Saravanan666/pypi-org huggingface dataset:
Package: labels2tables
labels2tables extracts keywords from a bibtex file, and uses them to generate an academic summary table comparing the articles.ExampleInput:@article{duch_quantifying_2010,
         keywords = {game:soccer, model:network:centrality, open-access}}
@article{yamamoto_common_2011,
         keywords = {game:soccer, model:network:scale-free, open-access}}
@article{yaari_hot_2011,
         keywords = {game:basketball, model:sequence, open-access}}Transformation:import labels2tables
labels = labels2tables.bib2labels("examples/sport.in.bib")
labels2tables.labels2txt(labels, "examples/sport.out.txt")Output:========================================================
game       model       open-access reference
========================================================
soccer     network
            centrality Y           duch_quantifying_2010
            scale-free Y           yamamoto_common_2011
basketball sequence    Y           yaari_hot_2011
========================================================AdvancedThe intermediate labels format encodes table data using standard Python dictionaries, lists and tuples. Seeexamples/*.spec.txtfor example tables, and how to describe them as a labels dictionary.AcknowledgementsPowered bybibtexparser
Package: labelspark
The Official Labelbox <> Databricks Python IntegrationLabelboxenables teams to maximize the value of their unstructured data with its enterprise-grade training data platform. For ML use cases, Labelbox has tools to deploy labelers to annotate data at massive scale, diagnose model performance to prioritize labeling, and plug in existing ML models to speed up labeling. For non-ML use cases, Labelbox has a powerful catalog with auto-computed similarity scores that users can leverage to label large amounts of data with a couple clicks.This library was designed to run in a Databricks environment, although it will function in any Spark environment with some modification.We strongly encourage collaboration - please free to fork this repo and tweak the code base to work for you own data, and make pull requests if you have suggestions on how to enhance the overall experience, add new features, or improve general performance.Please report any issues/bugs viaGithub Issues.Table of ContentsRequirementsSetupExample NotebooksRequirementsDatabricks: Runtime 10.4 LTS or LaterApache Spark: 3.1.2 or LaterLabelbox accountGenerate a Labelbox API keySetupSet up LabelSpark with the following lines of code:%pip install labelspark -q
import labelspark as ls

api_key = "" # Insert your Labelbox API key here
client = ls.Client(api_key)Once set up, you can run the following core functions:client.create_data_rows_from_table():   Creates Labelbox data rows (and metadata) given a Spark Table DataFrameclient.export_to_table():  Exports labels (and metadata) from a given Labelbox project and creates a Spark DataFrameExample NotebooksImporting DataNotebookGithubBasics: Data Rows from URLsData Rows with MetadataData Rows with AttachmentsData Rows with AnnotationsPutting it all TogetherExporting DataNotebookGithubExporting Data to a Spark TableWhile using LabelSpark, you will likely also use the Labelbox SDK (e.g. for programmatic ontology creation). These resources will help familiarize you with the Labelbox Python SDK:Visit our docsto learn how the SDK worksCheckout ournotebook examplesto follow along with interactive tutorialsView the LabelboxAPI reference.
Package: label-studio-evalme
label-studio-evalmeEvaluation metrics packageInstallationSimple installation from PyPIpipinstalllabel-studio-evalmeOther installation methodsPip from source
```bash
# with git
pip install git+https://github.com/heartexlabs/label-studio-evalme.git@master
```What is Evalme?Evalme is a collection of Label Studio evaluation metric implementations and an easy-to-use API to create custom metrics. It offers:A standardized interface to increase reproducibilityReduced boilerplateOptimized metrics for Label StudioGet started with EvalmeYou can use Evalme with any Label Studio versions or with Label Studio Enterprise.Load existing data from Label StudioUse the Label Studio REST API to load existing data from your instance of Label Studio or Label Studio Enterprise.Specify your Label Studio URL, access token and project ID in the parameters:fromevalme.matcherimportMatcherloader=Matcher(url="http://127.0.0.1:8000",token="ACCESS_TOKEN",project='1')loader.refresh()You can also load data from exported annotation files from Label Studio, exported usingthe APIor theLabel Studio UI:fromevalme.matcherimportMatcherloader=Matcher()loader.load('your_filename')After you load data, it is available in the_raw_datafield.Built-in metricsBy default there is a naive metric object. It evaluates annotation differences with a naive approach:
if an object is fully equal to another one, the evaluation method returns 1,
otherwise it returns 0.To use the built-in metrics, do the following:fromevalme.matcherimportMatcherloader=Matcher()loader.load('your_filename')# Run agreement_matrix method to get matrix for all your annotationsmatrix=loader.agreement_matrix()# print resultprint(matrix)Implement your own metricYou can implement your own metric by creating an evaluation function and registering it in Metrics class.For example, create an evaluation function with 2 parameters for compared objects:fromevalme.matcherimportMatcher# write your own evaluation function or use existing onedefnaive(x,y):"""Naive comparison of annotations"""iflen(x)!=len(y):result=0else:foriinrange(len(x)):ifx[i]['value']!=y[i]['value']:result=0breakelse:result=1returnresult# Register it in Metrics objectMetrics.register(name='naive',form=None,tag='all',func=naive,desc='Naive comparison of result dict')# create Matcher object from previous exampleloader=Matcher()loader.load('your_filename')matrix=loader.agreement_matrix(metric_name='naive')# print resultprint(matrix)Contribute!The Label Studio team is hard at work adding even more metrics, but we're looking for incredible contributors like you to submit new metrics and improve existing ones!Join ourSlack communityto get help becoming a contributor!CommunityFor help or questions, join our huge community onSlack!LicensePlease observe the MIT License that is listed in this repository.
Package: labelsync
The real purpose of this project is purely artificial. It serves as a task for MI-PYT course at CTU and will see no real use.With that out of the way, let’s pretend the project serves a real purpose from this moment on.Labelsync utility serves as a label synchronizer. It helps you maintain consistent label definitions across multiple git repositories
and allows for easy label definitions editing.Assignment featuresHosted athttp://novotkry.pythonanywhere.comSupports github and gitlab for managed repositoriesEasy to extend by extending a base classGit repository as an authority for labelsSimple html gui for label and master repo managementLogin data managed separately from label and repository dataInstallation and usage$ python setup.py install
$ export FLASK_APP=labelsync
$ flask runThen visit the web application on displayed address.LicenseThis project is licensed under the WTFPL License - see theLICENSEfile
for more details.
Package: labelu-ml
IntroductionQuickstartSetup environmentcondacreate-nlabelu-mlpython=3.7
condaactivatelabelu-mlInstall labelu-mlpipinstalllabelu-mlInitialize an ML application based on an example path:labelu-mlinitmy_ml_app--pathlabelu_ml/examples/the_simplest_app

pipinstall-rmy_ml_app/requirements.txtStart ML application serverlabelu-mlstartmy_ml_appUsageCommand line$ labelu-ml --help
                                                                                                                                                     
 Usage: labelu-ml [OPTIONS] COMMAND [ARGS]...                                                                                                        
                                                                                                                                                     
╭─ Options ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --install-completion          Install completion for the current shell.                                                                           │
│ --show-completion             Show completion for the current shell, to copy it or customize the installation.                                    │
│ --help                        Show this message and exit.                                                                                         │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ init                  Initailize an ML app from a example.                                                                                        │
│ start                 Start ML app server.                                                                                                        │
╰───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Package: labelutils
labelutilsPython scripts to perform conversions betweenlabelbuddy’s and other annotation tools’ file formats.InstallationCan be installed from PyPI withpip install labelutilsBut as this is WIP, it might be better at the moment to install from the Git repository to get a more up-to-date version:pip install "git+https://github.com/jeromedockes/labelutils.git"UsageAfter installing we can use the commandlabelutils_convert:inception → labelbuddy formatlabelutils_convert --from inception  --to labelbuddy --in_txt_dir /path/to/txt_dir --in_wtsv_dir /path/to/wtsv_dir --out_jsonl docs.jsonlWhere:/path/to/txt_diris a directory containing.txtfiles that have been imported into Inception, ie the contents of the documents.path/to/wtsv_diris a directory containing the.wtsvWebAnno files exported from Inception, whose names must match those of the.txtfiles except for the extension.labelbuddy → inception formatlabelutils_convert --from labelbuddy --to inception --in_jsonl docs.jsonl --in_wtsv_dir  /path/to/wtsv_dir --out_inception_dir inception_docsWhere:docs.jsonlcontains documents & annotations in labelbuddy format./path/to/wtsv_diris a directory containing.wtsvWebAnno files exported from Inception. If it contains any annotations, they are ignored, but this file is still needed to get Inception's tokenization of the documents.output will be stored ininception_docs
Package: label-wrapper
Label wrapperUser friendly image bootstraping framework.Label bootstrapping flowLabel wrapper enables label bootstrapping process:Load first data batchManually label first batchTrain first segmentation modelLoad second data batchUse first trained segmentation model to predict labelsReview labels and merge first and second labelled datatrain the second segmentation modelRepeat steps 4.-7. until out of raw data or review of labels is no longer required.Technical implementation exampleLoad data into datasetExport htmlLabelExport to jsonImport json and convert json to tfrecordsTrain on tfrecordsIntroduce new dataPredict with trained model to tf recordsImport stored tfrecords and convert to html with labelsReview stored labels and export to jsonJoin reviewed json and manual json (from step 4)Repeat 5 - 11 for n timesRun out of data to labelMeasure performanceTODOFinnish dual data dataset with gtiff (add test)mask to shapefile (geocoded)shapefile exportershapefile imporoter?example inference step with a pretrained segmentation cnn(maybe) constructor should take json and load it in postinit(maybe) Add via html tests with js (selenium?)ThanksLabel editor used isVIA 2.0.6.
Package: labelx
labelxLabel Creator for GitLab Projects.Free software: GNU General Public License v3Documentation:https://labelx.readthedocs.io.FeaturesShow package informationCreate labels for GitLab groups/projectsCreate badges for GitLab groups/projectsCustom label/badge from.yamlfileRequirementspython >= 3.6config.yamlat~/.config/labelx/---login:host:gitlab.company.comprotocol:httpstoken:KHGJIO**-dA76VGHs36CreditsSeeAUTHORS.rstThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History2.3.1 [30.03.2022]Fix dependency issue in setup.pyIssue #132.3.0 [30.03.2022]Updated documentationAdded group level support for labels and badgesUpdated testsFixed spelling mistakesMinor console output changes2.2.1 [28.03.2022]Add group level badges and labelsFix version upgrade bugUpdate documentation2.1.1 [19.06.2020]Fixed bug with license badgeUpdated readme and docs2.1.0 [19.06.2020]Fixed bug with link_urlChanged look and feel1.0.5 [19.06.2020]Create badges with default valuesAdded testsFixed formatting1.0.4 [02.04.2020]Create labels with default valuesShow package informationAdded more testsRe-newed documentation1.0.3 (unreleased) [30.03.2020]Show package informationAdded test cases
Package: labext
labextOverviewThis library provides pre-built widgets and integrates some additional JS libraries to provide an even better experience to work with data in Jupyter Lab.Features:Better display for pandas data frame.Pop-over content, allowing to show additional content when hovering over a DOM element.Widgets for interactive labeling/annotating data in Jupyter Lab.UsageSee theDemonotebook for how to use the library. Demo:Converting DataFrame to DataTablePop-over (Tippy)SliderAnnotatorInstallationDockerI provide aDockerfilethat create a base environment to run this library. To run thedemonotebook in docker, you can usedocker-composeto start a jupyter lab:docker-composeupManualTo use this with JupyterLab, you need to haverequirejsenable in Jupyter by installing either:https://github.com/DraTeots/jupyterlab_requirejs(jupyterlab version <= 1.*)https://github.com/binh-vu/jupyterlab_requirejs(jupyterlab version >= 2.*)Besiderequirejs, you also need to haveipywidgets,ipyevents,ipycallbackpipinstallipywidgetsipyeventsipycallback
jupyternbextensionenable--pywidgetsnbextension
jupyternbextensionenable--py--sys-prefixipyevents
jupyterlabextensioninstall@jupyter-widgets/jupyterlab-manageripyeventsipycallbackFinally, install this package directly frompypi:pipinstall-UlabextDevelopmentTo compile the typescript files you need to install:npminstall@types/jqueryThen compiles all.tsfiles, remove--watchif you don't want to monitor for changes.tsc--targetes2017--watch**/*.ts
Package: labfis
labfis.pyDescriptionSmall library for uncertainty calculations and error propagation.Error propagation:The uncertainty is calculated analytically in accordance with the gaussian propagation aproximation established by theInternational Bureau of Weights and Measures (BIPM):To compare two labfloats it is used the following methods:Assuming:If they are equal they must satisfy:If they are different they must satisfy:NOTE:Two labfloats can be not different and not equal at the same time by these methods.Made by and for Physics Laboratory students in IFSC, who can't use uncertainties.py because of mean’s absolute deviation used in its calculation.UsageJust import withfrom labfis import labfloatand create anlabfloatobject, as this exemple below:>>>fromlabfisimportlabfloat>>>a=labfloat(1,3)>>>b=labfloat(2,4)>>>a*b(2±7)Check theWikifor more details.InstalationIntstall main releases with:pip install labfisInstall development version with:pip install git+https://github.com/phisgroup/labfis.py@developmentReferencesKirchner, James."Data Analysis Toolkit #5: Uncertainty Analysis and Error Propagation"(PDF).Berkeley Seismology Laboratory. University of California. Retrieved  22 April  2016.Goodman, Leo(1960). "On the Exact Variance of Products".Journal of the American Statistical Association.55(292): 708–713.doi:10.2307/2281592.JSTOR2281592.Ochoa1,Benjamin; Belongie, Serge"Covariance Propagation for Guided Matching"Ku, H. H. (October 1966)."Notes on the use of propagation of error formulas".Journal of Research of the National Bureau of Standards.70C(4): 262.doi:10.6028/jres.070c.025.ISSN0022-4316. Retrieved  3 October  2012.Clifford, A. A. (1973).Multivariate error analysis: a handbook of error propagation and calculation in many-parameter systems. John Wiley & Sons.ISBN978-0470160558.Lee, S. H.; Chen, W. (2009). "A comparative study of uncertainty propagation methods for black-box-type problems".Structural and Multidisciplinary Optimization.37(3): 239–253.doi:10.1007/s00158-008-0234-7.Johnson, Norman L.; Kotz, Samuel; Balakrishnan, Narayanaswamy (1994).Continuous Univariate Distributions, Volume 1. Wiley. p. 171.ISBN0-471-58495-9.Lecomte, Christophe (May 2013). "Exact statistics of systems with uncertainties: an analytical theory of rank-one stochastic dynamic systems".Journal of Sound and Vibrations.332(11): 2750–2776.doi:10.1016/j.jsv.2012.12.009."A Summary of Error Propagation"(PDF). p. 2. Retrieved  2016-04-04."Propagation of Uncertainty through Mathematical Operations"(PDF). p. 5. Retrieved  2016-04-04."Strategies for Variance Estimation"(PDF). p. 37. Retrieved  2013-01-18.Harris, Daniel C. (2003),Quantitative chemical analysis(6th ed.), Macmillan, p. 56,ISBN978-0-7167-4464-1"Error Propagation tutorial"(PDF).Foothill College. October 9, 2009. Retrieved  2012-03-01.Helene,  O.;  Vanin,  V.. Tratamento  estatístico  de  dados  em  física experimental. São Paulo: Editora Edgard Blücher, 1981.Vuolo,  J.  E.. Fundamentos  da  teoria  de  erros.  2.  ed.  São  Paulo:  Editora Edgard Blücher, 1993.
Package: labflow
➤ nbsafetyAboutnbsafetyadds a layer of protection to computational notebooks by solving thestale dependency problemwhen executing cells out-of-order. Here's an
example in action:When the first cell is rerun, the second cell now contains a reference to an
updatedfand is suggested for re-execution with a turquoise highlight. The
third cell contains a reference to a staley--yis stale due to its
dependency on an old value off. As such, the third cell is marked as unsafe
for re-execution with a red highlight.  Once the second cell is rerun, it is
now suggested to re-execute the third cell in order to refresh its stale
output.nbsafetyaccomplishes its magic using a combination of a runtime tracer (to
build the implicit dependency graph) and a static checker (to provide warnings
before running a cell), both of which are deeply aware of Python's data model.
In particular,nbsafetyrequiresminimal to no changesin user
behavior, opting to get out of the way unless absolutely necessary and letting
you use notebooks the way you prefer.InstallpipinstallnbsafetyInterfaceThe kernel ships with an extension that highlights cells with live references
to stale symbols using red UI elements. It furthermore uses turquoise highlights
for cells with live references to updated symbols, as well as for cells that
resolve staleness.RunningTo run annbsafetykernel in Jupyter, select "Python 3 (nbsafety)" from the
list of notebook types in Jupyter's "New" dropdown dialogue. For JupyterLab,
similarly select "Python 3 (nbsafety)" from the list of available kernels in
the Launcher tab.Jupyter Notebook Entrypoint:Jupyter Lab Entrypoint:UninstallpipuninstallnbsafetyLicenseCode in this project licensed under theBSD-3-Clause License.➤ ContributorsStephen MackeRay GongShreya Shankar
Package: labfunctions
LabFunctionsDescriptionLabFunctions is a library and a service that allows you to run parametrized notebooks on demand.It was thought to empower different data roles to put notebooks into production whatever they do, this notebooks could be models, ETL process, crawlers, etc. This way of working should allow going backward and foreward in the process of building data products.Although this tool allow different workflows in a data project, we propose this one as an example:PhilosophyLabFunctions isn't a complete MLOps solution.We try hard to expose the right APIs to the user for the part of scheduling notebooks with reproducibility in mind.Whenever possible we try to use well established open source tools, projects and libraries to resolve common problems. Moreover we force some good practices like code versioning, and the use of containers to run wokrloadsThe idea comes from aNetflix postwhich suggest using notebooks like an interface or a some kind of DSL to orchestrate different workloads like Spark and so on. But it also could be used to run entire process as we said before.The benefits of this approach is that notebooks runned could be stored and inspected for good or for bad executions. If something fails, is easy to run in a classical way: cell by cell in a local pc or in a remote server.Status⚠️ Although the project is considered stable
please keep in mind that LabFunctions is still under active development
and therefore full backward compatibility is not guaranteed before reaching v1.0.0., APIS could change.FeaturesSome features can be used standalone, and others depend on each other.FeatureStatusNoteNotebook executionStable-Workflow schedulingBetaThis allow to schedule: every hour, every day, etcBuild RuntimesBetaBuild OCI compliance continers (Docker) and store it.Runtimes templatesStableGenereate Dockerfile based on templatesCreate and destroy serversAlphaCreate and delete Machines in different cloud providersGPU SupportBetaAllows to run workloads that requires GPUExecution HistoryAlphaTrack notebooks & workflows executionsGoogle Cloud supportBetaSupport google store and google cloud as providerSecrets managmentAlphaEncrypt and manager private data in a projectProject ManagmentAlphaMatch each git repostiroy to a projectCluster optionsIt is possible to run different cluster configurations with custom auto scalling policiesInstances inside a cluster could be created manually or automaticallySee a simple demo of a gpu cluster creationhttps://www.youtube.com/watch?v=-R7lJ4dGI9s:earth_americas: RoadmapSeeRoadmapdraft:post_office: Architecture:bookmark_tabs: References & inspirationsNotebook Innovation - NetflixTensorflow metastoreMaintainable and collaborative pipelinesThe magic of MerlinScale aware approachContributingBug reports and pull requests are welcome on GitHub at theissues
page. This project is intended to be
a safe, welcoming space for collaboration, and contributors are expected to
adhere to theContributor Covenantcode of
conduct.LicenseThis project is licensed under Apache 2.0. Refer toLICENSE.txt.
Package: labgraph-db
WarningThis project is still under development!This library defines a graph-based schema for storing materials science data.You can read the (evolving) documentationhere.Additional DependenciesASamplegraph can be plotted within Python usingSample.plot(). The defaultnetworkxplotting layouts can be pretty confusing to interpret. If you installgraphviz,labgraphwill instead use graphviz to design the graph layout in a hierarchical fashion. This only affects plotting, but if you are relying on this functionality it can make a big difference!