| Name | dimlpfidex JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | Discretized Interpretable Multi Layer Perceptron (DIMLP) and related algorithms |
| upload_time | 2024-09-19 09:33:20 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9.0 |
| license | BSD License (BSD-3-Clause) |
| keywords |
machine learning
xai
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# dimlpfidex 
Discretized Interpretable Multi Layer Perceptron (DIMLP) and related algorithms
## Installation
```
pip install dimlpfidex
```
## Contribution
### Get the project code
To get the latest source code, install [git](https://git-scm.com/) and [clone](https://docs.github.com/en/get-started/getting-started-with-git/about-remote-repositories) the repository:
```sh
$ git clone https://github.com/HES-XPLAIN/dimlpfidex.git
```
To download the required dependencies on your system, run:
```sh
$ git submodule init
$ git submodule update
```
### Install C++ toolchain
#### Linux, macOS, Windows/WSL
Install with your package manager:
* a C++ compiler (gcc/g++)
* the ninja build system (ninja or ninja-build)
* cmake
#### Windows
> [!TIP]
> Consider using Windows/WSL instead.
* Install [Visual Studio Community](https://visualstudio.microsoft.com/vs/) with the "Desktop development with C++" component:
```shell
winget install Microsoft.VisualStudio.2022.Community
```
The MSVC compiler is necessary for proper packaging of the bindings.
* Install [CMake](https://cmake.org/):
```shell
winget install Kitware.CMake
```
Ensure `cmake.exe` is accessible in the `$PATH` environment variable.
```shell
cmake.exe --version
```
> [!NOTE]
> You will have to restart your computer for the changes to take effect.
### Install Python
Install [Python](https://www.python.org/), version 3.9 or newer (3.11 is recommended):
* **Linux, macOS, Windows/WSL**: Use your package manager to install `python3` and `python3-dev`
* **Windows**: `winget install Python.Python.3.11`
> [!WARNING]
> On Windows, avoid installing Python through the Microsoft Store as the package has additional permission restrictions.
### Install Python dependencies
#### Using pip
```shell
python -m venv .venv
source .venv/bin/activate
pip install .
```
> [!NOTE]
> On Windows, use `.venv\Scripts\activate` instead.
### Work with virtualenv
To activate the virtualenv, use the standard methods:
* Unix: `source .venv/bin/activate`
* Windows: `.venv\Scripts\activate`
To leave the virtualenv, use `deactivate`.
#### Add dependencies
To add new dependencies to the project, add them to the `pyproject.toml` file.
To add them to the virtualenv, use:
```shell
pip install .
```
### Install Pre-commit hooks
Git hooks are used to ensure quality checks are run by all developers every time
before a commit.
Install with `pip install pre-commit`.
To enable pre-commit:
```shell
pre-commit install
```
Pre-commit hooks can be run manually with:
```shell
pre-commit run --all-files
```
### Compile
Compile:
```shell
mkdir build && cd build
cmake ..
cmake --build .
```
> [!NOTE]
> On Windows, you may have to use `cmake -DCMAKE_PREFIX_PATH="C:\<absolute\path\to>\.venv" ..` instead.
> [!TIP]
> To speed up the compilation process, you can also add `-j X` with `X` being your number of CPU cores.
> [!WARNING]
> If you need to rebuild the project, you must erase the content of the `build/` directory.
### Package
Create archives for distribution, from the root of the project:
```shell
python -m build
```
## Documentation
Install [Doxygen](https://www.doxygen.nl/):
* **Linux, macOS, Windows/WSL**: Use your package manager to install `doxygen`
* **Windows**: `winget install DimitriVanHeesch.Doxygen`
Create the documentation:
```shell
mkdir build && cd build
cmake -DBUILD_DOCUMENTATION=ON ..
cmake --build .
```
The generated HTML documentation will be found in `build/docs/sphinx`.
## Release
To publish the package on [PyPI](https://pypi.org/project/dimlpfidex/), refer to [RELEASE](RELEASE.md).
## Credits
Our test suite is using [Obesity or CVD risk dataset](https://www.kaggle.com/datasets/aravindpcoder/obesity-or-cvd-risk-classifyregressorcluster) from [AravindPCoder](https://www.kaggle.com/aravindpcoder) (under CC BY-SA 4.0 license)
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