hyperimpute.plugins.core.base_plugin module

class Plugin

Bases: Serializable

Base class for all plugins. Each derived class must implement the following methods:

type() - a static method that returns the type of the plugin. e.g., imputation, preprocessing, prediction, etc. subtype() - optional method that returns the subtype of the plugin. e.g. Potential subtypes:

• preprocessing: feature_scaling, dimensionality reduction

• prediction: classifiers, prediction, survival analysis

name() - a static method that returns the name of the plugin. e.g., EM, mice, etc. hyperparameter_space() - a static method that returns the hyperparameters that can be tuned during the optimization. The method will return a list of Params derived objects. _fit() - internal method, called by fit on each training set. _transform() - internal method, called by transform. Used by imputation or preprocessing plugins. _predict() - internal method, called by predict. Used by classification/prediction plugins.

If any method implementation is missing, the class constructor will fail.

_abc_impl = <_abc_data object>

abstract _fit(X: DataFrame, *args: Any, **kwargs: Any) → Plugin

abstract _predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

abstract _transform(X: DataFrame) → DataFrame

fit(X: DataFrame, *args: Any, **kwargs: Any) → Any

fit_predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

fit_transform(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

classmethod fqdn() → str

abstract static hyperparameter_space(*args: Any, **kwargs: Any) → List[Params]

classmethod hyperparameter_space_fqdn(*args: Any, **kwargs: Any) → List[Params]

abstract static name() → str

predict(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame

classmethod sample_hyperparameters(trial: Trial, *args: Any, **kwargs: Any) → Dict[str, Any]

classmethod sample_hyperparameters_fqdn(trial: Trial, *args: Any, **kwargs: Any) → Dict[str, Any]

classmethod sample_hyperparameters_np(*args: Any, **kwargs: Any) → Dict[str, Any]

abstract static subtype() → str

transform(X: DataFrame) → DataFrame

abstract static type() → str

class PluginLoader(plugins: list, expected_type: Type)

Bases: object

_load_single_plugin(plugin: str) → None

add(name: str, cls: Type) → PluginLoader

get(name: str, *args: Any, **kwargs: Any) → Any

get_type(name: str) → Type

list() → List[str]

reload() → PluginLoader

types() → List[Type]