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@@ -28,3 +28,7 @@ approx = "0.3" | |
[[bench]] | ||
name = "k_means" | ||
harness = false | ||
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[[bench]] | ||
name = "dbscan" | ||
harness = false |
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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; | ||
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fn dbscan_bench(c: &mut Criterion) { | ||
let mut rng = Isaac64Rng::seed_from_u64(40); | ||
let cluster_sizes = vec![10, 100, 1000, 10000]; | ||
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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, ¢roids, &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); | ||
} | ||
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criterion_group! { | ||
name = benches; | ||
config = Criterion::default(); | ||
targets = dbscan_bench | ||
} | ||
criterion_main!(benches); |
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use linfa_clustering::{generate_blobs, Dbscan, DbscanHyperParams}; | ||
use ndarray::array; | ||
use ndarray_npy::write_npy; | ||
use ndarray_rand::rand::SeedableRng; | ||
use rand_isaac::Isaac64Rng; | ||
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// 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); | ||
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// 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); | ||
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// Configure our training algorithm | ||
let min_points = 3; | ||
let hyperparams = DbscanHyperParams::new(min_points).tolerance(1e-5).build(); | ||
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// Infer an optimal set of centroids based on the training data distribution | ||
let cluster_memberships = Dbscan::predict(&hyperparams, &dataset); | ||
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// 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"); | ||
} |
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use crate::dbscan::hyperparameters::DbscanHyperParams; | ||
use ndarray::{Array1, ArrayBase, ArrayView, Axis, Data, Ix1, Ix2}; | ||
use ndarray_stats::DeviationExt; | ||
use serde::{Deserialize, Serialize}; | ||
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#[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; | ||
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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); | ||
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while !search_queue.is_empty() { | ||
let candidate = search_queue.remove(0); | ||
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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 | ||
} | ||
} | ||
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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) | ||
} | ||
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#[cfg(test)] | ||
mod tests { | ||
use super::*; | ||
use ndarray::{arr1, s, Array2}; | ||
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#[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(); | ||
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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); | ||
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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); | ||
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data.slice_mut(s![.., 40..]).fill(5.0); | ||
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let labels = Dbscan::predict(¶ms, &data); | ||
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assert!(labels.slice(s![..40]).iter().all(|x| x == &Some(0))); | ||
assert!(labels.slice(s![40..]).iter().all(|x| x == &Some(1))); | ||
} | ||
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#[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])); | ||
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let labels = Dbscan::predict(¶ms, &data); | ||
let expected = arr1(&[None, Some(0), Some(0), Some(0), Some(0)]); | ||
assert_eq!(labels, expected); | ||
} | ||
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#[test] | ||
fn dataset_too_small() { | ||
let params = DbscanHyperParams::new(4).build(); | ||
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let data: Array2<f64> = Array2::zeros((2, 3)); | ||
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let labels = Dbscan::predict(¶ms, &data); | ||
assert!(labels.iter().all(|x| x.is_none())); | ||
} | ||
} |
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use serde::{Deserialize, Serialize}; | ||
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#[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, | ||
} | ||
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/// Helper struct used to construct a set of hyperparameters for | ||
pub struct DbscanHyperParamsBuilder { | ||
tolerance: f64, | ||
min_points: usize, | ||
} | ||
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impl DbscanHyperParamsBuilder { | ||
/// Distance between points for them to be considered neighbours. | ||
pub fn tolerance(mut self, tolerance: f64) -> Self { | ||
self.tolerance = tolerance; | ||
self | ||
} | ||
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/// Return an instance of `DbscanHyperParams` after having performed | ||
/// validation checks on all hyperparameters. | ||
/// | ||
/// **Panics** if any of the validation checks fail. | ||
pub fn build(self) -> DbscanHyperParams { | ||
DbscanHyperParams::build(self.tolerance, self.min_points) | ||
} | ||
} | ||
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impl DbscanHyperParams { | ||
/// Minimum number of neighboring points a point needs to have to be a core | ||
/// point and not a noise point. | ||
/// | ||
/// Defaults are provided if the optional parameters are not specified: | ||
/// * `tolerance = 1e-4` | ||
pub fn new(min_points: usize) -> DbscanHyperParamsBuilder { | ||
DbscanHyperParamsBuilder { | ||
min_points, | ||
tolerance: 1e-4, | ||
} | ||
} | ||
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/// Two points are considered neighbors if the euclidean distance between | ||
/// them is below the tolerance | ||
pub fn tolerance(&self) -> f64 { | ||
self.tolerance | ||
} | ||
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/// Minimum number of a points in a neighborhood around a point for it to | ||
/// not be considered noise | ||
pub fn minimum_points(&self) -> usize { | ||
self.min_points | ||
} | ||
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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, | ||
} | ||
} | ||
} |
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mod algorithm; | ||
mod hyperparameters; | ||
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pub use algorithm::*; | ||
pub use hyperparameters::*; |
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