autoprognosis.plugins.prediction.risk_estimation.base module

class RiskEstimationPlugin(with_explanations: bool = False, explanations_model: Any | None = None, explanations_nepoch: int = 10000, explanations_nfolds: int = 5, **kwargs: Any)

Bases: PredictionPlugin

Base class for the survival analysis plugins.

It provides the implementation for plugin.Plugin’s subtype, _fit and _predict methods.

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 derived objects.

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

change_output(output: str) → None

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

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

Train the plugin

Parameters:

X – pd.DataFrame

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

Fit the model and predict the training data. Used by predictors.

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

Fit the model and transform the training data. Used by imputers and preprocessors.

classmethod fqdn() → str

The fully-qualified name of the plugin: type->subtype->name

get_args() → dict

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

The hyperparameter search domain, used for tuning.

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

The hyperparameter domain using they fully-qualified name.

is_fitted() → bool

Check if the model was trained

abstract classmethod load(buff: bytes) → Plugin

Load the plugin from bytes

abstract static name() → str

The name of the plugin, e.g.: xgboost

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

Run predictions for the input. Used by predictors.

Parameters:

X – pd.DataFrame

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

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

Sample hyperparameters for Optuna.

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

Sample hyperparameters using they fully-qualified name.

classmethod sample_hyperparameters_np(random_state: int = 0, *args: Any, **kwargs: Any) → Dict[str, Any]

Sample hyperparameters as a dict.

abstract save() → bytes

Save the plugin to bytes

score(X: DataFrame, y: DataFrame, metric: str = 'aucroc') → float

static subtype() → str

The type of the plugin, e.g.: classifier

transform(X: DataFrame) → DataFrame

Transform the input. Used by imputers and preprocessors.

Parameters:

X – pd.DataFrame

static type() → str

The type of the plugin, e.g.: prediction