sklears_impute/

neural.rs

//! Neural network-based imputation methods
//!
//! This module provides deep learning approaches to missing data imputation.
//! Note: Many advanced neural methods are currently stub implementations.

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

/// Autoencoder Imputer
#[derive(Debug, Clone)]
pub struct AutoencoderImputer {
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// activation
    pub activation: String,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for AutoencoderImputer {
    fn default() -> Self {
        Self {
            hidden_layers: vec![64, 32, 64],
            activation: "relu".to_string(),
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

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

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

/// Multi-Layer Perceptron Imputer
#[derive(Debug, Clone)]
pub struct MLPImputer {
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// activation
    pub activation: String,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for MLPImputer {
    fn default() -> Self {
        Self {
            hidden_layers: vec![100, 50],
            activation: "relu".to_string(),
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

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

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

/// Variational Autoencoder Imputer
#[derive(Debug, Clone)]
pub struct VAEImputer {
    /// latent_dim
    pub latent_dim: usize,
    /// hidden_layers
    pub hidden_layers: Vec<usize>,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
    /// kl_weight
    pub kl_weight: f64,
}

impl Default for VAEImputer {
    fn default() -> Self {
        Self {
            latent_dim: 10,
            hidden_layers: vec![64, 32],
            epochs: 100,
            learning_rate: 0.001,
            kl_weight: 1.0,
        }
    }
}

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

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

    pub fn predict_with_uncertainty(
        &self,
        _X: &ArrayView2<f64>,
    ) -> Result<(Array2<f64>, Array2<f64>), String> {
        Err("VAEImputer uncertainty prediction not implemented yet".to_string())
    }
}

/// Generative Adversarial Network Imputer
#[derive(Debug, Clone)]
pub struct GANImputer {
    /// generator_layers
    pub generator_layers: Vec<usize>,
    /// discriminator_layers
    pub discriminator_layers: Vec<usize>,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
    /// noise_dim
    pub noise_dim: usize,
}

impl Default for GANImputer {
    fn default() -> Self {
        Self {
            generator_layers: vec![64, 128, 64],
            discriminator_layers: vec![64, 32, 1],
            epochs: 100,
            learning_rate: 0.001,
            noise_dim: 10,
        }
    }
}

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

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

/// Normalizing Flow Imputer
#[derive(Debug, Clone)]
pub struct NormalizingFlowImputer {
    /// n_flows
    pub n_flows: usize,
    /// hidden_dim
    pub hidden_dim: usize,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for NormalizingFlowImputer {
    fn default() -> Self {
        Self {
            n_flows: 8,
            hidden_dim: 64,
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

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

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

/// Diffusion Model Imputer
#[derive(Debug, Clone)]
pub struct DiffusionImputer {
    /// n_timesteps
    pub n_timesteps: usize,
    /// hidden_dim
    pub hidden_dim: usize,
    /// epochs
    pub epochs: usize,
    /// learning_rate
    pub learning_rate: f64,
}

impl Default for DiffusionImputer {
    fn default() -> Self {
        Self {
            n_timesteps: 1000,
            hidden_dim: 128,
            epochs: 100,
            learning_rate: 0.001,
        }
    }
}

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

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

/// Neural ODE Imputer
#[derive(Debug, Clone)]
pub struct NeuralODEImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// time_steps
    pub time_steps: Vec<f64>,
    /// solver
    pub solver: String,
}

impl Default for NeuralODEImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 3,
            time_steps: vec![0.0, 1.0],
            solver: "dopri5".to_string(),
        }
    }
}

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

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

// Additional stub types that may be referenced

/// Transformer Imputer stub
#[derive(Debug, Clone)]
pub struct TransformerImputer {
    /// n_heads
    pub n_heads: usize,
    /// n_layers
    pub n_layers: usize,
    /// hidden_dim
    pub hidden_dim: usize,
}

impl Default for TransformerImputer {
    fn default() -> Self {
        Self {
            n_heads: 8,
            n_layers: 6,
            hidden_dim: 512,
        }
    }
}

/// RNN Imputer stub
#[derive(Debug, Clone)]
pub struct RNNImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// cell_type
    pub cell_type: String,
}

impl Default for RNNImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 2,
            cell_type: "lstm".to_string(),
        }
    }
}

/// LSTM Imputer stub
#[derive(Debug, Clone)]
pub struct LSTMImputer {
    /// hidden_dim
    pub hidden_dim: usize,
    /// n_layers
    pub n_layers: usize,
    /// bidirectional
    pub bidirectional: bool,
}

impl Default for LSTMImputer {
    fn default() -> Self {
        Self {
            hidden_dim: 64,
            n_layers: 2,
            bidirectional: false,
        }
    }
}

/// Attention Mechanism stub
#[derive(Debug, Clone)]
pub struct AttentionMechanism {
    /// attention_type
    pub attention_type: String,
    /// n_heads
    pub n_heads: usize,
}

/// Sequential Imputation stub
#[derive(Debug, Clone)]
pub struct SequentialImputation {
    /// sequence_length
    pub sequence_length: usize,
    /// overlap
    pub overlap: usize,
}

// Trained states (type aliases for simplicity)
pub type AutoencoderImputerTrained = AutoencoderImputer;
pub type MLPImputerTrained = MLPImputer;
pub type VAEImputerTrained = VAEImputer;
pub type GANImputerTrained = GANImputer;
pub type NormalizingFlowImputerTrained = NormalizingFlowImputer;
pub type DiffusionImputerTrained = DiffusionImputer;
pub type NeuralODEImputerTrained = NeuralODEImputer;