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_kernel_k_means.py
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_kernel_k_means.py
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"""Time series kernel kmeans."""
from typing import Dict, Union
import numpy as np
from numpy.random import RandomState
from aeon.clustering.base import BaseClusterer
from aeon.utils.validation._dependencies import _check_soft_dependencies
class TimeSeriesKernelKMeans(BaseClusterer):
"""Kernel K Means [1]_: wrapper of the ``tslearn`` implementation.
Parameters
----------
n_clusters: int, default=8
The number of clusters to form as well as the number of
centroids to generate.
kernel : string, or callable (default: "gak")
The kernel should either be "gak", in which case the Global Alignment
Kernel from [1]_ is used, or a value that is accepted as a metric
by `scikit-learn's pairwise_kernels
<https://scikit-learn.org/stable/modules/generated/\
sklearn.metrics.pairwise.pairwise_kernels.html>`_
n_init: int, default=10
Number of times the k-means algorithm will be run with different
centroid seeds. The final result will be the best output of ``n_init``
consecutive runs in terms of inertia.
kernel_params : dict or None (default: None)
Kernel parameters to be passed to the kernel function.
None means no kernel parameter is set.
For Global Alignment Kernel, the only parameter of interest is ``sigma``.
If set to 'auto', it is computed based on a sampling of the training
set
(cf :ref:`tslearn.metrics.sigma_gak <fun-tslearn.metrics.sigma_gak>`).
If no specific value is set for ``sigma``, its default to 1.
max_iter: int, default=300
Maximum number of iterations of the k-means algorithm for a single
run.
tol: float, default=1e-4
Relative tolerance with regards to Frobenius norm of the difference
in the cluster centers of two consecutive iterations to declare
convergence.
verbose: bool, default=False
Verbosity mode.
n_jobs : int or None, optional (default=None)
The number of jobs to run in parallel for GAK cross-similarity matrix
computations.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See scikit-learns'
`Glossary <https://scikit-learn.org/stable/glossary.html#term-n-jobs>`_
for more details.
random_state: int or np.random.RandomState instance or None, default=None
Determines random number generation for centroid initialization.
Attributes
----------
labels_: np.ndarray (1d array of shape (n_instance,))
Labels that is the index each time series belongs to.
inertia_: float
Sum of squared distances of samples to their closest cluster center, weighted by
the sample weights if provided.
n_iter_: int
Number of iterations run.
Reference
---------
.. [1] Kernel k-means, Spectral Clustering and Normalized Cuts. Inderjit S.
Dhillon, Yuqiang Guan, Brian Kulis. KDD 2004.
.. [2] Fast Global Alignment Kernels. Marco Cuturi. ICML 2011.
Examples
--------
>>> from aeon.clustering import TimeSeriesKernelKMeans
>>> from aeon.datasets import load_basic_motions
>>> # Load data
>>> X_train, y_train = load_basic_motions(split="TRAIN")[0:10]
>>> X_test, y_test = load_basic_motions(split="TEST")[0:10]
>>> # Example of KernelKMeans Clustering
>>> kkm = TimeSeriesKernelKMeans(n_clusters=3, kernel='rbf') # doctest: +SKIP
>>> kkm.fit(X_train) # doctest: +SKIP
TimeSeriesKernelKMeans(kernel='rbf', n_clusters=3)
>>> preds = kkm.predict(X_test) # doctest: +SKIP
"""
_tags = {
"capability:multivariate": True,
"python_dependencies": "tslearn",
}
def __init__(
self,
n_clusters: int = 8,
kernel: str = "gak",
n_init: int = 10,
max_iter: int = 300,
tol: float = 1e-4,
kernel_params: Union[dict, None] = None,
verbose: bool = False,
n_jobs: Union[int, None] = None,
random_state: Union[int, RandomState] = None,
):
self.kernel = kernel
self.n_init = n_init
self.max_iter = max_iter
self.tol = tol
self.kernel_params = kernel_params
self.verbose = verbose
self.n_jobs = n_jobs
self.random_state = random_state
self.cluster_centers_ = None
self.labels_ = None
self.inertia_ = None
self.n_iter_ = 0
self._tslearn_kernel_k_means = None
super().__init__(n_clusters=n_clusters)
def _fit(self, X, y=None):
"""Fit time series clusterer to training data.
Parameters
----------
X: np.ndarray, of shape (n_instances, n_channels, n_timepoints) or
(n_instances, n_timepoints)
A collection of time series instances.
y: ignored, exists for API consistency reasons.
Returns
-------
self:
Fitted estimator.
"""
_check_soft_dependencies("tslearn", severity="error")
from tslearn.clustering import KernelKMeans as TsLearnKernelKMeans
verbose = 0
if self.verbose is True:
verbose = 1
self._tslearn_kernel_k_means = TsLearnKernelKMeans(
n_clusters=self.n_clusters,
kernel=self.kernel,
max_iter=self.max_iter,
tol=self.tol,
n_init=self.n_init,
kernel_params=self.kernel_params,
n_jobs=self.n_jobs,
verbose=verbose,
random_state=self.random_state,
)
_X = X.swapaxes(1, 2)
self._tslearn_kernel_k_means.fit(_X)
self.labels_ = self._tslearn_kernel_k_means.labels_
self.inertia_ = self._tslearn_kernel_k_means.inertia_
self.n_iter_ = self._tslearn_kernel_k_means.n_iter_
def _predict(self, X, y=None) -> np.ndarray:
"""Predict the closest cluster each sample in X belongs to.
Parameters
----------
X: np.ndarray, of shape (n_instances, n_channels, n_timepoints) or
(n_instances, n_timepoints)
A collection of time series instances.
y: ignored, exists for API consistency reasons.
Returns
-------
np.ndarray (1d array of shape (n_instances,))
Index of the cluster each time series in X belongs to.
"""
_X = X.swapaxes(1, 2)
return self._tslearn_kernel_k_means.predict(_X)
@classmethod
def get_test_params(cls, parameter_set="default") -> Dict:
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return `"default"` set.
Returns
-------
params : dict or list of dict, default={}
Parameters to create testing instances of the class
Each dict are parameters to construct an "interesting" test instance, i.e.,
`MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
`create_test_instance` uses the first (or only) dictionary in `params`
"""
return {
"n_clusters": 2,
"kernel": "gak",
"n_init": 1,
"max_iter": 1,
"tol": 0.0001,
"kernel_params": None,
"verbose": False,
"n_jobs": 1,
"random_state": 1,
}
def _score(self, X, y=None) -> float:
return np.abs(self.inertia_)