Add new ensemble methods crate: linfa-ensemble

README.md

@@ -30,22 +30,23 @@ Where does `linfa` stand right now? [Are we learning yet?](http://www.arewelearn
 
 | Name | Purpose | Status | Category |  Notes | 
 | :--- | :--- | :---| :--- | :---| 
+| [bayes](algorithms/linfa-bayes/) | Naive Bayes | Tested | Supervised learning | Contains Bernouilli, Gaussian and Multinomial Naive Bayes |
 | [clustering](algorithms/linfa-clustering/) | Data clustering | Tested / Benchmarked  | Unsupervised learning | Clustering of unlabeled data; contains K-Means, Gaussian-Mixture-Model, DBSCAN and OPTICS | 
-| [kernel](algorithms/linfa-kernel/) | Kernel methods for data transformation  | Tested  | Pre-processing | Maps feature vector into higher-dimensional space| 
-| [linear](algorithms/linfa-linear/) | Linear regression | Tested  | Partial fit | Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM) | 
+| [ensemble](algorithms/linfa-ensemble/) | Ensemble methods | Tested | Supervised learning | Contains bagging |
 | [elasticnet](algorithms/linfa-elasticnet/) | Elastic Net | Tested | Supervised learning | Linear regression with elastic net constraints |
-| [logistic](algorithms/linfa-logistic/) | Logistic regression | Tested  | Partial fit | Builds two-class logistic regression models
-| [reduction](algorithms/linfa-reduction/) | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping, Principal Component Analysis (PCA), Random projections |
-| [trees](algorithms/linfa-trees/) | Decision trees | Tested / Benchmarked  | Supervised learning | Linear decision trees
-| [svm](algorithms/linfa-svm/) | Support Vector Machines | Tested  | Supervised learning | Classification or regression analysis of labeled datasets | 
+| [ftrl](algorithms/linfa-ftrl/) | Follow The Regularized Leader - proximal | Tested  / Benchmarked | Partial fit | Contains L1 and L2 regularization. Possible incremental update | 
 | [hierarchical](algorithms/linfa-hierarchical/) | Agglomerative hierarchical clustering | Tested | Unsupervised learning | Cluster and build hierarchy of clusters |
-| [bayes](algorithms/linfa-bayes/) | Naive Bayes | Tested | Supervised learning | Contains Gaussian Naive Bayes |
 | [ica](algorithms/linfa-ica/) | Independent component analysis | Tested | Unsupervised learning | Contains FastICA implementation |
-| [pls](algorithms/linfa-pls/) | Partial Least Squares | Tested | Supervised learning | Contains PLS estimators for dimensionality reduction and regression |
-| [tsne](algorithms/linfa-tsne/) | Dimensionality reduction| Tested | Unsupervised learning | Contains exact solution and Barnes-Hut approximation t-SNE |
-| [preprocessing](algorithms/linfa-preprocessing/) |Normalization & Vectorization| Tested / Benchmarked | Pre-processing | Contains data normalization/whitening and count vectorization/tf-idf |
+| [kernel](algorithms/linfa-kernel/) | Kernel methods for data transformation  | Tested  | Pre-processing | Maps feature vector into higher-dimensional space | 
+| [linear](algorithms/linfa-linear/) | Linear regression | Tested  | Partial fit | Contains Ordinary Least Squares (OLS), Generalized Linear Models (GLM) | 
+| [logistic](algorithms/linfa-logistic/) | Logistic regression | Tested  | Partial fit | Builds two-class logistic regression models |
 | [nn](algorithms/linfa-nn/) | Nearest Neighbours & Distances | Tested / Benchmarked | Pre-processing | Spatial index structures and distance functions |
-| [ftrl](algorithms/linfa-ftrl/) | Follow The Regularized Leader - proximal | Tested  / Benchmarked | Partial fit | Contains L1 and L2 regularization. Possible incremental update |
+| [pls](algorithms/linfa-pls/) | Partial Least Squares | Tested | Supervised learning | Contains PLS estimators for dimensionality reduction and regression |
+| [preprocessing](algorithms/linfa-preprocessing/) | Normalization & Vectorization| Tested / Benchmarked | Pre-processing | Contains data normalization/whitening and count vectorization/tf-idf |
+| [reduction](algorithms/linfa-reduction/) | Dimensionality reduction | Tested | Pre-processing | Diffusion mapping, Principal Component Analysis (PCA), Random projections |
+| [svm](algorithms/linfa-svm/) | Support Vector Machines | Tested  | Supervised learning | Classification or regression analysis of labeled datasets | 
+| [trees](algorithms/linfa-trees/) | Decision trees | Tested / Benchmarked  | Supervised learning | Linear decision trees |
+| [tsne](algorithms/linfa-tsne/) | Dimensionality reduction | Tested | Unsupervised learning | Contains exact solution and Barnes-Hut approximation t-SNE |
 
 We believe that only a significant community effort can nurture, build, and sustain a machine learning ecosystem in Rust - there is no other way forward.
 

algorithms/linfa-ensemble/Cargo.toml

@@ -0,0 +1,40 @@
+[package]
+name = "linfa-ensemble"
+version = "0.7.0"
+edition = "2018"
+authors = [
+    "James Knight <[email protected]>",
+    "James Kay <[email protected]>",
+]
+description = "A general method for creating ensemble classifiers"
+license = "MIT/Apache-2.0"
+
+repository = "https://github.com/rust-ml/linfa"
+readme = "README.md"
+
+keywords = ["machine-learning", "linfa", "ensemble"]
+categories = ["algorithms", "mathematics", "science"]
+
+[features]
+default = []
+serde = ["serde_crate", "ndarray/serde"]
+
+[dependencies.serde_crate]
+package = "serde"
+optional = true
+version = "1.0"
+default-features = false
+features = ["std", "derive"]
+
+[dependencies]
+ndarray = { version = "0.15", features = ["rayon", "approx"] }
+ndarray-rand = "0.14"
+rand = "0.8.5"
+
+linfa = { version = "0.7.1", path = "../.." }
+linfa-trees = { version = "0.7.1", path = "../linfa-trees" }
+
+[dev-dependencies]
+linfa-datasets = { version = "0.7.1", path = "../../datasets/", features = [
+    "iris",
+] }

algorithms/linfa-ensemble/README.md

@@ -0,0 +1,21 @@
+# Ensemble Learning
+
+`linfa-ensemble` provides pure Rust implementations of Ensemble Learning algorithms for the Linfa toolkit.
+
+## The Big Picture
+
+`linfa-ensemble` is a crate in the [`linfa`](https://crates.io/crates/linfa) ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's `scikit-learn`.
+
+## Current state
+
+`linfa-ensemble` currently provides an implementation of bootstrap aggregation (bagging) for other classifiers provided in linfa.
+
+## Examples
+
+You can find examples in the `examples/` directory. To run an bootstrap aggregation for ensemble of decision trees (a Random Forest) use:
+
+```bash
+$ cargo run --example randomforest_iris --release
+```
+
+

algorithms/linfa-ensemble/examples/bagging_iris.rs

@@ -0,0 +1,35 @@
+use linfa::prelude::{Fit, Predict, ToConfusionMatrix};
+use linfa_ensemble::EnsembleLearnerParams;
+use linfa_trees::DecisionTree;
+use ndarray_rand::rand::SeedableRng;
+use rand::rngs::SmallRng;
+
+fn main() {
+    // Number of models in the ensemble
+    let ensemble_size = 100;
+    // Proportion of training data given to each model
+    let bootstrap_proportion = 0.7;
+
+    // Load dataset
+    let mut rng = SmallRng::seed_from_u64(42);
+    let (train, test) = linfa_datasets::iris()
+        .shuffle(&mut rng)
+        .split_with_ratio(0.8);
+
+    // Train ensemble learner model
+    let model = EnsembleLearnerParams::new(DecisionTree::params())
+        .ensemble_size(ensemble_size)
+        .bootstrap_proportion(bootstrap_proportion)
+        .fit(&train)
+        .unwrap();
+
+    // Return highest ranking predictions
+    let final_predictions_ensemble = model.predict(&test);
+    println!("Final Predictions: \n{:?}", final_predictions_ensemble);
+
+    let cm = final_predictions_ensemble.confusion_matrix(&test).unwrap();
+
+    println!("{:?}", cm);
+    println!("Test accuracy: {} \n with default Decision Tree params, \n Ensemble Size: {},\n Bootstrap Proportion: {}",
+    100.0 * cm.accuracy(), ensemble_size, bootstrap_proportion);
+}

algorithms/linfa-ensemble/src/algorithm.rs

@@ -0,0 +1,103 @@
+use crate::EnsembleLearnerValidParams;
+use linfa::{
+    dataset::{AsTargets, AsTargetsMut, FromTargetArrayOwned, Records},
+    error::Error,
+    traits::*,
+    DatasetBase,
+};
+use ndarray::{Array2, Axis, Zip};
+use rand::Rng;
+use std::{cmp::Eq, collections::HashMap, hash::Hash};
+
+pub struct EnsembleLearner<M> {
+    pub models: Vec<M>,
+}
+
+impl<M> EnsembleLearner<M> {
+    // Generates prediction iterator returning predictions from each model
+    pub fn generate_predictions<'b, R: Records, T>(
+        &'b self,
+        x: &'b R,
+    ) -> impl Iterator<Item = T> + 'b
+    where
+        M: Predict<&'b R, T>,
+    {
+        self.models.iter().map(move |m| m.predict(x))
+    }
+}
+
+impl<F: Clone, T, M> PredictInplace<Array2<F>, T> for EnsembleLearner<M>
+where
+    M: PredictInplace<Array2<F>, T>,
+    <T as AsTargets>::Elem: Copy + Eq + Hash + std::fmt::Debug,
+    T: AsTargets + AsTargetsMut<Elem = <T as AsTargets>::Elem>,
+{
+    fn predict_inplace(&self, x: &Array2<F>, y: &mut T) {
+        let y_array = y.as_targets();
+        assert_eq!(
+            x.nrows(),
+            y_array.len_of(Axis(0)),
+            "The number of data points must match the number of outputs."
+        );
+
+        let predictions = self.generate_predictions(x);
+
+        // prediction map has same shape as y_array, but the elements are maps
+        let mut prediction_maps = y_array.map(|_| HashMap::new());
+
+        for prediction in predictions {
+            let p_arr = prediction.as_targets();
+            assert_eq!(p_arr.shape(), y_array.shape());
+            // Insert each prediction value into the corresponding map
+            Zip::from(&mut prediction_maps)
+                .and(&p_arr)
+                .for_each(|map, val| *map.entry(*val).or_insert(0) += 1);
+        }
+
+        // For each prediction, pick the result with the highest number of votes
+        let agg_preds = prediction_maps.map(|map| map.iter().max_by_key(|(_, v)| **v).unwrap().0);
+        let mut y_array = y.as_targets_mut();
+        for (y, pred) in y_array.iter_mut().zip(agg_preds.iter()) {
+            *y = **pred
+        }
+    }
+
+    fn default_target(&self, x: &Array2<F>) -> T {
+        self.models[0].default_target(x)
+    }
+}
+
+impl<D, T, P: Fit<Array2<D>, T::Owned, Error>, R: Rng + Clone> Fit<Array2<D>, T, Error>
+    for EnsembleLearnerValidParams<P, R>
+where
+    D: Clone,
+    T: FromTargetArrayOwned,
+    T::Elem: Copy + Eq + Hash,
+    T::Owned: AsTargets,
+{
+    type Object = EnsembleLearner<P::Object>;
+
+    fn fit(
+        &self,
+        dataset: &DatasetBase<Array2<D>, T>,
+    ) -> core::result::Result<Self::Object, Error> {
+        let mut models = Vec::new();
+        let mut rng = self.rng.clone();
+
+        let dataset_size =
+            ((dataset.records.nrows() as f64) * self.bootstrap_proportion).ceil() as usize;
+
+        let iter = dataset.bootstrap_samples(dataset_size, &mut rng);
+
+        for train in iter {
+            let model = self.model_params.fit(&train).unwrap();
+            models.push(model);
+
+            if models.len() == self.ensemble_size {
+                break;
+            }
+        }
+
+        Ok(EnsembleLearner { models })
+    }
+}

algorithms/linfa-ensemble/src/hyperparams.rs

@@ -0,0 +1,73 @@
+use linfa::{
+    error::{Error, Result},
+    ParamGuard,
+};
+use rand::rngs::ThreadRng;
+use rand::Rng;
+
+#[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 model parameters for the base model
+    pub model_params: P,
+    pub rng: R,
+}
+
+#[derive(Clone, Copy, Debug, PartialEq)]
+pub struct EnsembleLearnerParams<P, R>(EnsembleLearnerValidParams<P, R>);
+
+impl<P> EnsembleLearnerParams<P, ThreadRng> {
+    pub fn new(model_params: P) -> EnsembleLearnerParams<P, ThreadRng> {
+        Self::new_fixed_rng(model_params, rand::thread_rng())
+    }
+}
+
+impl<P, R: Rng + Clone> EnsembleLearnerParams<P, R> {
+    pub fn new_fixed_rng(model_params: P, rng: R) -> EnsembleLearnerParams<P, R> {
+        Self(EnsembleLearnerValidParams {
+            ensemble_size: 1,
+            bootstrap_proportion: 1.0,
+            model_params,
+            rng,
+        })
+    }
+
+    pub fn ensemble_size(mut self, size: usize) -> Self {
+        self.0.ensemble_size = size;
+        self
+    }
+
+    pub fn bootstrap_proportion(mut self, proportion: f64) -> Self {
+        self.0.bootstrap_proportion = proportion;
+        self
+    }
+}
+
+impl<P, R> ParamGuard for EnsembleLearnerParams<P, R> {
+    type Checked = EnsembleLearnerValidParams<P, R>;
+    type Error = Error;
+
+    fn check_ref(&self) -> Result<&Self::Checked> {
+        if self.0.bootstrap_proportion > 1.0 || self.0.bootstrap_proportion <= 0.0 {
+            Err(Error::Parameters(format!(
+                "Bootstrap proportion should be greater than zero and less than or equal to one, but was {}",
+                self.0.bootstrap_proportion
+            )))
+        } else if self.0.ensemble_size < 1 {
+            Err(Error::Parameters(format!(
+                "Ensemble size should be less than one, but was {}",
+                self.0.ensemble_size
+            )))
+        } else {
+            Ok(&self.0)
+        }
+    }
+
+    fn check(self) -> Result<Self::Checked> {
+        self.check_ref()?;
+        Ok(self.0)
+    }
+}

algorithms/linfa-ensemble/src/lib.rs

@@ -0,0 +1,77 @@
+//! # Ensemble Learning Algorithms
+//!
+//! 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.
+//!
+//! ## Bootstrap Aggregation (aka Bagging)
+//!
+//! A typical example of ensemble method is Bootstrapo AGgregation, which combines the predictions of
+//! several decision trees (see `linfa-trees`) trained on different samples subset of the training dataset.
+//!
+//! ## Reference
+//!
+//! * [Scikit-Learn User Guide](https://scikit-learn.org/stable/modules/ensemble.html)
+//!
+//! ## 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)
+//!     .bootstrap_proportion(0.7)
+//!     .fit(&train)
+//!     .unwrap();
+//!
+//! // Make predictions on the test set
+//! let predictions = bagging_model.predict(&test);
+//! ```
+//!
+mod algorithm;
+mod hyperparams;
+
+pub use algorithm::*;
+pub use hyperparams::*;
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use linfa::prelude::{Fit, Predict, ToConfusionMatrix};
+    use linfa_trees::DecisionTree;
+    use ndarray_rand::rand::SeedableRng;
+    use rand::rngs::SmallRng;
+
+    #[test]
+    fn test_ensemble_learner_accuracy_on_iris_dataset() {
+        let mut rng = SmallRng::seed_from_u64(42);
+        let (train, test) = linfa_datasets::iris()
+            .shuffle(&mut rng)
+            .split_with_ratio(0.8);
+
+        let model = EnsembleLearnerParams::new(DecisionTree::params())
+            .ensemble_size(100)
+            .bootstrap_proportion(0.7)
+            .fit(&train)
+            .unwrap();
+
+        let predictions = model.predict(&test);
+
+        let cm = predictions.confusion_matrix(&test).unwrap();
+        let acc = cm.accuracy();
+        assert!(acc >= 0.9, "Expected accuracy to be above 90%, got {}", acc);
+    }
+}