howso-engine 7.4.0

Howso Engine and Scikit Estimator for the interpretable Machine Learning and Artificial Intelligence API.

The Howso Engine™ is a natively and fully explainable ML engine, serving as an alternative to black box AI neural networks. It’s core features give users data exploration and machine learning capabilities through the creation and use of Trainees that help users store, explore, and analyze the relationships in their data. Howso™ leverages an instance-based learning approach with strong ties to the k-nearest neighbors algorithm and information theory to scale for real world applications.

At the core of Howso is the concept of a Trainee, a collection of cases that comprise knowledge. In traditional ML, this is typically referred to as a model, but a Trainee may additionally include metadata, parameters, details of feature attributes, with data lineage and provenance. Unlike traditional ML, Trainees are designed to be versatile, a single model that after training a dataset can do the following without the need to retrain:

• Perform classification on any target feature using any set of input features
• Perform regression on any target feature using any set of input features
• Perform anomaly detection based on any set of features
• Measure feature importance for predicting any target feature
• Synthesize data that maintains the same feature relationships of the original data while maintaining privacy

Furthermore, Trainees are auditable, debuggable, and editable.

• Debuggable: Every prediction of a Trainee can be drilled down to investigate which cases from the training data were used to make the prediction.
• Auditable: Trainees manage metadata about themselves including: when data is trained, when training data is edited, when data is removed, etc.
• Editable: Specific cases of training data can be removed, edited, and emphasized (through case weighting) without the need to retrain.

Resources

• Documentation
• Howso Engine Recipes (sample notebooks)
• Howso Playground

General Overview

This Repo provides the Python interface with Howso Engine that exposes the Howso Engine functionality. The Client objects directly interface with the engine API endpoints while the Trainee objects provides the python functionality for general users. Client functions may be called by the user but for most workflows the Trainee functionality is sufficient. Each Trainee represents an individual Machine Learning object or model that can perform functions like training and predicting, while a client may manage the API interface for multiple Trainees.

Supported Platforms

Compatible with Python versions: 3.8, 3.9, 3.10, and 3.11

Operating Systems

OS	x86_64	arm64
Windows	Yes	No
Linux	Yes	Yes
MacOS	Yes	Yes

Install

To install the current release:

pip install howso-engine

You can verify your installation is working by running the following command in your python environment terminal:

verify_howso_install

See the Howso Engine Install Guide for additional help and troubleshooting information.

Usage

The Howso Engine is designed to support users in the pursuit of many different machine learning tasks using Python.

Below is a very high-level set of steps recommended for using the Howso Engine:

1. Define the feature attributes of the data (Feature types, bounds, etc.)
2. Create a Trainee and set the feature attributes
3. Train the Trainee with the data
4. Call Analyze on the Trainee to find optimal hyperparameters
5. Explore your data!

Once the Trainee has been given feature attributes, trained, and analyzed, then the Trainee is ready to be used for all supported machine learning tasks. At this point one could start making predictions on unseen data, investigate the most noisy features, find the most anomalous training cases, and much more.

Please see the User Guide for basic workflows as well as additional information about:

• Anomaly detection
• Classification
• Regression
• Time-series forecasting
• Feature importance analysis
• Reinforcement learning
• Data synthesis
• Prediction auditing
• Measuring model performance (global or conditional)
• Bias mitigation
• Trainee editing
• ID-based privacy

There is also a set of basic Python examples that provide a complete set of Python scripts and Jupyter notebooks to run.

License

License

Contributing

Contributing