SelectByShuffling#

class feature_engine.selection.SelectByShuffling(estimator, scoring='roc_auc', cv=3, threshold=None, variables=None, random_state=None, confirm_variables=False)[source]#

SelectByShuffling() selects features by determining the drop in machine learning model performance when each feature’s values are randomly shuffled.

If the variables are important, a random permutation of their values will decrease dramatically the machine learning model performance. Contrarily, the permutation of the values should have little to no effect on the model performance metric we are assessing if the feature is not predictive.

The SelectByShuffling() first trains a machine learning model utilising all features. Next, it shuffles the values of 1 feature, obtains a prediction with the pre-trained model, and determines the performance drop (if any). If the drop in performance is bigger than a threshold then the feature is retained, otherwise removed. It continues until all features have been shuffled and examined.

The user can determine the model for which performance drop after feature shuffling should be assessed. The user also determines the threshold in performance under which a feature will be removed, and the performance metric to evaluate.

Model training and performance calculation are done with cross-validation.

More details in the User Guide.

Parameters
estimator: object

A Scikit-learn estimator for regression or classification.

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

threshold: float, int, default = 0.01

The value that defines whether a feature will be selected. Note that for metrics like the roc-auc, r2, and the accuracy, the threshold will be a float between 0 and 1. For metrics like the mean squared error and the root mean squared error, the threshold can take any number. The threshold must be defined by the user. With bigger thresholds, fewer features will be selected.

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

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

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.

random_state: int, default=None

Controls the randomness when shuffling features.

confirm_variables: bool, default=False

If set to True, variables that are not present in the input dataframe will be removed from the list of variables. Only used when passing a variable list to the parameter variables. See parameter variables for more details.

Attributes
initial_model_performance_:

The model’s performance when trained with the original dataset.

performance_drifts_:

Dictionary with the performance drift per shuffled feature.

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.

See also

sklearn.inspection.permutation_importance

Notes

This transformer is a similar concept to the permutation_importance from Scikit-learn. The function in Scikit-learn is used to evaluate feature importance instead of to select features.

Examples

>>> import pandas as pd
>>> from sklearn.ensemble import RandomForestClassifier
>>> from feature_engine.selection import SelectByShuffling
>>> X = pd.DataFrame(dict(x1 = [1000,2000,1000,1000,2000,3000],
>>>                     x2 = [2,4,3,1,2,2],
>>>                     x3 = [1,1,1,0,0,0],
>>>                     x4 = [1,2,1,1,0,1],
>>>                     x5 = [1,1,1,1,1,1]))
>>> y = pd.Series([1,0,0,1,1,0])
>>> sbs = SelectByShuffling(
>>>         RandomForestClassifier(random_state=42),
>>>         cv=2,
>>>         random_state=42,
>>>       )
>>> sbs.fit_transform(X, y)
   x2  x4  x5
0   2   1   1
1   4   2   1
2   3   1   1
3   1   1   1
4   2   0   1
5   2   1   1

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, sample_weight=None)[source]#

Find the important features.

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

The input dataframe.

y: array-like of shape (n_samples)

Target variable. Required to train the estimator.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights. If None, then samples are equally weighted.

fit_transform(X, y=None, **fit_params)[source]#

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)[source]#

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()[source]#

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)[source]#

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)[source]#

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_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') → SelectByShuffling[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns
selfobject

The updated object.

set_params(**params)[source]#

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)[source]#

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 ..