DropCorrelatedFeatures#

class feature_engine.selection.DropCorrelatedFeatures(variables=None, method='pearson', threshold=0.8, missing_values='ignore', confirm_variables=False)[source]#

DropCorrelatedFeatures() finds and removes correlated features. Correlation is calculated with pandas.corr(). Features are removed on first found, first removed basis, without any further insight.

DropCorrelatedFeatures() works only with numerical variables. Categorical variables will need to be encoded to numerical or will be excluded from the analysis.

To make the selector deterministic, features are sorted alphabetically before examining correlation.

More details in the User Guide.

Parameters
variables: str or list, default=None

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

Whether the missing values should be raised as error or ignored when determining correlation. Takes values ‘raise’ and ‘ignore’.

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
features_to_drop_:

Set with the correlated features that will be dropped.

correlated_feature_sets_:

Groups of correlated features. Each list is a group of correlated features.

correlated_feature_dict_: dict

Dictionary containing the correlated feature groups. The key is the feature against which all other features were evaluated. The values are the features correlated with the key. Key + values should be the same as the set found in correlated_feature_groups. We introduced this attribute in version 1.17.0 because from the set, it is not easy to see which feature will be retained and which ones will be removed. The key is retained, the values will be dropped.

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

pandas.corr
feature_engine.selection.SmartCorrelationSelection

Notes

If you want to select from each group of correlated features those that are perhaps more predictive or more complete, check Feature-engine’s SmartCorrelationSelection.

Examples

>>> import pandas as pd
>>> from feature_engine.selection import DropCorrelatedFeatures
>>> X = pd.DataFrame(dict(x1 = [1,2,1,1], x2 = [2,4,3,1], x3 = [1, 0, 0, 1]))
>>> dcf = DropCorrelatedFeatures(threshold=0.7)
>>> dcf.fit_transform(X)
    x1  x3
0   1   1
1   2   0
2   1   0
3   1   1

Methods

fit:

Find correlated 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:

Remove correlated features.

fit(X, y=None)[source]#

Find the correlated features.

Parameters
Xpandas dataframe of shape = [n_samples, n_features]

The training dataset.

ypandas series. Default = None

y is not needed in this transformer. You can pass y or None.

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_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 ..