Last active
January 4, 2024 11:45
-
-
Save mblondel/6230787 to your computer and use it in GitHub Desktop.
Kernel K-means.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
"""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]) |
Sure, go ahead!
How to get the cluster centers, any idea?
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
The code was written for Python 2 and you're using Python 3. Replace
xrange
byrange
andprint ...
byprint(...)
.