Name | pyreal JSON |
Version |
0.4.9
JSON |
| download |
home_page | https://sibyl-ml.dev/ |
Summary | Library for evaluating and deploying human readable machine learning explanations. |
upload_time | 2024-04-22 14:13:01 |
maintainer | MIT Data To AI Lab |
docs_url | None |
author | Alexandra Zytek |
requires_python | <3.12,>=3.9 |
license | None |
keywords |
pyreal
pyreal
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
<p align="left">
<img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” />
<i>An open source project from Data to AI Lab at MIT.</i>
</p>
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# Pyreal
An easier approach to understanding your model's predictions.
| Important Links | |
| --------------------------------------------- | -------------------------------------------------------------------- |
| :book: **[Documentation]** | Quickstarts and user guides |
| :memo: **[API Reference]** | Full library API |
| :apple: **[Tutorials]** | Notebooks with example usage |
| :scroll: **[License]** | This repository is published under the MIT License |
| :computer: **[Project Homepage]** | Check out the Sibyl project website for more information |
[Project Homepage]: https://sibyl-ml.dev/
[Documentation]: https://dtail.gitbook.io/pyreal/
[Tutorials]: https://github.com/sibyl-dev/pyreal/tree/dev/tutorials
[License]: https://github.com/sibyl-dev/pyreal/blob/dev/LICENSE
[Community]: https://join.slack.com/t/sibyl-ml/shared_invite/zt-2dyfwbgo7-2ALinuT2KDZpsVJ4rntJuA
[API Reference]: https://sibyl-ml.dev/pyreal/api_reference/index.html
# Overview
**Pyreal** gives you easy-to-understand explanations of your machine learning models in a low-code manner.
Pyreal wraps full ML pipelines in a RealApp object that makes it easy to use, understand, and interact with your ML model — regardless of your ML expertise.
# Install
## Requirements
**Pyreal** has been developed and tested on [Python 3.9, 3.10, and 3.11](https://www.python.org/downloads/)
The library uses Poetry for package management.
## Install from PyPI
We recommend using
[pip](https://pip.pypa.io/en/stable/) in order to install **Pyreal**:
```
pip install pyreal
```
This will pull and install the latest stable release from [PyPI](https://pypi.org/project/pyreal/).
## Install from source
If you do not have **poetry** installed, please head to [poetry installation guide](https://python-poetry.org/docs/#installation)
and install poetry according to the instructions.\
Run the following command to make sure poetry is activated. You may need to close and reopen the terminal.
```
poetry --version
```
Finally, you can clone this repository and install it from
source by running `poetry install`, with the optional `examples` extras if you'd like to run our tutorial scripts.
```
git clone https://github.com/sibyl-dev/pyreal.git
cd pyreal
poetry install
```
## Install for Development
If you want to contribute to the project, a few more steps are required to make the project ready
for development.
Please head to the [Contributing Guide](https://dtail.gitbook.io/pyreal/developer-guides/contributing-to-pyreal)
for more details about this process.
# Quickstart
In this short tutorial we will guide you through some steps to get your started with **Pyreal**.
We will use a RealApp object to get predictions and explanations on whether a passenger on the Titanic would have survived.
For a more detailed version of this tutorial, see [our documentation](https://dtail.gitbook.io/pyreal/getting-started/quickstart).
#### Load in the demo data and application
```python
import pyreal.sample_applications.titanic as titanic
real_app = titanic.load_app()
sample_data = titanic.load_data(n_rows=300)
```
#### Predict and produce explanation
```python
predictions = real_app.predict(sample_data)
explanation = real_app.produce_feature_contributions(sample_data)
```
#### Visualize explanation for one passenger
```python
passenger_id = 1
feature_bar_plot(explanation[passenger_id], prediction=predictions[passenger_id], show=False)
```
The output will be a bar plot showing the most contributing features, by absolute value.
![Quickstart](docs/images/titanic.png)
We can see here that the input passenger's predicted chance of survival was greatly reduced
because of their sex (male) and ticket class (3rd class).
### Migrating your application to Pyreal
To create a RealApp object for your own application,
see our [migration tutorial](https://github.com/sibyl-dev/pyreal/blob/dev/tutorials/migrating_to_pyreal.ipynb).
For basic applications built on `sklearn` pipelines, you may be able to simply use:
```python
from pyreal import RealApp
pipeline = # YOUR SKLEARN PIPELINE
X_train, y_train = # YOUR TRAINING DATA
real_app = RealApp.from_sklearn(pipeline, X_train=X_train, y_train=y_train)
```
# Next Steps
For more information on using **Pyreal** for your use case, head over to the full [documentation site](https://dtail.gitbook.io/pyreal/getting-started/next-steps).
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