autoprognosis.plugins.prediction.classifiers.plugin_bagging module

class BaggingPlugin(n_estimators: int = 10, max_samples: float = 1.0, max_features: float = 1.0, estimator: int = 0, calibration: int = 0, model: Optional[Any] = None, random_state: int = 0, **kwargs: Any)

Bases: autoprognosis.plugins.prediction.classifiers.base.ClassifierPlugin

Classification plugin based on the Bagging estimator.

Method:: A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions (either by voting or by averaging) to form a final prediction.

Parameters

n_estimators – int The number of base estimators in the ensemble.
max_samples – float The number of samples to draw from X to train each base estimator.
max_features – float The number of features to draw from X to train each base estimator.
estimator – int Base estimator to use. 0: HistGradientBoostingClassifier, 1: CatBoostClassifier, 2: LGBM, 3: LogisticRegression.

Example

>>> from autoprognosis.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("bagging")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y) # returns the probabilities for each class

base_estimators = [sklearn.ensemble.HistGradientBoostingClassifier, catboost.CatBoostClassifier, sklearn.base.ClassifierMixin, sklearn.linear_model.LogisticRegression]

change_output(output: str) → None

explain(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

fit(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → autoprognosis.plugins.core.base_plugin.Plugin

Train the plugin

Parameters: X – pd.DataFrame

fit_predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame: Fit the model and predict the training data. Used by predictors.

fit_transform(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.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[autoprognosis.plugins.core.params.Params]: The hyperparameter search domain, used for tuning.

classmethod hyperparameter_space_fqdn(*args: Any, **kwargs: Any) → List[autoprognosis.plugins.core.params.Params]: The hyperparameter domain using they fully-qualified name.

is_fitted() → bool: Check if the model was trained

classmethod load(buff: bytes) → autoprognosis.plugins.prediction.classifiers.plugin_bagging.BaggingPlugin: Load the plugin from bytes

static name() → str: The name of the plugin, e.g.: xgboost

predict(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.DataFrame

Run predictions for the input. Used by predictors.

Parameters: X – pd.DataFrame

predict_proba(X: pandas.core.frame.DataFrame, *args: Any, **kwargs: Any) → pandas.core.frame.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.

save() → bytes: Save the plugin to bytes

score(X: pandas.core.frame.DataFrame, y: pandas.core.frame.DataFrame, metric: str = 'aucroc') → float

static subtype() → str: The type of the plugin, e.g.: classifier

transform(X: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame

Transform the input. Used by imputers and preprocessors.

Parameters: X – pd.DataFrame

static type() → str: The type of the plugin, e.g.: prediction

plugin: alias of autoprognosis.plugins.prediction.classifiers.plugin_bagging.BaggingPlugin