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_ndarray of class labels, possibly strings
n_classes_integer, number of classes (length of classes_)
fit_time_integer, time (in milliseconds) for fit to run.
_class_dictionarydictionary mapping classes_ onto integers 0…n_classes_-1.
_threads_to_usenumber of threads to use in fit as determined by n_jobs.
state_infoAn 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_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 and dynamic tag overrides.

get_tags()

Get tags from estimator class and dynamic tag overrides.

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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.array of int, of shape [n_instances] - 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[numpy.ndarray, numpy.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.

Parameters
X3D np.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns
y1D np.array of int, of shape [n_instances] - 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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns
y1D np.array of int, of shape [n_instances] - 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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns
y2D array of shape [n_instances, 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[numpy.ndarray, numpy.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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

Returns
y2D array of shape [n_instances, 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.array (any number of dimensions, equal length series)

of shape [n_instances, n_dimensions, series_length]

or 2D np.array (univariate, equal length series)

of shape [n_instances, series_length]

or pd.DataFrame with each column a dimension, each cell a pd.Series

(any number of dimensions, equal or unequal length series)

or of any other supported Panel mtype

for list of mtypes, see datatypes.SCITYPE_REGISTER for specifications, see examples/AA_datatypes_and_datasets.ipynb

y1D np.ndarray of int, of shape [n_instances] - 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
estimatorestimator 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.

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

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 and dynamic tag overrides.

Parameters
tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; 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()
get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns
collected_tagsdict

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.

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
serialresult 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
serial1st 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 settting tag values in tag_dict as dynamic tags in self.