LogTransformer

API Reference

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.

Parameters
variables: list, default=None

The list of numerical variables to transform. If None, the transformer will 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.

n_features_in_:

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

Methods

fit:

This transformer does not learn parameters.

transform:

Transform the variables using the logarithm.

fit_transform:

Fit to data, then transform it.

inverse_transform:

Convert the data back to the original representation.

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.

Returns
self
Raises
TypeError
  • If the input is not a Pandas DataFrame

  • If any of the user provided variables are not numerical

ValueError
  • If there are no numerical variables in the df or the df is empty

  • If the variable(s) contain null values

  • If some variables contain zero or negative 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: pandas dataframe

The dataframe with the transformed variables.

rtype

DataFrame ..

Raises
TypeError

If the input is not a Pandas DataFrame

ValueError
  • If the variable(s) contain null values

  • If the df has different number of features than the df used in fit()

  • If some variables contain zero or negative values

transform(X)[source]

Transform the variables with the logarithm.

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

The data to be transformed.

Returns
X: pandas dataframe

The dataframe with the transformed variables.

rtype

DataFrame ..

Raises
TypeError

If the input is not a Pandas DataFrame

ValueError
  • If the variable(s) contain null values

  • If the df has different number of features than the df used in fit()

  • If some variables contain zero or negative values

Example

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine import transformation as vt

# Load dataset
data = pd.read_csv('houseprice.csv')

# Separate into train and test sets
X_train, X_test, y_train, y_test =  train_test_split(
            data.drop(['Id', 'SalePrice'], axis=1),
            data['SalePrice'], test_size=0.3, random_state=0)

# set up the variable transformer
tf = vt.LogTransformer(variables = ['LotArea', 'GrLivArea'])

# fit the transformer
tf.fit(X_train)

# transform the data
train_t= tf.transform(X_train)
test_t= tf.transform(X_test)

# un-transformed variable
X_train['LotArea'].hist(bins=50)
../_images/lotarearaw.png
# transformed variable
train_t['LotArea'].hist(bins=50)
../_images/lotarealog.png