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Open Source

featuretools

<p align="center"> <img width=50% src="https://www.featuretools.com/wp-content/uploads/2017/12/FeatureLabs-Logo-Tangerine-800.png" alt="Featuretools" /> </p> <p align="center"> <i>"One of the holy grails of machine learning is to automate more and more of the feature engineering process."</i> ― Pedro Domingos, <a href="https://bit.ly/things_to_know_ml">A Few Useful Things to Know about Machine Learning</a> </p> <p align="center"> <a href="https://github.com/alteryx/featuretools/actions/workflows/tests_with_latest_deps.yaml" alt="Tests" target="_blank"> <img src="https://github.com/alteryx/featuretools/actions/workflows/tests_with_latest_deps.yaml/badge.svg?branch=main" alt="Tests" /> </a> <a href="https://codecov.io/gh/alteryx/featuretools"> <img src="https://codecov.io/gh/alteryx/featuretools/branch/main/graph/badge.svg"/> </a> <a href='https://featuretools.alteryx.com/en/stable/?badge=stable'> <img src='https://readthedocs.com/projects/feature-labs-inc-featuretools/badge/?version=stable' alt='Documentation Status' /> </a> <a href="https://badge.fury.io/py/featuretools" target="_blank"> <img src="https://badge.fury.io/py/featuretools.svg?maxAge=2592000" alt="PyPI Version" /> </a> <a href="https://anaconda.org/conda-forge/featuretools" target="_blank"> <img src="https://anaconda.org/conda-forge/featuretools/badges/version.svg" alt="Anaconda Version" /> </a> <a href="https://stackoverflow.com/questions/tagged/featuretools" target="_blank"> <img src="http://img.shields.io/badge/questions-on_stackoverflow-blue.svg" alt="StackOverflow" /> </a> <a href="https://pepy.tech/project/featuretools" target="_blank"> <img src="https://static.pepy.tech/badge/featuretools/month" alt="PyPI Downloads" /> </a> </p> <hr> [Featuretools](https://www.featuretools.com) is a python library for automated feature engineering. See the [documentation](https://docs.featuretools.com) for more information. ## Installation Install with pip ``` python -m pip install featuretools ``` or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/featuretools): ``` 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]" ``` **Premium Primitives** - Use Premium Primitives from the premium-primitives repo ``` python -m pip install "featuretools[premium]" ``` **NLP Primitives** - Use Natural Language Primitives from the nlp-primitives repo ``` python -m pip install "featuretools[nlp]" ``` **Dask Support** - Use Dask to run DFS with njobs > 1 ``` python -m pip install "featuretools[dask]" ``` ## 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. ```python >> import featuretools as ft >> es = ft.demo.load_mock_customer(return_entityset=True) >> es.plot() ``` <img src="https://github.com/alteryx/featuretools/blob/main/docs/source/_static/images/entity_set.png?raw=true" width="350"> Featuretools can automatically create a single table of features for any "target dataframe" ```python >> 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](https://featuretools.alteryx.com/en/stable/getting_started/afe.html) 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](https://featuretools.alteryx.com/en/stable/getting_started/primitives.html#defining-custom-primitives). ## Demos **Predict Next Purchase** [Repository](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/) | [Notebook](https://github.com/alteryx/open_source_demos/blob/main/predict-next-purchase/Tutorial.ipynb) 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](https://www.featuretools.com/demos) page. ## Testing & Development The Featuretools community welcomes pull requests. Instructions for testing and development are available [here.](https://featuretools.alteryx.com/en/stable/install.html#development) ## 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](https://stackoverflow.com/questions/tagged/featuretools) with the `featuretools` tag. 2. For bugs, issues, or feature requests start a [Github issue](https://github.com/alteryx/featuretools/issues). 3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). 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.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/DSAA_DSM_2015.pdf) *IEEE DSAA 2015*. BibTeX entry: ```bibtex @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} } ``` ## Built at Alteryx **Featuretools** is an open source project maintained by [Alteryx](https://www.alteryx.com). To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch. <p align="center"> <a href="https://www.alteryx.com/open-source"> <img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/> </a> </p>

Analytics & BI ML Frameworks

7.7K Github Stars

Open Source

compose

<p align="center"><img width=50% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/compose.png" alt="Compose" /></p> <p align="center"><i>"Build better training examples in a fraction of the time."</i></p> <p align="center"> <a href="https://github.com/alteryx/compose/actions?query=workflow%3ATests" target="_blank"> <img src="https://github.com/alteryx/compose/workflows/Tests/badge.svg" alt="Tests" /> </a> <a href="https://codecov.io/gh/alteryx/compose"> <img src="https://codecov.io/gh/alteryx/compose/branch/main/graph/badge.svg?token=mDz4ueTUEO"/> </a> <a href="https://compose.alteryx.com/en/stable/?badge=stable" target="_blank"> <img src="https://readthedocs.com/projects/feature-labs-inc-compose/badge/?version=stable&token=5c3ace685cdb6e10eb67828a4dc74d09b20bb842980c8ee9eb4e9ed168d05b00" alt="ReadTheDocs" /> </a> <a href="https://badge.fury.io/py/composeml" target="_blank"> <img src="https://badge.fury.io/py/composeml.svg?maxAge=2592000" alt="PyPI Version" /> </a> <a href="https://stackoverflow.com/questions/tagged/compose-ml" target="_blank"> <img src="https://img.shields.io/badge/questions-on_stackoverflow-blue.svg?" alt="StackOverflow" /> </a> <a href="https://pepy.tech/project/composeml" target="_blank"> <img src="https://pepy.tech/badge/composeml/month" alt="PyPI Downloads" /> </a> </p> <hr> [Compose](https://compose.alteryx.com) is a machine learning tool for automated prediction engineering. It allows you to structure prediction problems and generate labels for supervised learning. An end user defines an outcome of interest by writing a *labeling function*, then runs a search to automatically extract training examples from historical data. Its result is then provided to [Featuretools](https://docs.featuretools.com/) for automated feature engineering and subsequently to [EvalML](https://evalml.alteryx.com/) for automated machine learning. The workflow of an applied machine learning engineer then becomes: <br><p align="center"><img width=90% src="https://raw.githubusercontent.com/alteryx/compose/main/docs/source/images/workflow.png" alt="Compose" /></p><br> By automating the early stage of the machine learning pipeline, our end user can easily define a task and solve it. See the [documentation](https://compose.alteryx.com) for more information. ## Installation Install with pip ``` python -m pip install composeml ``` or from the Conda-forge channel on [conda](https://anaconda.org/conda-forge/composeml): ``` conda install -c conda-forge composeml ``` ### Add-ons **Update checker** - Receive automatic notifications of new Compose releases ``` python -m pip install "composeml[update_checker]" ``` ## Example > Will a customer spend more than 300 in the next hour of transactions? In this example, we automatically generate new training examples from a historical dataset of transactions. ```python import composeml as cp df = cp.demos.load_transactions() df = df[df.columns[:7]] df.head() ``` <table border="0" class="dataframe"> <thead> <tr style="text-align: right;"> <th>transaction_id</th> <th>session_id</th> <th>transaction_time</th> <th>product_id</th> <th>amount</th> <th>customer_id</th> <th>device</th> </tr> </thead> <tbody> <tr> <td>298</td> <td>1</td> <td>2014-01-01 00:00:00</td> <td>5</td> <td>127.64</td> <td>2</td> <td>desktop</td> </tr> <tr> <td>10</td> <td>1</td> <td>2014-01-01 00:09:45</td> <td>5</td> <td>57.39</td> <td>2</td> <td>desktop</td> </tr> <tr> <td>495</td> <td>1</td> <td>2014-01-01 00:14:05</td> <td>5</td> <td>69.45</td> <td>2</td> <td>desktop</td> </tr> <tr> <td>460</td> <td>10</td> <td>2014-01-01 02:33:50</td> <td>5</td> <td>123.19</td> <td>2</td> <td>tablet</td> </tr> <tr> <td>302</td> <td>10</td> <td>2014-01-01 02:37:05</td> <td>5</td> <td>64.47</td> <td>2</td> <td>tablet</td> </tr> </tbody> </table> First, we represent the prediction problem with a labeling function and a label maker. ```python def total_spent(ds): return ds['amount'].sum() label_maker = cp.LabelMaker( target_dataframe_index="customer_id", time_index="transaction_time", labeling_function=total_spent, window_size="1h", ) ``` Then, we run a search to automatically generate the training examples. ```python label_times = label_maker.search( df.sort_values('transaction_time'), num_examples_per_instance=2, minimum_data='2014-01-01', drop_empty=False, verbose=False, ) label_times = label_times.threshold(300) label_times.head() ``` <table border="0" class="dataframe"> <thead> <tr style="text-align: right;"> <th>customer_id</th> <th>time</th> <th>total_spent</th> </tr> </thead> <tbody> <tr> <td>1</td> <td>2014-01-01 00:00:00</td> <td>True</td> </tr> <tr> <td>1</td> <td>2014-01-01 01:00:00</td> <td>True</td> </tr> <tr> <td>2</td> <td>2014-01-01 00:00:00</td> <td>False</td> </tr> <tr> <td>2</td> <td>2014-01-01 01:00:00</td> <td>False</td> </tr> <tr> <td>3</td> <td>2014-01-01 00:00:00</td> <td>False</td> </tr> </tbody> </table> We now have labels that are ready to use in [Featuretools](https://docs.featuretools.com/) to generate features. ## Support The Innovation Labs open source community is happy to provide support to users of Compose. Project support can be found in three places depending on the type of question: 1. For usage questions, use [Stack Overflow](https://stackoverflow.com/questions/tagged/compose-ml) with the `composeml` tag. 2. For bugs, issues, or feature requests start a Github [issue](https://github.com/alteryx/compose/issues/new). 3. For discussion regarding development on the core library, use [Slack](https://join.slack.com/t/alteryx-oss/shared_invite/zt-182tyvuxv-NzIn6eiCEf8TBziuKp0bNA). 4. For everything else, the core developers can be reached by email at [email protected] ## Citing Compose Compose is built upon a newly defined part of the machine learning process — prediction engineering. If you use Compose, please consider citing this paper: James Max Kanter, Gillespie, Owen, Kalyan Veeramachaneni. [Label, Segment,Featurize: a cross domain framework for prediction engineering.](https://dai.lids.mit.edu/wp-content/uploads/2017/10/Pred_eng1.pdf) IEEE DSAA 2016. BibTeX entry: ```bibtex @inproceedings{kanter2016label, title={Label, segment, featurize: a cross domain framework for prediction engineering}, author={Kanter, James Max and Gillespie, Owen and Veeramachaneni, Kalyan}, booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)}, pages={430--439}, year={2016}, organization={IEEE} } ``` ## Acknowledgements The open source development has been supported in part by DARPA's Data driven discovery of models program (D3M). ## Alteryx **Compose** is an open source project maintained by [Alteryx](https://www.alteryx.com). We developed Compose to enable flexible definition of the machine learning task. To see the other open source projects we’re working on visit [Alteryx Open Source](https://www.alteryx.com/open-source). If building impactful data science pipelines is important to you or your business, please get in touch. <p align="center"> <a href="https://www.alteryx.com/open-source"> <img src="https://alteryx-oss-web-images.s3.amazonaws.com/OpenSource_Logo-01.png" alt="Alteryx Open Source" width="800"/> </a> </p>

Data Labeling

513 Github Stars