WIP: Initial commit of DBScan

linfa-clustering/Cargo.toml

@@ -28,3 +28,7 @@ approx = "0.3"
 [[bench]]
 name = "k_means"
 harness = false
+
+[[bench]]
+name = "dbscan"
+harness = false

linfa-clustering/benches/dbscan.rs

@@ -0,0 +1,38 @@
+use criterion::{
+    black_box, criterion_group, criterion_main, AxisScale, Criterion, ParameterizedBenchmark,
+    PlotConfiguration,
+};
+use linfa_clustering::{generate_blobs, Dbscan, DbscanHyperParams};
+use ndarray::Array2;
+use ndarray_rand::rand::SeedableRng;
+use ndarray_rand::rand_distr::Uniform;
+use ndarray_rand::RandomExt;
+use rand_isaac::Isaac64Rng;
+
+fn dbscan_bench(c: &mut Criterion) {
+    let mut rng = Isaac64Rng::seed_from_u64(40);
+    let cluster_sizes = vec![10, 100, 1000, 10000];
+
+    let benchmark = ParameterizedBenchmark::new(
+        "dbscan",
+        move |bencher, &cluster_size| {
+            let min_points = 4;
+            let n_features = 3;
+            let centroids =
+                Array2::random_using((min_points, n_features), Uniform::new(-30., 30.), &mut rng);
+            let dataset = generate_blobs(cluster_size, &centroids, &mut rng);
+            let hyperparams = DbscanHyperParams::new(min_points).tolerance(1e-3).build();
+            bencher.iter(|| black_box(Dbscan::predict(&hyperparams, &dataset)));
+        },
+        cluster_sizes,
+    )
+    .plot_config(PlotConfiguration::default().summary_scale(AxisScale::Logarithmic));
+    c.bench("dbscan", benchmark);
+}
+
+criterion_group! {
+    name = benches;
+    config = Criterion::default();
+    targets = dbscan_bench
+}
+criterion_main!(benches);

linfa-clustering/examples/dbscan.rs

@@ -0,0 +1,33 @@
+use linfa_clustering::{generate_blobs, Dbscan, DbscanHyperParams};
+use ndarray::array;
+use ndarray_npy::write_npy;
+use ndarray_rand::rand::SeedableRng;
+use rand_isaac::Isaac64Rng;
+
+// A routine DBScan task: build a synthetic dataset, predict clusters for it
+// and save both training data and predictions to disk.
+fn main() {
+    // Our random number generator, seeded for reproducibility
+    let mut rng = Isaac64Rng::seed_from_u64(42);
+
+    // For each our expected centroids, generate `n` data points around it (a "blob")
+    let expected_centroids = array![[10., 10.], [1., 12.], [20., 30.], [-20., 30.],];
+    let n = 10000;
+    let dataset = generate_blobs(n, &expected_centroids, &mut rng);
+
+    // Configure our training algorithm
+    let min_points = 3;
+    let hyperparams = DbscanHyperParams::new(min_points).tolerance(1e-5).build();
+
+    // Infer an optimal set of centroids based on the training data distribution
+    let cluster_memberships = Dbscan::predict(&hyperparams, &dataset);
+
+    // Save to disk our dataset (and the cluster label assigned to each observation)
+    // We use the `npy` format for compatibility with NumPy
+    write_npy("clustered_dataset.npy", dataset).expect("Failed to write .npy file");
+    write_npy(
+        "clustered_memberships.npy",
+        cluster_memberships.map(|&x| x.map(|c| c as i64).unwrap_or(-1)),
+    )
+    .expect("Failed to write .npy file");
+}

linfa-clustering/src/dbscan/algorithm.rs

@@ -0,0 +1,188 @@
+use crate::dbscan::hyperparameters::DbscanHyperParams;
+use ndarray::{Array1, ArrayBase, ArrayView, Axis, Data, Ix1, Ix2};
+use ndarray_stats::DeviationExt;
+use serde::{Deserialize, Serialize};
+
+#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
+/// DBSCAN (Density-based Spatial Clustering of Applications with Noise)
+/// clusters together points which are close together with enough neighbors
+/// labelled points which are sparsely neighbored as noise. As points may be
+/// part of a cluster or noise the predict method returns
+/// `Array1<Option<usize>>`
+///
+/// As it groups together points in dense regions the number of clusters is
+/// determined by the dataset and distance tolerance not the user.
+///
+/// We provide an implemention of the standard O(N^2) query-based algorithm
+/// of which more details can be found in the next section or
+/// [here](https://en.wikipedia.org/wiki/DBSCAN).
+///
+/// The standard DBSCAN algorithm isn't iterative and therefore there's
+/// no fit method provided only predict.
+///
+/// ## The algorithm
+///
+/// The algorithm iterates over each point in the dataset and for every point
+/// not yet assigned to a cluster:
+/// - Find all points within the neighborhood of size `tolerance`
+/// - If the number of points in the neighborhood is below a minimum size label
+/// as noise
+/// - Otherwise label the point with the cluster ID and repeat with each of the
+/// neighbours
+///
+/// ## Tutorial
+///
+/// Let's do a walkthrough of an example running DBSCAN on some data.
+///
+/// ```
+/// use linfa_clustering::{DbscanHyperParams, Dbscan, generate_blobs};
+/// use ndarray::{Axis, array, s};
+/// use ndarray_rand::rand::SeedableRng;
+/// use rand_isaac::Isaac64Rng;
+/// use approx::assert_abs_diff_eq;
+///
+/// // Our random number generator, seeded for reproducibility
+/// let seed = 42;
+/// let mut rng = Isaac64Rng::seed_from_u64(seed);
+///
+/// // `expected_centroids` has shape `(n_centroids, n_features)`
+/// // i.e. three points in the 2-dimensional plane
+/// let expected_centroids = array![[0., 1.], [-10., 20.], [-1., 10.]];
+/// // Let's generate a synthetic dataset: three blobs of observations
+/// // (100 points each) centered around our `expected_centroids`
+/// let observations = generate_blobs(100, &expected_centroids, &mut rng);
+///
+/// // Let's configure and run our DBSCAN algorithm
+/// // We use the builder pattern to specify the hyperparameters
+/// // `min_points` is the only mandatory parameter.
+/// // If you don't specify the others (e.g. `tolerance`)
+/// // default values will be used.
+/// let min_points = 3;
+/// let hyperparams = DbscanHyperParams::new(min_points)
+///     .tolerance(1e-2)
+///     .build();
+/// // Let's run the algorithm!
+/// let clusters = Dbscan::predict(&hyperparams, &observations);
+/// // Points are `None` if noise `Some(id)` if belonging to a cluster.
+/// ```
+///
+pub struct Dbscan;
+
+impl Dbscan {
+    pub fn predict(
+        hyperparameters: &DbscanHyperParams,
+        observations: &ArrayBase<impl Data<Elem = f64>, Ix2>,
+    ) -> Array1<Option<usize>> {
+        let mut cluster_memberships = Array1::from_elem(observations.dim().1, None);
+        let mut current_cluster_id = 0;
+        for (i, obs) in observations.axis_iter(Axis(1)).enumerate() {
+            if cluster_memberships[i].is_some() {
+                continue;
+            }
+            let (neighbor_count, mut search_queue) = find_neighbors(
+                &obs,
+                observations,
+                hyperparameters.tolerance(),
+                &cluster_memberships,
+            );
+            if neighbor_count < hyperparameters.minimum_points() {
+                continue;
+            }
+            // Now go over the neighbours adding them to the cluster
+            cluster_memberships[i] = Some(current_cluster_id);
+
+            while !search_queue.is_empty() {
+                let candidate = search_queue.remove(0);
+
+                let (neighbor_count, mut neighbors) = find_neighbors(
+                    &candidate.1,
+                    observations,
+                    hyperparameters.tolerance(),
+                    &cluster_memberships,
+                );
+                if neighbor_count >= hyperparameters.minimum_points() {
+                    cluster_memberships[candidate.0] = Some(current_cluster_id);
+                    search_queue.append(&mut neighbors);
+                }
+            }
+            current_cluster_id += 1;
+        }
+        cluster_memberships
+    }
+}
+
+fn find_neighbors<'a>(
+    candidate: &ArrayBase<impl Data<Elem = f64>, Ix1>,
+    observations: &'a ArrayBase<impl Data<Elem = f64>, Ix2>,
+    eps: f64,
+    clusters: &Array1<Option<usize>>,
+) -> (usize, Vec<(usize, ArrayView<'a, f64, Ix1>)>) {
+    let mut res = vec![];
+    let mut count = 0;
+    for (i, (obs, cluster)) in observations
+        .axis_iter(Axis(1))
+        .zip(clusters.iter())
+        .enumerate()
+    {
+        if candidate.l2_dist(&obs).unwrap() < eps {
+            count += 1;
+            if cluster.is_none() {
+                res.push((i, obs));
+            }
+        }
+    }
+    (count, res)
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use ndarray::{arr1, s, Array2};
+
+    #[test]
+    fn nested_clusters() {
+        // Create a circuit of points and then a cluster in the centre
+        // and ensure they are identified as two separate clusters
+        let params = DbscanHyperParams::new(2).tolerance(1.0).build();
+
+        let mut data: Array2<f64> = Array2::zeros((2, 50));
+        let rising = Array1::linspace(0.0, 8.0, 10);
+        data.slice_mut(s![0, 0..10]).assign(&rising);
+        data.slice_mut(s![0, 10..20]).assign(&rising);
+        data.slice_mut(s![1, 20..30]).assign(&rising);
+        data.slice_mut(s![1, 30..40]).assign(&rising);
+
+        data.slice_mut(s![1, 0..10]).fill(0.0);
+        data.slice_mut(s![1, 10..20]).fill(8.0);
+        data.slice_mut(s![0, 20..30]).fill(0.0);
+        data.slice_mut(s![0, 30..40]).fill(8.0);
+
+        data.slice_mut(s![.., 40..]).fill(5.0);
+
+        let labels = Dbscan::predict(&params, &data);
+
+        assert!(labels.slice(s![..40]).iter().all(|x| x == &Some(0)));
+        assert!(labels.slice(s![40..]).iter().all(|x| x == &Some(1)));
+    }
+
+    #[test]
+    fn non_cluster_points() {
+        let params = DbscanHyperParams::new(4).build();
+        let mut data: Array2<f64> = Array2::zeros((2, 5));
+        data.slice_mut(s![.., 0]).assign(&arr1(&[10.0, 10.0]));
+
+        let labels = Dbscan::predict(&params, &data);
+        let expected = arr1(&[None, Some(0), Some(0), Some(0), Some(0)]);
+        assert_eq!(labels, expected);
+    }
+
+    #[test]
+    fn dataset_too_small() {
+        let params = DbscanHyperParams::new(4).build();
+
+        let data: Array2<f64> = Array2::zeros((2, 3));
+
+        let labels = Dbscan::predict(&params, &data);
+        assert!(labels.iter().all(|x| x.is_none()));
+    }
+}

linfa-clustering/src/dbscan/hyperparameters.rs

@@ -0,0 +1,60 @@
+use serde::{Deserialize, Serialize};
+
+#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
+/// The set of hyperparameters that can be specified for the execution of
+/// the [DBSCAN algorithm](struct.Dbscan.html).
+pub struct DbscanHyperParams {
+    /// Distance between points for them to be considered neighbours.
+    tolerance: f64,
+    /// Minimum number of neighboring points a point needs to have to be a core
+    /// point and not a noise point.
+    min_points: usize,
+}
+
+/// Helper struct used to construct a set of hyperparameters for
+pub struct DbscanHyperParamsBuilder {
+    tolerance: f64,
+    min_points: usize,
+}
+
+impl DbscanHyperParamsBuilder {
+    pub fn tolerance(mut self, tolerance: f64) -> Self {
+        self.tolerance = tolerance;
+        self
+    }
+
+    pub fn build(self) -> DbscanHyperParams {
+        DbscanHyperParams::build(self.tolerance, self.min_points)
+    }
+}
+
+impl DbscanHyperParams {
+    pub fn new(min_points: usize) -> DbscanHyperParamsBuilder {
+        DbscanHyperParamsBuilder {
+            min_points,
+            tolerance: 1e-4,
+        }
+    }
+
+    pub fn tolerance(&self) -> f64 {
+        self.tolerance
+    }
+
+    pub fn minimum_points(&self) -> usize {
+        self.min_points
+    }
+
+    fn build(tolerance: f64, min_points: usize) -> Self {
+        if tolerance <= 0. {
+            panic!("`tolerance` must be greater than 0!");
+        }
+        // There is always at least one neighbor to a point (itself)
+        if min_points <= 1 {
+            panic!("`min_points` must be greater than 1!");
+        }
+        Self {
+            tolerance,
+            min_points,
+        }
+    }
+}

linfa-clustering/src/dbscan/mod.rs

@@ -0,0 +1,5 @@
+mod algorithm;
+mod hyperparameters;
+
+pub use algorithm::*;
+pub use hyperparameters::*;

linfa-clustering/src/lib.rs

@@ -18,9 +18,11 @@
 //! Implementation choices, algorithmic details and a tutorial can be found [here](struct.KMeans.html).
 //!
 //! Check [here](https://github.com/LukeMathWalker/clustering-benchmarks) for extensive benchmarks against `scikit-learn`'s K-means implementation.
+mod dbscan;
 #[allow(clippy::new_ret_no_self)]
 mod k_means;
 mod utils;
 
+pub use dbscan::*;
 pub use k_means::*;
 pub use utils::*;