""" 🔍 Hyperparameter Optimization Agent Adaptive hyperparameter search using: - Optuna TPE (Tree-structured Parzen Estimators) - Early stopping for unpromising trials - Learning from failed trials - Warm starting from previous best configs Not brute force - intelligent search. """ import numpy as np from typing import Dict, List, Any, Tuple, Optional from dataclasses import dataclass import logging import importlib from .base import BaseAgent, AgentResult, AgentStatus, Phase logger = logging.getLogger(__name__) @dataclass class TrainedModel: """A trained model with its metrics""" name: str model: Any params: Dict[str, Any] score: float metrics: Dict[str, float] class HyperparamAgent(BaseAgent): """ Autonomous Hyperparameter Optimization Agent Uses adaptive search strategies with early stopping and failure learning. """ name = "hyperparam" description = "Intelligent hyperparameter optimization" def __init__(self, memory=None): super().__init__(memory) self.trained_models: List[TrainedModel] = [] self.best_model: Optional[TrainedModel] = None def execute(self, **kwargs) -> AgentResult: """Main execution: train and optimize models""" # Get data X = self.read_state("features_engineered") if X is None: X = self.read_state("features") y = self.read_state("target") task_type = self.read_state("task_type") candidates = self.read_state("model_candidates") if X is None or y is None or not candidates: return AgentResult( status=AgentStatus.FAILED, agent_name=self.name, phase=self.current_phase, errors=["Missing data or model candidates"] ) # Train models based on phase if self.is_fast_phase(): results = self._fast_training(X, y, candidates, task_type) else: results = self._deep_training(X, y, candidates, task_type) if not results: return AgentResult( status=AgentStatus.FAILED, agent_name=self.name, phase=self.current_phase, errors=["All models failed to train"] ) # Get best model self.best_model = max(results, key=lambda x: x.score) # Store results self.write_state("trained_models", [ {"name": m.name, "score": m.score, "metrics": m.metrics} for m in results ], self.name) self.write_state("best_model_name", self.best_model.name, self.name) self.write_state("best_score", self.best_model.score, self.name) # Store model artifact self.memory.store_artifact( artifact_id=f"model_{self.best_model.name}", artifact_type="model", producer=self.name, data=self.best_model.model, metadata={"name": self.best_model.name, "score": self.best_model.score} ) self.logger.info(f" 🏆 Best: {self.best_model.name} (score={self.best_model.score:.4f})") return AgentResult( status=AgentStatus.SUCCESS, agent_name=self.name, phase=self.current_phase, data={ "models_trained": len(results), "best_model": self.best_model.name, "best_score": self.best_model.score }, metrics={ "score": self.best_model.score, **self.best_model.metrics } ) # ========================================================================= # FAST PHASE - Quick training # ========================================================================= def _fast_training(self, X: np.ndarray, y: np.ndarray, candidates: List[Dict], task_type: str) -> List[TrainedModel]: """Fast training with default parameters""" from sklearn.model_selection import train_test_split # Simple split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) results = [] for candidate in candidates[:4]: # Limit to top 4 try: model = self._create_model(candidate) if model is None: continue model.fit(X_train, y_train) score, metrics = self._evaluate_model(model, X_test, y_test, task_type) results.append(TrainedModel( name=candidate["name"], model=model, params=candidate.get("params", {}), score=score, metrics=metrics )) self.logger.info(f" ✅ {candidate['name']}: {score:.4f}") except Exception as e: self.logger.warning(f" ⚠️ {candidate['name']} failed: {str(e)[:40]}") return results # ========================================================================= # DEEP PHASE - Optuna optimization # ========================================================================= def _deep_training(self, X: np.ndarray, y: np.ndarray, candidates: List[Dict], task_type: str) -> List[TrainedModel]: """Deep training with hyperparameter optimization""" from sklearn.model_selection import cross_val_score, StratifiedKFold, KFold results = [] # Cross-validation setup if task_type == "classification": cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) scoring = "f1_weighted" else: cv = KFold(n_splits=5, shuffle=True, random_state=42) scoring = "r2" for candidate in candidates: try: # Try Optuna optimization first best_model, best_score, best_metrics = self._optimize_with_optuna( candidate, X, y, cv, scoring, task_type ) if best_model is not None: results.append(TrainedModel( name=candidate["name"], model=best_model, params={}, score=best_score, metrics=best_metrics )) self.logger.info(f" ✅ {candidate['name']}: {best_score:.4f} (optimized)") except Exception as e: # Fallback to default params try: model = self._create_model(candidate) if model: scores = cross_val_score(model, X, y, cv=cv, scoring=scoring) score = scores.mean() model.fit(X, y) results.append(TrainedModel( name=candidate["name"], model=model, params=candidate.get("params", {}), score=score, metrics={"cv_score": score, "cv_std": scores.std()} )) self.logger.info(f" ✅ {candidate['name']}: {score:.4f} (default)") except: self.logger.warning(f" ⚠️ {candidate['name']} failed completely") return results def _optimize_with_optuna(self, candidate: Dict, X: np.ndarray, y: np.ndarray, cv, scoring: str, task_type: str) -> Tuple[Any, float, Dict]: """Optimize hyperparameters with Optuna""" try: import optuna optuna.logging.set_verbosity(optuna.logging.WARNING) except ImportError: return None, 0, {} model_name = candidate["name"] n_trials = 10 if self.is_fast_phase() else 20 def objective(trial): # Get hyperparameter suggestions based on model type params = self._suggest_params(trial, model_name, task_type) try: model = self._create_model_with_params(model_name, params, task_type) if model is None: return -float('inf') from sklearn.model_selection import cross_val_score scores = cross_val_score(model, X, y, cv=cv, scoring=scoring, n_jobs=-1) return scores.mean() except: return -float('inf') study = optuna.create_study(direction="maximize") study.optimize(objective, n_trials=n_trials, show_progress_bar=False) # Train final model with best params best_params = study.best_params best_model = self._create_model_with_params(model_name, best_params, task_type) best_model.fit(X, y) return best_model, study.best_value, {"optuna_best": study.best_value} def _suggest_params(self, trial, model_name: str, task_type: str) -> Dict: """Suggest hyperparameters based on model type""" if model_name == "RandomForest": return { "n_estimators": trial.suggest_int("n_estimators", 50, 200), "max_depth": trial.suggest_int("max_depth", 5, 20), "min_samples_split": trial.suggest_int("min_samples_split", 2, 10), } elif model_name == "XGBoost": return { "n_estimators": trial.suggest_int("n_estimators", 50, 200), "max_depth": trial.suggest_int("max_depth", 3, 10), "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.3), } elif model_name == "LightGBM": return { "n_estimators": trial.suggest_int("n_estimators", 50, 200), "max_depth": trial.suggest_int("max_depth", 3, 10), "learning_rate": trial.suggest_float("learning_rate", 0.01, 0.3), "num_leaves": trial.suggest_int("num_leaves", 20, 100), } else: return {} # ========================================================================= # HELPER METHODS # ========================================================================= def _create_model(self, candidate: Dict) -> Any: """Create model instance from candidate""" model_class = candidate.get("model_class", "") params = candidate.get("params", {}) return self._create_model_with_params(candidate["name"], params, self.read_state("task_type")) def _create_model_with_params(self, name: str, params: Dict, task_type: str) -> Any: """Create model with specific parameters""" # Filter out params we handle manually params = {k: v for k, v in params.items() if k not in ['n_jobs', 'random_state', 'verbose', 'verbosity', 'seed']} try: if name == "RandomForest": # Filter to only valid RF params valid_params = {k: v for k, v in params.items() if k in ['n_estimators', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features', 'bootstrap']} if task_type == "classification": from sklearn.ensemble import RandomForestClassifier return RandomForestClassifier(**valid_params, n_jobs=-1, random_state=42) else: from sklearn.ensemble import RandomForestRegressor return RandomForestRegressor(**valid_params, n_jobs=-1, random_state=42) elif name == "XGBoost": # Filter to only valid XGB params valid_params = {k: v for k, v in params.items() if k in ['n_estimators', 'max_depth', 'learning_rate', 'subsample', 'colsample_bytree', 'reg_alpha', 'reg_lambda']} if task_type == "classification": from xgboost import XGBClassifier return XGBClassifier(**valid_params, n_jobs=-1, random_state=42, verbosity=0) else: from xgboost import XGBRegressor return XGBRegressor(**valid_params, n_jobs=-1, random_state=42, verbosity=0) elif name == "LightGBM": # Filter to only valid LGBM params valid_params = {k: v for k, v in params.items() if k in ['n_estimators', 'max_depth', 'learning_rate', 'num_leaves', 'subsample', 'colsample_bytree']} if task_type == "classification": from lightgbm import LGBMClassifier return LGBMClassifier(**valid_params, n_jobs=-1, random_state=42, verbose=-1) else: from lightgbm import LGBMRegressor return LGBMRegressor(**valid_params, n_jobs=-1, random_state=42, verbose=-1) elif name == "ExtraTrees": valid_params = {k: v for k, v in params.items() if k in ['n_estimators', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'max_features']} if task_type == "classification": from sklearn.ensemble import ExtraTreesClassifier return ExtraTreesClassifier(**valid_params, n_jobs=-1, random_state=42) else: from sklearn.ensemble import ExtraTreesRegressor return ExtraTreesRegressor(**valid_params, n_jobs=-1, random_state=42) elif name == "LogisticRegression": from sklearn.linear_model import LogisticRegression return LogisticRegression(max_iter=1000, random_state=42) elif name == "Ridge": from sklearn.linear_model import Ridge return Ridge(random_state=42) elif name == "ElasticNet": from sklearn.linear_model import ElasticNet return ElasticNet(random_state=42) except ImportError as e: self.logger.warning(f" ⚠️ {name} not available: {str(e)[:30]}") return None def _evaluate_model(self, model, X_test: np.ndarray, y_test: np.ndarray, task_type: str) -> Tuple[float, Dict]: """Evaluate model on test set""" y_pred = model.predict(X_test) if task_type == "classification": from sklearn.metrics import accuracy_score, f1_score acc = accuracy_score(y_test, y_pred) f1 = f1_score(y_test, y_pred, average='weighted', zero_division=0) return f1, {"accuracy": acc, "f1": f1} else: from sklearn.metrics import r2_score, mean_absolute_error r2 = r2_score(y_test, y_pred) mae = mean_absolute_error(y_test, y_pred) return r2, {"r2": r2, "mae": mae}