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Fahri Can
2026-04-16 15:11:25 +03:00
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ai-engine/features/feature_adapter.py
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"""
Feature Adapter for XGBoost Inference
=====================================
Bridges the gap between V20 Engine outputs (CalculationContext) and XGBoost Models.
Constructs the exact 44-feature vector used in training.
"""
from __future__ import annotations
import os
from typing import Any
import psycopg2
from psycopg2.extensions import connection as PgConnection
import pandas as pd
import numpy as np
from data.db import get_clean_dsn
# Feature definitions (Must match train_xgboost_markets.py)
# NOTE: 68 features - matching the trained XGBoost models
FEATURES = [
# ELO
"home_overall_elo", "away_overall_elo", "elo_diff",
"home_home_elo", "away_away_elo", "form_elo_diff",
# Form
"home_goals_avg", "home_conceded_avg",
"away_goals_avg", "away_conceded_avg",
"home_clean_sheet_rate", "away_clean_sheet_rate",
"home_scoring_rate", "away_scoring_rate",
"home_winning_streak", "away_winning_streak",
# H2H
"h2h_home_win_rate", "h2h_draw_rate",
"h2h_avg_goals", "h2h_btts_rate", "h2h_over25_rate",
# Stats
"home_avg_possession", "away_avg_possession",
"home_avg_shots_on_target", "away_avg_shots_on_target",
"home_shot_conversion", "away_shot_conversion",
# Odds (Implicit market wisdom)
"odds_ms_h", "odds_ms_d", "odds_ms_a",
"implied_home", "implied_draw", "implied_away",
"odds_ht_ms_h", "odds_ht_ms_d", "odds_ht_ms_a",
"odds_ou05_o", "odds_ou05_u",
"odds_ou15_o", "odds_ou15_u",
"odds_ou25_o", "odds_ou25_u",
"odds_ou35_o", "odds_ou35_u",
"odds_ht_ou05_o", "odds_ht_ou05_u",
"odds_ht_ou15_o", "odds_ht_ou15_u",
"odds_btts_y", "odds_btts_n",
# League/Context
"league_avg_goals", "league_zero_goal_rate",
"home_xga", "away_xga",
# Upset features
"upset_atmosphere", "upset_motivation", "upset_fatigue", "upset_potential",
# Referee features
"referee_home_bias", "referee_avg_goals", "referee_cards_total",
"referee_avg_yellow", "referee_experience",
# Momentum features
"home_momentum_score", "away_momentum_score", "momentum_diff",
]
class FeatureAdapter:
"""
Adapter to convert V20 context into XGBoost-compatible features.
"""
def __init__(self) -> None:
self.conn: PgConnection | None = None
self._connect_db()
self.league_stats_cache: dict[str, dict[str, float]] = {}
def _connect_db(self) -> None:
try:
# FeatureAdapter uses DB only for optional league stats enrichment.
# Keep startup non-blocking when DB/tunnel is unavailable.
if not os.getenv("DATABASE_URL", "").strip():
return
self.conn = psycopg2.connect(get_clean_dsn())
except Exception as e:
print(f"⚠️ FeatureAdapter DB connection failed: {e}")
def get_features(self, ctx: Any) -> pd.DataFrame:
"""
Construct feature vector from CalculationContext.
Returns a DataFrame with 1 row and correct columns.
"""
raw = ctx.team_pred.raw_features
odds = ctx.odds_data or {}
upset_features = getattr(ctx, "upset_features", {}) or {}
momentum_features = getattr(ctx, "momentum_features", {}) or {}
referee_features = getattr(ctx, "referee_features", {}) or {}
# 1. Odds Features
ms_h = float(odds.get("ms_h") or 0)
ms_d = float(odds.get("ms_d") or 0)
ms_a = float(odds.get("ms_a") or 0)
implied_home, implied_draw, implied_away = 0.33, 0.33, 0.33
if ms_h > 0 and ms_d > 0 and ms_a > 0:
raw_sum = 1/ms_h + 1/ms_d + 1/ms_a
implied_home = (1/ms_h) / raw_sum
implied_draw = (1/ms_d) / raw_sum
implied_away = (1/ms_a) / raw_sum
# 2. League Features
# Using ctx.league_id if available, or just defaults
league_stats = self._get_league_stats(ctx.league_id)
# 3. Assemble Dictionary
row = {
# ELO (Explicit float casting)
"home_overall_elo": float(raw.get("home_overall_elo") or 1500),
"away_overall_elo": float(raw.get("away_overall_elo") or 1500),
"elo_diff": float(raw.get("elo_diff") or 0),
"home_home_elo": float(raw.get("home_home_elo") or 1500),
"away_away_elo": float(raw.get("away_away_elo") or 1500),
"form_elo_diff": float(raw.get("form_elo_diff") or 0),
# Form (Explicit float casting)
"home_goals_avg": float(raw.get("home_goals_avg") or 1.3),
"home_conceded_avg": float(raw.get("home_conceded_avg") or 1.2),
"away_goals_avg": float(raw.get("away_goals_avg") or 1.2),
"away_conceded_avg": float(raw.get("away_conceded_avg") or 1.4),
"home_clean_sheet_rate": float(raw.get("home_clean_sheet_rate") or 0.2),
"away_clean_sheet_rate": float(raw.get("away_clean_sheet_rate") or 0.2),
"home_scoring_rate": float(raw.get("home_scoring_rate") or 0.8),
"away_scoring_rate": float(raw.get("away_scoring_rate") or 0.8),
"home_winning_streak": float(raw.get("home_winning_streak") or 0),
"away_winning_streak": float(raw.get("away_winning_streak") or 0),
# H2H (Explicit float casting)
"h2h_home_win_rate": float(raw.get("h2h_home_win_rate") or 0.33),
"h2h_draw_rate": float(raw.get("h2h_draw_rate") or 0.33),
"h2h_avg_goals": float(raw.get("h2h_avg_goals") or 2.5),
"h2h_btts_rate": float(raw.get("h2h_btts_rate") or 0.5),
"h2h_over25_rate": float(raw.get("h2h_over25_rate") or 0.5),
# Stats (Explicit float casting to avoid XGBoost 'object' error)
"home_avg_possession": float(raw.get("home_avg_possession") or 0.5),
"away_avg_possession": float(raw.get("away_avg_possession") or 0.5),
"home_avg_shots_on_target": float(raw.get("home_avg_shots_on_target") or 4.0),
"away_avg_shots_on_target": float(raw.get("away_avg_shots_on_target") or 3.5),
"home_shot_conversion": float(raw.get("home_shot_conversion") or 0.1),
"away_shot_conversion": float(raw.get("away_shot_conversion") or 0.1),
# Odds
"odds_ms_h": ms_h,
"odds_ms_d": ms_d,
"odds_ms_a": ms_a,
"implied_home": implied_home,
"implied_draw": implied_draw,
"implied_away": implied_away,
"odds_ht_ms_h": float(odds.get("ht_ms_h") or 0.0),
"odds_ht_ms_d": float(odds.get("ht_ms_d") or 0.0),
"odds_ht_ms_a": float(odds.get("ht_ms_a") or 0.0),
"odds_ou05_o": float(odds.get("ou05_o") or 0.0),
"odds_ou05_u": float(odds.get("ou05_u") or 0.0),
"odds_ou15_o": float(odds.get("ou15_o") or 0.0),
"odds_ou15_u": float(odds.get("ou15_u") or 0.0),
"odds_ou25_o": float(odds.get("ou25_o") or 0.0),
"odds_ou25_u": float(odds.get("ou25_u") or 0.0),
"odds_ou35_o": float(odds.get("ou35_o") or 0.0),
"odds_ou35_u": float(odds.get("ou35_u") or 0.0),
"odds_ht_ou05_o": float(odds.get("ht_ou05_o") or 0.0),
"odds_ht_ou05_u": float(odds.get("ht_ou05_u") or 0.0),
"odds_ht_ou15_o": float(odds.get("ht_ou15_o") or 0.0),
"odds_ht_ou15_u": float(odds.get("ht_ou15_u") or 0.0),
"odds_btts_y": float(odds.get("btts_y") or 0.0),
"odds_btts_n": float(odds.get("btts_n") or 0.0),
# League/Def
"league_avg_goals": float(league_stats.get("avg_goals") or 2.7),
"league_zero_goal_rate": float(league_stats.get("zero_rate") or 0.07),
"home_xga": float(raw.get("home_xga") or 1.2),
"away_xga": float(raw.get("away_xga") or 1.4),
# Upset features (default values - computed separately in upset_engine_v2)
"upset_atmosphere": float(raw.get("upset_atmosphere") or 0.0),
"upset_motivation": float(raw.get("upset_motivation") or 0.0),
"upset_fatigue": float(raw.get("upset_fatigue") or 0.0),
"upset_potential": float(raw.get("upset_potential") or 0.0),
# Referee features (default values)
"referee_home_bias": float(raw.get("referee_home_bias") or 0.0),
"referee_avg_goals": float(raw.get("referee_avg_goals") or 2.5),
"referee_cards_total": float(raw.get("referee_cards_total") or 4.0),
"referee_avg_yellow": float(raw.get("referee_avg_yellow") or 3.0),
"referee_experience": float(raw.get("referee_experience") or 0),
# Momentum features (default values)
"home_momentum_score": float(raw.get("home_momentum_score") or 0.0),
"away_momentum_score": float(raw.get("away_momentum_score") or 0.0),
"momentum_diff": float(raw.get("momentum_diff") or 0.0),
}
# Return as DataFrame (cols sorted by FEATURES list to ensure alignment)
df = pd.DataFrame([row], columns=FEATURES)
return df
def _get_league_stats(self, league_id: str | None) -> dict[str, float]:
"""Get cached league stats or default."""
if not league_id:
return {"avg_goals": 2.7, "zero_rate": 0.07}
if league_id in self.league_stats_cache:
return self.league_stats_cache[league_id]
if self.conn:
try:
with self.conn.cursor() as cur:
cur.execute("""
SELECT AVG(score_home + score_away),
AVG(CASE WHEN score_home=0 AND score_away=0 THEN 1.0 ELSE 0.0 END)
FROM matches
WHERE league_id = %s AND status = 'FT'
AND mst_utc > EXTRACT(EPOCH FROM NOW() - INTERVAL '1 year')
""", (league_id,))
res = cur.fetchone()
if res and res[0]:
stats = {
"avg_goals": float(res[0]),
"zero_rate": float(res[1])
}
self.league_stats_cache[league_id] = stats
return stats
except Exception:
pass
# Default fallback
return {"avg_goals": 2.7, "zero_rate": 0.07}
# Singleton
_adapter: FeatureAdapter | None = None
def get_feature_adapter() -> FeatureAdapter:
global _adapter
if _adapter is None:
_adapter = FeatureAdapter()
return _adapter