WarpingSeriesTransformer¶
- class WarpingSeriesTransformer(series_index: int = 0, warping_path: list[tuple[int, int]] | None = None)[source]¶
Warping Path Transformer.
This transformer produces a longer version of the input series following the warping path produced by an elastic measure. The transformer assumes the path is pre-computed between the input series and another one.
- Parameters:
- series_indexint, default = 0
The index of the series, either 0 or 1 to choose from the warping path. Given the path is generated using two series, the user should choose which one is being transformed.
- warping_pathList[Tuple[int,int]], default = None
The warping path used to transform the series. If None, the output series is returned as is.
Examples
>>> from aeon.transformations.series import WarpingSeriesTransformer >>> from aeon.distances import dtw_alignment_path >>> import numpy as np >>> x = np.random.normal((2, 100)) >>> y = np.random.normal((2, 100)) >>> dtw_path, _ = dtw_alignment_path(x, y) >>> x_transformed = WarpingSeriesTransformer( ... series_index=0, warping_path=dtw_path).fit_transform(x) >>> y_transformed = WarpingSeriesTransformer( ... series_index=1, warping_path=dtw_path).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 from estimator class and all its parent classes.
get_fitted_params
([deep])Get fitted parameters.
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 totype(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 asself.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 asself.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 ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if
raise_error
is True andtag_name
is not inself.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 byset_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)[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 ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError
if raise_error is
True
andtag_name
is not inself.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__
orset_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 asself.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 totype(self)(**self.get_params(deep=False))
, assuming no other attributes were kept usingkeep
.- 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 ofget_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 asself.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