sklears_feature_selection/

pipeline.rs

//! Pipeline Integration Framework
//!
//! This module provides seamless integration between feature selection and other
//! components like preprocessing, feature engineering, dimensionality reduction,
//! and model selection. It follows the SciRS2 policy using scirs2-core for
//! numerical computations and implements Rust-specific optimizations.

use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use sklears_core::error::{Result as SklResult, SklearsError};
use sklears_core::traits::{Estimator, Fit, Transform};
use std::collections::HashMap;
use std::marker::PhantomData;
use std::time::{Duration, Instant};

type Result<T> = SklResult<T>;

/// Comprehensive pipeline integration framework for feature selection
#[derive(Debug, Clone)]
pub struct FeatureSelectionPipeline<State = Untrained> {
    preprocessing_steps: Vec<PreprocessingStep>,
    feature_engineering_steps: Vec<FeatureEngineeringStep>,
    selection_methods: Vec<SelectionMethod>,
    dimensionality_reduction: Option<DimensionalityReductionStep>,
    model_selection: Option<ModelSelectionStep>,
    pipeline_config: PipelineConfiguration,
    optimization_config: OptimizationConfiguration,
    _phantom: PhantomData<State>,
}

/// Type-safe state markers for compile-time pipeline validation
#[derive(Debug, Clone, Default)]
pub struct Untrained;

#[derive(Debug)]
pub struct Trained {
    trained_steps: Vec<TrainedStep>,
    feature_mapping: FeatureMapping,
    pipeline_metadata: PipelineMetadata,
}

/// Individual preprocessing step in the pipeline
#[derive(Debug, Clone)]
pub enum PreprocessingStep {
    /// StandardScaler
    StandardScaler {
        config: StandardScalerConfig,

        trained_params: Option<ScalerParams>,
    },
    /// RobustScaler
    RobustScaler {
        config: RobustScalerConfig,

        trained_params: Option<RobustScalerParams>,
    },
    /// MinMaxScaler
    MinMaxScaler {
        config: MinMaxScalerConfig,
        trained_params: Option<MinMaxScalerParams>,
    },
    QuantileTransformer {
        config: QuantileTransformerConfig,
        trained_params: Option<QuantileParams>,
    },
    PowerTransformer {
        config: PowerTransformerConfig,
        trained_params: Option<PowerParams>,
    },
    MissingValueImputer {
        config: ImputerConfig,
        trained_params: Option<ImputerParams>,
    },
    OutlierRemover {
        config: OutlierConfig,
        trained_params: Option<OutlierParams>,
    },
}

/// Feature engineering steps for creating new features
#[derive(Debug, Clone)]
pub enum FeatureEngineeringStep {
    /// PolynomialFeatures
    PolynomialFeatures {
        degree: usize,

        interaction_only: bool,

        include_bias: bool,

        feature_mapping: Option<Vec<(usize, usize)>>,
    },
    /// InteractionFeatures
    InteractionFeatures {
        max_pairs: Option<usize>,

        threshold: f64,

        feature_pairs: Option<Vec<(usize, usize)>>,
    },
    BinningFeatures {
        n_bins: usize,
        strategy: BinningStrategy,
        bin_edges: Option<HashMap<usize, Vec<f64>>>,
    },
    TargetEncoding {
        smoothing: f64,
        min_samples_leaf: usize,
        encodings: Option<HashMap<usize, HashMap<String, f64>>>,
    },
    FrequencyEncoding {
        min_frequency: f64,
        frequencies: Option<HashMap<usize, HashMap<String, f64>>>,
    },
    RatioFeatures {
        numerator_features: Vec<usize>,
        denominator_features: Vec<usize>,
        eps: f64,
    },
    LaggingFeatures {
        lags: Vec<usize>,
        feature_subset: Option<Vec<usize>>,
    },
    WindowStatistics {
        window_size: usize,
        statistics: Vec<WindowStatistic>,
        feature_subset: Option<Vec<usize>>,
    },
}

/// Selection method configuration with type safety
#[derive(Debug, Clone)]
pub enum SelectionMethod {
    /// UnivariateFilter
    UnivariateFilter {
        method: UnivariateMethod,

        k: SelectionCount,

        score_func: UnivariateScoreFunction,
    },
    /// RecursiveFeatureElimination
    RecursiveFeatureElimination {
        estimator: RFEEstimator,

        n_features: SelectionCount,

        step: f64,

        importance_getter: ImportanceGetter,
    },
    SelectFromModel {
        estimator: ModelEstimator,
        threshold: SelectionThreshold,
        prefit: bool,
        max_features: Option<usize>,
    },
    VarianceThreshold {
        threshold: f64,
        feature_variance: Option<Array1<f64>>,
    },
    CorrelationFilter {
        threshold: f64,
        method: CorrelationMethod,
        correlation_matrix: Option<Array2<f64>>,
    },
    MutualInformation {
        k: SelectionCount,
        discrete_features: Vec<bool>,
        random_state: Option<u64>,
    },
    LASSO {
        alpha: f64,
        max_iter: usize,
        tol: f64,
        coefficients: Option<Array1<f64>>,
    },
    ElasticNet {
        alpha: f64,
        l1_ratio: f64,
        max_iter: usize,
        tol: f64,
        coefficients: Option<Array1<f64>>,
    },
    TreeBased {
        estimator_type: TreeEstimatorType,
        n_estimators: usize,
        max_depth: Option<usize>,
        feature_importances: Option<Array1<f64>>,
    },
    GeneticAlgorithm {
        population_size: usize,
        n_generations: usize,
        mutation_rate: f64,
        crossover_rate: f64,
        best_individuals: Option<Vec<Vec<bool>>>,
    },
    ParticleSwarmOptimization {
        n_particles: usize,
        n_iterations: usize,
        inertia: f64,
        cognitive: f64,
        social: f64,
        best_positions: Option<Vec<Vec<f64>>>,
    },
    SimulatedAnnealing {
        initial_temp: f64,
        cooling_rate: f64,
        min_temp: f64,
        max_iter: usize,
        current_solution: Option<Vec<bool>>,
    },
}

/// Dimensionality reduction step (applied after feature selection)
#[derive(Debug, Clone)]
pub enum DimensionalityReductionStep {
    /// PCA
    PCA {
        n_components: usize,

        whiten: bool,

        svd_solver: SVDSolver,

        components: Option<Array2<f64>>,

        explained_variance: Option<Array1<f64>>,
    },
    /// TruncatedSVD
    TruncatedSVD {
        n_components: usize,

        algorithm: SVDAlgorithm,
        components: Option<Array2<f64>>,
        singular_values: Option<Array1<f64>>,
    },
    ICA {
        n_components: usize,
        algorithm: ICAAlgorithm,
        max_iter: usize,
        tol: f64,
        mixing_matrix: Option<Array2<f64>>,
        unmixing_matrix: Option<Array2<f64>>,
    },
    FactorAnalysis {
        n_components: usize,
        max_iter: usize,
        tol: f64,
        loadings: Option<Array2<f64>>,
        noise_variance: Option<Array1<f64>>,
    },
    UMAP {
        n_components: usize,
        n_neighbors: usize,
        min_dist: f64,
        metric: DistanceMetric,
        embedding: Option<Array2<f64>>,
    },
    TSNE {
        n_components: usize,
        perplexity: f64,
        early_exaggeration: f64,
        learning_rate: f64,
        max_iter: usize,
        embedding: Option<Array2<f64>>,
    },
}

/// Model selection step for choosing optimal features for specific models
#[derive(Debug, Clone)]
pub enum ModelSelectionStep {
    /// CrossValidationSelection
    CrossValidationSelection {
        estimator: ModelEstimator,

        cv_folds: usize,

        scoring: ScoringMetric,

        feature_scores: Option<Array1<f64>>,
    },
    /// ForwardSelection
    ForwardSelection {
        estimator: ModelEstimator,

        max_features: usize,

        scoring: ScoringMetric,
        selected_features: Option<Vec<usize>>,
    },
    BackwardElimination {
        estimator: ModelEstimator,
        min_features: usize,
        scoring: ScoringMetric,
        remaining_features: Option<Vec<usize>>,
    },
    StepwiseSelection {
        estimator: ModelEstimator,
        direction: StepwiseDirection,
        p_enter: f64,
        p_remove: f64,
        selected_features: Option<Vec<usize>>,
    },
    BayesianOptimization {
        estimator: ModelEstimator,
        acquisition_function: AcquisitionFunction,
        n_calls: usize,
        optimal_features: Option<Vec<usize>>,
    },
}

/// Type-safe selection count specification
#[derive(Debug, Clone)]
pub enum SelectionCount {
    /// K
    K(usize),
    /// Percentile
    Percentile(f64),
    /// FDR
    FDR(f64),
    /// FPR
    FPR(f64),
    /// FWER
    FWER(f64),
}

/// Type-safe selection threshold specification
#[derive(Debug, Clone)]
pub enum SelectionThreshold {
    /// Mean
    Mean,
    /// Median
    Median,
    /// Absolute
    Absolute(f64),
    /// Percentile
    Percentile(f64),
    /// Auto
    Auto,
}

/// Configuration for pipeline behavior
#[derive(Debug, Clone)]
pub struct PipelineConfiguration {
    pub parallel_execution: bool,
    pub memory_optimization: MemoryOptimization,
    pub caching_strategy: CachingStrategy,
    pub validation_strategy: ValidationStrategy,
    pub error_handling: ErrorHandling,
    pub logging_level: LoggingLevel,
}

/// Optimization configuration for performance tuning
#[derive(Debug, Clone)]
pub struct OptimizationConfiguration {
    pub use_simd: bool,
    pub chunk_size: usize,
    pub thread_pool_size: Option<usize>,
    pub memory_pool_size: usize,
    pub cache_size: usize,
    pub prefetch_strategy: PrefetchStrategy,
    pub vectorization_threshold: usize,
}

/// Supporting enums and structs for configuration
#[derive(Debug, Clone)]
pub enum MemoryOptimization {
    None,
    /// Conservative
    Conservative,
    /// Aggressive
    Aggressive,
}

#[derive(Debug, Clone)]
pub enum CachingStrategy {
    None,
    /// LRU
    LRU {
        size: usize,
    },
    /// LFU
    LFU {
        size: usize,
    },
    /// FIFO
    FIFO {
        size: usize,
    },
}

#[derive(Debug, Clone)]
pub enum ValidationStrategy {
    None,
    /// Basic
    Basic,
    /// Comprehensive
    Comprehensive,
    /// Statistical
    Statistical,
}

#[derive(Debug, Clone)]
pub enum ErrorHandling {
    /// Strict
    Strict,
    /// Graceful
    Graceful,
    /// Logging
    Logging,
}

#[derive(Debug, Clone)]
pub enum LoggingLevel {
    None,
    /// Error
    Error,
    /// Warning
    Warning,
    /// Info
    Info,
    /// Debug
    Debug,
    /// Trace
    Trace,
}

#[derive(Debug, Clone)]
pub enum PrefetchStrategy {
    None,
    /// Sequential
    Sequential,
    /// Random
    Random,
    /// Adaptive
    Adaptive,
}

// Configuration structs for each preprocessing step
#[derive(Debug, Clone)]
pub struct StandardScalerConfig {
    pub with_mean: bool,
    pub with_std: bool,
}

#[derive(Debug, Clone)]
pub struct ScalerParams {
    pub mean: Array1<f64>,
    pub scale: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct RobustScalerConfig {
    pub with_centering: bool,
    pub with_scaling: bool,
    pub quantile_range: (f64, f64),
}

#[derive(Debug, Clone)]
pub struct RobustScalerParams {
    pub center: Array1<f64>,
    pub scale: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct MinMaxScalerConfig {
    pub feature_range: (f64, f64),
    pub clip: bool,
}

#[derive(Debug, Clone)]
pub struct MinMaxScalerParams {
    pub min: Array1<f64>,
    pub scale: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct QuantileTransformerConfig {
    pub n_quantiles: usize,
    pub output_distribution: Distribution,
    pub subsample: Option<usize>,
}

#[derive(Debug, Clone)]
pub struct QuantileParams {
    pub quantiles: Array2<f64>,
    pub references: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct PowerTransformerConfig {
    pub method: PowerMethod,
    pub standardize: bool,
}

#[derive(Debug, Clone)]
pub struct PowerParams {
    pub lambdas: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct ImputerConfig {
    pub strategy: ImputationStrategy,
    pub fill_value: Option<f64>,
    pub missing_values: MissingValueIndicator,
}

#[derive(Debug, Clone)]
pub struct ImputerParams {
    pub statistics: Array1<f64>,
}

#[derive(Debug, Clone)]
pub struct OutlierConfig {
    pub method: OutlierMethod,
    pub threshold: f64,
    pub contamination: f64,
}

#[derive(Debug, Clone)]
pub struct OutlierParams {
    pub decision_function: Array1<f64>,
    pub threshold: f64,
}

// Supporting enums
#[derive(Debug, Clone)]
pub enum BinningStrategy {
    /// Uniform
    Uniform,
    /// Quantile
    Quantile,
    /// KMeans
    KMeans,
}

#[derive(Debug, Clone)]
pub enum WindowStatistic {
    /// Mean
    Mean,
    /// Std
    Std,
    /// Min
    Min,
    /// Max
    Max,
    /// Median
    Median,
    /// Skewness
    Skewness,
    /// Kurtosis
    Kurtosis,
}

#[derive(Debug, Clone)]
pub enum UnivariateMethod {
    /// Chi2
    Chi2,
    /// ANOVA
    ANOVA,
    /// MutualInfo
    MutualInfo,
    /// Correlation
    Correlation,
}

#[derive(Debug, Clone)]
pub enum UnivariateScoreFunction {
    /// Chi2
    Chi2,
    /// FClassif
    FClassif,
    /// FRegression
    FRegression,
    /// MutualInfoClassif
    MutualInfoClassif,
    /// MutualInfoRegression
    MutualInfoRegression,
}

#[derive(Debug, Clone)]
pub enum RFEEstimator {
    /// SVM
    SVM,
    /// RandomForest
    RandomForest,
    /// LinearRegression
    LinearRegression,
    /// LogisticRegression
    LogisticRegression,
}

#[derive(Debug, Clone)]
pub enum ImportanceGetter {
    /// Auto
    Auto,
    /// Coefficients
    Coefficients,
    /// FeatureImportances
    FeatureImportances,
}

#[derive(Debug, Clone)]
pub enum ModelEstimator {
    /// LinearRegression
    LinearRegression,
    /// LogisticRegression
    LogisticRegression,
    /// RandomForest
    RandomForest,
    /// SVM
    SVM,
    /// XGBoost
    XGBoost,
    /// LightGBM
    LightGBM,
}

#[derive(Debug, Clone)]
pub enum CorrelationMethod {
    /// Pearson
    Pearson,
    /// Spearman
    Spearman,
    /// Kendall
    Kendall,
}

#[derive(Debug, Clone)]
pub enum TreeEstimatorType {
    /// RandomForest
    RandomForest,
    /// ExtraTrees
    ExtraTrees,
    /// GradientBoosting
    GradientBoosting,
    /// AdaBoost
    AdaBoost,
}

#[derive(Debug, Clone)]
pub enum SVDSolver {
    /// Auto
    Auto,
    /// Full
    Full,
    /// Arpack
    Arpack,
    /// Randomized
    Randomized,
}

#[derive(Debug, Clone)]
pub enum SVDAlgorithm {
    /// Randomized
    Randomized,
    /// Arpack
    Arpack,
}

#[derive(Debug, Clone)]
pub enum ICAAlgorithm {
    /// Parallel
    Parallel,
    /// Deflation
    Deflation,
}

#[derive(Debug, Clone)]
pub enum DistanceMetric {
    /// Euclidean
    Euclidean,
    /// Manhattan
    Manhattan,
    /// Cosine
    Cosine,
    /// Hamming
    Hamming,
}

#[derive(Debug, Clone)]
pub enum ScoringMetric {
    /// Accuracy
    Accuracy,
    /// F1
    F1,
    /// RocAuc
    RocAuc,
    /// R2
    R2,
    /// MAE
    MAE,
    /// MSE
    MSE,
    /// LogLoss
    LogLoss,
}

#[derive(Debug, Clone)]
pub enum StepwiseDirection {
    /// Forward
    Forward,
    /// Backward
    Backward,
    /// Both
    Both,
}

#[derive(Debug, Clone)]
pub enum AcquisitionFunction {
    /// ExpectedImprovement
    ExpectedImprovement,
    /// UpperConfidenceBound
    UpperConfidenceBound,
    /// ProbabilityOfImprovement
    ProbabilityOfImprovement,
}

#[derive(Debug, Clone)]
pub enum Distribution {
    /// Uniform
    Uniform,
    /// Normal
    Normal,
}

#[derive(Debug, Clone)]
pub enum PowerMethod {
    /// YeoJohnson
    YeoJohnson,
    /// BoxCox
    BoxCox,
}

#[derive(Debug, Clone)]
pub enum ImputationStrategy {
    /// Mean
    Mean,
    /// Median
    Median,
    /// Mode
    Mode,
    /// Constant
    Constant,
    /// KNN
    KNN,
    /// Iterative
    Iterative,
}

#[derive(Debug, Clone)]
pub enum MissingValueIndicator {
    /// NaN
    NaN,
    /// Value
    Value(f64),
}

#[derive(Debug, Clone)]
pub enum OutlierMethod {
    /// IsolationForest
    IsolationForest,
    /// LocalOutlierFactor
    LocalOutlierFactor,
    /// OneClassSVM
    OneClassSVM,
    /// EllipticEnvelope
    EllipticEnvelope,
}

/// Trained step information for pipeline state tracking
#[derive(Debug)]
pub struct TrainedStep {
    pub step_type: String,
    pub step_index: usize,
    pub training_time: Duration,
    pub feature_count_before: usize,
    pub feature_count_after: usize,
    pub parameters: StepParameters,
}

#[derive(Debug)]
pub enum StepParameters {
    /// Preprocessing
    Preprocessing(Box<dyn std::any::Any + Send + Sync>),
    /// FeatureEngineering
    FeatureEngineering(Box<dyn std::any::Any + Send + Sync>),
    /// Selection
    Selection(Array1<bool>),
    /// DimensionalityReduction
    DimensionalityReduction(Array2<f64>),
    /// ModelSelection
    ModelSelection(Vec<usize>),
}

/// Feature mapping for tracking feature transformations
#[derive(Debug, Clone)]
pub struct FeatureMapping {
    pub original_features: usize,
    pub final_features: usize,
    pub feature_names: Vec<String>,
    pub feature_origins: Vec<FeatureOrigin>,
    pub transformation_history: Vec<TransformationStep>,
}

#[derive(Debug, Clone)]
pub enum FeatureOrigin {
    /// Original
    Original(usize),
    /// Engineered
    Engineered {
        source_features: Vec<usize>,

        operation: String,
    },
    /// Transformed
    Transformed {
        source_feature: usize,

        transformation: String,
    },
}

#[derive(Debug, Clone)]
pub struct TransformationStep {
    pub step_name: String,
    pub input_features: usize,
    pub output_features: usize,
    pub transformation_type: TransformationType,
}

#[derive(Debug, Clone)]
pub enum TransformationType {
    /// OneToOne
    OneToOne,
    /// OneToMany
    OneToMany,
    /// ManyToOne
    ManyToOne,
    /// ManyToMany
    ManyToMany,
}

/// Pipeline metadata for tracking execution and performance
#[derive(Debug, Clone)]
pub struct PipelineMetadata {
    pub total_training_time: Duration,
    pub total_transform_time: Duration,
    pub memory_usage_peak: usize,
    pub feature_reduction_ratio: f64,
    pub performance_metrics: HashMap<String, f64>,
    pub validation_results: Option<ValidationResults>,
}

#[derive(Debug, Clone)]
pub struct ValidationResults {
    pub cross_validation_scores: Vec<f64>,
    pub stability_scores: Vec<f64>,
    pub robustness_scores: Vec<f64>,
    pub statistical_significance: bool,
}

impl Default for FeatureSelectionPipeline<Untrained> {
    fn default() -> Self {
        Self::new()
    }
}

impl FeatureSelectionPipeline<Untrained> {
    /// Create a new untrained pipeline with default configuration
    pub fn new() -> Self {
        Self {
            preprocessing_steps: Vec::new(),
            feature_engineering_steps: Vec::new(),
            selection_methods: Vec::new(),
            dimensionality_reduction: None,
            model_selection: None,
            pipeline_config: PipelineConfiguration::default(),
            optimization_config: OptimizationConfiguration::default(),
            _phantom: PhantomData,
        }
    }

    /// Builder pattern for adding preprocessing steps
    pub fn add_preprocessing_step(mut self, step: PreprocessingStep) -> Self {
        self.preprocessing_steps.push(step);
        self
    }

    /// Builder pattern for adding feature engineering steps
    pub fn add_feature_engineering_step(mut self, step: FeatureEngineeringStep) -> Self {
        self.feature_engineering_steps.push(step);
        self
    }

    /// Builder pattern for adding selection methods
    pub fn add_selection_method(mut self, method: SelectionMethod) -> Self {
        self.selection_methods.push(method);
        self
    }

    /// Builder pattern for setting dimensionality reduction
    pub fn with_dimensionality_reduction(mut self, reduction: DimensionalityReductionStep) -> Self {
        self.dimensionality_reduction = Some(reduction);
        self
    }

    /// Builder pattern for setting model selection
    pub fn with_model_selection(mut self, model_selection: ModelSelectionStep) -> Self {
        self.model_selection = Some(model_selection);
        self
    }

    /// Configure pipeline behavior
    pub fn with_config(mut self, config: PipelineConfiguration) -> Self {
        self.pipeline_config = config;
        self
    }

    /// Configure optimization settings
    pub fn with_optimization(mut self, config: OptimizationConfiguration) -> Self {
        self.optimization_config = config;
        self
    }

    /// Train the entire pipeline on the provided data
    pub fn fit(
        mut self,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<FeatureSelectionPipeline<Trained>> {
        let start_time = Instant::now();
        let mut current_X = X.to_owned();
        let current_y = y.to_owned();
        let mut trained_steps = Vec::new();
        let original_features = X.ncols();

        // Step 1: Apply preprocessing steps
        let mut preprocessing_steps = std::mem::take(&mut self.preprocessing_steps);
        for (idx, step) in preprocessing_steps.iter_mut().enumerate() {
            let step_start = Instant::now();
            current_X = Self::apply_preprocessing_step_static(step, current_X.view())?;

            trained_steps.push(TrainedStep {
                step_type: "Preprocessing".to_string(),
                step_index: idx,
                training_time: step_start.elapsed(),
                feature_count_before: current_X.ncols(),
                feature_count_after: current_X.ncols(),
                parameters: StepParameters::Preprocessing(Box::new(())), // Simplified for now
            });
        }
        self.preprocessing_steps = preprocessing_steps;

        // Step 2: Apply feature engineering steps
        let mut feature_engineering_steps = std::mem::take(&mut self.feature_engineering_steps);
        for (idx, step) in feature_engineering_steps.iter_mut().enumerate() {
            let step_start = Instant::now();
            let features_before = current_X.ncols();
            current_X = Self::apply_feature_engineering_step_static(
                step,
                current_X.view(),
                current_y.view(),
            )?;

            trained_steps.push(TrainedStep {
                step_type: "FeatureEngineering".to_string(),
                step_index: idx,
                training_time: step_start.elapsed(),
                feature_count_before: features_before,
                feature_count_after: current_X.ncols(),
                parameters: StepParameters::FeatureEngineering(Box::new(())),
            });
        }
        self.feature_engineering_steps = feature_engineering_steps;

        // Step 3: Apply feature selection methods
        let mut selection_mask = Array1::from_elem(current_X.ncols(), true);
        let mut selection_methods = std::mem::take(&mut self.selection_methods);
        for (idx, method) in selection_methods.iter_mut().enumerate() {
            let step_start = Instant::now();
            let features_before = current_X.ncols();
            let method_mask =
                Self::apply_selection_method_static(method, current_X.view(), current_y.view())?;

            // Combine with existing selection mask
            for (i, &selected) in method_mask.iter().enumerate() {
                if !selected {
                    selection_mask[i] = false;
                }
            }

            trained_steps.push(TrainedStep {
                step_type: "Selection".to_string(),
                step_index: idx,
                training_time: step_start.elapsed(),
                feature_count_before: features_before,
                feature_count_after: selection_mask.iter().filter(|&&x| x).count(),
                parameters: StepParameters::Selection(method_mask),
            });
        }
        self.selection_methods = selection_methods;

        // Apply selection mask to reduce features
        let selected_indices: Vec<usize> = selection_mask
            .iter()
            .enumerate()
            .filter_map(|(i, &selected)| if selected { Some(i) } else { None })
            .collect();

        if !selected_indices.is_empty() {
            let mut selected_X = Array2::zeros((current_X.nrows(), selected_indices.len()));
            for (new_col, &old_col) in selected_indices.iter().enumerate() {
                for row in 0..current_X.nrows() {
                    selected_X[[row, new_col]] = current_X[[row, old_col]];
                }
            }
            current_X = selected_X;
        }

        // Step 4: Apply dimensionality reduction if specified
        if self.dimensionality_reduction.is_some() {
            let step_start = Instant::now();
            let features_before = current_X.ncols();
            let mut reduction = self.dimensionality_reduction.take().unwrap();
            current_X = self.apply_dimensionality_reduction(&mut reduction, current_X.view())?;
            self.dimensionality_reduction = Some(reduction);

            trained_steps.push(TrainedStep {
                step_type: "DimensionalityReduction".to_string(),
                step_index: 0,
                training_time: step_start.elapsed(),
                feature_count_before: features_before,
                feature_count_after: current_X.ncols(),
                parameters: StepParameters::DimensionalityReduction(Array2::zeros((1, 1))), // Simplified
            });
        }

        // Step 5: Apply model selection if specified
        if self.model_selection.is_some() {
            let step_start = Instant::now();
            let features_before = current_X.ncols();
            let mut model_sel = self.model_selection.take().unwrap();
            let selected_features =
                self.apply_model_selection(&mut model_sel, current_X.view(), current_y.view())?;
            self.model_selection = Some(model_sel);

            // Further reduce features based on model selection
            if !selected_features.is_empty() {
                let mut model_selected_X =
                    Array2::zeros((current_X.nrows(), selected_features.len()));
                for (new_col, &old_col) in selected_features.iter().enumerate() {
                    for row in 0..current_X.nrows() {
                        model_selected_X[[row, new_col]] = current_X[[row, old_col]];
                    }
                }
                current_X = model_selected_X;
            }

            trained_steps.push(TrainedStep {
                step_type: "ModelSelection".to_string(),
                step_index: 0,
                training_time: step_start.elapsed(),
                feature_count_before: features_before,
                feature_count_after: current_X.ncols(),
                parameters: StepParameters::ModelSelection(selected_features),
            });
        }

        // Create feature mapping
        let final_features = current_X.ncols();
        let _feature_mapping = FeatureMapping {
            original_features,
            final_features,
            feature_names: (0..final_features)
                .map(|i| format!("feature_{}", i))
                .collect(),
            feature_origins: (0..final_features).map(FeatureOrigin::Original).collect(),
            transformation_history: trained_steps
                .iter()
                .map(|step| TransformationStep {
                    step_name: step.step_type.clone(),
                    input_features: step.feature_count_before,
                    output_features: step.feature_count_after,
                    transformation_type: TransformationType::ManyToMany,
                })
                .collect(),
        };

        // Create pipeline metadata
        let total_training_time = start_time.elapsed();
        let feature_reduction_ratio = final_features as f64 / original_features as f64;

        let _pipeline_metadata = PipelineMetadata {
            total_training_time,
            total_transform_time: Duration::from_secs(0),
            memory_usage_peak: 0, // Would be calculated in real implementation
            feature_reduction_ratio,
            performance_metrics: HashMap::new(),
            validation_results: None,
        };

        Ok(FeatureSelectionPipeline {
            preprocessing_steps: self.preprocessing_steps,
            feature_engineering_steps: self.feature_engineering_steps,
            selection_methods: self.selection_methods,
            dimensionality_reduction: self.dimensionality_reduction,
            model_selection: self.model_selection,
            pipeline_config: self.pipeline_config,
            optimization_config: self.optimization_config,
            _phantom: PhantomData::<Trained>,
        })
    }

    // Helper methods for applying different types of steps
    fn apply_preprocessing_step(
        &self,
        step: &mut PreprocessingStep,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        Self::apply_preprocessing_step_static(step, X)
    }

    fn apply_preprocessing_step_static(
        step: &mut PreprocessingStep,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        match step {
            PreprocessingStep::StandardScaler {
                config,
                trained_params,
            } => Self::apply_standard_scaler_static(config, trained_params, X),
            PreprocessingStep::RobustScaler {
                config,
                trained_params,
            } => Self::apply_robust_scaler_static(config, trained_params, X),
            PreprocessingStep::MinMaxScaler {
                config,
                trained_params,
            } => Self::apply_minmax_scaler_static(config, trained_params, X),
            _ => {
                // For other preprocessing steps, return data unchanged for now
                Ok(X.to_owned())
            }
        }
    }

    fn apply_standard_scaler(
        &self,
        config: &StandardScalerConfig,
        trained_params: &mut Option<ScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        Self::apply_standard_scaler_static(config, trained_params, X)
    }

    fn apply_standard_scaler_static(
        config: &StandardScalerConfig,
        trained_params: &mut Option<ScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let mut result = X.to_owned();

        if trained_params.is_none() {
            // Training phase: compute mean and std
            let mut mean = Array1::zeros(X.ncols());
            let mut scale = Array1::ones(X.ncols());

            if config.with_mean {
                for col in 0..X.ncols() {
                    mean[col] = X.column(col).mean().unwrap_or(0.0);
                }
            }

            if config.with_std {
                for col in 0..X.ncols() {
                    let column = X.column(col);
                    let variance = column.var(1.0);
                    scale[col] = variance.sqrt().max(1e-8);
                }
            }

            *trained_params = Some(ScalerParams { mean, scale });
        }

        // Apply scaling
        if let Some(ref params) = trained_params {
            for col in 0..X.ncols() {
                for row in 0..X.nrows() {
                    if config.with_mean {
                        result[[row, col]] -= params.mean[col];
                    }
                    if config.with_std {
                        result[[row, col]] /= params.scale[col];
                    }
                }
            }
        }

        Ok(result)
    }

    fn apply_robust_scaler(
        &self,
        config: &RobustScalerConfig,
        trained_params: &mut Option<RobustScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        Self::apply_robust_scaler_static(config, trained_params, X)
    }

    fn apply_robust_scaler_static(
        config: &RobustScalerConfig,
        trained_params: &mut Option<RobustScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let mut result = X.to_owned();

        if trained_params.is_none() {
            // Training phase: compute median and IQR
            let mut center = Array1::zeros(X.ncols());
            let mut scale = Array1::ones(X.ncols());

            for col in 0..X.ncols() {
                let mut column_data: Vec<f64> = X.column(col).to_vec();
                column_data.sort_by(|a, b| a.partial_cmp(b).unwrap());

                let n = column_data.len();
                if config.with_centering {
                    center[col] = if n % 2 == 0 {
                        (column_data[n / 2 - 1] + column_data[n / 2]) / 2.0
                    } else {
                        column_data[n / 2]
                    };
                }

                if config.with_scaling {
                    let q1_idx = ((n - 1) as f64 * config.quantile_range.0) as usize;
                    let q3_idx = ((n - 1) as f64 * config.quantile_range.1) as usize;
                    let iqr = column_data[q3_idx] - column_data[q1_idx];
                    scale[col] = iqr.max(1e-8);
                }
            }

            *trained_params = Some(RobustScalerParams { center, scale });
        }

        // Apply scaling
        if let Some(ref params) = trained_params {
            for col in 0..X.ncols() {
                for row in 0..X.nrows() {
                    if config.with_centering {
                        result[[row, col]] -= params.center[col];
                    }
                    if config.with_scaling {
                        result[[row, col]] /= params.scale[col];
                    }
                }
            }
        }

        Ok(result)
    }

    fn apply_minmax_scaler(
        &self,
        config: &MinMaxScalerConfig,
        trained_params: &mut Option<MinMaxScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        Self::apply_minmax_scaler_static(config, trained_params, X)
    }

    fn apply_minmax_scaler_static(
        config: &MinMaxScalerConfig,
        trained_params: &mut Option<MinMaxScalerParams>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let mut result = X.to_owned();

        if trained_params.is_none() {
            // Training phase: compute min and scale
            let mut min = Array1::zeros(X.ncols());
            let mut scale = Array1::ones(X.ncols());

            for col in 0..X.ncols() {
                let column = X.column(col);
                let col_min = column.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                let col_max = column.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));

                min[col] = col_min;
                let range = col_max - col_min;
                if range > 1e-8 {
                    scale[col] = (config.feature_range.1 - config.feature_range.0) / range;
                }
            }

            *trained_params = Some(MinMaxScalerParams { min, scale });
        }

        // Apply scaling
        if let Some(ref params) = trained_params {
            for col in 0..X.ncols() {
                for row in 0..X.nrows() {
                    let scaled = (result[[row, col]] - params.min[col]) * params.scale[col]
                        + config.feature_range.0;
                    result[[row, col]] = if config.clip {
                        scaled
                            .max(config.feature_range.0)
                            .min(config.feature_range.1)
                    } else {
                        scaled
                    };
                }
            }
        }

        Ok(result)
    }

    fn apply_feature_engineering_step_static(
        _step: &mut FeatureEngineeringStep,
        X: ArrayView2<f64>,
        _y: ArrayView1<f64>,
    ) -> Result<Array2<f64>> {
        // Simplified static version - just return X for now
        Ok(X.to_owned())
    }

    fn apply_feature_engineering_step(
        &self,
        step: &mut FeatureEngineeringStep,
        X: ArrayView2<f64>,
        _y: ArrayView1<f64>,
    ) -> Result<Array2<f64>> {
        match step {
            FeatureEngineeringStep::PolynomialFeatures {
                degree,
                interaction_only,
                include_bias,
                feature_mapping,
            } => self.apply_polynomial_features(
                *degree,
                *interaction_only,
                *include_bias,
                feature_mapping,
                X,
            ),
            FeatureEngineeringStep::InteractionFeatures {
                max_pairs,
                threshold,
                feature_pairs,
            } => self.apply_interaction_features(*max_pairs, *threshold, feature_pairs, X),
            FeatureEngineeringStep::BinningFeatures {
                n_bins,
                strategy,
                bin_edges,
            } => self.apply_binning_features(*n_bins, strategy, bin_edges, X),
            _ => {
                // For other feature engineering steps, return data unchanged for now
                Ok(X.to_owned())
            }
        }
    }

    fn apply_polynomial_features(
        &self,
        degree: usize,
        interaction_only: bool,
        include_bias: bool,
        feature_mapping: &mut Option<Vec<(usize, usize)>>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let n_features = X.ncols();
        let mut new_features = Vec::new();
        let mut mapping = Vec::new();

        // Add bias term if requested
        if include_bias {
            let bias_feature = Array1::ones(X.nrows());
            new_features.push(bias_feature);
            mapping.push((0, 0)); // Bias has no source feature
        }

        // Add original features
        for i in 0..n_features {
            new_features.push(X.column(i).to_owned());
            mapping.push((i, 1));
        }

        // Add polynomial and interaction features
        if !interaction_only {
            // Add polynomial features (x^2, x^3, etc.)
            for d in 2..=degree {
                for i in 0..n_features {
                    let mut poly_feature = Array1::zeros(X.nrows());
                    for row in 0..X.nrows() {
                        poly_feature[row] = X[[row, i]].powi(d as i32);
                    }
                    new_features.push(poly_feature);
                    mapping.push((i, d));
                }
            }
        }

        // Add interaction features
        for d in 2..=degree {
            for i in 0..n_features {
                for j in (i + 1)..n_features {
                    let mut interaction_feature = Array1::zeros(X.nrows());
                    for row in 0..X.nrows() {
                        interaction_feature[row] = X[[row, i]] * X[[row, j]];
                    }
                    new_features.push(interaction_feature);
                    mapping.push((i * n_features + j, d));
                }
            }
        }

        *feature_mapping = Some(mapping);

        // Combine all features into a matrix
        let n_new_features = new_features.len();
        let mut result = Array2::zeros((X.nrows(), n_new_features));
        for (col, feature) in new_features.iter().enumerate() {
            for row in 0..X.nrows() {
                result[[row, col]] = feature[row];
            }
        }

        Ok(result)
    }

    fn apply_interaction_features(
        &self,
        max_pairs: Option<usize>,
        threshold: f64,
        feature_pairs: &mut Option<Vec<(usize, usize)>>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let n_features = X.ncols();
        let mut interactions = Vec::new();
        let pairs: Vec<(usize, usize)>;

        // Generate all possible pairs or use provided pairs
        if feature_pairs.is_none() {
            let mut candidate_pairs = Vec::new();
            for i in 0..n_features {
                for j in (i + 1)..n_features {
                    // Compute correlation to filter relevant interactions
                    let corr = self.compute_correlation(X.column(i), X.column(j));
                    if corr.abs() > threshold {
                        candidate_pairs.push((i, j, corr.abs()));
                    }
                }
            }

            // Sort by correlation and take top pairs
            candidate_pairs.sort_by(|a, b| b.2.partial_cmp(&a.2).unwrap());
            let limit = max_pairs.unwrap_or(candidate_pairs.len());
            pairs = candidate_pairs
                .into_iter()
                .take(limit)
                .map(|(i, j, _)| (i, j))
                .collect();
            *feature_pairs = Some(pairs.clone());
        } else {
            pairs = feature_pairs.as_ref().unwrap().clone();
        }

        // Create interaction features
        for &(i, j) in &pairs {
            let mut interaction = Array1::zeros(X.nrows());
            for row in 0..X.nrows() {
                interaction[row] = X[[row, i]] * X[[row, j]];
            }
            interactions.push(interaction);
        }

        // Combine original features with interactions
        let total_features = n_features + interactions.len();
        let mut result = Array2::zeros((X.nrows(), total_features));

        // Copy original features
        for col in 0..n_features {
            for row in 0..X.nrows() {
                result[[row, col]] = X[[row, col]];
            }
        }

        // Add interaction features
        for (idx, interaction) in interactions.iter().enumerate() {
            for row in 0..X.nrows() {
                result[[row, n_features + idx]] = interaction[row];
            }
        }

        Ok(result)
    }

    fn apply_binning_features(
        &self,
        n_bins: usize,
        strategy: &BinningStrategy,
        bin_edges: &mut Option<HashMap<usize, Vec<f64>>>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let mut result = X.to_owned();

        if bin_edges.is_none() {
            let mut edges_map = HashMap::new();

            for col in 0..X.ncols() {
                let column = X.column(col);
                let edges = match strategy {
                    BinningStrategy::Uniform => {
                        let min_val = column.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                        let max_val = column.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                        let step = (max_val - min_val) / n_bins as f64;
                        (0..=n_bins)
                            .map(|i| min_val + i as f64 * step)
                            .collect::<Vec<f64>>()
                    }
                    BinningStrategy::Quantile => {
                        let mut sorted_values: Vec<f64> = column.to_vec();
                        sorted_values.sort_by(|a, b| a.partial_cmp(b).unwrap());
                        let n = sorted_values.len();
                        (0..=n_bins)
                            .map(|i| {
                                let quantile = i as f64 / n_bins as f64;
                                let idx = ((n - 1) as f64 * quantile) as usize;
                                sorted_values[idx]
                            })
                            .collect::<Vec<f64>>()
                    }
                    BinningStrategy::KMeans => {
                        // Simplified K-means binning - would need proper clustering in real implementation
                        let min_val = column.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                        let max_val = column.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                        let step = (max_val - min_val) / n_bins as f64;
                        (0..=n_bins)
                            .map(|i| min_val + i as f64 * step)
                            .collect::<Vec<f64>>()
                    }
                };
                edges_map.insert(col, edges);
            }

            *bin_edges = Some(edges_map);
        }

        // Apply binning
        if let Some(ref edges_map) = bin_edges {
            for col in 0..X.ncols() {
                if let Some(edges) = edges_map.get(&col) {
                    for row in 0..X.nrows() {
                        let value = X[[row, col]];
                        let bin = edges
                            .iter()
                            .position(|&edge| value <= edge)
                            .unwrap_or(edges.len() - 1)
                            .min(n_bins - 1);
                        result[[row, col]] = bin as f64;
                    }
                }
            }
        }

        Ok(result)
    }

    fn apply_selection_method_static(
        _method: &mut SelectionMethod,
        X: ArrayView2<f64>,
        _y: ArrayView1<f64>,
    ) -> Result<Array1<bool>> {
        // Simplified static version - select all features for now
        Ok(Array1::from_elem(X.ncols(), true))
    }

    fn apply_selection_method(
        &self,
        method: &mut SelectionMethod,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<Array1<bool>> {
        match method {
            SelectionMethod::VarianceThreshold {
                threshold,
                feature_variance,
            } => self.apply_variance_threshold(*threshold, feature_variance, X),
            SelectionMethod::CorrelationFilter {
                threshold,
                method: corr_method,
                correlation_matrix,
            } => self.apply_correlation_filter(*threshold, corr_method, correlation_matrix, X),
            SelectionMethod::UnivariateFilter {
                method: uni_method,
                k,
                score_func,
            } => self.apply_univariate_filter(uni_method, k, score_func, X, y),
            _ => {
                // For other methods, select all features for now
                Ok(Array1::from_elem(X.ncols(), true))
            }
        }
    }

    fn apply_variance_threshold(
        &self,
        threshold: f64,
        feature_variance: &mut Option<Array1<f64>>,
        X: ArrayView2<f64>,
    ) -> Result<Array1<bool>> {
        if feature_variance.is_none() {
            let mut variances = Array1::zeros(X.ncols());
            for col in 0..X.ncols() {
                variances[col] = X.column(col).var(1.0);
            }
            *feature_variance = Some(variances);
        }

        let variances = feature_variance.as_ref().unwrap();
        let selection = variances.mapv(|v| v > threshold);
        Ok(selection)
    }

    fn apply_correlation_filter(
        &self,
        threshold: f64,
        corr_method: &CorrelationMethod,
        correlation_matrix: &mut Option<Array2<f64>>,
        X: ArrayView2<f64>,
    ) -> Result<Array1<bool>> {
        if correlation_matrix.is_none() {
            let n_features = X.ncols();
            let mut corr_matrix = Array2::zeros((n_features, n_features));

            for i in 0..n_features {
                for j in 0..n_features {
                    if i == j {
                        corr_matrix[[i, j]] = 1.0;
                    } else {
                        let corr = match corr_method {
                            CorrelationMethod::Pearson => {
                                self.compute_correlation(X.column(i), X.column(j))
                            }
                            _ => {
                                // For now, use Pearson for all methods
                                self.compute_correlation(X.column(i), X.column(j))
                            }
                        };
                        corr_matrix[[i, j]] = corr;
                    }
                }
            }
            *correlation_matrix = Some(corr_matrix);
        }

        // Remove features with high correlation
        let corr_matrix = correlation_matrix.as_ref().unwrap();
        let mut selection = Array1::from_elem(X.ncols(), true);

        for i in 0..X.ncols() {
            for j in (i + 1)..X.ncols() {
                if corr_matrix[[i, j]].abs() > threshold && selection[i] && selection[j] {
                    // Remove the feature with lower variance
                    let var_i = X.column(i).var(1.0);
                    let var_j = X.column(j).var(1.0);
                    if var_i < var_j {
                        selection[i] = false;
                    } else {
                        selection[j] = false;
                    }
                }
            }
        }

        Ok(selection)
    }

    fn apply_univariate_filter(
        &self,
        _method: &UnivariateMethod,
        k: &SelectionCount,
        score_func: &UnivariateScoreFunction,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<Array1<bool>> {
        // Compute scores for each feature
        let mut scores = Array1::zeros(X.ncols());

        for col in 0..X.ncols() {
            scores[col] = match score_func {
                UnivariateScoreFunction::Chi2 => self.compute_chi2_score(X.column(col), y),
                UnivariateScoreFunction::FClassif => self.compute_f_score(X.column(col), y),
                UnivariateScoreFunction::MutualInfoClassif => {
                    self.compute_mutual_info(X.column(col), y)
                }
                _ => {
                    // Default to correlation
                    self.compute_correlation(X.column(col), y).abs()
                }
            };
        }

        // Select features based on scores
        let selection = match k {
            SelectionCount::K(k_val) => {
                let mut indexed_scores: Vec<(usize, f64)> = scores
                    .iter()
                    .enumerate()
                    .map(|(i, &score)| (i, score))
                    .collect();
                indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

                let mut selection = Array1::from_elem(X.ncols(), false);
                for &(idx, _) in indexed_scores.iter().take(*k_val) {
                    selection[idx] = true;
                }
                selection
            }
            SelectionCount::Percentile(p) => {
                let k_val = ((X.ncols() as f64 * p / 100.0).round() as usize).max(1);
                let mut indexed_scores: Vec<(usize, f64)> = scores
                    .iter()
                    .enumerate()
                    .map(|(i, &score)| (i, score))
                    .collect();
                indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

                let mut selection = Array1::from_elem(X.ncols(), false);
                for &(idx, _) in indexed_scores.iter().take(k_val) {
                    selection[idx] = true;
                }
                selection
            }
            _ => {
                // For other selection methods, use top 50% for now
                let k_val = X.ncols() / 2;
                let mut indexed_scores: Vec<(usize, f64)> = scores
                    .iter()
                    .enumerate()
                    .map(|(i, &score)| (i, score))
                    .collect();
                indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

                let mut selection = Array1::from_elem(X.ncols(), false);
                for &(idx, _) in indexed_scores.iter().take(k_val) {
                    selection[idx] = true;
                }
                selection
            }
        };

        Ok(selection)
    }

    fn apply_dimensionality_reduction(
        &self,
        reduction: &mut DimensionalityReductionStep,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        match reduction {
            DimensionalityReductionStep::PCA {
                n_components,
                whiten,
                svd_solver,
                components,
                explained_variance,
            } => self.apply_pca(
                *n_components,
                *whiten,
                svd_solver,
                components,
                explained_variance,
                X,
            ),
            DimensionalityReductionStep::TruncatedSVD {
                n_components,
                algorithm,
                components,
                singular_values,
            } => self.apply_truncated_svd(*n_components, algorithm, components, singular_values, X),
            _ => {
                // For other methods, return first n_components columns for now
                let n_comp = match reduction {
                    DimensionalityReductionStep::ICA { n_components, .. } => *n_components,
                    DimensionalityReductionStep::FactorAnalysis { n_components, .. } => {
                        *n_components
                    }
                    DimensionalityReductionStep::UMAP { n_components, .. } => *n_components,
                    DimensionalityReductionStep::TSNE { n_components, .. } => *n_components,
                    _ => X.ncols().min(50),
                };
                let final_components = n_comp.min(X.ncols());
                let mut result = Array2::zeros((X.nrows(), final_components));
                for col in 0..final_components {
                    for row in 0..X.nrows() {
                        result[[row, col]] = X[[row, col]];
                    }
                }
                Ok(result)
            }
        }
    }

    fn apply_pca(
        &self,
        n_components: usize,
        _whiten: bool,
        _svd_solver: &SVDSolver,
        components: &mut Option<Array2<f64>>,
        explained_variance: &mut Option<Array1<f64>>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let n_comp = n_components.min(X.ncols()).min(X.nrows());

        // Center the data
        let mut centered_X = X.to_owned();
        let mut means = Array1::zeros(X.ncols());
        for col in 0..X.ncols() {
            means[col] = X.column(col).mean().unwrap_or(0.0);
            for row in 0..X.nrows() {
                centered_X[[row, col]] -= means[col];
            }
        }

        // Simplified PCA: just return first n_components columns
        // In a real implementation, this would compute SVD
        if components.is_none() {
            *components = Some(Array2::eye(X.ncols()));
            *explained_variance = Some(Array1::ones(n_comp));
        }

        let mut result = Array2::zeros((X.nrows(), n_comp));
        for col in 0..n_comp {
            for row in 0..X.nrows() {
                result[[row, col]] = centered_X[[row, col]];
            }
        }

        Ok(result)
    }

    fn apply_truncated_svd(
        &self,
        n_components: usize,
        _algorithm: &SVDAlgorithm,
        components: &mut Option<Array2<f64>>,
        singular_values: &mut Option<Array1<f64>>,
        X: ArrayView2<f64>,
    ) -> Result<Array2<f64>> {
        let n_comp = n_components.min(X.ncols()).min(X.nrows());

        // Simplified SVD: just return first n_components columns
        if components.is_none() {
            *components = Some(Array2::eye(X.ncols()));
            *singular_values = Some(Array1::ones(n_comp));
        }

        let mut result = Array2::zeros((X.nrows(), n_comp));
        for col in 0..n_comp {
            for row in 0..X.nrows() {
                result[[row, col]] = X[[row, col]];
            }
        }

        Ok(result)
    }

    fn apply_model_selection(
        &self,
        model_selection: &mut ModelSelectionStep,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<Vec<usize>> {
        match model_selection {
            ModelSelectionStep::CrossValidationSelection {
                estimator,
                cv_folds,
                scoring,
                feature_scores,
            } => self.apply_cv_selection(estimator, *cv_folds, scoring, feature_scores, X, y),
            ModelSelectionStep::ForwardSelection {
                estimator,
                max_features,
                scoring,
                selected_features,
            } => self.apply_forward_selection(
                estimator,
                *max_features,
                scoring,
                selected_features,
                X,
                y,
            ),
            _ => {
                // For other methods, return all features for now
                Ok((0..X.ncols()).collect())
            }
        }
    }

    fn apply_cv_selection(
        &self,
        _estimator: &ModelEstimator,
        _cv_folds: usize,
        _scoring: &ScoringMetric,
        feature_scores: &mut Option<Array1<f64>>,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<Vec<usize>> {
        if feature_scores.is_none() {
            // Simplified CV: compute correlation as proxy for CV score
            let mut scores = Array1::zeros(X.ncols());
            for col in 0..X.ncols() {
                scores[col] = self.compute_correlation(X.column(col), y).abs();
            }
            *feature_scores = Some(scores);
        }

        // Select top half of features based on scores
        if let Some(ref scores) = feature_scores {
            let mut indexed_scores: Vec<(usize, f64)> = scores
                .iter()
                .enumerate()
                .map(|(i, &score)| (i, score))
                .collect();
            indexed_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());

            let n_select = X.ncols() / 2;
            Ok(indexed_scores
                .into_iter()
                .take(n_select)
                .map(|(idx, _)| idx)
                .collect())
        } else {
            Ok((0..X.ncols()).collect())
        }
    }

    fn apply_forward_selection(
        &self,
        _estimator: &ModelEstimator,
        max_features: usize,
        _scoring: &ScoringMetric,
        selected_features: &mut Option<Vec<usize>>,
        X: ArrayView2<f64>,
        y: ArrayView1<f64>,
    ) -> Result<Vec<usize>> {
        if selected_features.is_none() {
            // Simplified forward selection: select features with highest correlation
            let mut scores = Vec::new();
            for col in 0..X.ncols() {
                let score = self.compute_correlation(X.column(col), y).abs();
                scores.push((col, score));
            }

            scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
            let features: Vec<usize> = scores
                .into_iter()
                .take(max_features.min(X.ncols()))
                .map(|(idx, _)| idx)
                .collect();

            *selected_features = Some(features.clone());
            Ok(features)
        } else {
            Ok(selected_features.as_ref().unwrap().clone())
        }
    }

    // Helper methods for statistical computations
    fn compute_correlation(&self, x: ArrayView1<f64>, y: ArrayView1<f64>) -> f64 {
        let n = x.len() as f64;
        if n < 2.0 {
            return 0.0;
        }

        let mean_x = x.mean().unwrap_or(0.0);
        let mean_y = y.mean().unwrap_or(0.0);

        let mut sum_xy = 0.0;
        let mut sum_x2 = 0.0;
        let mut sum_y2 = 0.0;

        for i in 0..x.len() {
            let dx = x[i] - mean_x;
            let dy = y[i] - mean_y;
            sum_xy += dx * dy;
            sum_x2 += dx * dx;
            sum_y2 += dy * dy;
        }

        let denom = (sum_x2 * sum_y2).sqrt();
        if denom < 1e-10 {
            0.0
        } else {
            sum_xy / denom
        }
    }

    fn compute_chi2_score(&self, x: ArrayView1<f64>, y: ArrayView1<f64>) -> f64 {
        // Simplified chi2 score - in real implementation would need proper contingency table
        self.compute_correlation(x, y).abs()
    }

    fn compute_f_score(&self, x: ArrayView1<f64>, y: ArrayView1<f64>) -> f64 {
        // Simplified F-score - in real implementation would compute ANOVA F-statistic
        self.compute_correlation(x, y).abs()
    }

    fn compute_mutual_info(&self, x: ArrayView1<f64>, y: ArrayView1<f64>) -> f64 {
        // Simplified mutual information - in real implementation would use proper MI estimation
        self.compute_correlation(x, y).abs()
    }
}

impl FeatureSelectionPipeline<Trained> {
    pub fn transform(&self, X: ArrayView2<f64>) -> Result<Array2<f64>> {
        let _start_time = Instant::now();
        let current_X = X.to_owned();

        // Apply all trained transformations in sequence
        // This would follow the same sequence as training but use stored parameters

        // For now, return the input data (full implementation would apply all transformations)
        Ok(current_X)
    }

    /// Get information about the trained pipeline
    pub fn get_pipeline_info(&self) -> PipelineInfo {
        PipelineInfo {
            n_preprocessing_steps: self.preprocessing_steps.len(),
            n_feature_engineering_steps: self.feature_engineering_steps.len(),
            n_selection_methods: self.selection_methods.len(),
            has_dimensionality_reduction: self.dimensionality_reduction.is_some(),
            has_model_selection: self.model_selection.is_some(),
            config: self.pipeline_config.clone(),
        }
    }
}

/// Information about a trained pipeline
#[derive(Debug, Clone)]
pub struct PipelineInfo {
    pub n_preprocessing_steps: usize,
    pub n_feature_engineering_steps: usize,
    pub n_selection_methods: usize,
    pub has_dimensionality_reduction: bool,
    pub has_model_selection: bool,
    pub config: PipelineConfiguration,
}

// Default implementations
impl Default for PipelineConfiguration {
    fn default() -> Self {
        Self {
            parallel_execution: true,
            memory_optimization: MemoryOptimization::Conservative,
            caching_strategy: CachingStrategy::LRU { size: 1000 },
            validation_strategy: ValidationStrategy::Basic,
            error_handling: ErrorHandling::Graceful,
            logging_level: LoggingLevel::Info,
        }
    }
}

impl Default for OptimizationConfiguration {
    fn default() -> Self {
        Self {
            use_simd: true,
            chunk_size: 1000,
            thread_pool_size: None,
            memory_pool_size: 1024 * 1024, // 1MB
            cache_size: 100,
            prefetch_strategy: PrefetchStrategy::Sequential,
            vectorization_threshold: 1000,
        }
    }
}

// Estimator trait implementation for type safety
impl Estimator for FeatureSelectionPipeline<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = f64;

    fn config(&self) -> &Self::Config {
        &()
    }
}

impl Fit<ArrayView2<'_, f64>, ArrayView1<'_, f64>> for FeatureSelectionPipeline<Untrained> {
    type Fitted = FeatureSelectionPipeline<Trained>;

    fn fit(self, X: &ArrayView2<'_, f64>, y: &ArrayView1<'_, f64>) -> Result<Self::Fitted> {
        self.fit(*X, *y)
    }
}

impl Transform<ArrayView2<'_, f64>, Array2<f64>> for FeatureSelectionPipeline<Trained> {
    fn transform(&self, X: &ArrayView2<'_, f64>) -> Result<Array2<f64>> {
        self.transform(*X)
    }
}