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k_shapes.py
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k_shapes.py
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# -*- coding: utf-8 -*-
"""Time series kshapes."""
from typing import 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 TimeSeriesKShapes(BaseClusterer):
"""Kshape algorithm: 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.
init_algorithm: str or np.ndarray, default='random'
Method for initializing cluster centres. Any of the following are valid:
['random']. Or a np.ndarray of shape (n_clusters, n_channels, n_timepoints)
and gives the initial cluster centres.
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.
max_iter: int, default=30
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 centres of two consecutive iterations to declare
convergence.
verbose: bool, default=False
Verbosity mode.
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_instances,))
Labels that is the index each time series belongs to.
inertia_: float
Sum of squared distances of samples to their closest cluster centre, weighted by
the sample weights if provided.
n_iter_: int
Number of iterations run.
"""
_tags = {
"capability:multivariate": True,
"python_dependencies": "tslearn",
}
def __init__(
self,
n_clusters: int = 8,
init_algorithm: Union[str, np.ndarray] = "random",
n_init: int = 10,
max_iter: int = 300,
tol: float = 1e-4,
verbose: bool = False,
random_state: Union[int, RandomState] = None,
):
self.init_algorithm = init_algorithm
self.n_init = n_init
self.max_iter = max_iter
self.tol = tol
self.verbose = verbose
self.random_state = random_state
self.cluster_centers_ = None
self.labels_ = None
self.inertia_ = None
self.n_iter_ = 0
self._tslearn_k_shapes = None
super(TimeSeriesKShapes, self).__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 KShape
self._tslearn_k_shapes = KShape(
n_clusters=self.n_clusters,
max_iter=self.max_iter,
tol=self.tol,
random_state=self.random_state,
n_init=self.n_init,
verbose=self.verbose,
init=self.init_algorithm,
)
_X = X.swapaxes(1, 2)
self._tslearn_k_shapes.fit(_X)
self._cluster_centers = self._tslearn_k_shapes.cluster_centers_
self.labels_ = self._tslearn_k_shapes.labels_
self.inertia_ = self._tslearn_k_shapes.inertia_
self.n_iter_ = self._tslearn_k_shapes.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_k_shapes.predict(_X)
@classmethod
def get_test_params(cls, parameter_set="default"):
"""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,
"init_algorithm": "random",
"n_init": 1,
"max_iter": 1,
"tol": 1e-4,
"verbose": False,
"random_state": 1,
}
def _score(self, X, y=None):
return np.abs(self.inertia_)