GaussianHMM#

class GaussianHMM(n_components: int = 1, covariance_type: str = 'diag', min_covar: float = 0.001, startprob_prior: float = 1.0, transmat_prior: float = 1.0, means_prior: float = 0, means_weight: float = 0, covars_prior: float = 0.01, covars_weight: float = 1, algorithm: str = 'viterbi', random_state: Optional[float] = None, n_iter: int = 10, tol: float = 0.01, verbose: bool = False, params: str = 'stmc', init_params: str = 'stmc', implementation: str = 'log')[source]#

Hidden Markov Model with Gaussian emissions.

Parameters

n_componentsint

Number of states

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.

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.

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_.

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 and ‘c’ for covars. 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

n_featuresint: Dimensionality of the Gaussian emissions.
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.
means_array, shape (n_components, n_features): Mean parameters for each state.
covars_array: Covariance parameters for each state. The shape depends on covariance_type: * (n_components, ) if “spherical”, * (n_components, n_features) if “diag”, * (n_components, n_features, n_features) if “full”, * (n_features, n_features) if “tied”.

Examples

>>> from sktime.annotation.hmm_learn import GaussianHMM 
>>> 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 = GaussianHMM(algorithm='viterbi', n_components=2) 
>>> 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 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 a class tag’s value.
`get_class_tags`()	Get class tags from the class and all its parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object’s parameter defaults.
`get_param_names`()	Get object’s parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`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 composed of other BaseObjects.
`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_config`(**config_dict)	Set config flags to given values.
`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')[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.

Raises

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)[source]#

Clone 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.

fit(X, Y=None)[source]#

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)[source]#

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)[source]#

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters

tag_namestr: Name of tag value.
tag_value_defaultany: 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 the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns

collected_tagsdict: Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

Returns

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

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 object’s parameter defaults.

Returns

default_dict: dict[str, Any]: 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 object’s parameter names.

Returns

param_names: list[str]: Alphabetically sorted list of parameter names of cls.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
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_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_valueAny: 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 composed of other BaseObjects.

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 an object has any parameters whose values are BaseObjects.

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)

predict(X)[source]#

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)[source]#

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()[source]#

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

hyper-parameters = arguments of __init__

object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

sample(n_samples=1, random_state=None, currstate=None)[source]#: Interface class which allows users to sample from their HMM.

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_config(**config_dict)[source]#

Set config flags to given values.

Parameters

config_dictdict: Dictionary of config name : config value pairs.

Returns

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

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

self: Reference 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.

update(X, Y=None)[source]#

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)[source]#

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 “_”.