PoissonHMM#

class PoissonHMM(n_components: int = 1, startprob_prior: float = 1.0, transmat_prior: float = 1.0, lambdas_prior: float = 0.0, lambdas_weight: float = 0.0, algorithm: str = 'viterbi', random_state: int = None, n_iter: int = 10, tol: float = 0.01, verbose: bool = False, params: str = 'stl', init_params: str = 'stl', implementation: str = 'log')[source]#

Hidden Markov Model with Poisson emissions.

Parameters:

n_componentsint: Number of states.
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_.
lambdas_prior, lambdas_weightarray, shape (n_components,), optional: The gamma prior on the lambda values using alpha-beta notation, respectively. If None, will be set based on the method of moments.
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, and ‘l’ for lambdas. 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:

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.
lambdas_array, shape (n_components, n_features): The expectation value of the waiting time parameters for each feature in a given state.

Examples

>>> from sktime.annotation.hmm_learn import PoissonHMM 
>>> from sktime.annotation.datagen import piecewise_poisson 
>>> data = piecewise_poisson( 
...    lambdas=[1, 2, 3], lengths=[2, 4, 8], random_state=7
...    ).reshape((-1, 1))
>>> model = PoissonHMM(n_components=3) 
>>> 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`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`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 detector to a sparse format.
`fit`(X[, y, Y])	Fit to training data.
`fit_predict`(X[, y, Y])	Fit to data, then predict it.
`fit_transform`(X[, y, Y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from 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 instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and 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 labels on test/deployment data.
`predict_points`(X)	Predict changepoints/anomalies on test/deployment data.
`predict_scores`(X)	Return scores for predicted labels 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 instance level tag overrides to given values.
`sparse_to_dense`(y_sparse, index)	Convert the sparse output from an detector to a dense format.
`transform`(X)	Create labels on test/deployment data.
`transform_scores`(X)	Return scores for predicted labels on test/deployment data.
`update`(X[, y, Y])	Update model with new data and optional ground truth labels.
`update_predict`(X[, y])	Update model with new data and create labels 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 of int, sorted ascendingly: A series containing the iloc indexes of change points.
startoptional, default=0: Starting point of the first segment. Must be before the first change point, i.e., < y_sparse[0].
endoptional, default=y_sparse[-1] + 1: End point of the last segment. Must be after the last change point, i.e., > y_sparse[-1].

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.detection.base import BaseDetector
>>> change_points = pd.Series([1, 2, 5])
>>> BaseDetector.change_points_to_segments(change_points, 0, 7)
[0, 1)    0
[1, 2)    1
[2, 5)    2
[5, 7)    3
dtype: int64

check_is_fitted(method_name=None)[source]#

Check if the estimator has been fitted.

Check if _is_fitted attribute is present and True. The is_fitted attribute should be set to True in calls to an object’s fit method.

If not, raises a NotFittedError.

Parameters:

method_namestr, optional: Name of the method that called this function. If provided, the error message will include this information.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters and config.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Equivalent to constructing a new instance of type(self), with parameters of self, that is, type(self)(**self.get_params(deep=False)).

If configs were set on self, the clone will also have the same configs as the original, equivalent to calling cloned_self.set_config(**self.get_config()).

Also equivalent in value to a call of self.reset, with the exception that clone returns a new object, instead of mutating self like reset.

Raises:

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

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

Clone tags from another object as dynamic override.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

clone_tags sets dynamic tag overrides from another object, estimator.

The clone_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

The dynamic tags are set to the values of the tags in estimator, with the names specified in tag_names.

The default of tag_names writes all tags from estimator to self.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

estimatorAn instance of :class:BaseObject or derived class
tag_namesstr or list of str, default = None: Names of tags to clone. The default (None) clones all tags from estimator.

Returns:

self: Reference to self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct an instance of the class, using first test parameter set.

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

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. The naming 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 detector 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, Y=None)[source]#

Fit to training data.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray

Training data to fit model to (time series).

ypd.DataFrame with RangeIndex, optional.

Known events for traininmg, in X, if detector is supervised.

Each row y is a known event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation with labels, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.

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, Y=None)[source]#

Fit to data, then predict it.

Fits model to X and Y with given detection parameters and returns the detection labels produced by the model.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray

Data to be transformed

ypd.DataFrame with RangeIndex, optional.

Known events for training, in X, if detector is supervised.

Each row y is a known event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation with labels, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.

Returns:

ypd.DataFrame with RangeIndex

Detected or predicted events.

Each row y is a detected or predicted event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation with labels, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.

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

Fit to data, then transform it.

Fits model to X and Y with given detection parameters and returns the detection labels 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:

ypd.DataFrame with same index as X

Labels for sequence X.

If task is "anomaly_detection", the values are integer labels. A value of 0 indicatesthat X, at the same time index, has no anomaly. Other values indicate an anomaly. Most detectors will return 0 or 1, but some may return more values, if they can detect different types of anomalies. indicating whether X, at the same index, is an anomaly, 0 for no, 1 for yes.
If task is "changepoint_detection", the values are integer labels, indicating labels for segments between changepoints. Possible labels are integers starting from 0.
If task is “segmentation”, the values are integer labels of the segments. Possible labels are integers starting from 0.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get class tag value from class, with tag level inheritance from parents.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tag method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns the value of the tag with name tag_name from the object, taking into account tag overrides, in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

To retrieve tag values with potential instance overrides, use the get_tag method instead.

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 class, with tag level inheritance from parent classes.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tags method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns a dictionary with keys being keys of any attribute of _tags set in the class or any of its parent classes.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Instances can override these tags depending on hyper-parameters.

To retrieve tags with potential instance overrides, use the get_tags method instead.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

For including overrides from dynamic tags, use get_tags.

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 clone_tags.

get_config()[source]#

Get config flags for self.

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

get_config returns dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

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 instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tag method retrieves the value of a single tag with name tag_name from the instance, taking into account tag overrides, in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

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, raises an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError, if raise_error is True.: The ValueError is then raised if tag_name is not in self.get_tags().keys().

get_tags()[source]#

Get tags from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tags method returns a dictionary of tags, with keys being keys of any attribute of _tags set in the class or any of its parent classes, or tags set via set_tags or clone_tags.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

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 BaseObject descendant instances.

property is_fitted[source]#

Whether fit has been called.

Inspects object’s _is_fitted` attribute that should initialize to ``False during object construction, and be set to True in calls to an object’s fit method.

Returns:

bool: Whether the estimator has been fit.

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 labels on test/deployment data.

This method returns a list-like type specific to the detection task, e.g., segments for segmentation, anomalies for anomaly detection.

The encoding varies by task and learning_type (tags), see below.

For returns that are type consistent across tasks, see predict_points and predict_segments.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray: Time series subject to detection, which will be assigned labels or scores.

Returns:

ypd.DataFrame with RangeIndex

Detected or predicted events.

Each row y is a detected or predicted event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation with labels, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.

predict_points(X)[source]#

Predict changepoints/anomalies on test/deployment data.

The main difference to predict is that this method always returns a pd.DataFrame with points of interest, even if the task is not anomaly or change point detection.

Parameters:

Xpd.DataFrame: Time series subject to detection, which will be assigned labels or scores.

Returns:

ypd.DataFrame with RangeIndex

pd.DataFrame with the following columns:

"ilocs" - always. Values are integers, iloc references to indices of X, signifying points of interest.
"labels" - if the task, by tags, is supervised or semi-supervised segmentation, or anomaly clustering.

The meaning of segments in the "ilocs" column and "labels" column is as follows:

If task is "anomaly_detection" or "change_point_detection", the values are integer indices of the changepoints/anomalies.
If task is "segmentation", the values are consecutive segment boundaries.

The "labels" are potential labels for the points of interest.

predict_scores(X)[source]#

Return scores for predicted labels on test/deployment data.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray: Data to label (time series).

Returns:

scorespd.DataFrame with same index as return of predict: Scores for prediction of sequence X.

predict_segments(X)[source]#

Predict segments on test/deployment data.

The main difference to predict is that this method always returns a pd.DataFrame with segments of interest, even if the task is not segmentation.

Parameters:

Xpd.DataFrame: Time series subject to detection, which will be assigned labels or scores.

Returns:

ypd.DataFrame with RangeIndex

pd.DataFrame with the following columns:

"ilocs" - always. Values are left-closed intervals with left/right values being iloc references to indices of X, signifying segments.
"labels" - if the task, by tags, is supervised or semi-supervised segmentation, or segment clustering.

The meaning of segments in the "ilocs" column and "labels" column is as follows:

If task is "anomaly_detection" or "change_point_detection", the intervals are intervals between changepoints/anomalies, and potential labels are consecutive integers starting from 0.
If task is "segmentation", the values are segmentation labels.

reset()[source]#

Reset the object to a clean post-init state.

Results in setting self to the state it had directly after the constructor call, with the same hyper-parameters. Config values set by set_config are also retained.

A reset call deletes any object attributes, except:

hyper-parameters = arguments of __init__ written to self, e.g., self.paramname where paramname is an argument of __init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of get_config before and after reset are equal.

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

Equivalent to clone, with the exception that reset mutates self instead of returning a new object.

After a self.reset() call, self is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.

Returns:

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

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. Index is RangeIndex, and values are iloc references to the start of each segment.

Examples

>>> import pandas as pd
>>> from sktime.detection.base import BaseDetector
>>> segments = pd.Series(
...     [3, -1, 2],
...     index=pd.IntervalIndex.from_breaks([2, 5, 7, 9], closed="left")
... )
>>> BaseDetector.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 skbase objects 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 self.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 self, depending on self_policy, and remaining component objects 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 object, 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 skbase object valued parameters, i.e., component estimators.

If False, will set only self’s random_state parameter, if exists.
If True, will set random_state parameters in component objects as well.

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

“copy” : self.random_state is set to input random_state
“keep” : self.random_state is kept as is
“new” : self.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 instance level tag overrides to given values.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

set_tags sets dynamic tag overrides to the values as specified in tag_dict, with keys being the tag name, and dict values being the value to set the tag to.

The set_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

**tag_dictdict: Dictionary of tag name: tag value pairs.

Returns:

Self: Reference to self.

static sparse_to_dense(y_sparse, index)[source]#

Convert the sparse output from an detector 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

Larger set of indices which contains event indices in y_sparse, to be used as the index of the returned series.

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.detection.base import BaseDetector
>>> y_sparse = pd.Series([2, 5, 7])  # Indices of changepoints/anomalies
>>> index = range(0, 8)
>>> BaseDetector.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)
>>> BaseDetector.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 labels on test/deployment data.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray: Time series subject to detection, which will be assigned labels or scores.

Returns:

ypd.DataFrame with same index as X

Labels for sequence X.

If task is "anomaly_detection", the values are integer labels. A value of 0 indicates that X, at the same time index, has no anomaly. Other values indicate an anomaly. Most detectors will return 0 or 1, but some may return more values, if they can detect different types of anomalies. indicating whether X, at the same index, is an anomaly, 0 for no, 1 for yes.
If task is "changepoint_detection", the values are integer labels, indicating labels for segments between changepoints. Possible labels are integers starting from 0.
If task is “segmentation”, the values are integer labels of the segments. Possible labels are integers starting from 0.

transform_scores(X)[source]#

Return scores for predicted labels on test/deployment data.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray: Data to label (time series).

Returns:

scorespd.DataFrame with same index as X: Scores for sequence X.

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

Update model with new data and optional ground truth labels.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray: Training data to update model with (time series).
ypd.Series, optional: Ground truth labels for training if detector is supervised.

Returns:

self: Reference to self.

Notes

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

update_predict(X, y=None)[source]#

Update model with new data and create labels for it.

Parameters:

Xpd.DataFrame, pd.Series or np.ndarray

Training data to update model with, time series.

ypd.DataFrame with RangeIndex, optional.

Known events for training, in X, if detector is supervised.

Each row y is a known event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation with labels, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.

Returns:

ypd.DataFrame with RangeIndex

Detected or predicted events.

Each row y is a detected or predicted event. Can have the following columns:

"ilocs" - always. Values encode where/when the event takes place, via iloc references to indices of X, or ranges ot indices of X, as below.
"label" - if the task, by tags, is supervised or semi-supervised segmentation, or segment clustering.

The meaning of entries in the "ilocs" column and "labels" column describe the event in a given row as follows:

If task is "anomaly_detection" or "change_point_detection", "ilocs" contains the iloc index at which the event takes place.
If task is "segmentation", "ilocs" contains left-closed intervals of iloc based segments, interpreted as the range of indices over which the event takes place.

Labels (if present) in the "labels" column indicate the type of event.