autoprognosis.plugins.preprocessors.dimensionality_reduction.plugin_pca module

class PCAPlugin(random_state: int = 0, model: Any | None = None, n_components: int = 2)

Bases: PreprocessorPlugin

Preprocessing plugin for dimensionality reduction based on the PCA method.

Method:: PCA is used to decompose a multivariate dataset in a set of successive orthogonal components that explain a maximum amount of the variance.
Reference:: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

Parameters:: n_components – int Number of components to use.

Example

>>> from autoprognosis.plugins.preprocessors import Preprocessors
>>> plugin = Preprocessors(category="dimensionality_reduction").get("pca")
>>> from sklearn.datasets import load_iris
>>> X, y = load_iris(return_X_y=True)
>>> plugin.fit_transform(X, y)

change_output(output: str) → None

static components_interval(*args: Any, **kwargs: Any) → Tuple[int, int]

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

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) → PCAPlugin: 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

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

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 PCAPlugin