Feature Logic

What is feature logic?

‍Feature logic is the series of steps that transform input data into the unencoded data value that represents the feature in the feature store. The encoding step, which is model-specific, is not typically considered part of the feature logic. 

‍How should you implement feature logic?

In feature engineering, we describe how your choice of framework depends on your feature freshness requirements and data processing volume - with popular frameworks including Pandas, Polars, Spark, Flink, and SQL. There are other specialized frameworks available, such as Facebook Prophet for time-series data or Feature Tools for automated feature engineering.

Below is an example of feature logic in Python for creating a feature that represents the number of words in a text document:

 import pandas as pd
import numpy as np

def word_count_feature(text_column):
    
    # Split text into words
    words = text_column.str.split()
    
    # Count the number of words in each document
    word_count = words.apply(lambda x: len(x))
        
    return word_count

# Load text data
data = pd.read_csv('text_data.csv')

# Create word count feature
data['word_count'] = word_count_feature(data['text_column'])

In this example, the word_count_feature function takes a column of text data as input, splits the text into words, and returns a count of the number of words in each document. The resulting feature values are then added to the original dataset as a new column. Later, you could write those values as part of a Pandas DataFrame to a feature group.