BaseSimiliaritySearch¶
- class BaseSimiliaritySearch(distance='euclidean', distance_args=None, normalize=False, store_distance_profile=False, speed_up=None)[source]¶
BaseSimilaritySearch.
- Parameters:
- 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.
Methods
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 from estimator class and all its parent classes.
get_fitted_params
([deep])Get fitted parameters.
Get metadata routing of this object.
Get parameter defaults for the object.
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.
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.
- final 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.
- final 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
- 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)
- instance of
- 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.
- 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_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 objectif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
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_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.
- 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()[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 totype(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)
- set_predict_request(*, apply_exclusion_to_result: bool | None | str = '$UNCHANGED$', exclusion_factor: bool | None | str = '$UNCHANGED$', q: bool | None | str = '$UNCHANGED$', q_index: bool | None | str = '$UNCHANGED$') BaseSimiliaritySearch [source]¶
Request metadata passed to the
predict
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed topredict
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it topredict
.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 inpredict
.- exclusion_factorstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
exclusion_factor
parameter inpredict
.- qstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
q
parameter inpredict
.- q_indexstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
q_index
parameter inpredict
.
- Returns:
- selfobject
The updated object.