hyperimpute.plugins.prediction package

class PredictionPlugin

Bases: Plugin

Base class for the prediction plugins.

It provides the implementation for plugin.Plugin.type() static method.

Each derived class must implement the following methods(inherited from plugin.Plugin):

name() - a static method that returns the name of the plugin. hyperparameter_space() - a static method that returns the hyperparameters that can be tuned during the optimization. The method will return a list of params.Params derived objects. _fit() - internal implementation, called by the fit method. _predict() - internal implementation, called by the predict method. _predict_proba() - internal implementation, called by the predict_proba method.

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

_abc_impl = <_abc_data object>
abstract _predict_proba(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame
_transform(X: DataFrame) → DataFrame
explain(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame
module_relative_path: Optional[Path]
predict_proba(X: DataFrame, *args: Any, **kwargs: Any) → DataFrame
score(X: DataFrame, y: DataFrame, metric: str = 'aucroc') → float
static type() → str
class Predictions(category: str = 'classifier')

Bases: object

add(name: str, cls: Type) → Predictions
get(name: str, *args: Any, **kwargs: Any) → PredictionPlugin
get_type(name: str) → Type
list() → List[str]
reload() → Predictions

Subpackages

Submodules