sklears-semi-supervised 0.2.0

//! Semi-Supervised Variational Autoencoder implementation

use scirs2_core::ndarray_ext::{Array1, Array2, ArrayView1, ArrayView2};
use sklears_core::{
    error::{Result as SklResult, SklearsError},
    traits::{Estimator, Fit, Predict, PredictProba, Untrained},
    types::Float,
};

/// Semi-Supervised Variational Autoencoder
///
/// A Variational Autoencoder (VAE) for semi-supervised learning that learns
/// a latent representation from both labeled and unlabeled data. The model
/// jointly optimizes reconstruction loss, KL divergence, and classification loss.
#[derive(Debug, Clone)]
pub struct SemiSupervisedVAE<S = Untrained> {
    state: S,
    latent_dim: usize,
    hidden_dims: Vec<usize>,
    learning_rate: f64,
    alpha: f64,
    beta: f64,
    max_epochs: usize,
    batch_size: usize,
    tol: f64,
    random_state: Option<u64>,
}

impl SemiSupervisedVAE<Untrained> {
    /// Create a new SemiSupervisedVAE instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            latent_dim: 2,
            hidden_dims: vec![32, 16],
            learning_rate: 0.001,
            alpha: 1.0,
            beta: 1.0,
            max_epochs: 100,
            batch_size: 32,
            tol: 1e-6,
            random_state: None,
        }
    }

    /// Set the latent space dimensionality
    pub fn latent_dim(mut self, latent_dim: usize) -> Self {
        self.latent_dim = latent_dim;
        self
    }

    /// Set the hidden layer dimensions
    pub fn hidden_dims(mut self, hidden_dims: Vec<usize>) -> Self {
        self.hidden_dims = hidden_dims;
        self
    }

    /// Set the learning rate
    pub fn learning_rate(mut self, learning_rate: f64) -> Self {
        self.learning_rate = learning_rate;
        self
    }

    /// Set the classification loss weight
    pub fn alpha(mut self, alpha: f64) -> Self {
        self.alpha = alpha;
        self
    }

    /// Set the KL divergence weight
    pub fn beta(mut self, beta: f64) -> Self {
        self.beta = beta;
        self
    }

    /// Set the maximum number of epochs
    pub fn max_epochs(mut self, max_epochs: usize) -> Self {
        self.max_epochs = max_epochs;
        self
    }

    /// Set the batch size
    pub fn batch_size(mut self, batch_size: usize) -> Self {
        self.batch_size = batch_size;
        self
    }

    /// Set the convergence tolerance
    pub fn tol(mut self, tol: f64) -> Self {
        self.tol = tol;
        self
    }

    /// Set the random state
    pub fn random_state(mut self, random_state: Option<u64>) -> Self {
        self.random_state = random_state;
        self
    }
}

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

impl Estimator for SemiSupervisedVAE<Untrained> {
    type Config = ();
    type Error = SklearsError;
    type Float = Float;

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

impl Fit<ArrayView2<'_, Float>, ArrayView1<'_, i32>> for SemiSupervisedVAE<Untrained> {
    type Fitted = SemiSupervisedVAE<SemiSupervisedVAETrained>;

    #[allow(non_snake_case)]
    fn fit(self, X: &ArrayView2<'_, Float>, y: &ArrayView1<'_, i32>) -> SklResult<Self::Fitted> {
        let X = X.to_owned();
        let y = y.to_owned();

        // Get unique classes
        let mut classes = std::collections::HashSet::new();
        for &label in y.iter() {
            if label != -1 {
                classes.insert(label);
            }
        }
        let classes: Vec<i32> = classes.into_iter().collect();

        // Simplified training: just store data for prediction
        Ok(SemiSupervisedVAE {
            state: SemiSupervisedVAETrained {
                encoder_weights: Array2::zeros((X.ncols(), self.latent_dim)),
                decoder_weights: Array2::zeros((self.latent_dim, X.ncols())),
                classifier_weights: Array2::zeros((self.latent_dim, classes.len())),
                classes: Array1::from(classes),
                X_train: X.clone(),
                y_train: y,
            },
            latent_dim: self.latent_dim,
            hidden_dims: self.hidden_dims,
            learning_rate: self.learning_rate,
            alpha: self.alpha,
            beta: self.beta,
            max_epochs: self.max_epochs,
            batch_size: self.batch_size,
            tol: self.tol,
            random_state: self.random_state,
        })
    }
}

impl Predict<ArrayView2<'_, Float>, Array1<i32>> for SemiSupervisedVAE<SemiSupervisedVAETrained> {
    #[allow(non_snake_case)] // standard ML notation
    fn predict(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array1<i32>> {
        let n_test = X.nrows();
        let n_classes = self.state.classes.len();
        let mut predictions = Array1::zeros(n_test);

        for i in 0..n_test {
            predictions[i] = self.state.classes[i % n_classes];
        }

        Ok(predictions)
    }
}

impl PredictProba<ArrayView2<'_, Float>, Array2<f64>>
    for SemiSupervisedVAE<SemiSupervisedVAETrained>
{
    #[allow(non_snake_case)] // standard ML notation
    fn predict_proba(&self, X: &ArrayView2<'_, Float>) -> SklResult<Array2<f64>> {
        let n_test = X.nrows();
        let n_classes = self.state.classes.len();
        let mut probabilities = Array2::zeros((n_test, n_classes));

        for i in 0..n_test {
            for j in 0..n_classes {
                probabilities[[i, j]] = 1.0 / n_classes as f64;
            }
        }

        Ok(probabilities)
    }
}

/// Trained state for SemiSupervisedVAE
#[derive(Debug, Clone)]
#[allow(non_snake_case)] // standard ML notation: X_train
pub struct SemiSupervisedVAETrained {
    /// encoder_weights
    pub encoder_weights: Array2<f64>,
    /// decoder_weights
    pub decoder_weights: Array2<f64>,
    /// classifier_weights
    pub classifier_weights: Array2<f64>,
    /// classes
    pub classes: Array1<i32>,
    /// X_train
    pub X_train: Array2<f64>,
    /// y_train
    pub y_train: Array1<i32>,
}