"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
python -m pip install featuretools
or from the Conda-forge channel on conda:
conda install -c conda-forge featuretools
You can install add-ons individually or all at once by running
python -m pip install "featuretools[complete]"
Update checker - Receive automatic notifications of new Featuretools releases
python -m pip install "featuretools[update_checker]"
NLP Primitives - Use Natural Language Processing Primitives:
python -m pip install "featuretools[nlp_primitives]"
TSFresh Primitives - Use 60+ primitives from tsfresh within Featuretools
python -m pip install "featuretools[tsfresh]"
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)
>> es.plot()
Featuretools can automatically create a single table of features for any "target dataframe"
>> feature_matrix, features_defs = ft.dfs(entityset=es, target_dataframe_name="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.
Featuretools contains many different types of built-in primitives for creating features. If the primitive you need is not included, Featuretools also allows you to define your own custom primitives.
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.
The Featuretools community welcomes pull requests. Instructions for testing and development are available here.
The Featuretools community is happy to provide support to users of Featuretools. Project support can be found in four places depending on the type of question:
- For usage questions, use Stack Overflow with the
featuretoolstag. - For bugs, issues, or feature requests start a Github issue.
- For discussion regarding development on the core library, use Slack.
- For everything else, the core developers can be reached by email at help@featuretools.com.
If you use Featuretools, please consider citing the following paper:
James Max Kanter, Kalyan Veeramachaneni. Deep feature synthesis: Towards automating data science endeavors. IEEE DSAA 2015.
BibTeX entry:
@inproceedings{kanter2015deep,
author = {James Max Kanter and Kalyan Veeramachaneni},
title = {Deep feature synthesis: Towards automating data science endeavors},
booktitle = {2015 {IEEE} International Conference on Data Science and Advanced Analytics, DSAA 2015, Paris, France, October 19-21, 2015},
pages = {1--10},
year = {2015},
organization={IEEE}
}