DropConstantFeatures¶
- class feature_engine.selection.DropConstantFeatures(variables=None, tol=1, missing_values='raise', confirm_variables=False)[source]¶
DropConstantFeatures() drops constant and quasi-constant variables from a dataframe. Constant variables show the same value in all the observations in the dataset. Quasi-constant variables show the same value in almost all the observations in the dataset.
This transformer works with numerical and categorical variables. The user can indicate a list of variables to examine. Alternatively, the transformer will evaluate all the variables in the dataset.
The transformer will first identify and store the constant and quasi-constant variables. Next, the transformer will drop these variables from a dataframe.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of variables to evaluate. If None, the transformer will evaluate all variables in the dataset.
- tol: float,int, default=1
Threshold to detect constant/quasi-constant features. Variables showing the same value in a percentage of observations greater than tol will be considered constant / quasi-constant and dropped. If tol=1, the transformer removes constant variables. Else, it will remove quasi-constant variables. For example, if tol=0.98, the transformer will remove variables that show the same value in 98% of the observations.
- missing_values: str, default=raises
Whether the missing values should be raised as error, ignored or included as an additional value of the variable. Takes values ‘raise’, ‘ignore’, ‘include’.
- 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_:
List with constant and quasi-constant features.
- 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.feature_selection.VarianceThreshold
Notes
This transformer is a similar concept to the VarianceThreshold from Scikit-learn, but it evaluates number of unique values instead of variance.
Methods
fit:
Find constant and quasi-constant 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.
transform:
Remove constant and quasi-constant features.
- fit(X, y=None)[source]¶
Find constant and quasi-constant features.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The input dataframe.
- y: None
y is not needed for 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
Xandywith optional parametersfit_paramsand 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.
- input_features: None
This parameter exists only for compatibility with the Scikit-learn pipeline, but has no functionality. You can pass a list of feature names or None.
- Returns
- feature_names_out: list
The feature names.
- :rtype:py:class:
~typing.List
- 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