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:
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’scross_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
andy
with optional parametersfit_params
and returns a transformed version ofX
.- 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
, thenfeature_names_in_
is used as feature names in.If an array or list, then
input_features
must matchfeature_names_in_
.
- Returns
- feature_names_out: list
Transformed feature names.
- 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. Ifindices
is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
- 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.