automl 0.3.0

Automated machine learning for classification and regression
Documentation 
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//! KNN parameters

use crate::utils::distance::{Distance, DistanceError, KNNRegressorDistance};
use smartcore::numbers::{floatnum::FloatNumber, realnum::RealNumber};
pub use smartcore::{algorithm::neighbour::KNNAlgorithmName, neighbors::KNNWeightFunction};

/// Parameters for k-nearest neighbor (KNN) algorithms
#[derive(serde::Serialize, serde::Deserialize)]
pub struct KNNParameters {
    /// Number of nearest neighbors to use
    pub(crate) k: usize,
    /// Weighting function to use with KNN algorithms
    pub(crate) weight: KNNWeightFunction,
    /// Search algorithm to use with KNN algorithms
    pub(crate) algorithm: KNNAlgorithmName,
    /// Distance metric to use with KNN algorithms
    pub(crate) distance: Distance,
}

impl KNNParameters {
    /// Define the number of nearest neighbors to use
    #[must_use]
    pub const fn with_k(mut self, k: usize) -> Self {
        self.k = k;
        self
    }

    /// Define the weighting function to use
    #[must_use]
    pub const fn with_weight(mut self, weight: KNNWeightFunction) -> Self {
        self.weight = weight;
        self
    }

    /// Define the search algorithm to use
    #[must_use]
    pub const fn with_algorithm(mut self, algorithm: KNNAlgorithmName) -> Self {
        self.algorithm = algorithm;
        self
    }

    /// Define the distance metric to use
    #[must_use]
    pub const fn with_distance(mut self, distance: Distance) -> Self {
        self.distance = distance;
        self
    }

    /// Convert to smartcore KNN classifier parameters using the configured distance metric.
    ///
    /// # Errors
    ///
    /// Returns [`DistanceError::UnsupportedDistance`] if the configured distance is not supported.
    pub fn to_classifier_params<INPUT: RealNumber + FloatNumber>(
        &self,
    ) -> Result<
        smartcore::neighbors::knn_classifier::KNNClassifierParameters<
            INPUT,
            KNNRegressorDistance<INPUT>,
        >,
        DistanceError,
    > {
        Ok(
            smartcore::neighbors::knn_classifier::KNNClassifierParameters::default()
                .with_k(self.k)
                .with_algorithm(self.algorithm.clone())
                .with_weight(self.weight.clone())
                .with_distance(KNNRegressorDistance::from(self.distance)?),
        )
    }

    /// Convert to smartcore KNN regressor parameters using the configured distance metric.
    ///
    /// # Errors
    ///
    /// Returns [`DistanceError::UnsupportedDistance`] if the configured distance is not supported.
    pub fn to_regressor_params<INPUT: RealNumber + FloatNumber>(
        &self,
    ) -> Result<
        smartcore::neighbors::knn_regressor::KNNRegressorParameters<
            INPUT,
            KNNRegressorDistance<INPUT>,
        >,
        DistanceError,
    > {
        Ok(
            smartcore::neighbors::knn_regressor::KNNRegressorParameters::default()
                .with_k(self.k)
                .with_algorithm(self.algorithm.clone())
                .with_weight(self.weight.clone())
                .with_distance(KNNRegressorDistance::from(self.distance)?),
        )
    }
}

impl Default for KNNParameters {
    fn default() -> Self {
        Self {
            k: 3,
            weight: KNNWeightFunction::Uniform,
            algorithm: KNNAlgorithmName::CoverTree,
            distance: Distance::Euclidean,
        }
    }
}