GMMHMM

class GMMHMM(n_components: int = 1, n_mix: int = 1, min_covar: float = 0.001, startprob_prior: float = 1.0, transmat_prior: float = 1.0, weights_prior: float = 1.0, means_prior: float = 0.0, means_weight: float = 0.0, covars_prior=None, covars_weight=None, algorithm: str = 'viterbi', covariance_type: str = 'diag', random_state=None, n_iter: int = 10, tol: float = 0.01, verbose: bool = False, params: str = 'stmcw', init_params: str = 'stmcw', implementation: str = 'log')

Hidden Markov Model with Gaussian mixture emissions.

Parameters

n_componentsint

Number of states in the model.

n_mixint

Number of states in the GMM.

covariance_type{“sperical”, “diag”, “full”, “tied”}, optional

The type of covariance parameters to use: * “spherical” — each state uses a single variance value that

applies to all features.

• “diag” — each state uses a diagonal covariance matrix

  (default).

• “full” — each state uses a full (i.e. unrestricted)

  covariance matrix.

• “tied” — all mixture components of each state use the same

  full covariance matrix (note that this is not the same as for GaussianHMM).

min_covarfloat, optional

Floor on the diagonal of the covariance matrix to prevent overfitting. Defaults to 1e-3.

startprob_priorarray, shape (n_components, ), optional

Parameters of the Dirichlet prior distribution for startprob_.

transmat_priorarray, shape (n_components, n_components), optional

Parameters of the Dirichlet prior distribution for each row of the transition probabilities transmat_.

weights_priorarray, shape (n_mix, ), optional

Parameters of the Dirichlet prior distribution for weights_.

means_prior, means_weightarray, shape (n_mix, ), optional

Mean and precision of the Normal prior distribtion for means_.

covars_prior, covars_weightarray, shape (n_mix, ), optional

Parameters of the prior distribution for the covariance matrix covars_. If covariance_type is “spherical” or “diag” the prior is the inverse gamma distribution, otherwise — the inverse Wishart distribution.

algorithm{“viterbi”, “map”}, optional

Decoder algorithm.

random_state: RandomState or an int seed, optional

A random number generator instance.

n_iterint, optional

Maximum number of iterations to perform.

tolfloat, optional

Convergence threshold. EM will stop if the gain in log-likelihood is below this value.

verbosebool, optional

Whether per-iteration convergence reports are printed to sys.stderr. Convergence can also be diagnosed using the monitor_ attribute.

params, init_paramsstring, optional

The parameters that get updated during (params) or initialized before (init_params) the training. Can contain any combination of ‘s’ for startprob, ‘t’ for transmat, ‘m’ for means, ‘c’ for covars, and ‘w’ for GMM mixing weights. Defaults to all parameters.

implementation: string, optional

Determines if the forward-backward algorithm is implemented with logarithms (“log”), or using scaling (“scaling”). The default is to use logarithms for backwards compatability.

Attributes

monitor_ConvergenceMonitor

Monitor object used to check the convergence of EM.

startprob_array, shape (n_components, )

Initial state occupation distribution.

transmat_array, shape (n_components, n_components)

Matrix of transition probabilities between states.

weights_array, shape (n_components, n_mix)

Mixture weights for each state.

means_array, shape (n_components, n_mix, n_features)

Mean parameters for each mixture component in each state.

covars_array

Covariance parameters for each mixture components in each state. The shape depends on covariance_type: * (n_components, n_mix) if “spherical”, * (n_components, n_mix, n_features) if “diag”, * (n_components, n_mix, n_features, n_features) if “full” * (n_components, n_features, n_features) if “tied”.

Examples

>>> from sktime.annotation.hmm_learn import GMMHMM 
>>> from sktime.annotation.datagen import piecewise_normal 
>>> data = piecewise_normal( 
...    means=[2, 4, 1], lengths=[10, 35, 40], random_state=7
...    ).reshape((-1, 1))
>>> model = GMMHMM(n_components=3) 
>>> model = model.fit(data) 
>>> labeled_data = model.predict(data) 

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 to training data.
fit_predict(X[, Y])	Fit to data, then predict it.
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()	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_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)	Create annotations on test/deployment data.
predict_scores(X)	Return scores for predicted annotations on test/deployment data.
reset()	Reset the object to a clean post-init state.
sample([n_samples, random_state, currstate])	Interface class which allows users to sample from their HMM.
save([path])	Save serialized self to bytes-like object or to (.zip) file.
set_params(**params)	Set the parameters of this object.
set_tags(**tag_dict)	Set dynamic tags to given values.
update(X[, Y])	Update model with new data and optional ground truth annotations.
update_predict(X)	Update model with new data and create annotations for it.

classmethod get_test_params(parameter_set='default')

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()

Check if the estimator has been fitted.

Raises

NotFittedError

If the estimator has not been fitted yet.

clone()

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)

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')

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')

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.

fit(X, Y=None)

Fit to training data.

Parameters

Xpd.DataFrame

Training data to fit model to (time series).

Ypd.Series, optional

Ground truth annotations for training if annotator is supervised.

Returns

self

Reference to self.

Notes

Creates fitted model that updates attributes ending in “_”. Sets _is_fitted flag to True.

fit_predict(X, Y=None)

Fit to data, then predict it.

Fits model to X and Y with given annotation parameters and returns the annotations made by the model.

Parameters

Xpd.DataFrame, pd.Series or np.ndarray

Data to be transformed

Ypd.Series or np.ndarray, optional (default=None)

Target values of data to be predicted.

Returns

selfpd.Series

Annotations for sequence X exact format depends on annotation type.

classmethod get_class_tag(tag_name, tag_value_default=None)

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()

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()

Get fitted parameters.

State required:

Requires state to be “fitted”.

Returns

fitted_paramsdict of fitted parameters, keys are str names of parameters

parameters of components are indexed as [componentname]__[paramname]

classmethod get_param_defaults()

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()

Get parameter names for the object.

Returns

param_names: list of str, alphabetically sorted list of parameter names of cls

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, tag_value_default=None, raise_error=True)

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()

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()

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

Whether fit has been called.

classmethod load_from_path(serial)

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)

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)

predict(X)

Create annotations on test/deployment data.

Parameters

Xpd.DataFrame

Data to annotate (time series).

Returns

Ypd.Series

Annotations for sequence X exact format depends on annotation type.

predict_scores(X)

Return scores for predicted annotations on test/deployment data.

Parameters

Xpd.DataFrame

Data to annotate (time series).

Returns

Ypd.Series

Scores for sequence X exact format depends on annotation type.

reset()

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

sample(n_samples=1, random_state=None, currstate=None)

Interface class which allows users to sample from their HMM.

save(path=None)

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)

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)

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.

update(X, Y=None)

Update model with new data and optional ground truth annotations.

Parameters

Xpd.DataFrame

Training data to update model with (time series).

Ypd.Series, optional

Ground truth annotations for training if annotator is supervised.

Returns

self

Reference to self.

Notes

Updates fitted model that updates attributes ending in “_”.

update_predict(X)

Update model with new data and create annotations for it.

Parameters

Xpd.DataFrame

Training data to update model with, time series.

Returns

Ypd.Series

Annotations for sequence X exact format depends on annotation type.

Notes

Updates fitted model that updates attributes ending in “_”.