StatsModelsACF¶

class StatsModelsACF(adjusted=False, n_lags=None, fft=False, missing='none')[source]¶

Bases: BaseSeriesTransformer

Auto-correlation wrapper for statsmodels.

The autocorrelation function measures how correlated a timeseries is with itself at different lags. The StatsModelsACF returns these values as a series for each lag up to the n_lags specified.

Parameters:

adjustedbool, default=False

If True, then denominators for autocovariance are n-k, otherwise n.

n_lagsint, default=None

Number of lags to return autocorrelation for. If None, statsmodels acf function uses min(10 * np.log10(nobs), nobs - 1).

fftbool, default=False

If True, computes the ACF via FFT.

missing{“none”, “raise”, “conservative”, “drop”}, default=”none”

How missing values are to be treated in autocorrelation function calculations.

“none” performs no checks or handling of missing values
“raise” raises an exception if NaN values are found.
“drop” removes the missing observations and then estimates the autocovariances treating the non-missing as contiguous.
“conservative” computes the autocovariance using nan-ops so that nans are removed when computing the mean and cross-products that are used to estimate the autocovariance. “n” in calculation is set to the number of non-missing observations.

See also

StatsModelsPACF

Notes

Capabilities ¶
Missing Values	No
Multithreading	No
Inverse Transform	No
Univariate	Yes
Multivariate	No

Provides wrapper around statsmodels acf function.

Examples

>>> from aeon.transformations.series import StatsModelsACF
>>> from aeon.datasets import load_airline
>>> y = load_airline()
>>> transformer = StatsModelsACF(n_lags=12)
>>> y_hat = transformer.fit_transform(y)

Methods

`clone`([random_state])	Obtain a clone of the object with the same hyperparameters.
`fit`(X[, y, axis])	Fit transformer to X, optionally using y if supervised.
`fit_transform`(X[, y, axis])	Fit to data, then transform it.
`get_class_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_fitted_params`([deep])	Get fitted parameters.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, raise_error, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator.
`reset`([keep])	Reset the object to a clean post-init state.
`set_params`(**params)	Set the parameters of this estimator.
`set_tags`(**tag_dict)	Set dynamic tags to given values.
`transform`(X[, y, axis])	Transform X and return a transformed version.

clone(random_state=None)[source]¶

Obtain a clone of the object with the same hyperparameters.

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)).

Parameters:

random_stateint, RandomState instance, or None, default=None: Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject: Instance of type(self), clone of self (see above)

fit(X, y=None, axis=1)[source]¶

Fit transformer to X, optionally using y if supervised.

Writes to self: - is_fitted : flag is set to True. - model attributes (ending in “_”) : dependent on estimator

Parameters:

XInput data: Time series to fit transform to, of type np.ndarray, pd.Series pd.DataFrame.
yTarget variable, default=None: Additional data, e.g., labels for transformation
axisint, default = 1: Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None, axis=1)[source]¶

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

Changes state to “fitted”. Model attributes (ending in “_”) : dependent on estimator.

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation
axisint, default = 1: Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

transformed version of X with the same axis as passed by the user, if axis
not None.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value: Value of the tag_name tag in cls. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError: if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()[source]¶

Get class tags from estimator class and all its parent classes.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)[source]¶

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True: If True, will return the fitted parameters for this estimator and contained subobjects that are estimators.

Returns:

fitted_paramsdict: Fitted parameter names mapped to their values.

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, raise_error=True, tag_value_default=None)[source]¶

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr: Name of tag to be retrieved.
raise_errorbool, default=True: Whether a ValueError is raised when the tag is not found.
tag_value_defaultany type, default=None: Default/fallback value if tag is not found and error is not raised.

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 and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()[source]¶

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict: Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

reset(keep=None)[source]¶

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed 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 hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument

Parameters:

keepNone, str, or list of str, default=None: If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject: Reference to self.

Raises:

TypeError: If ‘keep’ is not a string or a list of strings.

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_tags(**tag_dict)[source]¶

Set dynamic tags to given values.

Parameters:

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

Returns:

selfobject: Reference to self.

transform(X, y=None, axis=1)[source]¶

Transform X and return a transformed version.

State required:: Requires state to be “fitted”.

Parameters:

XInput data: Data to fit transform to, of valid collection type.
yTarget variable, default=None: Additional data, e.g., labels for transformation
axisint, default = 1: Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

transformed version of X with the same axis as passed by the user, if axis
not None.