Saravanan666/pypi-org huggingface dataset:
Package: cluster-lensing
This package includes tools for calculating a variety of galaxy cluster properties, as well as mass-richness and mass-concentration scaling relations, and weak lensing profiles. These include surface mass density (Sigma) and differential surface mass density (DeltaSigma) for NFW halos, both with and without the effects of cluster miscentering.
For more information, visithttp://jesford.github.io/cluster-lensing
Package: clusterlib
The goal of this library is to ease the creation, launch and management of
embarassingly parallel jobs on supercomputer such asSLURM. Some basic primitives (pure
python NO-SQL database) to work in distributed memory architecture are
provided.Aims:  simple, pure pythonIf you want to parallelize your python jobs in shared memory architecture, I
advise you to usejoblib.Getting the latest codeTo get the latest code using git, simply type:git clone git://github.com/clusterlib/clusterlib.gitIf you don’t have git installed, you can download a zip or tarball of the
latest code:https://github.com/clusterlib/clusterlib/archive/master.zipInstallingAs any Python packages, to install clusterlib, simply do:python setup.py installin the source code directory.How to contribute?To contribute to clusterlib, first create a github account. Then you can
fork theclusterlib repository.
Once this is done, you can make clone of your fork, make your changes and
whenever you are happy, send us a pull request to the main repository.Running the test suiteTo run the test suite, you need nosetests and the coverage modules.
Run the test suite using:nosetestsfrom the root of the project.DocumentationFor making the documentation, Sphinx==1.2.2 and sphinx-bootstrap-theme==0.4.0
are needed. Then, you can domake doc
Package: clusterlogger
Logging package for contextual information of clusters.FeaturesHazel Henlogging filter.GELFTCPHandler for sending logs toGraylogvia TCP.History0.1.0 (2015-07-17)First release on PyPI.0.1.1 (2015-07-18)Addsitenameandplatformattributes to the log record.0.2.0 (2015-07-28)GELFTCPHandler: Some clusters might not support outgoing UDP connections.
Package: clustermil
clustermilPython package for multiple instance learning (MIL) for large n_instance dataset.Featuressupport count-based multiple instance assumptions (seewikipedia)support multi-class settingsupport scikit-learn Clustering algorithms (such asMiniBatchKMeans)fast even if n_instance is largeInstallationpipinstallclustermilUsage# Prepare follwing dataset## - bags ... list of np.ndarray#            (num_instance_in_the_bag * num_features)# - lower_threshold ... np.ndarray (num_bags * num_classes)# - upper_threshold ... np.ndarray (num_bags * num_classes)## bags[i_bag] contains not less than lower_thrshold[i_bag, i_class]# i_class instances.# Prepare single-instance clustering algorithmsfromsklearn.clusterimportMiniBatchKMeansn_clusters=100clustering=MiniBatchKMeans(n_clusters=n_clusters)clusters=clustering.fit_predict(np.vstack(bags))# flatten bags into instances# Prepare one-hot encoderfromsklearn.preprocessingimportOneHotEncoderonehot_encoder=OneHotEncoder()onehot_encoder.fit(clusters)# generate ClusterMilClassifier with helper functionfromclustermilimportgenerate_mil_classifiermilclassifier=generate_mil_classifier(clustering,onehot_encoder,bags,lower_threshold,upper_threshold,n_clusters)# after multiple instance learning,# you can predict instance classmilclassifier.predict([instance_feature])Seetests/test_classification.pyfor an example of a fully working test data generation process.Licenseclustermil is available under the MIT License.
Package: cluster-model
cluster_modelPackage containing a wrapper classRiverfor performing Online Topic Modeling with BERTopic model as explained in therelated documentation.The reference documentation states that the packagerivercan be used for performing Online Topic Modeling in BERTopic. However it is necessary to create a class having a.partial_fitfunction and the possibility to extract labels through.labels.The module simply defines a Python classclass Riveras specified in the BERTopic guidelines for performing Online Topic Modeling, which allows to instantiate arivercluster model contaning also the.partial_fitfunction and the possibility to extract labels through.labels.
Package: cluster-over-sampling
cluster-over-samplingCategoryToolsDevelopmentPackageDocumentationCommunicationIntroductionA general interface for clustering based over-sampling algorithms.InstallationFor user installation,cluster-over-samplingis currently available on the PyPi's repository, and you can
install it viapip:pipinstallcluster-over-samplingDevelopment installation requires to clone the repository and then usePDMto install the
project as well as the main and development dependencies:gitclonehttps://github.com/georgedouzas/cluster-over-sampling.gitcdcluster-over-sampling
pdminstallSOM clusterer requires optional dependencies:pipinstallcluster-over-sampling[som]UsageAll the classes included incluster-over-samplingfollow theimbalanced-learnAPI using the functionality of the base
oversampler. Usingscikit-learnconvention, the data are represented as follows:Input dataX: 2D array-like or sparse matrices.Targetsy: 1D array-like.The clustering-based oversamplers implement afitmethod to learn fromXandy:clustering_based_oversampler.fit(X,y)They also implement afit_resamplemethod to resampleXandy:X_resampled,y_resampled=clustering_based_oversampler.fit_resample(X,y)ReferencesIf you usecluster-over-samplingin a scientific publication, we would appreciate citations to any of the following papers:G. Douzas, F. Bacao, "Self-Organizing Map Oversampling (SOMO) for imbalanced data set learning", Expert Systems with
Applications, vol. 82, pp. 40-52, 2017.G. Douzas, F. Bacao, F. Last, "Improving imbalanced learning through a heuristic oversampling method based on k-means and
SMOTE", Information Sciences, vol. 465, pp. 1-20, 2018.G. Douzas, F. Bacao, F. Last, "G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE", Expert
Systems with Applications, vol. 183,115230, 2021.
Package: cluster-pack
cluster-packcluster-pack is a library on top of eitherpexorconda-packto make your Python code easily available on a cluster.Its goal is to make your prod/dev Python code & libraries easiliy available on any cluster. cluster-pack supports HDFS/S3 as a distributed storage.The first examples useSkein(a simple library for deploying applications on Apache YARN) andPySparkwith HDFS storage. We intend to add more examples for other applications (likeDask,Ray) and S3 storage.An introducing blog post can be foundhere.InstallationInstall with Pip$pipinstallcluster-packInstall from source$gitclonehttps://github.com/criteo/cluster-pack
$cdcluster-pack
$pipinstall.Prerequisitescluster-pack supports Python ≥3.6.FeaturesShips a package with all the dependencies from your current virtual environment or your conda environmentStores metadata for an environmentSupports "under development" mode by taking advantage of pip'seditable installs mode, all editable requirements will be uploaded all the time, making local changes directly visible on the clusterInteractive (Jupyter notebook) modeProvides config helpers to directly use the uploaded zip file inside your applicationLaunching jobs from jobs by propagating all artifactsBasic examples withskeinInteractive modeSelf shipping projectBasic examples withPySparkPySpark with HDFS on YarnDocker with PySpark on S3
Package: clusterplot
clusterplotis a python tool to recognise trends in datasets. It uses different clustering algorithms on >=2-dimensional network histograms.
Getting Started
—————
For the start, have a look in the examples/ directory. Execute the mkfiles.py:
>>> python3 mkfiles.py
This will create 660 files in a subdirectory data/. Each file contains sine or cosine curves with a random period.
Two packets of sine functions with 20 files each have the same period within the packet. The periods can be seen in the file randinfo.txt. The set up goal for clusterplot is to identify the two sine periods, that occur more often.
Next, execute (step by step) the file xample.py:
>>>python3 xample.py
This should get you a basic idea of how Clusterplot works.### Prerequisites
- numpy
- networkx
- os### Installing
The project is available via pypi and can be installed with pip:
>>> pip install clusterplotIf you want to have an newer or older Version, clone the git repository, checkout the specific tag and do:
>>> pip install -e .
Changelog
———
### Version 0.1.0
First release, for functionality look into the documentation.LicenseCopyright 2018 Felix Meggendorfer, Carlos Andres Palma, Willi AuwaerterLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.AuthorsFelix Meggendorfer
Carlos A. Palma
Willi Auwaerter
Package: clusterpoint-api
#python-client-api - Clusterpoint Server Python API library#---##About##Clusterpoint Server is a scalable high-speed NoSQL database technologywith Google-like search.python-client-api is a Python API for Clusterpoint Server.##Useful links##* [Clusterpoint Ltd. web page](http://www.clusterpoint.com/)* [Clusterpoint documentation site](http://docs.clusterpoint.com/)* [Clusterpoint API reference includingPython](http://docs.clusterpoint.com/wiki/Reference)* [Clusterpoint Python API libraryoverview](http://docs.clusterpoint.com/wiki/Python_API_overview)* [python-client-api githubrepository](https://github.com/clusterpoint/python-client-api)##Installation##Use Python distutils to install Clusterpoint Python client from source:`sudo python2 setup.py install`##Support##Please feel free to contact the maintainer of this package onsuport@clusterpoint.com
Package: clusterpolate
Traditional approaches for inter- and extrapolation of scattered data
work on a filled rectangular area surrounding the data points or in
their filled convex hull. However, scattered data often consists of
different clusters of irregular shapes and usually contains areas where
there simply is no data. Forcing such data into a traditional inter-
or extrapolation scheme often does not lead to the desired results.Heatmaps, on the other hand, deal well with scattered data but often do
not provide real interpolation: Instead they usually use raw sums of
kernel functions which overestimate the target value in densely
populated areas.Clusterpolation is a hybrid inter- and extrapolation scheme to fix this.
It uses kernel functions for a weighted inter- and extrapolation of
local values, as well as for a density estimation of the data. The
latter is used to assign a membership degree to clusterpolated points:
Points with a low membership degree lie in an area where there’s just
not enough data.
Package: clusterprocessing
clusterprocessing is a … written in thePython Programming Language…This project is being developed for …The overall goal for this project is to …Exampleslocalcluster:# ...Features…InstallationThe simplest and recommended way to install clusterprocessing is with pip.
You may install the latest stable release from PyPI with pip:> pip install clusterprocessingIf you do not have pip, you may use easy_install:> easy_install clusterprocessingAlternatively, you may download the source package from thePyPI Pageor theDownloads pageon theProject Website;
extract it and install using:> python setup.py installYou can also install thelatest-development versionby usingpiporeasy_install:> pip install clusterprocessing==devor:> easy_install clusterprocessing==devFor further information see theclusterprocessing documentation.Changesclusterprocessing 0.0.1.devTBA
Package: clustersight
Clustering ExplorerThe Clustering Explorer allows users to interactively analyze which factors in a dataset are most associated with clusters. Users can lasso points of interest in a 2D plot of the data, which is created using Principal Component Analysis (PCA) for dimensionality reduction. The tool provides three modes of analysis: 'table', 'histogram', and 'explainer'.UsageCreate Lasso Toolcreate_lasso(df, mode='table', label_col=None, exclude_cols=[], num_factors = 10, dtreeviz_plot=True)The create_lasso function creates a lasso tool for data analysis. The parameters for this function are:df: A Pandas DataFrame of the data to be analyzedmode: The mode of analysis. Can be 'table', 'histogram', or 'explainer'label_col: The column name to be used for color coding of the plotexclude_cols: A list of columns to exclude from the analysisnum_factors: Number of factors to consider when mode is 'explainer'dtreeviz_plot: A boolean value to decide whether to plot decision tree using dtreeviz libraryThe mode parameter determines the type of analysis that will be performed:'table': shows a table of the selected points'histogram': shows an interactive histogram of each column's values among selected points compared with among all points'explainer': predicts which factors lead to the clustered selection with a decision treeThe dtreeviz_plot parameter is used when mode is 'explainer'. If dtreeviz_plot is True, the decision tree is plotted using the dtreeviz library. Otherwise, the decision tree is plotted using sklearn, which is faster.DependenciesPython 3.6+
numpy
pandas
sklearn
dtreeviz
plotly
ipywidgets
itertoolsNotesThe tool is designed for datasets that can fit in memory. For larger datasets, consider using a sampling method or dimensionality reduction techniques before using this tool.
Package: cluster-ss
cluster-ss: Cluster Silhouette SupportClustering Problems is hard and much more hard in Python, do not exists some packages to faster implementation of metrics and others helpful supports like cluster analysis, visualization, estimators training and dimensionality reduction.
Note, This package was heavily influenced by the lazypredict package, I loved the ideia of one comand line for fit multiple estimators.Based on this problemscluster-ssis a simple package to facilite implementation of this steps for clustering analysis, adding some simple ways to fit estimators and visualize one of my favorite metric, thesilhouette score.The package offers:Simple ways to visualize silhouette score and analysis.Fit functions from all sklearn cluster estimators.Setup multiple parameters to fit these estimators.This is a simple study proposed package by Me, however I invite the community to contribute. Please help by trying it out, reporting bugs, making improvments and other cool thigs. :)
Package: clustertools
clustertoolsclustertools is a Python package for analysing star cluster simulations. The package is built around the StarCluster class, which will store all the necessary information from a given star cluster simulation to be used for anaylsis. All functions within clustertools are then designed to act on a StarCluster. clustertools can be used for unit and coordinate transformations, the calculation of key structural and kinematic parameters, analysis of the cluster’s orbit and tidal tails (with the help of galpy , and measuring common cluster properties like its mass function, density profile, and velocity dispersion profile (among others). While originally designed with star clusters in mind, clustertools can be used to study other types of N-body systems, including stellar streams and dark matter sub-halos.The package contains functions for loading data from commonly used N-body codes, generic snapshots, and codes for generating initial conditions.clustertools is developed on Github. Please go tohttps://github.com/webbjj/clustertoolsto report issues or contribute to the code.Documentation for clustertools can be found athttps://clustertools.readthedocs.io/en/latest/Installationclustertools can be installed either directly from the GitHub repository or via pipTo install clustertools from GitHub, simply clone the repository and install via setup tools:git clonehttps://github.com/webbjj/clustertools.gitcd clustertools
python setup.py install
Please note that if you don’t have permission to write files to the default install location (often /usr/local/lib), you will either need to runsudo python setup.py install
orpython setup.py install --prefix='PATH'
where ‘PATH’ is a directory that you do have permission to write in and is in your PYTHONPATH.It is also possible to install clustertools using pip:pip install clustertools
however please note this version is not updated as frequently as the GitHub repository. Similarly, if permissions are an issue, you can use:pip install --user clustertools
orpip install clustertools --install-option="--prefix=PATH"Note: For users looking to take advantage of the plotting features available in clustertools, it may be necessary to install the cm-super and dvipng packages if they aren't installed by default.Requirementsclustertools requires the following python packages:galpy (https://docs.galpy.org/en/v1.7.2/index.html)
matplotlib
numpy
scipy
astropy
numbaOptional:
limepy (https://readthedocs.org/projects/limepy/)
AMUSE (https://amuse.readthedocs.io/en/latest/)
Package: clusterweb
![alt text](./docs/source/images/logo_color.png?raw=true "Icon")# ClusterWeb: Distributed System Python APIClusterWeb is a Python library for creating compute clusters out of any devices that use SSH.#### Documentation: <https://cluster-web.readthedocs.io/en/latest/>### Compatiable Devices- CPUs nodes- Desktops- GPUs workstations- Laptops- Raspberry Pi- Android phones### Requirements- Python 3.6 or above- SSH- Linux or OSX- pickle- cloudpickle (host machine)### Basic Usage:```pythonfrom clusterweb.pbs.qsub import Qsubimport timedef job(arg):a,b = 0,1for _ in range(int(arg)):a,b = b,a+breturn aq = Qsub(job,1e5)q.push()q.pull(thread=False)print(q.result)```
Package: clusterxplain
clusterxplainProvide feature relevance scores fo clustering using various explanation methods.InstallationClusterxplain can be easily installed via pip:pip install clusterxplainThe source coe is available at:https://github.com/AlexF1994/cluster-explainHow to use itThe official documentation can be found onhttps://cluster-explain.readthedocs.io/en/latest/index.html.But you can also jump right to the quickstartnotebook.ContributionsAll contributions, bug reports, bug fixes, documentation improvements, enhancements, ideas etc. are very welcome.Before contributing, please set up the pre-commit hooks to reduce errors and ensure consistencypip install -U pre-commit
pre-commit installWe usepytestas test framework. To execute the tests, please runpoetry run pytest testsTo run the tests with coverage information, please usepoetry run pytest tests --cov=src --cov-report=xmland have a look at thehtmlcovfolder, after the tests are done.ContactAlexander Fottner (alexander.fottner@uni-a.de)LicenseMIT
Package: clusterysis
ClusterysisClusterysis is a simple tool for a first cluster analysis that helps you visualize the difference between clusters.Usage:import clusterysis as clyJust pass a Dataframe with a cluster column to see several charts.cly.View(df=df, cluster_col="cluster")A table with the mean of each quantitative variable:Cluster composition of each categorical variable:Boxplot chart for quantitative variables:Parameters:dfandcluster_colare required, if you want you can explicit which are the quantitative and categorical variables (if you don't it will be assumed strings are categorical and numbers quantitative).
You can also ask to highlight a specif cluster.cly.View(
    df=df,
    cluster_col="cluster",
    cat_cols=["job", "education"],
    quant_cols=["age", "duration"],
    diff_cluster=0
)
Package: clusterzeug
Some functions for clusteringTested against Windows / Python 3.11 / Anacondapip install clusterzeugfromclusterzeugimport(birchcluster,gaussianmixture,opticscluster,hdbscancluster,dbscan,agglomerativeclustering,spectralclustering,kmeanscluster,minibatchkmeanscluster,afinity_propagation,mean_shift,)importnumpyasnpimportrandomdata=np.array([[random.randint(1,1000),random.randint(1,1000)]for_inrange(100)],dtype=np.int64,)a1=birchcluster(data,n_clusters=10)a2=gaussianmixture(data,n_components=5)a3=opticscluster(data,min_samples=5)a4=hdbscancluster(data,min_cluster_size=5)a5=dbscan(data,eps=0.5,min_samples=5)a6=agglomerativeclustering(data,n_clusters=10)a7=spectralclustering(data,n_clusters=10)res=kmeanscluster(data,n_clusters=10)print(res)res2=minibatchkmeanscluster(data,n_clusters=10)print(res2)aff=afinity_propagation(data,damping=0.5,preference=-10)print(aff)ms=mean_shift(data,bandwidth=2.0)print(ms)
Package: clusteval
clustevalclustevalis a python package that is developed to evaluate detected clusters and return the cluster labels that have most optimalclustering tendency,Number of clustersandclustering quality. Multiple evaluation strategies are implemented for the evaluation;silhouette,dbindex, andderivative, and four clustering methods can be used:agglomerative,kmeans,dbscanandhdbscan.⭐️ Star this repo if you like it ⭐️BlogsRead the blog to get a structured overview how you can useclusteval.Medium Blog: A step-by-step guide for clustering imagesIn case you want to detect identical images, you can also use hash functionalities.Medium Blog: Detection of Duplicate Images Using Image Hash FunctionsDocumentation pagesOn thedocumentation pagesyou can find detailed information about the working of theclustevalwith many examples.InstallationIt is advisable to create a new environment (e.g. with Conda).condacreate-nenv_clustevalpython=3.8
condaactivateclustevalInstall from PyPIpipinstallclustevalImport libraryfromclustevalimportclustevalExamplesA structured overview of all examples are now available on thedocumentation pages.Example: Cluster validation using Silhouette scoreExample: Determine the optimal number of clustersExample: Plot the dendrogramExample: Cluster validation using davies-boulin indexExample: Cluster validation using davies-boulin indexExample: Cluster validation using derivative evaluation methodExample: Cluster validation using dbscanExample: Cluster validation using hdbscanCitationPlease cite clusteval in your publications if this is useful for your research (see right top for citation).Other interesting techniques/blogsUse ARI when the ground truth clustering has large equal sized clustersUsa AMI when the ground truth clustering is unbalanced and there exist small clustershttps://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.htmlhttps://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-pyhttps://github.com/idealo/imagededuphttps://towardsdatascience.com/how-to-cluster-images-based-on-visual-similarity-cd6e7209fe34https://github.com/facebookresearch/deepclusterhttps://towardsdatascience.com/pca-on-hyperspectral-data-99c9c5178385https://machinelearningmastery.com/face-recognition-using-principal-component-analysis/MaintainerErdogan Taskesen, github:erdogantContributions are welcome.If you wish to buy me aCoffeefor this work, it is very appreciated :)
Star it if you like it!
Package: clusto
[![Build Status](https://secure.travis-ci.org/clusto/clusto.png)](http://travis-ci.org/clusto/clusto)What is Clusto?Clusto is a cluster management tool.  It helps you keep track of your
inventory, where it is, how it’s connected, and provides an abstracted
interface for interacting with the elements of the infrastructure.There are code examples in the unit tests in src/clusto/test/.To learn more, start with the wiki, here:https://github.com/clusto/clusto/wiki
Package: clustomatic
clustomaticclustomatic is a python package that displays a graph of clustered points with a dataframe in entryFree software: MIT licenseDocumentation:https://clustomatic.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2020-09-23)