autoprognosis.plugins.prediction.classifiers.plugin_ridge_classifier module

class RidgeClassifierPlugin(solver: int = 0, calibration: int = 0, random_state: int = 0, model: Optional[Any] = None, **kwargs: Any)

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

Classification plugin based on the Ridge classifier.

Method:

The RidgeClassifier converts the target values into {-1, 1} and then treats the problem as a regression task (multi-output regression in the multiclass case).

Parameters

  • solver – str Algorithm to use in the optimization problem: {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’}

  • 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("ridge_classifier")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_predict(X, y)
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_ridge_classifier.RidgeClassifierPlugin

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
solvers = ['auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg']
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_ridge_classifier.RidgeClassifierPlugin