Package: featuretools-MV
"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 LearningFeaturetoolsis a python library for automated feature engineering. See thedocumentationfor more information.This is a forked version ofFeaturetools. you can find original sourcehereInstallationInstall with pippython -m pip install featuretoolsor from the Conda-forge channel onconda:conda install -c conda-forge featuretoolsExampleBelow 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.>>importfeaturetoolsasft>>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_idFeaturetools 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 thedocumentation on Deep Feature Synthesisfor more examples.DemosPredict Next PurchaseRepository|NotebookIn 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 ourdemospage.Testing & DevelopmentThe Featuretools community welcomes pull requests. Instructions for testing and development are avialablehere.SupportThe 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, useStack Overflowwith thefeaturetoolstag.For bugs, issues, or feature requests start aGithub issue.For discussion regarding development on the core library, useSlack.For everything else, the core developers can be reached by email athelp@featuretools.com.Citing FeaturetoolsIf 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 LabsFeaturetools is an open source project created byFeature Labs. To see the other open source projects we're working on visit Feature LabsOpen Source. If building impactful data science pipelines is important to you or your business, pleaseget in touch.