LogTransformer#
- class feature_engine.transformation.LogTransformer(variables=None, base='e')[source]#
The LogTransformer() applies the natural logarithm or the base 10 logarithm to numerical variables. The natural logarithm is the logarithm in base e.
The LogTransformer() only works with positive values. If the variable contains a zero or a negative value the transformer will return an error.
A list of variables can be passed as an argument. Alternatively, the transformer will automatically select and transform all variables of type numeric.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.
- base: string, default=’e’
Indicates if the natural or base 10 logarithm should be applied. Can take values ‘e’ or ‘10’.
- Attributes
- variables_:
The group of variables that will be transformed.
- 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.
Methods
fit:
This transformer does not learn parameters.
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.
inverse_transform:
Convert the data back to the original representation.
transform:
Transform the variables using the logarithm.
- fit(X, y=None)[source]#
This transformer does not learn parameters.
Selects the numerical variables and determines whether the logarithm can be applied on the selected variables, i.e., it checks that the variables are positive.
- Parameters
- X: Pandas DataFrame of shape = [n_samples, n_features].
The training input samples. Can be the entire dataframe, not just the variables to transform.
- y: pandas Series, default=None
It 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
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. 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_featuresmust 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.
- inverse_transform(X)[source]#
Convert the data back to the original representation.
- Parameters
- X: Pandas DataFrame of shape = [n_samples, n_features]
The data to be transformed.
- Returns
- X_tr: pandas dataframe
The dataframe with the transformed variables.
- rtype
DataFrame..
- 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.