rating_gp.pipeline

rating_gp.pipeline#

Classes

LogPropagation()

Transformer for proper uncertainty propagation of log transforms

LogUncertaintyPipeline()

Pipeline to transform (propagate) uncertainty to log space
class rating_gp.pipeline.LogPropagation#

Transformer for proper uncertainty propagation of log transforms

Eq: f = ln(A) -> sigma_f = sigma_A / A

fit(A)#

Store data from A for later division in the transform.

Parameters:

A (ndarray)

set_fit_request(*, A: bool | None | str = '$UNCHANGED$') → LogPropagation#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Astr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for A parameter in fit.

selfobject

The updated object.

transform(X)#

Propagate the uncertainty.

Parameters:

X (ndarray)

Return type:

ndarray

class rating_gp.pipeline.LogUncertaintyPipeline#

Pipeline to transform (propagate) uncertainty to log space

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LogUncertaintyPipeline#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

selfobject

The updated object.

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