abd-clam 0.29.0

//! Meta-ML regressors built on the [automl] crate
//!
//! These relate to various aspects of machine learning and data analysis.

use std::path::Path;

use automl::SupervisedModel;

use super::metaml::{MetaMLDataset, MetaMLModel};

#[derive(Default)]
/// A metaml wrapper for an [automl] linear regressor
///
pub struct LinearRegressor {
    /// The underlying model for the Linear Regressor.
    #[allow(dead_code)]
    model: Option<SupervisedModel>,
}

impl LinearRegressor {
    /// Constructs a new `LinearRegressor`
    #[must_use]
    pub const fn new() -> Self {
        Self { model: None }
    }
}

impl MetaMLModel for LinearRegressor {
    /// Trains the linear regressor using the provided dataset.
    ///
    /// This function trains the linear regressor using the input `MetaMLDataset` and updates the
    /// model's internal parameters accordingly.
    ///
    /// # Arguments
    ///
    /// * `_data`: The dataset used for training the linear regressor.
    ///
    fn train(&mut self, _data: MetaMLDataset) {
        todo!()

        // Create the settings and model
        // let settings = automl::Settings::default_regression().only(Algorithm::Linear);
        // let mut model = SupervisedModel::new(data, settings);
        //
        // // Train the model
        // model.train();
        //
        // // Store the model
        // self.model = Some(model);
    }
    /// Predict the target value given input features.
    ///
    /// # Arguments
    /// * `features` - The input features for prediction.
    ///
    /// # Returns
    /// The predicted target value.
    fn predict(&self, _features: &[f32; 6]) -> f32 {
        todo!()

        // let model = self
        //     .model
        //     .as_ref()
        //     .expect("Model must be trained before making predictions");
        //
        // model.predict(vec![features.to_vec()])[0]
    }
    /// Load a trained model from a file.
    ///
    /// # Arguments
    ///
    /// * `path` - The path to the model file.
    ///
    /// # Returns
    ///
    /// A `Result` containing the loaded model if successful, or an error message if the operation fails.
    ///
    /// # Errors
    ///
    /// Returns an error message if any of the following conditions occur:
    /// * The provided `path` cannot be converted to a string.
    /// * Loading the model from the file fails for any reason.
    fn load(_path: &Path) -> Result<Self, String> {
        todo!()

        // let path_str = path.to_str().ok_or("Failed to convert path to a string")?;
        // let model = SupervisedModel::new_from_file(path_str);
        // Ok(LinearRegressor { model: Some(model) })
    }

    /// Save the trained model to a file.
    ///
    /// # Arguments
    ///
    /// * `path` - The path to save the model to.
    ///
    /// # Returns
    ///
    /// A `Result` indicating success if the model was saved successfully, or an error message if the operation fails.
    ///
    /// # Errors
    ///
    /// Returns an error message if any of the following conditions occur:
    /// * The provided `path` cannot be converted to a string.
    /// * The model has not been trained or is missing when attempting to save it.
    /// * Saving the model to the specified path fails for any reason.
    fn save(&self, _path: &Path) -> Result<(), String> {
        todo!()
        // let model = self.model.as_ref().expect("Model must be trained before being saved.");
        // let path_str = path.to_str().ok_or("Failed to convert path to a string")?;
        // model.save(path_str);
        //
        // Ok(())
    }
}

#[derive(Default)]
/// A metaml wrapper for an [automl] decision tree regressor
pub struct DecisionTreeRegressor {
    /// The underlying model for the Decision Tree Regressor.
    #[allow(dead_code)]
    model: Option<SupervisedModel>,
}

impl DecisionTreeRegressor {
    /// The maximum depth of the decision tree in a `DecisionTreeRegressor`
    #[allow(dead_code)]
    const MAX_DEPTH: u16 = 3;

    /// Constructs a new `DecisionTreeRegressor` with a specified max tree depth
    #[must_use]
    pub const fn new() -> Self {
        Self { model: None }
    }
}

impl MetaMLModel for DecisionTreeRegressor {
    /// Trains the decision tree regressor using the provided dataset.
    ///
    /// This function trains the decision tree regressor using the input `MetaMLDataset` and updates
    /// the model's internal parameters accordingly.
    ///
    /// # Arguments
    ///
    /// * `_data`: The dataset used for training the decision tree regressor.
    ///
    fn train(&mut self, _data: MetaMLDataset) {
        todo!()

        // Create the settings and model
        // let settings = automl::Settings::default_regression()
        //     .only(Algorithm::DecisionTreeRegressor)
        //     .with_decision_tree_regressor_settings(
        //         DecisionTreeRegressorParameters::default().with_max_depth(Self::MAX_DEPTH),
        //     );
        // let mut model = SupervisedModel::new(data, settings);
        //
        // // Train the model
        // model.train();
        //
        // // Store the model
        // self.model = Some(model);
    }

    /// Predict the target value given input features.
    ///
    /// # Arguments
    /// * `features` - The input features for prediction.
    ///
    /// # Returns
    /// The predicted target value.
    fn predict(&self, _features: &[f32; 6]) -> f32 {
        todo!()

        // let model = self
        //     .model
        //     .as_ref()
        //     .expect("Model must be trained before making predictions");
        //
        // model.predict(vec![features.to_vec()])[0]
    }

    /// Load a trained model from a file.
    ///
    /// # Arguments
    ///
    /// * `path` - The path to the model file.
    ///
    /// # Returns
    ///
    /// A `Result` containing the loaded model if successful, or an error message if the operation fails.
    ///
    /// # Errors
    ///
    /// Returns an error message if any of the following conditions occur:
    /// * The provided `path` cannot be converted to a string.
    /// * Loading the model from the file fails for any reason.
    fn load(_path: &Path) -> Result<Self, String> {
        todo!()

        // let path_str = path.to_str().ok_or("Failed to convert path to a string")?;
        // let model = SupervisedModel::new_from_file(path_str);
        // Ok(DecisionTreeRegressor { model: Some(model) })
    }

    /// Save the trained model to a file.
    ///
    /// # Arguments
    ///
    /// * `path` - The path to save the model to.
    ///
    /// # Returns
    ///
    /// A `Result` indicating success if the model was saved successfully, or an error message if the operation fails.
    ///
    /// # Errors
    ///
    /// Returns an error message if any of the following conditions occur:
    /// * The provided `path` cannot be converted to a string.
    /// * The model has not been trained or is missing when attempting to save it.
    /// * Saving the model to the specified path fails for any reason.
    fn save(&self, _path: &Path) -> Result<(), String> {
        todo!()
        // let model = self.model.as_ref().expect("Model must be trained before being saved");
        // let path_str = path.to_str().ok_or("Failed to convert path to a string")?;
        // model.save(path_str);
        // Ok(())
    }
}