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fast_hdbscan-0.1.0


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توضیحات

A fast multicore version of hdbscan for low dimensional euclidean spaces
ویژگی مقدار
سیستم عامل -
نام فایل fast_hdbscan-0.1.0
نام fast_hdbscan
نسخه کتابخانه 0.1.0
نگهدارنده []
ایمیل نگهدارنده []
نویسنده Leland McInnes
ایمیل نویسنده leland.mcinnes@gmail.com
آدرس صفحه اصلی https://github.com/TutteInstitute/fast_hdbscan
آدرس اینترنتی https://pypi.org/project/fast_hdbscan/
مجوز BSD 2-Clause License
.. -*- mode: rst -*- .. image:: doc/hdbscan_logo.png :width: 600 :alt: HDBSCAN logo :align: center ====================== Fast Multicore HDBSCAN ====================== Ahe ``fast_hdbscan`` library provides a simple implementation of the HDBSCAN clustering algorithm designed specifically for high performance on multicore machine with low dimensional data. The algorithm runs in parallel and can make effective use of as many cores as you wish to throw at a problem. It is thus ideal for large SMP systems, and even modern multicore laptops. This library provides a re-implementation of a subset of the HDBSCAN algorithm that is compatible with the `hdbscan <https://github.com/scikit-learn-contrib/hdbscan>`_ library for data that is Euclidean and low dimensional. The primary advantages of this library over the standard ``hdbscan`` library are: * this library can easily use all available cores to speed up computation; * this library has much faster implementations of tree condensing and cluster extraction; * this library is much simpler and more approachable for extending or using components from; * this library is built on numba and has less issues with binaries and compilation. This library does not support all the features and input formats available in the hdbscan library. ----------- Basic Usage ----------- The ``fast_hdbscan`` library follows the ``hdbscan`` library in using the sklearn API. You can use the ``fast_hdbscan`` class ``HDBSCAN`` exactly as you wuld that of the ``hdbscan`` library with the caveat that ``fast_hdbscan`` only supports a subset of the parameters and options of ``hdbscan``. Nonetheless, if you have low-dimensional Euclidean data (e.g. the output of UMAP), you can use this library as a straightforward drop in replacement for ``hdbscan``: .. code:: python import fast_hdbscan from sklearn.datasets import make_blobs data, _ = make_blobs(1000) clusterer = fast_hdbscan.HDBSCAN(min_cluster_size=10) cluster_labels = clusterer.fit_predict(data) ------------ Installation ------------ fast_hdbscan requires: * numba * numpy * scikit-learn fast_hdbscan can be installed via pip: .. code:: bash pip install fast-hdbscan To manually install this package: .. code:: bash wget https://github.com/TutteInstitute/fast_hdbscan/archive/main.zip unzip main.zip rm main.zip cd fast_hdbscan-main python setup.py install ---------- References ---------- The algorithm used here is an adaptation of the algorithms described in the papers: McInnes L, Healy J. *Accelerated Hierarchical Density Based Clustering* In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), IEEE, pp 33-42. 2017 `[pdf] <http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8215642>`_ R. Campello, D. Moulavi, and J. Sander, *Density-Based Clustering Based on Hierarchical Density Estimates* In: Advances in Knowledge Discovery and Data Mining, Springer, pp 160-172. 2013 ------- License ------- fast_hdbscan is BSD (2-clause) licensed. See the LICENSE file for details. ------------ Contributing ------------ Contributions are more than welcome! If you have ideas for features of projects please get in touch. Everything from code to notebooks to examples and documentation are all *equally valuable* so please don't feel you can't contribute. To contribute please `fork the project <https://github.com/TutteInstitute/fast_hdbscan/issues#fork-destination-box>`_ make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged in.


نحوه نصب


نصب پکیج whl fast_hdbscan-0.1.0:

    pip install fast_hdbscan-0.1.0.whl


نصب پکیج tar.gz fast_hdbscan-0.1.0:

    pip install fast_hdbscan-0.1.0.tar.gz