TopKSimilaritySearch¶

class TopKSimilaritySearch(k=1, distance='euclidean', distance_args=None, normalize=False, speed_up=None, store_distance_profile=False)[source]¶

Top-K similarity search method.

Finds the closest k series to the query series based on a distance function.

Parameters:

kint, default=1

The number of nearest matches from Q to return.

distancestr, default=”euclidean”: Name of the distance function to use. A list of valid strings can be found in the documentation for aeon.distances.get_distance_function. If a callable is passed it must either be a python function or numba function with nopython=True, that takes two 1d numpy arrays as input and returns a float.
distance_argsdict, default=None: Optional keyword arguments for the distance function.
normalizebool, default=False: Whether the distance function should be z-normalized.
store_distance_profilebool, default=False.: Whether to store the computed distance profile in the attribute “_distance_profile” after calling the predict method.
speed_upstr, default=None: Which speed up technique to use with for the selected distance function.

Attributes:

_Xarray, shape (n_cases, n_channels, n_timepoints): The input time series stored during the fit method.
distance_profile_functionfunction: The function used to compute the distance profile affected during the fit method based on the distance and normalize parameters.

Notes

For now, the multivariate case is only treated as independent. Distances are computed for each channel independently and then summed together.

Examples

>>> from aeon.similarity_search import TopKSimilaritySearch
>>> 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")
>>> clf = TopKSimilaritySearch(k=1)
>>> clf.fit(X_train, y_train)
TopKSimilaritySearch(...)
>>> q = X_test[0, :, 5:15]
>>> y_pred = clf.predict(q)

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 method: store the input data and get the distance profile function.
`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`(q[, q_index, exclusion_factor, ...])	Predict method: Check the shape of q and call _predict to perform the search.
`reset`()	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 object.
`set_predict_request`(*[, ...])	Request metadata passed to the `predict` method.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

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')[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, y=None)[source]¶

Fit method: store the input data and get the distance profile function.

Parameters:

Xarray, shape (n_cases, n_channels, n_timepoints): Input array to used as database for the similarity search
yoptional: Not used.

Returns:

self

Raises:

TypeError: If the input X array is not 3D raise an error.

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.

See also

get_tag: Get a single tag 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()
>>> tags = d.get_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.

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(q, q_index=None, exclusion_factor=2.0, apply_exclusion_to_result=False)[source]¶

Predict method: Check the shape of q and call _predict to perform the search.

If the distance profile function is normalized, it stores the mean and stds from q and _X.

Parameters:

qarray, shape (n_channels, query_length): Input query used for similarity search.
q_indexIterable: An Interable (tuple, list, array) of length two used to specify the index of the query q if it was extracted from the input data X given during the fit method. Given the tuple (id_sample, id_timestamp), the similarity search will define an exclusion zone around the q_index in order to avoid matching q with itself. If None, it is considered that the query is not extracted from X.
exclusion_factorfloat, default=2.: The factor to apply to the query length to define the exclusion zone. The exclusion zone is define from $id_timestamp - query_length//exclusion_factor$ to $id_timestamp + query_length//exclusion_factor$. This also applies to the matching conditions defined by child classes. For example, with TopKSimilaritySearch, the k best matches are also subject to the exclusion zone, but with $id_timestamp$ the index of one of the k matches.
apply_exclusion_to_result: bool, default=False: Wheter to apply the exclusion factor to the output of the similarity search. This means that two matches of the query from the same sample must be at least spaced by +/- $query_length//exclusion_factor$. This can avoid pathological matching where, for example if we extract the best two matches, there is a high chance that if the best match is located at $id_timestamp$, the second best match will be located at $id_timestamp$ +/- 1, as they both share all their values except one.

Returns:

array, shape (n_matches, 2): An array containing the indexes of the matches between q and _X. The decision of wheter a candidate of size query_length from _X is matched with Q depends on the subclasses that implent the _predict method (e.g. top-k, threshold, …). The first index for each match is the sample id, the second is the timestamp id.

Raises:

TypeError: If the input q array is not 2D raise an error.
ValueError: If the length of the query is greater

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.

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 predict 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 predict 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 predict.
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:

apply_exclusion_to_resultstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for apply_exclusion_to_result parameter in predict.
exclusion_factorstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for exclusion_factor parameter in predict.
qstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for q parameter in predict.
q_indexstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for q_index parameter in predict.

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.