RandomProjectionIndexANN

class RandomProjectionIndexANN(n_hash_funcs=128, hash_func_coverage=0.25, use_discrete_vectors=True, random_state=None, normalize=True, n_jobs=1)

Bases: BaseCollectionSimilaritySearch

Random Projection Locality Sensitive Hashing index with cosine similarity.

In this method based on SimHash, we define a hash function as a boolean operation such as, given a random vector V of shape (n_channels, L) and a time series X of shape (n_channels, n_timeponts) (with L<=n_timepoints), we compute X.V > 0 to obtain the boolean result. In the case where L<n_timepoints, each hash function is affected a random starting point s (between [0, n_timepoints - L]) to compute the dot product as X[:, s:s+L].V

Note that this method will not provide exact results, but will perform approximate searches. This also ignore any temporal correlation and consider series as high dimensional points due to the cosine similarity distance.

Parameters:

n_hash_funcsint, optional

Number of random hashing function to use to index series. The default is 128.

hash_func_coveragefloat, optional

A value in the interval ]0,1] which defines the size L of the random vectors relative to the size of the input time series. The default is 0.25.

use_discrete_vectors: bool, optional,

Whether to use discrete vectors with values -1 or 1 as random vector. If false, the values of the random vectors are drawn uniformly between [-1,1].

random_state: int, optional

A random seed to seed the index building. The default is None.

normalize: bool, optional

Whether to z-normalize the input the series during fit and predict before indexing them.

n_jobs: int, optional

Number of parallel threads to use when computing boolean hashes.

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y])	Fit method: data preprocessing and storage.
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, **kwargs)	Predict function.
reset([keep])	Reset the object to a clean post-init state.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.

Notes

Capabilities

Missing Values	No
Multithreading	Yes
Univariate	Yes
Multivariate	Yes
Unequal Length	No

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)

fit(X: ndarray, y=None)

Fit method: data preprocessing and storage.

Parameters:

Xnp.ndarray, 3D array of shape (n_cases, n_channels, n_timepoints)

Input array to be used as database for the similarity search. If it is an unequal length collection, it should be a list of 2d numpy arrays.

yoptional

Not used.

Returns:

self

Raises:

TypeError

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

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, **kwargs)

Predict function.

Parameters:

Xnp.ndarray, 3D array of shape = (n_cases, n_channels, n_timepoints)

Collections of series to predict on.

kwargsdict, optional

Additional keyword arguments to be passed to the _predict function of the estimator.

Returns:

indexesnp.ndarray, shape = (n_cases, k)

Indexes of series in the that are similar to X.

distancesnp.ndarray, shape = (n_cases, k)

Distance of the matches to each series

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_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.