TimeSeriesKernelKMeans#

class TimeSeriesKernelKMeans(n_clusters: int = 8, kernel: str = 'gak', n_init: int = 10, max_iter: int = 300, tol: float = 0.0001, kernel_params: Optional[dict] = None, verbose: bool = False, n_jobs: Optional[int] = None, random_state: Optional[Union[int, numpy.random.mtrand.RandomState]] = None)[source]#

Kernel algorithm wrapper tslearns implementation.

Parameters
n_clusters: int, defaults = 8

The number of clusters to form as well as the number of centroids to generate.

kernelstring, or callable (default: “gak”)

The kernel should either be “gak”, in which case the Global Alignment Kernel from [2]_ is used or a value that is accepted as a metric by scikit-learn’s pairwise_kernels

n_init: int, defaults = 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_paramsdict 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 tslearn.metrics.sigma_gak). If no specific value is set for sigma, its defaults to 1.

max_iter: int, defaults = 300

Maximum number of iterations of the k-means algorithm for a single run.

tol: float, defaults = 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, defaults = False

Verbosity mode.

n_jobsint 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 joblib.parallel_backend context. -1 means using all processors. See scikit-learns’ Glossary for more details.

random_state: int or np.random.RandomState instance or None, defaults = 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.

Methods

check_is_fitted()

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters.

clone_tags(estimator[, tag_names])

clone/mirror tags from another estimator as dynamic override.

create_test_instance([parameter_set])

Construct Estimator instance if possible.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

fit(X[, y])

Fit time series clusterer to training data.

fit_predict(X[, y])

Compute cluster centers and predict cluster index for each time series.

get_class_tag(tag_name[, tag_value_default])

Get tag value from estimator class (only class tags).

get_class_tags()

Get class tags from estimator class and all its parent classes.

get_fitted_params()

Get fitted parameters.

get_param_defaults()

Get parameter defaults for the object.

get_param_names()

Get parameter names for the object.

get_params([deep])

Get parameters for this estimator.

get_tag(tag_name[, tag_value_default, …])

Get tag value from estimator class and dynamic tag overrides.

get_tags()

Get tags from estimator class and dynamic tag overrides.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

is_composite()

Check if the object is composite.

predict(X[, y])

Predict the closest cluster each sample in X belongs to.

predict_proba(X)

Predicts labels probabilities for sequences in X.

reset()

Reset the object to a clean post-init state.

score(X[, y])

Score the quality of the clusterer.

set_params(**params)

Set the parameters of this object.

set_tags(**tag_dict)

Set dynamic tags to given values.

classmethod get_test_params(parameter_set='default') Dict[source]#

Return testing parameter settings for the estimator.

Parameters
parameter_setstr, 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
paramsdict 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

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises
NotFittedError

If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]#

clone/mirror tags from another estimator as dynamic override.

Parameters
estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters
parameter_setstr, 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
instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters
parameter_setstr, 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
objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

parameter_setstr, 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.

fit(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) sktime.base._base.BaseEstimator[source]#

Fit time series clusterer to training data.

Parameters
XTraining time series instances to cluster. np.ndarray (2d or 3d array of
shape (n_instances, series_length) or shape (n_instances, n_dimensions,
series_length)) or pd.DataFrame (where each column is a dimension, each cell
is a pd.Series (any number of dimensions, equal or unequal length series)).
Converted to type _tags[“X_inner_mtype”]
y: ignored, exists for API consistency reasons.
Returns
self:

Fitted estimator.

fit_predict(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) numpy.ndarray[source]#

Compute cluster centers and predict cluster index for each time series.

Convenience method; equivalent of calling fit(X) followed by predict(X)

Parameters
Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape

(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to train clusterer and then have indexes each belong to return.

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.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters
tag_namestr

Name of tag value.

tag_value_defaultany type

Default/fallback value if tag is not found.

Returns
tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from estimator class and all its parent classes.

Returns
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params()[source]#

Get fitted parameters.

State required:

Requires state to be “fitted”.

Returns
fitted_paramsdict of fitted parameters, keys are str names of parameters

parameters of components are indexed as [componentname]__[paramname]

classmethod get_param_defaults()[source]#

Get parameter defaults for the object.

Returns
default_dict: dict with str keys

keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__

classmethod get_param_names()[source]#

Get parameter names for the object.

Returns
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters
tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns
tag_value

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises
ValueError if raise_error is True i.e. if tag_name is not in self.get_tags(
).keys()
get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns
composite: bool, whether self contains a parameter which is BaseObject
property is_fitted[source]#

Whether fit has been called.

predict(X: Union[pandas.core.frame.DataFrame, numpy.ndarray], y=None) numpy.ndarray[source]#

Predict the closest cluster each sample in X belongs to.

Parameters
Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape

(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to predict their cluster indexes.

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.

predict_proba(X)[source]#

Predicts labels probabilities for sequences in X.

Default behaviour is to call _predict and set the predicted class probability to 1, other class probabilities to 0. Override if better estimates are obtainable.

Parameters
Xguaranteed to be of a type in self.get_tag(“X_inner_mtype”)
if self.get_tag(“X_inner_mtype”) = “numpy3D”:

3D np.ndarray of shape = [n_instances, n_dimensions, series_length]

if self.get_tag(“X_inner_mtype”) = “nested_univ”:

pd.DataFrame with each column a dimension, each cell a pd.Series

for list of other mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns
y2D array of shape [n_instances, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

reset()[source]#

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

score(X, y=None) float[source]#

Score the quality of the clusterer.

Parameters
Xnp.ndarray (2d or 3d array of shape (n_instances, series_length) or shape

(n_instances, n_dimensions, series_length)) or pd.DataFrame (where each column is a dimension, each cell is a pd.Series (any number of dimensions, equal or unequal length series)). Time series instances to train clusterer and then have indexes each belong to return.

y: ignored, exists for API consistency reasons.
Returns
scorefloat

Score of the clusterer.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

BaseObject parameters

Returns
selfreference to self (after parameters have been set)
set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters
tag_dictdict

Dictionary of tag name : tag value pairs.

Returns
Self

Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.