autoprognosis.plugins.preprocessors.dimensionality_reduction.plugin_variance_threshold module
- class VarianceThresholdPlugin(random_state: int = 0, model: Optional[Any] = None, threshold: float = 0.001)
Bases:
autoprognosis.plugins.preprocessors.base.PreprocessorPluginPreprocessing plugin for dimensionality reduction based on removing features with low variance.
- Method:
VarianceThreshold is a simple baseline approach to feature selection. It removes all features whose variance doesn’t meet some threshold. By default, it removes all zero-variance features, i.e. features that have the same value in all samples.
- Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
- Parameters
threshold – float Features with a training-set variance lower than this threshold will be removed.
Example
>>> from autoprognosis.plugins.preprocessors import Preprocessors >>> plugin = Preprocessors(category="dimensionality_reduction").get("variance_threshold", threshold=1.0) >>> 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: 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
- 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.preprocessors.dimensionality_reduction.plugin_variance_threshold.VarianceThresholdPlugin
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
- 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: 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