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| #!/usr/bin/env python3 | |
| """ | |
| Expand seed JSON files into the full challenge dataset. | |
| Usage: | |
| python generate_dataset.py --out ./expanded | |
| Reads from: | |
| categories/*.json — already-complete category contexts (5) | |
| merchants_seed.json — 10 representative merchants (2 per category) | |
| customers_seed.json — 15 representative customers | |
| triggers_seed.json — 25 representative triggers | |
| Writes to ./expanded/: | |
| categories/{slug}.json (5 files, copied as-is) | |
| merchants/m_NNN_*.json (50 files — seeds + 40 generated) | |
| customers/c_NNN_*.json (200 files — seeds + 185 generated) | |
| triggers/trg_NNN_*.json (100 files — seeds + 75 generated) | |
| test_pairs.json (30 canonical (merchant, trigger) pairs all | |
| candidates produce a message for) | |
| Deterministic — fixed seed, same output for everyone. | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| import os | |
| import random | |
| from pathlib import Path | |
| SEED = 20260426 # fixed so every candidate gets the same expanded dataset | |
| # Indian city + locality pool for variation | |
| LOCALITIES = { | |
| "Delhi": ["Lajpat Nagar", "Saket", "Karol Bagh", "Pitampura", "Dwarka", "Rohini", "Greater Kailash", "Vasant Kunj", "Connaught Place", "Hauz Khas"], | |
| "Mumbai": ["Andheri West", "Bandra", "Borivali", "Powai", "Lower Parel", "Goregaon", "Thane", "Vile Parle", "Juhu", "Worli"], | |
| "Bangalore": ["HSR Layout", "Indiranagar", "Whitefield", "Koramangala", "JP Nagar", "Marathahalli", "Bellandur", "Jayanagar", "BTM Layout", "Sarjapur"], | |
| "Hyderabad": ["Kapra", "Kondapur", "Madhapur", "Banjara Hills", "Jubilee Hills", "Kukatpally", "Gachibowli", "Begumpet", "Secunderabad", "LB Nagar"], | |
| "Chennai": ["Mylapore", "Adyar", "Velachery", "T Nagar", "Anna Nagar", "Tambaram", "OMR", "Nungambakkam", "Porur", "Besant Nagar"], | |
| "Pune": ["Aundh", "Baner", "Hadapsar", "Kothrud", "Wakad", "Hinjewadi", "Viman Nagar", "Kharadi", "Pimpri", "Magarpatta"], | |
| "Chandigarh": ["Sector 17", "Sector 22", "Sector 35", "Mohali", "Panchkula", "Sector 9", "Sector 11", "Manimajra", "Sector 8", "Sector 26"], | |
| "Jaipur": ["Malviya Nagar", "Vaishali Nagar", "Mansarovar", "Tonk Road", "C-Scheme", "Raja Park", "Civil Lines", "Jhotwara", "Bani Park", "Sodala"], | |
| "Lucknow": ["Gomti Nagar", "Hazratganj", "Indira Nagar", "Aliganj", "Aminabad", "Vibhuti Khand", "Mahanagar", "Aashiana", "Alambagh", "Janakipuram"], | |
| "Ahmedabad": ["Satellite", "Bodakdev", "Vastrapur", "Maninagar", "Naranpura", "Bopal", "SG Highway", "Navrangpura", "Thaltej", "Chandkheda"], | |
| } | |
| NAME_BANKS = { | |
| "dentists": [ | |
| ("Dr. Asha", "Asha Dental Care"), | |
| ("Dr. Vikram", "Smile Crafters"), | |
| ("Dr. Neha", "Pearl Dental Studio"), | |
| ("Dr. Rajan", "City Dental Clinic"), | |
| ("Dr. Priya", "Family Dental Centre"), | |
| ("Dr. Sameer", "Bright Smile Dental"), | |
| ("Dr. Tara", "Crown Dental"), | |
| ("Dr. Karthik", "Apex Dental Care"), | |
| ], | |
| "salons": [ | |
| ("Renu", "Beauty Lounge by Renu"), | |
| ("Karim", "Karim's Salon"), | |
| ("Anita", "Anita's Beauty Studio"), | |
| ("Salim", "Studio Cuts"), | |
| ("Manish", "Aesthetic Hair Studio"), | |
| ("Geeta", "Glow Up Salon"), | |
| ("Paras", "Paras Hair & Beauty"), | |
| ("Sushma", "The Beauty Bar"), | |
| ], | |
| "restaurants": [ | |
| ("Suresh", "Madras Express"), | |
| ("Anand", "Chai Point Cafe"), | |
| ("Karim", "Kabab Junction"), | |
| ("Sandeep", "Tandoor Treats"), | |
| ("Ravi", "Veg Bowl"), | |
| ("Imran", "Biryani House"), | |
| ("Mukesh", "Pizza Spot"), | |
| ("Lalit", "Family Diner"), | |
| ], | |
| "gyms": [ | |
| ("Karan", "Iron Forge Fitness"), | |
| ("Sneha", "Pulse Studio"), | |
| ("Akash", "Fit Republic"), | |
| ("Roshni", "Active Life Gym"), | |
| ("Vivek", "Strength Co."), | |
| ("Manisha", "Vyayam Yoga"), | |
| ("Deepak", "Body Mechanics"), | |
| ("Pooja", "Bend & Burn"), | |
| ], | |
| "pharmacies": [ | |
| ("Anil", "Healthwell Pharmacy"), | |
| ("Rajesh", "MedPlus Express"), | |
| ("Sunita", "Reliable Medicos"), | |
| ("Vinod", "Family Health Pharmacy"), | |
| ("Bharti", "Wellness Cart"), | |
| ("Sanjay", "TrueCare Medicos"), | |
| ("Mohit", "QuickRx Pharmacy"), | |
| ("Komal", "Daily Care Medicos"), | |
| ], | |
| } | |
| def load_seeds(seed_dir: Path): | |
| categories = {} | |
| for f in (seed_dir / "categories").glob("*.json"): | |
| with open(f) as fp: | |
| data = json.load(fp) | |
| categories[data["slug"]] = data | |
| with open(seed_dir / "merchants_seed.json") as fp: | |
| merchants = json.load(fp)["merchants"] | |
| with open(seed_dir / "customers_seed.json") as fp: | |
| customers = json.load(fp)["customers"] | |
| with open(seed_dir / "triggers_seed.json") as fp: | |
| triggers = json.load(fp)["triggers"] | |
| return categories, merchants, customers, triggers | |
| def expand_merchants(seeds: list[dict], rnd: random.Random) -> list[dict]: | |
| """Generate 8 additional merchants per category (10 total per category, 50 overall).""" | |
| expanded = list(seeds) | |
| by_cat = {} | |
| for m in seeds: | |
| by_cat.setdefault(m["category_slug"], []).append(m) | |
| next_idx = len(seeds) + 1 | |
| for cat_slug in NAME_BANKS: | |
| existing = len(by_cat.get(cat_slug, [])) | |
| need = 10 - existing | |
| for i in range(need): | |
| owner_first, biz_name = rnd.choice(NAME_BANKS[cat_slug]) | |
| city = rnd.choice(list(LOCALITIES.keys())) | |
| locality = rnd.choice(LOCALITIES[city]) | |
| mid = f"m_{next_idx:03d}_{owner_first.lower().replace(' ', '_').replace('dr.', 'dr')}_{cat_slug.rstrip('s')}_{city.lower()}" | |
| views = rnd.randint(400, 6000) | |
| calls = rnd.randint(2, max(3, views // 80)) | |
| ctr = round(rnd.uniform(0.015, 0.060), 3) | |
| verified = rnd.random() > 0.25 | |
| sub_status = rnd.choices(["active", "expired", "trial"], weights=[7, 2, 1])[0] | |
| expanded.append({ | |
| "merchant_id": mid, | |
| "category_slug": cat_slug, | |
| "identity": { | |
| "name": biz_name, "city": city, "locality": locality, | |
| "place_id": f"ChIJ_{locality.upper().replace(' ', '_')}_{cat_slug.upper()}_{next_idx:03d}", | |
| "verified": verified, | |
| "languages": ["en", "hi"] + (["mr"] if city == "Mumbai" else ["ta"] if city == "Chennai" else ["te"] if city == "Hyderabad" else ["kn"] if city == "Bangalore" else []), | |
| "owner_first_name": owner_first, | |
| "established_year": rnd.randint(2010, 2023), | |
| }, | |
| "subscription": {"status": sub_status, "plan": "Pro" if sub_status != "trial" else "Trial", | |
| "days_remaining": rnd.randint(5, 300) if sub_status == "active" else (rnd.randint(1, 14) if sub_status == "trial" else 0), | |
| "days_since_expiry": rnd.randint(7, 90) if sub_status == "expired" else None}, | |
| "performance": {"window_days": 30, "views": views, "calls": calls, | |
| "directions": calls * 2 + rnd.randint(0, 30), | |
| "ctr": ctr, "leads": rnd.randint(0, calls), | |
| "delta_7d": {"views_pct": round(rnd.uniform(-0.30, 0.30), 2), | |
| "calls_pct": round(rnd.uniform(-0.30, 0.30), 2)}}, | |
| "offers": [], | |
| "conversation_history": [], | |
| "customer_aggregate": {"total_unique_ytd": rnd.randint(50, 2000)}, | |
| "signals": [], | |
| "review_themes": [], | |
| }) | |
| next_idx += 1 | |
| return expanded | |
| def expand_customers(seeds: list[dict], merchants: list[dict], rnd: random.Random) -> list[dict]: | |
| """Generate ~3-5 customers per merchant up to 200 total.""" | |
| expanded = list(seeds) | |
| next_idx = len(seeds) + 1 | |
| target_per_merchant = 4 | |
| have_per_merchant = {} | |
| for c in seeds: | |
| have_per_merchant[c["merchant_id"]] = have_per_merchant.get(c["merchant_id"], 0) + 1 | |
| customer_names = ["Aarav", "Vivaan", "Aditya", "Vihaan", "Arjun", "Ishaan", "Reyansh", "Aryan", "Ananya", "Aadhya", "Saanvi", "Kavya", "Diya", "Ira", "Myra", "Anika", "Riya", "Tara"] | |
| for m in merchants: | |
| cur = have_per_merchant.get(m["merchant_id"], 0) | |
| for i in range(max(0, target_per_merchant - cur)): | |
| if next_idx > 200 + len(seeds): break | |
| name = rnd.choice(customer_names) | |
| cid = f"c_{next_idx:03d}_{name.lower()}_for_{m['merchant_id']}" | |
| visits = rnd.randint(1, 12) | |
| state = rnd.choices( | |
| ["new", "active", "lapsed_soft", "lapsed_hard", "churned"], | |
| weights=[1, 4, 2, 1, 1] | |
| )[0] | |
| expanded.append({ | |
| "customer_id": cid, | |
| "merchant_id": m["merchant_id"], | |
| "identity": {"name": name, "phone_redacted": "<phone>", | |
| "language_pref": rnd.choice(["en", "hi-en mix", "hi"]), | |
| "age_band": rnd.choice(["20-25", "25-35", "30-40", "40-50", "50-65"])}, | |
| "relationship": {"first_visit": "2025-09-01", "last_visit": "2026-04-01", | |
| "visits_total": visits, "services_received": [], | |
| "lifetime_value": visits * rnd.randint(200, 1500)}, | |
| "state": state, | |
| "preferences": {"channel": "whatsapp", "reminder_opt_in": rnd.random() > 0.2}, | |
| "consent": {"opted_in_at": "2025-09-01", "scope": ["promotional_offers"]}, | |
| }) | |
| next_idx += 1 | |
| return expanded | |
| def expand_triggers(seeds: list[dict], merchants: list[dict], customers: list[dict], rnd: random.Random) -> list[dict]: | |
| """Generate ~75 additional triggers spread across kinds + merchants.""" | |
| expanded = list(seeds) | |
| next_idx = len(seeds) + 1 | |
| additional_kinds = [ | |
| ("research_digest", "external", "merchant", 1), | |
| ("perf_dip", "internal", "merchant", 3), | |
| ("perf_spike", "internal", "merchant", 1), | |
| ("milestone_reached", "internal", "merchant", 1), | |
| ("dormant_with_vera", "internal", "merchant", 2), | |
| ("review_theme_emerged", "internal", "merchant", 3), | |
| ("competitor_opened", "external", "merchant", 2), | |
| ("festival_upcoming", "external", "merchant", 1), | |
| ("recall_due", "internal", "customer", 3), | |
| ("customer_lapsed_soft", "internal", "customer", 3), | |
| ("appointment_tomorrow", "internal", "customer", 2), | |
| ("chronic_refill_due", "internal", "customer", 2), | |
| ("trial_followup", "internal", "customer", 2), | |
| ("renewal_due", "internal", "merchant", 4), | |
| ("curious_ask_due", "internal", "merchant", 1), | |
| ] | |
| for kind, source, scope, urgency in additional_kinds: | |
| for _ in range(5): # 5 of each kind | |
| if next_idx > 100: break | |
| m = rnd.choice(merchants) | |
| cust = None | |
| if scope == "customer": | |
| m_customers = [c for c in customers if c["merchant_id"] == m["merchant_id"]] | |
| if not m_customers: continue | |
| cust = rnd.choice(m_customers) | |
| expanded.append({ | |
| "id": f"trg_{next_idx:03d}_{kind}_{m['merchant_id'][:20]}", | |
| "scope": scope, "kind": kind, "source": source, | |
| "merchant_id": m["merchant_id"], | |
| "customer_id": cust["customer_id"] if cust else None, | |
| "payload": {"placeholder": True, "metric_or_topic": kind}, | |
| "urgency": urgency, "suppression_key": f"{kind}:{m['merchant_id']}:gen_{next_idx}", | |
| "expires_at": "2026-06-30T00:00:00Z", | |
| }) | |
| next_idx += 1 | |
| return expanded[:100] | |
| def write_outputs(out_dir: Path, categories, merchants, customers, triggers): | |
| out_dir.mkdir(parents=True, exist_ok=True) | |
| (out_dir / "categories").mkdir(exist_ok=True) | |
| for slug, data in categories.items(): | |
| with open(out_dir / "categories" / f"{slug}.json", "w") as f: | |
| json.dump(data, f, indent=2, ensure_ascii=False) | |
| (out_dir / "merchants").mkdir(exist_ok=True) | |
| for m in merchants: | |
| with open(out_dir / "merchants" / f"{m['merchant_id']}.json", "w") as f: | |
| json.dump(m, f, indent=2, ensure_ascii=False) | |
| (out_dir / "customers").mkdir(exist_ok=True) | |
| for c in customers: | |
| with open(out_dir / "customers" / f"{c['customer_id']}.json", "w") as f: | |
| json.dump(c, f, indent=2, ensure_ascii=False) | |
| (out_dir / "triggers").mkdir(exist_ok=True) | |
| for t in triggers: | |
| with open(out_dir / "triggers" / f"{t['id']}.json", "w") as f: | |
| json.dump(t, f, indent=2, ensure_ascii=False) | |
| def write_test_pairs(out_dir: Path, triggers, rnd: random.Random): | |
| """Pick 30 (merchant, trigger) pairs covering all kinds. Same set for everyone.""" | |
| by_kind = {} | |
| for t in triggers: | |
| by_kind.setdefault(t["kind"], []).append(t) | |
| pairs = [] | |
| test_id = 1 | |
| for kind, ts in sorted(by_kind.items()): | |
| for t in ts[:2]: # take up to 2 per kind | |
| pairs.append({"test_id": f"T{test_id:02d}", "trigger_id": t["id"], | |
| "merchant_id": t["merchant_id"], "customer_id": t.get("customer_id")}) | |
| test_id += 1 | |
| if len(pairs) >= 30: break | |
| if len(pairs) >= 30: break | |
| with open(out_dir / "test_pairs.json", "w") as f: | |
| json.dump({"pairs": pairs[:30]}, f, indent=2) | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--seed-dir", default=".", help="Directory containing the seed JSON files") | |
| parser.add_argument("--out", default="./expanded", help="Output directory") | |
| args = parser.parse_args() | |
| rnd = random.Random(SEED) | |
| seed_dir = Path(args.seed_dir).resolve() | |
| out_dir = Path(args.out).resolve() | |
| print(f"Reading seeds from {seed_dir}") | |
| print(f"Writing to {out_dir}") | |
| categories, m_seeds, c_seeds, t_seeds = load_seeds(seed_dir) | |
| print(f" Loaded {len(categories)} categories, {len(m_seeds)} merchant seeds, " | |
| f"{len(c_seeds)} customer seeds, {len(t_seeds)} trigger seeds") | |
| merchants = expand_merchants(m_seeds, rnd) | |
| customers = expand_customers(c_seeds, merchants, rnd) | |
| triggers = expand_triggers(t_seeds, merchants, customers, rnd) | |
| print(f" Expanded to {len(merchants)} merchants, {len(customers)} customers, {len(triggers)} triggers") | |
| write_outputs(out_dir, categories, merchants, customers, triggers) | |
| write_test_pairs(out_dir, triggers, rnd) | |
| print(f"Done. Run: ls {out_dir}") | |
| if __name__ == "__main__": | |
| main() | |