ProbeFeatureSelection

class feature_engine.selection.ProbeFeatureSelection(estimator, variables=None, scoring='roc_auc', n_probes=1, distribution='normal', cv=5, random_state=0, confirm_variables=False)

ProbeFeatureSelection() generates one or more probe features based on the user-selected distribution. The distribution options are ‘normal’, ‘binomial’, ‘uniform’, or ‘all’. ‘all’ creates at least one distribution for each of the three aforementioned distributions.

Using cross validation, the class fits a Scikit-learn estimator to the provided dataset’s variables and the probe features.

The class derives the feature importance for each variable and probe feature. In the case of there being more than one probe feature, ProbeFeatureSelection() calculates the average feature importance of all the probe features.

The variables that have a feature importance less than the feature importance or average feature importance of the probe feature(s) are dropped from the dataset.

More details in the User Guide.

Parameters

estimator: object

A Scikit-learn estimator for regression or classification. The estimator must have either a feature_importances or a coef_ attribute after fitting.

variables: str or list, default=None

The list of variables to evaluate. If None, the transformer will evaluate all numerical features in the dataset.

scoring: str, default=’roc_auc’

Metric to evaluate the performance of the estimator. Comes from sklearn.metrics. See the model evaluation documentation for more options: https://scikit-learn.org/stable/modules/model_evaluation.html

n_probes: int, default=1

Number of probe features to be created. If distribution is ‘all’, n_probes must be a multiple of 3.

distribution: str, default=’normal’

The distribution used to create the probe features. The options are ‘normal’, ‘binomial’, ‘uniform’, and ‘all’. ‘all’ creates at least 1 or more probe features comprised of each distribution type, i.e., normal, binomial, and uniform. The remaining options create n_probes features of the selected distribution.

cv: int, cross-validation generator or an iterable, default=3

Determines the cross-validation splitting strategy. Possible inputs for cv are:

• None, to use cross_validate’s default 5-fold cross validation

• int, to specify the number of folds in a (Stratified)KFold,

• CV splitter: (https://scikit-learn.org/stable/glossary.html#term-CV-splitter)

• An iterable yielding (train, test) splits as arrays of indices.

For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls. For more details check Scikit-learn’s cross_validate’s documentation.

Attributes

probe_features_:

A dataframe comprised of the pseudo-randomly generated features based on the selected distribution.

feature_importances_:

Pandas Series with the feature importance.

features_to_drop_:

List with the features that will be removed.

variables_:

The variables that will be considered for the feature selection procedure.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

References

1

Stoppiglia, et al. “Ranking a Random Feature for Variable and Feature Selection”. JMLR: 1399-1414, 2003 https://jmlr.org/papers/volume3/stoppiglia03a/stoppiglia03a.pdf

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from feature_engine.selection import ProbeFeatureSelection
>>> X, y = load_breast_cancer(return_X_y=True, as_frame=True)
>>> sel = ProbeFeatureSelection(
>>>     estimator=LogisticRegression(),
>>>     scoring="roc_auc",
>>>     n_probes=3,
>>>     distribution="normal",
>>>     cv=3,
>>>     random_state=150,
>>> )
>>> X_tr = sel.fit_transform(X, y)
print(X.shape, X_tr.shape)

Methods

fit:	Find the important features.
fit_transform:	Fit to data, then transform it.
get_feature_names_out:	Get output feature names for transformation.
get_params:	Get parameters for this estimator.
set_params:	Set the parameters of this estimator.
get_support:	Get a mask, or integer index, of the features selected.
transform:	Reduce X to the selected features.

fit(X, y)

Find the important features.

Parameters

X: pandas dataframe of shape = [n_samples, n_features]

y: array-like of shape (n_samples)

Target variable. Required to train the estimator.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

Get output feature names for transformation. In other words, returns the variable names of transformed dataframe.

Parameters

input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

• If None, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

get_support(indices=False)

Get a mask, or integer index, of the features selected.

Parameters

indicesbool, default=False

If True, the return value will be an array of integers, rather than a boolean mask.

Returns

supportarray

An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True if its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Return dataframe with selected features.

Parameters

X: pandas dataframe of shape = [n_samples, n_features].

The input dataframe.

Returns

X_new: pandas dataframe of shape = [n_samples, n_selected_features]

Pandas dataframe with the selected features.

rtype

DataFrame ..