autoprognosis.plugins.prediction.regression package

class Regression

Bases: PluginLoader

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

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

get_type(name: str) → Type

list() → List[str]

list_available() → List[str]

reload() → PluginLoader

types() → List[Type]

class RegressionPlugin(**kwargs: Any)

Bases: PredictionPlugin

Base class for the regression 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) → RegressionPlugin

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

Submodules