first (part 2: other directories)

2026-04-16 15:11:25 +03:00
parent 7814e0bc6b
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+"""
+VQWEN Deep Model Training Script (Final Version)
+================================================
+Includes: ELO, Contextual Goals, Rest Days, Player Participation.
+"""
+
+import os
+import sys
+import json
+import time
+import pickle
+import psycopg2
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+import lightgbm as lgb
+
+AI_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = os.path.dirname(AI_DIR)
+sys.path.insert(0, ROOT_DIR)
+
+def get_clean_dsn() -> str:
+    return "postgresql://suggestbet:SuGGesT2026SecuRe@localhost:15432/boilerplate_db"
+
+def train_vqwen_deep():
+    print("🧠 VQWEN DEEP MODEL EĞİTİMİ (ELO + REST + CONTEXT)")
+    print("="*60)
+
+    dsn = get_clean_dsn()
+    conn = psycopg2.connect(dsn)
+    cur = conn.cursor()
+
+    # ─── 1. GELİŞMİŞ VERİ SORGUSU ───
+    # ELO, Dinlenme Süresi, İç Saha/Deplasman Performansı
+    query = """
+        SELECT 
+            m.id, m.home_team_id, m.away_team_id, m.score_home, m.score_away, m.mst_utc,
+            
+            -- ELO Ratings
+            COALESCE(maf.home_elo, 1500) as home_elo,
+            COALESCE(maf.away_elo, 1500) as away_elo,
+
+            -- Contextual Goals (Home Team at Home, Away Team Away)
+            COALESCE((SELECT AVG(m2.score_home) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as h_home_goals,
+            COALESCE((SELECT AVG(m2.score_away) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc), 1.2) as a_away_goals,
+            
+            -- Rest Days (Yorgunluk)
+            COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.home_team_id = m.home_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as h_rest,
+            COALESCE(EXTRACT(EPOCH FROM (to_timestamp(m.mst_utc/1000) - (SELECT MAX(to_timestamp(m2.mst_utc/1000)) FROM matches m2 WHERE m2.away_team_id = m.away_team_id AND m2.status = 'FT' AND m2.mst_utc < m.mst_utc)) / 86400), 7) as a_rest,
+
+            -- Squad Participation
+            COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.home_team_id AND mp.is_starting = true), 11) as h_xi,
+            COALESCE((SELECT COUNT(*) FROM match_player_participation mp WHERE mp.match_id = m.id AND mp.team_id = m.away_team_id AND mp.is_starting = true), 11) as a_xi,
+            
+            -- Cards
+            COALESCE((SELECT COUNT(*) FROM match_player_events mpe WHERE mpe.match_id = m.id AND mpe.event_type = 'card'), 4) as cards,
+
+            -- Odds
+            (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '1' LIMIT 1) as oh,
+            (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = 'X' LIMIT 1) as od,
+            (SELECT os.odd_value FROM odd_categories oc JOIN odd_selections os ON os.odd_category_db_id = oc.db_id WHERE oc.match_id = m.id AND oc.name ILIKE 'Maç Sonucu' AND os.name = '2' LIMIT 1) as oa
+
+        FROM matches m
+        LEFT JOIN football_ai_features maf ON maf.match_id = m.id
+        WHERE m.status = 'FT' AND m.score_home IS NOT NULL AND m.sport = 'football'
+          AND EXISTS (SELECT 1 FROM odd_categories oc WHERE oc.match_id = m.id)
+        ORDER BY m.mst_utc DESC
+        LIMIT 150000
+    """
+
+    print("📊 Veri çekiliyor...")
+    start = time.time()
+    cur.execute(query)
+    rows = cur.fetchall()
+    print(f"✅ {len(rows)} maç çekildi ({time.time()-start:.1f}s)")
+
+    df = pd.DataFrame(rows, columns=[
+        'id', 'h_id', 'a_id', 'sh', 'sa', 'utc',
+        'h_elo', 'a_elo',
+        'h_home_goals', 'a_away_goals',
+        'h_rest', 'a_rest',
+        'h_xi', 'a_xi', 'cards',
+        'oh', 'od', 'oa'
+    ])
+
+    # Temizlik
+    for col in df.columns[2:]:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    df = df.fillna(df.median(numeric_only=True))
+    df = df[(df['oh'] > 1.0) & (df['oa'] > 1.0)]
+
+    # ─── 2. ÖZELLİK MÜHENDİSLİĞİ ───
+    
+    # 1. ELO Farkı
+    df['elo_diff'] = df['h_elo'] - df['a_elo']
+
+    # 2. Yorgunluk Faktörü (Dinlenme < 3 günse performans düşer)
+    # xG hesaplamasında kullanacağız
+    def fatigue_factor(rest):
+        if rest < 3: return 0.85
+        if rest < 5: return 0.95
+        return 1.0
+
+    df['h_fatigue'] = df['h_rest'].apply(fatigue_factor)
+    df['a_fatigue'] = df['a_rest'].apply(fatigue_factor)
+
+    # 3. xG (Contextual Goals * Fatigue)
+    df['h_xg'] = df['h_home_goals'] * df['h_fatigue']
+    df['a_xg'] = df['a_away_goals'] * df['a_fatigue']
+    df['total_xg'] = df['h_xg'] + df['a_xg']
+    df['rest_diff'] = df['h_rest'] - df['a_rest']
+
+    # 4. Form (ELO bazlı power rating)
+    df['h_pow'] = (df['h_elo'] / 100) * df['h_fatigue']
+    df['a_pow'] = (df['a_elo'] / 100) * df['a_fatigue']
+    df['pow_diff'] = df['h_pow'] - df['a_pow']
+
+    # Oranlar
+    margin = (1/df['oh']) + (1/df['od']) + (1/df['oa'])
+    df['imp_h'] = (1/df['oh']) / margin
+    df['imp_d'] = (1/df['od']) / margin
+    df['imp_a'] = (1/df['oa']) / margin
+
+    # Hedefler
+    df['t_ms'] = df.apply(lambda r: 0 if r['sh']>r['sa'] else (2 if r['sh']<r['sa'] else 1), axis=1)
+    df['t_ou'] = ((df['sh'] + df['sa']) > 2.5).astype(int)
+    df['t_btts'] = ((df['sh'] > 0) & (df['sa'] > 0)).astype(int)
+
+    # ─── 3. MODEL EĞİTİMİ ───
+    # Yeni Özellik Seti
+    feats = ['elo_diff', 'h_xg', 'a_xg', 'total_xg', 'pow_diff', 'rest_diff', 'h_fatigue', 'a_fatigue',
+             'imp_h', 'imp_d', 'imp_a', 'h_xi', 'a_xi', 'cards']
+
+    # MS
+    print("🤖 MS...")
+    X_ms, y_ms = df[feats], df['t_ms']
+    X_tr, X_te, y_tr, y_te = train_test_split(X_ms, y_ms, test_size=0.15, random_state=42)
+    model_ms = lgb.train({'objective': 'multiclass', 'num_class': 3, 'verbose': -1, 'num_leaves': 63}, 
+                         lgb.Dataset(X_tr, y_tr), num_boost_round=1000, 
+                         valid_sets=[lgb.Dataset(X_te, y_te)], callbacks=[lgb.early_stopping(50)])
+
+    # OU2.5
+    print("🤖 OU2.5...")
+    model_ou = lgb.train({'objective': 'binary', 'verbose': -1}, 
+                         lgb.Dataset(df[feats], df['t_ou']), num_boost_round=500)
+
+    # BTTS
+    print("🤖 BTTS...")
+    model_btts = lgb.train({'objective': 'binary', 'verbose': -1}, 
+                           lgb.Dataset(df[feats], df['t_btts']), num_boost_round=500)
+
+    # ─── 4. KAYDET ───
+    mdir = os.path.join(ROOT_DIR, 'models', 'vqwen')
+    os.makedirs(mdir, exist_ok=True)
+    for nm, md in [('ms', model_ms), ('ou25', model_ou), ('btts', model_btts)]:
+        p = os.path.join(mdir, f'vqwen_{nm}.pkl')
+        with open(p, 'wb') as f: pickle.dump(md, f)
+        print(f"✅ vqwen_{nm}.pkl")
+
+    print("\n🎉 VQWEN DEEP EĞİTİMİ BİTTİ!")
+    cur.close()
+    conn.close()
+
+if __name__ == "__main__":
+    train_vqwen_deep()