Object and estimator tags#
Every first-class object in sktime
is tagged with a set of tags that describe its properties and capabilities,
or control its behavior.
Tags are key-value pairs, where the key is a string with the name of the tag. The value of the tag can have arbitrary type, and describes a property, capability, or controls behaviour of the object, depending on the value.
For instance, a forecaster may have the tag "capability:pred_int": True
if it can
make probabilistic predictions.
Users can find all forecasters that can make probabilistic predictions by filtering
for this tag.
This API reference lists all tags available in sktime
, and key utilities
for their usage.
To search estimators by tags on the sktime
webpage, use the
Estimator Search Page
Inspecting tags, retrieving by tags#
Tags can be inspected at runtime using the following utilities:
to get the tags of an object, use the
get_tags
method. An object’s tags can depend on its hyper-parameters.to get the tags of a class, use the
get_class_tags
method of the class. A class’s tags are static and do not depend on its hyper-parameters. By default, class tags that may vary for instances take the most “capable” value, in the case of capabilities.to programmatically retrieve all tags available in
sktime
or for a particular type of object, at runtime, use theregistry.all_tags
utilityto programmatically retrieve all objects or estimators in
sktime
, filtered for values of tags, use theregistry.all_estimators
utility
|
List all tags in sktime, for objects of a certain type. |
|
List all estimators or objects in sktime, by scitype or tag. |
General tags, packaging#
This section lists tags that are general and apply to all objects in sktime
.
These tags are typically used for typing, packaging and documentation purposes.
Scientific type of the object. |
|
Current maintainers of the object, GitHub IDs. |
|
Authors of the object, GitHub IDs. |
|
Python version requirement specifier for the object (PEP 440). |
|
Python package dependency requirement specifiers for the object (PEP 440). |
|
Environment marker requirement for the object (PEP 508). |
|
Whether the object requires a C compiler present, such as libomp, gcc. |
Forecaster tags#
This section lists tags applying to forecasters ("forecaster"
type).
These tags are used to describe capabilities, properties, and behavior of forecasters.
Capability: the forecaster can use exogeneous data. |
|
Capability: the forecaster can make in-sample predictions. |
|
Capability: the forecaster can make probabilistic or interval forecasts. |
|
Capability: the forecaster can make in-sample probabilistic forecasts. |
|
Capability: the estimator can handle missing data, e.g,, NaNs. |
|
Capability: If estimator can handle categorical natively in exogeneous(X) data. |
|
Behaviour flag: forecaster requires forecasting horizon in fit. |
|
Property: Whether the estimator has an empty fit method. |
Tags for classifiers, regressors, clustering#
This section lists tags applying to time series classifiers, regressors,
and clusterers ("classifier"
, "regressor"
, "clusterer"
types).
These tags are used to describe capabilities, properties, and behavior of
these types of objects.
Capability: the estimator can handle multivariate time series. |
|
Capability: the estimator can handle multi-output time series. |
|
Capability: the estimator can handle missing data, e.g,, NaNs. |
|
Capability: the estimator can handle unequal length time series. |
|
Capability: the estimator can make probabilistic predictions. |
|
Capability: the estimator can provide feature importance. |
|
Capability: the estimator can be asked to satisfy a maximum time contract. |
|
Capability: the algorithm can estimate its performance on the training set. |
Tags for ordinary transformers#
This section lists tags applying to ordinary transformers, i.e., objects that
transform a single time series object ("transformer"
type).
The scitype of the input data for the transformer. |
|
The scitype of the input data for the transformer. |
|
The scitype of the target data for the transformer, if required. |
|
Behaviour flag: transformer requires X in fit and transform. |
|
Behaviour flag: transformer requires y in fit. |
|
Capability: the estimator can handle missing data, e.g,, NaNs. |
|
Capability: the estimator can handle unequal length time series. |
|
Capability: the transformer can carry out an inverse transform. |
|
Capability: whether the inverse transform is an exact inverse to the transform. |
|
Capability: the domain of invertibility of the transform. |
|
Property: Whether the estimator has an empty fit method. |
|
Property: transformer returns same time index as input. |
Tags for detectors#
This section lists tags applying to time series detectors ("detector"
types).
These tags are used to describe capabilities, properties, and behavior of
detectors.
Subtype tag for detectors: type of detection task. |
|
Learning type of the detection task. |
|
Capability: whether the estimator can be run in stream or on-line mode. |
|
Capability: the estimator can handle multivariate time series. |
|
Capability: the estimator can handle missing data, e.g,, NaNs. |
Common developer tags#
This section lists tags that are used to control internal behaviour of objects, e.g., the boilerplate layer.
These are primarily useful for power users using the extension
templates to create sktime
compatible objects.
The tags below have limited use in retrieval or inspection of objects.
The machine type(s) the transformer can deal with internally for X. |
|
The machine type(s) the transformer can deal with internally for y. |
|
How to display html representation of a meta-estimator in a jupyter notebook. |