whylogs 0.7.0

Profile and monitor your ML data pipeline end-to-end

The open source standard for data logging

Documentation • Slack Community • Python Quickstart

What is whylogs

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

1. Track changes in their dataset
2. Create data constraints to know whether their data looks they way it should
3. Quickly visualize key summary statistics about their datasets

These three functionalities enable a variety of use cases for data scientists, machine learning engineers, and data engineers:

• Detecting data drift (and resultant ML model performance degradation)
• Data quality validation
• Exploratory data analysis via data profiling
• Tracking data for ML experiments
• And many more

whylogs can be run in Python or Apache Spark (both PySpark and Scala) environments on a variety of data types. We integrate with lots of other tools including Pandas, AWS Sagemaker, MLflow, Flask, Ray, RAPIDS, Apache Kafka, and more.

If you have any questions, comments, or just want to hang out with us, please join our Slack Community. In addition to joining the Slack Community, you can also help this project by giving us a ⭐ in the upper right corner of this page.

Python Quickstart

Install whylogs

Install whylogs using the pip package manager by running:

pip install whylogs

Log some data

Profiling dataframes, Python dicts, or images is as easy as running:

from whylogs import get_or_create_session
import pandas as pd

session = get_or_create_session()

with session.logger(dataset_name="my_dataset") as logger:
    logger.log_dataframe(df)

Table of Contents

• whylogs Profiles
• Visualizing Profiles
• Features
• Data Types
• Integrations
• Examples
• Community
• Roadmap
• Contribute

whylogs Profiles

What are profiles

whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond simple mean, median, and standard deviation measures), the number of missing values, and a wide range of configurable custom metrics. By capturing these summary statistics, we are able to accurately represent the data and enable all of the use cases described in the introduction.

whylogs profiles have three properties that make them ideal for data logging: they are descriptive, lightweight, and mergeable.

Descriptive: whylogs profiles describe the dataset that they represent. This high fidelity representation of datasets is what enables whylogs profiles to be effective snapshots of the data. They are better at capturing the characteristics of a dataset than a sample would be, as discussed in our Data Logging: Sampling versus Profiling blog post.

Lightweight: In addition to being a high fidelity representation of data, whylogs profiles also have high information density. You can easily profile terabytes or even petabytes of data in profiles that are only megabytes large. Because whylogs profiles are lightweight, they are very inexpensive to store, transport, and interact with.

Mergeable: One of the most powerful features of whylogs profiles is their mergeability. Mergeability means that whylogs profiles can be combined together to form new profiles which represent the aggregate of their constituent profiles. This enables logging for distributed and streaming systems, and allows users to view aggregated data across any time granularity.

How do you generate profiles

Once whylogs is installed, it's easy to generate profiles in both Python and Java environments.

To generate a profile from a Pandas dataframe in Python, simply run:

from whylogs import get_or_create_session
import pandas as pd

session = get_or_create_session()

df = pd.read_csv("path/to/file.csv")

with session.logger(dataset_name="my_dataset") as logger:
    logger.log_dataframe(df)

For images, replace logger.log_dataframe(df) with logger.log_image("path/to/image.png"). Similarly, you can profile Python dicts by running logger.log(dict) directly on the dict object.

What can you do with profiles

Once you’ve generated whylogs profiles, a few things can be done with them:

In your local Python environment, you can set data constraints or visualize your profiles. Setting data constraints on your profiles allows you to get notified when your data don’t match your expectations, allowing you to do data unit testing and some baseline data monitoring. With the Profile Visualizer, you can visually explore your data, allowing you to understand it and ensure that your ML models are ready for production.

In addition, you can send whylogs profiles to the SaaS ML monitoring and AI observability platform WhyLabs. With WhyLabs, you can automatically set up monitoring for your machine learning models, getting notified on both data quality and data change issues (such as data drift). If you’re interested in trying out WhyLabs, check out the always free Starter edition, which allows you to experience the entire platform’s capabilities with no credit card required.

Visualizing Profiles

Multiple profile plots

To view your logger profiles you can use, methods within whylogs.viz:

vizualization = ProfileVisualizer()
vizualization.set_profiles([profile_day_1, profile_day_2])
figure= vizualization.plot_distribution("<feature_name>")
figure.savefig("/my/image/path.png")

Individual profiles can be saved to local disk, uploaded to AWS S3, or sent to the WhyLabs AI Observability Platform via the WhyLabs API. This can be set up to happen automatically when loggers are closed, per the configuration found in the Session configuration.

Current profiles from active loggers can be loaded from memory with:

profile = logger.profile()

Profile viewer

You can also load a local profile viewer, where you upload the json summary file. The default path for the json files is set as output/{dataset_name}/{session_id}/json/dataset_profile.json.

from whylogs.viz import profile_viewer
profile_viewer()

This will open a viewer on your default browser where you can load a profile json summary, using the Select JSON profile button: Once the json is selected you can view your profile's features and associated statistics.

Features

whylogs collects approximate statistics and sketches of data on a column-basis into a statistical profile. These metrics include:

• Simple counters: boolean, null values, data types.
• Summary statistics: sum, min, max, median, variance.
• Unique value counter or cardinality: tracks an approximate unique value of your feature using HyperLogLog algorithm.
• Histograms for numerical features. whyLogs binary output can be queried to with dynamic binning based on the shape of your data.
• Top frequent items (default is 128). Note that this configuration affects the memory footprint, especially for text features.

Some other key features about whylogs:

• Accurate data profiling: whylogs calculates statistics from 100% of the data, never requiring sampling, ensuring an accurate representation of data distributions
• Lightweight runtime: whylogs utilizes approximate statistical methods to achieve minimal memory footprint that scales with the number of features in the data
• Any architecture: whylogs scales with your system, from local development mode to live production systems in multi-node clusters, and works well with batch and streaming architectures
• Configuration-free: whylogs infers the schema of the data, requiring zero manual configuration to get started
• Tiny storage footprint: whylogs turns data batches and streams into statistical fingerprints, 10-100MB uncompressed
• Unlimited metrics: whylogs collects all possible statistical metrics about structured or unstructured data

Data Types

whylogs supports both structured and unstructured data, specifically:

Data type	Features	Notebook Example
Structured Data	Distribution, cardinality, schema, counts, missing values	Getting started with structured data
Images	exif metadata, derived pixels features, bounding boxes	Getting started with images
Video	In development	Github Issue #214
Tensors	derived 1d features (more in developement)	Github Issue #216
Text	top k values, counts, cardinality	String Features
Audio	In developement	Github Issue #212

Integrations

Integration	Features	Resources
Spark	Run whylogs in Apache Spark environment	Code Example
Pandas	Log and monitor any pandas dataframe	Notebook Example whylogs: Embrace Data Logging
Kafka	Log and monitor Kafka topics with whylogs	Notebook Example Integrating whylogs into your Kafka ML Pipeline
MLflow	Enhance MLflow metrics with whylogs:	Notebook Example Streamlining data monitoring with whylogs and MLflow
Github actions	Unit test data with whylogs and github actions	Notebook Example
RAPIDS	Use whylogs in RAPIDS environment	Notebook Example Monitoring High-Performance Machine Learning Models with RAPIDS and whylogs
Java	Run whylogs in Java environment	Notebook Example
Docker	Run whylogs as in Docker	Rest Container
AWS S3	Store whylogs profiles in S3	S3 example

Examples

For a full set of our examples, please check out the examples folder.

Check out our example notebooks with Binder:

• Getting Started notebook
• Logging Example notebook
• Logging Images
• MLflow Integration

Roadmap

whylogs is maintained by WhyLabs.

Community

If you have any questions, comments, or just want to hang out with us, please join our Slack channel.

Contribute

How to Contribute

We welcome contributions to whylogs. Please see our contribution guide and our development guide for details.

Contributors

Made with contrib.rocks.