Installation#
Dependencies#
numpy(>=1.23)scipy(>=1.5.0)scikit-learn(>=1.3)joblib(>=1.0.0)matplotlib(optional)
scikit-tree supports Python >= 3.9.
Installing with pip#
scikit-tree is available [on PyPI](https://pypi.org/project/scikit-tree/). Just run
pip install sktree
Installing from source with Meson#
To install scikit-tree from source, first clone [the repository](neurodata/scikit-tree):
git clone https://github.com/neurodata/scikit-tree.git
cd scikit-tree
# ideally, you should always start within a virtual environment
conda create -n sklearn-dev python=3.9
conda activate sklearn-dev
Then run installation of build packages
pip install -r build_requirements.txt
pip install spin
# use spin CLI to run Meson build locally
./spin build -j 2
# you can now run tests
./spin test
via pip, you will be able to install in editable mode (pending Meson-Python support).
pip install -e .
# if editing Cython files
pip install --verbose --no-build-isolation --editable .
pip install --user -U https://api.github.com/repos/neurodata/scikit-tree/zipball/master
Conda (Recommended)#
First, create a virtual environment using Conda.
conda create -n sklearn-dev python=3.9
# activate the virtual environment and install necessary packages to build from source
conda activate sklearn-dev conda install -c conda-forge numpy scipy cython joblib threadpoolctl pytest compilers llvm-openmp poetry
Next, sktree from source:
pip install -e .
# if editing Cython files pip install –verbose –no-build-isolation –editable .
To install the package from github, clone the repository and then cd into the directory. You can then use poetry to install:
poetry install
# if you would like an editable install of dodiscover for dev purposes pip install -e .
pip install https://api.github.com/repos/neurodata/scikit-tree/zipball/main
pip install https://api.github.com/repos/neurodata/scikit-learn/zipball/obliquepr
Note that currently, we need to build the development version of scikit-learn with oblique trees within this [PR](scikit-learn/scikit-learn#22754).
Checkout this PR code, and build from source, using scikit-learn’s build from source page instructions.