189 lines
6.4 KiB
Python
189 lines
6.4 KiB
Python
"""
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XGBoost Model Training (Advanced Basketball V21)
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================================================
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Trains XGBoost models for Match Winner (ML), Totals (O/U), and Spread.
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Builds upon 60+ deep tactical features (Rebounds, FG%, Q1/Q2 pacing, advanced odds).
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Usage:
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python3 scripts/train_advanced_basketball.py
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"""
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import os
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import sys
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import pandas as pd
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import numpy as np
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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, precision_score, recall_score, f1_score
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from datetime import datetime
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# Configuration
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AI_ENGINE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, AI_ENGINE_DIR)
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DATA_PATH = os.path.join(AI_ENGINE_DIR, "data", "advanced_basketball_training_data.csv")
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MODEL_DIR = os.path.join(AI_ENGINE_DIR, "models", "bin")
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os.makedirs(MODEL_DIR, exist_ok=True)
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# -----------------------------------------------------------------------------
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# Deep Statistical Feature Matrix (54 Features)
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# -----------------------------------------------------------------------------
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FEATURES = [
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# Form
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"home_winning_streak", "away_winning_streak",
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"home_win_rate", "away_win_rate",
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# Home Team Offense
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"home_pts_avg", "home_reb_avg", "home_ast_avg", "home_stl_avg", "home_blk_avg", "home_tov_avg",
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"home_fg_pct", "home_3pt_pct", "home_ft_pct",
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"home_q1_avg", "home_q2_avg", "home_q3_avg", "home_q4_avg",
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# Home Team Defense
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"home_conc_pts", "home_conc_reb", "home_conc_ast", "home_conc_tov",
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"home_conc_fg_pct", "home_conc_3pt_pct",
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# Away Team Offense
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"away_pts_avg", "away_reb_avg", "away_ast_avg", "away_stl_avg", "away_blk_avg", "away_tov_avg",
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"away_fg_pct", "away_3pt_pct", "away_ft_pct",
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"away_q1_avg", "away_q2_avg", "away_q3_avg", "away_q4_avg",
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# Away Team Defense
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"away_conc_pts", "away_conc_reb", "away_conc_ast", "away_conc_tov",
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"away_conc_fg_pct", "away_conc_3pt_pct",
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# H2H Features
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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 Features
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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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# -----------------------------------------------------------------------------
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# Core Training Function
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# -----------------------------------------------------------------------------
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def train_model(df, target_col, model_name, params=None):
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print(f"\n--- Training {model_name} ---")
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# For Totals and Spread we need to drop purely empty lines if odds aren't matched
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if target_col in ["label_tot", "label_spread"]:
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# If line implies 0 and wasn't populated heavily, we may want to skip
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if target_col == "label_tot":
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df_filtered = df[(df["odds_tot_line"] > 50) & (df["odds_tot_line"] < 300)].copy()
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elif target_col == "label_spread":
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df_filtered = df[(abs(df["odds_spread_line"]) > 0.0) | (df["odds_spread_h"] != 1.9)].copy()
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else:
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df_filtered = df.copy()
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X = df_filtered[FEATURES]
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y = df_filtered[target_col]
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print(f"Data Shape: {X.shape}")
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)
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# Defaults for XGBoost
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if params is None:
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params = {
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'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'max_depth': 6,
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'learning_rate': 0.05,
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'n_estimators': 300,
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'subsample': 0.8,
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'colsample_bytree': 0.8,
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'random_state': 42
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}
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clf = xgb.XGBClassifier(**params)
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clf.fit(
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X_train, y_train,
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eval_set=[(X_train, y_train), (X_test, y_test)],
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verbose=50
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)
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y_pred = clf.predict(X_test)
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acc = accuracy_score(y_test, y_pred)
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prec = precision_score(y_test, y_pred, zero_division=0)
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rec = recall_score(y_test, y_pred, zero_division=0)
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print(f"\n[{model_name}] Metrics:")
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print(f"Accuracy : {acc:.4f}")
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if len(np.unique(y_train)) == 2:
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print(f"Precision: {prec:.4f}")
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print(f"Recall : {rec:.4f}")
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# Display Top 10 Feature Importances
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importances = clf.feature_importances_
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sorted_idx = np.argsort(importances)[::-1]
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print("\nTop 10 Feature Importances:")
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for i in range(10):
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print(f" {i+1}. {FEATURES[sorted_idx[i]]}: {importances[sorted_idx[i]]:.4f}")
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# Save
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save_path = os.path.join(MODEL_DIR, f"{model_name}.json")
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clf.save_model(save_path)
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print(f"Saved to: {save_path}")
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return clf
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if __name__ == "__main__":
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if not os.path.exists(DATA_PATH):
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print(f"ERROR: Training data not found at {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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# ---------------------------------------------------------
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# 1. Match Winner (Moneyline)
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# ---------------------------------------------------------
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ml_params = {
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'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'max_depth': 5,
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'learning_rate': 0.03,
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'n_estimators': 250,
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'subsample': 0.85,
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'colsample_bytree': 0.8,
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'random_state': 42
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}
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train_model(df, "label_ml", "basketball_v21_ml", ml_params)
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# ---------------------------------------------------------
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# 2. Match Totals (Over / Under)
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# ---------------------------------------------------------
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# Finding O/U against dynamic line needs complex relationships
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tot_params = {
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'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'max_depth': 6,
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'learning_rate': 0.05,
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'n_estimators': 350,
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'subsample': 0.8,
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'colsample_bytree': 0.8,
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'random_state': 42
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}
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train_model(df, "label_tot", "basketball_v21_tot", tot_params)
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# ---------------------------------------------------------
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# 3. Spread (Handicap Cover)
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# ---------------------------------------------------------
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spread_params = {
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'objective': 'binary:logistic',
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'eval_metric': 'logloss',
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'max_depth': 6,
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'learning_rate': 0.04,
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'n_estimators': 300,
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'subsample': 0.8,
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'colsample_bytree': 0.8,
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'random_state': 42
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}
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train_model(df, "label_spread", "basketball_v21_spread", spread_params)
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print("\n🏁 Advanced V21 Basketball Models trained successfully.")
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