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: 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{“spherical”, “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 distribution 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 compatibility.

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

`change_points_to_segments`(y_sparse[, start, end])	Convert an series of change point indexes to segments.
`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.
`dense_to_sparse`(y_dense)	Convert the dense output from an annotator to a sparse format.
`fit`(X[, Y])	Fit to training data.
`fit_predict`(X[, Y])	Fit to data, then predict it.
`fit_transform`(X[, Y])	Fit to data, then transform 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`([sort])	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_points`(X)	Predict changepoints/anomalies on test/deployment data.
`predict_scores`(X)	Return scores for predicted annotations on test/deployment data.
`predict_segments`(X)	Predict segments 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, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`segments_to_change_points`(y_sparse)	Convert segments to change points.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`sparse_to_dense`(y_sparse, index)	Convert the sparse output from an annotator to a dense format.
`transform`(X)	Create annotations on test/deployment data.
`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

static change_points_to_segments(y_sparse, start=None, end=None)[source]#

Convert an series of change point indexes to segments.

Parameters:

y_sparsepd.Series: A series containing the indexes of change points.
startoptional: Starting point of the first segment.
endoptional: Ending point of the last segment

Returns:

pd.Series: A series with an interval index indicating the start and end points of the segments. The values of the series are the labels of the segments.

Examples

>>> import pandas as pd
>>> from sktime.annotation.base._base import BaseSeriesAnnotator
>>> change_points = pd.Series([1, 2, 5])
>>> BaseSeriesAnnotator.change_points_to_segments(change_points, 0, 7)
[0, 1)   -1
[1, 2)    1
[2, 5)    2
[5, 7)    3
dtype: int64

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__}

static dense_to_sparse(y_dense)[source]#

Convert the dense output from an annotator to a sparse format.

Parameters:

y_densepd.Series

If y_sparse contains only 1’s and 0’s, the 1’s represent change points or anomalies.
If y_sparse contains only contains integers greater than 0, it is an an array of segments.

Returns:

pd.Series

If y_sparse is a series of changepoints/anomalies, a pandas series will be returned containing the indexes of the changepoints/anomalies
If y_sparse is a series of segments, a series with an interval datatype index will be returned. The values of the series will be the labels of segments.

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.

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

Fit to data, then transform 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(sort=True)[source]#

Get object’s parameter names.

Parameters:

sortbool, default=True: Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]: List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

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_points(X)[source]#

Predict changepoints/anomalies on test/deployment data.

Parameters:

Xpd.DataFrame: Data to annotate, time series.

Returns:

Ypd.Series: A series whose values are the changepoints/anomalies in X.

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.

predict_segments(X)[source]#

Predict segments on test/deployment data.

Parameters:

Xpd.DataFrame: Data to annotate, time series.

Returns:

Ypd.Series: A series with an index of intervals. Each interval is the range of a segment and the corresponding value is the label of the segment.

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, serialization_format='pickle')[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/.
serialization_format: str, default = “pickle”: Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file

static segments_to_change_points(y_sparse)[source]#

Convert segments to change points.

Parameters:

y_sparsepd.DataFrame: A series of segments. The index must be the interval data type and the values should be the integer labels of the segments.

Returns:

pd.Series: A series containing the indexes of the start of each segment.

Examples

>>> import pandas as pd
>>> from sktime.annotation.base._base import BaseSeriesAnnotator
>>> segments = pd.Series(
...     [3, -1, 2],
...     index=pd.IntervalIndex.from_breaks([2, 5, 7, 9], closed="left")
... )
>>> BaseSeriesAnnotator.segments_to_change_points(segments)
0    2
1    5
2    7
dtype: int64

set_config(**config_dict)[source]#

Set config flags to given values.

Parameters:

config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

“diagram” = html box diagram representation
“text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

“on” = will raise warnings from sktime
“off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel
“joblib”: custom and 3rd party joblib backends, e.g., spark
“dask”: uses dask, requires dask package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel:

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler

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 composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : estimator.random_state is set to input random_state
“keep” : estimator.random_state is kept as is
“new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

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.

static sparse_to_dense(y_sparse, index)[source]#

Convert the sparse output from an annotator to a dense format.

Parameters:

y_sparsepd.Series

If y_sparse is a series with an index of intervals, it should represent segments where each value of the series is label of a segment. Unclassified intervals should be labelled -1. Segments must never have the label 0.
If the index of y_sparse is not a set of intervals, the values of the series should represent the indexes of changepoints/anomalies.

indexarray-like

Indices that are to be annotated according to y_sparse.

Returns:

pd.Series

A series with an index of index is returned. * If y_sparse is a series of changepoints/anomalies then the returned

series is labelled 0 and 1 dependendy on whether the index is associated with an anomaly/changepoint. Where 1 means anomaly/changepoint.

If y_sparse is a series of segments then the returned series is labelled depending on the segment its indexes fall into. Indexes that fall into no segments are labelled -1.

Examples

>>> import pandas as pd
>>> from sktime.annotation.base._base import BaseSeriesAnnotator
>>> y_sparse = pd.Series([2, 5, 7])  # Indices of changepoints/anomalies
>>> index = range(0, 8)
>>> BaseSeriesAnnotator.sparse_to_dense(y_sparse, index=index)
0    0
1    0
2    1
3    0
4    0
5    1
6    0
7    1
dtype: int64
>>> y_sparse = pd.Series(
...     [1, 2, 1],
...     index=pd.IntervalIndex.from_arrays(
...         [0, 4, 6], [4, 6, 10], closed="left"
...     )
... )
>>> index = range(10)
>>> BaseSeriesAnnotator.sparse_to_dense(y_sparse, index=index)
0    1
1    1
2    1
3    1
4    2
5    2
6    1
7    1
8    1
9    1
dtype: int64

transform(X)[source]#

Create annotations on test/deployment data.

Parameters:

Xpd.DataFrame: Data to annotate (time series).

Returns:

Ypd.Series: Annotations for sequence X. The returned annotations will be in the dense format.

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 “_”.