Metadata-Version: 2.1
Name: sibyl-api
Version: 0.1.0
Summary: Explanation tool for machine learning
Home-page: https://github.com/DAI-Lab/sibyl-api
License: MIT
Keywords: sibyl-api,sibyl,api
Author: MIT Data To AI Lab
Requires-Python: >=3.9, !=2.7.*, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*, !=3.7.*, !=3.8.*
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: black[jupyter] (>=23.7.0,<24.0.0)
Requires-Dist: click (>=8.1.3,<9.0.0)
Requires-Dist: gevent (>=23.9.0.post1,<24.0.0)
Requires-Dist: greenlet (>=2.0.2,<3.0.0)
Requires-Dist: pyreal (>=0.4.7,<0.5.0)
Requires-Dist: pyyaml (>=6.0,<7.0)
Requires-Dist: streamlit (>=1.29.0,<2.0.0)
Requires-Dist: tqdm (>=4.66.1,<5.0.0)
Project-URL: Repository, https://github.com/DAI-Lab/sibyl-api
Description-Content-Type: text/markdown

<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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# Sibyl-API

APIs for explainable ML.

-   API Documentation: [https://sibyl-ml.dev/sibyl-api/](https://sibyl-ml.dev/sibyl-api/)

# Overview

Interpretability is perhaps most impactful in situations where humans make decisions with input from amachine learning model. In such situations, humans have traditionally made decisions without ML models, and as such use the ML model predictions as an aideto improve their effectiveness or speed.
In these cases, explanations can serve many functions. They may help build user trust in the model, identify possible mistakes in the model’s prediction, expedite decisionmaking, maintain accountability, validate their hypotheses, or satisfy curiosity.

Sibylapp is an online interactive tool built on the top of Sibyl (python library) to provide explanations to predictive models on tabular data.

# Install

## Requirements

**Sibyl-API** has been developed and tested on [Python 3.9, 3.10, and 3.11](https://www.python.org/downloads/), and on [MongoDB version 6](https://www.mongodb.com/try/download/community).

This library uses Poetry for package management.

## 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.

```bash
poetry --version
```

Finally, you can clone this repository and install it from
source by running `poetry install`:

```bash
git clone https://github.com/sibyl-dev/sibyl-api.git
cd sibyl-api
poetry install
```

Sibyl-API runs using MongoDB, tested for versions 5 and 6. To install, follow the instructions
[here](https://www.mongodb.com/docs/manual/administration/install-community/).

## Quickstart
Follow these steps to get started with the built-in Ames Housing dataset example.
You can prepare and load the Ames Housing dataset by running:
```bash
poetry run invoke load-housing-data
```

Alternatively, you can manually prepare and load the database by running teh following two commands:
```bash
poetry run python sibyl/sample_applications/prepare_housing_application.py   # Prepare model and realapp
poetry run python sibyl/db/preprocessing.py sibyl/sample_applications/housing_config.yml   # Load in database
```

You can test your APIs with the housing dataset by running `sibyl/test_apis_on_database.ipynb`.
You can also automatically run all unit tests and the testing script with:
```bash
poetry run invoke test
```

To run Sibyl-API, make sure the `db` parameter under `mongodb` in `sibyl/config.yml` is set to `housing`, and then run  Sibyl-API with:
```bash
poetry run sibyl run -v
```

Once Sibyl-API is running, you can access and test your APIs manually at `localhost:3000/apidocs`

## Preparing database
Sibyl-API uses a MongoDB-based database system. We offer several methods to setup your database.

### With the prepare-db script
You can fill the database using the `preprocessing.py` script by
following these steps. Be sure to `start` your mongodb service before using the database.

First, if it doesn't already exist, add a `dbdata` directory in the top-level `sibyl-api` directory.

Next, add a directory in `dbdata` named after your domain, and fill it with your data files. You should end with a file
structure that looks like:
```
sibyl-api
|---dbdata
   |---domain_name
        |---entities.csv
        |   feature.csv
        |   realapp.pkl
        |   ...
```

Next, copy `sibyl/db/config_template.yml` and fill it in with your file names.

Finally, run the preprocessing script with:
```bash
poetry run python preprocessing.py [CONFIG_NAME].yml
```

### Running the Setup Wizard
First, install the optional setup dependencies with
```bash
poetry install --with setup
```
Then, run the setup wizard with
```bash
poetry run streamlit run setup-wizard/main.py
```

## Running APIs

Once the library has been installed, you can run the APIs locally with:

```bash
poetry run sibyl run -v
```

Or, to run in development mode:
```bash
poetry shell

sibyl run -E development -v
```

You can then access your APIs locally at http://localhost:3000/apidocs

# Contributing Guide
We appreciate contributions of all kinds! To contribute code to the repo please follow these steps:
1. Clone and install the library and load in your test database(s) following the instructions above.
2. Make a new branch off of `dev` with a descriptive name describing your change.
3. Make changes to that branch, committing and pushing code as you go.
4. Run the following commands to ensure your code passed required code style guidelines and tests:
```
# Run all tests
poetry run invoke test

# Run unit tests only
poetry run invoke test-unit

# Fix most linting errors
poetry run invoke fix-lint

# Ensure no linting errors remain
poetry run invoke lint
```
5. You can manually run `sibyl/test_apis_on_database.ipynb` on your database(s) to test further.
6. Before making a PR with your final changes, update the api docs by running Sibyl with the -G flag, ie.
```
# Generate docs
poetry run sibyl run -G
```
8. Once all tests/linting pass, push all code and make a pull request. One all checks pass and the PR has been approved, merge your code and delete the branch.

