136 lines
4.2 KiB
Python
136 lines
4.2 KiB
Python
"""
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XGBoost Market Model Trainer (Basketball)
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=========================================
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Trains specialized XGBoost models for basketball betting markets.
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Models:
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1. ML (Match Result) - Binary (Home Win / Away Win)
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2. Totals (Over/Under) - Binary (Over / Under dynamic line)
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3. Spread (Handicap) - Binary (Home Cover / Away Cover)
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Usage:
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python3 scripts/train_basketball_markets.py
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"""
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import os
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import sys
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import pickle
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import pandas as pd
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import xgboost as xgb
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, classification_report, roc_auc_score
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# Config
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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", "basketball_training_data.csv")
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MODELS_DIR = os.path.join(AI_ENGINE_DIR, "models", "xgboost", "basketball")
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os.makedirs(MODELS_DIR, exist_ok=True)
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# Feature Columns
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FEATURES = [
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# Form
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"home_points_avg", "home_conceded_avg",
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"away_points_avg", "away_conceded_avg",
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"home_winning_streak", "away_winning_streak",
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"home_win_rate", "away_win_rate",
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# H2H
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"h2h_total_matches", "h2h_home_win_rate",
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"h2h_avg_points", "h2h_over140_rate",
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# Odds
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"odds_ml_h", "odds_ml_a",
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"odds_tot_o", "odds_tot_u", "odds_tot_line",
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"odds_spread_h", "odds_spread_a", "odds_spread_line"
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]
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def load_data():
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if not os.path.exists(DATA_PATH):
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print(f"❌ Data file not found: {DATA_PATH}")
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sys.exit(1)
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print(f"📦 Loading data from {DATA_PATH}...")
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df = pd.read_csv(DATA_PATH)
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df.fillna(0, inplace=True)
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print(f" Shape: {df.shape}")
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return df
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def train_binary_model(df, target_col, model_name):
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"""Generic trainer for Binary XGBoost models (ML, Totals, Spread)."""
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print(f"\n🚀 Training {model_name} (Target: {target_col})...")
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valid_df = df[df[target_col].notna()].copy()
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if valid_df.empty:
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print(f" ⚠️ No valid data for {target_col}, skipping.")
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return
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X = valid_df[FEATURES]
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y = valid_df[target_col].astype(int)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42, stratify=y
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)
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params = {
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'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'eta': 0.05,
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'max_depth': 6,
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'subsample': 0.8,
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'colsample_bytree': 0.8,
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'nthread': 4,
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'seed': 42
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}
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model = xgb.XGBClassifier(**params, n_estimators=1000, early_stopping_rounds=50)
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model.fit(
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X_train, y_train,
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eval_set=[(X_test, y_test)],
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verbose=False
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)
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y_pred = model.predict(X_test)
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y_prob = model.predict_proba(X_test)[:, 1]
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acc = accuracy_score(y_test, y_pred)
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try:
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auc = roc_auc_score(y_test, y_prob)
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except:
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auc = 0.0
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print(f" ✅ Finished! Best Iteration: {model.best_iteration}")
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print(f" 📊 Accuracy: {acc:.4f} | ROC AUC: {auc:.4f}")
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print(classification_report(y_test, y_pred, zero_division=0))
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# Save Model
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model_path = os.path.join(MODELS_DIR, f"{model_name}.pkl")
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with open(model_path, "wb") as f:
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pickle.dump(model, f)
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print(f" 💾 Saved to {model_path}")
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# Save Top Features
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try:
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booster = model.get_booster()
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importance = booster.get_score(importance_type="gain")
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sorted_imp = sorted(importance.items(), key=lambda x: x[1], reverse=True)[:5]
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print(" 🔍 Top 5 Features (Gain):")
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for ft, score in sorted_imp:
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print(f" - {ft}: {score:.2f}")
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except Exception as e:
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print(f" ⚠️ Could not extract feature importance: {e}")
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if __name__ == "__main__":
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df = load_data()
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# 1. Moneyline (ML) Model -> Targets Home Win (0) vs Away Win (1)
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train_binary_model(df, "label_ml", "basketball_ml_v1")
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# 2. Totals (Over/Under) Model -> Targets Under (0) vs Over (1) against 'odds_tot_line'
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train_binary_model(df, "label_tot", "basketball_tot_v1")
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# 3. Spread (Handicap) Model -> Targets Away Cover (0) vs Home Cover (1) against 'odds_spread_line'
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train_binary_model(df, "label_spread", "basketball_spread_v1")
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print("\n🎉 All Basketball Models Trained Successfully!")
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