"""
AutoML Agent

Automated machine learning for model selection, feature engineering,
and hyperparameter tuning. Implements AutoML best practices.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class AutoMLAgent(BaseAgent):
    """
    Automated machine learning pipeline builder.

    Features:
    - Automated model selection
    - Automated feature engineering
    - Automated hyperparameter tuning
    - Neural architecture search
    - Ensemble model creation
    - Auto-sklearn, H2O AutoML, TPOT integration
    - Pipeline optimization
    - Multi-objective optimization
    """

    def __init__(self):
        super().__init__(
            name='automl-agent',
            description='Automated machine learning with model selection and tuning',
            category='ai_ml',
            version='1.0.0',
            tags=['ml', 'automl', 'automation', 'optimization', 'ensemble']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Run AutoML pipeline.

        Args:
            params: {
                'data_config': {
                    'train_data': str,
                    'test_data': str,
                    'target_column': str,
                    'task_type': 'classification|regression|clustering|time_series',
                    'metric': 'accuracy|f1|auc|rmse|r2|custom'
                },
                'automl_config': {
                    'framework': 'auto_sklearn|h2o|tpot|autokeras|ludwig',
                    'time_budget_minutes': int,
                    'max_trials': int,
                    'ensemble_size': int,
                    'algorithms': List[str],  # Optional: limit to specific algorithms
                    'optimization_metric': str
                },
                'search_space': {
                    'models': [
                        'random_forest', 'xgboost', 'lightgbm', 'catboost',
                        'neural_network', 'svm', 'logistic_regression'
                    ],
                    'preprocessing': [
                        'scaling', 'encoding', 'imputation', 'feature_selection'
                    ],
                    'feature_engineering': {
                        'enabled': bool,
                        'techniques': ['polynomial', 'interactions', 'binning']
                    }
                },
                'constraints': {
                    'max_model_size_mb': float,
                    'max_inference_time_ms': float,
                    'min_accuracy': float,
                    'interpretability_required': bool
                },
                'compute_config': {
                    'n_jobs': int,
                    'gpu_enabled': bool,
                    'memory_limit_gb': int
                },
                'advanced': {
                    'early_stopping': bool,
                    'warm_start': bool,
                    'incremental_learning': bool,
                    'meta_learning': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'automl_id': str,
                'best_model': {
                    'algorithm': str,
                    'hyperparameters': Dict[str, Any],
                    'score': float,
                    'pipeline': List[str],
                    'model_path': str
                },
                'leaderboard': List[Dict[str, Any]],
                'search_summary': {
                    'total_trials': int,
                    'successful_trials': int,
                    'failed_trials': int,
                    'best_trial_number': int,
                    'total_time_minutes': float
                },
                'model_ensemble': {
                    'enabled': bool,
                    'n_models': int,
                    'ensemble_method': 'voting|stacking|blending',
                    'ensemble_score': float,
                    'member_models': List[str]
                },
                'feature_engineering': {
                    'original_features': int,
                    'engineered_features': int,
                    'selected_features': int,
                    'importance_scores': Dict[str, float]
                },
                'preprocessing_pipeline': List[Dict[str, Any]],
                'performance_analysis': {
                    'train_score': float,
                    'validation_score': float,
                    'test_score': float,
                    'cross_validation_scores': List[float],
                    'overfitting_score': float
                },
                'model_characteristics': {
                    'model_size_mb': float,
                    'inference_time_ms': float,
                    'training_time_minutes': float,
                    'interpretability_score': float
                },
                'recommendations': List[str]
            }
        """
        data_config = params.get('data_config', {})
        automl_config = params.get('automl_config', {})
        search_space = params.get('search_space', {})

        task_type = data_config.get('task_type', 'classification')
        time_budget = automl_config.get('time_budget_minutes', 60)

        self.logger.info(
            f"Running AutoML for {task_type} task with {time_budget} minute budget"
        )

        # Mock AutoML results
        leaderboard = [
            {
                'rank': 1,
                'algorithm': 'XGBoost',
                'score': 0.9712,
                'training_time': 234.5,
                'hyperparameters': {
                    'max_depth': 7,
                    'learning_rate': 0.05,
                    'n_estimators': 500
                }
            },
            {
                'rank': 2,
                'algorithm': 'LightGBM',
                'score': 0.9689,
                'training_time': 178.3,
                'hyperparameters': {
                    'num_leaves': 31,
                    'learning_rate': 0.03,
                    'n_estimators': 600
                }
            },
            {
                'rank': 3,
                'algorithm': 'RandomForest',
                'score': 0.9634,
                'training_time': 456.2,
                'hyperparameters': {
                    'n_estimators': 300,
                    'max_depth': 15,
                    'min_samples_split': 5
                }
            },
            {
                'rank': 4,
                'algorithm': 'CatBoost',
                'score': 0.9623,
                'training_time': 312.1,
                'hyperparameters': {
                    'depth': 8,
                    'learning_rate': 0.04,
                    'iterations': 400
                }
            },
            {
                'rank': 5,
                'algorithm': 'NeuralNetwork',
                'score': 0.9589,
                'training_time': 678.9,
                'hyperparameters': {
                    'hidden_layers': [256, 128, 64],
                    'learning_rate': 0.001,
                    'dropout': 0.3
                }
            }
        ]

        return {
            'status': 'success',
            'automl_id': f'automl_{task_type}_{automl_config.get("framework", "auto_sklearn")}',
            'framework': automl_config.get('framework', 'auto_sklearn'),
            'task_type': task_type,
            'best_model': {
                'algorithm': 'XGBoost',
                'hyperparameters': {
                    'max_depth': 7,
                    'learning_rate': 0.05,
                    'n_estimators': 500,
                    'subsample': 0.8,
                    'colsample_bytree': 0.8,
                    'min_child_weight': 3,
                    'gamma': 0.1
                },
                'score': 0.9712,
                'pipeline': [
                    'imputer',
                    'scaler',
                    'feature_selector',
                    'xgboost_classifier'
                ],
                'model_path': '/models/automl/best_model.pkl',
                'config_path': '/models/automl/best_config.json'
            },
            'leaderboard': leaderboard,
            'search_summary': {
                'total_trials': 150,
                'successful_trials': 142,
                'failed_trials': 8,
                'best_trial_number': 87,
                'total_time_minutes': time_budget,
                'avg_trial_time_seconds': (time_budget * 60) / 150,
                'trials_per_algorithm': {
                    'XGBoost': 35,
                    'LightGBM': 32,
                    'RandomForest': 28,
                    'CatBoost': 25,
                    'NeuralNetwork': 22,
                    'Others': 8
                }
            },
            'model_ensemble': {
                'enabled': True,
                'n_models': 5,
                'ensemble_method': 'stacking',
                'ensemble_score': 0.9734,
                'improvement_over_best': 0.0022,
                'member_models': [
                    'XGBoost',
                    'LightGBM',
                    'RandomForest',
                    'CatBoost',
                    'NeuralNetwork'
                ],
                'meta_learner': 'LogisticRegression',
                'ensemble_path': '/models/automl/ensemble_model.pkl'
            },
            'feature_engineering': {
                'original_features': 50,
                'engineered_features': 87,
                'selected_features': 65,
                'feature_creation_methods': [
                    'polynomial_features',
                    'interaction_features',
                    'statistical_features'
                ],
                'importance_scores': {
                    'feature_1': 0.156,
                    'poly_2_3': 0.134,
                    'interaction_1_5': 0.112,
                    'feature_7': 0.098
                }
            },
            'preprocessing_pipeline': [
                {
                    'step': 'missing_value_imputation',
                    'method': 'iterative',
                    'features_affected': 12
                },
                {
                    'step': 'categorical_encoding',
                    'method': 'target_encoding',
                    'features_encoded': 8
                },
                {
                    'step': 'scaling',
                    'method': 'robust_scaler',
                    'features_scaled': 50
                },
                {
                    'step': 'feature_selection',
                    'method': 'mutual_information',
                    'features_selected': 65
                }
            ],
            'performance_analysis': {
                'train_score': 0.9856,
                'validation_score': 0.9712,
                'test_score': 0.9689,
                'cross_validation_scores': [0.9678, 0.9712, 0.9689, 0.9723, 0.9698],
                'cross_validation_mean': 0.9700,
                'cross_validation_std': 0.0016,
                'overfitting_score': 0.0144,  # train - validation
                'generalization_gap': 0.0023  # validation - test
            },
            'model_characteristics': {
                'model_size_mb': 45.3,
                'inference_time_ms': 12.4,
                'training_time_minutes': 3.91,
                'interpretability_score': 0.72,
                'complexity': 'medium',
                'production_ready': True
            },
            'optimization_insights': {
                'best_performing_family': 'Gradient Boosting',
                'feature_engineering_impact': '+4.2% accuracy',
                'ensemble_benefit': '+0.22% accuracy',
                'optimal_complexity': 'medium',
                'convergence_reached': True
            },
            'artifacts': {
                'best_model_path': '/models/automl/best_model.pkl',
                'ensemble_path': '/models/automl/ensemble_model.pkl',
                'pipeline_path': '/models/automl/pipeline.pkl',
                'leaderboard_path': '/models/automl/leaderboard.json',
                'report_path': '/models/automl/automl_report.html'
            },
            'recommendations': [
                'XGBoost is the best single model with 97.12% accuracy',
                'Ensemble model provides slight improvement to 97.34%',
                'Model shows minimal overfitting (1.44% gap)',
                'Feature engineering contributed 4.2% accuracy improvement',
                'Inference time of 12.4ms meets production requirements',
                'Consider gradient boosting algorithms for similar problems',
                'Model is production-ready with good interpretability',
                'Use ensemble for maximum accuracy, XGBoost for speed',
                'Set up retraining pipeline to maintain performance'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate AutoML parameters."""
        if 'data_config' not in params:
            self.logger.error("Missing required field: data_config")
            return False

        data_config = params['data_config']
        required_fields = ['train_data', 'target_column', 'task_type']
        for field in required_fields:
            if field not in data_config:
                self.logger.error(f"Missing required field: data_config.{field}")
                return False

        valid_tasks = ['classification', 'regression', 'clustering', 'time_series']
        if data_config['task_type'] not in valid_tasks:
            self.logger.error(f"Invalid task type: {data_config['task_type']}")
            return False

        return True
