ferrolearn-preprocess 0.2.0

//! Target encoder: encode categorical features using target statistics.
//!
//! [`TargetEncoder`] replaces each category with the mean of the target variable
//! for that category, regularised toward the global mean using smoothing.
//!
//! This is especially useful for high-cardinality categorical features where
//! one-hot encoding would produce too many columns.
//!
//! # Smoothing
//!
//! The encoded value for category `c` is:
//!
//! ```text
//! encoded(c) = (count(c) * mean_c + smooth * global_mean) / (count(c) + smooth)
//! ```
//!
//! where `smooth` controls the degree of regularisation.

use ferrolearn_core::error::FerroError;
use ferrolearn_core::traits::{Fit, Transform};
use ndarray::{Array1, Array2};
use num_traits::Float;
use std::collections::HashMap;

// ---------------------------------------------------------------------------
// TargetEncoder (unfitted)
// ---------------------------------------------------------------------------

/// An unfitted target encoder.
///
/// Takes a matrix of categorical integer features and a continuous (or binary)
/// target vector at fit time. Each category is encoded as the smoothed mean of
/// the target for that category.
///
/// # Parameters
///
/// - `smooth` — smoothing factor (default 1.0). Higher values regularise more
///   toward the global mean. Set to 0 for no smoothing.
///
/// # Examples
///
/// ```
/// use ferrolearn_preprocess::target_encoder::TargetEncoder;
/// use ferrolearn_core::traits::{Fit, Transform};
/// use ndarray::array;
///
/// let enc = TargetEncoder::<f64>::new(1.0);
/// let x = array![[0usize, 1], [0, 0], [1, 1], [1, 0]];
/// let y = array![1.0, 2.0, 3.0, 4.0];
/// let fitted = enc.fit(&x, &y).unwrap();
/// let out = fitted.transform(&x).unwrap();
/// assert_eq!(out.shape(), &[4, 2]);
/// ```
#[must_use]
#[derive(Debug, Clone)]
pub struct TargetEncoder<F> {
    /// Smoothing factor.
    smooth: F,
}

impl<F: Float + Send + Sync + 'static> TargetEncoder<F> {
    /// Create a new `TargetEncoder` with the given smoothing factor.
    pub fn new(smooth: F) -> Self {
        Self { smooth }
    }

    /// Return the smoothing factor.
    #[must_use]
    pub fn smooth(&self) -> F {
        self.smooth
    }
}

impl<F: Float + Send + Sync + 'static> Default for TargetEncoder<F> {
    fn default() -> Self {
        Self::new(F::one())
    }
}

// ---------------------------------------------------------------------------
// FittedTargetEncoder
// ---------------------------------------------------------------------------

/// A fitted target encoder holding per-feature, per-category encoding values.
///
/// Created by calling [`Fit::fit`] on a [`TargetEncoder`].
#[derive(Debug, Clone)]
pub struct FittedTargetEncoder<F> {
    /// Per-feature mapping from category → encoded value.
    category_maps: Vec<HashMap<usize, F>>,
    /// Global target mean (used for unseen categories).
    global_mean: F,
}

impl<F: Float + Send + Sync + 'static> FittedTargetEncoder<F> {
    /// Return the encoding maps per feature.
    #[must_use]
    pub fn category_maps(&self) -> &[HashMap<usize, F>] {
        &self.category_maps
    }

    /// Return the global target mean.
    #[must_use]
    pub fn global_mean(&self) -> F {
        self.global_mean
    }
}

// ---------------------------------------------------------------------------
// Trait implementations
// ---------------------------------------------------------------------------

impl<F: Float + Send + Sync + 'static> Fit<Array2<usize>, Array1<F>> for TargetEncoder<F> {
    type Fitted = FittedTargetEncoder<F>;
    type Error = FerroError;

    /// Fit the encoder by computing smoothed target means per category.
    ///
    /// # Errors
    ///
    /// - [`FerroError::InsufficientSamples`] if the input has zero rows.
    /// - [`FerroError::ShapeMismatch`] if `x` rows and `y` length differ.
    /// - [`FerroError::InvalidParameter`] if `smooth` is negative.
    fn fit(&self, x: &Array2<usize>, y: &Array1<F>) -> Result<FittedTargetEncoder<F>, FerroError> {
        let n_samples = x.nrows();
        if n_samples == 0 {
            return Err(FerroError::InsufficientSamples {
                required: 1,
                actual: 0,
                context: "TargetEncoder::fit".into(),
            });
        }
        if y.len() != n_samples {
            return Err(FerroError::ShapeMismatch {
                expected: vec![n_samples],
                actual: vec![y.len()],
                context: "TargetEncoder::fit — y must have same length as x rows".into(),
            });
        }
        if self.smooth < F::zero() {
            return Err(FerroError::InvalidParameter {
                name: "smooth".into(),
                reason: "smoothing factor must be non-negative".into(),
            });
        }

        let n_features = x.ncols();
        let global_mean = y.iter().copied().fold(F::zero(), |a, v| a + v)
            / F::from(n_samples).unwrap_or(F::one());

        let mut category_maps = Vec::with_capacity(n_features);

        for j in 0..n_features {
            // Collect (sum, count) per category
            let mut cat_stats: HashMap<usize, (F, usize)> = HashMap::new();
            for i in 0..n_samples {
                let cat = x[[i, j]];
                let entry = cat_stats.entry(cat).or_insert((F::zero(), 0));
                entry.0 = entry.0 + y[i];
                entry.1 += 1;
            }

            // Compute smoothed mean per category
            let mut cat_map: HashMap<usize, F> = HashMap::new();
            for (&cat, &(sum, count)) in &cat_stats {
                let count_f = F::from(count).unwrap_or(F::one());
                let cat_mean = sum / count_f;
                let encoded =
                    (count_f * cat_mean + self.smooth * global_mean) / (count_f + self.smooth);
                cat_map.insert(cat, encoded);
            }

            category_maps.push(cat_map);
        }

        Ok(FittedTargetEncoder {
            category_maps,
            global_mean,
        })
    }
}

impl<F: Float + Send + Sync + 'static> Transform<Array2<usize>> for FittedTargetEncoder<F> {
    type Output = Array2<F>;
    type Error = FerroError;

    /// Encode categorical features using the learned target statistics.
    ///
    /// Unseen categories are encoded as the global target mean.
    ///
    /// # Errors
    ///
    /// Returns [`FerroError::ShapeMismatch`] if the number of columns differs
    /// from the number of features seen during fitting.
    fn transform(&self, x: &Array2<usize>) -> Result<Array2<F>, FerroError> {
        let n_features = self.category_maps.len();
        if x.ncols() != n_features {
            return Err(FerroError::ShapeMismatch {
                expected: vec![x.nrows(), n_features],
                actual: vec![x.nrows(), x.ncols()],
                context: "FittedTargetEncoder::transform".into(),
            });
        }

        let n_samples = x.nrows();
        let mut out = Array2::zeros((n_samples, n_features));

        for j in 0..n_features {
            let cat_map = &self.category_maps[j];
            for i in 0..n_samples {
                let cat = x[[i, j]];
                out[[i, j]] = *cat_map.get(&cat).unwrap_or(&self.global_mean);
            }
        }

        Ok(out)
    }
}

// ---------------------------------------------------------------------------
// Tests
// ---------------------------------------------------------------------------

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_abs_diff_eq;
    use ndarray::array;

    #[test]
    fn test_target_encoder_basic() {
        let enc = TargetEncoder::<f64>::new(0.0); // no smoothing
        // Category 0: targets [1.0, 2.0], mean = 1.5
        // Category 1: targets [3.0, 4.0], mean = 3.5
        let x = array![[0usize], [0], [1], [1]];
        let y = array![1.0, 2.0, 3.0, 4.0];
        let fitted = enc.fit(&x, &y).unwrap();
        let out = fitted.transform(&x).unwrap();
        assert_abs_diff_eq!(out[[0, 0]], 1.5, epsilon = 1e-10);
        assert_abs_diff_eq!(out[[1, 0]], 1.5, epsilon = 1e-10);
        assert_abs_diff_eq!(out[[2, 0]], 3.5, epsilon = 1e-10);
        assert_abs_diff_eq!(out[[3, 0]], 3.5, epsilon = 1e-10);
    }

    #[test]
    fn test_target_encoder_smoothing() {
        let enc = TargetEncoder::<f64>::new(2.0);
        // Category 0: targets [1.0], mean = 1.0, count = 1
        // Category 1: targets [3.0, 5.0], mean = 4.0, count = 2
        // Global mean = (1 + 3 + 5) / 3 = 3.0
        let x = array![[0usize], [1], [1]];
        let y = array![1.0, 3.0, 5.0];
        let fitted = enc.fit(&x, &y).unwrap();
        let out = fitted.transform(&x).unwrap();
        // Cat 0: (1 * 1.0 + 2 * 3.0) / (1 + 2) = 7/3 ≈ 2.333
        let expected_0 = (1.0 * 1.0 + 2.0 * 3.0) / (1.0 + 2.0);
        assert_abs_diff_eq!(out[[0, 0]], expected_0, epsilon = 1e-10);
        // Cat 1: (2 * 4.0 + 2 * 3.0) / (2 + 2) = 14/4 = 3.5
        let expected_1 = (2.0 * 4.0 + 2.0 * 3.0) / (2.0 + 2.0);
        assert_abs_diff_eq!(out[[1, 0]], expected_1, epsilon = 1e-10);
    }

    #[test]
    fn test_target_encoder_unseen_category() {
        let enc = TargetEncoder::<f64>::new(1.0);
        let x = array![[0usize], [0], [1], [1]];
        let y = array![1.0, 2.0, 3.0, 4.0];
        let fitted = enc.fit(&x, &y).unwrap();
        // Transform with unseen category 2
        let x_new = array![[2usize]];
        let out = fitted.transform(&x_new).unwrap();
        // Unseen category → global mean = 2.5
        assert_abs_diff_eq!(out[[0, 0]], 2.5, epsilon = 1e-10);
    }

    #[test]
    fn test_target_encoder_multi_feature() {
        let enc = TargetEncoder::<f64>::new(0.0);
        let x = array![[0usize, 1], [0, 0], [1, 1], [1, 0]];
        let y = array![1.0, 2.0, 3.0, 4.0];
        let fitted = enc.fit(&x, &y).unwrap();
        let out = fitted.transform(&x).unwrap();
        assert_eq!(out.shape(), &[4, 2]);
    }

    #[test]
    fn test_target_encoder_zero_rows_error() {
        let enc = TargetEncoder::<f64>::new(1.0);
        let x: Array2<usize> = Array2::zeros((0, 2));
        let y: Array1<f64> = Array1::zeros(0);
        assert!(enc.fit(&x, &y).is_err());
    }

    #[test]
    fn test_target_encoder_shape_mismatch_fit() {
        let enc = TargetEncoder::<f64>::new(1.0);
        let x = array![[0usize], [1]];
        let y = array![1.0]; // wrong length
        assert!(enc.fit(&x, &y).is_err());
    }

    #[test]
    fn test_target_encoder_shape_mismatch_transform() {
        let enc = TargetEncoder::<f64>::new(1.0);
        let x = array![[0usize, 1], [1, 0]];
        let y = array![1.0, 2.0];
        let fitted = enc.fit(&x, &y).unwrap();
        let x_bad = array![[0usize]]; // wrong number of columns
        assert!(fitted.transform(&x_bad).is_err());
    }

    #[test]
    fn test_target_encoder_negative_smooth_error() {
        let enc = TargetEncoder::<f64>::new(-1.0);
        let x = array![[0usize]];
        let y = array![1.0];
        assert!(enc.fit(&x, &y).is_err());
    }

    #[test]
    fn test_target_encoder_default() {
        let enc = TargetEncoder::<f64>::default();
        assert_abs_diff_eq!(enc.smooth(), 1.0, epsilon = 1e-10);
    }

    #[test]
    fn test_target_encoder_global_mean_accessor() {
        let enc = TargetEncoder::<f64>::new(0.0);
        let x = array![[0usize], [1]];
        let y = array![2.0, 4.0];
        let fitted = enc.fit(&x, &y).unwrap();
        assert_abs_diff_eq!(fitted.global_mean(), 3.0, epsilon = 1e-10);
    }

    #[test]
    fn test_target_encoder_f32() {
        let enc = TargetEncoder::<f32>::new(1.0f32);
        let x = array![[0usize], [0], [1]];
        let y: Array1<f32> = array![1.0f32, 2.0, 3.0];
        let fitted = enc.fit(&x, &y).unwrap();
        let out = fitted.transform(&x).unwrap();
        assert!(!out[[0, 0]].is_nan());
    }
}