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This PR:
- Support for additional `extras` installed into `howso/resources/` to
combined multiple `extras.yml` into one, merged set of
`client_extra_config` passed into the client initializer.
- Updates the README.md to include a better SVG image that should work
better with PyPI.org
- Updates the description in the pyproject.toml file to better reflect
Howso's vision
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](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) and [information theory](https://en.wikipedia.org/wiki/Information_theory) to scale for real world applications.
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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:
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- Perform **classification** on any target feature using any set of input features
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The Howso Engine™ is a natively and fully explainable ML engine, serving
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as an alternative to black box AI neural networks. Its core features give users
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data exploration and machine learning capabilities through the creation and use
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of Trainees that help users store, explore, and analyze the relationships in
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their data. Howso™ leverages an instance-based learning approach with
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strong ties to the [k-nearest neighbors
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algorithm](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) and
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[information theory](https://en.wikipedia.org/wiki/Information_theory) to scale
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for real world applications.
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At the core of Howso is the concept of a Trainee, a collection of cases that
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comprise knowledge. In traditional ML, this is typically referred to as a model,
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but a Trainee may additionally include metadata, parameters, details of feature
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attributes, with data lineage and provenance. Unlike traditional ML, Trainees
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are designed to be versatile, a single model that after training a dataset can
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do the following without the need to retrain:
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- Perform **classification** on any target feature using any set of
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input features
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- Perform **regression** on any target feature using any set of input features
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- Perform **anomaly detection** based on any set of features
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- Measure **feature importance** for predicting any target feature
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-**Synthesize** data that maintains the same feature relationships of the original data while maintaining privacy
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-**Synthesize** data that maintains the same feature relationships of the
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original data while maintaining privacy
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Furthermore, Trainees are auditable, debuggable, and editable.
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-**Debuggable**: Every prediction of a Trainee can be drilled down to investigate which cases from the training data were used to make the prediction.
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-**Auditable**: Trainees manage metadata about themselves including: when data is trained, when training data is edited, when data is removed, etc.
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-**Editable**: Specific cases of training data can be removed, edited, and emphasized (through case weighting) without the need to retrain.
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-**Debuggable**: Every prediction of a Trainee can be drilled down to
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investigate which cases from the training data were used to make
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the prediction.
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-**Auditable**: Trainees manage metadata about themselves including: when data
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is trained, when training data is edited, when data is removed, etc.
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-**Editable**: Specific cases of training data can be removed, edited, and
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emphasized (through case weighting) without the need to retrain.
This Repo provides the Python interface with [Howso Engine](https://github.com/howsoai/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.
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This Repo provides the Python interface with
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[Howso Engine](https://github.com/howsoai/howso-engine) that exposes the Howso
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Engine functionality. The Client objects directly interface with the engine API
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endpoints while the Trainee objects provides the python functionality for
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general users. Client functions may be called by the user but for most workflows
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the Trainee functionality is sufficient. Each Trainee represents an individual
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Machine Learning object or model that can perform functions like training and
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predicting, while a client may manage the API interface for multiple Trainees.
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## Supported Platforms
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pip install howso-engine
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```
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You can verify your installation is working by running the following command in your python environment terminal:
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You can verify your installation is working by running the following command in
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your python environment terminal:
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```bash
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verify_howso_install
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```
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See the Howso Engine [Install Guide](https://docs.howso.com/getting_started/installing.html) for additional help and troubleshooting information.
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See the Howso Engine
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[Install Guide](https://docs.howso.com/getting_started/installing.html) for
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additional help and troubleshooting information.
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## Usage
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The Howso Engine is designed to support users in the pursuit of many different machine learning tasks using Python.
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The Howso Engine is designed to support users in the pursuit of many different
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machine learning tasks using Python.
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Below is a very high-level set of steps recommended for using the Howso Engine:
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4. Call Analyze on the Trainee to find optimal hyperparameters
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5. Explore your data!
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Once the Trainee has been given feature attributes, trained, and analyzed, then the Trainee is ready
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to be used for all supported machine learning tasks. At this point one could start making predictions
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on unseen data, investigate the most noisy features, find the most anomalous training cases, and much more.
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Once the Trainee has been given feature attributes, trained, and analyzed, then
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the Trainee is ready to be used for all supported machine learning tasks. At
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this point one could start making predictions on unseen data, investigate the
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most noisy features, find the most anomalous training cases, and much more.
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Please see the [User Guide](https://docs.howso.com/user_guide/index.html) for basic workflows as well as additional information about:
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Please see the [User Guide](https://docs.howso.com/user_guide/index.html) for
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basic workflows as well as additional information about:
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- Anomaly detection
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- Classification
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- Regression
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- Trainee editing
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- ID-based privacy
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There is also a set of basic [Python examples](https://docs.howso.com/examples/index.html) that provide a complete set of Python scripts and [Jupyter notebooks](https://jupyter.org/) to run.
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There is also a set of basic [Jupyter notebooks](https://jupyter.org/) to run
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that provides a
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[complete set of examples](https://docs.howso.com/examples/index.html) of how
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to use Howso Engine.
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## License
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