"""
Hyperparameter Tuning for Neural Network Surrogates

This module provides automatic hyperparameter optimization for MLP surrogates
using Optuna, with proper train/validation splits and early stopping.

Key Features:
1. Optuna-based hyperparameter search
2. K-fold cross-validation
3. Early stopping to prevent overfitting
4. Ensemble model support
5. Proper uncertainty quantification

Usage:
    from optimization_engine.surrogate_tuner import SurrogateHyperparameterTuner

    tuner = SurrogateHyperparameterTuner(
        input_dim=11,
        output_dim=3,
        n_trials=50
    )
    best_config = tuner.tune(X_train, Y_train)
    model = tuner.create_tuned_model(best_config)
"""

import logging
import numpy as np
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass, field
from pathlib import Path

logger = logging.getLogger(__name__)

try:
    import torch
    import torch.nn as nn
    from torch.utils.data import DataLoader, TensorDataset
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False
    logger.warning("PyTorch not installed")

try:
    import optuna
    from optuna.samplers import TPESampler
    from optuna.pruners import MedianPruner
    OPTUNA_AVAILABLE = True
except ImportError:
    OPTUNA_AVAILABLE = False
    logger.warning("Optuna not installed")


@dataclass
class SurrogateConfig:
    """Configuration for a tuned surrogate model."""
    hidden_dims: List[int] = field(default_factory=lambda: [128, 256, 128])
    dropout: float = 0.1
    activation: str = 'relu'
    use_batch_norm: bool = True
    learning_rate: float = 1e-3
    weight_decay: float = 1e-4
    batch_size: int = 16
    max_epochs: int = 500
    early_stopping_patience: int = 30

    # Normalization stats (filled during training)
    input_mean: Optional[np.ndarray] = None
    input_std: Optional[np.ndarray] = None
    output_mean: Optional[np.ndarray] = None
    output_std: Optional[np.ndarray] = None

    # Validation metrics
    val_loss: float = float('inf')
    val_r2: Dict[str, float] = field(default_factory=dict)


class TunableMLP(nn.Module):
    """Flexible MLP with configurable architecture."""

    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        hidden_dims: List[int],
        dropout: float = 0.1,
        activation: str = 'relu',
        use_batch_norm: bool = True
    ):
        super().__init__()

        self.input_dim = input_dim
        self.output_dim = output_dim

        # Activation function
        activations = {
            'relu': nn.ReLU(),
            'leaky_relu': nn.LeakyReLU(0.1),
            'elu': nn.ELU(),
            'selu': nn.SELU(),
            'gelu': nn.GELU(),
            'swish': nn.SiLU()
        }
        act_fn = activations.get(activation, nn.ReLU())

        # Build layers
        layers = []
        prev_dim = input_dim

        for hidden_dim in hidden_dims:
            layers.append(nn.Linear(prev_dim, hidden_dim))
            if use_batch_norm:
                layers.append(nn.BatchNorm1d(hidden_dim))
            layers.append(act_fn)
            if dropout > 0:
                layers.append(nn.Dropout(dropout))
            prev_dim = hidden_dim

        layers.append(nn.Linear(prev_dim, output_dim))
        self.network = nn.Sequential(*layers)

        self._init_weights()

    def _init_weights(self):
        """Initialize weights using Kaiming initialization."""
        for m in self.modules():
            if isinstance(m, nn.Linear):
                nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)

    def forward(self, x):
        return self.network(x)


class EarlyStopping:
    """Early stopping to prevent overfitting."""

    def __init__(self, patience: int = 20, min_delta: float = 1e-5):
        self.patience = patience
        self.min_delta = min_delta
        self.counter = 0
        self.best_loss = float('inf')
        self.best_model_state = None
        self.should_stop = False

    def __call__(self, val_loss: float, model: nn.Module) -> bool:
        if val_loss < self.best_loss - self.min_delta:
            self.best_loss = val_loss
            self.best_model_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}
            self.counter = 0
        else:
            self.counter += 1
            if self.counter >= self.patience:
                self.should_stop = True

        return self.should_stop

    def restore_best(self, model: nn.Module):
        """Restore model to best state."""
        if self.best_model_state is not None:
            model.load_state_dict(self.best_model_state)


class SurrogateHyperparameterTuner:
    """
    Automatic hyperparameter tuning for neural network surrogates.

    Uses Optuna for Bayesian optimization of:
    - Network architecture (layers, widths)
    - Regularization (dropout, weight decay)
    - Learning rate and batch size
    - Activation functions
    """

    def __init__(
        self,
        input_dim: int,
        output_dim: int,
        n_trials: int = 50,
        n_cv_folds: int = 5,
        device: str = 'auto',
        seed: int = 42,
        timeout_seconds: Optional[int] = None
    ):
        """
        Initialize hyperparameter tuner.

        Args:
            input_dim: Number of input features (design variables)
            output_dim: Number of outputs (objectives)
            n_trials: Number of Optuna trials for hyperparameter search
            n_cv_folds: Number of cross-validation folds
            device: Computing device ('cuda', 'cpu', or 'auto')
            seed: Random seed for reproducibility
            timeout_seconds: Optional timeout for tuning
        """
        if not TORCH_AVAILABLE:
            raise ImportError("PyTorch required for surrogate tuning")
        if not OPTUNA_AVAILABLE:
            raise ImportError("Optuna required for hyperparameter tuning")

        self.input_dim = input_dim
        self.output_dim = output_dim
        self.n_trials = n_trials
        self.n_cv_folds = n_cv_folds
        self.seed = seed
        self.timeout = timeout_seconds

        if device == 'auto':
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        else:
            self.device = torch.device(device)

        self.best_config: Optional[SurrogateConfig] = None
        self.study: Optional[optuna.Study] = None

        # Set seeds
        np.random.seed(seed)
        torch.manual_seed(seed)
        if torch.cuda.is_available():
            torch.cuda.manual_seed(seed)

    def _suggest_hyperparameters(self, trial: optuna.Trial) -> SurrogateConfig:
        """Suggest hyperparameters for a trial."""

        # Architecture
        n_layers = trial.suggest_int('n_layers', 2, 5)
        hidden_dims = []
        for i in range(n_layers):
            dim = trial.suggest_int(f'hidden_dim_{i}', 32, 512, step=32)
            hidden_dims.append(dim)

        # Regularization
        dropout = trial.suggest_float('dropout', 0.0, 0.5)
        weight_decay = trial.suggest_float('weight_decay', 1e-6, 1e-2, log=True)

        # Training
        learning_rate = trial.suggest_float('learning_rate', 1e-5, 1e-2, log=True)
        batch_size = trial.suggest_categorical('batch_size', [8, 16, 32, 64])

        # Activation
        activation = trial.suggest_categorical('activation',
            ['relu', 'leaky_relu', 'elu', 'gelu', 'swish'])

        # Batch norm
        use_batch_norm = trial.suggest_categorical('use_batch_norm', [True, False])

        return SurrogateConfig(
            hidden_dims=hidden_dims,
            dropout=dropout,
            activation=activation,
            use_batch_norm=use_batch_norm,
            learning_rate=learning_rate,
            weight_decay=weight_decay,
            batch_size=batch_size
        )

    def _train_fold(
        self,
        config: SurrogateConfig,
        X_train: np.ndarray,
        Y_train: np.ndarray,
        X_val: np.ndarray,
        Y_val: np.ndarray,
        trial: Optional[optuna.Trial] = None
    ) -> Tuple[float, Dict[str, float]]:
        """Train model on one fold and return validation metrics."""

        # Create model
        model = TunableMLP(
            input_dim=self.input_dim,
            output_dim=self.output_dim,
            hidden_dims=config.hidden_dims,
            dropout=config.dropout,
            activation=config.activation,
            use_batch_norm=config.use_batch_norm
        ).to(self.device)

        # Prepare data
        X_train_t = torch.tensor(X_train, dtype=torch.float32, device=self.device)
        Y_train_t = torch.tensor(Y_train, dtype=torch.float32, device=self.device)
        X_val_t = torch.tensor(X_val, dtype=torch.float32, device=self.device)
        Y_val_t = torch.tensor(Y_val, dtype=torch.float32, device=self.device)

        train_dataset = TensorDataset(X_train_t, Y_train_t)
        train_loader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True)

        # Optimizer and scheduler
        optimizer = torch.optim.AdamW(
            model.parameters(),
            lr=config.learning_rate,
            weight_decay=config.weight_decay
        )
        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
            optimizer, T_max=config.max_epochs
        )

        early_stopping = EarlyStopping(patience=config.early_stopping_patience)

        # Training loop
        for epoch in range(config.max_epochs):
            model.train()
            for X_batch, Y_batch in train_loader:
                optimizer.zero_grad()
                pred = model(X_batch)
                loss = nn.functional.mse_loss(pred, Y_batch)
                loss.backward()
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()

            scheduler.step()

            # Validation
            model.eval()
            with torch.no_grad():
                val_pred = model(X_val_t)
                val_loss = nn.functional.mse_loss(val_pred, Y_val_t).item()

            # Early stopping
            if early_stopping(val_loss, model):
                break

            # Optuna pruning (only report once per epoch across all folds)
            if trial is not None and epoch % 10 == 0:
                trial.report(val_loss, epoch // 10)
                if trial.should_prune():
                    raise optuna.TrialPruned()

        # Restore best model
        early_stopping.restore_best(model)

        # Final validation metrics
        model.eval()
        with torch.no_grad():
            val_pred = model(X_val_t).cpu().numpy()
            Y_val_np = Y_val_t.cpu().numpy()

            val_loss = float(np.mean((val_pred - Y_val_np) ** 2))

            # R² per output
            r2_scores = {}
            for i in range(self.output_dim):
                ss_res = np.sum((Y_val_np[:, i] - val_pred[:, i]) ** 2)
                ss_tot = np.sum((Y_val_np[:, i] - Y_val_np[:, i].mean()) ** 2)
                r2 = 1 - ss_res / ss_tot if ss_tot > 0 else 0
                r2_scores[f'output_{i}'] = r2

        return val_loss, r2_scores

    def _cross_validate(
        self,
        config: SurrogateConfig,
        X: np.ndarray,
        Y: np.ndarray,
        trial: Optional[optuna.Trial] = None
    ) -> Tuple[float, Dict[str, float]]:
        """Perform k-fold cross-validation."""

        n_samples = len(X)
        indices = np.random.permutation(n_samples)
        fold_size = n_samples // self.n_cv_folds

        fold_losses = []
        fold_r2s = {f'output_{i}': [] for i in range(self.output_dim)}

        for fold in range(self.n_cv_folds):
            # Split indices
            val_start = fold * fold_size
            val_end = val_start + fold_size if fold < self.n_cv_folds - 1 else n_samples

            val_indices = indices[val_start:val_end]
            train_indices = np.concatenate([indices[:val_start], indices[val_end:]])

            X_train, Y_train = X[train_indices], Y[train_indices]
            X_val, Y_val = X[val_indices], Y[val_indices]

            # Skip fold if too few samples
            if len(X_train) < 10 or len(X_val) < 2:
                continue

            val_loss, r2_scores = self._train_fold(
                config, X_train, Y_train, X_val, Y_val, trial
            )

            fold_losses.append(val_loss)
            for key, val in r2_scores.items():
                fold_r2s[key].append(val)

        mean_loss = np.mean(fold_losses)
        mean_r2 = {k: np.mean(v) for k, v in fold_r2s.items()}

        return mean_loss, mean_r2

    def tune(
        self,
        X: np.ndarray,
        Y: np.ndarray,
        output_names: Optional[List[str]] = None
    ) -> SurrogateConfig:
        """
        Tune hyperparameters using Optuna.

        Args:
            X: Input features [n_samples, input_dim]
            Y: Outputs [n_samples, output_dim]
            output_names: Optional names for outputs (for logging)

        Returns:
            Best SurrogateConfig found
        """
        logger.info(f"Starting hyperparameter tuning with {self.n_trials} trials...")
        logger.info(f"Data: {len(X)} samples, {self.n_cv_folds}-fold CV")

        # Normalize data
        self.input_mean = X.mean(axis=0)
        self.input_std = X.std(axis=0) + 1e-8
        self.output_mean = Y.mean(axis=0)
        self.output_std = Y.std(axis=0) + 1e-8

        X_norm = (X - self.input_mean) / self.input_std
        Y_norm = (Y - self.output_mean) / self.output_std

        def objective(trial: optuna.Trial) -> float:
            config = self._suggest_hyperparameters(trial)
            val_loss, r2_scores = self._cross_validate(config, X_norm, Y_norm, trial)

            # Log R² scores
            for key, val in r2_scores.items():
                trial.set_user_attr(f'r2_{key}', val)

            return val_loss

        # Create study
        self.study = optuna.create_study(
            direction='minimize',
            sampler=TPESampler(seed=self.seed, n_startup_trials=10),
            pruner=MedianPruner(n_startup_trials=5, n_warmup_steps=20)
        )

        self.study.optimize(
            objective,
            n_trials=self.n_trials,
            timeout=self.timeout,
            show_progress_bar=True,
            catch=(RuntimeError,)  # Catch GPU OOM errors
        )

        # Build best config
        best_trial = self.study.best_trial
        self.best_config = self._suggest_hyperparameters_from_params(best_trial.params)
        self.best_config.val_loss = best_trial.value
        self.best_config.val_r2 = {
            k.replace('r2_', ''): v
            for k, v in best_trial.user_attrs.items()
            if k.startswith('r2_')
        }

        # Store normalization
        self.best_config.input_mean = self.input_mean
        self.best_config.input_std = self.input_std
        self.best_config.output_mean = self.output_mean
        self.best_config.output_std = self.output_std

        # Log results
        logger.info(f"\nBest hyperparameters found:")
        logger.info(f"  Hidden dims: {self.best_config.hidden_dims}")
        logger.info(f"  Dropout: {self.best_config.dropout:.3f}")
        logger.info(f"  Activation: {self.best_config.activation}")
        logger.info(f"  Batch norm: {self.best_config.use_batch_norm}")
        logger.info(f"  Learning rate: {self.best_config.learning_rate:.2e}")
        logger.info(f"  Weight decay: {self.best_config.weight_decay:.2e}")
        logger.info(f"  Batch size: {self.best_config.batch_size}")
        logger.info(f"  Validation loss: {self.best_config.val_loss:.6f}")

        if output_names:
            for i, name in enumerate(output_names):
                r2 = self.best_config.val_r2.get(f'output_{i}', 0)
                logger.info(f"  {name} R² (CV): {r2:.4f}")

        return self.best_config

    def _suggest_hyperparameters_from_params(self, params: Dict[str, Any]) -> SurrogateConfig:
        """Reconstruct config from Optuna params dict."""
        n_layers = params['n_layers']
        hidden_dims = [params[f'hidden_dim_{i}'] for i in range(n_layers)]

        return SurrogateConfig(
            hidden_dims=hidden_dims,
            dropout=params['dropout'],
            activation=params['activation'],
            use_batch_norm=params['use_batch_norm'],
            learning_rate=params['learning_rate'],
            weight_decay=params['weight_decay'],
            batch_size=params['batch_size']
        )

    def create_tuned_model(
        self,
        config: Optional[SurrogateConfig] = None
    ) -> TunableMLP:
        """Create a model with tuned hyperparameters."""
        if config is None:
            config = self.best_config
        if config is None:
            raise ValueError("No config available. Run tune() first.")

        return TunableMLP(
            input_dim=self.input_dim,
            output_dim=self.output_dim,
            hidden_dims=config.hidden_dims,
            dropout=config.dropout,
            activation=config.activation,
            use_batch_norm=config.use_batch_norm
        )


class TunedEnsembleSurrogate:
    """
    Ensemble of tuned surrogate models for better uncertainty quantification.

    Trains multiple models with different random seeds and aggregates predictions.
    """

    def __init__(
        self,
        config: SurrogateConfig,
        input_dim: int,
        output_dim: int,
        n_models: int = 5,
        device: str = 'auto'
    ):
        """
        Initialize ensemble surrogate.

        Args:
            config: Tuned configuration to use for all models
            input_dim: Number of input features
            output_dim: Number of outputs
            n_models: Number of models in ensemble
            device: Computing device
        """
        self.config = config
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.n_models = n_models

        if device == 'auto':
            self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
        else:
            self.device = torch.device(device)

        self.models: List[TunableMLP] = []
        self.trained = False

    def train(self, X: np.ndarray, Y: np.ndarray, val_split: float = 0.2):
        """Train all models in the ensemble."""
        logger.info(f"Training ensemble of {self.n_models} models...")

        # Normalize using config stats
        X_norm = (X - self.config.input_mean) / self.config.input_std
        Y_norm = (Y - self.config.output_mean) / self.config.output_std

        # Split data
        n_val = int(len(X) * val_split)
        indices = np.random.permutation(len(X))
        train_idx, val_idx = indices[n_val:], indices[:n_val]

        X_train, Y_train = X_norm[train_idx], Y_norm[train_idx]
        X_val, Y_val = X_norm[val_idx], Y_norm[val_idx]

        X_train_t = torch.tensor(X_train, dtype=torch.float32, device=self.device)
        Y_train_t = torch.tensor(Y_train, dtype=torch.float32, device=self.device)
        X_val_t = torch.tensor(X_val, dtype=torch.float32, device=self.device)
        Y_val_t = torch.tensor(Y_val, dtype=torch.float32, device=self.device)

        train_dataset = TensorDataset(X_train_t, Y_train_t)

        self.models = []

        for i in range(self.n_models):
            torch.manual_seed(42 + i)

            model = TunableMLP(
                input_dim=self.input_dim,
                output_dim=self.output_dim,
                hidden_dims=self.config.hidden_dims,
                dropout=self.config.dropout,
                activation=self.config.activation,
                use_batch_norm=self.config.use_batch_norm
            ).to(self.device)

            optimizer = torch.optim.AdamW(
                model.parameters(),
                lr=self.config.learning_rate,
                weight_decay=self.config.weight_decay
            )
            scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
                optimizer, T_max=self.config.max_epochs
            )

            train_loader = DataLoader(
                train_dataset,
                batch_size=self.config.batch_size,
                shuffle=True
            )
            early_stopping = EarlyStopping(patience=self.config.early_stopping_patience)

            for epoch in range(self.config.max_epochs):
                model.train()
                for X_batch, Y_batch in train_loader:
                    optimizer.zero_grad()
                    pred = model(X_batch)
                    loss = nn.functional.mse_loss(pred, Y_batch)
                    loss.backward()
                    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                    optimizer.step()

                scheduler.step()

                model.eval()
                with torch.no_grad():
                    val_pred = model(X_val_t)
                    val_loss = nn.functional.mse_loss(val_pred, Y_val_t).item()

                if early_stopping(val_loss, model):
                    break

            early_stopping.restore_best(model)
            model.eval()
            self.models.append(model)

            logger.info(f"  Model {i+1}/{self.n_models}: val_loss = {early_stopping.best_loss:.6f}")

        self.trained = True
        logger.info("Ensemble training complete")

    def predict(self, X: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
        """
        Predict with uncertainty estimation.

        Args:
            X: Input features [n_samples, input_dim]

        Returns:
            Tuple of (mean_predictions, std_predictions)
        """
        if not self.trained:
            raise RuntimeError("Ensemble not trained. Call train() first.")

        # Normalize input
        X_norm = (X - self.config.input_mean) / self.config.input_std
        X_t = torch.tensor(X_norm, dtype=torch.float32, device=self.device)

        # Collect predictions from all models
        predictions = []
        for model in self.models:
            model.eval()
            with torch.no_grad():
                pred = model(X_t).cpu().numpy()
                # Denormalize
                pred = pred * self.config.output_std + self.config.output_mean
                predictions.append(pred)

        predictions = np.array(predictions)  # [n_models, n_samples, output_dim]

        mean_pred = predictions.mean(axis=0)
        std_pred = predictions.std(axis=0)

        return mean_pred, std_pred

    def predict_single(self, params: Dict[str, float], var_names: List[str]) -> Tuple[Dict[str, float], float]:
        """
        Predict for a single point with uncertainty.

        Args:
            params: Dictionary of input parameters
            var_names: List of variable names in order

        Returns:
            Tuple of (predictions dict, total uncertainty)
        """
        X = np.array([[params[name] for name in var_names]])
        mean, std = self.predict(X)

        pred_dict = {f'output_{i}': mean[0, i] for i in range(self.output_dim)}
        uncertainty = float(np.sum(std[0]))

        return pred_dict, uncertainty

    def save(self, path: Path):
        """Save ensemble to disk."""
        state = {
            'config': {
                'hidden_dims': self.config.hidden_dims,
                'dropout': self.config.dropout,
                'activation': self.config.activation,
                'use_batch_norm': self.config.use_batch_norm,
                'learning_rate': self.config.learning_rate,
                'weight_decay': self.config.weight_decay,
                'batch_size': self.config.batch_size,
                'input_mean': self.config.input_mean.tolist(),
                'input_std': self.config.input_std.tolist(),
                'output_mean': self.config.output_mean.tolist(),
                'output_std': self.config.output_std.tolist(),
            },
            'n_models': self.n_models,
            'model_states': [m.state_dict() for m in self.models]
        }
        torch.save(state, path)
        logger.info(f"Saved ensemble to {path}")

    def load(self, path: Path):
        """Load ensemble from disk."""
        state = torch.load(path, map_location=self.device)

        # Restore config
        cfg = state['config']
        self.config = SurrogateConfig(
            hidden_dims=cfg['hidden_dims'],
            dropout=cfg['dropout'],
            activation=cfg['activation'],
            use_batch_norm=cfg['use_batch_norm'],
            learning_rate=cfg['learning_rate'],
            weight_decay=cfg['weight_decay'],
            batch_size=cfg['batch_size'],
            input_mean=np.array(cfg['input_mean']),
            input_std=np.array(cfg['input_std']),
            output_mean=np.array(cfg['output_mean']),
            output_std=np.array(cfg['output_std'])
        )

        self.n_models = state['n_models']
        self.models = []

        for model_state in state['model_states']:
            model = TunableMLP(
                input_dim=self.input_dim,
                output_dim=self.output_dim,
                hidden_dims=self.config.hidden_dims,
                dropout=self.config.dropout,
                activation=self.config.activation,
                use_batch_norm=self.config.use_batch_norm
            ).to(self.device)
            model.load_state_dict(model_state)
            model.eval()
            self.models.append(model)

        self.trained = True
        logger.info(f"Loaded ensemble with {self.n_models} models from {path}")


def tune_surrogate_for_study(
    fea_data: List[Dict],
    design_var_names: List[str],
    objective_names: List[str],
    n_tuning_trials: int = 50,
    n_ensemble_models: int = 5
) -> TunedEnsembleSurrogate:
    """
    Convenience function to tune and create ensemble surrogate.

    Args:
        fea_data: List of FEA results with 'params' and 'objectives' keys
        design_var_names: List of design variable names
        objective_names: List of objective names
        n_tuning_trials: Number of Optuna trials
        n_ensemble_models: Number of models in ensemble

    Returns:
        Trained TunedEnsembleSurrogate
    """
    # Prepare data
    X = np.array([[d['params'][name] for name in design_var_names] for d in fea_data])
    Y = np.array([[d['objectives'][name] for name in objective_names] for d in fea_data])

    logger.info(f"Tuning surrogate on {len(X)} samples...")
    logger.info(f"Input: {len(design_var_names)} design variables")
    logger.info(f"Output: {len(objective_names)} objectives")

    # Tune hyperparameters
    tuner = SurrogateHyperparameterTuner(
        input_dim=len(design_var_names),
        output_dim=len(objective_names),
        n_trials=n_tuning_trials,
        n_cv_folds=5
    )

    best_config = tuner.tune(X, Y, output_names=objective_names)

    # Create and train ensemble
    ensemble = TunedEnsembleSurrogate(
        config=best_config,
        input_dim=len(design_var_names),
        output_dim=len(objective_names),
        n_models=n_ensemble_models
    )

    ensemble.train(X, Y)

    return ensemble