Description:
howsoengine 29.0.4
The Howso Engine™ is a natively and fully explainable ML engine, serving as an alternative to black box AI neural networks. Its core functionality gives 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, as well as make understandable, debuggable predictions. 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 data elements that comprise knowledge. In traditional ML, this is typically referred to as a model, but a Trainee is original training data coupled with metadata, parameters, details of feature attributes, with data lineage and provenance. Unlike traditional ML, Trainees are designed to be versatile so that after a single training instance (no re-training required!), they can:
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 online and reinforcement learning
Perform anomaly detection based on any set of features
Measure feature importance for predicting any target feature
Identify counterfactuals
Understand increases and decreases in accuracy for features and individual cases
Forecast time series
Synthesize data that maintains the same feature relationships of the original data while maintaining privacy
And more!
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: 3.9, 3.10, 3.11, and 3.12.
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:
Define the feature attributes of the data (Feature types, bounds, etc.)
Create a Trainee and set the feature attributes
Train the Trainee with the data
Call Analyze on the Trainee to find optimal hyperparameters
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 Jupyter notebooks to run
that provides a
complete set of examples of how
to use Howso Engine.
License
License
Contributing
Contributing
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
Customer Reviews
There are no reviews.
