ProjectionDepth#

class skfda.exploratory.depth.multivariate.ProjectionDepth[source]#

Computes Projection depth.

It is defined as the depth induced by the Stahel-Donoho outlyingness.

See also

StahelDonohoOutlyingness: Stahel-Donoho outlyingness.

References

Zuo, Y., Cui, H., & He, X. (2004). On the Stahel-Donoho estimator and depth-weighted means of multivariate data. Annals of Statistics, 32(1), 167–188. https://doi.org/10.1214/aos/1079120132

Methods

fit(X[, y])

Learn the distribution from the observations.

fit_transform(X[, y])

Compute the depth or outlyingness of each observation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_output(*[, transform])

Set output container.

set_params(**params)

Set the parameters of this estimator.

transform(X)

Compute the depth or outlyingness inside the learned distribution.
fit(X, y=None)[source]#

Learn the distribution from the observations.

Parameters:

  • X (T) – Functional dataset from which the distribution of the data is inferred.

  • y (object) – Unused. Kept only for convention.

Returns:

Fitted estimator.

Return type:

OutlyingnessBasedDepth[T]

fit_transform(X, y=None)[source]#

Compute the depth or outlyingness of each observation.

This computation is done with respect to the whole dataset.

Parameters:

  • X (Input) – Dataset.

  • y (object) – Unused. Kept only for convention.

Returns:

Depth of each observation.

Return type:

ndarray[tuple[int, …], dtype[floating[Any]]]

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routing – A MetadataRequest encapsulating routing information.

Return type:

MetadataRequest

get_params(deep=True)#

Get parameters for this estimator.

Parameters:

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params – Parameter names mapped to their values.

Return type:

dict

set_output(*, transform=None)#

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform ({"default", "pandas", "polars"}, default=None) –

Configure output of transform and fit_transform.

  • ”default”: Default output format of a transformer

  • ”pandas”: DataFrame output

  • ”polars”: Polars output

  • None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

self – Estimator instance.

Return type:

estimator instance

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:

**params (dict) – Estimator parameters.

Returns:

self – Estimator instance.

Return type:

estimator instance

transform(X)[source]#

Compute the depth or outlyingness inside the learned distribution.

Parameters:

X (T) – Points whose depth is going to be evaluated.

Returns:

Depth of each observation.

Return type:

ndarray[tuple[int, …], dtype[floating[Any]]]