Saravanan666/pypi-org huggingface dataset:
Package: regressor
regressorThis is a small library that tries to run simple linear regression quickly on
modern x86 hardware. This uses vectorized operations to speed up calculating dot
products and means. The input numpy arrays need to be 1D with 32-bit floats.As a result, this is ~20X faster than scipy.stats.linregress, but only runs
on x86-64 hardware with AVX extensions (most desktops and servers as of 2020).InstallpipinstallregressorUsage>>>importnumpyasnp>>>fromregressorimportlinregress>>>x=np.array([1.0,2.0,3.0,4.0,5.0],dtype=np.float32)>>>y=np.array([5.0,4.0,3.0,2.0,1.0],dtype=np.float32)>>>linregress(x,y)LinregressResult(slope=-1.0,intercept=6.0,rvalue=-1.0,pvalue=1.2e-30,stderr=0.0)PerformanceThe graph below compares the times required for simple linear regressions between
this package and scipy.stats.linregress, starting from arrays with 10 elements
up to arrays with 100 million elements. This was run on a 2.6 GHz Skylake CPU.
In general, this package performs simple linear regression in about 1/20th of
the time required by scipy.stats.linregress.ReliabilityThe regression results from this package match scipy.stats.linregress to within
4 decimal places (for the slope, intercept, r-value, p-value and standard error).
The graphs below demonstrate this consistency by comparing betas, r-values and
p-values from this package vs scipy.stats.linregress. These used randomly
sampled values with varying correlation between the X and Y arrays to assess
reliability across a wide range of P-values.
Package: regressormetricgraphplot
Common Evaluation metrics graph plot for RegressionDescriptionPython implementations for comparing different Regression Models and Plotting with their most common evaluation metrics.The purpose of this package is to help users plot the graph at ease with different widely used metrics for regression model evaluation for comparing them at a glanceFigure: Model evaluation plot with widely used metricsTable of ContentsCompare Regression Model Metrics PlotInstallationExamplesDemo)ContactInstallation$ pip install regressormetricgraphplot

     OR

$ git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot
$ cd RegressorMetricGraphPlot
$ python setup.py installNotebook!git clone https://github.com/ajayarunachalam/RegressorMetricGraphPlot.git
cd RegressorMetricGraphPlot/

Just replace the line 'from CompareModels import *' with 'from regressioncomparemetricplot import CompareModels'Follow the rest as demonstrated in the demo example [here] -- (https://github.com/ajayarunachalam/RegressorMetricGraphPlot/blob/main/regressormetricgraphplot/demo.ipynb)Installation with AnacondaIf you installed your Python with Anacoda you can run the following commands to get started:# Clone the repositorygitclonehttps://github.com/ajayarunachalam/RegressorMetricGraphPlot.gitcdRegressorMetricGraphPlot# Create new conda environment with Python 3.6condacreate--newyour-env-namepython=3.6# Activate the environmentcondaactivateyour-env-name# Install conda dependenciescondainstall--yes--fileconda_requirements.txt# Instal pip dependenciespipinstallrequirements.txtExamplesNavigate to the demo example in a form of iPython notebooks: --hereDemo* demo.ipynbContactIf there's some implementation you would like to see here or add in some examples feel free to do so. You can reach me atemail
Package: regressors
Easy utilities for fitting various regressors, extracting stats, and making relevant plotsFree software: ISC licenseDocumentation:https://regressors.readthedocs.org.FeaturesTODOCreditsTools used in rendering this package:Cookiecuttercookiecutter-pypackageHistory0.0.1 (2015-11-24)First release on PyPI.0.0.2 (2015-12-08)Remove old LinearRegression class.Add residuals, qq, scree, and principal components pairwise plots.Add stats function for calculating SSE, adjusted R2, coefficients standard
error, t-values, p-values, F-statistic, and residuals.Add a summary function for summarizing stats on linear models.Add a PCR class for performing principal components regression.0.0.3 (2015-12-08)Fix typo of ‘principle components’ to ‘principal components’.
Package: regressout
Regress Out Covariates (WIP)TODOs: Configuring this templateCreate a Netlify site for your repository, then turn off automatic builds in Netlify settings.Add these CI secrets:PYPI_API_TOKEN,NETLIFY_AUTH_TOKEN(Netlify user settings - personal access tokens),DEV_NETLIFY_SITE_ID,PROD_NETLIFY_SITE_ID(API ID from Netlify site settings)Set up Codecov at TODOOverviewInstallationpipinstallregressoutUsageDevelopmentSubmit PRs againstdevelopbranch, then make a release pull request tomaster.# Optional: set up a pyenv virtualenvpyenvvirtualenv3.9regressout-3.9echo"regressout-3.9">.python-version
pyenvversion# Install requirementspipinstall--upgradepipwheel
pipinstall-rrequirements_dev.txt# Install local packagepipinstall-e.# Install pre-commitpre-commitinstall# Run testsmaketest# Run lintmakelint# bump version before submitting a PR against master (all master commits are deployed)bump2versionpatch# possible: major / minor / patch# also ensure CHANGELOG.md updatedChangelog0.0.1First release on PyPI.
Package: regressproxy
Proxy classes for regression analysisOverviewThis package provides classes to easily model proxies for regression analysis.
Originally intended to be used for (trace gas density) time series,
but it might be useful in other cases too.
The classes allow for a lag and finite proxy lifetime to be include when
modelling the time series.Development happens on githubhttps://github.com/st-bender/pyregressproxy.InstallPrerequisitesAll dependencies will be automatically installed when usingpip installorpython setup.py, see below.
However, to speed up the install or for use
within acondaenvironment, it may be advantageous to
install some of the important packages beforehand:numpyat least version 1.13.0 for general numerics,celeriteat least version 0.3.0 for the "standard" model interface,PyMC3, optional, for the PyMC3 model interface, andPyMC4, optional, for the PyMC4 model interface.numpyshould probably be installed first becauseceleriteneeds it for setup.
It may also be a good idea to installpybind11beforehand
becauseceleriteuses its interface.Depending on the setup,numpyandpybind11can be installed viapip:pipinstallnumpypybind11orconda:condainstallnumpypybind11regressproxyOfficial releases are available aspippackages from the main package repository,
to be found athttps://pypi.org/project/regressproxy/, and which can be installed with:$pipinstallregressproxyThe latest development version of regressproxy can be installed withpipdirectly from github
(seehttps://pip.pypa.io/en/stable/reference/pip_install/#vcs-supportandhttps://pip.pypa.io/en/stable/reference/pip_install/#git):$pipinstall[-e]git+https://github.com/st-bender/pyregressproxy.gitThe other option is to use a local clone:$gitclonehttps://github.com/st-bender/pyregressproxy.git
$cdpyregressproxyand then usingpip(optionally using-e, seehttps://pip.pypa.io/en/stable/reference/pip_install/#install-editable):$pipinstall[-e].or usingsetup.py:$pythonsetup.pyinstallUsageThe module is imported as usual:>>>importregressproxyBasic class and method documentation is accessible viapydoc:$pydocregressproxyLicenseThis python package is free software: you can redistribute it or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, version 2 (GPLv2), seelocal copyoronline version.
Package: regret
regretis a library for deprecating functionality in Python
libraries and applications.Its documentation lives onRead the Docs.Deprecationsregretcan deprecate:[x] callables[x] functions[x] classes[ ] subclassable classes[ ] attributes[ ] of modules[ ] of classes (& methods)[ ] of instances[ ] descriptors[ ] classmethod[ ] modules[ ] current module[ ] other module[ ] parameters to callables[x] previously required parameters that will be removed[x] optional parameters that are now required[ ] deprecated values for parameters[ ] type changes for parameters[ ] mutual exclusion[ ] interfaces[ ] PEP 544 protocols[ ]zope.interfaces[x] inheritability of a classDesign Goalsregretis meant to cover all of the deprecations an author may encounter.It is intended to:be versioning system agnostic (i.e.SemVer,CalVer,HipsTer,
etc.), because deprecations originate from a version, a point in
time, or both.be documentation system aware (i.e.Sphinx,epydoc,Plaintext, etc.), because
deprecations need communication.be itself fully tested, because deprecations must not break the
code they deprecatesupport removal date indication, and likely “policies” which
automate choosing default removal dates, because deprecations
ultimately intend some ultimate changemake “clean code” trivially easy to deprecate, and make complex
codepossibleto deprecate, because the deprecation process is
fraught with edge cases and unforeseen necessity.minimize the amount of deprecation-related code required for
authors, since deprecations are boring, and we all want to focus on
developing our libraries instead.In particular, as a lofty first milestone, it is intended to cover all
of the specific deprecations required for thesejsonschema issues,
and with luck, to subsume all the functionality present intwisted.python.deprecate.
Package: regrez
This is a simple Python package that aims to make using linear regression easier for programmers.You can create a simple linear regression model as following:from regrez import Models

m = Models.Simple("path/to/csv", "label for column that'll be used for x axis", "label for column that'll be used for y axis")After that, you can train your model usingm.Train()and test usingm.Test(test_x, test_y). Alternatively, there is a function calledm.TrainAndTest()you can use if you only want to see how accurate would the model work. It separates 20% of the data for testing, trains the model with the rest of it, tests the model with separated data and shows how accurate your model is. You can usem.Visualize()after training if you want to see a plot showing both data points and the line to see how relative your variables are.You can create a multiple regression model as following:from regrez import Models
m = Models.Multiple("path/to/csv", ["X Label 1", "X Label 2", "X Label 3"], ["Y Label"])After that, you can train your model usingm.Train()and test usingm.Test(test_x, test_y). Alternatively, there is a function calledm.TrainAndTest()you can use if you only want to see how accurate would the model work. It separates 20% of the data for testing, trains the model with the rest of it, tests the model with separated data and shows how accurate your model is. There is nom.Visualize()for multiple linear regression models.
Package: regridcart
regridcartRegridding lat/lon to local Cartesian coordinatesThis package takes care of regridding data defined with latitude/longitude
coordinates onto a local Cartesian grid of a fixed resolution. To use it you
simply define the domain you want to regrid onto and then call theresamplemethod.Usageimportregridcartasrcimportxarayasxrda_src=xr.open_dataarray("...")target_domain=rc.LocalCartesianDomain(central_latitude=lat0,central_longitude=lon0,l_meridional=1000.0e3,l_zonal=3000.0e3,)dx=1.0e3# new resoluion 1kmda_regridded=rc.resample(target_domain,da=da_src,dx=dx)The provided data-array is assumed to have latitude/longitude
coordinates defined by one of the following:latandloncoordinates along which the data is aligned, i.e.latandlonare given as 1D arrayslatandlonare given as auxilliary variables so that the data isn't
aligned along the lat/lon directions, but rather thelatandlonof
every datapoint is giventhe data-array has projection information defined in a CF-compliant
manner using thegrid_mappingattribute
(http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch05s06.html)the data-array was loaded from a raster-file usingrioxarray.open_rasterioso that the projection information is
available viada.rio.crsThe package also implements cropping (rc.crop_field_to_domain), plotting
domain outline (domain.plot_outline) and can also with data already on a
Cartesian grid withrc.CartesianDomain. Seenotebooks/examples.ipynbfor detailed examples.Installationregridcartcan be installed withpipfrompypi, but it
relies oncartopyandxesmfwhich in turn rely onprojandemsf, these
can most easily be installed withconda:condainstallxarraycartopyxesmf-cconda-forge
pipinstallregridcart
Package: regrippy
RegRippyis a framework for reading and extracting useful forensics data from Windows registry hives. It is an alternative toRegRipperdeveloped in modern Python 3. It makes use of William Ballenthin'spython-registryto access the raw registry hives.The goal of this project is to provide a framework for quickly and easily developing your own plugins in an incident response scenario.By default, the script will look for the various hives by reading theREG_SYSTEM,REG_SOFTWARE,REG_SAM,REG_NTUSERandREG_USRCLASSenvironment variables. This allows the analyst to simplyexportthese in their current shell session and not have to worry about specifying them every time they invoke the script.
Alternatively, you can use the--rootswitch to specify the path to the root of theC:drive. RegRippy will automatically look into the right places depending on which hive each plugin needs.All plugins should also support both a human-readable and machine-readable output (theBodyfileformat), allowing easy piping tomactimeor other tools.
Package: regru-cloudapi
REG.RU CloudAPIНеофициальная библиотека для работы с API услугиReg.ruОблачные VPS.Документация по API Reg.ru Облачные VPS -developers.cloudvps.reg.ruПриступая к работеУстановка при помощи PyPiУстановка вручнуюЗависимостиБыстрый стартДокументацияЛицензияПриступая к работеУстановка при помощи PyPi:pip3installregru_cloudapiУстановка вручную:gitclonehttps://github.com/plvskiy/regru_cloudapi.gitcdregru_cloudapi
pythonsetup.pyinstallЗависимости:setuptools
requestsБыстрый стартСперва импортируем модульfromregru_cloudapiimportCloudAPIДалее авторизуемся:api=CloudAPI('token')Токен можно получить в Личном Кабинете -> Услуга Облачные сервера ->
Настройки -> Токен для APIПодробно -https://developers.cloudvps.reg.ru/getting-started/authentication.html.Следующим шагом используем нужные нам функции. Например, запросим список тарифов:api.get_tariffs()На выходе мы получим список тарифов в JSON. Пример:{"prices":[{"plan":"cloud-1-1018","price":"0.32","price_month":215,"type":"reglet","unit":"hour"}]}Полный код:fromregru_cloudapiimportCloudAPIapi=CloudAPI('token')api.get_tariffs()Документация -DOCUMENTATION.mdЛицензия -LICENSE
Package: reg-stat-inference
Regression and Statistical Inference ToolkitWelcome to thereg_stat_inferencetoolkit!This toolkit provides functions for treating multicollinearity and performing feature selection based on p-values in linear regression and logistic regression models. It leverages thestatsmodelslibrary for model analysis.PurposeThe purpose of this toolkit is to offer a collection of Python functions that streamline the process of dealing with multicollinearity and feature selection in regression models. It aims to simplify the analysis of complex datasets by automating tasks like variance inflation factor (VIF) calculation and p-value-based feature removal.reg_stat_inferencereg_stat_inferenceis a Python package that provides functions for treating multicollinearity and performing statistical inference in regression models. It offers tools to identify and address multicollinearity issues and to iteratively drop features based on p-values. This can help you build more interpretable and robust regression models.InstallationYou can installreg_stat_inferenceusing pip:pipinstallreg-stat-inferenceUsageTo use the toolkit, import the functions in your scripts or notebooks:importpandasaspdimportstatsmodels.apiassmfromreg_stat_inferenceimporttreat_regression_model# Create sample dataX=pd.DataFrame({'feature1':[1,2,3],'feature2':[4,5,6]})y=pd.DataFrame({'target':[0,1,0]})# Use the treat_regression_model function with OLS regressionresult=treat_regression_model(X,y,threshhold_vif=5,threshold_pval=0.05,reg_type='OLS')# Print the treated feature list and the model summaryprint("Treated Features:",result.metric_list)print(result.model.summary())You can then apply these functions to your dataset to enhance the quality of your regression models.Git RepoFor detailed documentation and usage examples, please refer to theGitHub repository.ContributingContributions are welcome! If you have suggestions, bug reports, or improvements, please open an issue or submit a pull request.
Package: regta
regtaProduction-ready scheduler with async, multithreading and multiprocessing support for Python.Core FeaturesVarious Job Types- Create async, thread-based,
or process-based jobs depending on your goals.Flexible Intervals- Use standardtimedeltaor specially designedPeriodfor highly responsible jobs.Multi-Paradigm- Design OOP styled
or functional styled jobs.CLI Interface- Regta provides a CLI tool
to start, list and create jobs by template.Professional Logging- Redefine standard logger
and define your own. ANSI coloring is supported.You may discover scheduling alternatives and find the comparison with Regta onregta.alinsky.tech/alternativesInstallationInstall usingpip install regtaorpoetry add regta.If you use python < 3.9, then also install backports:pip install "backports.zoneinfo[tzdata]".You can check if Regta was installed correctly with the following commandregta --version.ExampleTo write async job just use@regta.async_job()decorator.# jobs/my_jobs.pyfromdatetimeimporttimedeltafromregtaimportasync_job,Period@async_job(Period().every(10).seconds)asyncdefmy_period_based_job():return"1. Hello world! This is just a log message."@async_job(timedelta(seconds=10))asyncdefmy_timedelta_based_job():return"2. You may use `timedelta` or `Period` as interval."@async_job(Period().on.sunday.at("18:35").by("Asia/Almaty"))asyncdefmy_sunday_job():return"3. `Period` is recommended for highly responsible jobs because it does not accumulate shift."Read more about various job typeshere.Start UpTo start jobs useregta runcommand:$regtarun
>[3]jobswerefound.
>2023-01-0818:31:00,005[jobs.my_jobs:my_period_based_job][INFO]-1.Helloworld!Thisisjustalogmessage.
>2023-01-0818:31:05,622[jobs.my_jobs:my_timedelta_based_job][INFO]-2.Youmayuse`timedelta`or`Period`asinterval.
...
>2023-01-0818:34:50,002[jobs.my_jobs:my_period_based_job][INFO]-1.Helloworld!Thisisjustalogmessage.
>2023-01-0818:34:55,689[jobs.my_jobs:my_timedelta_based_job][INFO]-2.Youmayuse`timedelta`or`Period`asinterval.
>2023-01-0818:35:00,001[jobs.my_jobs:my_sunday_job][INFO]-3.`Period`isrecommendedforhighlyresponsiblejobsbecauseitdoesnotaccumulateshift.
...Read CLI referencehere.Full documentation and reference are available onregta.alinsky.tech
Package: reg-tables
DescriptionA simplelinearmodelsextension to run panel regressions with different specifications and export the results in a professional-looking latex tableInstallationpip install reg_tablesSample Usagefromreg_tablesimport*# Generate Random panelN=10**3df=pd.DataFrame({'x1':np.random.randn(N),'x2':np.random.randn(N),})df['entity']=np.random.randint(0,10,N)df['time']=np.random.randint(0,50,N)# Generate the `y` variabledf['y']=2*df['x1']-0.5*df['x2']+np.random.randn(N)# Generate the `y2`, with some fixed effectsdf['y2']=df['y']+(df['entity']%3)*10+np.where(df['time']>10,-50,0)# Set the panel's double-indexdf=df.set_index(['entity','time'])# Define the baseline specificationbaseline=Spec(df,'y2',['x1','x2'],double_cluster=True)# The renaming dictionaryrename={'y2':'Salary','x1':'Education','x2':'Age',}# Create the modelmodel=Model(baseline,rename_dict=rename)# Define some other regression specificationsmodel.add_spec(y='y2',entity_effects=True)model.add_spec(y='y2',time_effects=True)model.add_spec(y='y2',entity_effects=True,time_effects=True)# Run all the specificationsres=model.run()resClasses and MethodsThis package consists of two classes:SpecandModel.Specdefines the regression specifications, including the panel dataset, the independent variable, and the independent variables. Optional arguments for this class include specifying whether the regressions should be performed with entity effects, time effects or both (entity_effects,time_effectsorall_effectsarguments respectively). Methods forSpecclass include.run, which runs the regression and.renameâ€“ a method to rename variable according to the dictionary passed.TheModelclass is a wrapper around thecomparefunction of linearmodels. When creatingModel, one has to specify the baseline regression specification, passed as aSpecobject. Optional arguments include passing arename_dict, according to which the variables are going to be renamed, as well as setting anall_effectsBoolean variable, which will add the four versions of baseline Spec object with all possible combinations of entity and time effects to the model. TheModelclass has.rename,.add_specand.remove_specmethods. The latter has a mandatory index argument and second optional index argument, which, if passed would work as a end point for slice.  The.runmethod executes all Spec objects within Model and outputs them to a table. Optional argumentcoeff_decimalsallows to specify the number of decimals for coefficient estimates and t-values, whilelatex_pathallows to save the output table to a disk.
Package: regtest
Regression test enhancement for the PythonunittestframeworkWriting tests is important
(seehere).
And when it comes to an existing and running application even more.
Existing results must at any chance be reproduced (like-for-liketests).An easy way to add many test cases
is by invoking the application and its subroutines many times.
But taking notes (and hardcoding) of all results is annoying.Hereregtestmight help.Simply, write routines that invoke our application.
The initial run will collect and store return values in files.
The next time (and at any time these routines run) the return values
will be checked against the stored ones.To reset a routine simply remove the corresponding file
(named accordingly) of stored reference data.
The next time the reference data will be rebuild.
Package: regtester
# RegEx TesterThis project will help to have a simple module that will help to test regexes in a simple and structured way.# INSTALLATIONpip install regtester# Usageregtester folder/file path to execute one or many tests in a folder
every test has to inherit the class RegExTest inside the module and also finish with Test.py
The class name must be the same as the file name without the .pyClass tes
# TestThe module can be tested using the command : regtester-test
Package: regtricks
RegtricksTools for manipulating, combining and applying image transformations.Install via pip:pip install regtricksDocumentation:https://regtricks.readthedocs.io/en/latest/Further readingAn explanation of the FSL coordinate system:https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/FAQAcknowledgementsThe FLIRT matrix adjustment code was supplied by Tim Coalson and Martin Craig, adapted from (https://github.com/Washington-University/workbench/blob/9c34187281066519e78841e29dc14bef504776df/src/Nifti/NiftiHeader.cxx#L168) and (https://github.com/Washington-University/workbench/blob/335ad0c910ca9812907ea92ba0e5563225eee1e6/src/Files/AffineFile.cxx#L144)ContactTom Kirk, 2020.
Institute of Biomedical Engineering, University of Oxford.thomas.kirk@eng.ox.ac.uk