autoprognosis.plugins.prediction.classifiers.plugin_adaboost module

class AdaBoostPlugin(estimator: int = 0, n_estimators: int = 10, learning_rate: float = 0.1, 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 AdaBoost estimator.

Method:: An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

Parameters

estimator – int Base Learner to use. 0: HistGradientBoostingClassifier, 1: CatBoostClassifier, 2: LGBM, 3: LogisticRegression
n_estimators – int The maximum number of estimators at which boosting is terminated.
learning_rate – float Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learning_rate and n_estimators parameters.
calibration – int Enable/disable calibration. 0: disabled, 1 : sigmoid, 2: isotonic.
random_state – int, default 0 Random seed

Example

>>> from autoprognosis.plugins.prediction import Predictions
>>> plugin = Predictions(category="classifiers").get("adaboost")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y)

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

calibrations = ['none', 'sigmoid', 'isotonic']

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_adaboost.AdaBoostPlugin: 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_adaboost.AdaBoostPlugin