Retaining the Grouping Column in Featuretools: Understanding the Change and Workarounds
Featuretools, a powerful library for automated feature engineering, has undergone changes in recent versions that affect the behavior of the groupby parameter within the dfs function. This change has led to the dropping of the grouping column in the resulting DataFrame. While this may seem counterintuitive, it's actually a deliberate design choice that aligns with the broader philosophy of Featuretools and its focus on generating features for predictive modeling.
Problem Scenario:
Let's consider a simple example using the classic "Customers and Transactions" dataset:
import featuretools as ft
# Define entities and relationships
entities = {
"customers": ft.Entity(id="customer_id", dataframe=customers_df),
"transactions": ft.Entity(id="transaction_id", dataframe=transactions_df)
}
relationships = [
ft.Relationship(entities["customers"], entities["transactions"],
parent_variable="customer_id", child_variable="customer_id")
]
# Create a feature matrix using dfs
feature_matrix = ft.dfs(entities=entities, relationships=relationships,
target_entity="transactions",
agg_primitives=["sum", "mean"],
groupby="customer_id")
The Problem:
Previously, in older versions of Featuretools, running this code would return a feature matrix with the "customer_id" column included. However, in newer versions, this column is dropped by default. This might seem unexpected, especially if you rely on that column for further analysis or data manipulation.
Why the Change?
The core purpose of Featuretools is to create features for machine learning models. Typically, these models don't require the grouping column, as it's already encoded within the generated features (e.g., "mean_amount_by_customer"). Keeping the grouping column would introduce redundancy and potentially complicate downstream analysis.
Workarounds:
While Featuretools doesn't retain the grouping column by default, you have a few options to achieve your desired outcome:
-
Manual Column Creation: You can manually create a new column with the grouping values after generating the feature matrix:
feature_matrix["customer_id"] = feature_matrix.index.get_level_values("customer_id") -
Custom Primitive: If you need the grouping column frequently, consider creating a custom primitive that explicitly returns the grouping value. You can then apply this primitive within the
dfsfunction. -
groupbyfor Direct Aggregation: You can use thegroupbyfunction directly on the original DataFrame before generating features if you only need aggregated values for a specific column:grouped_transactions = transactions_df.groupby("customer_id")["amount"].agg(["sum", "mean"])
Conclusion:
The change in Featuretools' dfs function to drop the grouping column is a deliberate design choice aimed at simplifying feature generation for machine learning. While it may require adjustments in your workflow, the workarounds provided offer solutions to maintain the grouping information if necessary.
Key Resources:
- Featuretools Documentation: The official documentation provides extensive resources and examples for using Featuretools.
- Featuretools GitHub Repository: The repository hosts the source code, issue tracking, and community discussions.
By understanding the reason behind this change and exploring the available solutions, you can leverage Featuretools effectively for feature engineering while achieving your desired results.
