"""
Ensemble Manager for Advanced Decision Tree
Copyright (c) 2024, Bleu.js
"""

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import mlflow
import numpy as np
import optuna
import ray
import structlog
from mlflow.tracking import MlflowClient
from ray import tune
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
from tensorflow import keras


@dataclass
class EnsembleConfig:
    """Configuration for ensemble learning."""

    n_models: int = 10
    model_types: List[str] = None
    voting_method: str = "soft"  # 'soft', 'hard', 'weighted'
    diversity_metrics: List[str] = None
    enable_hyperparameter_tuning: bool = True
    enable_distributed_training: bool = True
    enable_model_pruning: bool = True
    enable_auto_weighting: bool = True


class EnsembleManager:
    """
    Advanced ensemble manager that provides sophisticated ensemble learning
    capabilities with model diversity and automatic weighting.
    """

    def __init__(
        self,
        model_types: Optional[List[str]] = None,
        diversity_metrics: Optional[List[str]] = None,
        config: EnsembleConfig = EnsembleConfig(),
    ):
        self.config = config
        self.logger = structlog.get_logger()
        self.models = []
        self.weights = None
        self.diversity_scores = {}
        self.model_metrics = {}

        if model_types is None:
            self.config.model_types = [
                "random_forest",
                "gradient_boosting",
                "neural_network",
            ]
        else:
            self.config.model_types = model_types

        if diversity_metrics is None:
            self.config.diversity_metrics = [
                "q_statistic",
                "correlation",
                "entropy",
                "kappa",
            ]
        else:
            self.config.diversity_metrics = diversity_metrics

        # Initialize MLflow tracking
        self.mlflow_client = MlflowClient()
        self.experiment_name = "ensemble_learning"
        mlflow.set_experiment(self.experiment_name)

        # Initialize Ray for distributed computing if enabled
        if config.enable_distributed_training:
            if not ray.is_initialized():
                ray.init(ignore_reinit_error=True)

    async def initialize(self) -> None:
        """Initialize the ensemble manager and its components."""
        self.logger.info("initializing_ensemble_manager")

        try:
            # Initialize models
            await self._initialize_models()

            self.logger.info("ensemble_manager_initialized")

        except Exception as e:
            self.logger.error("initialization_failed", error=str(e))
            raise

    async def _initialize_models(self) -> None:
        """Initialize ensemble models."""
        for i in range(self.config.n_models):
            model_type = self.config.model_types[i % len(self.config.model_types)]

            if model_type == "random_forest":
                model = RandomForestClassifier(
                    n_estimators=100, max_depth=10, random_state=i
                )
            elif model_type == "gradient_boosting":
                model = GradientBoostingClassifier(
                    n_estimators=100, max_depth=5, random_state=i
                )
            else:  # neural_network
                model = self._create_neural_network()

            self.models.append(model)

    def _create_neural_network(self) -> keras.Model:
        """Create a neural network model."""
        model = keras.Sequential(
            [
                keras.layers.Dense(64, activation="relu", input_shape=(None,)),
                keras.layers.Dropout(0.2),
                keras.layers.Dense(32, activation="relu"),
                keras.layers.Dropout(0.2),
                keras.layers.Dense(1, activation="sigmoid"),
            ]
        )

        model.compile(
            optimizer=keras.optimizers.Adam(learning_rate=0.001),
            loss="binary_crossentropy",
            metrics=["accuracy"],
        )

        return model

    async def create_ensemble(
        self,
        X: np.ndarray,
        y: np.ndarray,
        feature_importance: Optional[Dict[str, np.ndarray]] = None,
    ) -> None:
        """
        Create and train an ensemble of models.
        """
        self.logger.info("creating_ensemble", data_shape=X.shape)

        try:
            # Start MLflow run
            with mlflow.start_run():
                # Log parameters
                mlflow.log_params(self.config.__dict__)

                # Train models with hyperparameter tuning if enabled
                if self.config.enable_hyperparameter_tuning:
                    await self._tune_hyperparameters(X, y)
                else:
                    await self._train_models(X, y)

                # Calculate model diversity
                self.diversity_scores = await self._calculate_diversity(X)
                mlflow.log_metrics(self.diversity_scores)

                # Calculate model weights if auto-weighting is enabled
                if self.config.enable_auto_weighting:
                    self.weights = await self._calculate_weights(X, y)
                    mlflow.log_params({"model_weights": self.weights.tolist()})

                # Log model metrics
                mlflow.log_metrics(self.model_metrics)

            self.logger.info("ensemble_created")

        except Exception as e:
            self.logger.error("ensemble_creation_failed", error=str(e))
            raise

    async def _tune_hyperparameters(self, X: np.ndarray, y: np.ndarray) -> None:
        """Perform hyperparameter tuning for ensemble models."""
        for i, model in enumerate(self.models):
            if isinstance(model, (RandomForestClassifier, GradientBoostingClassifier)):
                # Use Optuna for scikit-learn models
                study = optuna.create_study(direction="maximize")
                study.optimize(
                    lambda trial: self._objective(trial, model, X, y), n_trials=50
                )

                # Update model with best parameters
                best_params = study.best_params
                model.set_params(**best_params)
                model.fit(X, y)

            else:  # neural network
                # Use Ray Tune for neural networks
                analysis = tune.run(
                    lambda config: self._train_neural_network(config, X, y),
                    config=self._get_neural_network_config(),
                    num_samples=50,
                    resources_per_trial={"cpu": 2},
                )

                # Update model with best parameters
                best_config = analysis.get_best_config(metric="mean_accuracy")
                model = self._create_neural_network_with_config(best_config)
                model.fit(X, y, epochs=100, batch_size=32, verbose=0)

    async def _train_models(self, X: np.ndarray, y: np.ndarray) -> None:
        """Train ensemble models without hyperparameter tuning."""
        for model in self.models:
            if isinstance(model, keras.Model):
                model.fit(X, y, epochs=100, batch_size=32, verbose=0)
            else:
                model.fit(X, y)

    async def _calculate_diversity(self, X: np.ndarray) -> Dict[str, float]:
        """Calculate diversity metrics for the ensemble."""
        diversity_scores = {}

        if "q_statistic" in self.config.diversity_metrics:
            diversity_scores["q_statistic"] = await self._calculate_q_statistic(X)

        if "correlation" in self.config.diversity_metrics:
            diversity_scores["correlation"] = await self._calculate_correlation(X)

        if "entropy" in self.config.diversity_metrics:
            diversity_scores["entropy"] = await self._calculate_entropy(X)

        if "kappa" in self.config.diversity_metrics:
            diversity_scores["kappa"] = await self._calculate_kappa(X)

        return diversity_scores

    async def _calculate_q_statistic(self, X: np.ndarray) -> float:
        """Calculate Q-statistic as a measure of diversity."""
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)
        q_statistic = 0
        n_pairs = 0

        for i in range(len(self.models)):
            for j in range(i + 1, len(self.models)):
                agreement = np.mean(predictions[i] == predictions[j])
                q_statistic += 1 - 2 * agreement
                n_pairs += 1

        return q_statistic / n_pairs

    async def _calculate_correlation(self, X: np.ndarray) -> float:
        """Calculate correlation between model predictions."""
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)
        correlation_matrix = np.corrcoef(predictions)

        # Calculate average correlation excluding diagonal
        mask = np.ones(correlation_matrix.shape, dtype=bool)
        np.fill_diagonal(mask, 0)
        return np.mean(correlation_matrix[mask])

    async def _calculate_entropy(self, X: np.ndarray) -> float:
        """Calculate entropy of ensemble predictions."""
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)
        mean_pred = np.mean(predictions, axis=0)
        entropy = -np.sum(mean_pred * np.log2(mean_pred + 1e-10))
        return float(entropy)

    async def _calculate_kappa(self, X: np.ndarray) -> float:
        """Calculate Kappa statistic as a measure of diversity."""
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)
        agreement = np.mean(
            [
                np.mean(predictions[i] == predictions[j])
                for i in range(len(self.models))
                for j in range(i + 1, len(self.models))
            ]
        )

        random_agreement = np.mean(
            [
                np.mean(predictions[i] == predictions[j])
                for i in range(len(self.models))
                for j in range(i + 1, len(self.models))
                if i != j
            ]
        )

        return (agreement - random_agreement) / (1 - random_agreement)

    async def _calculate_weights(self, X: np.ndarray, y: np.ndarray) -> np.ndarray:
        """Calculate optimal weights for ensemble models."""
        # Get predictions from all models
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)

        # Calculate individual model performance
        scores = []
        for pred in predictions:
            if isinstance(pred, np.ndarray):
                score = accuracy_score(y, pred)
            else:
                score = f1_score(y, pred)
            scores.append(score)

        # Normalize scores to get weights
        weights = np.array(scores)
        weights = weights / np.sum(weights)

        return weights

    async def predict(
        self, X: np.ndarray, return_uncertainty: bool = False
    ) -> Union[np.ndarray, Tuple[np.ndarray, np.ndarray]]:
        """
        Make predictions using the ensemble.
        """
        # Get predictions from all models
        predictions = []
        for model in self.models:
            if isinstance(model, keras.Model):
                pred = model.predict(X, verbose=0)
            else:
                pred = model.predict(X)
            predictions.append(pred)

        predictions = np.array(predictions)

        # Apply voting method
        if self.config.voting_method == "soft":
            if self.weights is not None:
                ensemble_pred = np.average(predictions, axis=0, weights=self.weights)
            else:
                ensemble_pred = np.mean(predictions, axis=0)

        elif self.config.voting_method == "hard":
            if self.weights is not None:
                ensemble_pred = np.average(
                    predictions > 0.5, axis=0, weights=self.weights
                )
            else:
                ensemble_pred = np.mean(predictions > 0.5, axis=0)

        else:  # weighted
            if self.weights is not None:
                ensemble_pred = np.average(predictions, axis=0, weights=self.weights)
            else:
                ensemble_pred = np.mean(predictions, axis=0)

        if return_uncertainty:
            uncertainty = np.std(predictions, axis=0)
            return ensemble_pred, uncertainty

        return ensemble_pred

    async def get_diversity(self) -> float:
        """Get ensemble diversity score."""
        if not self.diversity_scores:
            return 0.0

        # Combine diversity metrics
        diversity_score = np.mean(list(self.diversity_scores.values()))
        return float(diversity_score)

    async def save_state(self, path: str) -> None:
        """Save the current state of the ensemble manager."""
        import joblib

        state = {
            "config": self.config,
            "models": self.models,
            "weights": self.weights,
            "diversity_scores": self.diversity_scores,
            "model_metrics": self.model_metrics,
        }

        joblib.dump(state, path)
        self.logger.info("ensemble_manager_state_saved", path=path)

    async def load_state(self, path: str) -> None:
        """Load a saved state of the ensemble manager."""
        import joblib

        state = joblib.load(path)
        self.config = state["config"]
        self.models = state["models"]
        self.weights = state["weights"]
        self.diversity_scores = state["diversity_scores"]
        self.model_metrics = state["model_metrics"]

        self.logger.info("ensemble_manager_state_loaded", path=path)

    def get_ensemble_info(self) -> Dict[str, Any]:
        """Get ensemble information."""
        return {
            "n_models": len(self.models),
            "weights": self.weights,
            "metrics": self.model_metrics,
        }

    def get_metrics(self) -> Dict[str, float]:
        """Get ensemble metrics."""
        return self.model_metrics

    def get_weights(self) -> Dict[str, float]:
        """Get model weights."""
        return self.weights

    def train(self, features: np.ndarray, labels: np.ndarray) -> Dict[str, Any]:
        # ... existing code ...
        pass

    def predict(self, features: np.ndarray) -> np.ndarray:
        # ... existing code ...
        pass

    def _calculate_diversity(self, features: np.ndarray) -> float:
        # ... existing code ...
        pass

    def _optimize_weights(self, features: np.ndarray, labels: np.ndarray) -> np.ndarray:
        # ... existing code ...
        pass

    def _validate_features(self, features: np.ndarray) -> None:
        # ... existing code ...
        pass

    def _validate_labels(self, labels: np.ndarray) -> None:
        # ... existing code ...
        pass

    def _check_model_compatibility(self, features: np.ndarray) -> None:
        # ... existing code ...
        pass

    def _get_model_predictions(self, features: np.ndarray) -> np.ndarray:
        # ... existing code ...
        pass

    def _combine_predictions(
        self, predictions: np.ndarray, weights: np.ndarray
    ) -> np.ndarray:
        # ... existing code ...
        pass

    def _calculate_metrics(
        self, features: np.ndarray, labels: np.ndarray
    ) -> Dict[str, float]:
        # ... existing code ...
        pass

    def _update_model_weights(self, features: np.ndarray, labels: np.ndarray) -> None:
        # ... existing code ...
        pass

    def _validate_weights(self, weights: np.ndarray) -> None:
        # ... existing code ...
        pass
