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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

Mithilesh Tiwari
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1 Answers1

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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.

Jean-Christophe Bianic
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