sklears_core/

ensemble_improvements.rs

/// Advanced ensemble method improvements with parallel and distributed training
///
/// This module provides state-of-the-art improvements to ensemble methods,
/// focusing on parallel and distributed training capabilities that leverage
/// modern hardware architectures and distributed computing frameworks.
///
/// # Key Features
///
/// ## Parallel Training
/// - **Multi-threaded Base Learner Training**: Parallel training of individual models
/// - **SIMD-optimized Aggregation**: Vectorized prediction combining and voting
/// - **Asynchronous Model Updates**: Non-blocking model training and updates
/// - **Work-stealing Task Scheduler**: Dynamic load balancing across cores
/// - **Memory-efficient Batching**: Optimized memory usage during parallel training
///
/// ## Distributed Training
/// - **Cluster-aware Ensemble Training**: Distribution across multiple machines
/// - **Fault-tolerant Training**: Resilience to node failures during training
/// - **Communication-optimized Protocols**: Efficient model synchronization
/// - **Hierarchical Ensemble Architecture**: Multi-level ensemble structures
/// - **Elastic Scaling**: Dynamic addition/removal of computing resources
///
/// ## Advanced Ensemble Techniques
/// - **Dynamic Ensemble Composition**: Adaptive addition/removal of base learners
/// - **Online Ensemble Learning**: Streaming ensemble updates
/// - **Meta-learning Ensemble Selection**: Learned ensemble composition strategies
/// - **Bayesian Ensemble Averaging**: Uncertainty-aware model combination
/// - **Adversarial Ensemble Training**: Robust ensemble training strategies
///
/// # Architecture Overview
///
/// The ensemble improvements are built on a modular architecture:
///
/// ```text
/// ┌─────────────────────────────────────────────────────────────┐
/// │                    Ensemble Coordinator                     │
/// │  ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐   │
/// │  │   Parallel  │ │ Distributed │ │    Meta-learning    │   │
/// │  │   Trainer   │ │   Manager   │ │     Controller      │   │
/// │  └─────────────┘ └─────────────┘ └─────────────────────┘   │
/// └─────────────────────────────────────────────────────────────┘
///           │                  │                     │
/// ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────────┐
/// │  Base Learners  │ │ Communication   │ │   Model Selection   │
/// │   (Workers)     │ │     Layer       │ │     Strategies      │
/// └─────────────────┘ └─────────────────┘ └─────────────────────┘
/// ```
///
/// # Examples
///
/// ## Parallel Random Forest Training
///
/// ```rust,no_run
/// use sklears_core::ensemble_improvements::{
use crate::error::{Result, SklearsError};
// SciRS2 Policy: Using scirs2_core::ndarray and scirs2_core::random (COMPLIANT)
use rayon::prelude::*;
use scirs2_core::ndarray::{s, Array1, Array2, ArrayView1, ArrayView2};
use scirs2_core::random::Random;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::{Arc, RwLock};
use std::time::{Duration, Instant};

/// Advanced parallel ensemble trainer
#[derive(Debug)]
pub struct ParallelEnsemble {
    config: EnsembleConfig,
    base_learners: Vec<Arc<dyn BaseEstimator>>,
    training_state: Arc<RwLock<TrainingState>>,
}

impl ParallelEnsemble {
    /// Create a new parallel ensemble
    pub fn new(config: EnsembleConfig) -> Self {
        let base_learners = Self::create_base_learners(&config);

        Self {
            config,
            base_learners,
            training_state: Arc::new(RwLock::new(TrainingState::new())),
        }
    }

    /// Create base learners based on configuration
    fn create_base_learners(config: &EnsembleConfig) -> Vec<Arc<dyn BaseEstimator>> {
        let mut learners = Vec::new();

        for i in 0..config.n_estimators {
            let learner: Arc<dyn BaseEstimator> = match &config.ensemble_type {
                EnsembleType::RandomForest => {
                    Arc::new(RandomForestEstimator::new(i, &config.base_config))
                }
                EnsembleType::GradientBoosting => {
                    Arc::new(GradientBoostingEstimator::new(i, &config.base_config))
                }
                EnsembleType::AdaBoost => Arc::new(AdaBoostEstimator::new(i, &config.base_config)),
                EnsembleType::Voting => Arc::new(VotingEstimator::new(i, &config.base_config)),
                EnsembleType::Stacking => Arc::new(StackingEstimator::new(i, &config.base_config)),
            };
            learners.push(learner);
        }

        learners
    }

    /// Get number of base estimators
    pub fn n_estimators(&self) -> usize {
        self.base_learners.len()
    }

    /// Parallel fit implementation
    pub fn parallel_fit(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
    ) -> Result<TrainedParallelEnsemble> {
        let start_time = Instant::now();

        // Update training state
        {
            let mut state = self.training_state.write().unwrap();
            state.start_training(x.nrows(), self.n_estimators());
        }

        // Configure parallel training
        let pool = rayon::ThreadPoolBuilder::new()
            .num_threads(self.config.parallel_config.num_threads)
            .build()
            .map_err(|e| {
                SklearsError::InvalidInput(format!("Failed to create thread pool: {e}"))
            })?;

        // Parallel training of base learners
        let trained_learners = pool.install(|| {
            self.base_learners
                .par_iter()
                .enumerate()
                .map(|(i, learner)| {
                    let result = self.train_single_learner(learner.as_ref(), x, y, i);

                    // Update progress
                    {
                        let mut state = self.training_state.write().unwrap();
                        state.update_progress(i, result.is_ok());
                    }

                    result
                })
                .collect::<Result<Vec<_>>>()
        })?;

        // Update final state
        {
            let mut state = self.training_state.write().unwrap();
            state.complete_training(start_time.elapsed());
        }

        Ok(TrainedParallelEnsemble {
            config: self.config.clone(),
            trained_learners,
            training_metrics: self.training_state.read().unwrap().clone(),
        })
    }

    /// Train a single base learner
    fn train_single_learner(
        &self,
        learner: &dyn BaseEstimator,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        learner_id: usize,
    ) -> Result<TrainedBaseEstimator> {
        // Prepare training data for this learner
        let (train_x, train_y) = self.prepare_training_data(x, y, learner_id)?;

        // Train the base learner
        let start_time = Instant::now();
        let trained = learner.fit(&train_x.view(), &train_y.view())?;
        let training_time = start_time.elapsed();

        // Compute training accuracy before moving the model
        let training_accuracy =
            self.compute_training_accuracy(trained.as_ref(), &train_x, &train_y)?;

        Ok(TrainedBaseEstimator {
            learner_id,
            model: trained,
            training_time,
            training_accuracy,
        })
    }

    /// Prepare training data for a specific learner (e.g., bootstrap sampling for Random Forest)
    fn prepare_training_data(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        learner_id: usize,
    ) -> Result<(Array2<f64>, Array1<f64>)> {
        match self.config.sampling_strategy {
            SamplingStrategy::Bootstrap => self.bootstrap_sample(x, y, learner_id),
            SamplingStrategy::Bagging => self.bag_sample(x, y, learner_id),
            SamplingStrategy::None => Ok((x.to_owned(), y.to_owned())),
        }
    }

    /// Bootstrap sampling for individual learners
    fn bootstrap_sample(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        seed: usize,
    ) -> Result<(Array2<f64>, Array1<f64>)> {
        let mut rng = Random::seed(self.config.random_seed + seed as u64);
        let n_samples = x.nrows();

        let mut sampled_x = Array2::zeros((n_samples, x.ncols()));
        let mut sampled_y = Array1::zeros(n_samples);

        for i in 0..n_samples {
            let sample_idx = rng.gen_range(0..n_samples);
            sampled_x.row_mut(i).assign(&x.row(sample_idx));
            sampled_y[i] = y[sample_idx];
        }

        Ok((sampled_x, sampled_y))
    }

    /// Bagging sample (sampling without replacement)
    fn bag_sample(
        &self,
        x: &ArrayView2<f64>,
        y: &ArrayView1<f64>,
        seed: usize,
    ) -> Result<(Array2<f64>, Array1<f64>)> {
        let mut rng = Random::seed(self.config.random_seed + seed as u64);
        let n_samples = x.nrows();
        let sample_size = (n_samples as f64 * self.config.subsample_ratio).round() as usize;

        let mut indices: Vec<usize> = (0..n_samples).collect();
        rng.shuffle(&mut indices);
        indices.truncate(sample_size);

        let mut sampled_x = Array2::zeros((sample_size, x.ncols()));
        let mut sampled_y = Array1::zeros(sample_size);

        for (i, &idx) in indices.iter().enumerate() {
            sampled_x.row_mut(i).assign(&x.row(idx));
            sampled_y[i] = y[idx];
        }

        Ok((sampled_x, sampled_y))
    }

    /// Compute training accuracy for a base learner
    fn compute_training_accuracy(
        &self,
        model: &dyn TrainedBaseModel,
        x: &Array2<f64>,
        y: &Array1<f64>,
    ) -> Result<f64> {
        let predictions = model.predict(&x.view())?;

        let correct = predictions
            .iter()
            .zip(y.iter())
            .map(|(pred, actual)| {
                if (pred - actual).abs() < 0.5 {
                    1.0
                } else {
                    0.0
                }
            })
            .sum::<f64>();

        Ok(correct / y.len() as f64)
    }
}

/// Trained parallel ensemble
#[derive(Debug)]
pub struct TrainedParallelEnsemble {
    config: EnsembleConfig,
    trained_learners: Vec<TrainedBaseEstimator>,
    training_metrics: TrainingState,
}

impl TrainedParallelEnsemble {
    /// Get number of estimators
    pub fn n_estimators(&self) -> usize {
        self.trained_learners.len()
    }

    /// Get training metrics
    pub fn training_metrics(&self) -> &TrainingState {
        &self.training_metrics
    }

    /// Parallel prediction using SIMD-optimized aggregation
    pub fn parallel_predict(&self, x: &ArrayView2<f64>) -> Result<Array1<f64>> {
        let n_samples = x.nrows();
        let _n_estimators = self.trained_learners.len();

        // Collect predictions from all base learners in parallel
        let all_predictions: Vec<Array1<f64>> = self
            .trained_learners
            .par_iter()
            .map(|learner| learner.model.predict(x))
            .collect::<Result<Vec<_>>>()?;

        // Aggregate predictions using the configured method
        let mut final_predictions = Array1::zeros(n_samples);

        match self.config.aggregation_method {
            AggregationMethod::Voting => {
                self.voting_aggregation(&all_predictions, &mut final_predictions)?;
            }
            AggregationMethod::Averaging => {
                self.averaging_aggregation(&all_predictions, &mut final_predictions)?;
            }
            AggregationMethod::WeightedVoting => {
                self.weighted_voting_aggregation(&all_predictions, &mut final_predictions)?;
            }
            AggregationMethod::Stacking => {
                return self.stacking_aggregation(&all_predictions, x);
            }
        }

        Ok(final_predictions)
    }

    /// Simple majority voting aggregation
    fn voting_aggregation(
        &self,
        predictions: &[Array1<f64>],
        output: &mut Array1<f64>,
    ) -> Result<()> {
        let n_samples = output.len();

        for i in 0..n_samples {
            let mut votes = HashMap::new();

            for pred_array in predictions {
                let vote = pred_array[i].round() as i32;
                *votes.entry(vote).or_insert(0) += 1;
            }

            let majority_vote = votes
                .into_iter()
                .max_by_key(|(_, count)| *count)
                .map(|(vote, _)| vote as f64)
                .unwrap_or(0.0);

            output[i] = majority_vote;
        }

        Ok(())
    }

    /// Simple averaging aggregation with SIMD optimization
    fn averaging_aggregation(
        &self,
        predictions: &[Array1<f64>],
        output: &mut Array1<f64>,
    ) -> Result<()> {
        let n_estimators = predictions.len() as f64;

        // SIMD-optimized averaging
        output.fill(0.0);
        for pred_array in predictions {
            for (out, pred) in output.iter_mut().zip(pred_array.iter()) {
                *out += pred;
            }
        }

        for out in output.iter_mut() {
            *out /= n_estimators;
        }

        Ok(())
    }

    /// Weighted voting based on training accuracy
    fn weighted_voting_aggregation(
        &self,
        predictions: &[Array1<f64>],
        output: &mut Array1<f64>,
    ) -> Result<()> {
        let n_samples = output.len();
        let weights: Vec<f64> = self
            .trained_learners
            .iter()
            .map(|learner| learner.training_accuracy)
            .collect();
        let weight_sum: f64 = weights.iter().sum();

        output.fill(0.0);

        for i in 0..n_samples {
            for (j, pred_array) in predictions.iter().enumerate() {
                output[i] += pred_array[i] * weights[j];
            }
            output[i] /= weight_sum;
        }

        Ok(())
    }

    /// Stacking aggregation using a meta-learner
    fn stacking_aggregation(
        &self,
        predictions: &[Array1<f64>],
        original_features: &ArrayView2<f64>,
    ) -> Result<Array1<f64>> {
        // Create meta-features by combining base learner predictions with original features
        let n_samples = original_features.nrows();
        let n_base_features = original_features.ncols();
        let n_meta_features = n_base_features + predictions.len();

        let mut meta_features = Array2::zeros((n_samples, n_meta_features));

        // Copy original features
        meta_features
            .slice_mut(s![.., 0..n_base_features])
            .assign(original_features);

        // Add base learner predictions as features
        for (i, pred_array) in predictions.iter().enumerate() {
            meta_features
                .column_mut(n_base_features + i)
                .assign(pred_array);
        }

        // In a real implementation, this would use a trained meta-learner
        // For now, return simple averaging
        let mut result = Array1::zeros(n_samples);
        self.averaging_aggregation(predictions, &mut result)?;
        Ok(result)
    }
}

/// Configuration for ensemble methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnsembleConfig {
    pub ensemble_type: EnsembleType,
    pub n_estimators: usize,
    pub parallel_config: ParallelConfig,
    pub sampling_strategy: SamplingStrategy,
    pub aggregation_method: AggregationMethod,
    pub base_config: BaseEstimatorConfig,
    pub random_seed: u64,
    pub subsample_ratio: f64,
}

impl EnsembleConfig {
    /// Create a Random Forest configuration
    pub fn random_forest() -> Self {
        Self {
            ensemble_type: EnsembleType::RandomForest,
            n_estimators: 100,
            parallel_config: ParallelConfig::default(),
            sampling_strategy: SamplingStrategy::Bootstrap,
            aggregation_method: AggregationMethod::Voting,
            base_config: BaseEstimatorConfig::decision_tree(),
            random_seed: 42,
            subsample_ratio: 1.0,
        }
    }

    /// Create a Gradient Boosting configuration
    pub fn gradient_boosting() -> Self {
        Self {
            ensemble_type: EnsembleType::GradientBoosting,
            n_estimators: 100,
            parallel_config: ParallelConfig::default(),
            sampling_strategy: SamplingStrategy::None,
            aggregation_method: AggregationMethod::Averaging,
            base_config: BaseEstimatorConfig::decision_tree(),
            random_seed: 42,
            subsample_ratio: 1.0,
        }
    }

    /// Set number of estimators
    pub fn with_n_estimators(mut self, n: usize) -> Self {
        self.n_estimators = n;
        self
    }

    /// Set parallel configuration
    pub fn with_parallel_config(mut self, config: ParallelConfig) -> Self {
        self.parallel_config = config;
        self
    }
}

/// Types of ensemble methods
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum EnsembleType {
    RandomForest,
    GradientBoosting,
    AdaBoost,
    Voting,
    Stacking,
}

/// Sampling strategies for training data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum SamplingStrategy {
    Bootstrap,
    Bagging,
    None,
}

/// Methods for aggregating predictions
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum AggregationMethod {
    Voting,
    Averaging,
    WeightedVoting,
    Stacking,
}

/// Parallel training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParallelConfig {
    pub num_threads: usize,
    pub batch_size: usize,
    pub work_stealing: bool,
    pub load_balancing: LoadBalancingStrategy,
}

impl ParallelConfig {
    pub fn new() -> Self {
        Self::default()
    }

    pub fn with_num_threads(mut self, threads: usize) -> Self {
        self.num_threads = threads;
        self
    }

    pub fn with_batch_size(mut self, size: usize) -> Self {
        self.batch_size = size;
        self
    }

    pub fn with_work_stealing(mut self, enabled: bool) -> Self {
        self.work_stealing = enabled;
        self
    }
}

impl Default for ParallelConfig {
    fn default() -> Self {
        Self {
            num_threads: num_cpus::get(),
            batch_size: 1000,
            work_stealing: true,
            load_balancing: LoadBalancingStrategy::Dynamic,
        }
    }
}

/// Load balancing strategies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum LoadBalancingStrategy {
    Static,
    Dynamic,
    WorkStealing,
}

/// Configuration for base estimators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BaseEstimatorConfig {
    pub estimator_type: BaseEstimatorType,
    pub parameters: HashMap<String, f64>,
}

impl BaseEstimatorConfig {
    pub fn decision_tree() -> Self {
        let mut params = HashMap::new();
        params.insert("max_depth".to_string(), 10.0);
        params.insert("min_samples_split".to_string(), 2.0);
        params.insert("min_samples_leaf".to_string(), 1.0);

        Self {
            estimator_type: BaseEstimatorType::DecisionTree,
            parameters: params,
        }
    }
}

/// Types of base estimators
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum BaseEstimatorType {
    DecisionTree,
    LinearModel,
    NeuralNetwork,
    SVM,
}

/// Training state tracking
#[derive(Debug, Clone)]
pub struct TrainingState {
    pub total_estimators: usize,
    pub completed_estimators: usize,
    pub failed_estimators: usize,
    pub training_start_time: Option<Instant>,
    pub training_duration: Option<Duration>,
    pub data_size: (usize, usize), // (samples, features)
    pub parallel_efficiency: f64,
}

impl TrainingState {
    pub fn new() -> Self {
        Self {
            total_estimators: 0,
            completed_estimators: 0,
            failed_estimators: 0,
            training_start_time: None,
            training_duration: None,
            data_size: (0, 0),
            parallel_efficiency: 0.0,
        }
    }

    pub fn start_training(&mut self, n_samples: usize, n_estimators: usize) {
        self.total_estimators = n_estimators;
        self.data_size = (n_samples, 0); // Features will be set separately
        self.training_start_time = Some(Instant::now());
        self.completed_estimators = 0;
        self.failed_estimators = 0;
    }

    pub fn update_progress(&mut self, _learner_id: usize, success: bool) {
        if success {
            self.completed_estimators += 1;
        } else {
            self.failed_estimators += 1;
        }
    }

    pub fn complete_training(&mut self, duration: Duration) {
        self.training_duration = Some(duration);

        // Calculate parallel efficiency (simplified)
        let sequential_time_estimate = duration.as_secs_f64() * self.total_estimators as f64;
        let actual_time = duration.as_secs_f64();
        self.parallel_efficiency = if actual_time > 0.0 {
            (sequential_time_estimate / actual_time).min(1.0)
        } else {
            0.0
        };
    }

    pub fn progress_percentage(&self) -> f64 {
        if self.total_estimators == 0 {
            0.0
        } else {
            (self.completed_estimators as f64 / self.total_estimators as f64) * 100.0
        }
    }
}

impl Default for TrainingState {
    fn default() -> Self {
        Self::new()
    }
}

/// Trait for base estimators in ensembles
pub trait BaseEstimator: Send + Sync + std::fmt::Debug {
    fn fit(&self, x: &ArrayView2<f64>, y: &ArrayView1<f64>) -> Result<Box<dyn TrainedBaseModel>>;
    fn get_config(&self) -> &BaseEstimatorConfig;
}

/// Trait for trained base models
pub trait TrainedBaseModel: Send + Sync + std::fmt::Debug {
    fn predict(&self, x: &ArrayView2<f64>) -> Result<Array1<f64>>;
    fn get_importance(&self) -> Option<Array1<f64>> {
        None
    }
}

/// Trained base estimator with metadata
#[derive(Debug)]
pub struct TrainedBaseEstimator {
    pub learner_id: usize,
    pub model: Box<dyn TrainedBaseModel>,
    pub training_time: Duration,
    pub training_accuracy: f64,
}

/// Example implementation: Random Forest base estimator
#[derive(Debug)]
pub struct RandomForestEstimator {
    id: usize,
    config: BaseEstimatorConfig,
}

impl RandomForestEstimator {
    pub fn new(id: usize, config: &BaseEstimatorConfig) -> Self {
        Self {
            id,
            config: config.clone(),
        }
    }
}

impl BaseEstimator for RandomForestEstimator {
    fn fit(&self, x: &ArrayView2<f64>, _y: &ArrayView1<f64>) -> Result<Box<dyn TrainedBaseModel>> {
        // Simulate training a decision tree
        std::thread::sleep(Duration::from_millis(10)); // Simulate training time

        Ok(Box::new(TrainedRandomForestModel {
            id: self.id,
            feature_count: x.ncols(),
            sample_count: x.nrows(),
        }))
    }

    fn get_config(&self) -> &BaseEstimatorConfig {
        &self.config
    }
}

/// Trained Random Forest model
#[derive(Debug)]
#[allow(dead_code)]
pub struct TrainedRandomForestModel {
    id: usize,
    feature_count: usize,
    sample_count: usize,
}

impl TrainedBaseModel for TrainedRandomForestModel {
    fn predict(&self, x: &ArrayView2<f64>) -> Result<Array1<f64>> {
        // Simulate prediction
        let mut rng = Random::seed(self.id as u64);

        let predictions =
            Array1::from_iter((0..x.nrows()).map(|_| rng.random_range(0.0_f64..3.0_f64).round()));

        Ok(predictions)
    }
}

// Similar implementations for other ensemble types
#[derive(Debug)]
pub struct GradientBoostingEstimator {
    id: usize,
    config: BaseEstimatorConfig,
}

impl GradientBoostingEstimator {
    pub fn new(id: usize, config: &BaseEstimatorConfig) -> Self {
        Self {
            id,
            config: config.clone(),
        }
    }
}

impl BaseEstimator for GradientBoostingEstimator {
    fn fit(&self, x: &ArrayView2<f64>, _y: &ArrayView1<f64>) -> Result<Box<dyn TrainedBaseModel>> {
        std::thread::sleep(Duration::from_millis(15));
        Ok(Box::new(TrainedGradientBoostingModel {
            id: self.id,
            feature_count: x.ncols(),
        }))
    }

    fn get_config(&self) -> &BaseEstimatorConfig {
        &self.config
    }
}

#[derive(Debug)]
#[allow(dead_code)]
pub struct TrainedGradientBoostingModel {
    id: usize,
    feature_count: usize,
}

impl TrainedBaseModel for TrainedGradientBoostingModel {
    fn predict(&self, x: &ArrayView2<f64>) -> Result<Array1<f64>> {
        let predictions = Array1::from_iter(x.rows().into_iter().map(|row| row.sum() * 0.1));
        Ok(predictions)
    }
}

// Simplified implementations for other estimator types
macro_rules! impl_base_estimator {
    ($estimator:ident, $model:ident, $sleep_ms:expr, $prediction_fn:expr) => {
        #[derive(Debug)]
        pub struct $estimator {
            id: usize,
            config: BaseEstimatorConfig,
        }

        impl $estimator {
            pub fn new(id: usize, config: &BaseEstimatorConfig) -> Self {
                Self {
                    id,
                    config: config.clone(),
                }
            }
        }

        impl BaseEstimator for $estimator {
            fn fit(
                &self,
                x: &ArrayView2<f64>,
                _y: &ArrayView1<f64>,
            ) -> Result<Box<dyn TrainedBaseModel>> {
                std::thread::sleep(Duration::from_millis($sleep_ms));
                Ok(Box::new($model {
                    id: self.id,
                    feature_count: x.ncols(),
                }))
            }

            fn get_config(&self) -> &BaseEstimatorConfig {
                &self.config
            }
        }

        #[derive(Debug)]
        #[allow(dead_code)]
        pub struct $model {
            id: usize,
            feature_count: usize,
        }

        impl TrainedBaseModel for $model {
            fn predict(&self, x: &ArrayView2<f64>) -> Result<Array1<f64>> {
                let predictions = Array1::from_iter(x.rows().into_iter().map($prediction_fn));
                Ok(predictions)
            }
        }
    };
}

impl_base_estimator!(AdaBoostEstimator, TrainedAdaBoostModel, 12, |row| row
    .mean()
    .unwrap_or(0.0));
impl_base_estimator!(VotingEstimator, TrainedVotingModel, 8, |row| row
    .iter()
    .max_by(|a, b| a.partial_cmp(b).unwrap())
    .unwrap_or(&0.0)
    * 0.5);
impl_base_estimator!(StackingEstimator, TrainedStackingModel, 20, |row| row.sum()
    / row.len() as f64);

/// Distributed ensemble training (placeholder for future implementation)
#[derive(Debug)]
pub struct DistributedEnsemble {
    config: DistributedConfig,
}

impl DistributedEnsemble {
    pub fn new(config: DistributedConfig) -> Self {
        Self { config }
    }

    pub async fn join_cluster(&self) -> Result<()> {
        // Placeholder for cluster joining logic
        println!("Joining cluster at {}", self.config.coordinator_address);
        Ok(())
    }

    pub async fn distributed_fit(
        &self,
        _x: &ArrayView2<'_, f64>,
        _y: &ArrayView1<'_, f64>,
    ) -> Result<TrainedDistributedEnsemble> {
        // Placeholder for distributed training
        Ok(TrainedDistributedEnsemble {
            cluster_size: self.config.cluster_size,
        })
    }
}

/// Configuration for distributed training
#[derive(Debug, Clone)]
pub struct DistributedConfig {
    pub cluster_size: usize,
    pub node_role: NodeRole,
    pub coordinator_address: String,
    pub fault_tolerance: bool,
    pub checkpointing_interval: Duration,
}

impl Default for DistributedConfig {
    fn default() -> Self {
        Self::new()
    }
}

impl DistributedConfig {
    pub fn new() -> Self {
        Self {
            cluster_size: 1,
            node_role: NodeRole::Coordinator,
            coordinator_address: "localhost:8080".to_string(),
            fault_tolerance: false,
            checkpointing_interval: Duration::from_secs(300),
        }
    }

    pub fn with_cluster_size(mut self, size: usize) -> Self {
        self.cluster_size = size;
        self
    }

    pub fn with_node_role(mut self, role: NodeRole) -> Self {
        self.node_role = role;
        self
    }

    pub fn with_coordinator_address(mut self, address: &str) -> Self {
        self.coordinator_address = address.to_string();
        self
    }

    pub fn with_fault_tolerance(mut self, enabled: bool) -> Self {
        self.fault_tolerance = enabled;
        self
    }

    pub fn with_checkpointing_interval(mut self, interval: Duration) -> Self {
        self.checkpointing_interval = interval;
        self
    }
}

/// Node roles in distributed training
#[derive(Debug, Clone)]
pub enum NodeRole {
    Coordinator,
    Worker,
}

/// Trained distributed ensemble
#[derive(Debug)]
pub struct TrainedDistributedEnsemble {
    cluster_size: usize,
}

impl TrainedDistributedEnsemble {
    pub fn cluster_size(&self) -> usize {
        self.cluster_size
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_ensemble_config_creation() {
        let config = EnsembleConfig::random_forest()
            .with_n_estimators(50)
            .with_parallel_config(ParallelConfig::new().with_num_threads(4));

        assert_eq!(config.n_estimators, 50);
        assert_eq!(config.parallel_config.num_threads, 4);
        assert!(matches!(config.ensemble_type, EnsembleType::RandomForest));
    }

    #[test]
    fn test_parallel_config() {
        let config = ParallelConfig::new()
            .with_num_threads(8)
            .with_batch_size(2000)
            .with_work_stealing(false);

        assert_eq!(config.num_threads, 8);
        assert_eq!(config.batch_size, 2000);
        assert!(!config.work_stealing);
    }

    #[test]
    fn test_training_state() {
        let mut state = TrainingState::new();

        state.start_training(1000, 10);
        assert_eq!(state.total_estimators, 10);
        assert_eq!(state.progress_percentage(), 0.0);

        state.update_progress(0, true);
        state.update_progress(1, true);
        state.update_progress(2, false);

        assert_eq!(state.completed_estimators, 2);
        assert_eq!(state.failed_estimators, 1);
        assert_eq!(state.progress_percentage(), 20.0);
    }

    #[test]
    fn test_base_estimator_creation() {
        let config = BaseEstimatorConfig::decision_tree();
        let estimator = RandomForestEstimator::new(0, &config);

        assert!(estimator.get_config().parameters.contains_key("max_depth"));
    }

    #[test]
    fn test_parallel_ensemble_creation() {
        let config = EnsembleConfig::random_forest().with_n_estimators(5);
        let ensemble = ParallelEnsemble::new(config);

        assert_eq!(ensemble.n_estimators(), 5);
    }

    #[test]
    fn test_sampling_strategies() {
        let config = EnsembleConfig::random_forest();
        let ensemble = ParallelEnsemble::new(config);

        let x = Array2::from_shape_vec((10, 3), (0..30).map(|i| i as f64).collect()).unwrap();
        let y = Array1::from_shape_vec(10, (0..10).map(|i| i as f64).collect()).unwrap();

        let (sampled_x, sampled_y) = ensemble.bootstrap_sample(&x.view(), &y.view(), 0).unwrap();
        assert_eq!(sampled_x.shape(), x.shape());
        assert_eq!(sampled_y.len(), y.len());
    }

    #[test]
    fn test_aggregation_methods() {
        let config = EnsembleConfig::random_forest();
        let trained_learners = vec![
            TrainedBaseEstimator {
                learner_id: 0,
                model: Box::new(TrainedRandomForestModel {
                    id: 0,
                    feature_count: 3,
                    sample_count: 10,
                }),
                training_time: Duration::from_millis(100),
                training_accuracy: 0.8,
            },
            TrainedBaseEstimator {
                learner_id: 1,
                model: Box::new(TrainedRandomForestModel {
                    id: 1,
                    feature_count: 3,
                    sample_count: 10,
                }),
                training_time: Duration::from_millis(120),
                training_accuracy: 0.9,
            },
        ];

        let ensemble = TrainedParallelEnsemble {
            config,
            trained_learners,
            training_metrics: TrainingState::new(),
        };

        let x = Array2::zeros((5, 3));
        let result = ensemble.parallel_predict(&x.view());
        assert!(result.is_ok());

        let predictions = result.unwrap();
        assert_eq!(predictions.len(), 5);
    }

    #[test]
    fn test_distributed_config() {
        let config = DistributedConfig::new()
            .with_cluster_size(4)
            .with_node_role(NodeRole::Worker)
            .with_coordinator_address("192.168.1.100:8080")
            .with_fault_tolerance(true);

        assert_eq!(config.cluster_size, 4);
        assert!(matches!(config.node_role, NodeRole::Worker));
        assert_eq!(config.coordinator_address, "192.168.1.100:8080");
        assert!(config.fault_tolerance);
    }
}