# ScaledLogitTransformer¶

class ScaledLogitTransformer(lower_bound=None, upper_bound=None)[source]

Scaled logit transform or Log transform.

If both lower_bound and upper_bound are not None, a scaled logit transform is applied to the data. Otherwise, the transform applied is a log transform variation that ensures the resulting values from the inverse transform are bounded accordingly. The transform is applied to all scalar elements of the input array individually.

Combined with an aeon.forecasting.compose.TransformedTargetForecaster, it ensures that the forecast stays between the specified bounds (lower_bound, upper_bound).

Default is lower_bound = upper_bound = None, i.e., the identity transform.

The logarithm transform is obtained for lower_bound = 0, upper_bound = None.

Parameters:
lower_boundfloat, optional, default=None

lower bound of inverse transform function

upper_boundfloat, optional, default=None

upper bound of inverse transform function

Attributes:
is_fitted

Whether fit has been called.

aeon.transformations.boxcox.LogTransformer

Transformer input data using natural log. Can help normalize data and compress variance of the series.

aeon.transformations.boxcox.BoxCoxTransformer

Applies Box-Cox power transformation. Can help normalize data and compress variance of the series.

aeon.transformations.exponent.ExponentTransformer

Transform input data by raising it to an exponent. Can help compress variance of series if a fractional exponent is supplied.

aeon.transformations.exponent.SqrtTransformer

Transform input data by taking its square root. Can help compress variance of input series.

Notes

The scaled logit transform is applied if both upper_bound and lower_bound are
not None:
$$log(\frac{x - a}{b - x})$$, where a is the lower and b is the upper bound.
If upper_bound is None and lower_bound is not None the transform applied is
a log transform of the form:
$$log(x - a)$$
If lower_bound is None and upper_bound is not None the transform applied is
a log transform of the form:
$$- log(b - x)$$

References

[1]

Hyndsight - Forecasting within limits: https://robjhyndman.com/hyndsight/forecasting-within-limits/

[2]

Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on January 24th 2022.

Examples

>>> import numpy as np
>>> from aeon.transformations.scaledlogit import ScaledLogitTransformer
>>> from aeon.forecasting.trend import PolynomialTrendForecaster
>>> from aeon.forecasting.compose import TransformedTargetForecaster
>>> fcaster = TransformedTargetForecaster([
...     ("scaled_logit", ScaledLogitTransformer(0, 650)),
...     ("poly", PolynomialTrendForecaster(degree=2))
... ])
>>> fcaster.fit(y)
TransformedTargetForecaster(...)
>>> y_pred = fcaster.predict(fh = np.arange(32))


Methods

 Check if the estimator has been fitted. Obtain a clone of the object with same hyper-parameters. clone_tags(estimator[, tag_names]) Clone/mirror 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. fit(X[, y]) Fit transformer to X, optionally to y. fit_transform(X[, y]) Fit to data, then transform it. get_class_tag(tag_name[, tag_value_default]) 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. Get metadata routing of this object. Get parameter defaults for the object. Get parameter names for the object. get_params([deep]) Get parameters for this estimator. get_tag(tag_name[, tag_value_default, ...]) Get tag value from estimator class. Get tags from estimator class. get_test_params([parameter_set]) Return testing parameter settings for the estimator. inverse_transform(X[, y]) Inverse transform X and return an inverse transformed version. Check if the object is composite. load_from_path(serial) Load object from file location. load_from_serial(serial) Load object from serialized memory container. Reset the object to a clean post-init state. save([path]) Save serialized self to bytes-like object or to (.zip) file. set_params(**params) Set the parameters of this object. set_tags(**tag_dict) Set dynamic tags to given values. transform(X[, y]) Transform X and return a transformed version. update(X[, y, update_params]) Update transformer with X, optionally y.
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, default = {}

Parameters to create testing instances of the class Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params

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. Equal in value to type(self)(**self.get_params(deep=False)).

Returns:
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]

Clone/mirror tags from another estimator as dynamic override.

Parameters:
estimatorobject

Estimator 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__}.

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.

fit(X, y=None)[source]

Fit transformer to X, optionally to y.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True possibly coerced to inner type or update_data compatible type by reference, when possible model attributes (ending in “_”) : dependent on estimator

Parameters:
XSeries or Panel, any supported type
Data to fit transform to, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame, nested pd.DataFrame, or pd.DataFrame in long/wide format.

ySeries or Panel, default=None

Additional data, e.g., labels for transformation.

Returns:
selfa fitted instance of the estimator
fit_transform(X, y=None)[source]

Fit to data, then transform it.

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

State change: changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator

Parameters:
XSeries or Panel, any supported type
Data to be transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns:
transformed version of X
type depends on type of X and output_data_type tag:
X | tf-output | type of return |

|__________|______________|________________________| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same mtype Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

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

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

Parameters:
tag_namestr

Name of tag value.

tag_value_defaultany type

Returns:
tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

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 : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

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

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

A MetadataRequest encapsulating routing information.

classmethod get_param_defaults()[source]

Get parameter defaults for the object.

Returns:
default_dict: dict with str keys

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()[source]

Get parameter names for the object.

Returns:
param_names: list of str, alphabetically sorted list of parameter names of cls
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, tag_value_default=None, raise_error=True)[source]

Get tag value from estimator class.

Uses dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, default=None

raise_errorbool

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 i.e. if tag_name is not in self.get_tags(
).keys()

get_tags

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

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

get_tags()[source]

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:
dict

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.

get_tag

Get a single tag from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

inverse_transform(X, y=None)[source]

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags

“input_data_type”=”Series”, “output_data_type”=”Series”,

have an inverse_transform.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:
XSeries or Panel, any supported type
Data to be inverse transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns:
inverse transformed version of X

of the same type as X, and conforming to mtype format specifications

is_composite()[source]

Check if the object is composite.

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 self contains a parameter which is BaseObject.

property is_fitted[source]

Whether fit has been called.

Parameters:
serialobject

Result of ZipFile(path).open(“object).

Returns:
deserialized self resulting in output at path, of cls.save(path)

Load object from serialized memory container.

Parameters:
serialobject

First element of output of cls.save(None).

Returns:
deserialized self resulting in output serial, of cls.save(None).
reset()[source]

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail 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 hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[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/.

Returns:
if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.
set_params(**params)[source]

Set the parameters of this object.

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

Parameters:
**paramsdict

BaseObject parameters

Returns:
selfreference to self (after parameters have been set)
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.

transform(X, y=None)[source]

Transform X and return a transformed version.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:
XSeries or Panel, any supported type
Data to be transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

Returns:
transformed version of X
type depends on type of X and output_data_type tag:
| transform | |
X | -output | type of return |

|__________|______________|________________________| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same type Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

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

Update transformer with X, optionally y.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : accessed by _update and by update_data, if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _update

Writes to self: _X : updated by values in X, via update_data, if remember_data tag is True fitted model attributes (ending in “_”) : only if update_params=True

type and nature of update are dependent on estimator

Parameters:
XSeries or Panel, any supported type
Data to fit transform to, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

ySeries or Panel, 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.

Returns:
selfa fitted instance of the estimator