Metadata-Version: 2.1
Name: feast
Version: 0.10.1
Summary: Python SDK for Feast
Home-page: https://github.com/feast-dev/feast
Author: Feast
License: Apache
Description: <p align="center">
            <a href="https://feast.dev/">
              <img src="docs/assets/feast_logo.png" width="550">
            </a>
        </p>
        <br />
        
        [![unit-tests](https://github.com/feast-dev/feast/actions/workflows/unit_tests.yml/badge.svg?branch=master&event=push)](https://github.com/feast-dev/feast/actions/workflows/unit_tests.yml)
        [![integration-tests](https://github.com/feast-dev/feast/actions/workflows/integration_tests.yml/badge.svg?branch=master&event=push)](https://github.com/feast-dev/feast/actions/workflows/integration_tests.yml)
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        [![Docs Latest](https://img.shields.io/badge/docs-latest-blue.svg)](https://docs.feast.dev/)
        [![Python API](https://img.shields.io/readthedocs/feast/master?label=Python%20API)](http://rtd.feast.dev/)
        [![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://github.com/feast-dev/feast/blob/master/LICENSE)
        [![GitHub Release](https://img.shields.io/github/v/release/feast-dev/feast.svg?style=flat&sort=semver&color=blue)](https://github.com/feast-dev/feast/releases)
        
        ## Overview
        
        Feast is an open source feature store for machine learning. Feast is the fastest path to productionizing analytic data for model training and online inference.
        
        Please see our [documentation](https://docs.feast.dev/) for more information about the project.
        
        ## Architecture
        <img src="https://i.imgur.com/IYUMF3Q.png" width="700">
        
        The above architecture is the minimal Feast deployment. Want to run the full Feast on Kubernetes? Click [here](https://docs.feast.dev/feast-on-kubernetes/getting-started).
        
        ## Getting Started
        
        ### 1. Install Feast
        ```commandline
        pip install feast
        ```
        
        ### 2. Create a feature repository
        ```commandline
        feast init my_feature_repo
        cd my_feature_repo
        ```
        
        ### 3. Register your feature definitions and set up your feature store
        ```commandline
        feast apply
        ```
        
        ### 4. Build a training dataset
        ```python
        from feast import FeatureStore
        import pandas as pd
        from datetime import datetime
        
        entity_df = pd.DataFrame.from_dict({
            "driver_id": [1001, 1002, 1003, 1004],
            "event_timestamp": [
                datetime(2021, 4, 12, 10, 59, 42),
                datetime(2021, 4, 12, 8,  12, 10),
                datetime(2021, 4, 12, 16, 40, 26),
                datetime(2021, 4, 12, 15, 1 , 12)
            ]
        })
        
        store = FeatureStore(repo_path=".")
        
        training_df = store.get_historical_features(
            entity_df=entity_df, 
            feature_refs = [
                'driver_hourly_stats:conv_rate',
                'driver_hourly_stats:acc_rate',
                'driver_hourly_stats:avg_daily_trips'
            ],
        ).to_df()
        
        print(training_df.head())
        
        # Train model
        # model = ml.fit(training_df)
        ```
        ```commandline
              event_timestamp  driver_id  driver_hourly_stats__conv_rate  driver_hourly_stats__acc_rate
          2021-04-12 08:12:10       1002                        0.497279                       0.357702
          2021-04-12 10:59:42       1001                        0.979747                       0.008166
          2021-04-12 15:01:12       1004                        0.151432                       0.551748
          2021-04-12 16:40:26       1003                        0.951506                       0.753572
        
        ```
        
        ### 5. Load feature values into your online store
        ```commandline
        CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
        feast materialize-incremental $CURRENT_TIME
        ```
        
        ```commandline
        Materializing feature view driver_hourly_stats from 2021-04-14 to 2021-04-15 done!
        ```
        
        ### 6. Read online features at low latency
        ```python
        from pprint import pprint
        from feast import FeatureStore
        
        store = FeatureStore(repo_path=".")
        
        feature_vector = store.get_online_features(
            feature_refs=[
                'driver_hourly_stats:conv_rate',
                'driver_hourly_stats:acc_rate',
                'driver_hourly_stats:avg_daily_trips'
            ],
            entity_rows=[{"driver_id": 1001}]
        ).to_dict()
        
        pprint(feature_vector)
        
        # Make prediction
        # model.predict(feature_vector)
        ```
        ```json
        {
            "driver_id": [1001],
            "driver_hourly_stats__conv_rate": [0.49274],
            "driver_hourly_stats__acc_rate": [0.92743],
            "driver_hourly_stats__avg_daily_trips": [72]
        }
        ```
        
        ## Important resources
        
        Please refer to the official documentation at [Documentation](https://docs.feast.dev/)
         * [Quickstart](https://docs.feast.dev/quickstart)
         * [Roadmap](https://docs.feast.dev/roadmap)
         * [Feast on Kubernetes](https://docs.feast.dev/feast-on-kubernetes/getting-started)
         * [Change Log](https://github.com/feast-dev/feast/blob/master/CHANGELOG.md)
         * [Slack (#Feast)](https://slack.feast.dev/)
        
        ## Contributing
        Feast is a community project and is still under active development. Please have a look at our contributing and development guides if you want to contribute to the project:
        - [Contribution Process for Feast](https://docs.feast.dev/contributing/contributing)
        - [Development Guide for Feast](https://docs.feast.dev/contributing/development-guide)
        - [Development Guide for the Main Feast Repository](./CONTRIBUTING.md)
        
        ## Contributors ✨
        
        Thanks goes to these incredible people:
        
        <a href="https://github.com/feast-dev/feast/graphs/contributors">
          <img src="https://contrib.rocks/image?repo=feast-dev/feast" />
        </a>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: ci
