YeoJohnsonTransformer¶
The Yeo-Johnson transformation is defined as:

where Y is the response variable and λ is the transformation parameter.
API Reference¶
-
class
feature_engine.transformation.
YeoJohnsonTransformer
(variables=None)[source]¶ The YeoJohnsonTransformer() applies the Yeo-Johnson transformation to the numerical variables.
The Yeo-Johnson transformation implemented by this transformer is that of SciPy.stats: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html
The YeoJohnsonTransformer() works only with numerical variables.
A list of variables can be passed as an argument. Alternatively, the transformer will automatically select and transform all numerical variables.
- Parameters
- variableslist, default=None
The list of numerical variables that will be transformed. If None, the transformer will automatically find and select all numerical variables.
Attributes
lambda_dict_ :
Dictionary containing the best lambda for the Yeo-Johnson per variable.
References
- 1
Weisberg S. “Yeo-Johnson Power Transformations”. https://www.stat.umn.edu/arc/yjpower.pdf
Methods
fit:
Learn the optimal lambda for the Yeo-Johnson transformation.
transform:
Apply the Yeo-Johnson transformation.
fit_transform:
Fit to data, then transform it.
-
fit
(X, y=None)[source]¶ Learn the optimal lambda for the Yeo-Johnson transformation.
- Parameters
- Xpandas dataframe of shape = [n_samples, n_features]
The training input samples. Can be the entire dataframe, not just the variables to transform.
- ypandas 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
-
transform
(X)[source]¶ Apply the Yeo-Johnson transformation.
- Parameters
- XPandas DataFrame of shape = [n_samples, n_features]
The data to be transformed.
- Returns
- Xpandas 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 dataframe not of the same size as that used in fit().
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 = 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.YeoJohnsonTransformer(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)

# transformed variable
train_t['LotArea'].hist(bins=50)
