PowerTransformer¶
API Reference¶
- class feature_engine.transformation.PowerTransformer(variables=None, exp=0.5)[source]¶
The PowerTransformer() applies power or exponential transformations to numerical variables.
The PowerTransformer() 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
- variables: list, default=None
The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.
- exp: float or int, default=0.5
The power (or exponent).
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:
Apply the power transformation to the variables.
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.
- 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
- 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 power 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()
- transform(X)[source]¶
Apply the power transformation to the variables.
- Parameters
- X: Pandas DataFrame of shape = [n_samples, n_features]
The data to be transformed.
- Returns
- X: pandas Dataframe
The dataframe with the power 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()
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.PowerTransformer(variables = ['LotArea', 'GrLivArea'], exp=0.5)
# 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)
