223 lines
8.0 KiB
Python
Executable File
223 lines
8.0 KiB
Python
Executable File
"""
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V20 Pro Model Trainer
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=====================
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Advanced training pipeline for Suggest-Bet V20 Ensemble.
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Features:
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1. Optuna Hyperparameter Optimization
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2. Stratified K-Fold Cross-Validation
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3. Probability Calibration (Isotonic Regression)
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4. Market-specific weight handling for reversals (1/2, 2/1)
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Usage:
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python3 scripts/train_xgboost_pro.py
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"""
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import os
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import sys
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import json
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import pickle
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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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import optuna
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from optuna.samplers import TPESampler
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from sklearn.model_selection import StratifiedKFold, train_test_split
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from sklearn.metrics import accuracy_score, log_loss, brier_score_loss, classification_report
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from sklearn.calibration import CalibratedClassifierCV, calibration_curve
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import matplotlib.pyplot as plt
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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", "training_data.csv")
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MODELS_DIR = os.path.join(AI_ENGINE_DIR, "models", "xgboost")
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REPORTS_DIR = os.path.join(AI_ENGINE_DIR, "reports", "training_v20")
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os.makedirs(MODELS_DIR, exist_ok=True)
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os.makedirs(REPORTS_DIR, exist_ok=True)
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# Feature Columns (Must match extraction + inference)
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FEATURES = [
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# ELO
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"home_overall_elo", "away_overall_elo", "elo_diff",
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"home_home_elo", "away_away_elo", "form_elo_diff",
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# Form
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"home_goals_avg", "home_conceded_avg",
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"away_goals_avg", "away_conceded_avg",
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"home_clean_sheet_rate", "away_clean_sheet_rate",
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"home_scoring_rate", "away_scoring_rate",
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"home_winning_streak", "away_winning_streak",
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# H2H
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"h2h_home_win_rate", "h2h_draw_rate",
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"h2h_avg_goals", "h2h_btts_rate", "h2h_over25_rate",
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# Stats
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"home_avg_possession", "away_avg_possession",
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"home_avg_shots_on_target", "away_avg_shots_on_target",
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"home_shot_conversion", "away_shot_conversion",
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# Odds (Implicit market wisdom)
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"odds_ms_h", "odds_ms_d", "odds_ms_a",
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"implied_home", "implied_draw", "implied_away",
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# League/Context
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"league_avg_goals", "league_zero_goal_rate",
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"home_xga", "away_xga"
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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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class MarketTrainer:
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def __init__(self, df, target_col, market_name, is_multi=False, num_class=None, weights=None):
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self.df = df[df[target_col].notna()].copy()
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self.target_col = target_col
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self.market_name = market_name
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self.is_multi = is_multi
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self.num_class = num_class
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self.weights = weights
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self.X = self.df[FEATURES]
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self.y = self.df[target_col].astype(int)
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# Split for final evaluation hold-out
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self.X_train, self.X_holdout, self.y_train, self.y_holdout = train_test_split(
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self.X, self.y, test_size=0.15, random_state=42, stratify=self.y
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)
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def optimize(self, n_trials=50):
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print(f"\n🔍 Tuning {self.market_name} with Optuna ({n_trials} trials)...")
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study = optuna.create_study(direction="minimize", sampler=TPESampler(seed=42))
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study.optimize(self.objective, n_trials=n_trials)
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print(f" Best params: {study.best_params}")
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print(f" Best Cross-Validation LogLoss: {study.best_value:.4f}")
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return study.best_params
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def objective(self, trial):
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params = {
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"verbosity": 0,
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"objective": "multi:softprob" if self.is_multi else "binary:logistic",
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"eval_metric": "mlogloss" if self.is_multi else "logloss",
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"booster": "gbtree",
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"lambda": trial.suggest_float("lambda", 1e-8, 1.0, log=True),
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"alpha": trial.suggest_float("alpha", 1e-8, 1.0, log=True),
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"max_depth": trial.suggest_int("max_depth", 3, 9),
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"eta": trial.suggest_float("eta", 1e-3, 0.1, log=True),
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"gamma": trial.suggest_float("gamma", 1e-8, 1.0, log=True),
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"grow_policy": trial.suggest_categorical("grow_policy", ["depthwise", "lossguide"]),
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"subsample": trial.suggest_float("subsample", 0.5, 1.0),
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"colsample_bytree": trial.suggest_float("colsample_bytree", 0.5, 1.0),
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"n_estimators": trial.suggest_int("n_estimators", 100, 1000),
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"early_stopping_rounds": 20,
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"n_jobs": 4,
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"random_state": 42
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}
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if self.is_multi:
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params["num_class"] = self.num_class
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skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
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losses = []
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for train_idx, val_idx in skf.split(self.X_train, self.y_train):
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X_t, X_v = self.X_train.iloc[train_idx], self.X_train.iloc[val_idx]
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y_t, y_v = self.y_train.iloc[train_idx], self.y_train.iloc[val_idx]
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# Apply weights if available
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w_t = None
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if self.weights:
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w_t = y_t.map(self.weights).fillna(1.0)
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model = xgb.XGBClassifier(**params)
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model.fit(X_t, y_t, sample_weight=w_t, eval_set=[(X_v, y_v)], verbose=False)
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preds = model.predict_proba(X_v)
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loss = log_loss(y_v, preds)
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losses.append(loss)
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return np.mean(losses)
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def train_final(self, best_params):
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print(f"🚀 Training final calibrated {self.market_name} model...")
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# Add core params
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best_params["objective"] = "multi:softprob" if self.is_multi else "binary:logistic"
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best_params["eval_metric"] = "mlogloss" if self.is_multi else "logloss"
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if self.is_multi:
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best_params["num_class"] = self.num_class
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base_model = xgb.XGBClassifier(**best_params)
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# Sample weights for training
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w_train = None
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if self.weights:
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w_train = self.y_train.map(self.weights).fillna(1.0)
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# Calibration using Cross-Validation
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calibrated_model = CalibratedClassifierCV(base_model, method='isotonic', cv=5)
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calibrated_model.fit(self.X_train, self.y_train, sample_weight=w_train)
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# Evaluate on Hold-out
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holdout_preds_raw = calibrated_model.predict_proba(self.X_holdout)
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holdout_preds_class = calibrated_model.predict(self.X_holdout)
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acc = accuracy_score(self.y_holdout, holdout_preds_class)
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loss = log_loss(self.y_holdout, holdout_preds_raw)
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print(f"📊 Hold-out Results for {self.market_name}:")
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print(f" Accuracy: {acc:.4f} | LogLoss: {loss:.4f}")
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print(classification_report(self.y_holdout, holdout_preds_class))
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# Save model
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model_path = os.path.join(MODELS_DIR, f"xgb_{self.market_name.lower()}.pkl")
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with open(model_path, "wb") as f:
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pickle.dump(calibrated_model, f)
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print(f"💾 Calibrated model saved to {model_path}")
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return calibrated_model
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def main():
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df = load_data()
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# 1. MS (1X2)
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ms_trainer = MarketTrainer(df, "label_ms", "MS", is_multi=True, num_class=3)
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ms_params = ms_trainer.optimize(n_trials=50)
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ms_trainer.train_final(ms_params)
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# 2. OU 2.5
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ou25_trainer = MarketTrainer(df, "label_ou25", "OU25")
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ou25_params = ou25_trainer.optimize(n_trials=30)
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ou25_trainer.train_final(ou25_params)
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# 3. BTTS
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btts_trainer = MarketTrainer(df, "label_btts", "BTTS")
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btts_params = btts_trainer.optimize(n_trials=30)
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btts_trainer.train_final(btts_params)
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# 4. HT/FT SURPRISE HUNTER
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htft_weights = {
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0: 1.0, 1: 3.0, 2: 20.0, # 1/1, 1/X, 1/2 (MAX WEIGHT)
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3: 2.0, 4: 2.0, 5: 2.0,
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6: 20.0, 7: 3.0, 8: 1.0 # 2/1 (MAX WEIGHT)
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}
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htft_trainer = MarketTrainer(df, "label_ht_ft", "HT_FT", is_multi=True, num_class=9, weights=htft_weights)
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htft_params = htft_trainer.optimize(n_trials=50)
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htft_trainer.train_final(htft_params)
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print("\n✅ Advanced V20 Model Training Complete!")
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if __name__ == "__main__":
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main()
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