ReciprocalTransformer¶
API Reference¶
-
class
feature_engine.transformation.
ReciprocalTransformer
(variables=None)[source]¶ The ReciprocalTransformer() applies the reciprocal transformation 1 / x to numerical variables.
The ReciprocalTransformer() only works with numerical variables with non-zero values. If a variable contains the value 0, the transformer will raise an error.
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.
Methods
fit:
This transformer does not learn parameters.
transform:
Apply the reciprocal 1 / x transformation.
fit_transform:
Fit to data, then transform it.
-
fit
(X, y=None)[source]¶ This transformer does not learn parameters.
- 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
If some variables contain zero as values
-
transform
(X)[source]¶ Apply the reciprocal 1 / x 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().
If some variables contain zero 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 = 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.ReciprocalTransformer(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)
