"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

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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 Conda-forge channel on conda:

conda install -c conda-forge featuretools

Add-ons

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]"

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.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.