Improve linfa_ensemble documentation

algorithms/linfa-ensemble/src/algorithm.rs

@@ -10,8 +10,63 @@ use ndarray::{Array2, Axis, Zip};
 use rand::Rng;
 use std::{cmp::Eq, collections::HashMap, hash::Hash};
 
+/// A fitted ensemble of [Decision Trees](DecisionTree) trained on a random subset of features.
+///
+/// Check out [EnsembleLearner] documentation for more information regarding [RandomForest] interface.
 pub type RandomForest<F, L> = EnsembleLearner<DecisionTree<F, L>>;
 
+/// A fitted ensemble of learners for classification.
+///
+/// ## Structure
+///
+/// An Ensemble Learner is composed of a collection of fitted models of type `M`.
+///
+/// ## Fitting Algorithm
+///
+/// Given a [DatasetBase](DatasetBase) denoted as `D`,
+/// 1. Create as many distinct bootstrapped subset of the original dataset `D` as number of
+///    distinct model to fit.
+/// 2. Fit each distinct model on a distinct bootstrapped subset of `D`.
+///
+/// Note that the subset size, as well as the subset of feature to use in each training subset can
+/// be specified in the [parameters](crate::EnsembleLearnerParams).
+///
+/// ## Prediction Algorithm
+///
+/// The prediction result is the result of majority voting across the fitted learners.
+///
+/// ## Example
+///
+/// This example shows how to train a bagging model using 100 decision trees,
+/// each trained on 70% of the training data (bootstrap sampling).
+/// ```no_run
+/// use linfa::prelude::{Fit, Predict};
+/// use linfa_ensemble::EnsembleLearnerParams;
+/// use linfa_trees::DecisionTree;
+/// use ndarray_rand::rand::SeedableRng;
+/// use rand::rngs::SmallRng;
+///
+/// // Load Iris dataset
+/// let mut rng = SmallRng::seed_from_u64(42);
+/// let (train, test) = linfa_datasets::iris()
+///     .shuffle(&mut rng)
+///     .split_with_ratio(0.8);
+///
+/// // Train the model on the iris dataset
+/// let bagging_model = EnsembleLearnerParams::new(DecisionTree::params())
+///     .ensemble_size(100)        // Number of Decision Tree to fit
+///     .bootstrap_proportion(0.7) // Select only 70% of the data via bootstrap
+///     .fit(&train)
+///     .unwrap();
+///
+/// // Make predictions on the test set
+/// let predictions = bagging_model.predict(&test);
+/// ```
+///
+/// ## References
+///
+/// * [Scikit-Learn User Guide](https://scikit-learn.org/stable/modules/ensemble.html)
+/// * [An Introduction to Statistical Learning](https://www.statlearning.com/)
 pub struct EnsembleLearner<M> {
     pub models: Vec<M>,
     pub model_features: Vec<Vec<usize>>,

algorithms/linfa-ensemble/src/hyperparams.rs

@@ -6,22 +6,26 @@ use linfa_trees::DecisionTreeParams;
 use rand::rngs::ThreadRng;
 use rand::Rng;
 
+/// The set of valid hyper-parameters that can be specified for the fitting procedure of the
+/// [Ensemble Learner](crate::EnsembleLearner).
 #[derive(Clone, Copy, Debug, PartialEq)]
 pub struct EnsembleLearnerValidParams<P, R> {
     /// The number of models in the ensemble
     pub ensemble_size: usize,
     /// The proportion of the total number of training samples that should be given to each model for training
     pub bootstrap_proportion: f64,
-    /// The proportion of the total number of training feature that should be given to each model for training
+    /// The proportion of the total number of training features that should be given to each model for training
     pub feature_proportion: f64,
     /// The model parameters for the base model
     pub model_params: P,
     pub rng: R,
 }
 
+/// A helper struct for building a set of [Ensemble Learner](crate::EnsembleLearner) hyper-parameters.
 #[derive(Clone, Copy, Debug, PartialEq)]
 pub struct EnsembleLearnerParams<P, R>(EnsembleLearnerValidParams<P, R>);
 
+/// A helper struct for building a set of [Random Forest](crate::RandomForest) hyper-parameters.
 pub type RandomForestParams<F, L, R> = EnsembleLearnerParams<DecisionTreeParams<F, L>, R>;
 
 impl<P> EnsembleLearnerParams<P, ThreadRng> {
@@ -41,16 +45,25 @@ impl<P, R: Rng + Clone> EnsembleLearnerParams<P, R> {
         })
     }
 
+    /// Specifies the number of models to fit in the ensemble.
     pub fn ensemble_size(mut self, size: usize) -> Self {
         self.0.ensemble_size = size;
         self
     }
 
+    /// Sets the proportion of the total number of training samples that should be given to each model for training
+    ///
+    /// Note that the `proportion` should be in the interval (0, 1] in order to pass the  
+    /// parameter validation check.
     pub fn bootstrap_proportion(mut self, proportion: f64) -> Self {
         self.0.bootstrap_proportion = proportion;
         self
     }
 
+    /// Sets the proportion of the total number of training features that should be given to each model for training
+    ///
+    /// Note that the `proportion` should be in the interval (0, 1] in order to pass the
+    /// parameter validation check.
     pub fn feature_proportion(mut self, proportion: f64) -> Self {
         self.0.feature_proportion = proportion;
         self

algorithms/linfa-ensemble/src/lib.rs

@@ -3,14 +3,18 @@
 //! Ensemble methods combine the predictions of several base estimators built with a given
 //! learning algorithm in order to improve generalizability / robustness over a single estimator.
 //!
+//! This crate (`linfa-ensemble`), provides pure Rust implementations of popular ensemble techniques, such as
+//! * [Boostrap Aggregation](EnsembleLearner)
+//! * [Random Forest](RandomForest)
+//!
 //! ## Bootstrap Aggregation (aka Bagging)
 //!
 //! A typical example of ensemble method is Bootstrap Aggregation, which combines the predictions of
-//! several decision trees (see `linfa-trees`) trained on different samples subset of the training dataset.
+//! several decision trees (see [`linfa-trees`](linfa_trees)) trained on different samples subset of the training dataset.
 //!
 //! ## Random Forest
 //!
-//! A special case of Bootstrap Aggregation using decision trees (see `linfa-trees`) with random feature
+//! A special case of Bootstrap Aggregation using decision trees (see  [`linfa-trees`](linfa_trees)) with random feature
 //! selection. A typical number of random prediction to be selected is $\sqrt{p}$ with $p$ being
 //! the number of available features.
 //!
@@ -48,7 +52,7 @@
 //! let predictions = bagging_model.predict(&test);
 //! ```
 //!
-//! This example shows how to train a Random Forest model using 100 decision trees,
+//! This example shows how to train a [Random Forest](RandomForest) model using 100 decision trees,
 //! each trained on 70% of the training data (bootstrap sampling) and using only
 //! 30% of the available features.
 //!
@@ -66,15 +70,15 @@
 //!     .split_with_ratio(0.8);
 //!
 //! // Train the model on the iris dataset
-//! let bagging_model = RandomForestParams::new(DecisionTree::params())
+//! let random_forest = RandomForestParams::new(DecisionTree::params())
 //!     .ensemble_size(100)        // Number of Decision Tree to fit
 //!     .bootstrap_proportion(0.7) // Select only 70% of the data via bootstrap
 //!     .feature_proportion(0.3)   // Select only 30% of the feature
 //!     .fit(&train)
 //!     .unwrap();
 //!
 //! // Make predictions on the test set
-//! let predictions = bagging_model.predict(&test);
+//! let predictions = random_forest.predict(&test);
 //! ```
 
 mod algorithm;