"One of the holy grails of machine learning is to automate more and more of the feature engineering process." ― Pedro Domingos, A Few Useful Things to Know about Machine Learning
Featuretools is a python library for automated feature engineering. See the documentation for more information.
Install with pip
pip install featuretools Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, we apply DFS to a multi-table dataset consisting of timestamped customer transactions.
>> import featuretools as ft >> es = ft.demo.load_mock_customer(return_entityset=True) >> esEntityset: transactions Entities: customers (shape = [5, 3]) sessions (shape = [35, 4]) products (shape = [5, 2]) transactions (shape = [500, 5]) Relationships: transactions.product_id -> products.product_id transactions.session_id -> sessions.session_id sessions.customer_id -> customers.customer_id Featuretools can automatically create a single table of features for any "target entity"
>> feature_matrix, features_defs = ft.dfs(entityset=es, target_entity="customers") >> feature_matrix.head(5) zip_code COUNT(transactions) COUNT(sessions) SUM(transactions.amount) MODE(sessions.device) MIN(transactions.amount) MAX(transactions.amount) YEAR(join_date) SKEW(transactions.amount) DAY(join_date) ... SUM(sessions.MIN(transactions.amount)) MAX(sessions.SKEW(transactions.amount)) MAX(sessions.MIN(transactions.amount)) SUM(sessions.MEAN(transactions.amount)) STD(sessions.SUM(transactions.amount)) STD(sessions.MEAN(transactions.amount)) SKEW(sessions.MEAN(transactions.amount)) STD(sessions.MAX(transactions.amount)) NUM_UNIQUE(sessions.DAY(session_start)) MIN(sessions.SKEW(transactions.amount)) customer_id ... 1 60091 131 10 10236.77 desktop 5.60 149.95 2008 0.070041 1 ... 169.77 0.610052 41.95 791.976505 175.939423 9.299023 -0.377150 5.857976 1 -0.395358 2 02139 122 8 9118.81 mobile 5.81 149.15 2008 0.028647 20 ... 114.85 0.492531 42.96 596.243506 230.333502 10.925037 0.962350 7.420480 1 -0.470007 3 02139 78 5 5758.24 desktop 6.78 147.73 2008 0.070814 10 ... 64.98 0.645728 21.77 369.770121 471.048551 9.819148 -0.244976 12.537259 1 -0.630425 4 60091 111 8 8205.28 desktop 5.73 149.56 2008 0.087986 30 ... 83.53 0.516262 17.27 584.673126 322.883448 13.065436 -0.548969 12.738488 1 -0.497169 5 02139 58 4 4571.37 tablet 5.91 148.17 2008 0.085883 19 ... 73.09 0.830112 27.46 313.448942 198.522508 8.950528 0.098885 5.599228 1 -0.396571 [5 rows x 69 columns] We now have a feature vector for each customer that can be used for machine learning. See the documentation on Deep Feature Synthesis for more examples.
Predict Next Purchase
In this demonstration, we use a multi-table dataset of 3 million online grocery orders from Instacart to predict what a customer will buy next. We show how to generate features with automated feature engineering and build an accurate machine learning pipeline using Featuretools, which can be reused for multiple prediction problems. For more advanced users, we show how to scale that pipeline to a large dataset using Dask.
For more examples of how to use Featuretools, check out our demos page.
For installation or usage questions, please reach out on Gitter.
Featuretools was created by the developers at Feature Labs. If building impactful data science pipelines is important to you or your business, please get in touch.