meta_estimators

Module Contents

Classes

EstimatorSwitch

A Meta Estimator that can turn on or off an estimator in a scikit-learn Pipeline.

class meta_estimators.EstimatorSwitch(estimator: Any, *, apply: bool = True)

Bases: sklearn.base.TransformerMixin, sklearn.base.BaseEstimator

A Meta Estimator that can turn on or off an estimator in a scikit-learn Pipeline.

Parameters

• estimator (Any) – The estimator to turn on or off.

• apply (bool) – To apply the estimator, by default True.

Examples

>>> import numpy as np
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.impute import SimpleImputer, MissingIndicator
>>> from extra_ds_tools.ml.sklearn.meta_estimators import EstimatorSwitch
>>> X = np.array([np.nan, 10] * 2).reshape(-1, 1)
>>> # The SimpleImputer should transform the nans to the mean, which is 10.
>>> pipeline = make_pipeline(EstimatorSwitch(SimpleImputer(), apply=False))
>>> print(pipeline.fit_transform(X))
[[nan]
[10.]
[nan]
[10.]]

fit(X: Union[pandas.DataFrame, pandas.Series, numpy.array], y: Optional[Union[pandas.Series, numpy.array]] = None, **fit_params)

Fits the estimator on the data if self.apply == True.

Parameters

• X (Union[pd.DataFrame, pd.Series, np.array]) – Train data.

• y (Optional[Union[pd.Series, np.array]], optional) – Target data, by default None.

Returns

EstimatorSwitch with a fitted estimator if self.apply == True.

Return type

EstimatorSwitch

transform(X: Union[pandas.DataFrame, pandas.Series, numpy.array], y: Optional[Union[pandas.Series, numpy.array]] = None, **fit_params)

Returns the transformed X if self.apply == True.

Parameters

• X (Union[pd.DataFrame, pd.Series, np.array]) – Train data.

• y (Optional[Union[pd.Series, np.array]], optional) – Target data, by default None.

Returns

Transformed X.

Return type

Union[pd.DataFrame, pd.Series, np.array]