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Concurrent_AP-1.4
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مشاهده بیشترتوضیحات
Scalable and parallel programming implementation of Affinity Propagation clustering
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | Concurrent_AP-1.4 |
| نام | Concurrent_AP |
| نسخه کتابخانه | 1.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Gregory Giecold |
| ایمیل نویسنده | ggiecold@jimmy.harvard.edu |
| آدرس صفحه اصلی | https://github.com/GGiecold/Concurrent_AP |
| آدرس اینترنتی | https://pypi.org/project/Concurrent_AP/ |
| مجوز | MIT License |
Concurrent\_AP
==============
Overview
--------
A scalable and concurrent programming implementation of Affinity
Propagation clustering.
Affinity Propagation is a clustering algorithm based on passing messages
between data-points.
Storing and updating matrices of 'affinities', 'responsibilities' and
'similarities' between samples can be memory-intensive. We address this
issue through the use of an HDF5 data structure, allowing Affinity
Propagation clustering of arbitrary large data-sets, where other Python
implementations would return a MemoryError on most machines.
We also significantly speed up the computations by splitting them up
across subprocesses, thereby taking full advantage of the resources of
multi-core processors and bypassing the Global Interpreter Lock of the
standard Python interpreter, CPython.
Installation and Requirements
-----------------------------
Concurrent\_AP requires Python 2.7 along with the following packages and
a few modules from the Standard Python Library:
- NumPy >= 1.9
- psutil
- PyTables
- scikit-learn
- setuptools
It is suggested that you check that the required dependencies are installed, although the ``pip`` command below should do this automatically for you. You can indeed most conveniently download Concurrent_AP from the official Python Package Index (PyPI) as follows:
- open a terminal window;
- type in the command ``pip install Concurrent_AP``.
The code herewith has been tested on Fedora, OS X and Ubuntu and should
work fine on any other member of the Unix-like family of operating systems.
Usage and Command Line Options
------------------------------
See the docstrings associated to each function of the Concurrent\_AP
module for more information and an understanding of how different tasks
are organized and shared among subprocesses.
Usage: ``Concurrent_AP [options] file_name``, where ``file_name``
denotes the path where the data to be processed by Affinity Propagation
clustering is held. The data must consist in tab-separated rows of samples, each column corresponding to a particular feature.
- ``-c`` or ``--convergence``: specify the number of iterations without
change in the number of clusters that signals convergence (defaults
to 15);
- ``-d`` or ``--damping``: the damping parameter of Affinity
Propagation (defaults to 0.5);
- ``-f`` or ``--file``: option to specify the file name or file handle
of the hierarchical data format where the matrices involved in
Affinity Propagation clustering will be stored (defaults to a
temporary file);
- ``-i`` or ``--iterations``: maximum number of message-passing
iterations (defaults to 200);
- ``-m`` or ``--multiprocessing``: the number of processes to use;
- ``-p`` or ``--preference``: the preference parameter of Affinity
Propagation (if not specified, will be determined as the median of
the distribution of pairwise L2 Euclidean distances between samples);
- ``-s`` or ``--similarities``: determine if a similarity matrix has
been pre-computed and stored in the HDF5 data structure accessible at
the location specified through the command line option ``-f`` or
``--file`` (see above);
- ``-v`` or ``--verbose``: whether to be verbose.
Demo of Concurrent\_AP
----------------------
The following few lines illustrate the use of Concurrent\_AP on the
'Iris data-set' from the UCI Machine Learning Repository. While the
number of samples is here way too small for the benefits of the present
multi-tasking implementation and the use of an HDF5 data structure to
come fully into play, this data-set has the advantage of being
well-known and prone to a quick comparison with scikit-learn's version
of Affinity Propagation clustering.
- In a Python interpreter console, enter the following few lines, whose
purpose is to create a file containing the Iris data-set that will be
later subjected to Affinity Propagation clustering via
Concurrent\_AP:
::
>>> import numpy as np
>>> from sklearn import datasets
>>> iris = datasets.load_iris()
>>> data = iris.data
>>> with open('./iris_data.txt', 'w') as f:
np.savetxt(f, data, fmt = '%.4f', delimiter = '\t')
- Open a terminal window.
- Type in ``Concurrent_AP --preference 5.47 --v iris_data.txt`` or
simply ``Concurrent_AP iris_data.txt``.
The latter will automatically compute a preference parameter from the
data-set.
When the rounds of message-passing among data-points have completed, a
folder containing a file of cluster labels and a file of cluster centers
indices both in tab-separated format is created in your current working
directory.
Reference
---------
Brendan J. Frey and Delbert Dueck. "Clustering by Passing Messages
between Data Points", Science Feb. 2007
نحوه نصب
نصب پکیج whl Concurrent_AP-1.4:
pip install Concurrent_AP-1.4.whl
نصب پکیج tar.gz Concurrent_AP-1.4:
pip install Concurrent_AP-1.4.tar.gz