qafs 0.1.1

Quality Aware Feature Store.

Quality Aware Feature Store

Simple and scalable feature store with data quality checks.

feature store aim to solve the data management problems when building Machine Learning applications. However the data quality is a component which data teams need integrate and handle as separated component. This project join both concepts keeping the data quality closely coupled with data transformations making necessary a minimal data verification check and possibiliting the data/transformations check evolve during the projects.

For that qafs have a strong dependecy with pandera to build the data validations.

Features

• Pandas-like API
• Features information stored in database along with metadata.
• Dask to process large datasets in a cluster enviroment.
• Data is stored as timeseries in Parquet format, store in filesystem or object storage services.
• Store transformations as feature.

Get Started

Installing the python package through pip:

$ pip install qafs

Bellow is an example of usage qafs where we'll create a feature store and register numbers feature and an squared feature transformation. First we need import the packages and create the feature store, for this example we are using sqlite database and persisting the features in the filesystem:

import qafs
import pandas as pd
import pandera as pa
from pandera import Check, Column, DataFrameSchema
from pandera import io


fs = qafs.FeatureStore(
    connection_string='sqlite:///test.sqlite',
    url='/tmp/featurestore/example'
)

Features could be stored in namespaces, it help organize the data. When creating numbers we specify the 'example/numbers' feature to point the feature numbersat that namespace example however we can use the arguments name='numbers', namespace='example' as well. The we specify the data validation using pandera telling that feature is Integer and the values should be greater than 0:

fs.create_namespace('example', description='Example datasets')
fs.create_feature(
    'example/numbers',
    description='Timeseries of numbers',
    check=Column(pa.Int, Check.greater_than(0))
)


dts = pd.date_range('2020-01-01', '2021-02-09')
df = pd.DataFrame({'time': dts, 'numbers': list(range(1, len(dts) + 1))})

fs.save_dataframe(df, name='numbers', namespace='example')

To register our squared transformation feature we're using the annotation fs.transform and fetching the data from the numbers feature applying the same data validation from numbers:

@fs.transform(
    'example/squared',
    from_features=['example/numbers'],
    check=Column(pa.Int, Check.greater_than(0))
)
def squared(df):
    return df ** 2

When fetch our features we should see:

df_query = fs.load_dataframe(
    ['example/numbers', 'example/squared'], 
    from_date='2021-01-01',
    to_date='2021-01-31'
)
print(df_query.tail(1))
##----
#             example/numbers  example/squared
# time                                        
# 2021-01-31              397           157609
##----

Contributing

Please follow the Contributing guide.

License

GPL-3.0 License

This project started using the as base bytehub feature store and is under the same license.