tadkit.utils package

Subpackages

• tadkit.utils.widget package
  • Module contents

Submodules

tadkit.utils.decomposable_tadlearner module

tadkit.utils.match_formalizer_learners module

tadkit.utils.match_formalizer_learners.match_formalizer_learners(formalizer: Formalizer, learners: Sequence[TADLearner])

tadkit.utils.parameter_distributions module

tadkit.utils.parameter_distributions.domain_mapper(param_description)

tadkit.utils.parameter_distributions.numerical_domain_mapper(param_description)

tadkit.utils.parameter_distributions.param_distributions(params_description: Dict[str, Any]) → Dict[str, Any]

tadkit.utils.print_learner_catalog module

tadkit.utils.print_learner_catalog.print_catalog_classes(learner_classes, detailed=False)

tadkit.utils.synthetic_ornstein_uhlenbeck module

tadkit.utils.synthetic_ornstein_uhlenbeck.synthetise_ornstein_uhlenbeck_data(n_rows=1000, n_cols_x=5)

tadkit.utils.tadlearner_factory module

tadkit.utils.tadlearner_factory.tadlearner_factory(Model: Type[Estimator], required_properties: Sequence[str], params_description: Dict[str, Dict[str, Number | str | datetime | Sequence[Number] | Sequence[str] | Sequence[datetime]]], name: str | None = None) → Type[TADLearner]

Wrap a sklearn anomaly detection model to a TADLearner.

Parameters:

• Model – sklearn type model- to wrapp, possessing get_params, fit and score_samples methods.

• required_properties – The properties that the input data must satisfies.

• params_description – Description of the kwargs of the __init__ method that is exposed.

• name – The Name of the class. Default name if None.

Returns:

A subclass of TADLearner wrapping Model with given required_properties, params_description

and __name__.

Module contents