tadkit.catalog.learners package

Module contents

tadkit.catalog.learners.IsolationForestLearner

alias of IsolationForest

tadkit.catalog.learners.KernelDensityLearner

alias of KernelDensity

class tadkit.catalog.learners.ScaledKernelDensityLearner(scaling='standard')

Bases: Pipeline

Learner class wrapped from scikit-learn’s KernelDensity class, with a scaler preprocessor.

params_description = {'scaling': {'description': 'Scaling method', 'family': 'scaling', 'set': ['quantile_normal', 'standard'], 'value_type': 'choice'}}

required_properties = []

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

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.

New 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.

steps: List[Any]