sklears_impute/

advanced.rs

//! Advanced imputation methods
//!
//! This module provides sophisticated imputation strategies including matrix factorization,
//! tree-based methods, and other advanced statistical approaches.

use scirs2_core::ndarray::{Array2, ArrayView2};

// Note: Re-exports removed due to missing modules
// TODO: Implement MatrixFactorizationImputer and DecisionTreeImputer in their own modules

/// Placeholder for advanced imputation implementations
/// Many advanced methods are implemented in specialized modules:
/// - Matrix methods in matrix_factorization.rs
/// - Tree methods in tree_methods.rs
/// - Kernel methods in kernel.rs
/// - Neural methods in neural.rs
/// - Bayesian methods in bayesian.rs
// Temporary stub implementations for types referenced in lib.rs that don't exist yet
///
///   Kernel Density Estimation Imputer
#[derive(Debug, Clone)]
pub struct KDEImputer {
    /// bandwidth
    pub bandwidth: f64,
    /// kernel
    pub kernel: String,
}

impl Default for KDEImputer {
    fn default() -> Self {
        Self {
            bandwidth: 1.0,
            kernel: "gaussian".to_string(),
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("KDEImputer not fully implemented yet".to_string())
    }
}

/// Local Linear Regression Imputer
#[derive(Debug, Clone)]
pub struct LocalLinearImputer {
    /// n_neighbors
    pub n_neighbors: usize,
    /// degree
    pub degree: usize,
}

impl Default for LocalLinearImputer {
    fn default() -> Self {
        Self {
            n_neighbors: 5,
            degree: 1,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("LocalLinearImputer not fully implemented yet".to_string())
    }
}

/// LOWESS (Locally Weighted Scatterplot Smoothing) Imputer
#[derive(Debug, Clone)]
pub struct LowessImputer {
    /// frac
    pub frac: f64,
    /// it
    pub it: usize,
}

impl Default for LowessImputer {
    fn default() -> Self {
        Self {
            frac: 0.6667,
            it: 3,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("LowessImputer not fully implemented yet".to_string())
    }
}

/// Robust Regression Imputer
#[derive(Debug, Clone)]
pub struct RobustRegressionImputer {
    /// method
    pub method: String,
    /// max_iter
    pub max_iter: usize,
}

impl Default for RobustRegressionImputer {
    fn default() -> Self {
        Self {
            method: "huber".to_string(),
            max_iter: 100,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("RobustRegressionImputer not fully implemented yet".to_string())
    }
}

/// Trimmed Mean Imputer
#[derive(Debug, Clone)]
pub struct TrimmedMeanImputer {
    /// trim_fraction
    pub trim_fraction: f64,
}

impl Default for TrimmedMeanImputer {
    fn default() -> Self {
        Self { trim_fraction: 0.1 }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("TrimmedMeanImputer not fully implemented yet".to_string())
    }
}

/// Multivariate Normal Imputer
#[derive(Debug, Clone)]
pub struct MultivariateNormalImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
}

impl Default for MultivariateNormalImputer {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-6,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("MultivariateNormalImputer not fully implemented yet".to_string())
    }
}

/// Copula-based Imputer
#[derive(Debug, Clone)]
pub struct CopulaImputer {
    /// copula_type
    pub copula_type: String,
    /// n_samples
    pub n_samples: usize,
}

impl Default for CopulaImputer {
    fn default() -> Self {
        Self {
            copula_type: "gaussian".to_string(),
            n_samples: 1000,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("CopulaImputer not fully implemented yet".to_string())
    }
}

/// Copula Parameters
#[derive(Debug, Clone, Default)]
pub struct CopulaParameters {
    /// correlation_matrix
    pub correlation_matrix: Option<Array2<f64>>,
    /// marginal_distributions
    pub marginal_distributions: Vec<String>,
}

/// Factor Analysis Imputer
#[derive(Debug, Clone)]
pub struct FactorAnalysisImputer {
    /// n_components
    pub n_components: usize,
    /// max_iter
    pub max_iter: usize,
}

impl Default for FactorAnalysisImputer {
    fn default() -> Self {
        Self {
            n_components: 2,
            max_iter: 1000,
        }
    }
}

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

    pub fn fit_transform(&self, _X: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Err("FactorAnalysisImputer not fully implemented yet".to_string())
    }
}

/// Empirical CDF
#[derive(Debug, Clone)]
pub struct EmpiricalCDF {
    /// values
    pub values: Vec<f64>,
}

impl EmpiricalCDF {
    pub fn new(values: Vec<f64>) -> Self {
        Self { values }
    }

    pub fn evaluate(&self, _x: f64) -> f64 {
        0.5 // Placeholder
    }
}

/// Empirical Quantile function
#[derive(Debug, Clone)]
pub struct EmpiricalQuantile {
    /// values
    pub values: Vec<f64>,
}

impl EmpiricalQuantile {
    pub fn new(values: Vec<f64>) -> Self {
        Self { values }
    }

    pub fn evaluate(&self, _p: f64) -> f64 {
        self.values.first().cloned().unwrap_or(0.0) // Placeholder
    }
}

/// Breakdown point analysis
#[derive(Debug, Clone)]
pub struct BreakdownPointAnalysis {
    /// breakdown_point
    pub breakdown_point: f64,
    /// robust_estimates
    pub robust_estimates: Vec<f64>,
}

/// Analyze breakdown point of robust estimators
pub fn analyze_breakdown_point(_X: &ArrayView2<f64>) -> BreakdownPointAnalysis {
    BreakdownPointAnalysis {
        breakdown_point: 0.5,
        robust_estimates: Vec::new(),
    }
}