sklears_ensemble/stacking/

blending.rs

//! Blending classifier implementation
//!
//! This module provides a blending ensemble classifier that uses a holdout validation
//! approach instead of cross-validation to generate meta-features for training a meta-learner.

use scirs2_core::ndarray::{Array1, Array2};
use sklears_core::{
    error::{Result, SklearsError},
    prelude::Predict,
    traits::{Fit, Trained, Untrained},
    types::Float,
};
use std::marker::PhantomData;

/// Blending Classifier
///
/// Uses a holdout validation approach to train base estimators on part of the data
/// and generate meta-features on the remaining data for training a meta-learner.
#[derive(Debug)]
pub struct BlendingClassifier<State = Untrained> {
    pub(crate) holdout_ratio: f64,
    pub(crate) random_state: Option<u64>,
    pub(crate) state: PhantomData<State>,
    // Fitted attributes
    pub(crate) n_base_estimators_: Option<usize>,
    pub(crate) classes_: Option<Array1<i32>>,
    pub(crate) n_features_in_: Option<usize>,
}

impl BlendingClassifier<Untrained> {
    /// Create a new blending classifier
    pub fn new(n_base_estimators: usize) -> Self {
        Self {
            holdout_ratio: 0.2, // 20% holdout by default
            random_state: None,
            state: PhantomData,
            n_base_estimators_: Some(n_base_estimators),
            classes_: None,
            n_features_in_: None,
        }
    }

    /// Set the holdout ratio for validation set
    pub fn holdout_ratio(mut self, ratio: f64) -> Self {
        if ratio <= 0.0 || ratio >= 1.0 {
            panic!("Holdout ratio must be between 0 and 1");
        }
        self.holdout_ratio = ratio;
        self
    }

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

impl Fit<Array2<Float>, Array1<i32>> for BlendingClassifier<Untrained> {
    type Fitted = BlendingClassifier<Trained>;

    fn fit(self, x: &Array2<Float>, y: &Array1<i32>) -> Result<Self::Fitted> {
        if x.nrows() != y.len() {
            return Err(SklearsError::ShapeMismatch {
                expected: format!("{} samples", x.nrows()),
                actual: format!("{} samples", y.len()),
            });
        }

        let n_features = x.ncols();

        // Get unique classes
        let mut classes: Vec<i32> = y.to_vec();
        classes.sort_unstable();
        classes.dedup();
        let classes_array = Array1::from_vec(classes);

        // Placeholder implementation - in practice would split data and train estimators

        Ok(BlendingClassifier {
            holdout_ratio: self.holdout_ratio,
            random_state: self.random_state,
            state: PhantomData,
            n_base_estimators_: self.n_base_estimators_,
            classes_: Some(classes_array),
            n_features_in_: Some(n_features),
        })
    }
}

impl Predict<Array2<Float>, Array1<i32>> for BlendingClassifier<Trained> {
    fn predict(&self, x: &Array2<Float>) -> Result<Array1<i32>> {
        if x.ncols() != self.n_features_in_.unwrap() {
            return Err(SklearsError::FeatureMismatch {
                expected: self.n_features_in_.unwrap(),
                actual: x.ncols(),
            });
        }

        // Placeholder implementation
        let n_samples = x.nrows();
        let classes = self.classes_.as_ref().unwrap();

        // Simple prediction: assign first class to all samples
        let predictions = Array1::from_elem(n_samples, classes[0]);

        Ok(predictions)
    }
}

impl BlendingClassifier<Trained> {
    /// Get the classes
    pub fn classes(&self) -> &Array1<i32> {
        self.classes_.as_ref().unwrap()
    }

    /// Get the number of features in the training data
    pub fn n_features_in(&self) -> usize {
        self.n_features_in_.unwrap()
    }

    /// Get the number of base estimators
    pub fn n_base_estimators(&self) -> usize {
        self.n_base_estimators_.unwrap()
    }

    /// Get the holdout ratio used for validation
    pub fn holdout_ratio(&self) -> f64 {
        self.holdout_ratio
    }

    /// Get the random state used for reproducibility
    pub fn random_state(&self) -> Option<u64> {
        self.random_state
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_blending_creation() {
        let blending = BlendingClassifier::new(2)
            .holdout_ratio(0.3)
            .random_state(42);

        assert_eq!(blending.holdout_ratio, 0.3);
        assert_eq!(blending.random_state, Some(42));
        assert_eq!(blending.n_base_estimators_.unwrap(), 2);
    }

    #[test]
    fn test_blending_fit_predict() {
        let x = array![
            [1.0, 2.0],
            [3.0, 4.0],
            [5.0, 6.0],
            [7.0, 8.0],
            [9.0, 10.0],
            [11.0, 12.0],
            [13.0, 14.0],
            [15.0, 16.0],
            [17.0, 18.0],
            [19.0, 20.0],
            [21.0, 22.0],
            [23.0, 24.0]
        ];
        let y = array![0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1];

        let blending = BlendingClassifier::new(2);
        let fitted_model = blending.fit(&x, &y).unwrap();

        assert_eq!(fitted_model.n_features_in(), 2);
        assert_eq!(fitted_model.classes().len(), 2);

        let predictions = fitted_model.predict(&x).unwrap();
        assert_eq!(predictions.len(), 12);
    }

    #[test]
    #[should_panic(expected = "Holdout ratio must be between 0 and 1")]
    fn test_invalid_holdout_ratio() {
        let _blending = BlendingClassifier::new(2).holdout_ratio(1.5);
    }

    #[test]
    fn test_shape_mismatch() {
        let x = array![[1.0, 2.0], [3.0, 4.0]];
        let y = array![0]; // Wrong length

        let blending = BlendingClassifier::new(1);
        let result = blending.fit(&x, &y);

        assert!(result.is_err());
        assert!(result.unwrap_err().to_string().contains("Shape mismatch"));
    }

    #[test]
    fn test_feature_mismatch() {
        let x_train = array![
            [1.0, 2.0],
            [3.0, 4.0],
            [5.0, 6.0],
            [7.0, 8.0],
            [9.0, 10.0],
            [11.0, 12.0]
        ];
        let y_train = array![0, 1, 0, 1, 0, 1];
        let x_test = array![[1.0, 2.0, 3.0]]; // Wrong number of features

        let blending = BlendingClassifier::new(1);
        let fitted_model = blending.fit(&x_train, &y_train).unwrap();
        let result = fitted_model.predict(&x_test);

        assert!(result.is_err());
        assert!(result.unwrap_err().to_string().contains("Feature"));
    }
}