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توضیحات

A package for determining the consensus clustering from an ensemble of partitions

ویژگی	مقدار
سیستم عامل	-
نام فایل	Cluster_Ensembles-1.16
نام	Cluster_Ensembles
نسخه کتابخانه	1.16
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Gregory Giecold
ایمیل نویسنده	ggiecold@jimmy.harvard.edu
آدرس صفحه اصلی	https://github.com/GGiecold/Cluster_Ensembles
آدرس اینترنتی	https://pypi.org/project/Cluster_Ensembles/
مجوز	MIT License

Cluster\_Ensembles ================== A package for combining multiple partitions into a consolidated clustering. The combinatorial optimization problem of obtaining such a consensus clustering is reformulated in terms of approximation algorithms for graph or hyper-graph partitioning. Installation ------------ Cluster\_Ensembles is written in Python and in C. You need Python 2.7, its Standard Library and the following packages: - numexpr (version 2.4.0 or later) - NumPy (version 1.9.0 or any ulterior version) - SciPy - scikit-learn - setuptools - PyTables The ``pip install Cluster_Ensembles`` command mentioned below should automatically detect and, if applicable, install or update any of the afore-mentioned dependencies. As yet another prelimiary to running Cluster\_Ensembles, you should also follow the few more instructions below. On CentOS, Fedora or some Red Hat Linux distribution: - open a terminal console; - type in: ``sudo dnf install glibc.i686``. This will install the GNU C library that is required to run a 32-bit executable binary with a 64-bit Linux kernel. This executable is tasked with hyper-graph partitioning. Skipping this step would result in a ``bad ELF interpreter`` error message when subsequently trying to run the Cluster\_Ensembles package. On a Debian or Ubuntu platform, the following commands should yield the same outcome: - open a terminal console; - type in: ``sudo dpkg --add-architecture i386`` to add support for the i386 architecture; - enter: ``sudo apt-get install libc6:i386``. Upon completion of the steps outlined above, install Cluster\_Ensembles by sending a request to the Python Package Index (PyPI) as follows: - open a terminal console; - enter ``pip install Cluster_Ensembles``. Any missing third-party dependency should be automatically resolved. Please note that as part of the installation of this package, some code written in C that will later on be required by the Cluster\_Ensembles package to determine a graph partition is automatically compiled under the hood and according to the specifications of your machine. You therefore need to ensure availability of ``CMake`` and ``GNU make`` on your operating system. Usage ----- Say that you have an array of shape (M, N) where each row corresponds to a vector reporting the cluster label of each of the N samples comprising your dataset. It is possible that some of those samples have been left out of consideration from some of those M clusterings; in this case, the corresponding entry is tagged as NaN (``numpy.nan``). The few lines below illustrate how to submit consensus clustering analysis such an cluster_runs (M, N) array of cluster labels. A vector holding the consensus clustering identities for each of the N samples in your dataset, ``consensus_clustering_labels``, is returned. Please note that those M vectors of clustering labels can correspond to partitions of the samples into distinct numbers of overall clusters. Cluster_Ensembles therefore offers the possibility of seeking a consensus clustering from the aggregation of a clustering of your dataset into, say, 10 groups, another clustering of a fraction of your samples into 5 clusters, yet another partition of your dataset into 20 clusters, etc. Those choices are entirely up to you. Pretty much all that is required for Cluster_Ensembles is an array of clustering vectors. :: >>> import numpy as np >>> import Cluster_Ensembles as CE >>> cluster_runs = np.random.randint(0, 50, (50, 15000)) >>> consensus_clustering_labels = CE.cluster_ensembles(cluster_runs, verbose = True, N_clusters_max = 50) References ---------- - Giecold, G., Marco, E., Trippa, L. and Yuan, G.-C., “Robust Lineage Reconstruction from High-Dimensional Single-Cell Data”. ArXiv preprint [q-bio.QM, stat.AP, stat.CO, stat.ML]: http://arxiv.org/abs/1601.02748 - A. Strehl and J. Ghosh, "Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions". In: Journal of Machine Learning Research, 3, pp. 583-617. 2002

نحوه نصب

نصب پکیج whl Cluster_Ensembles-1.16:

pip install Cluster_Ensembles-1.16.whl

نصب پکیج tar.gz Cluster_Ensembles-1.16:

pip install Cluster_Ensembles-1.16.tar.gz