| Name | sktree JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | |
| Summary | Modern decision trees in Python |
| upload_time | 2023-07-05 23:35:30 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | |
| keywords |
tree
oblique
trees
manifold-learning
scikit-learn
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/psf/black)
[](https://circleci.com/gh/neurodata/scikit-tree/tree/main)
[](https://github.com/neurodata/scikit-tree/actions/workflows/main.yml)
[](http://mypy-lang.org/)
[](https://codecov.io/gh/neurodata/scikit-tree)
scikit-tree
===========
scikit-tree is a scikit-learn compatible API for building state-of-the-art decision trees. These include unsupervised trees, oblique trees, uncertainty trees, quantile trees and causal trees.
Tree-models have withstood the test of time, and are consistently used for modern-day data science and machine learning applications. They especially perform well when there are limited samples for a problem and are flexible learners that can be applied to a wide variety of different settings, such as tabular, images, time-series, genomics, EEG data and more.
We welcome contributions for modern tree-based algorithms. We use Cython to achieve fast C/C++ speeds, while abiding by a scikit-learn compatible (tested) API. Moreover, our Cython internals are easily extensible because they follow the internal Cython API of scikit-learn as well.
**Submodule dependency on a fork of scikit-learn**
Due to the current state of scikit-learn's internal Cython code for trees, we have to instead leverage a maintained fork of scikit-learn at https://github.com/neurodata/scikit-learn, where specifically, the `fork` branch is used to build and install this repo. We keep that fork well-maintained and up-to-date with respect to the main sklearn repo. The only difference is the refactoring of the `tree/` submodule. This fork is used internally under the namespace ``sktree._lib.sklearn``. It is necessary to use this fork for anything related to:
- `RandomForest*`
- `ExtraTrees*`
- or any importable items from the `tree/` submodule, whether it is a Cython or Python object
If you are developing for scikit-tree, we will always depend on the most up-to-date commit of `https://github.com/neurodata/scikit-learn/submodulev2` as a submodule within scikit-tee. This branch is consistently maintained for changes upstream that occur in the scikit-learn tree submodule. This ensures that our fork maintains consistency and robustness due to bug fixes and improvements upstream.
Documentation
=============
See here for the documentation for our dev version: https://docs.neurodata.io/scikit-tree/dev/index.html
Why oblique trees and why trees beyond those in scikit-learn?
=============================================================
In 2001, Leo Breiman proposed two types of Random Forests. One was known as ``Forest-RI``, which is the axis-aligned traditional random forest. One was known as ``Forest-RC``, which is the random oblique linear combinations random forest. This leveraged random combinations of features to perform splits. [MORF](1) builds upon ``Forest-RC`` by proposing additional functions to combine features. Other modern tree variants such as Canonical Correlation Forests (CCF), or unsupervised random forests are also important at solving real-world problems using robust decision tree models.
Installation
============
Our installation will try to follow scikit-learn installation as close as possible, as we contain Cython code subclassed, or inspired by the scikit-learn tree submodule.
AS OF NOW, scikit-tree is in development stage and the installation is still finicky due to the upstream scikit-learn's stalled refactoring PRs of the tree submodule. Once those are merged, the installation will be simpler. The current recommended installation is done locally with meson.
Dependencies
------------
We minimally require:
* Python (>=3.8)
* numpy
* scipy
* scikit-learn >= 1.3
Building locally with Meson (RECOMMENDED)
-----------------------------------------
Make sure you have the necessary packages installed
# install build dependencies
pip install numpy scipy meson ninja meson-python Cython scikit-learn scikit-learn-tree
# you may need these optional dependencies to build scikit-learn locally
conda install -c conda-forge joblib threadpoolctl pytest compilers llvm-openmp
We use the ``spin`` CLI to abstract away build details:
# run the build using Meson/Ninja
./spin build
# you can run the following command to see what other options there are
./spin --help
./spin build --help
# For example, you might want to start from a clean build
./spin build --clean
# or build in parallel for faster builds
./spin build -j 2
# you will need to double check the build-install has the proper path
# this might be different from machine to machine
export PYTHONPATH=${PWD}/build-install/usr/lib/python3.9/site-packages
# run specific unit tests
./spin test -- sktree/tree/tests/test_tree.py
# you can bring up the CLI menu
./spin --help
You can also do the same thing using Meson/Ninja itself. Run the following to build the local files:
# generate ninja make files
meson build --prefix=$PWD/build
# compile
ninja -C build
# install scikit-tree package
meson install -C build
export PYTHONPATH=${PWD}/build/lib/python3.9/site-packages
# to check installation, you need to be in a different directory
cd docs;
python -c "from sktree import tree"
python -c "import sklearn; print(sklearn.__version__);"
Alternatively, you can use editable installs
pip install --no-build-isolation --editable .
References
==========
[1]: [`Li, Adam, et al. "Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks." arXiv preprint arXiv:1909.11799 (2019)`](https://arxiv.org/abs/1909.11799)
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It is necessary to use this fork for anything related to:\n\n- `RandomForest*`\n- `ExtraTrees*`\n- or any importable items from the `tree/` submodule, whether it is a Cython or Python object\n\nIf you are developing for scikit-tree, we will always depend on the most up-to-date commit of `https://github.com/neurodata/scikit-learn/submodulev2` as a submodule within scikit-tee. This branch is consistently maintained for changes upstream that occur in the scikit-learn tree submodule. This ensures that our fork maintains consistency and robustness due to bug fixes and improvements upstream.\n\nDocumentation\n=============\n\nSee here for the documentation for our dev version: https://docs.neurodata.io/scikit-tree/dev/index.html\n\nWhy oblique trees and why trees beyond those in scikit-learn?\n=============================================================\nIn 2001, Leo Breiman proposed two types of Random Forests. 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The current recommended installation is done locally with meson.\n\nDependencies\n------------\n\nWe minimally require:\n\n * Python (>=3.8)\n * numpy\n * scipy\n * scikit-learn >= 1.3\n\nBuilding locally with Meson (RECOMMENDED)\n-----------------------------------------\nMake sure you have the necessary packages installed\n\n # install build dependencies\n pip install numpy scipy meson ninja meson-python Cython scikit-learn scikit-learn-tree\n\n # you may need these optional dependencies to build scikit-learn locally\n conda install -c conda-forge joblib threadpoolctl pytest compilers llvm-openmp\n\nWe use the ``spin`` CLI to abstract away build details:\n\n # run the build using Meson/Ninja\n ./spin build\n \n # you can run the following command to see what other options there are\n ./spin --help\n ./spin build --help\n \n # For example, you might want to start from a clean build\n ./spin build --clean\n \n # or build in parallel for faster builds\n ./spin build -j 2\n\n # you will need to double check the build-install has the proper path \n # this might be different from machine to machine\n export PYTHONPATH=${PWD}/build-install/usr/lib/python3.9/site-packages\n\n # run specific unit tests\n ./spin test -- sktree/tree/tests/test_tree.py\n\n # you can bring up the CLI menu\n ./spin --help\n\nYou can also do the same thing using Meson/Ninja itself. 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