autoprognosis.plugins.prediction.classifiers.plugin_gaussian_process module

class GaussianProcessPlugin(calibration: int = 0, model: Any | None = None, **kwargs: Any)

Bases: ClassifierPlugin

Classification plugin based on Gaussian processes.

Method:: The plugin uses GaussianProcessClassifier, which implements Gaussian processes for classification purposes, more specifically for probabilistic classification, where test predictions take the form of class probabilities.

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("gaussian_process")
>>> plugin.fit_predict(...)

change_output(output: str) → None

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

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

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

classmethod load(buff: bytes) → GaussianProcessPlugin: Load the plugin from bytes

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.

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

plugin: alias of GaussianProcessPlugin