LogCpTransformer

class feature_engine.transformation.LogCpTransformer(variables=None, base='e', C='auto')[source]

The LogCpTransformer() applies the transformation log(x + C), where C is a positive constant, to the input variable. It applies the natural logarithm or the base 10 logarithm, where the natural logarithm is logarithm in base e.

The logarithm can only be applied to numerical non-negative values. If the variable contains a zero or a negative value after adding a constant C, 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 find and select all numerical variables. If C is a dictionary, then this parameter is ignored and the variables to transform are selected from the dictionary keys.

base: string, default=’e’

Indicates if the natural or base 10 logarithm should be applied. Can take values ‘e’ or ‘10’.

C: “auto”, int or dict, default=”auto”

The constant C to add to the variable before the logarithm, i.e., log(x + C).

  • If int, then log(x + C)

  • If “auto”, then C = abs(min(x)) + 1

  • If dict, dictionary mapping the constant C to apply to each variable.

Note, when C is a dictionary, the parameter variables is ignored.

Attributes
variables_:

The group of variables that will be transformed.

C_:

The constant C to add to each variable. If C = “auto” a dictionary with C = abs(min(variable)) + 1.

n_features_in_:

The number of features in the train set used in fit.

Methods

fit:

Learn the constant C.

transform:

Transform the variables with the logarithm of x plus C.

fit_transform:

Fit to data, then transform it.

inverse_transform:

Convert the data back to the original representation.

fit(X, y=None)[source]

Learn the constant C to add to the variable before the logarithm transformation if C=”auto”.

Select the numerical variables or check that the variables entered by the user are numerical. Then check that the selected variables are positive after addition of C.

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 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_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.

transform(X)[source]

Transform the variables with the logarithm of x plus a constant C.

Parameters
X: Pandas DataFrame of shape = [n_samples, n_features]

The data to be transformed.

Returns
X_new: pandas dataframe

The dataframe with the transformed variables.

rtype

DataFrame ..