licsh / featuretools

An open source python framework for automated feature engineering

"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.

Installation

Install with pip

python -m pip install featuretools

or from the Featuretools channel on conda:

conda install -c featuretools featuretools

Example

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

Entityset: 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.

Demos

Predict Next Purchase

Repository | Notebook

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.

Support

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:

1. For usage questions, use Stack Overflow with the featuretools tag.
2. For bugs, issues, or feature requests start a Github issue.
3. For discussion regarding development on the core library, use gitter.
4. For everything else, the core developers can be reached by email at [email protected].

Citing Featuretools

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}
}

Feature Labs

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.