RandomIntervalSpectralEnsembleClassifier¶

class RandomIntervalSpectralEnsembleClassifier(base_estimator=None, n_estimators=200, min_interval_length=3, max_interval_length=inf, acf_lag=100, acf_min_values=4, time_limit_in_minutes=None, contract_max_n_estimators=500, use_pyfftw=False, random_state=None, n_jobs=1, parallel_backend=None)[source]¶

Random Interval Spectral Ensemble (RISE) classifier.

Input: n series length m For each tree

sample a random intervals

take the ACF and PS over this interval, and concatenate features

build a tree on new features

Ensemble the trees through averaging probabilities.

Parameters:

base_estimatorBaseEstimator or None, default=None

scikit-learn BaseEstimator used to build the interval ensemble. If None, use a simple decision tree.

n_estimatorsint, default=200

Number of estimators to build for the ensemble.

min_interval_lengthint, float, list, or tuple, default=3

Minimum length of intervals to extract from series. float inputs take a proportion of the series length to use as the minimum interval length.

Different minimum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.

max_interval_lengthint, float, list, or tuple, default=np.inf

Maximum length of intervals to extract from series. float inputs take a proportion of the series length to use as the maximum interval length.

Different maximum interval lengths for each series_transformers series can be specified using a list or tuple. Any list or tuple input must be the same length as the number of series_transformers.

Ignored for supervised interval_selection_method inputs.

acf_lagint or callable, default=100

The maximum number of autocorrelation terms to use. If callable, the function should take a 3D numpy array of shape (n_cases, n_channels, n_timepoints) and return an integer.

acf_min_valuesint, default=0

Never use fewer than this number of terms to find a correlation unless the series length is too short. This will reduce n_lags if needed.

time_limit_in_minutesint, default=0

Time contract to limit build time in minutes, overriding n_estimators. Default of 0 means n_estimators are used.

contract_max_n_estimatorsint, default=500

Max number of estimators when time_limit_in_minutes is set.

use_pyfftwbool, default=False

Whether to use the pyfftw library for FFT calculations. Requires the pyfftw package to be installed.

random_stateint, RandomState instance or None, default=None

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

parallel_backendstr, ParallelBackendBase instance or None, default=None

Specify the parallelisation backend implementation in joblib, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

Attributes:

n_cases_int: The number of train cases in the training set.
n_channels_int: The number of dimensions per case in the training set.
n_timepoints_int: The length of each series in the training set.
n_classes_int: Number of classes. Extracted from the data.
classes_ndarray of shape (n_classes_): Holds the label for each class.
total_intervals_int: Total number of intervals per tree from all representations.
estimators_list of shape (n_estimators) of BaseEstimator: The collections of estimators trained in fit.
intervals_list of shape (n_estimators) of TransformerMixin: Stores the interval extraction transformer for all estimators.

See also

RandomIntervalSpectralEnsembleRegressor

Notes

For the Java version, see TSML.

References

[1]

Jason Lines, Sarah Taylor and Anthony Bagnall, “Time Series Classification with HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles”, ACM Transactions on Knowledge and Data Engineering, 12(5): 2018

Examples

>>> from aeon.classification.interval_based import (
...     RandomIntervalSpectralEnsembleClassifier
... )
>>> from aeon.testing.data_generation import make_example_3d_numpy
>>> X, y = make_example_3d_numpy(n_cases=10, n_channels=1, n_timepoints=12,
...                              return_y=True, random_state=0)
>>> clf = RandomIntervalSpectralEnsembleClassifier(n_estimators=10, random_state=0)
>>> clf.fit(X, y)
RandomIntervalSpectralEnsembleClassifier(n_estimators=10, random_state=0)
>>> clf.predict(X)
array([0, 1, 0, 1, 0, 0, 1, 1, 1, 0])

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 classifier to training data.
`fit_predict`(X, y, **kwargs)	Fits the classifier and predicts class labels for X.
`fit_predict_proba`(X, y, **kwargs)	Fits the classifier and predicts class label probabilities for X.
`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`([deep])	Get fitted parameters.
`get_metadata_routing`()	Get metadata routing of this object.
`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.
`get_tags`()	Get tags from estimator class.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts class labels for time series in X.
`predict_proba`(X)	Predicts class label probabilities for time series in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y[, metric, use_proba, metric_params])	Scores predicted labels against ground truth labels on X.
`set_params`(**params)	Set the parameters of this object.
`set_score_request`(*[, metric, ...])	Request metadata passed to the `score` method.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`temporal_importance_curves`([return_dict, ...])	Calculate the temporal importance curves for each feature.

classmethod get_test_params(parameter_set='default')[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. RandomIntervalSpectralEnsembleClassifier provides the following special sets:

“results_comparison” - used in some classifiers to compare against
previously generated results where the default set of parameters cannot produce suitable probability estimates

“contracting” - used in classifiers that set the
“capability:contractable” tag to True to test contacting functionality

“train_estimate” - used in some classifiers that set the
“capability:train_estimate” tag to True to allow for more efficient testing when relevant parameters are available

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:

estimatorobject: Estimator 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', return_first=True)[source]¶

Construct Estimator instance if possible.

Calls the get_test_params method and returns an instance or list of instances using the returned dict or list of dict.

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.
return_firstbool, default=True: If True, return the first instance of the list of instances. If False, return the list of instances.

Returns:

instanceBaseEstimator or list of BaseEstimator: Instance of the class with default parameters. If return_first is False, returns list of instances.

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, y) → BaseCollectionEstimator[source]¶

Fit time series classifier to training data.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

Different estimators have different capabilities to handle different types of input. If self.get_tag("capability:multivariate") is False, they cannot handle multivariate series, so either n_channels == 1 is true or X is 2D of shape (n_cases, n_timepoints). If self.get_tag( "capability:unequal_length") is False, they cannot handle unequal length input. In both situations, a ValueError is raised if X has a characteristic that the estimator does not have the capability for is passed.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

Returns:

selfBaseClassifier: Reference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

fit_predict(X, y, **kwargs) → ndarray[source]¶

Fits the classifier and predicts class labels for X.

fit_predict produces prediction estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

key word arguments to configure the default cross validation if the base class default fit_predict is used (i.e. if function _fit_predict is not overridden. If _fit_predict is overridden, kwargs may not function as expected. If _fit_predict is not overridden, valid input is cv_size integer, which is the number of cross validation folds to use to estimate train data. If cv_size is not passed, the default is 10. If cv_size is greater than the minimum number of samples in any class, it is set to this minimum.

Returns:

predictionsnp.ndarray: shape [n_cases] - predicted class labels indices correspond to instance indices in

fit_predict_proba(X, y, **kwargs) → ndarray[source]¶

Fits the classifier and predicts class label probabilities for X.

fit_predict_proba produces probability estimates using just the train data. By default, this is through 10x cross validation, although some estimators may utilise specialist techniques such as out-of-bag estimates or leave-one-out cross-validation.

Classifiers which override _fit_predict_proba will have the capability:train_estimate tag set to True.

Generally, this will not be the same as fitting on the whole train data then making train predictions. To do this, you should call fit(X,y).predict_proba(X)

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

kwargsdict

Returns:

probabilitiesnp.ndarray: 2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated probability that i-th instance is of class j

classmethod get_class_tag(tag_name, tag_value_default=None, raise_error=False)[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.
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()

See also

get_tag: Get a single tag from an object.
get_tags: Get all tags from an object.
get_class_tag: Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

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(deep=True)[source]¶

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc.

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

Uses dynamic tag overrides.

Parameters:

tag_namestr: Name of tag to be retrieved.
tag_value_defaultany type, 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()

See also

get_tags: Get all tags from an object.
get_clas_tags: Get all tags from a class.
get_class_tag: Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()[source]¶

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:

dict: 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.

See also

get_tag: Get a single tag from an object.
get_class_tags: Get all tags from a class.
get_class_tag: Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

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.

classmethod load_from_path(serial)[source]¶

Load object from file location.

Parameters:

serialobject: Result of ZipFile(path).open(“object).

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]¶

Load object from serialized memory container.

Parameters:

serialobject: First element of output of cls.save(None).

Returns:

deserialized self resulting in output serial, of cls.save(None).

predict(X) → ndarray[source]¶

Predicts class labels for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Returns:

predictionsnp.ndarray: 1D np.array of float, of shape (n_cases) - predicted class labels indices correspond to instance indices in X

predict_proba(X) → ndarray[source]¶

Predicts class label probabilities for time series in X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Returns:

probabilitiesnp.ndarray: 2D array of shape (n_cases, n_classes) - predicted class probabilities First dimension indices correspond to instance indices in X, second dimension indices correspond to class labels, (i, j)-th entry is estimated 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

save(path=None)[source]¶

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path).: if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.

score(X, y, metric='accuracy', use_proba=False, metric_params=None) → float[source]¶

Scores predicted labels against ground truth labels on X.

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

ynp.ndarray

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X.

metricUnion[str, callable], default=”accuracy”,

Defines the scoring metric to test the fit of the model. For supported strings arguments, check sklearn.metrics.get_scorer_names.

use_probabool, default=False,

Argument to check if scorer works on probability estimates or not.

metric_paramsdict, default=None,

Contains parameters to be passed to the scoring function. If None, no parameters are passed.

Returns:

scorefloat: Accuracy score of predict(X) vs y.

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)

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

metricstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric parameter in score.
metric_paramsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for metric_params parameter in score.
use_probastr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for use_proba parameter in score.

Returns:

selfobject: The updated object.

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 setting tag values in tag_dict as dynamic tags in self.

temporal_importance_curves(return_dict=False, normalise_time_points=False)[source]¶

Calculate the temporal importance curves for each feature.

Can be finicky with transformers currently.

Parameters:

return_dictbool, default=False: If True, return a dictionary of curves. If False, return a list of names and a list of curves.
normalise_time_pointsbool, default=False: If True, normalise the time points for each feature to the number of splits that used that feature. If False, return the sum of the information gain for each split.

Returns:

nameslist of str: The names of the features.
curveslist of np.ndarray: The temporal importance curves for each feature.