Science Score: 41.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 9 DOI reference(s) in README -
○Academic publication links
-
✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (17.3%) to scientific vocabulary
Keywords
machine-learning
quantum-computing
Last synced: 6 months ago
·
JSON representation
·
Repository
Quantum decision tree classifiers for binary data.
Basic Info
Statistics
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Topics
machine-learning
quantum-computing
Created over 3 years ago
· Last pushed about 2 years ago
Metadata Files
Readme
License
Citation
README.rst
******************************
Quantum Decision Trees (qtree)
******************************
.. image:: https://github.com/RaoulHeese/qtree/actions/workflows/tests.yml/badge.svg
:target: https://github.com/RaoulHeese/qtree/actions/workflows/tests.yml
:alt: GitHub Actions
.. image:: https://readthedocs.org/projects/qtree/badge/?version=latest
:target: https://qtree.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://img.shields.io/pypi/v/quantum-tree
:target: https://pypi.org/project/quantum-tree/
:alt: PyPI - Project
.. image:: https://img.shields.io/badge/license-MIT-lightgrey
:target: https://github.com/RaoulHeese/qtree/blob/main/LICENSE
:alt: MIT License
This Python package implements quantum decision tree classifiers for binary data. The details of the method can be found in `Representation of binary classification trees with binary features by quantum circuits `_.
.. image:: https://raw.githubusercontent.com/RaoulHeese/qtree/master/docs/source/_static/title.png
:target: https://doi.org/10.22331/q-2022-03-30-676
:alt: Title
**Installation**
Install via ``pip`` or clone this repository. In order to use ``pip``, type:
.. code-block:: sh
$ pip install quantum-tree
The package is tested with Python 3.8 and Python 3.9.
🌳 **Usage**
Minimal working example:
.. code-block:: python
# create quantum tree instance
from qtree.qtree import QTree
qtree = QTree(max_depth=1)
# create simple training data
import numpy as np
X = np.array([[1,0,0], [0,1,0], [0,0,1]]) # features
y = np.array([[0,0], [0,1], [1,1]]) # labels
# fit quantum tree
qtree.fit(X, y)
# make quantum tree prediction
qtree.predict([[0,0,1]])
**Documentation**
Documentation is available on ``_.
Demo notebooks can be found in the ``examples/`` directory.
📖 **Citation**
If you find this code useful in your research, please consider citing `Representation of binary classification trees with binary features by quantum circuits `_:
.. code-block:: tex
@article{Heese2022representationof,
doi = {10.22331/q-2022-03-30-676},
url = {https://doi.org/10.22331/q-2022-03-30-676},
title = {Representation of binary classification trees with binary features by quantum circuits},
author = {Heese, Raoul and Bickert, Patricia and Niederle, Astrid Elisa},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
volume = {6},
pages = {676},
month = {3},
year = {2022}
}
*This project is currently not under development and is not actively maintained.*
Owner
- Login: RaoulHeese
- Kind: user
- Repositories: 4
- Profile: https://github.com/RaoulHeese
Citation (CITATION.bib)
@article{Heese2022representationof,
doi = {10.22331/q-2022-03-30-676},
url = {https://doi.org/10.22331/q-2022-03-30-676},
title = {Representation of binary classification trees with binary features by quantum circuits},
author = {Heese, Raoul and Bickert, Patricia and Niederle, Astrid Elisa},
journal = {{Quantum}},
issn = {2521-327X},
publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}},
volume = {6},
pages = {676},
month = {3},
year = {2022}
}
GitHub Events
Total
- Watch event: 2
Last Year
- Watch event: 2
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 23
- Total Committers: 2
- Avg Commits per committer: 11.5
- Development Distribution Score (DDS): 0.217
Top Committers
| Name | Commits | |
|---|---|---|
| RaoulHeese | r****e@g****m | 18 |
| Raoul Heese | r****e@i****e | 5 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 8 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
-
Total downloads:
- pypi 25 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 3
- Total maintainers: 1
pypi.org: quantum-tree
Quantum decision trees with binary features and binary classes
- Homepage: https://github.com/RaoulHeese/qtree
- Documentation: https://quantum-tree.readthedocs.io/
- License: MIT
-
Latest release: 1.2
published about 2 years ago
Rankings
Dependent packages count: 6.6%
Forks count: 30.5%
Dependent repos count: 30.6%
Average: 31.0%
Stargazers count: 39.1%
Downloads: 48.1%
Maintainers (1)
Last synced:
6 months ago
Dependencies
docs/requirements.txt
pypi
- deap *
- numpy *
- qiskit *
- scikit-learn *
.github/workflows/tests.yml
actions
- actions/checkout v3 composite
- actions/setup-python v4 composite
setup.py
pypi