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#
# maturin generate-ci github
#
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matrix:
platform:
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python-version: 3.x
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path: dist
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runs-on: ubuntu-latest
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uses: PyO3/maturin-action@v1
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# Byte-compiled / optimized / DLL files
__pycache__/
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*.so
# Distribution / packaging
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.venv/
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[package]
name = "kmeans_rs"
version = "0.1.0"
edition = "2024"
rust-version = "1.94.0"
authors = ["Wim Pomp <w.pomp@nki.nl>"]
license = "MIT OR Apache-2.0"
description = "Python wrapper for Rust kmeans library."
homepage = "https://git.wimpomp.nl/wim/kmeans_rs"
repository = "https://git.wimpomp.nl/wim/kmeans_rs"
readme = "README.md"
keywords = ["kmeans"]
categories = ["science"]
# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
[lib]
name = "kmeans_rs"
crate-type = ["cdylib"]
[dependencies]
color-eyre = "0.6"
console = "0.16"
indicatif = { version = "0.18", features = ["rayon"] }
kmeans = "2"
ndarray = "0.17"
numpy = "0.28"
pyo3 = { version = "0.28", features = ["abi3-py310", "anyhow", "eyre"] }
pyo3-stub-gen = "0.22"
rayon = "1"
thiserror = "2"
LICENSE-APACHE
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LICENSE-MIT
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Copyright (c) 2015 - 2021 Ulrik Sverdrup "bluss",
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README.md
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### KMeans
A small library wrapping the unaffiliated Rust kmeans library: https://crates.io/crates/kmeans,
kmeans is fast for big datasets due because of the use of multicore processing and SIMD.
Building requires rust nightly.
py/kmeans_rs/__init__.py
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import os
import sys
from importlib.metadata import version
from pathlib import Path
os.environ["RUST_BACKTRACE"] = "full"
os.environ["COLORBT_SHOW_HIDDEN"] = "1"
from .kmeans_rs import * # noqa
try:
__version__ = version(Path(__file__).parent.name)
except (Exception,):
__version__ = "unknown"
try:
with open(Path(__file__).parent.parent / ".git" / "HEAD") as g:
head = g.read().split(":")[1].strip()
with open(Path(__file__).parent.parent / ".git" / head) as h:
__git_commit_hash__ = h.read().rstrip("\n")
except (Exception,):
__git_commit_hash__ = "unknown"
def kmeans_generate_stub():
if len(sys.argv) > 1:
path = Path(sys.argv[1]).resolve()
else:
path = Path.cwd().resolve()
if (path / "py" / "kmeans_rs" / "__init__.py").exists():
generate_stub(str(path)) # noqa
else:
raise ModuleNotFoundError(str(path / "py" / "kmeans_rs" / "__init__.py"))
py/kmeans_rs/__init__.pyi
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# This file is automatically generated by pyo3_stub_gen
# ruff: noqa: E501, F401, F403, F405
import builtins
import numpy
import numpy.typing
import typing
__all__ = [
"KMeans",
"KMeansAlgorithm",
"KMeansInit",
"silhouette",
]
@typing.final
class KMeans:
r"""
Compute kmeans clustering
this implementation is supposed to be faster than scipy or scikit-learn
when dealing with a lot of points
## Arguments
- **points**: Numpy array #points x dimensions
- **k**: Amount of clusters to search for
- **max_iter**: Limit the maximum amount of iterations (just pass a high number for infinite)
- **init**: initialization method
- **algorithm**: algorithm to use
"""
@property
def ndim(self) -> builtins.int:
r"""
number of dimensions
"""
@property
def k(self) -> builtins.int:
r"""
number of clusters
"""
@property
def distance_sum(self) -> builtins.float:
r"""
sum of all distances, cost measure
"""
@property
def centroids(self) -> numpy.typing.NDArray[numpy.float64]:
r"""
centroid coordinates
"""
@property
def centroid_frequency(self) -> builtins.list[builtins.int]:
r"""
centroid frequencies
"""
@property
def assignments(self) -> builtins.list[builtins.int]:
r"""
to which cluster each of the points is assigned
"""
@property
def centroid_distances(self) -> builtins.list[builtins.float]:
r"""
distances of all points to the center it's assigned to
"""
def __new__(cls, points: numpy.typing.ArrayLike, k: builtins.int, max_iter: builtins.int = 300, init: typing.Optional[KMeansInit] = None, algorithm: typing.Optional[KMeansAlgorithm] = None) -> KMeans: ...
@staticmethod
def init_plusplus() -> KMeansInit:
r"""
K-Means++ initialization method, as implemented in Matlab
## Description
This initialization method starts by selecting one sample as first centroid.
Proceeding from there, the method iteratively selects one new centroid (per iteration) by calculating
each sample's probability of "being a centroid". This probability is bigger, the farther away a sample
is from its centroid. Then, one sample is randomly selected, while taking their probability of being
the next centroid into account. This leads to a tendency of selecting centroids, that are far away from
their currently assigned cluster's centroid.
(see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
"""
@staticmethod
def init_random_partition() -> KMeansInit:
r"""
Random-Parition initialization method
## Description
This initialization method randomly partitions the samples into k partitions, and then calculates these partion's means.
These means are then used as initial clusters.
"""
@staticmethod
def init_random_sample() -> KMeansInit:
r"""
Random sample initialization method (a.k.a. Forgy)
## Description
This initialization method randomly selects k centroids from the samples as initial centroids.
"""
@staticmethod
def init_precomputed(centroids: numpy.typing.ArrayLike) -> KMeansInit:
r"""
Precomputed centroids initialization method
## Description
This initialization method requires a precomputed list of k centroids to use as initial
centroids.
"""
@staticmethod
def algo_lloyd() -> KMeansAlgorithm:
r"""
Normal K-Means algorithm implementation. This is the same algorithm as implemented in Matlab (one-phase).
(see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
"""
@staticmethod
def algo_mini_batch(batch_size: builtins.int) -> KMeansAlgorithm:
r"""
Mini-Batch k-Means implementation.
(see: https://dl.acm.org/citation.cfm?id=1772862)
## Arguments
- **batch_size**: Amount of samples to use per iteration (higher -> better approximation but slower)
"""
def predict(self, points: numpy.typing.ArrayLike) -> tuple[builtins.list[builtins.int], builtins.list[builtins.float]]:
r"""
find the closest cluster and the distance for each point
"""
def silhouette_simple(self, points: numpy.typing.ArrayLike, assignments: numpy.typing.ArrayLike = None) -> builtins.float:
r"""
calculate the mean simple (using centroids) silhouette score for a set of points,
assignments must be specified if they do not correspond to the assignments in the KMeans instance
"""
class KMeansAlgorithm:
r"""
Specify a kmeans algorithm using lloyd or mini_batch.
"""
@staticmethod
def lloyd() -> KMeansAlgorithm:
r"""
Normal K-Means algorithm implementation. This is the same algorithm as implemented in Matlab (one-phase).
(see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
"""
@staticmethod
def mini_batch(batch_size: builtins.int) -> KMeansAlgorithm:
r"""
Mini-Batch k-Means implementation.
(see: https://dl.acm.org/citation.cfm?id=1772862)
## Arguments
- **batch_size**: Amount of samples to use per iteration (higher -> better approximation but slower)
"""
@typing.final
class Lloyd(KMeansAlgorithm):
__match_args__ = ()
def __new__(cls) -> KMeansAlgorithm.Lloyd: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
@typing.final
class MiniBatch(KMeansAlgorithm):
__match_args__ = ("_0",)
@property
def _0(self) -> builtins.int: ...
def __new__(cls, _0: builtins.int) -> KMeansAlgorithm.MiniBatch: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
class KMeansInit:
r"""
Specify an initialization method using plusplus, random_partition, random_sample or precomputed.
"""
@staticmethod
def plusplus() -> KMeansInit:
r"""
K-Means++ initialization method, as implemented in Matlab
## Description
This initialization method starts by selecting one sample as first centroid.
Proceeding from there, the method iteratively selects one new centroid (per iteration) by calculating
each sample's probability of "being a centroid". This probability is bigger, the farther away a sample
is from its centroid. Then, one sample is randomly selected, while taking their probability of being
the next centroid into account. This leads to a tendency of selecting centroids, that are far away from
their currently assigned cluster's centroid.
(see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
"""
@staticmethod
def random_partition() -> KMeansInit:
r"""
Random-Partition initialization method
## Description
This initialization method randomly partitions the samples into k partitions, and then calculates these partion's means.
These means are then used as initial clusters.
"""
@staticmethod
def random_sample() -> KMeansInit:
r"""
Random sample initialization method (a.k.a. Forgy)
## Description
This initialization method randomly selects k centroids from the samples as initial centroids.
"""
@staticmethod
def precomputed(centroids: numpy.typing.ArrayLike) -> KMeansInit:
r"""
Precomputed centroids initialization method
## Description
This initialization method requires a precomputed list of k centroids to use as initial
centroids.
"""
@typing.final
class PlusPlus(KMeansInit):
__match_args__ = ()
def __new__(cls) -> KMeansInit.PlusPlus: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
@typing.final
class RandomPartition(KMeansInit):
__match_args__ = ()
def __new__(cls) -> KMeansInit.RandomPartition: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
@typing.final
class RandomSample(KMeansInit):
__match_args__ = ()
def __new__(cls) -> KMeansInit.RandomSample: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
@typing.final
class Precomputed(KMeansInit):
__match_args__ = ("_0",)
@property
def _0(self) -> builtins.list[builtins.float]: ...
def __new__(cls, _0: typing.Sequence[builtins.float]) -> KMeansInit.Precomputed: ...
def __len__(self) -> builtins.int: ...
def __getitem__(self, key: builtins.int) -> typing.Any: ...
def silhouette(points: numpy.typing.ArrayLike, assignments: numpy.typing.ArrayLike) -> builtins.float:
r"""
calculate the mean silhouette score for a set of points
"""
pyproject.toml
+35
View File
@@ -0,0 +1,35 @@
[build-system]
requires = ["maturin>=1.9.4,<2.0"]
build-backend = "maturin"
[project]
name = "kmeans"
dynamic = ["version"]
authors = [
{ name = "Wim Pomp", email = "w.pomp@nki.nl" },
]
readme = "README.md"
keywords = ["kmeans"]
description = "Python wrapper for Rust kmeans library."
requires-python = ">=3.8"
classifiers = [
"License :: OSI Approved :: MIT License",
"License :: OSI Approved :: Apache Software License",
"Programming Language :: Rust",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
]
[tool.maturin]
python-source = "py"
module-name = "kmeans_rs"
[project.scripts]
kmeans_generate_stub = "kmeans_rs:kmeans_generate_stub"
[tool.ruff]
line-length = 119
indent-width = 4
[tool.isort]
line_length = 119
rust-toolchain.toml
+2
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@@ -0,0 +1,2 @@
[toolchain]
channel = "nightly"
src/lib.rs
+696
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@@ -0,0 +1,696 @@
use console::Term;
use indicatif::{ProgressBar, ProgressDrawTarget, ProgressStyle};
use kmeans::*;
use ndarray::{Array2, AsArray, Ix1, Ix2};
use numpy::{AllowTypeChange, IntoPyArray, PyArray2, PyArrayLike1, PyArrayLike2};
use pyo3::exceptions::PyTypeError;
use pyo3::prelude::*;
use pyo3_stub_gen::derive::*;
use pyo3_stub_gen::{StubGenConfig, StubInfo};
use rayon::prelude::*;
use std::collections::{HashMap, HashSet};
use std::hash::Hash;
use std::path::PathBuf;
use std::sync::Arc;
use std::time::Duration;
#[derive(Debug, thiserror::Error)]
pub enum Error {
#[error(transparent)]
ProgressBarTemplate(#[from] indicatif::style::TemplateError),
#[error("shape mismatch: {0} != {1}")]
ShapeMismatch(usize, usize),
#[error("no centroids defined")]
NoCentroidsDefined,
}
impl From<Error> for PyErr {
fn from(err: Error) -> PyErr {
color_eyre::eyre::Report::from(err).into()
}
}
/// a progress bar with an ok style that when py::detach is used also works in jupyter
pub fn get_bar(count: Option<usize>) -> Result<ProgressBar, Error> {
let style = ProgressStyle::with_template(
"{spinner:.green} {percent}% [{wide_bar:.green/lime}] {pos:>7}/{len:7} [{elapsed}/{eta}, {per_sec:<5}]",
)?.progress_chars("#>-");
let bar = ProgressBar::with_draw_target(
count.map(|i| i as u64),
ProgressDrawTarget::term_like_with_hz(Box::new(Term::buffered_stdout()), 20),
)
.with_style(style);
bar.enable_steady_tick(Duration::from_millis(100));
Ok(bar)
}
trait Predict<T> {
type Error;
fn predict<'a, A>(&self, points: A) -> Result<(Vec<usize>, Vec<T>), Self::Error>
where
A: AsArray<'a, T, Ix2>,
T: 'a;
fn silhouette_simple<'p, 'a, P, A>(
&self,
points: P,
assignments: Option<A>,
) -> Result<f64, Self::Error>
where
P: AsArray<'p, f64, Ix2>,
A: AsArray<'a, usize, Ix1>;
}
/// Specify an initialization method using plusplus, random_partition, random_sample or precomputed.
#[gen_stub_pyclass_complex_enum]
#[pyclass(name = "KMeansInit", module = "kmeans_rs", from_py_object)]
#[derive(Clone, Debug)]
pub(crate) enum PyKMeansInit {
PlusPlus(),
RandomPartition(),
RandomSample(),
Precomputed(Vec<f64>),
}
#[gen_stub_pymethods]
#[pymethods]
impl PyKMeansInit {
/// K-Means++ initialization method, as implemented in Matlab
///
/// ## Description
/// This initialization method starts by selecting one sample as first centroid.
/// Proceeding from there, the method iteratively selects one new centroid (per iteration) by calculating
/// each sample's probability of "being a centroid". This probability is bigger, the farther away a sample
/// is from its centroid. Then, one sample is randomly selected, while taking their probability of being
/// the next centroid into account. This leads to a tendency of selecting centroids, that are far away from
/// their currently assigned cluster's centroid.
/// (see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
#[staticmethod]
pub(crate) fn plusplus() -> Self {
Self::PlusPlus()
}
/// Random-Partition initialization method
///
/// ## Description
/// This initialization method randomly partitions the samples into k partitions, and then calculates these partion's means.
/// These means are then used as initial clusters.
#[staticmethod]
pub(crate) fn random_partition() -> Self {
Self::RandomPartition()
}
/// Random sample initialization method (a.k.a. Forgy)
///
/// ## Description
/// This initialization method randomly selects k centroids from the samples as initial centroids.
#[staticmethod]
pub(crate) fn random_sample() -> Self {
Self::RandomSample()
}
/// Precomputed centroids initialization method
///
/// ## Description
/// This initialization method requires a precomputed list of k centroids to use as initial
/// centroids.
#[staticmethod]
pub(crate) fn precomputed(
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
centroids: PyArrayLike2<f64, AllowTypeChange>,
) -> Self {
Self::Precomputed(centroids.as_array().flatten().to_vec())
}
}
/// Specify a kmeans algorithm using lloyd or mini_batch.
#[gen_stub_pyclass_complex_enum]
#[pyclass(name = "KMeansAlgorithm", module = "kmeans_rs", from_py_object)]
#[derive(Clone, Debug)]
pub(crate) enum PyKMeansAlgorithm {
Lloyd(),
MiniBatch(usize),
}
#[gen_stub_pymethods]
#[pymethods]
impl PyKMeansAlgorithm {
/// Normal K-Means algorithm implementation. This is the same algorithm as implemented in Matlab (one-phase).
/// (see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
#[staticmethod]
pub(crate) fn lloyd() -> Self {
Self::Lloyd()
}
/// Mini-Batch k-Means implementation.
/// (see: https://dl.acm.org/citation.cfm?id=1772862)
///
/// ## Arguments
/// - **batch_size**: Amount of samples to use per iteration (higher -> better approximation but slower)
#[staticmethod]
pub(crate) fn mini_batch(batch_size: usize) -> Self {
Self::MiniBatch(batch_size)
}
}
/// Compute kmeans clustering
/// this implementation is supposed to be faster than scipy or scikit-learn
/// when dealing with a lot of points
///
/// ## Arguments
/// - **points**: Numpy array #points x dimensions
/// - **k**: Amount of clusters to search for
/// - **max_iter**: Limit the maximum amount of iterations (just pass a high number for infinite)
/// - **init**: initialization method
/// - **algorithm**: algorithm to use
#[gen_stub_pyclass]
#[pyclass(name = "KMeans", module = "kmeans_rs", from_py_object)]
#[derive(Clone, Debug)]
pub(crate) struct PyKMeans {
ndim: usize,
inner: KMeansState<f64>,
}
#[gen_stub_pymethods]
#[pymethods]
impl PyKMeans {
#[new]
#[pyo3(signature = (points, k, max_iter=300, init=None, algorithm=None))]
pub(crate) fn new(
py: Python,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
points: PyArrayLike2<f64, AllowTypeChange>,
k: usize,
max_iter: usize,
init: Option<PyKMeansInit>,
algorithm: Option<PyKMeansAlgorithm>,
) -> Self {
let points = points.as_array();
py.detach(|| {
let shape = points.shape();
let kmeans = if let Some(s) = points.as_slice() {
KMeans::<f64, 8, _>::new(s, shape[0], shape[1], EuclideanDistance)
} else {
let v = points.flatten().to_vec();
KMeans::<f64, 8, _>::new(v.as_slice(), shape[0], shape[1], EuclideanDistance)
};
let init = if let Some(init) = init {
init
} else {
PyKMeansInit::PlusPlus()
};
let algorithm = if let Some(algorithm) = algorithm {
algorithm
} else {
PyKMeansAlgorithm::Lloyd()
};
let config = KMeansConfig::default();
match algorithm {
PyKMeansAlgorithm::Lloyd() => PyKMeans {
ndim: shape[1],
inner: match init {
PyKMeansInit::PlusPlus() => {
kmeans.kmeans_lloyd(k, max_iter, KMeans::init_kmeanplusplus, &config)
}
PyKMeansInit::RandomPartition() => {
kmeans.kmeans_lloyd(k, max_iter, KMeans::init_random_partition, &config)
}
PyKMeansInit::RandomSample() => {
kmeans.kmeans_lloyd(k, max_iter, KMeans::init_random_sample, &config)
}
PyKMeansInit::Precomputed(centroids) => kmeans.kmeans_lloyd(
k,
max_iter,
KMeans::init_precomputed(centroids),
&config,
),
},
},
PyKMeansAlgorithm::MiniBatch(size) => PyKMeans {
ndim: shape[1],
inner: match init {
PyKMeansInit::PlusPlus() => kmeans.kmeans_minibatch(
size,
k,
max_iter,
KMeans::init_kmeanplusplus,
&config,
),
PyKMeansInit::RandomPartition() => kmeans.kmeans_minibatch(
size,
k,
max_iter,
KMeans::init_random_partition,
&config,
),
PyKMeansInit::RandomSample() => kmeans.kmeans_minibatch(
size,
k,
max_iter,
KMeans::init_random_sample,
&config,
),
PyKMeansInit::Precomputed(centroids) => kmeans.kmeans_minibatch(
size,
k,
max_iter,
KMeans::init_precomputed(centroids),
&config,
),
},
},
}
})
}
/// K-Means++ initialization method, as implemented in Matlab
///
/// ## Description
/// This initialization method starts by selecting one sample as first centroid.
/// Proceeding from there, the method iteratively selects one new centroid (per iteration) by calculating
/// each sample's probability of "being a centroid". This probability is bigger, the farther away a sample
/// is from its centroid. Then, one sample is randomly selected, while taking their probability of being
/// the next centroid into account. This leads to a tendency of selecting centroids, that are far away from
/// their currently assigned cluster's centroid.
/// (see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
#[staticmethod]
pub(crate) fn init_plusplus() -> PyKMeansInit {
PyKMeansInit::PlusPlus()
}
/// Random-Parition initialization method
///
/// ## Description
/// This initialization method randomly partitions the samples into k partitions, and then calculates these partion's means.
/// These means are then used as initial clusters.
#[staticmethod]
pub(crate) fn init_random_partition() -> PyKMeansInit {
PyKMeansInit::RandomPartition()
}
/// Random sample initialization method (a.k.a. Forgy)
///
/// ## Description
/// This initialization method randomly selects k centroids from the samples as initial centroids.
#[staticmethod]
pub(crate) fn init_random_sample() -> PyKMeansInit {
PyKMeansInit::RandomSample()
}
/// Precomputed centroids initialization method
///
/// ## Description
/// This initialization method requires a precomputed list of k centroids to use as initial
/// centroids.
#[staticmethod]
pub(crate) fn init_precomputed(
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
centroids: PyArrayLike2<f64, AllowTypeChange>,
) -> PyKMeansInit {
PyKMeansInit::Precomputed(centroids.as_array().flatten().to_vec())
}
/// Normal K-Means algorithm implementation. This is the same algorithm as implemented in Matlab (one-phase).
/// (see: https://uk.mathworks.com/help/stats/kmeans.html#bueq7aj-5 Section: More About)
#[staticmethod]
pub(crate) fn algo_lloyd() -> PyKMeansAlgorithm {
PyKMeansAlgorithm::Lloyd()
}
/// Mini-Batch k-Means implementation.
/// (see: https://dl.acm.org/citation.cfm?id=1772862)
///
/// ## Arguments
/// - **batch_size**: Amount of samples to use per iteration (higher -> better approximation but slower)
#[staticmethod]
pub(crate) fn algo_mini_batch(batch_size: usize) -> PyKMeansAlgorithm {
PyKMeansAlgorithm::MiniBatch(batch_size)
}
/// find the closest cluster and the distance for each point
pub(crate) fn predict(
&self,
py: Python,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
points: PyArrayLike2<f64, AllowTypeChange>,
) -> PyResult<(Vec<usize>, Vec<f64>)> {
let points = points.as_array();
Ok(py.detach(|| self.inner.predict(points))?)
}
/// calculate the mean simple (using centroids) silhouette score for a set of points,
/// assignments must be specified if they do not correspond to the assignments in the KMeans instance
#[pyo3(signature = (points, assignments = None))]
pub(crate) fn silhouette_simple(
&self,
py: Python,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
points: PyArrayLike2<f64, AllowTypeChange>,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
assignments: Option<PyArrayLike1<usize, AllowTypeChange>>,
) -> PyResult<f64> {
let points = points.as_array();
let assignments = assignments.as_ref().map(|a| a.as_array());
Ok(py.detach(|| self.inner.silhouette_simple(points, assignments))?)
}
/// number of dimensions
#[getter]
pub(crate) fn ndim(&self) -> usize {
self.ndim
}
/// number of clusters
#[getter]
pub(crate) fn k(&self) -> usize {
self.inner.k
}
/// sum of all distances, cost measure
#[getter]
pub(crate) fn distance_sum(&self) -> f64 {
self.inner.distsum
}
/// centroid coordinates
#[getter]
pub(crate) fn centroids<'py>(&self, py: Python<'py>) -> PyResult<Bound<'py, PyArray2<f64>>> {
let v = self.inner.centroids.to_vec();
Ok(Array2::from_shape_vec((v.len() / self.ndim, self.ndim), v)
.map_err(|e| PyErr::new::<PyTypeError, String>(e.to_string()))?
.into_pyarray(py))
}
/// centroid frequencies
#[getter]
pub(crate) fn centroid_frequency(&self) -> Vec<usize> {
self.inner.centroid_frequency.clone()
}
/// to which cluster each of the points is assigned
#[getter]
pub(crate) fn assignments(&self) -> Vec<usize> {
self.inner.assignments.clone()
}
/// distances of all points to the center it's assigned to
#[getter]
pub(crate) fn centroid_distances(&self) -> Vec<f64> {
self.inner.centroid_distances.clone()
}
}
impl Predict<f64> for KMeansState<f64> {
type Error = Error;
fn predict<'a, A>(&self, points: A) -> Result<(Vec<usize>, Vec<f64>), Self::Error>
where
A: AsArray<'a, f64, Ix2>,
{
let centroids = self.centroids.to_vec();
let ndim = centroids.len() / self.k;
let points = points.into();
let shape = points.shape();
if shape[1] != ndim {
return Err(Error::ShapeMismatch(shape[1], ndim));
}
if centroids.is_empty() {
return Err(Error::NoCentroidsDefined);
}
let fill = vec![0.0; 8 - ndim % 8];
let e = EuclideanDistance;
let dist = |s: &[f64]| {
s.par_chunks_exact(ndim)
.map(|point| {
let (i, d) = centroids
.par_chunks_exact(ndim)
.enumerate()
.fold(
|| (usize::MAX, f64::INFINITY),
|(i, a), (j, centroid)| {
let b = <EuclideanDistance as DistanceFunction<f64, 8>>::distance(
&e,
&[point, &fill].concat(),
&[centroid, &fill].concat(),
);
if a <= b { (i, a) } else { (j, b) }
},
)
.reduce(
|| (usize::MAX, f64::INFINITY),
|(i, a), (j, b)| {
if a <= b { (i, a) } else { (j, b) }
},
);
(i, d.sqrt())
})
.collect::<(Vec<_>, Vec<_>)>()
};
Ok(if let Some(s) = points.as_slice() {
dist(s)
} else {
let s = points.flatten().to_vec();
dist(&s)
})
}
fn silhouette_simple<'p, 'a, P, A>(
&self,
points: P,
assignments: Option<A>,
) -> Result<f64, Self::Error>
where
P: AsArray<'p, f64, Ix2>,
A: AsArray<'a, usize, Ix1>,
{
let points = points.into();
let shape = points.shape();
let centroids = Arc::new(self.centroids.to_vec());
let ndim = centroids.len() / self.k;
if shape[1] != ndim {
return Err(Error::ShapeMismatch(shape[1], ndim));
}
if centroids.is_empty() {
return Err(Error::NoCentroidsDefined);
}
let assignments = if let Some(assignments) = assignments {
assignments.into().to_vec()
} else {
self.assignments.to_vec()
};
let k = self.k;
let mut clusters = vec![Vec::new(); k];
for (point, assignment) in points.rows().into_iter().zip(assignments) {
clusters[assignment].extend(point.to_vec());
}
let fill = vec![0.0; 8 - ndim % 8];
let a = clusters
.par_iter()
.zip(centroids.clone().par_chunks_exact(ndim))
.flat_map(|(points, centroid)| {
let c = [centroid, &fill].concat();
let fill = fill.clone();
let e = EuclideanDistance;
points.par_chunks_exact(ndim).map(move |point| {
<EuclideanDistance as DistanceFunction<f64, 8>>::distance(
&e,
&c,
&[point, &fill].concat(),
)
.sqrt()
})
})
.collect::<Vec<_>>();
let b = clusters
.par_iter()
.enumerate()
.flat_map(|(i, points)| {
let centroids = centroids.clone();
let fill = fill.clone();
let e = EuclideanDistance;
points.par_chunks_exact(ndim).map(move |point| {
centroids
.par_chunks_exact(ndim)
.enumerate()
.map(|(j, centroid)| {
if i == j {
f64::INFINITY
} else {
<EuclideanDistance as DistanceFunction<f64, 8>>::distance(
&e,
&[centroid, &fill].concat(),
&[point, &fill].concat(),
)
.sqrt()
}
})
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(f64::INFINITY)
})
})
.collect::<Vec<_>>();
Ok(a.into_iter()
.zip(b)
.map(|(a, b)| (b - a) / a.max(b))
.sum::<f64>()
/ points.shape()[0] as f64)
}
}
fn silhouette<'p, 'a, P, A, K>(points: P, assignments: A) -> Result<f64, Error>
where
P: AsArray<'p, f64, Ix2>,
A: AsArray<'a, K, Ix1>,
K: 'a + Eq + Hash,
{
let points = points.into();
let assignments = assignments.into();
let shape = points.shape();
let n = shape[0];
let ndim = shape[1];
let labels = assignments
.iter()
.collect::<HashSet<_>>()
.into_iter()
.enumerate()
.map(|(k, v)| (v, k))
.collect::<HashMap<_, _>>();
let assignments = assignments.iter().map(|k| labels[k]).collect::<Vec<_>>();
let k = labels.len();
let mut clusters = vec![Vec::new(); k];
for (point, assignment) in points.rows().into_iter().zip(assignments) {
clusters[assignment].extend(point.to_vec());
}
let bar = get_bar(Some(k * n + k * n * k))?;
let fill = vec![0.0; 8 - ndim % 8];
let e = EuclideanDistance;
let a = clusters
.par_iter()
.flat_map(|points| {
let c = (points.len() / ndim - 1) as f64;
points
.par_chunks_exact(ndim)
.map(|i| {
let q = points
.par_chunks_exact(ndim)
.map(|j| {
<EuclideanDistance as DistanceFunction<f64, 8>>::distance(
&e,
&[i, &fill].concat(),
&[j, &fill].concat(),
)
.sqrt()
})
.sum::<f64>()
/ c;
bar.inc(1);
q
})
.collect::<Vec<_>>()
})
.collect::<Vec<_>>();
let b = clusters
.par_iter()
.enumerate()
.flat_map(|(i, points_i)| {
points_i
.par_chunks_exact(ndim)
.map(|a| {
clusters
.par_iter()
.enumerate()
.map(|(j, points_j)| {
let c = (points_j.len() / ndim) as f64;
let q = if i == j {
f64::INFINITY
} else {
points_j
.par_chunks_exact(ndim)
.map(|b| {
<EuclideanDistance as DistanceFunction<f64, 8>>::distance(
&e,
&[a, &fill].concat(),
&[b, &fill].concat(),
)
.sqrt()
})
.sum::<f64>()
/ c
};
bar.inc(1);
q
})
.min_by(|a, b| a.total_cmp(b))
.unwrap_or(f64::INFINITY)
})
.collect::<Vec<_>>()
})
.collect::<Vec<_>>();
bar.finish();
Ok(a.into_iter()
.zip(b)
.map(|(a, b)| (b - a) / a.max(b))
.sum::<f64>()
/ points.shape()[0] as f64)
}
/// calculate the mean silhouette score for a set of points
#[gen_stub_pyfunction(module = "kmeans_rs")]
#[pyfunction(name = "silhouette")]
pub(crate) fn py_silhouette(
py: Python,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
points: PyArrayLike2<f64, AllowTypeChange>,
#[gen_stub(override_type(type_repr="numpy.typing.ArrayLike", imports=("numpy", "numpy.typing")))]
assignments: PyArrayLike1<usize, AllowTypeChange>,
) -> PyResult<f64> {
let points = points.as_array();
let assignments = assignments.as_array();
Ok(py.detach(|| silhouette(points, assignments))?)
}
/// generates kmeans/__init__.pyi
#[pyfunction]
fn generate_stub(dest_path: String) -> PyResult<()> {
Ok(StubInfo::from_project_root(
"kmeans_rs".to_string(),
PathBuf::from(dest_path).join("py"),
true,
StubGenConfig::default(),
)?
.generate()?)
}
#[pymodule]
#[pyo3(name = "kmeans_rs")]
mod kmeans_rs {
use pyo3::prelude::*;
#[pymodule_export]
use super::generate_stub;
#[pymodule_export]
use super::PyKMeans;
#[pymodule_export]
use super::PyKMeansInit;
#[pymodule_export]
use super::PyKMeansAlgorithm;
#[pymodule_export]
use super::py_silhouette;
#[pymodule_init]
fn init(_: &Bound<'_, PyModule>) -> PyResult<()> {
Ok(color_eyre::install()?)
}
}