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"""
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League Model Backtest — Son 100+ Maç
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======================================
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Her lig için en son 100-200 maçı (eğitim datasından bağımsız, test seti)
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lig bazlı modelle tahmin eder ve gerçek sonuçla karşılaştırır.
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Usage:
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python scripts/backtest_league_models.py
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python scripts/backtest_league_models.py --min-matches 150
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"""
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import os, sys, json, warnings, argparse
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import numpy as np
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import pandas as pd
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import xgboost as xgb
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from sklearn.metrics import accuracy_score
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warnings.filterwarnings("ignore")
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from models.league_model import get_league_model_loader, MARKET_META, FILE_TO_SIGNAL
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AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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DATA_PATH = os.path.join(AI_ENGINE_DIR, "data", "training_data.csv")
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REPORTS_DIR = os.path.join(AI_ENGINE_DIR, "reports")
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QL_PATH = os.path.join(os.path.dirname(AI_ENGINE_DIR), "qualified_leagues.json")
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# Gerçek label kolonları (CSV'den)
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LABEL_COLS = {
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"MS": "label_ms",
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"OU15": "label_ou15",
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"OU25": "label_ou25",
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"OU35": "label_ou35",
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"BTTS": "label_btts",
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"HT": "label_ht_result",
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"HT_OU05": "label_ht_ou05",
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"HT_OU15": "label_ht_ou15",
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"HTFT": "label_ht_ft",
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"OE": "label_odd_even",
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"CARDS": "label_cards_ou45",
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"HCAP": "label_handicap_ms",
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}
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# Model dosya adı → signal key eşlemesi
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SIGNAL_TO_FILE = {v: k for k, v in FILE_TO_SIGNAL.items()}
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SKIP_COLS = {
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"match_id","home_team_id","away_team_id","league_id","mst_utc",
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"score_home","score_away","total_goals","ht_score_home","ht_score_away","ht_total_goals",
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"label_ms","label_ou05","label_ou15","label_ou25","label_ou35","label_btts",
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"label_ht_result","label_ht_ou05","label_ht_ou15","label_ht_ft",
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"label_odd_even","label_yellow_cards","label_cards_ou45","label_handicap_ms",
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}
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def backtest_league(
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league_id: str,
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df_league: pd.DataFrame,
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feature_cols: list,
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league_model,
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n_test: int,
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) -> dict:
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"""Son n_test maçı backtest et, her market için doğruluk döndür."""
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df_sorted = df_league.sort_values("mst_utc")
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df_test = df_sorted.tail(n_test)
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X = df_test[feature_cols].fillna(0)
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results = {}
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for sig_key, mfile_key in SIGNAL_TO_FILE.items():
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label_col = LABEL_COLS.get(sig_key)
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if not label_col or label_col not in df_test.columns:
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continue
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y_true = df_test[label_col].dropna().values
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if len(y_true) < 30:
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continue
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# League-specific model varsa kullan
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if league_model and league_model.has_market(mfile_key):
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probs_list = []
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preds = []
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for _, row in df_test.iterrows():
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feat = row[feature_cols].fillna(0).to_dict()
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probs = league_model.predict_market(mfile_key, feat)
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if probs:
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best = max(probs, key=probs.__getitem__)
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meta = MARKET_META[mfile_key]
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labels = meta[1]
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pred_idx = labels.index(best)
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preds.append(pred_idx)
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probs_list.append(list(probs.values()))
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if not preds:
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continue
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y_valid = df_test[label_col].dropna()
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if len(preds) != len(y_valid):
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min_len = min(len(preds), len(y_valid))
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preds = preds[:min_len]
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y_valid = y_valid.values[:min_len]
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else:
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y_valid = y_valid.values
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acc = accuracy_score(y_valid, preds)
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results[sig_key] = {
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"accuracy": round(acc, 4),
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"n": len(preds),
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"source": "league_specific",
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}
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return results
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def backtest_with_general_v25(
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df_test: pd.DataFrame,
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feature_cols: list,
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) -> dict:
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"""Genel V25 modeli ile backtest."""
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try:
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from models.v25_ensemble import get_v25_predictor
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v25 = get_v25_predictor()
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if not v25._loaded:
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v25.load_models()
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except Exception as e:
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return {}
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X = df_test[feature_cols].fillna(0)
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results = {}
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mkey_map = {
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"MS": ("ms", {"1": 0, "X": 1, "2": 2}),
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"OU15": ("ou15", {"Over": 0, "Under": 1}),
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"OU25": ("ou25", {"Over": 0, "Under": 1}),
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"OU35": ("ou35", {"Over": 0, "Under": 1}),
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"BTTS": ("btts", {"Yes": 0, "No": 1}),
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}
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for sig_key, (mkey, label_to_idx) in mkey_map.items():
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label_col = LABEL_COLS.get(sig_key)
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if not label_col or label_col not in df_test.columns:
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continue
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y_true = df_test[label_col].dropna().values
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if len(y_true) < 30 or not v25.has_market(mkey):
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continue
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try:
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dmat = xgb.DMatrix(X.values, feature_names=feature_cols)
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models_v25 = v25.models.get(mkey, {})
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if "xgb" not in models_v25:
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continue
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raw = models_v25["xgb"].predict(dmat)
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num_class = list(MARKET_META.get(mkey, (2,)))[0]
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if num_class > 2:
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raw = raw.reshape(-1, num_class)
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preds = np.argmax(raw, axis=1)
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else:
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preds = (raw >= 0.5).astype(int)
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acc = accuracy_score(y_true, preds)
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results[sig_key] = {
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"accuracy": round(acc, 4),
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"n": len(preds),
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"source": "general_v25",
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}
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except Exception:
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continue
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return results
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--min-matches", type=int, default=100)
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parser.add_argument("--test-size", type=int, default=150,
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help="Son kaç maçı test için kullan (min 100)")
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args = parser.parse_args()
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n_test = max(args.min_matches, args.test_size)
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print(f"Loading training data ...")
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df = pd.read_csv(DATA_PATH, low_memory=False)
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feature_cols = [c for c in df.columns if c not in SKIP_COLS]
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print(f" {len(df):,} maç | {len(feature_cols)} feature")
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qualified = json.load(open(QL_PATH)) if os.path.exists(QL_PATH) else []
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loader = get_league_model_loader()
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try:
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import psycopg2
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from data.db import get_clean_dsn
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conn = psycopg2.connect(get_clean_dsn())
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cur = conn.cursor()
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cur.execute("SELECT id, name FROM leagues WHERE id = ANY(%s)", (qualified,))
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league_names = {r[0]: r[1] for r in cur.fetchall()}
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conn.close()
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except Exception:
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league_names = {}
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counts = df[df["league_id"].isin(qualified)].groupby("league_id").size()
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leagues_to_test = counts[counts >= n_test].index.tolist()
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print(f"\nBacktest: {len(leagues_to_test)} lig (>={n_test} maç) | son {n_test} maç kullanılacak\n")
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all_results = []
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markets_order = ["MS", "OU15", "OU25", "OU35", "BTTS", "HT", "HT_OU05", "HT_OU15", "HTFT", "OE", "CARDS", "HCAP"]
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header = f"{'Liga':<35} {'Maç':>5} | " + " | ".join(f"{m:>7}" for m in markets_order)
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print(header)
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print("-" * len(header))
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for league_id in leagues_to_test:
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df_league = df[df["league_id"] == league_id].copy()
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name = league_names.get(league_id, league_id[:20])
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league_model = loader.get(league_id)
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if league_model and league_model.models:
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# Batch predict from CSV features (fast)
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df_test = df_league.sort_values("mst_utc").tail(n_test)
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X = df_test[feature_cols].fillna(0)
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mkt_results = {}
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for mfile_key in list(league_model.models.keys()):
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sig_key = FILE_TO_SIGNAL.get(mfile_key)
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if not sig_key:
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continue
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label_col = LABEL_COLS.get(sig_key)
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if not label_col or label_col not in df_test.columns:
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continue
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y_true = df_test[label_col].dropna().values
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if len(y_true) < 30:
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continue
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try:
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dmat = xgb.DMatrix(X.values, feature_names=feature_cols)
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raw = league_model.models[mfile_key].predict(dmat)
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nc = MARKET_META[mfile_key][0]
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if nc > 2:
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preds = np.argmax(raw.reshape(-1, nc), axis=1)
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else:
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preds = (raw >= 0.5).astype(int)
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acc = accuracy_score(y_true[:len(preds)], preds[:len(y_true)])
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mkt_results[sig_key] = {"accuracy": round(float(acc), 4), "n": len(preds), "source": "league_xgb"}
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except Exception as e:
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mkt_results[sig_key] = {"error": str(e)}
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# Fill missing markets with general V25
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missing_mkts_df = df_league.sort_values("mst_utc").tail(n_test)
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gen_results = backtest_with_general_v25(missing_mkts_df, feature_cols)
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for k, v in gen_results.items():
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if k not in mkt_results:
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mkt_results[k] = {**v, "source": "general_v25_fallback"}
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else:
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# No league model — use general V25
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df_test = df_league.sort_values("mst_utc").tail(n_test)
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mkt_results = backtest_with_general_v25(df_test, feature_cols)
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for k in mkt_results:
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mkt_results[k]["source"] = "general_v25"
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n_used = min(n_test, len(df_league))
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# Print row
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accs = []
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for m in markets_order:
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r = mkt_results.get(m, {})
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if "accuracy" in r:
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accs.append(f"{r['accuracy']*100:>6.1f}%")
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else:
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accs.append(f"{'—':>7}")
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print(f"{name:<35} {n_used:>5} | " + " | ".join(accs))
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all_results.append({
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"league_id": league_id,
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"league_name": name,
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"n_tested": n_used,
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"markets": mkt_results,
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})
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# ── Özet ──────────────────────────────────────────────────────
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print("\n" + "=" * len(header))
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print("ORTALAMA DOĞRULUK (tüm ligler):")
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for m in markets_order:
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accs = [r["markets"][m]["accuracy"] for r in all_results if m in r["markets"] and "accuracy" in r["markets"][m]]
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if accs:
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print(f" {m:<10}: {np.mean(accs)*100:.1f}% (min={min(accs)*100:.1f}% max={max(accs)*100:.1f}% n_leagues={len(accs)})")
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# En iyi / en kötü MS ligleri
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ms_sorted = sorted(
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[(r["league_name"], r["markets"].get("MS",{}).get("accuracy",0), r["n_tested"])
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for r in all_results if "MS" in r["markets"] and "accuracy" in r["markets"]["MS"]],
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key=lambda x: x[1], reverse=True
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)
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print("\nEN İYİ MS (Top 10):")
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for name, acc, n in ms_sorted[:10]:
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print(f" {name:<35} {acc*100:.1f}% ({n} maç)")
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print("\nEN KÖTÜ MS (Bottom 10):")
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for name, acc, n in ms_sorted[-10:]:
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print(f" {name:<35} {acc*100:.1f}% ({n} maç)")
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# Save
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report = {"generated_at": pd.Timestamp.now().isoformat(), "n_test_per_league": n_test, "results": all_results}
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out_path = os.path.join(REPORTS_DIR, "backtest_league_results.json")
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with open(out_path, "w") as f:
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json.dump(report, f, indent=2)
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print(f"\nRapor: {out_path}")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user