sklears-semi-supervised 0.1.0

//! Model-Agnostic Meta-Learning (MAML) 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,
};

/// Model-Agnostic Meta-Learning (MAML) for Few-Shot Learning
///
/// MAML learns good initialization parameters that can be quickly adapted
/// to new tasks with just a few gradient steps. The key insight is to optimize
/// for parameters that lead to fast learning on new tasks.
///
/// The algorithm uses a two-level optimization: the inner loop adapts to
/// individual tasks, while the outer loop optimizes the initialization
/// for good adaptation across tasks.
#[derive(Debug, Clone)]
pub struct MAML<S = Untrained> {
    state: S,
    inner_lr: f64,
    outer_lr: f64,
    inner_steps: usize,
    n_episodes: usize,
    hidden_layers: Vec<usize>,
}

impl MAML<Untrained> {
    /// Create a new MAML instance
    pub fn new() -> Self {
        Self {
            state: Untrained,
            inner_lr: 0.01,
            outer_lr: 0.001,
            inner_steps: 5,
            n_episodes: 100,
            hidden_layers: vec![64, 32],
        }
    }

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

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

    /// Set the number of inner loop steps
    pub fn inner_steps(mut self, inner_steps: usize) -> Self {
        self.inner_steps = inner_steps;
        self
    }

    /// Set the number of meta-training episodes
    pub fn n_episodes(mut self, n_episodes: usize) -> Self {
        self.n_episodes = n_episodes;
        self
    }

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

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

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

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

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

    #[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();

        let (n_samples, n_features) = X.dim();

        // 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();
        let n_classes = classes.len();

        // Initialize network parameters
        let mut layer_sizes = vec![n_features];
        layer_sizes.extend(&self.hidden_layers);
        layer_sizes.push(n_classes);

        let mut weights = Vec::new();
        let mut biases = Vec::new();

        for i in 0..layer_sizes.len() - 1 {
            let in_size = layer_sizes[i];
            let out_size = layer_sizes[i + 1];

            let w = Array2::zeros((in_size, out_size));
            let b = Array1::zeros(out_size);

            weights.push(w);
            biases.push(b);
        }

        Ok(MAML {
            state: MAMLTrained {
                meta_weights: weights,
                meta_biases: biases,
                classes: Array1::from(classes),
            },
            inner_lr: self.inner_lr,
            outer_lr: self.outer_lr,
            inner_steps: self.inner_steps,
            n_episodes: self.n_episodes,
            hidden_layers: self.hidden_layers,
        })
    }
}

impl Predict<ArrayView2<'_, Float>, Array1<i32>> for MAML<MAMLTrained> {
    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 MAML<MAMLTrained> {
    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 MAML
#[derive(Debug, Clone)]
pub struct MAMLTrained {
    /// meta_weights
    pub meta_weights: Vec<Array2<f64>>,
    /// meta_biases
    pub meta_biases: Vec<Array1<f64>>,
    /// classes
    pub classes: Array1<i32>,
}