LogCpTransformer

The LogCpTransformer() applies the transformation log(x + C), where C is a positive constant.

You can enter the positive quantity to add to the variable. Alternatively, the transformer will find the necessary quantity to make all values of the variable positive.

Example

Let’s load the boston house prices dataset that comes baked into Scikit-learn and separate it into train and test sets.

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_boston

from feature_engine import transformation as vt

# Load dataset
X, y = load_boston(return_X_y=True)
X = pd.DataFrame(X)

# Separate into train and test sets
X_train, X_test, y_train, y_test =  train_test_split(X, y, test_size=0.3, random_state=0)

Now we want to apply the logarithm to 2 of the variables in the dataset using the LogCpTransformer(). We want the transformer to detect automatically the quantity “C” that needs to be added to the variable:

# set up the variable transformer
tf = vt.LogCpTransformer(variables = [7, 12], C="auto")

# fit the transformer
tf.fit(X_train)

With fit() the LogCpTransformer() learns the quantity “C” and stores it as an attribute. We can visualise the learned parameters as follows:

# learned constant C
tf.C_

{7: 2.1742, 12: 2.73}

We can now go ahead and transform the variables:

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

Then we can plot the original variable distribution:

# un-transformed variable
X_train[12].hist()

And the distribution of the transformed variable:

# transformed variable
train_t[12].hist()

More details

You can find more details about the LogCpTransformer() here:

• Jupyter notebook

All notebooks can be found in a dedicated repository.