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توضیحات

The Density Peak Advanced packages.

ویژگی	مقدار
سیستم عامل	-
نام فایل	DPA-0.0.3
نام	DPA
نسخه کتابخانه	0.0.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Maria d'Errico
ایمیل نویسنده	-
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/DPA/
مجوز	new BSD

Density Peaks Advanced clustering ================================= Status of the `scikit-learn`_ compatibility test: .. image:: https://github.com/mariaderrico/DPA/actions/workflows/runpytest.yml/badge.svg?branch=master :alt: scikit-learn compatibility test status on GitHub Actions :target: https://github.com/mariaderrico/DPA/actions/workflows/runpytest.yml The DPA package implements the Density Peaks Advanced (DPA) clustering algorithm as introduced in the paper "Automatic topography of high-dimensional data sets by non-parametric Density Peak clustering", published on `M. d'Errico, E. Facco, A. Laio, A. Rodriguez, Information Sciences, Volume 560, June 2021, 476-492`_ (also available on `arXiv`_). The package offers the following features: * Intrinsic dimensionality estimation by means of the `TWO-NN` algorithm, published in the `Estimating the intrinsic dimension of datasets by a minimal neighborhood information`_ paper. * Adaptive k-NN Density estimation by means of the `PAk` algorithm, published in the `Computing the free energy without collective variables`_ paper. * Advanced version of the `DP` clustering algorithm, published in the `Clustering by fast search and find of density peaks`_ paper, which includes an automatic search of cluster centers and assessment of statistical significance of the clusters .. contents:: Top-level directory layout ------------------------------ :: cd DPA ls -l :: . |-- DP/ # Auxiliary package with the DP clustering implementation. |-- docs/ # Documentation files. |-- Examples/ # Auxiliary scripts for the examples generations. |-- DPA_analysis.ipynb # Use-case example for DPA. |-- DPA_comparison-all.ipynb # Performance comparison with other clustering methods. |-- README.rst |-- compile.sh |-- setup.py |-- src/ # Source files for DPA, PAk and twoNN algorithms. Source files ------------ The source Python codes are stored inside the ``src`` folder. :: . |-- ... |-- src/ | |-- Pipeline/ | |-- __init__.py | |-- DPA.py # Python module implementing the DPA | | # clustering algorithm. | | | |-- _DPA.pyx # Cython extension of the DPA module. | | | |-- PAk.py # Python module implementing the PAk | | # density estimator. | | | |-- _PAk.pyx # Cython extension of the PAk module. | | | |-- twoNN.py # Python module implementing the TWO-NN | # algorithm for the ID calculation. | |-- ... Documentation files ------------------- Full documentation about the Python codes developed and the how-to instructions is created in the ``docs`` folder using `Sphinx`. Complete documentation for DPA is available on the `Read The Docs <https://dpaclustering.readthedocs.org>`_ website. Jupyter notebooks ----------------- Examples of how-to run the ``DPA``, ``PAk`` and ``twoNN`` modules are provided as Jupyter notebook in ``DPA_analysis.ipynb``. Additional useful use-cases are available in ``DPA_comparison-all.ipynb``, which include a performance comparison with the following clustering methods: Bayesian Gaussian Mixture, HDBSCAN, Spectral Clustering and Density Peaks. Both jupyter notebooks are also available as Python script (saved using `jupytext`_) in the ``jupytext`` folder. :: . |-- ... |-- DPA_analysis.ipynb # Use-case example for DPA. |-- DPA_comparison-all.ipynb # Performance comparison with | # other clustering methods. | |-- ... |-- jupytext/ | |-- DPA_analysis.py # DPA_analysis.ipynb saved as | | # Python script. | |-- DPA_comparison-all.py # DPA_comparison-all.ipynb | # saved as Python script. Getting started --------------- The source code of DPA is on `github DPA repository`_. You need the ``git`` command in order to be able to clone it, and we suggest you to use Python virtual environment in order to create a controlled environment in which you can install DPA as normal user avoiding conflicts with system files or Python libraries. The following section documents the steps required to install DPA on a Linux or Windows/Mac computer. Debian/Ubuntu ^^^^^^^^^^^^^ Run the following commands to create and activate a Python virtual environment with *python virtualenv*:: apt-get install git python-dev virtualenv* virtualenv -p python3 venvdpa . venvdpa/bin/activate Windows ^^^^^^^ A possible setup makes use of `Anaconda`_. It has preinstalled and configured packages for data analysis and it is available on all major platforms. It uses *conda* as package manager, in addition to the standard pip. A versioning control can be installed by downloading `git`_. Run the following commands to activate the conda virtual environment:: conda create -n venvdpa conda activate venvdpa to list the available environments you can type ``conda info --envs``, and to deactivate an active environment use ``source deactivate``. Installation ------------ Install required dependencies ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The DPA package depends on ``easycython``, that can be installed using ``conda`` or ``pip``. Note that it is possible to check which packages are installed with the ``pip freeze`` command. Installing released code from GitHub ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Install the latest version from the GitHub repository via:: pip install git+https://github.com/mariaderrico/DPA Installing development code from GitHub ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Run the following commands to download the DPA source code:: git clone https://github.com/mariaderrico/DPA.git Install DPA with the following commands:: cd DPA . compile.sh Citing ------ If you have used this codebase in a scientific publication and wish to cite the algorithm, please cite our paper in Information Sciences. `M. d'Errico, E. Facco, A. Laio, A. Rodriguez, Information Sciences, Volume 560, June 2021, 476-492`_ .. code:: bibtex @article{DERRICO2021476, title = {Automatic topography of high-dimensional data sets by non-parametric density peak clustering}, journal = {Information Sciences}, volume = {560}, pages = {476-492}, year = {2021}, issn = {0020-0255}, doi = {https://doi.org/10.1016/j.ins.2021.01.010}, url = {https://www.sciencedirect.com/science/article/pii/S0020025521000116}, author = {Maria d’Errico and Elena Facco and Alessandro Laio and Alex Rodriguez}, } .. References .. _`scikit-learn`: https://scikit-learn.org/stable/ .. _`M. d'Errico, E. Facco, A. Laio, A. Rodriguez, Information Sciences, Volume 560, June 2021, 476-492`: https://www.sciencedirect.com/science/article/pii/S0020025521000116?dgcid=author .. _`arXiv`: https://arxiv.org/abs/1802.10549v2 .. _`Computing the free energy without collective variables`: https://pubs.acs.org/doi/full/10.1021/acs.jctc.7b00916 .. _`Estimating the intrinsic dimension of datasets by a minimal neighborhood information`: https://export.arxiv.org/pdf/1803.06992 .. _`Clustering by fast search and find of density peaks`: http://science.sciencemag.org/content/344/6191/1492.full.pdf .. _`github DPA repository`: https://github.com/mariaderrico/DPA.git .. _`Anaconda`: https://www.anaconda.com/download/#windows .. _`git`: https://git-scm.com .. _`jupytext`: https://pypi.org/project/jupytext/

نیازمندی

مقدار	نام
==0.24.*	scikit-learn
-	matplotlib
==4.0.2	sphinx
-	sphinx-gallery
-	sphinx-rtd-theme
==1.3.0	nbsphinx-link
-	numpydoc
-	matplotlib
-	scipy
-	jupyter
-	py-cpuinfo
-	hdbscan
-	pytest
-	pytest-cov
-	pandas

نحوه نصب

نصب پکیج whl DPA-0.0.3:

pip install DPA-0.0.3.whl

نصب پکیج tar.gz DPA-0.0.3:

pip install DPA-0.0.3.tar.gz