Saravanan666/pypi-org huggingface dataset:
Package: skops
SKOPSskopsis a Python library helping you share yourscikit-learnbased models and put them in production.
At the moment, it includes tools to easily integrate models on the Hugging Face
Hub, which allows you to share your models, make them discoverable, and use the
Hub’s API inference and widgets to get outputs of the model without having to
download or load the model.skops.hub_utils: tools to create a model repository to be stored onHugging Face Hub, mainly throughskops.hub_utils.initandskops.hub_utils.push. You can see all the
models uploaded to the hub using this libraryhere. Find out morehere.skops.card: tools to create a model card explaining what the model does
and how it should be used. The model card can then be stored as theREADME.mdfile on the Hugging Face Hub, with pre-populated metadata to
help Hub understand the model. More information can be foundhere.skops.io: Secure persistence of sklearn estimators and more, without usingpickle. Visitthe docsfor more
information.Please refer to ourdocumentationon using the library as user, which includes user guides on the above topics as
well as complete examples explaining how the features can be used.If you want to contribute to the library, please refer to ourcontributingguidelines.InstallationYou can install this library using:python-mpipinstallskopsBug Reports and QuestionsPlease send all your questions and report issues onthis repository’s issue
trackeras an issue. Try to look
for existing ones before you create a new one.
Package: skorecard
skorecardskorecardis a scikit-learn compatible python package that helps streamline the development of credit risk acceptance models (scorecards).Scorecards are ‘traditional’ models used by banks in the credit decision process. Internally, scorecards are Logistic Regression models that make use of features that are binned into different groups. The process of binning is usually done manually by experts, andskorecardprovides tools to makes this process easier.skorecardis built on top ofscikit-learnas well as other excellent open source projects likeoptbinning,dashandplotly.:point_right: Read theblogpost introducing skorecardFeatures ⭐Automate bucketing of features inside scikit-learn pipelines.Dash webapp to help manually tweak bucketing of features with business knowledgeExtension tosklearn.linear_model.LogisticRegressionthat is also able to report p-valuesPlots and reports to speed up analysis and writing technical documentation.Quick demoskorecardoffers a range ofbucketers:importpandasaspdfromskorecard.bucketersimportEqualWidthBucketerdf=pd.DataFrame({'column':range(100)})ewb=EqualWidthBucketer(n_bins=5)ewb.fit_transform(df)ewb.bucket_table('column')#>    bucket                       label  Count  Count (%)#> 0      -1                     Missing    0.0        0.0#> 1       0                (-inf, 19.8]   20.0       20.0#> 2       1                (19.8, 39.6]   20.0       20.0#> 3       2  (39.6, 59.400000000000006]   20.0       20.0#> 4       3  (59.400000000000006, 79.2]   20.0       20.0#> 5       4                 (79.2, inf]   20.0       20.0That also support a dash app to explore and update bucket boundaries:ewb.fit_interactive(df)#> Dash app running on http://127.0.0.1:8050/Installationpip3installskorecardDocumentationSeeing-bank.github.io/skorecard/.PresentationsTitleHostDateSpeaker(s)Skorecard: Making logistic regressions great againING Data Science Meetup10 June 2021Daniel Timbrell, Sandro Bjelogrlic, Tim Vink
Package: skorfmann-cdktf-provider-aws
Terraform CDK AWS ProviderThis repo builds and publishes the Terraform AWS Provider bindings forcdktf.Current build targets are:npmPypiVersioningThis project is explicitly not tracking the Terraform AWS Provider version 1:1. In fact, it always trackslatestof~> 2.0with every release. If there scenarios where you explicitly have to pin your provider version, you can do so by generating theprovider constructs manually.These are the upstream dependencies:Terraform CDKTerraform AWS ProviderTerraform EngineIf there are breaking changes (backward incompatible) in any of the above, the major version of this project will be bumped. While the Terraform Engine and the Terraform AWS Provider are relatively stable, the Terraform CDK is in an early stage. Therefore, it's likely that there will be breaking changes.Features / Issues / BugsPlease report bugs and issues to theterraform cdkproject:Create bug reportCreate feature requestContributingprojenThis is mostly based onprojen, which takes care of generating the entire repository.cdktf-provider-project based on projenThere's a customproject builderwhich encapsulate the common settings for allcdktfproviders.provider versionThe provider version can be adjusted in./.projenrc.js.
Package: skorovarka
SkorovarkaSkorovarka takes a template project and prompts you to fill in the gaps via command line. It prepares the most basic files for you, so you don't need to fill everything out manually. It is compatible with two types of syntax:${1:label}or$1-> the VSCode snippets syntax|||1:label|||or|||1|||-> because it's rare that a triple pipe is an actual syntactical feature of any language and thus is more resistant to situations where the${}notation might not be suitable (e.g. Bash scripts).UsageRunskvk cook --input "./Python" --output "./MyProj".Fill in the values when you're asked.Recipe fileA recipie file contains hints on what the fields being filled in actually mean. This file is optional but when you provide it, it should be in the form of:tox.ini:-Resources File YAML Path-Coverage options and adapter to run tests withALLOWED_EXTENSIONS:-allAll-caps fields are obligatory to be present.ALLOWED_EXTENSIONS-> optionally constrains the extensions being considered by the tool. If the first element of the list isall(orALLoraLL, casing doesn't matter), all extensions will be allowed.Remaining fields are Unix-style paths relative to the template folder directory (passed with-iflag), e.g. iftox.iniexisted undertests, it should be named in the YAML:tests/tox.ini. Please don't use ugly Windows slashes in paths at least not in the recipe file.Any future configuration will be extended via the recipe YAML.To use the recipe file, provide a path to it via--recipeflag.To Do[] Recipe modification ->skvk recipe file.txt 4-> changes label on tag4infile.txt.CaveatsThe tool doesn't support mixed-line-ending scenario. You either have a CRLF or an LF file. Period.
Package: skosify
Python script for converting toSKOS.This program accepts a thesaurus-like vocabulary expressed as RDFS, OWL or
SKOS as input. It produces a clean SKOS representation, which attempts to
represent the input data losslessly using SKOS best practices. When given
SKOS as input, it will be cleaned up, validated and enriched to follow
the SKOS specification and related best practices.InstallationSkosify requires Python 3.6+.pip install --upgrade skosifyUsageAs command line script:skosify myvoc.owl -o myvoc-skos.ttl --label "My Ontology"This will read the filemyvoc.owlin RDF/XML format and write SKOS filemyvoc-skos.ttlin Turtle format, setting the name of the Concept Scheme toMy Ontology.Runskosify--helpfor more usage information.As Python library:importskosify# contains skosify, config, and infervoc=skosify.skosify('myontology.owl',label='My Ontology')voc.serialize(destination='myontology-skos.rdf',format='xml')rdf=Graph()rdf.parse('myontology.owl')config=skosify.config('owl2skos.cfg')voc=skosify.skosify(rdf,**config)skosify.infer.skos_related(rdf)skosify.infer.skos_topConcept(rdf):skosify.infer.skos_hierarchical(rdf,narrower=True)skosify.infer.skos_transitive(rdf,narrower=True)skosify.infer.rdfs_classes(rdf)skosify.infer.rdfs_properties(rdf)Seethe API Referencefor documentation of the public API of this module. Everything not listed there might change in a future version.Additional documentation can be foundin the GitHub project wikiAdditional scriptsThescriptsdirectory contains two additional scripts to be used together with Skosify:skosify.cgia web application to use Skosifysparqldump.pya command line client to download RDF via a SPARQL endpointAuthor and ContributorsOsma SuominenJakob VoßDan Michael O. HeggøAlex KourijokiSee alsoSeebackgroundfor history, related works, publications etc.
Package: skosprovider
This library helps abstract vocabularies (thesauri, controlled lists, authority
files). It depends heavily on theSKOSspecification, but adds elements
of other specifications such as theISO 25964 SKOS extensionwhere deemed useful.Building the docsMore information about this library can be found indocs. The docs can be
built usingSphinx.Please make sure you have installed Sphinx in the same environment where
skosprovider is present.# activate your virtual environment$pipinstall-rrequirements.txt$pythonsetup.pydevelop$cddocs$makehtml
Package: skosprovider-getty
Skosproviderimplementation of theGetty Vocabularies, can be used in conjunction with theAtramhasis SKOS editorto allow
linking your own vocabularies to the Getty thesauri and importing from them.Supported Getty thesauri:Art & Architecture Thesaurus (AAT)The Getty Thesaurus of Geographic Names (TGN)Union List of Artist Names (ULAN)1.2.0 (2023-11-08)Include the three Getty Conceptschemes in the code to make instantiating
the providers less dependent on the Getty services being up and running. (#86)Drop support for Python 3.8 and add support for 3.111.1.0 (2022-08-17)Drop python 3.6, 3.7 support and add support for 3.8, 3.9 and 3.10Upgrade RDFLib to 6.2.01.0.0 (2021-12-14)Drop python 2 supportUpgrade all requirements (#86)0.5.1 (2020-10-06)Prevent get_by_uri erroring on non-Getty URI’s (#77)Remove reference to nose.collectorRemove pyup integration0.5.0 (2020-08-06)Compatibile withSkosProvider 0.7.0. (#59)Prevent unnecessary loading of conceptschemes. (#56)Update to RDFlib 5.0.0 (#69)Supports Python 2.7, 3.6, 3.7 and 3.8. Last version to support Python 2.0.4.2 (2017-09-06)Really stop loading the conceptscheme while initialising the provider.0.4.1 (2017-09-06)Stop loading the conceptscheme while initialising the provider.0.4.0 (2017-07-15)Stop collecting SKOS Concept and Collection subclasses. They are now included
in the code base since they seem to have become rather stable and this reduces
the startup time of the provider significantly. (#28)Add support for python 3.6 when testing.0.3.1 (2016-09-14)Handle a bug with private language tags. Currently not recognised by the
language_tags library. The Getty services do use them. When encountered, we
fall back to the undeterminded language. (#26, #27)0.3.0 (2016-08-11)Upgrade to skosprovider 0.6.0. (#13)Add support for theULANvocabulary. (#22)Add support for sorting. (#24)Allow configuring the requests session in use. (#25)0.2.1 (2015-03-10)Introduce language support. Until now it was impossible to pass in a language
parameter to certain methods. This was not only a missing feature, but also a
bug since the VocabularyProvider interface requires that a client can pass in
extra keywords. (#16)iso-thes:superordinates get fetched from the SPARQL store. (#17)All network requests now go through requests. (#13)Some documentation improvements. (#15)0.2.0 (2014-12-22)Compatibile withSkosProvider 0.5.x.Now uses the IANA language codeundfor labels in an unknown language.Now throws a ProviderUnavailableException when the Getty vocab services can’t
be reached.Handle superordinates and subordinate arrays.Error handling and bugfixes.0.1.0 (2014-09-30)Initial versionContains providers forAATandTGNvocabularies.Compatible withSkosProvider 0.3.0.
Package: skosprovider-heritagedata
Skosproviderimplementation of theHeritagedata Vocabularies(Historic England,
Historic Environment Scotland, Royal Commission on the Ancient and
Historical Monuments of Wales, …), can be used in conjunction with theAtramhasis SKOS editorto allow
linking your own vocabularies to the Heritagedata thesauri and importing from them.Building the docsMore information about this library can be found indocs. The docs can be
built usingSphinx.Please make sure you have installed Sphinx in the same environment where
skosprovider_heritagedata is present.# activate your virtual env$pipinstallsphinx$pythonsetup.pydevelop$cddocs$makehtml
Package: skosprovider_oe
This library implements aSKOS provider <https://github.com/koenedaele/skosprovider>for the thesaurus webservice ofhttps://inventaris.onroerenderfgoed.be/thesaurus.Usagefromskosprovider_oe.providersimportOnroerendErfgoedProvidertypologie=OnroerendErfgoedProvider({'id':'TYPOLOGIE'},'https://inventaris.onroerenderfgoed.be/thesaurus/typologie')concepts=typologie.get_all()0.5.0 (2016-08-12)Make compatible with skosprovider 0.6.x0.4.2 (2016-02-22)Update to latest version of services.0.4.1 (2015-03-02)Make sure a Concept or Collection gets passed a ConceptScheme from the
provider. (#7) [dieuska]0.4.0 (2015-01-25)Make compatible with skosprovider 0.4.xDrop support for Python 2.6 and 3.2Add support for subordinate arrays and superordinate concepts.Make notes available through the provider.0.3.0 (2014-06-24)Make compatible with skosprovider 0.3.xSwitch to a requests.Session for handling requests.Switch to py.testAdd support for Coveralls0.2.1 (2013-12-06)Fix dependency on skosprovider < 0.3.00.2.0 (2013-05-16)Upgrade to maintain compatibility with skosprovider 0.2.00.1.1 (2013-??-??)Some doc fixes0.1.0 (2013-03-11)Initial version
Package: skosprovider-sqlalchemy
A SQLAlchemy implementation of theskosproviderinterface.Migrating to skosprovider_sqlalchemy 2.0.0A change in the models has been made which requires a database upgrade.
The “concept” table’s “concept_id” column has changed from being an int to a string.Existing databases will therefor require a small change to update table scheme.
Typically this will look like:ALTER TABLE concept ALTER COLUMN concept_id TEXT NOT NULL;Building the docsMore information about this library can be found indocs. The docs can be
built usingSphinx.Please make sure you have installed Sphinx in the same environment where
skosprovider_sqlalchemy is present.# activate your virtual env$pipinstall-rrequirements-dev.txt$cddocs$makehtml
Package: skoy
Example PackageThis is a simple example package. You can useGithub-flavored Markdownto write your content.seq -f "Line %05g" 10000 > sample.txt
curl -F "file=@sample.txt" -F "resumable=2"http://httpbin.org/postcurl -F "file=@sample.txt" -F "resumable=2"https://hookb.in/r1L3ZdxyzLcQnP1o2lqbcurl -X POST -H "Content-Type: application/json" -d '{"name": "John"}'https://hookb.in/r1L3ZdxyzLcQnP1o2lqbcurl --trace trace.txt -F "file=@sample.txt" -F "resumable=2"http://httpbin.org/post
Package: skpar
SKPAR is a software tool intended to automate the optimisation of
parameters for the Density Functional Tight Binding (DFTB) theory
as implemented in dftb+ (BCCMS Bremen), lodestar (The University
of Hong Kong) and other computer codes. SKPAR was formerly SKOPT.Documentation:http://skpar.readthedocs.ioSKPAR is under MIT licence.
Package: skpomade
skpomade: PrObabilistic MAtrix DEcompositions from Halko et al., 2011Python implementation of algorithms from paperFinding Structure
with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix
Decompositions, by N. Halko, P. G. Martinsson and J. A. Tropp, SIAM review,
53 (2), 2011.InstallInstall the current release withpip:pip install skpomadeFor additional details, see doc/install.rst.UsageSee thedocumentation.BugsPlease report any bugs that you find through theskpomade GitLab project.You can also fork the repository and create a merge request.Source codeThe source code of skpomade is available via itsGitLab project.You can clone the git repository of the project using the command:git clone git@gitlab.lis-lab.fr:valentin.emiya/skpomade.gitCopyright © 2019-2020Laboratoire d’Informatique et SystèmesUniversité d’Aix-MarseilleCentre National de la Recherche ScientifiqueUniversité de ToulonContributorsValentin EmiyaLicenseReleased under the GNU General Public License version 3 or later
(seeLICENSE.txt).
Package: skprofiling
# Scikit Profiling
Profiling of machine learning models based on scikit-learning interface## ExamplesThe following examples can give you an impression of what the package can do:[Binary Classification](https://github.com/tiagohcalves/scikit-profiling/blob/master/notebooks/lib-testing-binary.ipynb)## InstallationYou can install directly from PyPI:pip install skprofilingOr from source:` git clonehttps://github.com/tiagohcalves/scikit-profilingcd datapipe pip install . `
Package: skql
Deploying a Machine Learning model is always a resource and cost intensive task.This package converts a scikit-learn based machine learning models to SQL query, thereby allowing a model to be executed on a target data-set with the already existing SQL based database.The current version is capable of conerting:Decision Tree Classifier modelsinto a series of case when statements, where the resultant value could be the resultant class or the probability value associatd with a class.Other model-type conversions will be released in subsequent versions.Free software: MIT licenseUsageInstall the packagepip install skqlImport skql packageimport skqlInitialize the ‘DecisionTreeClassifierConverter’ instance with a valid scikit-learn DecisionTreeClassifier model object.dtConverter = skql.DecisionTreeClassifierConverter( clf )Classes of the tree are:
[‘“Setosa”’ ‘“Versicolor”’ ‘“Virginica”’]
Choose class index accordingly if probability values are required. :)A message is displayed after initialization, as shown above.
The message shows the classes, the model wa tarined with in a particular order.
If a SQL query is to be generated that outputs the probability value for a particular class,
the index of the class for which probability values are needed is slected from this list. starting from 0.
For example of the SQL query to be generated need propabilty output for class “Versicolor”, the index to be entered is 1.Get the list of columns that were used to train the data-set (without the target valriable)used_columns = iris.drop([‘variety’], axis=1).columns[‘sepal_length’, ‘sepal_width’, ‘petal_length’, ‘petal_width’]If a SQL query is to be generated that classifies the dataset into respective classes.sql = dtConverter.fit( cols = used_columns )print(sql)If a SQL query needs to be generated that gives probability values for class “Setosa”,
given that that class order is [‘“Setosa”’ ‘“Versicolor”’ ‘“Virginica”’] as shown when initialsing the DecisionTreeClassifierConverter instance.sql = dtConverter.fit( cols = used_columns, vbh_classes = False, class_index = 0 )print(sql)CreditsCreated and manageed by:
Aditya Kumaradityakumar3008@gmail.comhttps://www.linkedin.com/in/nullp0inter/This package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: skquantreg
Scikit wrapper for quantreg============================Scikit wrapper for quantregInstallation / Usage----------------------------To install use pip:```$ pip install skquantreg```Or clone the repo:```$ git clone https://github.com/Marco-Santoni/skquantreg.git$ python setup.py install```Usage:```from skquantreg import QuantileRegressorimport numpy as npX = np.random.rand(10,2)y = np.random.rand(10)X_test = np.random.rand(5,2)model = QuantileRegressor(q=0.5)model.fit(X, y)model.predict(X_test)```
Package: skqulacs
scikit-qulacsscikit-qulacs is a library for quantum neural network. This library is based on qulacs and named after scikit-learn.RequirementspipPython >= 3.7InstallationpippipinstallskqulacsBuild from SourceInstallpoetrybefore building package.gitclonehttps://github.com/Qulacs-Osaka/scikit-qulacs.gitcdscikit-qulacs
poetryinstallDocumentationTutorial:scikit-qulacs チュートリアル(Only Japanese version is available now)API Documentation and example notebooks:scikit-qulacs documentation
Package: skreep
SkreepIt's fun data scraping with just a few lines of code. Basically Skreep is a function to make it easier to run selenium.Examplefrom skreep.skreep import Skreep
from skreep.download import dl
from skreep.datasheet import sheet
from skreep.save import Save
from skreep.display import display

sk = Skreep()
sv = Save(name='output')
datasheet = sheet('datasheet')

for i in datasheet:
    sk.get(i, sc=5)
    title = sk.tag('h1')
    print(display(title))
    sv.save(display(title))

sk.quit()DependencyseleniumDriverChromehttps://chromedriver.chromium.org/downloadsDonateBTC37rkr9cpVVcxg8vUpF65Cp9Mjgu1WrKD3dor
PaypalHere
Package: sk-regex-maker
Variable HandlerThis is a Python package that provides aVariableHandlerclass for extracting variable values from a string of variable declarations.InstallationTo use theVariableHandlerpackage, you need to install it first. You can install it using pip:pipinstallsk_variable_handlerUsageTo extract variable values from a string of variable declarations, follow these steps:Import theVariableHandlerclass from thevariable_handlermodule:fromsk_variable_handlerimportVariableHandlerCreate an instance of theVariableHandlerclass:variable_handler=VariableHandler()Call theget_valuesmethod of theVariableHandlerinstance, passing the string of variable declarations as an argument. It returns a dictionary containing the variable names as keys and their corresponding values as values.declarations="Your variable declarations"values=variable_handler.get_values(declarations)ExampleHere is an example that demonstrates the usage of theVariableHandler:fromvariable_handlerimportVariableHandlervariable_handler=VariableHandler()declarations="$x=1+2;$y=2+1;$var=12+223+(222+2)+sin(90);$var2=$x+$y;$xy=($var2+$x+$y);$yx=$xy+$var2"expected_result={'$x':'3.0','$y':'3.0','$var':'460.0','$var2':'6.0','$xy':'12.0','$yx':'18.0'}result=variable_handler.get_values(declarations)assertresult==expected_resultTest CasesThe package includes a set of unit tests to verify the functionality of theVariableHandlerclass. The tests cover various scenarios and edge cases to ensure the correct extraction of variable values.To run the tests, execute the script as a standalone Python program:python<filename>.pyThe output will indicate whether all the tests have passed or if there are any failures.Please note that the tests are implemented using theunittestframework. Each test case is independent and tests a specific aspect of theVariableHandlerfunctionality.ContributingContributions to theVariableHandlerpackage are welcome! If you encounter any issues, have suggestions for improvements, or would like to add new features, please create a pull request or open an issue on the GitHub repository.LicenseTheVariableHandlerpackage is distributed under theMIT License. See theLICENSEfile for more information.
Package: skretrieval
The University of Saskatchewan Retrieval Frameworkskretrievalis an atmospheric retrieval framework developed at the University of Saskatchewan.
It has been successfully used to retrieved atmospheric properties from satellite instruments such as OSIRIS
and OMPS-LP, as well as numerous.  At it's core,skretrievalis an optimal estimation framework built around
theSASKTRAN Radiative Transfer Model.  Future versions of the framework
are being developed aroundVersion 2 of the SASKTRAN Model.InstallationThe preferred method to install SASKTRAN2 is through conda packageconda install -c usask-arg -c conda-forge skretrievalalternatively throughpip,pip install skretrievalThe package is tested on Python versions >= 3.10, on Windows/Linux/MacOS platforms.UsageDocumentation can be found athttps://sasktran2.readthedocs.io/Licenseskretrieval is made available under the MIT license subject to the Commons Clause condition (see license.md). Effectively this is a MIT license restricted for academic, educational, and/or non-profit use, for commercial use please contact the package authors. Commerical level support may also be available for specific applications.