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//! Meta-ML regressors built on the [automl] crate
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(())
}
}