Metadata-Version: 2.1
Name: blurr-dev
Version: 0.394
Summary: Data aggregation pipeline for running real-time predictive models
Home-page: https://github.com/productml/blurr
Author: productml.com
Author-email: info@productml.com
License: UNKNOWN
Description: ![Blurr](logo.png)
        
        >We believe in a world where everyone is a data engineer. Or a data scientist. Or an ML engineer. The lines are blurred (*cough*). Just like development and operations became DevOps over time
        
        >--- Blurr authors
        
        [![CircleCI](https://circleci.com/gh/productml/blurr/tree/master.svg?style=svg)](https://circleci.com/gh/productml/blurr/tree/master)
        [![Documentation Status](https://readthedocs.org/projects/productml-blurr/badge/?version=latest)](http://productml-blurr.readthedocs.io/en/latest/?badge=latest)
        
        Blurr transforms `raw data` into `features` for model training and prediction using a `high-level expressive YAML-based language` called the Data Transform Configuration (DTC).
        
        For production ML applications, __experimentation__ and __iteration speed__ is important. Working directly with raw data provides the most flexibility. Blurr allows product teams to iterate quickly during ML dev and provides a self-service way to take experiments to production.
        
        ![Data Transformer](docs/images/data-transformer.png)
        
        >Coming up with features is difficult, time-consuming, requires expert knowledge. 'Applied machine learning' is basically feature engineering
        
        >--- Andrew Ng
        
        # Table of contents
        
        - [DTC at a glance](#dtc-at-a-glance)
        - [Tutorial & Docs](#tutorial-and-docs)
        - [Install](#use-blurr)
        - [Contribute](#contribute-to-blurr)
        - [Data Science 'Joel Test'](#data-science-joel-test)
        - [Roadmap](#roadmap)
        
        # DTC at a glance
        
        Raw data like this
        
        ```javascript
        { "user_id": "09C1", "session_id": "915D", "country" : "US", "event_id": "game_start" }
        { "user_id": "09C1", "session_id": "915D", "country" : "US", "event_id": "game_end", "won": 1 }
        { "user_id": "09C1", "session_id": "915D", "country" : "US", "event_id": "game_start" }
        { "user_id": "09C1", "session_id": "915D", "country" : "US", "event_id": "game_end", "won": 1 }
        { "user_id": "B6FA", "session_id": "D043", "country" : "US", "event_id": "game_start" }
        { "user_id": "B6FA", "session_id": "D043", "country" : "US", "event_id": "game_end", "won": 1 }
        { "user_id": "09C1", "session_id": "T8KA", "country" : "UK", "event_id": "game_start" }
        { "user_id": "09C1", "session_id": "T8KA", "country" : "UK", "event_id": "game_end", "won": 1 }
        ```
        
        turns into
        
        session_id |  user_id | games_played | games_won
        --- | ------------ | -------------- | --------
        915D | 09C1 | 2 | 2
        D043 | B6FA | 1 | 1
        T8KA | 09C1 | 1 | 1
        
        using this DTC
        
        ```yaml
        
        Type: Blurr:Streaming
        Version: '2018-03-07'
        
        Store:
           - Type: Blurr:Store:MemoryStore
             Name: hello_world_store
        
        Identity: source.user_id
        
        DataGroups:
        
         - Type: Blurr:DataGroup:BlockAggregate
           Name: session_stats
           Store: hello_world_store
           Split: source.session_id != session_stats.session_id
        
           Fields:
        
             - Name: session_id
               Type: string
               Value: source.session_id
        
             - Name: games_played
               Type: integer
               Value: session_stats.games_played + 1
               When: source.event_id == 'game_start'
        
             - Name: games_won
               Type: integer
               Value: session_stats.games_won + 1
               When: source.event_id == 'game_end' and source.won == '1'
        
        ```
        
        # Tutorial and Docs
        
        [Read the docs](http://productml-blurr.readthedocs.io/en/latest/)
        
        [Streaming DTC Tutorial](http://productml-blurr.readthedocs.io/en/latest/Streaming%20dtc%20tutorial/) |
        [Window DTC Tutorial](http://productml-blurr.readthedocs.io/en/latest/Window%20dtc%20tutorial/)
        
        Preparing data for specific use cases using Blurr
        
        [Dynamic in-game offers (Offer AI)](examples/offer-ai/offer-ai-walkthrough.md) | [Frequently Bought Together](examples/frequently-bought-together/fbt-walkthrough.md)
        
        # Use Blurr
        
        We interact with Blurr using a Command Line Interface (CLI). Blurr is installed via pip:
        
        `$ pip install blurr`
        
        Transform data
        
        ```
        $ blurr transform \
             --streaming-dtc ./dtcs/sessionize-dtc.yml \
             --window-dtc ./dtcs/windowing-dtc.yml \
             --source file://path
        ```
        
        [CLI documentation](http://productml-blurr.readthedocs.io/en/latest/Blurr%20CLI/)
        
        # Contribute to Blurr
        
        Welcome to the Blurr community! We are so glad that you share our passion for making data management and machine learning accessible to everyone.
        
        Please create a [new issue](https://github.com/productml/blurr/issues/new) to begin a discussion. Alternatively, feel free to pick up an existing issue!
        
        Please sign the [Contributor License Agreement](https://docs.google.com/forms/d/e/1FAIpQLSeUP5RFuXH0Kbi4CnV6V3IZ-xyJmd3KQP_2Ij-pTvN-_h7wUg/viewform) before raising a pull request.
        
        # Data Science 'Joel Test'
        
        Inspired by the (old school) [Joel Test](https://www.joelonsoftware.com/2000/08/09/the-joel-test-12-steps-to-better-code/) to rate software teams, here's our version for data science teams. What's your score? We'd love to know!
        
        1. Data pipelines are versioned and reproducible
        2. Pipelines (re)build in one step
        3. Deploying to production needs minimal engineering help
        4. Successful ML is a long game. You play it like it is
        5. Kaizen. Experimentation and iterations are a way of life
        
        __Stay in touch!__ Star this project or email hello@blurr.ai
        
        # Roadmap
        
        Blurr is all about enabling machine learning and AI teams to run faster.
        
        **Developer Preview 0**: Local transformations only
        
        **Developer Preview 1**: S3-S3 data transformations
        
        **Developer Preview 2**: Add DynamoDB as a Store + Features server for ML production use
        
        Ingestion connectors to Kafka and Spark
        
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.6
Description-Content-Type: text/markdown
