BaseEarlyClassifier#

class BaseEarlyClassifier[source]#

Abstract base class for early time series classifiers.

The base classifier specifies the methods and method signatures that all early classifiers have to implement. Attributes with an underscore suffix are set in the method fit.

Parameters:
classes_np.ndarray

Class labels, possibly strings.

n_classes_int

Number of classes (length of classes_).

fit_time_int

Time (in milliseconds) for fit to run.

_class_dictionarydict

dictionary mapping classes_ onto integers 0…n_classes_-1.

_n_jobsint, default=1

Number of threads to use in fit as determined by n_jobs.

state_infoarray-like, default=None

An array containing the state info for each decision in X.

Attributes:
is_fitted

Whether fit has been called.

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.

filter_X(X, decisions)

Remove True cases from X given a boolean array of decisions.

filter_X_y(X, y, decisions)

Remove True cases from X and y given a boolean array of decisions.

fit(X, y)

Fit time series classifier to training data.

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_state_info()

Return the state information generated from the last predict/update call.

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)

Predicts labels for sequences in X.

predict_proba(X)

Predicts labels probabilities for sequences in X.

reset()

Reset the object to a clean post-init state.

reset_state_info()

Reset the state information used in update methods.

save([path])

Save serialized self to bytes-like object or to (.zip) file.

score(X, y)

Scores predicted labels against ground truth labels on X.

set_params(**params)

Set the parameters of this object.

set_tags(**tag_dict)

Set dynamic tags to given values.

split_indices(indices, decisions)

Split a list of indices given a boolean array of decisions.

split_indices_and_filter(X, indices, decisions)

Remove True cases and split a list of indices given an array of decisions.

update_predict(X)

Update label prediction for sequences in X at a larger series length.

update_predict_proba(X)

Update label probabilities for sequences in X at a larger series length.

fit(X, y)[source]#

Fit time series classifier to training data.

Parameters:
X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. Other types are allowed and converted into one of the above.

np.array

shape (n_cases) - class labels for fitting indices correspond to instance indices in X.

Returns:
selfReference to self.

Notes

Changes state by creating a fitted model that updates attributes ending in “_” and sets is_fitted flag to True.

predict(X) Tuple[ndarray, ndarray][source]#

Predicts labels for sequences in X.

Early classifiers can predict at series lengths shorter than the train data series length.

Predict will return -1 for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i other types are allowed and converted into one of the above.

Returns:
ynp.array

shape [n_cases] - predicted class labels indices correspond to instance indices in X.

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use.

update_predict(X) Tuple[ndarray, ndarray][source]#

Update label prediction for sequences in X at a larger series length.

Uses information stored in the classifiers state from previous predictions and updates at shorter series lengths. Update will only accept cases which have not yet had a decision made, cases which have had a positive decision should be removed from the input with the row ordering preserved.

If no state information is present, predict will be called instead.

Prediction updates will return -1 for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:
X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Returns:
y1D np.array of int, of shape [n_cases] - predicted class labels

indices correspond to instance indices in X

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use

predict_proba(X) Tuple[ndarray, ndarray][source]#

Predicts labels probabilities for sequences in X.

Early classifiers can predict at series lengths shorter than the train data series length.

Probability predictions will return [-1]*n_classes_ for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:
X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Returns:
y2D array of shape [n_cases, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use

update_predict_proba(X) Tuple[ndarray, ndarray][source]#

Update label probabilities for sequences in X at a larger series length.

Uses information stored in the classifiers state from previous predictions and updates at shorter series lengths. Update will only accept cases which have not yet had a decision made, cases which have had a positive decision should be removed from the input with the row ordering preserved.

If no state information is present, predict_proba will be called instead.

Probability predictions updates will return [-1]*n_classes_ for cases which it cannot make a decision on yet. The output is only guaranteed to return a valid class label for all cases when using the full series length.

Parameters:
X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

Returns:
y2D array of shape [n_cases, n_classes] - predicted class probabilities

1st dimension indices correspond to instance indices in X 2nd dimension indices correspond to possible labels (integers) (i, j)-th entry is predictive probability that i-th instance is of class j

decisions1D bool array

An array of booleans, containing the decision of whether a prediction is safe to use or not. i-th entry is the classifier decision that i-th instance safe to use

score(X, y) Tuple[float, float, float][source]#

Scores predicted labels against ground truth labels on X.

Parameters:
X3D np.ndarray

Input data, any number of channels, equal length series of shape ( n_cases, n_channels, n_timepoints) or 2D np.array (univariate, equal length series) of shape (n_cases, n_timepoints) or list of numpy arrays (any number of channels, unequal length series) of shape [n_cases], 2D np.array (n_channels, n_timepoints_i), where n_timepoints_i is length of series i. other types are allowed and converted into one of the above.

y1D np.ndarray of int, of shape [n_cases] - class labels (ground truth)

indices correspond to instance indices in X

Returns:
Tuple of floats, harmonic mean, accuracy and earliness scores of predict(X) vs y
get_state_info()[source]#

Return the state information generated from the last predict/update call.

Returns:
An array containing the state info for each decision in X from update and
predict methods. Contains classifier dependant information for future decisions
on the data and information on when a cases decision has been made. Each row
contains information for a case from the latest decision on its safety made in
update/predict. Successive updates are likely to remove rows from the
state_info, as it will only store as many rows as there are input instances to
update/predict.
reset_state_info()[source]#

Reset the state information used in update methods.

static filter_X(X, decisions)[source]#

Remove True cases from X given a boolean array of decisions.

static filter_X_y(X, y, decisions)[source]#

Remove True cases from X and y given a boolean array of decisions.

static split_indices(indices, decisions)[source]#

Split a list of indices given a boolean array of decisions.

static split_indices_and_filter(X, indices, decisions)[source]#

Remove True cases and split a list of indices given an array of decisions.

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.

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 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()[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.

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).
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)
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.