flytekitplugins-whylogs

Flytekit whylogs Plugin

whylogs is an open source library for logging any kind of data. With whylogs, you are able to generate summaries of datasets (called whylogs profiles) which can be used to:

• Create data constraints to know whether your data looks the way it should
• Quickly visualize key summary statistics about a dataset
• Track changes in a dataset over time

pip install flytekitplugins-whylogs

To generate profiles, you can add a task like the following:

import whylogs as why
from whylogs.core import DatasetProfileView

import pandas as pd

from flytekit import task

@task
def profile(df: pd.DataFrame) -> DatasetProfileView:
    result = why.log(df) # Various overloads for different common data types exist
    profile_view = result.view()
    return profile

NOTE: You'll be passing around DatasetProfileView from tasks, not DatasetProfile.

Validating Data

A common step in data pipelines is data validation. This can be done in whylogs through the constraint feature. You'll be able to create failure tasks if the data in the workflow doesn't conform to some configured constraints, like min/max values on features, data types on features, etc.

from whylogs.core.constraints.factories import greater_than_number, mean_between_range

@task
def validate_data(profile_view: DatasetProfileView):
    builder = ConstraintsBuilder(dataset_profile_view=profile_view)
    builder.add_constraint(greater_than_number(column_name="my_column", number=0.14))
    builder.add_constraint(mean_between_range(column_name="my_other_column", lower=2, upper=3))
    constraint = builder.build()
    valid = constraint.validate()

    if valid is False:
        print(constraint.report())
        raise Exception("Invalid data found")

If you want to learn more about whylogs, check out our example notebooks.