Installation

This page documents InsideForest 0.4.3. The package can be installed from PyPI or from source; use an isolated environment to keep dependencies controlled.

Prerequisites

• Python 3.9 or newer.
• An up-to-date pip: python -m pip install --upgrade pip.
• C/C++ build tools (for example, build-essential on Linux) in case scikit-learn needs compilation.

Install from PyPI

The quickest way to get started is to install the published package:

python -m pip install InsideForest==0.4.3

Verify that the package imports correctly:

python -c "import InsideForest; print(InsideForest.__version__)"

Install from source

Clone the repository to access notebooks, experiments, and extra examples.

git clone https://github.com/jcval94/InsideForest.git
cd InsideForest
pip install -e .

The editable mode (-e) lets you modify the code and test changes without reinstalling.

Development dependencies

Install the dedicated dependency file to run automated tests and reproducible notebooks:

python -m pip install -r requirements-dev.txt

Verify your setup

Confirm that InsideForest works by running a basic test:

python -m pytest tests -q

This command validates the categorical and continuous region-clustering contracts, persistence, documentation examples, and the packaged notebook.

Troubleshooting

• Build errors: ensure the compiler toolchain is installed and that numpy and scikit-learn versions match the requirements.
• Dependency conflicts: create an isolated environment with python -m venv .venv or conda create -n insideforest python=3.11.
• GPU not required: InsideForest runs on CPU, so no special accelerators are needed.

Continue with the Quick API to run your first supervised clustering workflow.