InformationGainSegmentation#

class InformationGainSegmentation(k_max: int = 10, step: int = 5)[source]#

Information Gain based Temporal Segmentation (IGTS) Estimator.

IGTS is a n unsupervised method for segmenting multivariate time series into non-overlapping segments by locating change points that for which the information gain is maximized.

Information gain (IG) is defined as the amount of entropy lost by the segmentation. The aim is to find the segmentation that have the maximum information gain for a specified number of segments.

IGTS uses top-down search method to greedily find the next change point location that creates the maximum information gain. Once this is found, it repeats the process until it finds k_max splits of the time series.

Note

IGTS does not work very well for univariate series but it can still be used if the original univariate series are augmented by an extra feature dimensions. A technique proposed in the paper [1] us to subtract the series from it’s largest element and append to the series.

Parameters
k_max: int, default=10

Maximum number of change points to find. The number of segments is thus k+1.

step:int, default=5

Step size, or stride for selecting candidate locations of change points. Fox example a step=5 would produce candidates [0, 5, 10, …]. Has the same meaning as step in range function.

Attributes
change_points_: list of int

Locations of change points as integer indexes. By convention change points include the identity segmentation, i.e. first and last index + 1 values.

intermediate_results_: list of `ChangePointResult`

Intermediate segmentation results for each k value, where k=1, 2, …, k_max

Notes

Based on the work from [1]. - alt. py implementation: https://github.com/cruiseresearchgroup/IGTS-python - MATLAB version: https://github.com/cruiseresearchgroup/IGTS-matlab - paper available at:

References

1(1,2)

Sadri, Amin, Yongli Ren, and Flora D. Salim. “Information gain-based metric for recognizing transitions in human activities.”, Pervasive and Mobile Computing, 38, 92-109, (2017). https://www.sciencedirect.com/science/article/abs/pii/S1574119217300081

Examples

>>> from sktime.annotation.datagen import piecewise_normal_multivariate
>>> from sklearn.preprocessing import MinMaxScaler
>>> X = piecewise_normal_multivariate(
... lengths=[10, 10, 10, 10],
... means=[[0.0, 1.0], [11.0, 10.0], [5.0, 3.0], [2.0, 2.0]],
... variances=0.5,
... )
>>> X_scaled = MinMaxScaler(feature_range=(0, 1)).fit_transform(X)
>>> from sktime.annotation.igts import InformationGainSegmentation
>>> igts = InformationGainSegmentation(k_max=3, step=2)
>>> y = igts.fit_predict(X_scaled)

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.

fit(X[, y])

Fit method for compatibility with sklearn-type estimator interface.

fit_predict(X[, y])

Perform segmentation.

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])

Return initialization parameters.

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[, y])

Perform segmentation.

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(**parameters)

Set the parameters of this object.

set_tags(**tag_dict)

Set dynamic tags to given values.

to_classification(change_points)

Convert change point locations to a classification vector.

to_clusters(change_points)

Convert change point locations to a clustering vector.

fit(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Optional[Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None)[source]#

Fit method for compatibility with sklearn-type estimator interface.

It sets the internal state of the estimator and returns the initialized instance.

Parameters
X: array_like

2D array_like representing time series with sequence index along the first dimension and value series as columns.

y: array_like

Placeholder for compatibility with sklearn-api, not used, default=None.

predict(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Optional[Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None) Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]][source]#

Perform segmentation.

Parameters
X: array_like

2D array_like representing time series with sequence index along the first dimension and value series as columns.

y: array_like

Placeholder for compatibility with sklearn-api, not used, default=None.

Returns
y_predarray_like

1D array with predicted segmentation of the same size as the first dimension of X. The numerical values represent distinct segments labels for each of the data points.

fit_predict(X: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], y: Optional[Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]] = None) Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]][source]#

Perform segmentation.

A convenience method for compatibility with sklearn-like api.

Parameters
X: array_like

2D array_like representing time series with sequence index along the first dimension and value series as columns.

y: array_like

Placeholder for compatibility with sklearn-api, not used, default=None.

Returns
y_predarray_like

1D array with predicted segmentation of the same size as the first dimension of X. The numerical values represent distinct segments labels for each of the data points.

get_params(deep: bool = True) Dict[source]#

Return initialization parameters.

Parameters
deep: bool

Dummy argument for compatibility with sklearn-api, not used.

Returns
params: dict

Dictionary with the estimator’s initialization parameters, with keys being argument names and values being argument values.

set_params(**parameters)[source]#

Set the parameters of this object.

Parameters
parametersdict

Initialization parameters for th estimator.

Returns
selfreference to self (after parameters have been set)
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
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_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.

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_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.

to_classification(change_points: List[int]) Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]][source]#

Convert change point locations to a classification vector.

Change point detection results can be treated as classification with true change point locations marked with 1’s at position of the change point and remaining non-change point locations being 0’s.

For example change points [2, 8] for a time series of length 10 would result in: [0, 0, 1, 0, 0, 0, 0, 0, 1, 0].

to_clusters(change_points: List[int]) Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]][source]#

Convert change point locations to a clustering vector.

Change point detection results can be treated as clustering with each segment separated by change points assigned a distinct dummy label.

For example change points [2, 8] for a time series of length 10 would result in: [0, 0, 1, 1, 1, 1, 1, 1, 2, 2].