MultiRocket¶

class MultiRocket(num_kernels=6250, max_dilations_per_kernel=32, n_features_per_kernel=4, normalise=False, n_jobs=1, random_state=None)[source]¶

Multi RandOm Convolutional KErnel Transform (MultiRocket).

MultiRocket [1] uses the same set of kernels as MiniRocket on both the raw series and the first order differenced series representation. It uses a different set of dilations and used for each representation. In addition to percentage of positive values (PPV), MultiRocket adds 3 pooling operators: Mean of Positive Values (MPV); Mean of Indices of Positive Values (MIPV); and Longest Stretch of Positive Values (LSPV).

Parameters:

num_kernelsint, default = 6,250: Number of random convolutional kernels. The calculated number of features is the nearest multiple of n_features_per_kernel(default 4)*84=336 < 50,000 (2*n_features_per_kernel(default 4)*num_kernels(default 6,250)).
max_dilations_per_kernelint, default = 32: Maximum number of dilations per kernel.
n_features_per_kernelint, default = 4: Number of features per kernel.
normalisebool, default False: Whether or not to normalise the input time series per instance.
n_jobsint, default=1: The number of jobs to run in parallel for transform. -1 means using all processors.
random_stateNone or int, default = None: Seed for random number generation.

Attributes:

parametertuple: Parameter (dilations, n_features_per_dilation, biases) for transformation of input X.
parameter1tuple: Parameter (dilations, n_features_per_dilation, biases) for transformation of input X1 = np.diff(X, 1).

See also

MiniRocket, Rocket

References

[1]

Tan, Chang Wei and Dempster, Angus and Bergmeir, Christoph and

Webb, Geoffrey I, “MultiRocket: Multiple pooling operators and transformations for fast and effective time series classification”,2022, https://link.springer.com/article/10.1007/s10618-022-00844-1 https://arxiv.org/abs/2102.00457

Examples

>>> from aeon.transformations.collection.convolution_based import MultiRocket
>>> from aeon.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train")
>>> X_test, y_test = load_unit_test(split="test")
>>> trf = MultiRocket(num_kernels=512)
>>> trf.fit(X_train)
MultiRocket(num_kernels=512)
>>> X_train = trf.transform(X_train)
>>> X_test = trf.transform(X_test)

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`([random_state])	Obtain a clone of the object with the same hyperparameters.
`create_test_instance`([parameter_set, ...])	Construct Estimator instance if possible.
`fit`(X[, y])	Fit transformer to X, optionally using y if supervised.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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_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.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`([keep])	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`set_params`(**params)	Set the parameters of this estimator.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`transform`(X[, y])	Transform X and return a transformed version.

check_is_fitted()[source]¶

Check if the estimator has been fitted.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone(random_state=None)[source]¶

Obtain a clone of the object with the same hyperparameters.

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)).

Parameters:

random_stateint, RandomState instance, or None, default=None: Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject: Instance of type(self), clone of self (see above)

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:

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

fit(X, y=None)[source]¶

Fit transformer to X, optionally using y if supervised.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator

Parameters:

Xnp.ndarray or list

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, 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. 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 to handle.

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None)[source]¶

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:: Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. model attributes (ending in “_”) : dependent on estimator.

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X

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 and tag_name is not in self.get_tags().keys()

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 and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

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 (= BaseAeonEstimator-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
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.

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.

Includes dynamic and overridden tags.

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 and tag_name is not in self.get_tags().keys()

Examples

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

get_tags()[source]¶

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

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.

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.

inverse_transform(X, y=None)[source]¶

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags: “input_data_type”=”Series”, “output_data_type”=”Series”,

can have an inverse_transform.

State required:: Requires state to be “fitted”.
Accesses in self:: _is_fitted : must be True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

inverse transformed version of X: of the same type as X

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).

reset(keep=None)[source]¶

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed 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 hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None: If None, all attributes are removed except hyper-parameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

self: Reference to self.

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.

set_params(**params)[source]¶

Set the parameters of this estimator.

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

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name and tag value pairs.

Returns:

self: Reference to self.

transform(X, y=None)[source]¶

Transform X and return a transformed version.

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

Accesses in self: _is_fitted : must be True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:

Xnp.ndarray or list

Data to fit transform to, of valid collection type.

ynp.ndarray, default=None

1D np.array of float or str, of shape (n_cases) - class labels (ground truth) for fitting indices corresponding to instance indices in X. If None, no labels are used in fitting.

Returns:

transformed version of X