Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Kernel K-means.
"""Kernel K-means"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator, ClusterMixin
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils import check_random_state
class KernelKMeans(BaseEstimator, ClusterMixin):
"""
Kernel K-means
Reference
---------
Kernel k-means, Spectral Clustering and Normalized Cuts.
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis.
KDD 2004.
"""
def __init__(self, n_clusters=3, max_iter=50, tol=1e-3, random_state=None,
kernel="linear", gamma=None, degree=3, coef0=1,
kernel_params=None, verbose=0):
self.n_clusters = n_clusters
self.max_iter = max_iter
self.tol = tol
self.random_state = random_state
self.kernel = kernel
self.gamma = gamma
self.degree = degree
self.coef0 = coef0
self.kernel_params = kernel_params
self.verbose = verbose
@property
def _pairwise(self):
return self.kernel == "precomputed"
def _get_kernel(self, X, Y=None):
if callable(self.kernel):
params = self.kernel_params or {}
else:
params = {"gamma": self.gamma,
"degree": self.degree,
"coef0": self.coef0}
return pairwise_kernels(X, Y, metric=self.kernel,
filter_params=True, **params)
def fit(self, X, y=None, sample_weight=None):
n_samples = X.shape[0]
K = self._get_kernel(X)
sw = sample_weight if sample_weight else np.ones(n_samples)
self.sample_weight_ = sw
rs = check_random_state(self.random_state)
self.labels_ = rs.randint(self.n_clusters, size=n_samples)
dist = np.zeros((n_samples, self.n_clusters))
self.within_distances_ = np.zeros(self.n_clusters)
for it in xrange(self.max_iter):
dist.fill(0)
self._compute_dist(K, dist, self.within_distances_,
update_within=True)
labels_old = self.labels_
self.labels_ = dist.argmin(axis=1)
# Compute the number of samples whose cluster did not change
# since last iteration.
n_same = np.sum((self.labels_ - labels_old) == 0)
if 1 - float(n_same) / n_samples < self.tol:
if self.verbose:
print "Converged at iteration", it + 1
break
self.X_fit_ = X
return self
def _compute_dist(self, K, dist, within_distances, update_within):
"""Compute a n_samples x n_clusters distance matrix using the
kernel trick."""
sw = self.sample_weight_
for j in xrange(self.n_clusters):
mask = self.labels_ == j
if np.sum(mask) == 0:
raise ValueError("Empty cluster found, try smaller n_cluster.")
denom = sw[mask].sum()
denomsq = denom * denom
if update_within:
KK = K[mask][:, mask] # K[mask, mask] does not work.
dist_j = np.sum(np.outer(sw[mask], sw[mask]) * KK / denomsq)
within_distances[j] = dist_j
dist[:, j] += dist_j
else:
dist[:, j] += within_distances[j]
dist[:, j] -= 2 * np.sum(sw[mask] * K[:, mask], axis=1) / denom
def predict(self, X):
K = self._get_kernel(X, self.X_fit_)
n_samples = X.shape[0]
dist = np.zeros((n_samples, self.n_clusters))
self._compute_dist(K, dist, self.within_distances_,
update_within=False)
return dist.argmin(axis=1)
if __name__ == '__main__':
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=1000, centers=5, random_state=0)
km = KernelKMeans(n_clusters=5, max_iter=100, random_state=0, verbose=1)
print km.fit_predict(X)[:10]
print km.predict(X[:10])
@amueller
Copy link

amueller commented Aug 28, 2015

How did this compare to spectral clustering in practice? Do you think it is worth adding to scikit-learn?

@mblondel
Copy link
Author

mblondel commented Oct 9, 2015

@amueller Sorry for late reply (why no notifcations in gists?!). I haven't compared it to other algorithms but I am open to inclusion in scikit-learn if someone wants to work on it.

@vishalbelsare
Copy link

vishalbelsare commented Oct 18, 2015

@mblondel , how related (or not) is this to Graclus? [ http://www.cs.utexas.edu/users/dml/Software/graclus.html ] Being able to work with an out-of-core kernel matrix would be a fantastic addition to scikit-learn.

@lyndaKhiali
Copy link

lyndaKhiali commented Aug 4, 2016

Bonjour, est-il possible que votre algorithme reçois comme paramètre une matrice de distance au lieu d'une matrice(N_samples,N_Features).
Si oui, quel paramètre permet de spécifier le type de la matrice d'entré. merci

Copy link

ghost commented Nov 9, 2016

it gives me the same result of scikit kmeans. How is it possible? I have set kernel='rbf'

@jdbsilva
Copy link

jdbsilva commented Oct 27, 2018

This is great! Many thanks

@merveydn
Copy link

merveydn commented Jun 6, 2019

What other kernels can we use? Could you give some info on how to use different kernels?

@ajilling
Copy link

ajilling commented Jun 19, 2019

Is there a way to make this not fail when an empty cluster is found?

@ravi2k1
Copy link

ravi2k1 commented Nov 15, 2019

How can I use kmeans++ using the same code?

@lonevetad
Copy link

lonevetad commented Jan 30, 2020

Important not for everyone:
If You are looking for "gaussian kernel" just pass 'rbf' as "metric".
Sometimes I forget it.

@arnab-007
Copy link

arnab-007 commented Aug 27, 2020

Any advice on how to extend this to multi-kernel k-means?

@mblondel
Copy link
Author

mblondel commented Nov 16, 2020

The code was written for Python 2 and you're using Python 3. Replace xrange by range and print ... by print(...).

@nancychenxizhong
Copy link

nancychenxizhong commented Aug 24, 2022

@amueller Sorry for late reply (why no notifcations in gists?!). I haven't compared it to other algorithms but I am open to inclusion in scikit-learn if someone wants to work on it.

@mblondel I am happy to work on it. Are you still open to inclusion of this gist to scikit-learn?

@mblondel
Copy link
Author

mblondel commented Aug 24, 2022

Sure, go ahead!

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment