PCASeriesTransformer

class PCASeriesTransformer(n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, n_oversamples=10, power_iteration_normalizer='auto', iterated_power='auto', random_state=None)[source]

Principal Components Analysis applied as transformer.

Provides a simple wrapper around sklearn.decomposition.PCA.

Parameters:
n_componentsint, float or ‘mle’, default=None

Number of components to keep. if n_components is not set all components are kept:

n_components == min(n_samples, n_features)

If n_components == 'mle' and svd_solver == 'full', Minka’s MLE is used to guess the dimension. Use of n_components == 'mle' will interpret svd_solver == 'auto' as svd_solver == 'full'. If 0 < n_components < 1 and svd_solver == 'full', select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components. If svd_solver == 'arpack', the number of components must be strictly less than the minimum of n_features and n_samples. Hence, the None case results in:

n_components == min(n_samples, n_features) - 1
copybool, default=True

If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead.

whitenbool, default=False

When True (False by default) the components_ vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances. Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.

svd_solver{‘auto’, ‘full’, ‘arpack’, ‘randomized’}, default=’auto’
If auto :

The solver is selected by a default policy based on X.shape and n_components: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient ‘randomized’ method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards.

If full :

run exact full SVD calling the standard LAPACK solver via scipy.linalg.svd and select the components by postprocessing

If arpack :

run SVD truncated to n_components calling ARPACK solver via scipy.sparse.linalg.svds. It requires strictly 0 < n_components < min(X.shape)

If randomized :

run randomized SVD by the method of Halko et al.

tolfloat, default=0.0

Tolerance for singular values computed by svd_solver == ‘arpack’. Must be of range [0.0, infinity).

iterated_powerint or ‘auto’, default=’auto’

Number of iterations for the power method computed by svd_solver == ‘randomized’. Must be of range [0, infinity).

n_oversamplesint, default=10

This parameter is only relevant when svd_solver=”randomized”. It corresponds to the additional number of random vectors to sample the range of X so as to ensure proper conditioning. See randomized_svd for more details.

power_iteration_normalizer{‘auto’, ‘QR’, ‘LU’, ‘none’}, default=’auto’

Power iteration normalizer for randomized SVD solver. Not used by ARPACK. See randomized_svd for more details.

random_stateint, RandomState instance or None, default=None

Used when the ‘arpack’ or ‘randomized’ solvers are used. Pass an int for reproducible results across multiple function calls.

Attributes:
pca_sklearn.decomposition.PCA

The fitted PCA object

References

# noqa: E501 .. [R469025a6a4b6-1] https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

Examples

>>> # skip DOCTEST if Python < 3.8
>>> import sys, pytest
>>> if sys.version_info < (3, 8):
...     pytest.skip("PCATransformer requires Python >= 3.8")
>>>
>>> from aeon.transformations.series._pca import PCASeriesTransformer
>>> from aeon.datasets import load_longley
>>> data = load_longley(return_array=False)
>>> transformer = PCASeriesTransformer(n_components=2)
>>> X_hat = transformer.fit_transform(data)

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_metadata_routing()

Sklearn metadata routing.

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.

inverse_transform(X[, y, axis])

Inverse transform X and return an inverse transformed version.

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.

update(X[, y, update_params, axis])

Update transformer with X, optionally y.

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.

State change:

Changes state to “fitted”.

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_metadata_routing()[source]

Sklearn metadata routing.

Not supported by aeon estimators.

get_params(deep=True)[source]

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.

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

Inverse transform X and return an inverse 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:
inverse transformed version of X

of the same type as X

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.

set_params(**params)[source]

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.
update(X, y=None, update_params=True, axis=1)[source]

Update transformer with X, optionally y.

Parameters:
Xdata to update of valid series type.
yTarget variable, default=None

Additional data, e.g., labels for transformation

update_paramsbool, default=True

whether the model is updated. Yes if true, if false, simply skips call. argument exists for compatibility with forecasting module.

axisint, default=None

axis along which to update. If None, uses self.axis.

Returns:
selfa fitted instance of the estimator