Saravanan666/pypi-org huggingface dataset:
Package: record-recommender
The Record Recommender creates recommendations for Invenio.
By reading page views and downloads from Elasticsearch calculating the
recommendations and storing them in Redis to be retrieve and displayed
by Invenio.UsageThe workflow to generate the recommendations is:recommender fetch 24to cache the last 24 weeks of page views and downloads
from Elasticsearch.recommender profiles 24generate the user profiles from the page views
and downloads. For the not logged in users profiles based on the
ip-address and user agent are created.recommender build 50calculates the recommendations using 50 processes
and stores them in the specified Redis server.Alternative the recommendations can be automatically be fetched, the profiles
generated and the recommendations calculated all this with one command:recommender update_recommender 24 50for the last 24 weeks and using
50 processes.ConfigurationThe configuration file is expected to be in/etc/record_recommender.ymlotherwise the path to the config file can be defined by using the
command line option--config_path.# Record-Recommender configuration.elasticsearch:es_index:['index-2014','index-2015','index-2016']es_user:useres_password:es_host:localhostes_port:443recommendation_version:1# Sentry connection stringsentry:redis:host:localhostport:6379db:0prefix:'Reco_1::'cache:base_path:cache/cache_file_prefix:''logging:version:1disable_existing_loggers:Falseformatters:simple:format:'%(asctime)s-%(name)s-%(levelname)s-%(message)s'handlers:console:class:logging.StreamHandlerlevel:INFOformatter:simplestream:ext://sys.stdoutsentry:class:raven.handlers.logging.SentryHandlerlevel:WARNdsn:file:class:logging.FileHandlerformatter:simplelevel:DEBUGfilename:log_recommender.logloggers:record_recommender:level:DEBUGhandlers:[console,file,sentry]propagate:no# elasticsearch:#   level: WARN#   handlers: [console]#   propagate: noroot:level:ERRORhandlers:[console,file,sentry]Additional to the configuration options found in the config file
environment variables, which overwrite the ones from the config file can be set.RECOMMENDER_ES_PASSWORDto set the Elasticsearch password.RECOMMENDER_SENTRYto set the Sentry connection string.Command lineUsage: recommender [OPTIONS] COMMAND [ARGS]...

Record-Recommender command line version.

Options:
-v, --verbose           Enables verbose mode.
-c, --config_path TEXT  Path to the configuration file.
--help                  Show this message and exit.

Commands:
debug               Debug the application and recommender.
fetch               Fetch newest PageViews and Downloads.
build               Calculate all recommendations.
profiles            Number of weeks to build.
update_recommender  Download and build the recommendations.Debugging the RecommendationsAs first step look into the created user profiles in the definedcachefolder.To explore the graph with all loaded data use therecommender debugcommand to get a interactive python shell.
Package: recordsearch-data-scraper
RecordSearch Data ScraperThe National Archives of Australia’s online database, RecordSearch,
contains lots of rich, historical data. Unfortunately there’s no API, so
we have to resort to screen scrapers to get it out in reusable form.
This is a library of scrapers to extract data about the main entities in
RecordSearch – Items, Series, and Agencies – from both individual
records, and search results.The main classes are:RSItem()– an individual itemRSItemSearch()– an advanced search for itemsRSSeries()– an individual seriesRSSeriesSearch()– an advanced search for seriesRSAgency()– an individual agencyRSAgencySearch()– an advanced search for agenciesRecordSearch makes use of an odd assortment of sessions, redirects, and
hidden forms, which make scraping a challenge. Please let me know if
something isn’t working as expected, as problems can be difficult to pin
down!This is a replacement for the original Recordsearch_tools library. The
main changes are:Requirements have been updated (dropping RoboBrowser which seems to be
no longer maintained)The full range of search parameters are now supported for Items,
Series, and AgenciesThere’s a built-in cache for improved efficiency and speedSee thedocumentationfor
more details. And check out theRecordSearch
sectionof the GLAM Workbench
for examples of what’s possible.Installpip install recordsearch-data-scraperHow to useRetrieve an item using its Item ID.fromrecordsearch_data_scraper.scrapersimport*item=RSItem('3445411')View the item data.item.data{'title': 'WRAGGE Clement Lionel Egerton : SERN 647 : POB Cheadle England : POE Enoggera QLD : NOK  (Father) WRAGGE Clement Lindley',
 'identifier': '3445411',
 'series': 'B2455',
 'control_symbol': 'WRAGGE C L E',
 'digitised_status': True,
 'digitised_pages': 47,
 'access_status': 'Open',
 'access_decision_reasons': [],
 'location': 'Canberra',
 'retrieved': '2021-04-25T21:12:22.620414+10:00',
 'contents_date_str': '1914 - 1920',
 'contents_start_date': '1914',
 'contents_end_date': '1920',
 'access_decision_date_str': '12 Apr 2001',
 'access_decision_date': '2001-04-12'}Search for items.search=RSItemSearch(kw='wragge')View the total number of items in the results set.search.total_results209Access the first page of results.items=search.get_results()View the first result.items['results'][0]{'series': 'A2479',
 'control_symbol': '17/1306',
 'title': 'The Wragge Estate. Property for sale.',
 'identifier': '149309',
 'access_status': 'Open',
 'location': 'Canberra',
 'contents_date_str': '1917 - 1917',
 'contents_start_date': '1917',
 'contents_end_date': '1917',
 'digitised_status': True}The Series and Agency classes follow exactly the same pattern. See thedocsfor more
examples.
Package: recordset
recordsetFeaturesYou can userset configureset the profile information of Tencent cloud through a simple commandYou can use therset rlistcommand to query all record records under DNSPodYou can add a record using therset raddcommandYou can be deleted byrset rdelcommandRequirementsA terminal with Python and pi'p installed:install py environment and python version >= 3.8install tencentcloud-sdk-python packageInstallation~→pip3installrecordsetcommand help:~→rset--help
Usage:rset[OPTIONS]COMMAND[ARGS]...

Options:--versionShowtheversionandexit.--helpShowthismessageandexit.

Commands:configureraddrdelrlistUninstall the recordset~→pip3uninstallrecordsetFeedback & QuestionsLicenseOSI Approved :: MIT License
Package: recorpy
ReCorPyA python's package for reordering/clustering correlation matricesrecorpyreorder correlation matrixes to get better visual analysis. All it is made with the original dataframe. And the result
is a new dataframe with columns reordered, to let you re-use it wherever you want.Input parameters:method-model- , default:cluster- , default:InstallationpipinstallrecorpyUsage>>>df=pd.DataFrame(np.random.rand(3,3))>>>ReorderCorr(df)Development setup$python3-mvenvenv
$.env/bin/activate
$makedeps
$toxContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Make sure to add or update tests as appropriate.UseBlackfor code formatting andConventional Commitsfor commit messages.ChangelogLicenseGPL
Package: recoverpy
Recover overwritten or deleted data.DemoInstallationDependenciesRun with pipxInstallation from pipInstallation from AURUsageTipsContributingRecoverPyRecoverPy is a powerful tool that leverages your system capabilities to recover lost files.Unlike others, you can not only recover deleted files but alsooverwrittendata.Every block of your partition will be scanned. You can even find a string in binary files.DemoInstallation:penguin: RecoverPy is currently only available on Linux systems.:red_circle:You must be root or use sudo.DependenciesMandatory:To list and search through your partitions, recoverpy usesgrep,dd, andlsblkcommands. Although, if you're running a major Linux distrucition these tools should already be installed.Optional:To display real time grep progress, you can installprogress.To install all dependencies:Debian-like:apt install grep coreutils util-linux progressArch:pacman -S grep coreutils util-linux progressFedora:dnf install grep coreutils util-linux progressRun with pipxYou candirectly run recoverpy with pipx in an isolated environmentwithout installing it.To install pipx, follow theofficial documentation.To run recoverpy with pipx, simply run:sudo pipx run recoverpyInstallation from pippython3 -m pip install recoverpyInstallation from AURyay -S python-recoverpyUsagepython3-mrecoverpySelect the system partitionin which your file was. If you are out of luck, you can alternatively search in your home partition, maybe your IDE, text editor, etc. made a backup at some point.Type a text string to search. See tips below for better results.Start search, Results will appear in the left-hand box.Select a result.Once you have found your precious,selectOpen.You can now either save this block individually or explore neighboring blocks for the remaining parts of the file. You could then save it all in one file.TipsAlways do backups! Yes, maybe too late...Unmount your partition before you do anything!Although you can search with your partition still mounted, it is highly recommended to unmount your partition to avoid any alteration to your file.Regarding the searched string:Be concise, find something that could be unique to your file.Stay simple, your string is escaped but exotic characters may affect your results.Try to remember the last edit you have made to your file.When you have found your file:You might see multiple results. Your system often use different partion blocks to save successive versions of a file. Make sure you've found the last version.Try exploring neighboring blocks to be sure to save your whole file.ContributingThank you for considering contributing to RecoverPy.
Any request, bug report or PR are welcome. Please read thecontributing guide.
Package: recpermissions
What is RecPermissions?It’s a script to change Linux permissions and ownership in one step. It can delete empty directories when necessary.This script doesn’t work on Windows.UsageHere you have a console video example:![English howto](https://raw.githubusercontent.com/Turulomio/recpermissions/master/doc/ttyrec/recpermissions_howto_en.gif)Once installed, you can see man documentation withman recpermissionsLinksDoxygen documentation:http://turulomio.users.sourceforge.net/doxygen/recpermissions/Pypi web page:https://pypi.org/project/recpermissions/Gentoo ebuildIf you use Gentoo you can find a ebuild inhttps://github.com/Turulomio/myportage/tree/master/app-admin/recpermissionsDependencieshttps://www.python.org/, as the main programming language.https://pypi.org/project/colorama/, to give console colors.Changelog1.10.0Now user is informed when he tries to change ownership with uid or gid1.9.0Solved bug when using Default None arguments.1.8.0Improved colorized output.1.7.0Fixed critical bug with symlinks1.6.0Octal permissions string is now validated in –files and –directories#15 Absoute path directory given as a paramater changes its permissios too1.5.0Added frech translation1.4.0Improved man pagesIf recpermissions is executed on Windows, just exists script, instead of crash.mangenerator is not needed for setup.py main script1.3.0If file owner isn’t in /etc/passwd now remains its uid, and desn’t crashCode of conduct is added to the projectAdded localized integers in summaryAdded files to french translationAdded –only parameter funcionality to allow change ownership and permissions of one file or directory1.2.0Due to a boolean logic error, some changes didn’t took place1.1.0Added 30 seconds to reload video in howto.pyNothing is changed if –user –group –files or –directories is not set.1.0.0Version fully operationalAdded howto video in English and SpanishMan pages and spanish translation have been improvedAdded summary and added io error exception catching0.2.1Solved critical bug. Directory now is set tu absolut_path parameter0.2.0Added absolute path parameter to avoid errors and wrong changes0.1.1Solved bug in current path directory0.1.0Creating infrastructure
Package: recreatedb
# recreatedb## Useful for Testing Postgres Databases for Django DeploymentsCommand Line Interface tool that looks for a database backup in the form of an .sql file, removes a local Postgres database, recreates it, then adds the database data to the newly created one.To use it assumes you have the Postgres command line tools available on your bash config/profile. If you do not have these tools follow the _Install Postgres App_ section.## Install recreatedbTo add `recreatedb` to the your path, clone it then run the following to create a simlink.```bash# Commandspip3 install recreatedb```## Configure```bash# Commandsrecreatedb configure```This will create a JSON configuration file at `~/.recreatedb`:```{"DATABASE_NAME": "<databse>","DATABASE_USER": "<user>","DUMP_LOCATION": "<location of SQL file>"}```Please update this with your supplied data.## UsageTo use `recreatedb` please ensure you have installed Postgres, have updated the `.recreatedb` configuration file, and have a SQL file readily available you wish to load.```bash# Commandsrecreatedb start```## Install Postgres AppThis is a GUI interface for PostgreSQL databases. Find it here: https://postgresapp.com/Post installation it is important to add Postgres's tools to your system path. Based on the official docs run the following:```bash# Commandsudo mkdir -p /etc/paths.d &&echo /Applications/Postgres.app/Contents/Versions/latest/bin | sudo tee /etc/paths.d/postgresapp```Use which psql to confirm installation while in the virtualenv.## Development*   To create edits please branch and clone the repository.*   Cd into the directory and install + activate a new Pipenv.*   To package use setup.py upload
Package: recruiter-notification-app
PROJECT HUSH RECRUITER NOTIFICATION APPLICATIONPython packagingpython -n buildtwine upload -r testpypi dist/*Docker commands used:docker build -t hushrecruiternotifyimage .docker run --env-file ./env_variables.list -p 5000:5000 hushrecruiternotifyimageSending test link to the selected candidates via e-mailOnce candidates are selected through an algorithm, selected candidates receive a test link with unique token via mail. The test link contains 3 coding questions which need to be submitted within a specified time and the submitted answers will be stored in the mongoDB, otherwise the link will expire.Procedure to run flask web application to send e-mailSet following env variablesMAIL_USERNAME='your_email'MAIL_PASSWORD='your_password'MAIL_PORT='server_port'MAIL_SERVER='smtp_your_mail_server'export FLASK_APP=notification_appexport FLASK_ENV=development
Run application usingflask runorflask --app notification_app --debug runcommandTo run via docker , run below commanddocker run --env-file ./env_variables.list -p 5000:5000 hushrecruiternotifyimage
Package: recs
Contains one essential thing, and a few other minorly useful bits.ImportAllTest: test that all modules import successfullyNot every file is covered by unit tests, particularly scripts;  and unit tests
won’t report any new warnings that occur.ImportAllTestautomatically loads each Python file or module reachable from
your root directory.  Warnings are by default treated as errors.I drop this into each new project as my first test.  It inevitably catches a
host of problems, particularly when I’m in rapid development and running a
subset of tests.In the most common usage, just add this tiny fileall_test.py.
anywhere in your unit test directory:importimport_allclassImportAllTest(import_all.TestCase):passYou can customize how warnings are treated here.all_test.py.
Package: recsim
RecSim: A Configurable Recommender Systems Simulation PlatformRecSim is a configurable platform for authoring simulation environments for
recommender systems (RSs) that naturally supportssequential interactionwith users. RecSim allows the creation of new environments that reflect
particular aspects of user behavior and item structure at a level of abstraction
well-suited to pushing the limits of current reinforcement learning (RL) and RS
techniques in sequential interactive recommendation problems. Environments can
be easily configured that vary assumptions about: user preferences and item
familiarity; user latent state and its dynamics; and choice models and other
user response behavior. We outline how RecSim offers value to RL and RS
researchers and practitioners, and how it can serve as a vehicle for
academic-industrial collaboration. For a detailed description of the RecSim
architecture please readIe et al. Please
cite the paper if you use the code from this repository in your work.Bibtex@article{ie2019recsim,
    title={RecSim: A Configurable Simulation Platform for Recommender Systems},
    author={Eugene Ie and Chih-wei Hsu and Martin Mladenov and Vihan Jain and Sanmit Narvekar and Jing Wang and Rui Wu and Craig Boutilier},
    year={2019},
    eprint={1909.04847},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}DisclaimerThis is not an officially supported Google product.What's new12/13/2019:Added (abstract) classes for both multi-user environments
and agents. Added bandit algorithms for generalized linear models.Installation and Sample UsageIt is recommended to install RecSim using (https://pypi.org/project/recsim/):pipinstallrecsimHowever, the latest version of Dopamine is not in PyPI as of December, 2019. We
want to install the latest version from Dopamine's repository like the following
before we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is
the final 1.x release including GPU support for Ubuntu and Windows.pip install git+https://github.com/google/dopamine.gitHere are some sample commands you could use for testing the installation:git clone https://github.com/google-research/recsim
cd recsim/recsim
python main.py --logtostderr \
  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
  --agent_name=full_slate_q \
  --environment_name=interest_exploration \
  --episode_log_file='episode_logs.tfrecord' \
  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5You could then start a tensorboard and view the outputtensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecordTutorialsTo get started, please check out our Colab tutorials. InRecSim: Overview,
we give a brief overview about RecSim. We then talk about each configurable
component:environmentandrecommender agent.DocumentationPlease refer to thewhite paperfor the
high-level design.
Package: recsim-ng
RecSim NG: Toward Principled Uncertainty Modeling for Recommender EcosystemsRecSim NG, a probabilistic platform for multi-agent recommender systems
simulation. RecSimNG is a scalable, modular, differentiable simulator
implemented in Edward2 and TensorFlow. It offers: a powerful, general
probabilistic programming language for agent-behavior specification; an
XLA-based vectorized execution model for running simulations on accelerated
hardware; and tools for probabilistic inference and latent-variable model
learning, backed by automatic differentiation and tracing. We describe RecSim NG
and illustrate how it can be used to create transparent, configurable,
end-to-end models of a recommender ecosystem. Specifically, we present a
collection of use cases that demonstrate how the functionality described above
can help both researchers and practitioners easily develop and train novel
algorithms for recommender systems. Please refer toMladenov et alfor the
high-level design of RecSim NG. Please cite the paper if you use the code from
this repository in your work.Bibtex@article{mladenov2021recsimng,
    title = {RecSim {NG}: Toward Principled Uncertainty Modeling for Recommender Ecosystems},
    author = {Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier}
    year = {2021},
    eprint={2103.08057},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}DisclaimerThis is not an officially supported Google product.Installation and Sample UsageIt is recommended to install RecSim NG using
(https://pypi.org/project/recsim_ng).pipinstallrecsim_ngHere are some sample commands you could use for testing the installation:git clone https://github.com/google-research/recsim_ng
cd recsim_ng/recsim_ng/applications/ecosystem_simulation
python ecosystem_simulation_demo.pyTutorialsTo get started, please check out our Colab tutorials. InRecSim NG: Basics,
we introduce the RecSim NG model and corresponding modeling APIs and runtime
library. We then demonstrate how we define a simulation usingentities,behaviors, andstories. Finally, we illustrate differentiable
simulation including model learning and inference.InRecSim NG: Dealing With Uncertainty,
we explicitly address the stochastics of the Markov process captured by a DBN.
We demonstrate how to use Edward2 in RecSim NG and show how to use the
corresponding RecSim NG APIs for inference and learning tasks. Finally, we
showcase how the uncertainty APIs of RecSim NG can be used within a
recommender-system model-learning application.DocumentationPlease refer to thewhite paperfor the high-level design.
Package: recsim-no-tf
RecSim: A Configurable Recommender Systems Simulation PlatformRecSim is a configurable platform for authoring simulation environments for
recommender systems (RSs) that naturally supportssequential interactionwith users. RecSim allows the creation of new environments that reflect
particular aspects of user behavior and item structure at a level of abstraction
well-suited to pushing the limits of current reinforcement learning (RL) and RS
techniques in sequential interactive recommendation problems. Environments can
be easily configured that vary assumptions about: user preferences and item
familiarity; user latent state and its dynamics; and choice models and other
user response behavior. We outline how RecSim offers value to RL and RS
researchers and practitioners, and how it can serve as a vehicle for
academic-industrial collaboration.DisclaimerThis is not an officially supported Google product.What's new12/13/2019:Added (abstract) classes for both multi-user environments and
agents. Added bandit algorithms for generalized linear models.Installation and Sample UsageIt is recommended to install RecSim using (https://pypi.org/project/recsim/):pipinstallrecsimHowever, the latest version of Dopamine is not in PyPI as of December, 2019. We
want to install the latest version from Dopamine's repository like the following
before we install RecSim. Note that Dopamine requires Tensorflow 1.15.0 which is
the final 1.x release including GPU support for Ubuntu and Windows.pip install git+https://github.com/google/dopamine.gitHere are some sample commands you could use for testing the installation:git clone https://github.com/google-research/recsim
cd recsim/recsim
python main.py --logtostderr \
  --base_dir="/tmp/recsim/interest_exploration_full_slate_q" \
  --agent_name=full_slate_q \
  --environment_name=interest_exploration \
  --episode_log_file='episode_logs.tfrecord' \
  --gin_bindings=simulator.runner_lib.Runner.max_steps_per_episode=100 \
  --gin_bindings=simulator.runner_lib.TrainRunner.num_iterations=10 \
  --gin_bindings=simulator.runner_lib.TrainRunner.max_training_steps=100 \
  --gin_bindings=simulator.runner_lib.EvalRunner.max_eval_episodes=5You could then start a tensorboard and view the outputtensorboard --logdir=/tmp/recsim/interest_exploration_full_slate_q/ --port=2222You could also find the simulated logs in /tmp/recsim/episode_logs.tfrecordTutorialsTo get started, please check out our Colab tutorials. InRecSim:
Overview, we give a brief overview about
RecSim. We then talk about each configurable component:environmentandrecommender agent.DocumentationPlease refer to thewhite paperfor the
high-level design.