BaseDistanceProfileSearch

class BaseDistanceProfileSearch(length: int)

Bases: BaseSubsequenceSearch

Base class for distance-profile-based subsequence search.

This class provides shared logic for search algorithms that compute full distance profiles (e.g., MASS, brute force). Algorithms that use other approaches should inherit directly from BaseSubsequenceSearch instead or create their own base class.

Subclasses must implement compute_distance_profile which computes distances from a query to all candidate subsequences in the fitted collection.

Parameters:

lengthint

The length of the subsequences to use for the search.

See also

BaseSubsequenceSearch

Parent class for all subsequence search methods.

MASS

FFT-based distance profile computation.

NaiveSubsequenceSearch

Naive pairwise distance computation.

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
compute_distance_profile(X)	Compute the distance profile of X to all subsequences in X_.
fit(X[, y])	Fit estimator to X.
get_class_tag(tag_name[, raise_error, ...])	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_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
predict(X[, k, axis])	Find the k nearest neighbors to X in the fitted collection.
reset([keep])	Reset the object to a clean post-init state.
set_params(**params)	Set the parameters of this estimator.
set_predict_request(*[, axis, k])	Configure whether metadata should be requested to be passed to the predict method.
set_tags(**tag_dict)	Set dynamic tags to given values.

clone(random_state=None)

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)

abstractmethod compute_distance_profile(X: ndarray)

Compute the distance profile of X to all subsequences in X_.

Parameters:

Xnp.ndarray, 2D array of shape (n_channels, length)

The query to use to compute the distance profiles.

Returns:

distance_profilesnp.ndarray, 2D array of shape (n_cases, n_candidates)

The distance profile of X to all subsequences in all series of X_. The n_candidates value is equal to n_timepoints - length + 1.

fit(X: ndarray, y=None)

Fit estimator to X.

Parameters:

Xnp.ndarray shape (n_cases, n_channels, n_timepoints)

Input data to store and use as database against the query given when calling predict.

y: ignored, exists for API consistency reasons.

Returns:

selfa fitted instance of the estimator

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

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

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

Value of the tag_name tag in cls. 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()

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)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

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

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

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, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

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()

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.

predict(X: ndarray, k: int = 1, axis: int = 1, **kwargs)

Find the k nearest neighbors to X in the fitted collection.

Returns the indexes and distances of the best matches to X. It is possible that fewer than k indexes are returned if fewer than k admissible matches exist.

Parameters:

Xnp.ndarray, 2D array of shape (n_channels, n_timepoints)

Query series for which to find the nearest neighbors in the database. For subsequence search, n_timepoints should equal the length parameter. For whole series search, n_timepoints should match the series length in the fitted collection.

kint or np.inf, default=1

Number of best matches to return. Must be a positive integer, or the sentinel np.inf to return all matches (supported by whole series estimators).

axisint, default=1

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).

**kwargsdict, optional

Additional search options passed to the estimator’s _predict method. The accepted options (e.g. dist_threshold, inverse_distance, X_index, allow_trivial_matches, exclusion_factor) and their defaults differ per estimator; see the docstring of each concrete estimator for the options it supports.

Returns:

indexesnp.ndarray

Indexes of the best matches. The shape and meaning depend on the search type:

• Subsequence search: shape (n_matches, 2) with (i_case, i_timestamp) pairs indicating which series and at what position the match was found.

• Whole series search: shape (n_matches,) with case indices indicating which series in the fitted collection are nearest neighbors.

distancesnp.ndarray, shape (n_matches,)

Distances of the matches to the query. Lower values indicate better matches (unless inverse_distance=True is used).

reset(keep=None)

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 hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

set_params(**params)

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_predict_request(*, axis: bool | None | str = '$UNCHANGED$', k: bool | None | str = '$UNCHANGED$') → BaseDistanceProfileSearch

Configure whether metadata should be requested to be passed to the predict method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the 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.

Parameters:

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

Metadata routing for axis parameter in predict.

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

Metadata routing for k 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 and tag value pairs.

Returns:

selfobject

Reference to self.