optimize_fast.py

"""Fast optimization using random search."""
import json
import random
from pathlib import Path

import sys
sys.path.insert(0, '/home/brettanthonysjoberg179/trifecta-bro-hf-space')

from trifecta_bro.data.models import RaceModel, RunnerModel
from trifecta_bro.model.scoring import score_runner
from trifecta_bro.model.pace_analysis import classify_pace
from trifecta_bro.model.distance_profiles import classify_distance

DATA_DIR = Path('/home/brettanthonysjoberg179/trifecta-bro-hf-space/data')
CACHE_DIR = DATA_DIR / 'cache'
RESULTS_DIR = DATA_DIR / 'results'

random.seed(42)


def load_cached_races(date: str) -> list[dict]:
    races = []
    for f in CACHE_DIR.glob('*.json'):
        try:
            with open(f) as fp:
                data = json.load(fp)
            if data.get('date') == date:
                races.append(data)
        except:
            continue
    return races


def load_results(date: str) -> dict:
    result_lookup = {}
    result_path = RESULTS_DIR / f"{date}-ra.json"
    if not result_path.exists():
        result_path = RESULTS_DIR / f"{date}.json"
    if not result_path.exists():
        return result_lookup
    
    with open(result_path) as f:
        rdata = json.load(f)
    
    if isinstance(rdata, dict):
        if 'tracks' in rdata:
            for track, races in rdata['tracks'].items():
                if isinstance(races, dict):
                    for rn, r in races.items():
                        runners = r.get('runners', [])
                        winners = []
                        for runner in runners:
                            pos = runner.get('position')
                            if pos and pos <= 3:
                                winners.append((pos, runner['number']))
                        winners.sort()
                        actual = [w[1] for w in winners]
                        if actual:
                            result_lookup[(track, int(rn))] = actual
                elif isinstance(races, list):
                    for r in races:
                        if 'trifecta' in r:
                            result_lookup[(track, r.get('race', 0))] = [int(str(x).replace('e','')) for x in r['trifecta']]
        else:
            for track, races in rdata.items():
                if isinstance(races, list):
                    for r in races:
                        if 'trifecta' in r:
                            result_lookup[(track, r['race'])] = [int(str(x).replace('e','')) for x in r['trifecta']]
    
    return result_lookup


def score_race(race_data: dict, weights: dict) -> list[int] | None:
    try:
        runners = []
        for r in race_data.get('runners', []):
            if r.get('scratched'):
                continue
            runner = RunnerModel(
                number=r['number'], name=r.get('name', ''),
                jockey=r.get('jockey'), trainer=r.get('trainer'),
                weight=r.get('weight'), barrier=r.get('barrier'),
                age=r.get('age'), sex=r.get('sex'),
                form=r.get('form', ''), last20_starts=r.get('last20Starts', ''),
                stats=r.get('stats', {}),
            )
            runners.append(runner)
        
        if len(runners) < 3:
            return None
        
        race = RaceModel(
            date=race_data.get('date', ''), track=race_data.get('track', ''),
            track_slug=race_data.get('slug', race_data.get('track', '').lower()),
            race_number=race_data.get('raceNumber', 0),
            race_name=race_data.get('raceName', ''),
            distance=race_data.get('distance'),
            condition=race_data.get('condition'),
            race_class=race_data.get('raceClass'),
            abandoned=race_data.get('abandoned', False),
            start_time=race_data.get('startTime'),
            prize_money=str(race_data.get('prizeMoney', '')),
            number_of_runners=race_data.get('numberOfRunners', len(runners)),
            runners=runners,
        )
        
        pace = classify_pace(race, runners)
        scored = []
        for r in runners:
            sc = score_runner(r, race, pace, weights)
            scored.append((r.number, sc['score']))
        
        scored.sort(key=lambda x: x[1], reverse=True)
        return [s[0] for s in scored[:3]]
    except:
        return None


def evaluate_date(date: str, weights: dict) -> dict:
    result_lookup = load_results(date)
    races = load_cached_races(date)
    stats = {"total": 0, "top1": 0, "exact": 0, "box": 0}
    
    for race_data in races:
        track = race_data.get('track', '')
        race_num = race_data.get('raceNumber', 0)
        actual = result_lookup.get((track, race_num))
        if not actual:
            continue
        
        pred = score_race(race_data, weights)
        if not pred:
            continue
        
        stats["total"] += 1
        if pred == actual:
            stats["exact"] += 1
        if set(pred) == set(actual):
            stats["box"] += 1
        if pred[0] == actual[0]:
            stats["top1"] += 1
    
    if stats["total"] > 0:
        stats["top1_pct"] = stats["top1"] / stats["total"] * 100
    return stats


def evaluate_all(dates: list[str], weights: dict) -> dict:
    agg = {"total": 0, "top1": 0, "exact": 0, "box": 0}
    for date in dates:
        stats = evaluate_date(date, weights)
        agg["total"] += stats["total"]
        agg["top1"] += stats["top1"]
        agg["exact"] += stats["exact"]
        agg["box"] += stats["box"]
    agg["top1_pct"] = agg["top1"] / agg["total"] * 100 if agg["total"] > 0 else 0
    return agg


def random_weights() -> dict:
    """Generate random weights that sum to ~1.0."""
    keys = ["form", "class", "distance", "track", "track_distance", 
            "condition", "jockey", "fitness", "barrier", "weight", "pace"]
    vals = [random.random() for _ in keys]
    total = sum(vals)
    return {k: v / total for k, v in zip(keys, vals)}


def main():
    dates = ["2026-08-07", "2026-08-08", "2026-08-09", "2026-08-14"]
    
    print("=" * 60)
    print("FAST OPTIMIZATION (Random Search)")
    print("=" * 60)
    
    # Base weights
    base = {
        "form": 0.18, "class": 0.14, "distance": 0.10, "track": 0.10,
        "track_distance": 0.10, "condition": 0.05, "jockey": 0.06,
        "fitness": 0.08, "barrier": 0.08, "weight": 0.08, "pace": 0.03,
    }
    
    base_stats = evaluate_all(dates, base)
    print(f"Base: {base_stats['top1_pct']:.1f}% ({base_stats['top1']}/{base_stats['total']})")
    
    # Random search - 100 iterations
    best_pct = base_stats['top1_pct']
    best_weights = base
    
    for i in range(100):
        weights = random_weights()
        stats = evaluate_all(dates, weights)
        
        if stats['top1_pct'] > best_pct:
            best_pct = stats['top1_pct']
            best_weights = weights
            print(f"  New best at iter {i+1}: {best_pct:.1f}%")
    
    print(f"\nBest: {best_pct:.1f}%")
    w = best_weights
    print(f"form={w['form']:.2f}, class={w['class']:.2f}, dist={w['distance']:.2f}, "
          f"track={w['track']:.2f}, td={w['track_distance']:.2f}, "
          f"cond={w['condition']:.2f}, jockey={w['jockey']:.2f}, "
          f"fitness={w['fitness']:.2f}, barrier={w['barrier']:.2f}, "
          f"weight={w['weight']:.2f}, pace={w['pace']:.2f}")
    
    # Per-date breakdown
    print("\nPer-date breakdown:")
    for date in dates:
        stats = evaluate_date(date, best_weights)
        if stats['total'] > 0:
            print(f"  {date}: {stats['top1']}/{stats['total']} ({stats['top1_pct']:.1f}%)")
    
    # Per-distance breakdown
    print("\nPer-distance breakdown:")
    for dist_class in ["sprint", "middle", "staying"]:
        dist_stats = {"total": 0, "top1": 0}
        for date in dates:
            result_lookup = load_results(date)
            races = load_cached_races(date)
            for race_data in races:
                if classify_distance(race_data.get('distance', '1200m')) == dist_class:
                    track = race_data.get('track', '')
                    race_num = race_data.get('raceNumber', 0)
                    actual = result_lookup.get((track, race_num))
                    if not actual:
                        continue
                    pred = score_race(race_data, best_weights)
                    if not pred:
                        continue
                    dist_stats["total"] += 1
                    if pred[0] == actual[0]:
                        dist_stats["top1"] += 1
        
        if dist_stats["total"] > 0:
            print(f"  {dist_class}: {dist_stats['top1']}/{dist_stats['total']} ({dist_stats['top1']/dist_stats['total']*100:.1f}%)")
    
    return best_weights


if __name__ == "__main__":
    main()