SmartCorrelatedSelection#
- class feature_engine.selection.SmartCorrelatedSelection(variables=None, method='pearson', threshold=0.8, missing_values='ignore', selection_method='missing_values', estimator=None, scoring='roc_auc', cv=3)[source]#
SmartCorrelatedSelection() finds groups of correlated features and then selects, from each group, a feature following certain criteria:
Feature with least missing values
Feature with most unique values
Feature with highest variance
Feature with highest importance according to an estimator
SmartCorrelatedSelection() returns a dataframe containing from each group of correlated features, the selected variable, plus all original features that were not correlated to any other.
Correlation is calculated with
pandas.corr()
.SmartCorrelatedSelection() works only with numerical variables. Categorical variables will need to be encoded to numerical or will be excluded from the analysis.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of variables to evaluate. If None, the transformer will evaluate all numerical variables in the dataset.
- method: string or callable, default=’pearson’
Can take ‘pearson’, ‘spearman’, ‘kendall’ or callable. It refers to the correlation method to be used to identify the correlated features.
‘pearson’: standard correlation coefficient
‘kendall’: Kendall Tau correlation coefficient
‘spearman’: Spearman rank correlation
callable: callable with input two 1d ndarrays and returning a float.
For more details on this parameter visit the
pandas.corr()
documentation.- threshold: float, default=0.8
The correlation threshold above which a feature will be deemed correlated with another one and removed from the dataset.
- missing_values: str, default=ignore
Takes values ‘raise’ and ‘ignore’. Whether the missing values should be raised as error or ignored when determining correlation.
- selection_method: str, default= “missing_values”
Takes the values “missing_values”, “cardinality”, “variance” and “model_performance”.
“missing_values”: keeps the feature from the correlated group with least missing observations
“cardinality”: keeps the feature from the correlated group with the highest cardinality.
“variance”: keeps the feature from the correlated group with the highest variance.
“model_performance”: trains a machine learning model using the correlated feature group and retains the feature with the highest importance.
- estimator: object, default = None
A Scikit-learn estimator for regression or classification.
- scoring: str, default=’roc_auc’
Desired metric to optimise 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
- 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,
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.
- Attributes
- correlated_feature_sets_:
Groups of correlated features. Each list is a group of correlated features.
- features_to_drop_:
The correlated features to remove from the dataset.
- variables_:
The variables that will be considered for the feature selection.
- n_features_in_:
The number of features in the train set used in fit.
See also
pandas.corr
feature_engine.selection.DropCorrelatedFeatures
Notes
For brute-force correlation selection, check Feature-engine’s DropCorrelatedFeatures().
Methods
fit:
Find best feature from each correlated groups.
transform:
Return selected features.
fit_transform:
Fit to the data. Then transform it.
- fit(X, y=None)[source]#
Find the correlated feature groups. Determine which feature should be selected from each group.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training dataset.
- y: pandas series. Default = None
y is needed if selection_method == ‘model_performance’.
- 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_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.
- 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:py:class:
~pandas.core.frame.DataFrame