.jsonl")
if not audio_manifest.exists():
log.warning("Audio manifest not found. Run 03_download_audio_corpora.py first.")
# Generate placeholder examples
return [
{
"persona_id": "arjun",
"pictogram_sequence": ["water", "want"],
"audio_path": "PLACEHOLDER",
"audio_transcript_hint": "pani",
"target_sentence": "Mujhe pani chahida",
"intent_class": "request_food_drink",
}
]
with open(audio_manifest) as f:
audio_clips = [json.loads(l) for l in f if l.strip()]
random.shuffle(audio_clips)
personas = ["ananya", "arjun", "priya", "rohan", "zara"]
records = []
for i, clip in enumerate(audio_clips[:n]):
pid = personas[i % len(personas)]
persona_data = load_persona_jsonl(pid)
if not persona_data:
continue
base = random.choice(persona_data)
records.append({
"persona_id": pid,
"pictogram_sequence": base.get("seq", []),
"audio_path": clip["path"],
"audio_transcript_hint": clip.get("transcript", ""),
"target_sentence": base.get("target", ""),
"intent_class": _classify_intent(base.get("seq", [])),
})
return records
def build_visual_ground_eval(n: int = 150) -> list[dict]:
"""
Build visual grounding eval set.
Each example: (home_photo + pictogram_seq) → sentence with visual reference.
Uses placeholder image paths since real home photos aren't available.
"""
personas = ["ananya", "arjun", "priya", "rohan", "zara"]
records = []
for i in range(n):
pid = personas[i % len(personas)]
persona_data = load_persona_jsonl(pid)
if not persona_data:
continue
base = random.choice(persona_data)
records.append({
"persona_id": pid,
"pictogram_sequence": base.get("seq", []),
"image_path": f"data/raw/home_photos/{pid}/sample_{i:03d}.jpg", # placeholder
"target_sentence": base.get("target", ""),
"visual_reference": f"object in image #{i % 5 + 1}",
})
return records
def build_adapter_specificity_eval() -> list[dict]:
"""
Build the fixed prompt set used for adapter-specificity evaluation.
100 prompts, all same prompts applied to each persona adapter.
"""
# These are universal prompts that any CP child might use
UNIVERSAL_PROMPTS = [
{"seq": ["mother", "want"], "context": "morning"},
{"seq": ["water", "give"], "context": "afternoon"},
{"seq": ["pain", "help"], "context": "general"},
{"seq": ["food", "more"], "context": "lunch"},
{"seq": ["sleep", "want"], "context": "evening"},
{"seq": ["play", "want"], "context": "afternoon"},
{"seq": ["toilet", "go"], "context": "general"},
{"seq": ["doctor", "no"], "context": "hospital"},
{"seq": ["happy", "today"], "context": "morning"},
{"seq": ["love", "mother"], "context": "bedtime"},
{"seq": ["school", "go"], "context": "morning"},
{"seq": ["friend", "come"], "context": "afternoon"},
{"seq": ["TV", "watch"], "context": "evening"},
{"seq": ["cold", "milk", "want"], "context": "afternoon"},
{"seq": ["medicine", "no", "want"], "context": "evening"},
{"seq": ["tired", "bed"], "context": "night"},
{"seq": ["hot", "stop"], "context": "bathing"},
{"seq": ["book", "read"], "context": "bedtime"},
{"seq": ["father", "come", "now"], "context": "general"},
{"seq": ["outside", "go", "want"], "context": "afternoon"},
] * 5 # 20 prompts × 5 = 100
records = []
for i, p in enumerate(UNIVERSAL_PROMPTS[:100]):
records.append({
"prompt_id": f"spec_{i:03d}",
"pictogram_sequence": p["seq"],
"context": p["context"],
"expected_persona_styles": {
"ananya": "Tamil/English style, short",
"arjun": "Punjabi/Hindi/English mix",
"priya": "Bengali/English, very short (4yo)",
"rohan": "Hindi/English, older child register",
"zara": "Marathi/English, 7yo register",
},
})
return records
def _classify_intent(seq: list[str]) -> str:
"""Simple rule-based intent classifier for audio eval labels."""
seq_lower = [s.lower() for s in seq]
if any(w in seq_lower for w in ["food", "eat", "drink", "water", "milk", "more", "hungry"]):
return "request_food_drink"
if any(w in seq_lower for w in ["pain", "hurt", "stop", "help"]):
return "report_discomfort"
if any(w in seq_lower for w in ["play", "fun", "friend", "TV", "outside"]):
return "request_activity"
if any(w in seq_lower for w in ["love", "happy", "hug", "kiss"]):
return "social_emotional"
if any(w in seq_lower for w in ["toilet", "bath", "sleep"]):
return "personal_care"
return "general"
def save_jsonl(records: list[dict], path: Path):
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
for r in records:
f.write(json.dumps(r, ensure_ascii=False) + "\n")
log.info(f"Saved {len(records)} records → {path}")
def main():
random.seed(RANDOM_SEED)
EVAL_DIR.mkdir(parents=True, exist_ok=True)
log.info("Building eval_pictogram_to_sentence...")
p2s = build_pictogram_to_sentence_eval(100)
save_jsonl(p2s, EVAL_DIR / "eval_pictogram_to_sentence.jsonl")
log.info("Building eval_audio_disambig...")
audio = build_audio_disambig_eval(200)
save_jsonl(audio, EVAL_DIR / "eval_audio_disambig.jsonl")
log.info("Building eval_visual_ground...")
visual = build_visual_ground_eval(150)
save_jsonl(visual, EVAL_DIR / "eval_visual_ground.jsonl")
log.info("Building eval_adapter_specificity...")
spec = build_adapter_specificity_eval()
save_jsonl(spec, EVAL_DIR / "eval_adapter_specificity.jsonl")
log.info("\nEval sets complete:")
log.info(f" pictogram_to_sentence: {len(p2s)}")
log.info(f" audio_disambig: {len(audio)}")
log.info(f" visual_ground: {len(visual)}")
log.info(f" adapter_specificity: {len(spec)}")
if __name__ == "__main__":
main()
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