New Feature in Scikit-Learn Pipeline - Interaction between two existing Features - feature-engineering

Question

I have two features in my data set: height and Area. I want to create a new feature by Interacting Area and Height using pipeline in scikit-learn.
Can anyone please guide me on how I can achieve this?
Thanks

Answer 1

You can achieve this with a custom transformer, implementing a fit and transform method. Optionnaly you can make it inherit from sklearn TransformerMixin for bullet-profing.
from sklearn.base import TransformerMixin
class CustomTransformer(TransformerMixin):
def fit(self, X, y=None):
"""The fit method doesn't do much here,
but it still required if your pipeline
ever need to be fit. Just returns self."""
return self
def transform(self, X, y=None):
"""This is where the actual transformation occurs.
Assuming you want to compute the product of your feature
height and area.
"""
# Copy X to avoid mutating the original dataset
X_ = X.copy()
# change new_feature and right member according to your needs
X_["new_feature"] = X_["height"] * X_["area"]
# you then return the newly transformed dataset. It will be
# passed to the next step of your pipeline
return X_
You can test it with this code :
import pandas as pd
from sklearn.pipeline import Pipeline
# Instantiate fake DataSet, your Transformer and Pipeline
X = pd.DataFrame({"height": [10, 23, 34], "area": [345, 33, 45]})
custom = CustomTransformer()
pipeline = Pipeline([("heightxarea", custom)])
# Test it
pipeline.fit(X)
pipeline.transform(X)
For such a simple processing, it might seem like an overkill, but it is a good practice to put any dataset manipulations into Transformers. They are more reproducible that way.