trifecta_bro/model/scoring.py

"""Configurable runner scoring engine.

Each component yields a 0..100 sub-score. The final score is a weighted blend
of the configured weights. When a component has insufficient data for a runner,
its weight is redistributed across the components that DO have data (so a
runner missing jockey/trainer stats isn't unfairly zeroed). This keeps the model
transparent and degrades gracefully on incomplete FormFav data.

v2.0 changes:
- Distance-specific weight profiles (sprint/middle/staying)
- Market component placeholder (SP odds when available via Betfair API)
- track_distance component
- fitness component (replaces trainer proxy)
"""
from __future__ import annotations

import logging
from pathlib import Path
from typing import Optional

import yaml

from ..config import settings
from ..data.models import RaceModel, RunnerModel
from .feature_engine import form_quality_score
from .pace_analysis import classify_pace, pace_adjustment
from .distance_profiles import get_profile, parse_distance, classify_distance

logger = logging.getLogger("trifecta_bro.scoring")

# Base weights used as fallback
DEFAULT_WEIGHTS = {
    "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,
}


def load_weights(path: Optional[Path] = None) -> dict:
    p = Path(path or settings.weights_path)
    if not p.exists():
        return dict(DEFAULT_WEIGHTS)
    data = yaml.safe_load(p.read_text()) or {}
    w = data.get("weights", {})
    merged = {**DEFAULT_WEIGHTS, **{k: float(v) for k, v in w.items()}}
    return merged


def _safe_ratio(places: float, starts: float, cap: float, weight: float) -> float:
    if not starts:
        return 0.0
    return min((places / starts) * 100 * (weight / cap), cap)


def _form_component(r: RunnerModel) -> float:
    score, _ = form_quality_score(r.form)
    return score


def _class_component(r: RunnerModel, race: RaceModel) -> float:
    wp = r.win_percent
    pp = r.place_percent
    return min(100.0, wp * 1.5 + pp * 0.5)


def _distance_component(r: RunnerModel) -> float:
    d = r.stats.get("distance", {})
    return _safe_ratio(d.get("places", 0), d.get("starts", 0), 100.0, 100.0)


def _track_component(r: RunnerModel) -> float:
    t = r.stats.get("track", {})
    return _safe_ratio(t.get("places", 0), t.get("starts", 0), 100.0, 100.0)


def _track_distance_component(r: RunnerModel) -> float:
    """Use FormFav trackDistance stats — the most specific signal."""
    td = r.stats.get("trackDistance", {})
    if not td or not td.get("starts"):
        return 0.0
    return _safe_ratio(td.get("places", 0), td.get("starts", 0), 100.0, 100.0)


def _condition_component(r: RunnerModel, race: RaceModel) -> float:
    cond = (race.condition or "").lower()
    cond_stats = r.stats.get("conditions", {})
    if not cond_stats:
        return 0.0
    best = 0.0
    for key, data in cond_stats.items():
        if cond and cond[:4] in key.lower():
            best = max(best, _safe_ratio(data.get("places", 0), data.get("starts", 0), 100.0, 100.0))
    if best == 0.0:
        vals = [_safe_ratio(v.get("places", 0), v.get("starts", 0), 100.0, 100.0) for v in cond_stats.values()]
        best = sum(vals) / len(vals) if vals else 0.0
    return best


def _jockey_component(r: RunnerModel) -> float:
    # Form consistency (low variance = reliable)
    if not r.form:
        return 0.0
    positions = []
    for ch in r.form:
        if ch.isdigit():
            val = int(ch)
            if val <= 9:
                positions.append(val)
    if len(positions) < 2:
        return 0.0
    mean = sum(positions) / len(positions)
    variance = sum((p - mean) ** 2 for p in positions) / len(positions)
    stddev = variance ** 0.5
    score = max(0.0, 100 - stddev * 30)
    return score


def _fitness_component(r: RunnerModel) -> float:
    """Combines first-up and second-up strike rates as fitness proxy."""
    first_up = r.stats.get("firstUp", {})
    second_up = r.stats.get("secondUp", {})
    
    fu_starts = first_up.get("starts", 0) or 0
    fu_wins = first_up.get("wins", 0) or 0
    su_starts = second_up.get("starts", 0) or 0
    su_wins = second_up.get("wins", 0) or 0
    
    total_starts = fu_starts + su_starts
    if total_starts == 0:
        return 0.0
    
    fu_rate = (fu_wins / fu_starts) if fu_starts > 0 else 0
    su_rate = (su_wins / su_starts) if su_starts > 0 else 0
    
    if fu_starts > 0 and su_starts > 0:
        score = (fu_rate * 0.6 + su_rate * 0.4) * 100
    elif fu_starts > 0:
        score = fu_rate * 100
    else:
        score = su_rate * 100
    
    return min(100.0, score)


def _barrier_component(r: RunnerModel, race: RaceModel, pace: dict) -> float:
    barrier = r.barrier
    if barrier is None:
        return 0.0
    
    n = max(race.number_of_runners, 8)
    distance = race.distance or ""
    
    dist_num = 1200
    try:
        dist_num = int(''.join(filter(str.isdigit, distance))) if distance else 1200
    except:
        pass
    
    # Distance-aware barrier logic
    if dist_num <= 1200:
        spread = abs(barrier - (n / 2.0 + 0.5))
        base = max(0.0, 100.0 - spread * 12.0)
    elif dist_num <= 1600:
        spread = abs(barrier - (n / 2.0 + 0.5))
        base = max(0.0, 100.0 - spread * 8.0)
    else:
        spread = abs(barrier - (n / 2.0 + 0.5))
        base = max(0.0, 100.0 - spread * 5.0)
    
    role = pace["roles"].get(r.number, "midfield")
    if pace["label"] == "SLOW":
        base += (5 - barrier) * 1.2 if barrier <= 5 else 0.0
    elif pace["label"] in ("VERY FAST", "FAST"):
        base += (barrier - 5) * 0.8 if barrier > 5 else 0.0
    
    return max(0.0, min(100.0, base))


def _weight_component(r: RunnerModel, race: RaceModel) -> float:
    if r.weight is None:
        return 0.0
    w = r.weight
    score = 40 + (w - 52) * 4.0
    return max(0.0, min(100.0, score))


def _market_component(r: RunnerModel) -> float:
    """Market component — placeholder for SP odds.
    
    Returns 0 until Betfair API key is configured.
    When available, this will fetch SP odds and convert to implied probability.
    """
    return 0.0


def score_runner(r: RunnerModel, race: RaceModel, pace: dict, weights: dict | None = None) -> dict:
    """Score a runner using distance-specific weights if no weights provided."""
    if weights is None:
        weights = get_profile(race)
    
    comp = {
        "form": _form_component(r),
        "class": _class_component(r, race),
        "distance": _distance_component(r),
        "track": _track_component(r),
        "track_distance": _track_distance_component(r),
        "condition": _condition_component(r, race),
        "jockey": _jockey_component(r),
        "fitness": _fitness_component(r),
        "barrier": _barrier_component(r, race, pace),
        "weight": _weight_component(r, race),
        "pace": 50.0 + pace_adjustment(r, pace),
        "market": _market_component(r),
    }
    
    available = {}
    total_w = 0.0
    for k, w in weights.items():
        if comp[k] > 0 or k in ("form", "pace"):
            available[k] = w
            total_w += w
    
    if total_w == 0:
        return {"sub_scores": comp, "score": 0.0, "available": {}, "completeness": r.data_completeness}
    
    norm = {k: w / total_w for k, w in available.items()}
    score = sum(comp[k] * norm[k] for k in norm)
    
    return {
        "sub_scores": comp,
        "score": round(score, 1),
        "available": {k: round(v, 3) for k, v in norm.items()},
        "completeness": r.data_completeness,
    }