QuerySearch

class QuerySearch(k: int = 1, threshold: float = inf, distance: str = 'euclidean', distance_args: dict | None = None, inverse_distance: bool = False, normalize: bool = False, speed_up: str = 'fastest', n_jobs: int = 1, store_distance_profiles: bool = False)

Query search estimator.

The query search estimator will return a set of matches of a query in a search space , which is defined by a time series dataset given during fit. Depending on the k and/or threshold parameters, which condition what is considered a valid match during the search, the number of matches will vary. If k is used, at most k matches (the k best) will be returned, if threshold is used and k is set to np.inf, all the candidates which distance to the query is inferior or equal to threshold will be returned. If both are used, the k best matches to the query with distance inferior to threshold will be returned.

Parameters:

kint, default=1

The number of best matches to return during predict for a given query.

thresholdfloat, default=np.inf

The number of best matches to return during predict for a given query.

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.

speed_upstr, default=’fastest’

Which speed up technique to use with for the selected distance function. By default, the fastest algorithm is used. A list of available algorithm for each distance can be obtained by calling the get_speedup_function_names function of the child classes.

inverse_distancebool, default=False

If True, the matching will be made on the inverse of the distance, and thus, the worst matches to the query will be returned instead of the best ones.

n_jobsint, default=1

Number of parallel jobs to use.

store_distance_profilesbool, default=False.

Whether to store the computed distance profiles in the attribute “distance_profiles_” after calling the predict method. It will store the raw distance profile, meaning without potential inversion or thresholding applied.

Attributes:

X_array, shape (n_cases, n_channels, n_timepoints)

The input time series stored during the fit method. This is the database we search in when given a query.

distance_profile_functionfunction

The function used to compute the distance profile. This is determined 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.

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: data preprocessing and storage.
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_speedup_function_names()	Get available speedup for similarity search in aeon.
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[, axis, X_index, ...])	Predict method: Check the shape of X 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(*[, X_index, ...])	Request metadata passed to the predict method.
set_tags(**tag_dict)	Set dynamic tags to given values.

final predict(X, axis=1, X_index=None, exclusion_factor=2.0, apply_exclusion_to_result=False)

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

If the distance profile function is normalized, it stores the mean and stds from X and X_, with X_ the training data.

Parameters:

Xarray, shape (n_channels, query_length)

Input query used for similarity search.

axis: int

The time point axis of the input series if it is 2D. If axis==0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints,n_channels). axis==1 indicates the time series are in rows, i.e. the shape of the data is (n_channels,n_timepoints).

X_indexIterable

An Interable (tuple, list, array) of length two used to specify the index of the query X 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 X_index in order to avoid matching X 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 X and X_. The decision of wheter a candidate of size query_length from X_ is matched with X 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 X array is not 2D raise an error.

ValueError

If the length of the query is greater

classmethod get_speedup_function_names()

Get available speedup for similarity search in aeon.

The returned structure is a dictionnary that contains the names of all avaialble speedups for normalized and non-normalized distance functions.

Returns:

dict

The available speedups name that can be used as parameters in similarity search classes.

check_is_fitted()

Check if the estimator has been fitted.

Raises:

NotFittedError

If the estimator has not been fitted yet.

clone()

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)

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)

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')

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)

Fit method: data preprocessing and storage.

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)

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_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()

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)

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()

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()

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()

Get parameter names for the object.

Returns:

param_names: list of str, alphabetically sorted list of parameter names of cls

get_params(deep=True)

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)

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()

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()

classmethod get_test_params(parameter_set='default')

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()

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

Whether fit has been called.

classmethod load_from_path(serial)

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)

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()

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)

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)

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_predict_request(*, X_index: bool | None | str = '$UNCHANGED$', apply_exclusion_to_result: bool | None | str = '$UNCHANGED$', axis: bool | None | str = '$UNCHANGED$', exclusion_factor: bool | None | str = '$UNCHANGED$') → QuerySearch

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:

X_indexstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for X_index parameter in predict.

apply_exclusion_to_resultstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for apply_exclusion_to_result parameter in predict.

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in predict.

exclusion_factorstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for exclusion_factor parameter in predict.

Returns:

selfobject

The updated object.

set_tags(**tag_dict)

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.