sklears_impute/

bayesian.rs

//! Bayesian imputation methods
//!
//! This module provides comprehensive Bayesian approaches to missing data imputation.
//! Note: Many advanced Bayesian methods are currently stub implementations.

use scirs2_core::ndarray::{Array1, Array2, ArrayView2};

/// Bayesian Linear Imputer
#[derive(Debug, Clone)]
pub struct BayesianLinearImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
    /// alpha
    pub alpha: f64,
    /// beta
    pub beta: f64,
}

impl Default for BayesianLinearImputer {
    fn default() -> Self {
        Self {
            max_iter: 1000,
            tol: 1e-6,
            alpha: 1.0,
            beta: 1.0,
        }
    }
}

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

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

/// Bayesian Logistic Imputer
#[derive(Debug, Clone)]
pub struct BayesianLogisticImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
}

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

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

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

/// Bayesian Multiple Imputer
#[derive(Debug, Clone)]
pub struct BayesianMultipleImputer {
    /// n_imputations
    pub n_imputations: usize,
    /// max_iter
    pub max_iter: usize,
}

impl Default for BayesianMultipleImputer {
    fn default() -> Self {
        Self {
            n_imputations: 5,
            max_iter: 1000,
        }
    }
}

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

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

/// Hierarchical Bayesian Imputer
#[derive(Debug, Clone)]
pub struct HierarchicalBayesianImputer {
    /// n_levels
    pub n_levels: usize,
    /// max_iter
    pub max_iter: usize,
}

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

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

/// Variational Bayes Imputer
#[derive(Debug, Clone)]
pub struct VariationalBayesImputer {
    /// max_iter
    pub max_iter: usize,
    /// tol
    pub tol: f64,
}

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

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

// Stub types for compatibility

/// Bayesian Model trait
pub trait BayesianModel: Send + Sync {
    fn log_likelihood(&self, X: &ArrayView2<f64>) -> f64;
    fn sample_posterior(&mut self, X: &ArrayView2<f64>) -> Result<(), String>;
}

/// Bayesian Model Averaging
#[derive(Debug, Clone, Default)]
pub struct BayesianModelAveraging {
    /// models
    pub models: Vec<String>,
    /// weights
    pub weights: Vec<f64>,
}

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

/// Bayesian Model Averaging Results
#[derive(Debug, Clone)]
pub struct BayesianModelAveragingResults {
    /// predictions
    pub predictions: Array2<f64>,
    /// weights
    pub weights: Array1<f64>,
    /// model_probabilities
    pub model_probabilities: Array1<f64>,
}

/// Convergence Diagnostics
#[derive(Debug, Clone, Default)]
pub struct ConvergenceDiagnostics {
    /// rhat
    pub rhat: Vec<f64>,
    /// ess
    pub ess: Vec<f64>,
    /// converged
    pub converged: bool,
}

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

/// Pooled Results for multiple imputation
#[derive(Debug, Clone)]
pub struct PooledResults {
    /// estimates
    pub estimates: Array1<f64>,
    /// standard_errors
    pub standard_errors: Array1<f64>,
    /// degrees_of_freedom
    pub degrees_of_freedom: f64,
}

impl PooledResults {
    pub fn new(estimates: Array1<f64>) -> Self {
        let n = estimates.len();
        Self {
            estimates,
            standard_errors: Array1::zeros(n),
            degrees_of_freedom: 0.0,
        }
    }
}

/// Hierarchical Bayesian Sample
#[derive(Debug, Clone)]
pub struct HierarchicalBayesianSample {
    /// global_parameters
    pub global_parameters: Array1<f64>,
    /// local_parameters
    pub local_parameters: Array2<f64>,
    /// hyperparameters
    pub hyperparameters: Array1<f64>,
}

/// Bayesian Regression Sample
#[derive(Debug, Clone)]
pub struct BayesianRegressionSample {
    /// coefficients
    pub coefficients: Array1<f64>,
    /// intercept
    pub intercept: f64,
    /// sigma
    pub sigma: f64,
}

// Additional stub types that may be referenced in lib.rs

/// MCMC Sampler stub
#[derive(Debug, Clone)]
pub struct MCMCsampler {
    /// n_samples
    pub n_samples: usize,
    /// burn_in
    pub burn_in: usize,
}

/// Gibbs Sampler stub
#[derive(Debug, Clone)]
pub struct GibbsSampler {
    /// n_samples
    pub n_samples: usize,
}

/// Metropolis-Hastings Sampler stub
#[derive(Debug, Clone)]
pub struct MetropolisHastingsSampler {
    /// n_samples
    pub n_samples: usize,
}

/// Prior Specification stub
#[derive(Debug, Clone)]
pub struct PriorSpecification {
    /// prior_type
    pub prior_type: String,
    /// parameters
    pub parameters: Vec<f64>,
}

/// Conjugate Prior stub
#[derive(Debug, Clone)]
pub struct ConjugatePrior {
    /// alpha
    pub alpha: f64,
    /// beta
    pub beta: f64,
}

/// Non-conjugate Prior stub
#[derive(Debug, Clone)]
pub struct NonConjugatePrior {
    /// distribution
    pub distribution: String,
    /// parameters
    pub parameters: Vec<f64>,
}

/// Variational Parameters stub
#[derive(Debug, Clone)]
pub struct VariationalParameters {
    /// mean
    pub mean: Array1<f64>,
    /// variance
    pub variance: Array1<f64>,
}

/// ELBO Components stub
#[derive(Debug, Clone)]
pub struct ELBOComponents {
    /// log_likelihood
    pub log_likelihood: f64,
    /// kl_divergence
    pub kl_divergence: f64,
}

// Trained states (stubs)
pub type BayesianLinearImputerTrained = BayesianLinearImputer;
pub type BayesianLogisticImputerTrained = BayesianLogisticImputer;
pub type BayesianMultipleImputerTrained = BayesianMultipleImputer;
pub type HierarchicalBayesianImputerTrained = HierarchicalBayesianImputer;
pub type VariationalBayesImputerTrained = VariationalBayesImputer;