Saravanan666/pypi-org huggingface dataset:
Package: recmetrics-pyspark
recmetrics-pyspark: recommender systems metrics for big datarecmetrics-pysparkobtains the most relevant internal metrics for items recommendations from pySpark DataFrames. It efficiently handles huge amounts of data. Most routines are adapted from therecmetricslibrary which works with pandas DataFrames.DISCLAIMER: recmetrics-pyspark is not affiliated nor endorsed by recmetrics or its authors.
Some routines have been adapted from recmetrics to work with pySpark DataFrames
and/or to handle bigger datasets. Therefore, some chunks of code have been copied verbatim,
and functions and parameters names have been kept the same (as much as possible) for better usability.Furthermore, if you are dealing with small datasets, we recommend to use the recmetrics library (https://github.com/statisticianinstilettos/recmetrics) instead, as it most efficiently handles smaller datasets.Where to get itThe source code is currently hosted on GitHub at:https://github.com/camiloakv/recmetrics-pysparkBinary installers for the latest released version are available at thePython
Package Index (PyPI).pipinstallrecmetrics-pysparkAvailable metrics as of version 0.0.1:long_tail_plotcoverageNovelty:novelty_refacA small refactoring of recmetrics' implementation.novelty_pandasSimilar implementation to novelty_refac but using pandas DataFrames as inputsnoveltypySpark implementationpersonalizationintra_list_similarities
Package: recnetlogin
RecNet-LoginThis is a Python package that allows you to acquire yourRecNetbearer token and more with your account credentials!
This is the rewritten version! For the older version,visit the legacy branch.FeaturesAutomatically renewing tokenSupports 2FA accountsDecoding the bearer tokenDetailed exceptionsInstallationDone via pip:pipinstall-UrecnetloginUsageCheck theexamplesfolder for useful examples to get started with the package.Alternative ways of getting your bearer tokenRun this JS script on your browser's DevTools console.url=window.location.hrefif(url.includes("rec.net")){try{b_token="Bearer "+JSON.parse(localStorage.getItem("oidc.user:https://auth.rec.net:recnet"))["access_token"]console.log(b_token)alert("Successfully printed bearer token on console!")}catch(err){alert("Not logged in on RecNet!")}}else{alert("Not in rec.net")}Generate from cookieRec Room API Tool Boxmade byzigzatuzoo
Package: recnetpy
RecNetPyRecNetPyis an API wrapper built in Python for pulling data fromRecNet.RecNetPyaims to be easy to use yet powerful. It's the same wrapper used to powerRecNetBot!InstallationAll platforms viapip:pipinstall-UrecnetpyQuickstartCreating an instance of RecNetPy:importrecnetpyRecNet=recnetpy.Client(api_key="...")An example that showcases how to fetch an account by username and acquire its bio:importrecnetpy# Import the moduleimportasyncioasyncdefmain():# Create a new RecNetPy client instanceRecNet=recnetpy.Client(api_key="...")# Fetch the user from the AccountManager with the "get" methoduser=awaitRecNet.accounts.get("ColinXYZ")# Fetch the bio from the Account dataclassbio=awaituser.get_bio()# Print and close the clientprint(bio)awaitRecNet.close()asyncio.run(main())For more examples and usage, please refer to theexamples. More documentation can be foundHere.AuthorizationIn order to use most of the endpoints, you need an API key. You may acquire one fromhttps://devportal.rec.net/. Endpoints that require an API key are marked in function docstrings.For more information and guidance, refer tohttps://recroom.zendesk.com/hc/en-us/articles/16543324225303-Third-Party-API-Access-and-Usage.Development setupTo install a local build run the following command.pipinstall.MetaDistributed under the MIT license. SeeLICENSEfor more information.ContributingFork it (https://github.com/RecNetBot-Development/RecNetPy/fork)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: recoder
НазначениеПакет нужен чтобы чинить “кракозябры” (или “краказябры”) в читаемый текст. Например: “õîğîøèé òåêñò” => “хооший текст”.Установка$ git clone https://bitbucket.org/dkuryakin/recoder.git
$ cd recoder && python setup.py installили$ pip install recoderПолезные командыИспользование как консольная тулза.$ echo "Îñíîâíàÿ Îëèìïèéñêàÿ äåðåâíÿ â" | python -mrecoder [coding]По умолчанию, coding=utf-8.Использование в кодеЧаще всего с кракозябрами справится такой базовый пример:fromrecoder.cyrillicimportRecoderrec=Recoder()broken_text=u'Îñíîâíàÿ Îëèìïèéñêàÿ äåðåâíÿ â'fixed_text=rec.fix_common(broken_text)printfixed_text.encode('utf-8')Если базовый пример не справился, можно поиграться с настройками:fromrecoder.cyrillicimportRecoderrec=Recoder(depth=4)broken_text=u'...'fixed_text=rec.fix(broken_text)# fix работает дольше и сложнее чем fix_common...Можно использовать частоупотребимые слова (и, на, к, в, …) как индикатор успеха перекодировки. Но в этом случае текст починится только если в нём есть эти слова:fromrecoder.cyrillicimportRecoderrec=Recoder(use_plus_words=True)...ЗамечанияВ данный момент поддерживается только кириллица.РасширениеЕсли хочется расширить библиотеку не только кириллицей, предусмотренна удобная тулза:$ cat some_learning_text.txt | python -mrecoder.builder [coding]По-умолчанию, coding=utf-8. На stdin подавать текстовку для обучения. На выходе получится 2 файлика: 3grams.json и plus_words.json. Далее всё делается по аналогии с recoder.cyrillic.ТестыТут всё просто:$ git clone https://bitbucket.org/dkuryakin/recoder.git
$ cd recoder && python setup.py testChangelogv0.1.0Реалиизация базовой функциональности.v0.2.0Добавлние декодеров. Теперь умеет декодить такие кракозябры (взял примеры на 2cyr.com):&egrave;&eth;&egrave;&euml;&egrave;&ouml;&agrave;%D0%A2%D0%BE%D0%B2%D0%B0+%D0%B5+%D0%BA&#229;&#228;&#237;&#224; &#227;&#238;&#228;&#232;&#1080;&#1088;&#1080;&#1083;&#1080;&#1094;&#1072;v0.3.0Добавлена поддержка python3.v0.3.1Удаление из зависимостей пакета regex.Минорные фиксы.
Package: recognize
RecognizesThis CLI allows you to upload files to the ML Pipeline and query results.First StepsYou first need to make sure that you have the requirements needed to install the CLI as well as install the CLI itself.Make sure that you havepythonandpipinstalled. It is recommended that you use a virtual environment although not
a requirement.Then to install the CLI run the following command in your shell:pipinstallrecognizeUsageAfter installing the CLI run the following command:recognize--helpThis should show you a list of all the available commands with a short description of what they do. You can append
the--helpoption to each command for more details about the command. For example:recognizeupload--helpThis should give you a list of all the subcommands for this command, the options, and the order in which to write them.ExamplesTo upload a directory of videos, accepted file types aremp4andmov:recognizeuploaddirectorypath/to/directory/of/videos--tagsome_custom_tagThe tag is useful for if you want to organise the uploads. You can search for labels associated with the tag and you can
upload files using this tag at a later stage, and they will be automatically grouped.After uploading videos it will take a few minutes to process them all. After which you can start querying.recognizesearchkeywords--outputresults.csvsomeinterestingwordsThe--outputargument is optional, if ommited you will be asked what to name the output interactively instead.You can also search for specific entry types returned by AWS Rekognition. For example to which resources might have
potentially harmful, violent, or offensive content run the following command:recognizesearchentries--outputresults.csvmoderationFinally, to search for a particular face run the following command, accepted file types arepngandjpeg:recognizesearchfaces--outputresults.csvfile/path/to/image/of/faceResultsFor keyword and entry search the columns in the csv (or keys in the records for the json output) will be:ColumnDescriptionidThe unique identifier for the entry.average_confidenceThe average confidence score.confidencesThe list of confidence scores.entry_typeThe type of entry. Either label, alias, category, parent, moderation.nameThe value associated with the entry.timestampsThe timestamps related to the entry.urlThe S3 bucket URL of the associated video.tagThe tag related to the entry.The faces search will have the following columns (or keys):ColumnDescriptionfaceIdThe unique identifier for the face.imageIdThe unique identifier for the image.externalImageIdThe path to the resource in the S3 bucket with metadata.similarityThe similarity of the face to the input image.searchedFaceConfidenceConfidence in whether a face was detected in the input.timestampThe timestamp of the face. In milliseconds.
Package: recognizer
reCognizer v1.3reCognizer is a free-to-use AI basedreCaptchaSolver.Usable with an easy-to-use API, also available for Async and Sync Playwright.You can pass almost any format into the Challenger, from full-page screenshots, only-captcha images and no-border images to single images in a list.Note: You Should use an undetected browser engine likeBotrightto solve the Captchas consistently.reCaptcha detects normal Playwright easily and you probably wont get any successful solves despite correct recognitions.Install it from PyPIpipinstallrecognizerExamplesPossible Image InputsExample Solve Video (Good IP & Botright)https://github.com/Vinyzu/recognizer/assets/50874994/95a713e3-bb46-474b-994f-cb3dacae9279Basic Usage# Only for Type-HintsfromtypingimportTypeVar,Sequence,UnionfrompathlibimportPathfromosimportPathLikeaccepted_image_types=TypeVar("accepted_image_types",Path,Union[PathLike[str],str],bytes,Sequence[Path],Sequence[Union[PathLike[str],str]],Sequence[bytes])# Real CodefromrecognizerimportDetectordetector=Detector()task_type:str="bicycle"images:accepted_image_types="recaptcha_image.png"area_captcha:bool=Falseresponse,coordinates=detector.detect(task_type,images,area_captcha=area_captcha)Playwright UsageSync Playwrightfromplaywright.sync_apiimportsync_playwright,Playwrightfromrecognizer.agents.playwrightimportSyncChallengerdefrun(playwright:Playwright):browser=playwright.chromium.launch()page=browser.new_page()challenger=SyncChallenger(page)page.goto("https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox-explicit.php")challenger.solve_recaptcha()browser.close()withsync_playwright()asplaywright:run(playwright)Async Playwrightimportasynciofromplaywright.async_apiimportasync_playwright,Playwrightfromrecognizer.agents.playwrightimportAsyncChallengerasyncdefrun(playwright:Playwright):browser=awaitplaywright.chromium.launch()page=awaitbrowser.new_page()challenger=AsyncChallenger(page)awaitpage.goto("https://recaptcha-demo.appspot.com/recaptcha-v2-checkbox-explicit.php")awaitchallenger.solve_recaptcha()awaitbrowser.close()asyncdefmain():asyncwithasync_playwright()asplaywright:awaitrun(playwright)asyncio.run(main())Copyright and License©VinyzuGNU GPL(Commercial Usage is allowed, but source, license and copyright has to made available. reCaptcha Challenger does not provide and Liability or Warranty)Thanks toQIN2DIM(For basic project structure)
Package: recogpipe
DeepSpeech as a (Docker) Service for IBusThis repository includes the following:a Docker container that can run DeepSpeech hardware-accelerated by your
host OS's (nVidia) graphics card; the container reads audio from a pipe and
reports results to an eventan IBus Engine that allows the reslts of the recognition to be treated as
regular input to the (Linux) host operating systemInstallation/Setupgit clone https://github.com/mcfletch/deepspeech-docker.git
cd deepspeech-dockerStarting the daemon in a docker container:./recogpipe/dockersetup.py
# when docker container is started...
./recogpipe/daemon.pyFeeding some data into the daemon from your microphone:ffmpeg-audio.shFeeding some data into the daemon from a wav file:ffmpeg-sample.sh path/to/your/file.wavRunning the IBus daemon on your desktop:./recogpipe/ibusengine.pyTesting bits out...Sending audio to the daemon./ffmpeg-audio.sh hw:1,1
./ffmpeg-sample.sh /path/to/some.wavViewing the raw transcripts from the daemonnc -U /tmp/dspipe/eventsRunning the ibus engine interactively:./ibusengine.py
Package: recohut
recohuta python library for building recommender systems.Explore the docs »View Demo·Report Bug·Request FeatureAbout The Project(back to top)Built WithPythonPyTorchLightningnbdev(back to top)Getting StartedTo get a local copy up and running follow these simple example steps.Prerequisitespytorchpipinstalltorchlightningpipinstallpytorch-lightningInstallationpip install recohut(back to top)Usage# import the required modulesfromrecohut.datasets.movielensimportML1mDataModulefromrecohut.models.nmfimportNMFfromrecohut.trainers.pl_trainerimportpl_trainer# build the datasetclassArgs:def__init__(self):self.data_dir='/content/data'self.min_rating=4self.num_negative_samples=99self.min_uc=5self.min_sc=5self.val_p=0.2self.test_p=0.2self.num_workers=2self.normalize=Falseself.batch_size=32self.seed=42self.shuffle=Trueself.pin_memory=Trueself.drop_last=Falseself.split_type='stratified'args=Args()ds=ML1mDataModule(**args.__dict__)ds.prepare_data()# build the modelmodel=NMF(n_items=ds.data.num_items,n_users=ds.data.num_users,embedding_dim=20)# train and evaluate the matrix factorization modelpl_trainer(model,ds,max_epochs=5)Checkthisquick tutorial.For more examples, please refer to theDocumentationandTutorials.(back to top)Roadmap[] RecSys Model Deployment and MLOps features[] RL agents and environment specific to recommender systems[] Visualization utilities and EDASee theopen issuesfor a full list of proposed features (and known issues).(back to top)ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make aregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! Thanks again!Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull Request(back to top)LicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.(back to top)ContactSparsh A.@sparsh-ai(back to top)Acknowledgmentsnbdev teamfor providing supporting tools to build this library.colab teamfor providing running VMs instances for development and testing.(back to top)
Package: recombinant
IntroductionThis library is meant to supplement the presentationPhonotactic well-formedness is recombinant, given at the55th Annual Meeting of the SLE.It learns typologically diverse phonotactics from positive examples alone.Installationpip install recombinantExampleHere, we generalize the set /miememei, oumumo, momomu, iememi/ and then elicit ratings for /meem, memo, mumo/.fromrecombinantimportLearnerL=Learner(['miememei','oumumo','momomu','iememi',])L.optimize()print(L.rate('meem'))# => 0.4873949579831933print(L.rate('memo'))# => 0print(L.rate('mumo'))# => 0.7899159663865546print(L.basis)# => ['momu', 'momo', 'oumo', 'memi', 'mi', 'emi', ...]The actual learning takes place during the call toL.optimize(). Sometimes, running this optimization routine more than once gives better results.
Package: recomb-to-s3
Recomb to S3InstallationInstalling from PyPI is as easy as doing:pipinstallrecomb-to-s3create the following environment variables in your settings:AWS_ACCESS_KEY_ID="__your_secret_id__"AWS_SECRET_ACCESS_KEY="__your_secret_key__"AWS_STORAGE_BUCKET_NAME="__your_bucket_name__"AWS_S3_SIGNATURE_VERSION="s3v4"AWS_S3_REGION_NAME="__your_region_name__"AWS_S3_FILE_OVERWRITE=False# true if you want to write over the file in s3AWS_DEFAULT_ACL="public-read"# to have access by the django adminDEFAULT_FILE_STORAGE="storages.backends.s3boto3.S3Boto3Storage"add recomb_to_s3 and storages in your installed appsINSTALLED_APPS=["storages","recomb_to_s3",]run the migrationpythonmanage.pymigrateAboutThe library aims to facilitate the sending of python dictionaries to an amazon s3 backet, however it is possible to send any type of file, or you can import the AbstractRecombToS3 class and add or remove fields according to your needs.How To Usethe most basic way to use this package is very simple, just import the "send_dict_to_s3" function and use it as in the example below.fromrecomb_to_s3.contribimportsend_dict_to_s3data={"test":"test"}my_model=send_dict_to_s3(data=data,file_name="my_file.json",author=None)Dependencies[tool.dependencies]python="^3.10"boto3="^1.26.47"django-storages="^1.13.2"django="^4.1"ContributingAlexandre Jastrow da Cruz
Package: recommendation-model-server
inference serviceflowchart TD
    A[ModelArtifact] -->B(Model Instance)
    G[InferenceFeatures] -->  B
    B --> C[VenueRatings]
    C -->D(Search List)Training Pipeline Source Code:https://github.com/ra312/personalizationSource Code:https://github.com/ra312/model-serverA service to rate venuesInstallationpython3-mpipinstallrecommendation-model-serverRunning locally on hostIf you choose to use pre-trained model in artifacts/rate_venues.picklepython3-mrecommendation_model_server\--host0.0.0.0\--port8000\--recommendation-model-pathartifacts/rate_venues.pickleIn separate tab, please runcurl-X'POST'\'http://0.0.0.0:8000/predict'\-H'accept: application/json'\-H'Content-Type: application/json'\-d'[{"venue_id": -4202398962129790000,"conversions_per_impression": 0.3556765815,"price_range": 1,"rating": 8.6,"popularity": 4.4884057024,"retention_rate": 8.6,"session_id_hashed": 3352618370338455600,"position_in_list": 31,"is_from_order_again": 0,"is_recommended": 0}]'Running in containerdockerpullakylzhanov/my-fastapi-app
dockerrun-d--namemy-fastapi-container-p8000:8000--rmakylzhanov/my-fastapi-appDevelopmentClone this repositoryRequirements:PoetryPython 3.8.1+Create a virtual environment and install the dependenciespoetryinstallActivate the virtual environmentpoetryshellTestingpytesttestsPre-commitPre-commit hooks run all the auto-formatters (e.g.black,isort), linters (e.g.mypy,flake8), and other quality
checks to make sure the changeset is in good shape before a commit/push happens.You can install the hooks with (runs for each commit):pre-commitinstallOr if you want them to run only for each push:pre-commitinstall-tpre-pushOr if you want e.g. want to run all checks manually for all files:pre-commitrun--all-filesHow to run load testsStart service locally,python3-mrecommendation_model_server\--host0.0.0.0\--port8000\--recommendation-model-pathartifacts/rate_venues.pickleRun load test with locust 1million users with spawn rate 100 users per second, i.e.poetryshell&&pytesttests/test_invokust_load.py-sThe output is similar to (the time is in milliseconds)Rampingto1000000usersatarateof100.00persecond
TypeName# reqs      # fails |    Avg     Min     Max    Med |   req/s  failures/s--------||-------|-------------|-------|-------|-------|-------|--------|-----------
POST/predict14530(0.00%)|44851948390|167.830.00
--------||-------|-------------|-------|-------|-------|-------|--------|-----------Aggregated14530(0.00%)|44851948390|167.830.00This project was generated using thewolt-python-package-cookiecuttertemplate.
Package: recommender-engine
Recommender is a recommendation application using either item-based or user-based approaches.Recommender is at versionv0.3.0, also seechange logfor more details on release history.If you like this project, feel fee to leave a few words of appreciation hereBuildQualitySupportPlatformTable of contentsUsageContributionLicenseUsageInstall with pip$ pip install recommender-engineAPImake_recommendation(person_to_recommend, preference_space, recommender_approach='user_based', number_of_items_to_recommend=10, similarity_measure='euclidean_distance')Return list of recommendation items based on the chosen approach and similarity emasure

	Parameters
	--------------
	person_to_recommend (str): user id/name to recommend to

	preference_space (dict):  keys are user id/name and values are dictionary of items and ratings

	recommender_approach (str): support 'user_based' (default) or 'item_based'

	number_of_items_to_recommend (int): number of items to recommend (default=10)

	similarity_measure (str): similarity measurement method , support 'euclidean_distance' (default), 'cosine' or 'pearson_correlation'Example>>> from recommender_engine import make_recommendation
>>>	result = make_recommendation(person_to_recommend = "userA",
								 preference_space = preference_space,
								 recommender_approach = 'user_based',
								 number_of_items_to_recommend = 10,
								 similarity_measure = 'euclidean_distance')The preference space is dictionary data structure where keys are users and values are dictionary of items and ratingspreference_space = {
					'userA : {
							 'item1' : 'ratingA1, 
							 'item2' : 'ratingA2',
							  ..., 
							  'itemn' : 'ratingAn
							  }, 
					..., 
					'userZ:{
							'item1' : 'ratingZ1,
							 'item2' : 'ratingZ2',
							  ...,
							 'itemn' : 'ratingZn
							}
				    }Tested DatasetsThe project has been tested on these DatasetsJesterMovieLensContributionPlease follow our contribution convention atcontribution instructionandcode of conductPlease check out theissue filefor list of issues that required helps.AppreciationFeel free to add your name into thelist of contributors. You will automatically be inducted into Hall of Fame as a way to show my appreciation for your contributionsHall of FameLicenseSee theLICENSEfile for license rights and limitations (Apache License 2.0).
Package: recommender-pkg
Recommender PackageImplementations of explicit and implicit recommenders with Numpy and Keras.$pipinstallrecommender-pkgAuthor:Mian UddinAdvisor:Dr. Paul AndersonCourse(s):CSC 491, CSC 492School:Cal Poly, San Luis ObispoUsageimportrecpkgSee thedocumentationfor
more.BuildUse the following command to build a wheel of this package.$python3-mbuildTestUse the following command to run unit tests.$python3-munittesttestsDocumentUse the following command to build the documentation.$sphinx-apidoc-f-odocs/sourcerecommender_pkg/
$(cddocs/&&makehtml)ReleaseUse the following command to distribute to PyPi.$python3-mtwineuploaddist/*