Saravanan666/pypi-org huggingface dataset:
Package: scikit-gstat
Info: scikit-gstat needs Python >= 3.6!How to citeIn case you use SciKit-GStat in other software or scientific publications,
please reference this module. There is aGMDpublication. Please cite it like:Mälicke, M.: SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python, Geosci. Model Dev., 15, 2505–2532,https://doi.org/10.5194/gmd-15-2505-2022, 2022.The code itself is published and has a DOI. It can be cited as:Mirko Mälicke, Romain Hugonnet, Helge David Schneider, Sebastian Müller, Egil Möller, & Johan Van de Wauw. (2022). mmaelicke/scikit-gstat: Version 1.0 (v1.0.0). Zenodo.https://doi.org/10.5281/zenodo.5970098Full DocumentationThe full documentation can be found at:https://mmaelicke.github.io/scikit-gstatDescriptionSciKit-Gstat is a scipy-styled analysis module for geostatistics. It includes
two base classesVariogramandOrdinaryKriging. Additionally, various
variogram classes inheriting fromVariogramare available for solving
directional or space-time related tasks.
The module makes use of a rich selection of semi-variance
estimators and variogram model functions, while being extensible at the same
time.
The estimators include:matheroncressiedowdgentonentropytwo experimental ones: quantiles, minmaxThe models include:spericalexponentialgaussiancubicstablematérnwith all of them in a nugget and no-nugget variation. All the estimator are
implemented using numba’s jit decorator. The usage of numba might be subject
to change in future versions.InstallationPyPIpipinstallscikit-gstatNote:It can happen that the installation of numba or numpy is failing using pip. Especially on Windows systems.
Usually, a missing Dll (see eg.#31) or visual c++ redistributable is the reason.GIT:gitclonehttps://github.com/mmaelicke/scikit-gstat.gitcdscikit-gstatpipinstall-rrequirements.txtpipinstall-e.Conda-Forge:From Version0.5.5onscikit-gstatis also available on conda-forge.
Note that for versions< 1.0conda-forge will not always be up to date, but
from1.0on, each minor release will be available.condainstall-cconda-forgescikit-gstatQuickstartTheVariogramclass needs at least a list of coordiantes and values.
All other attributes are set by default.
You can easily set up an example by using theskgstat.datasub-module,
that includes a growing list of sample data.importskgstatasskg# the data functions return a dict of 'sample' and 'description'coordinates,values=skg.data.pancake(N=300).get('sample')V=skg.Variogram(coordinates=coordinates,values=values)print(V)sphericalVariogram-------------------Estimator:matheronEffectiveRange:353.64Sill:1512.24Nugget:0.00All variogram parameters can be changed in place and the class will automatically
invalidate and update dependent results and parameters.V.model='exponential'V.n_lags=15V.maxlag=500# plot - matplotlib and plotly are available backendsfig=V.plot()
Package: scikit-guess
scikit-guessThis scikit contains methods for computing fast, non-iterative estimates of
fitting parameters for common functions. The estimates may be used as-is on
their own, or refined through non-linear optimization algorithms. The name of
the scikit comes from the fact that estimates are a good initial guess for the
optimal fitting parameters.Documentation available on Read the Docs:https://scikit-guess.readthedocs.io/en/latest/.Changelog0.0.1a0 (2021-02-01)First release on PyPI. Still a WIP, but wanted to hog the package name.
Package: scikit-hep
Project infoTheScikit-HEP projectis a community-driven and community-oriented project
with the aim of providing Particle Physics at large with an ecosystem for data analysis in Python
embracing all major topics involved in a physicist’s work.
The project started in Autumn 2016 and its packages are actively developed and maintained.It is not just about providing core and common tools for the community.
It is also about improving the interoperability between HEP tools and the Big Data scientific ecosystem in Python,
and about improving on discoverability of utility packages and projects.For what concerns the project grand structure, it should be seen as atoolsetrather than a toolkit.Getting in touchThere are various ways toget in touchwith project admins and/or users and developers.scikit-hep packagescikit-hepis a metapackage for the Scikit-HEP project.InstallationYou can install this metapackage from PyPI withpip:python-mpipinstallscikit-hepor you can use Conda through conda-forge:condainstall-cconda-forgescikit-hepAll the normal best-practices for Python apply; you should be in a virtual environment, etc.Package version and dependenciesPlease check thesetup.cfgandrequirements.txtfiles for the list
of Python versions supported and the list of Scikit-HEP project packages
and dependencies included, respectively.For any installedscikit-hepthe following displays the actual versions
of all Scikit-HEP dependent packages installed, for example:>>>importskhep>>>skhep.show_versions()System:python:3.10.10|packagedbyconda-forge|(main,Mar242023,20:08:06)[GCC11.3.0]executable:/srv/conda/envs/notebook/bin/pythonmachine:Linux-5.15.0-72-generic-x86_64-with-glibc2.27Pythondependencies:pip:23.1.2numpy:1.24.3scipy:1.10.1pandas:2.0.2matplotlib:3.7.1Scikit-HEPpackageversionanddependencies:awkward:2.2.2boost_histogram:1.3.2decaylanguage:0.15.3hepstats:0.6.1hepunits:2.3.2hist:2.6.3histoprint:2.4.0iminuit:2.21.3mplhep:0.3.28particle:0.22.0pylhe:0.6.0resample:1.6.0skhep:2023.06.09uproot:5.0.8vector:1.0.0Note on the versioning system:This package usesCalendar Versioning(CalVer).
Package: scikit-highs
Cython wrappers overHiGHS.  This project is a fork of the main repo:fork.The package is available on pypi asscikit-highsand is tested on the following:Ubuntu 18, gcc 7.5.0, Python 3.6.9 and 3.7Windows 10, VS 2019, Python 3.8.2 x64It has dependencies on numpy and Cython that require these to be installed before installation of scikit-highs.  Once numpy and Cython are installed, install using pip like this:pipinstallscikit-highsExample usage from script:from_highs.highs_wrapperimporthighs_wrapperfrom_highs.highs_wrapperimportCONST_INF,CONST_I_INF# Call like this (usually using numpy arrays and A is a scipy.sparse.csc_matrix):res=highs_wrapper(c,A.indptr,A.indices,A.data,lhs,rhs,lb,ub,options)optionsis a dict that supports all options of the HiGHS C++ API.It will take a second to compile when installing, I did not create any wheels, etc, so always compiles from source.
Package: scikit-ika
scikit-ikaA real-time adaptive predictive system for evolving data streams.Learn MoreRequirementsMake sure the following dependencies are installed:Python ≥ 3.6C++ toolchain supporting C++14 (g++ 7+, CMake)InstallingFrom PyPIpipinstallscikit-ikaFrom sourcegitclonehttps://github.com/scikit-ika/scikit-ika.git--recursive
pipinstall./scikit-ikaNotescikit-ikais still in its infancy. The APIs are unstable and the documentation is scarse.Guides on usages and parameter tunning are on its way. Meanwhile, please feel free to
raise issues or email us if you have any questions.AcknowledgementThis project is inspired by and has dependencies on the following projects:scikit-multiflowstreamDM in C++
Package: scikit-image
scikit-image: Image processing in PythonWebsite (including documentation):https://scikit-image.org/Documentation:https://scikit-image.org/docs/stable/User forum:https://forum.image.sc/tag/scikit-imageDeveloper forum:https://discuss.scientific-python.org/c/contributor/skimageSource:https://github.com/scikit-image/scikit-imageInstallationpip:pip install scikit-imageconda:conda install -c conda-forge scikit-imageAlso seeinstallingscikit-image.LicenseSeeLICENSE.txt.CitationIf you find this project useful, please cite:Stéfan van der Walt, Johannes L. Schönberger, Juan Nunez-Iglesias,
François Boulogne, Joshua D. Warner, Neil Yager, Emmanuelle
Gouillart, Tony Yu, and the scikit-image contributors.scikit-image: Image processing in Python. PeerJ 2:e453 (2014)https://doi.org/10.7717/peerj.453
Package: scikit-ipp
scikit-IPP (skipp)scikit-ippis optimization of open-source image processing libraryscikit-imageby using Intel® Integrated Performance Primitives (Intel® IPP) library.scikit-ippis a standalone package, provided scikit-image-like API to some of Intel® IPP functions.DocumentationSource CodeAbout Intel® IPPGetting startedscikit-ippis easily built from source with the majority of the necessary prerequisites available on conda.  The instructions below detail how to gather the prerequisites, setting one's build environment, and finally building and installing the completed package.scikit-ippcan be built for all three major platforms (Windows, Linux, macOS).The build-process (using setup.py) happens in 2 stages:Running cython on C and Cython sourcesCompiling and linkingBuilding scikit-ipp using conda-buildThe easiest way to buildscikit-ippis using the conda-build with the provided recipe.PrerequisitesPython version >= 3.6conda-build version >= 3C compilerBuildingscikit-ippcd <checkout-dir>
conda build -c intel conda-recipeThis will build the conda package and tell you where to find it (.../scikit-ipp*.tar.bz2).Installing the built scikit-ipp conda packageconda install <path-to-conda-package-as-built-above>To actually use yourscikit-ipp, dependent packages need to be installed. To ensure, doLinux or Windows:conda install -c intel numpy ippBuilding documentation for scikit-ippPrerequisites for creating documentationsphinx >= 3.0sphinx_rtd_theme >= 0.4sphinx-gallery >= 0.3.1matplotlib > = 3.0.1Building documentationInstall scikit-ipp into your python environmentcd doc && make htmlThe documentation will be indoc/_build/htmlExamplesIntroductory examples forscikit-ipplink
Package: scikit-labs
OverviewBest-subset selection plays a vital role in regression analysis, aiming to identify a parsimonious subset of variables that maximizes prediction accuracy within the resulting linear model. This process is important in various scientific fields, including physics, biology, and medicine, where extensive datasets are routinely generated. Nevertheless, the computational complexity of selecting the best subset from massive datasets presents a formidable challenge, given the problem’s well-known NP-hard nature.To address this challenge, we introduce a new tuning-free iterative algorithmscikit-labsthat capitalizes on a novel subset splicing procedure. Remarkably, under mild conditions, our algorithm demonstrates provable identification of the best subset while maintaining a linear time complexity, achieving optimality in computation and statistics simultaneously. The power ofscikit-labsis numerically certified by extensive test cases.Quick StartInstallationInstall the stablescikit-labsPython package from Pypi:pipinstallscikit-labsAnd then the package can be imported as:importsklabsExampleBest subset selection for linear regression on a simulated dataset in Python:fromsklabs.datasetsimportmake_glm_datafromsklabs.linearimportLinearRegressionsim_dat=make_glm_data(n=350,p=500,k=6,family="gaussian")model=LinearRegression()model.fit(sim_dat.x,sim_dat.y)Open source softwarescikit-labsis a free software and its source code are publicly available in Github. The core framework is programmed in C++, and user-friendly Python interfaces are offered. You can redistribute it and/or modify it under the terms of the GPL-v3 License. We welcome contributions forscikit-labs, especially stretchingscikit-labsto the other best subset selection problems.CitationIf you usescikit-labsor reference our tutorials in a presentation or publication, we would appreciate citations of our library.@article{scikit-labs,
    title   ={Selecting the Best Subset in Regression in Linear Time},
    author  ={Jin Zhu and Junxian Zhu and Junhao Huang and Xueqin Wang and Heping Zhang},
    journal ={Submitted},
    year    ={2023},}
Package: scikit-learn-extra
scikit-learn-extra - A set of useful tools compatible with scikit-learnscikit-learn-extra is a Python module for machine learning that extends scikit-learn. It includes algorithms that are useful but do not satisfy the scikit-learninclusion criteria, for instance due to their novelty or lower citation number.InstallationDependenciesscikit-learn-extra requires,Python (>=3.7)scikit-learn (>=0.24), and its dependenciesInstallationLatest release can be installed with conda,conda install -c conda-forge scikit-learn-extraor from PyPi with,pip install scikit-learn-extraLicenseThis package is released under the 3-Clause BSD license.
Package: scikit-learn-pipeline-utils
Helpful Package For Custom Transformers To Use In Sklearn PipelinesInstallationpip install scikit-learn-pipeline-utilsQuick Startfrom sklearn_pipeline_utils import DFSelector
from sklearn.pipeline import Pipeline
import pandas as pd

df = pd.DataFrame({
    "col1": ["a", "b", "a"],
    "col2": [1, 2, 3]
})

pipeline = Pipeline([
    ("dfselectcol1", DFSelector("col1"))
    ])

print(pipeline.transform(df))
'''
expected result:
  col1
0    a
1    b
2    a
'''Dataframe Transformers ListDFSelectorDFObjectSelectorDFFeatureUnionDFImputerDFImputerMostFrequentDFOrdinalEncoderDFStandardScaler
Package: scikit-learn-progestimator
A scikit-learn estimator which wraps another estimator to provide facilities for time series problems where previous predictions are used
as features.DescriptionWhen callingmodel.fit(X,y),ywith time lag 1 is appended toXbefore fitting the model.
When callingmodel.predict(X), for each sample inX, the prediction uses the previous known value fory(either true or predicted) as an additional feature.UsageThis estimator implements the standardestimatorAPI. As
such, it should play nice with other scikit-learn objectsExample of wrapping an existing estimator:>>>fromsklearn.linear_modelimportLinearRegressionfromprogestimator.prog_regressionimportProgressiveRegressiony=np.array([[1.0],[3.0],[4.0],[7.0],[15.0],[31.0]])X=np.ones(([1.0],[1.0],[1.0],[1.0],[1.0],[1.0]])model=ProgressiveRegression(LinearRegression())model.fit(X,y)>>>model.predict(([1.0],[1.0],[1.0],[1.0],[1.0],[1.0]]))array([[64.98224852],[137.08896047],[290.09172322],[614.74728963],[1303.63182285],[2765.37143003]])
Package: scikit-learn_runnr
scikit-learnscikit-learn is a Python module for machine learning built on top of
SciPy and distributed under the 3-Clause BSD license.The project was started in 2007 by David Cournapeau as a Google Summer
of Code project, and since then many volunteers have contributed. See
the AUTHORS.rst file for a complete list of contributors.It is currently maintained by a team of volunteers.Notescikit-learnwas previously referred to asscikits.learn.Important linksOfficial source code repo:https://github.com/scikit-learn/scikit-learnHTML documentation (stable release):http://scikit-learn.orgHTML documentation (development version):http://scikit-learn.org/dev/Download releases:http://sourceforge.net/projects/scikit-learn/files/Issue tracker:https://github.com/scikit-learn/scikit-learn/issuesMailing list:https://lists.sourceforge.net/lists/listinfo/scikit-learn-generalIRC channel:#scikit-learnatirc.freenode.netDependenciesscikit-learn is tested to work under Python 2.6, Python 2.7, and Python 3.4.
(using the same codebase thanks to an embedded copy ofsix). It should also work with Python 3.3.The required dependencies to build the software are NumPy >= 1.6.1,
SciPy >= 0.9 and a working C/C++ compiler.For running the examples Matplotlib >= 1.1.1 is required and for running the
tests you need nose >= 1.1.2.This configuration matches the Ubuntu Precise 12.04 LTS release from April
2012.scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
Subprograms library. scikit-learn comes with a reference implementation, but
the system CBLAS will be detected by the build system and used if present.
CBLAS exists in many implementations; seeLinear algebra librariesfor known issues.InstallThis package uses distutils, which is the default way of installing
python modules. To install in your home directory, use:python setup.py install --userTo install for all users on Unix/Linux:python setup.py build
sudo python setup.py installDevelopmentCodeGITYou can check the latest sources with the command:git clone https://github.com/scikit-learn/scikit-learn.gitor if you have write privileges:git clone git@github.com:scikit-learn/scikit-learn.gitContributingQuick tutorial on how to go about setting up your environment to
contribute to scikit-learn:https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.mdBefore opening a Pull Request, have a look at the
full Contributing page to make sure your code complies
with our guidelines:http://scikit-learn.org/stable/developers/index.htmlTestingAfter installation, you can launch the test suite from outside the
source directory (you will need to have thenosepackage installed):$ nosetests -v sklearnUnder Windows, it is recommended to use the following command (adjust the path
to thepython.exeprogram) as using thenosetests.exeprogram can badly
interact with tests that usemultiprocessing:C:\Python34\python.exe -c "import nose; nose.main()" -v sklearnSee the web pagehttp://scikit-learn.org/stable/install.html#testingfor more information.Random number generation can be controlled during testing by setting
theSKLEARN_SEEDenvironment variable.
Package: scikit-learn-whiskers
scikit-learn-whiskersA collection (only one at this time) of tools aimed to help with some tasks of machine learning and datascience studies.These tools are intended to be compatible with scikit-learn utilities, and work properly inside a Pipeline.WhiskerOutliersA class to mark asoutliersthe values that can visually be identified as outliers from a typicalbox and whiskersplot.This class implements.fit,transformandfit_transform, as well asget_paramsandset_paramsmethods as any standard scikit-learn implementation.StandardOutliersA class to mark asoutliersthe values outside the rangethreshold* standard deviationaround themean.This class implements.fit,transformandfit_transform, as well asget_paramsandset_paramsmethods as any standard scikit-learn implementation.Requisites:NumPyPandasScikit-LearnInstallationTo install it:pip git+https://github.com/ayaranitram/scikit-learn-whiskers
Package: scikit-llm
Scikit-LLM: Scikit-Learn Meets Large Language ModelsSeamlessly integrate powerful language models like ChatGPT into scikit-learn for enhanced text analysis tasks.Installation 💾pipinstallscikit-llmSupport us 🤝You can support the project in the following ways:⭐ Star Scikit-LLM on GitHub (click the star button in the top right corner)💡 Provide your feedback or propose ideas in theissuessection orDiscord📰 Post about Scikit-LLM on LinkedIn or other platforms🔗 Check out our other projects (cards below are clickable):Quick Start & Documentation 📚Quick start example of zero-shot text classification using GPT:# Import the necessary modulesfromskllm.datasetsimportget_classification_datasetfromskllm.configimportSKLLMConfigfromskllm.models.gpt.classification.zero_shotimportZeroShotGPTClassifier# Configure the credentialsSKLLMConfig.set_openai_key("<YOUR_KEY>")SKLLMConfig.set_openai_org("<YOUR_ORGANIZATION_ID>")# Load a demo datasetX,y=get_classification_dataset()# labels: positive, negative, neutral# Initialize the model and make the predictionsclf=ZeroShotGPTClassifier(model="gpt-4")clf.fit(X,y)clf.predict(X)For more information please refer to thedocumentation.CitationYou can cite Scikit-LLM using the following BibTeX:@software{ScikitLLM,
  author = {Iryna Kondrashchenko and Oleh Kostromin},
  year = {2023},
  publisher = {beastbyte.ai},
  address = {Linz, Austria},
  title = {Scikit-LLM: Scikit-Learn Meets Large Language Models},
  url = {https://github.com/iryna-kondr/scikit-llm }
}
Package: scikit-lr
scikit-lrscikit-lris a Python module integrating Machine Learning algorithms for Label Ranking problems and distributed under MIT license.InstallationDependenciesscikit-lrrequires:* Python>=3.6
* Numpy>=1.15.2
* SciPy>=1.1.0LinuxorMac OS Xoperating systems.Windowsis not currently supported.User installationThe easiest way to installscikit-lris usingpippackage:pip install -U scikit-lrDevelopmentFeel free to contribute to the package, but be sure that the standards are followed.Source codeThe latest sources can be obtained with the command:git clone https://github.com/alfaro96/scikit-lr.gitSetting up a development environmentTo setup the development environment, it is strongly recommended to usedockertools (seehttps://github.com/alfaro96/docker-scikit-lrfor details).Alternatively, one can usePythonvirtual environments (seehttps://docs.python.org/3/library/venv.htmlfor details).TestingAfter installation the test suite can be executed from outside the source directory, with (you will need to havepytest>=4.6.4installed):pytest sklrAuthors* Alfaro Jiménez, Juan Carlos
* Aledo Sánchez, Juan Ángel
* Gámez Martín, José AntonioLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: scikit-MDR
A sklearn-compatible Python implementation of Multifactor Dimensionality Reduction (MDR) for feature construction.ContactIf you have any questions or comments about scikit-MDR, please feel free to contact us via:E-mail:rso@randalolson.comor Twitter:https://twitter.com/randal_olsonThis project is hosted athttps://github.com/EpistasisLab/scikit-mdr
Package: scikit-mine
Scikit-mine : pattern mining in PythonDescriptive analysis, leading tointerpretable, concise descriptions using theMinimum Description Length PrincipleFastAlgorithmsSimple, extendable API, inspired byscikit-learnResourcesFree software: BSD licenseGitHub:https://github.com/scikit-mine/scikit-mineDocumentation:https://scikit-mine.github.io/scikit-mine/Quickstartscikit-mine is a Python module for pattern mining built on top of
Pandas/Numpy/SciPy and is distributed under the 3-Clause BSD license.It is currently maintained by a team of volunteers.See examples in the tutorials; the notebooks are availablehere. To execute the tutorials, you will have to install jupyter notebook or jupyterlab (https://jupyter.org/install)Dependenciesscikit-mine requires Python>=3.8,
and some extra dependenciesscipy>=1.2.1pandas>=1.0.0pyroaring>=0.3.4joblib>=0.11.1sortedcontainers>=2.1.0dataclasses>=0.6networkxwget>=3.2scikit-learngraphvizmatplotlibpydotDevelopmentThis project is funded by theINRIA center in Rennes, Brittany, France.We welcome new contributors of all experience levels.Internal ContributorsRémi Adon (https://github.com/remiadon)Luis Galárraga (https://github.com/lgalarra)Josie Signe (https://github.com/Darlysia)Esther Galbrun (https://github.com/nurblageij)Hermann Courteille (https://github.com/hermann74)Cyril Regan (https://github.com/cyril-data)Thomas Betton (https://github.com/thomasbtnfr)