Saravanan666/pypi-org huggingface dataset:
Package: hundred-hammers
Hundred Hammers"At leastoneof them is bound to do the trick."Hundred Hammersis a Python package that helps you batch-test ML models in a dataset. It can be used out-of-the-box
to run most popular ML models and metrics, or it can be easily extended to include your own.Supports both classification and regression.Already comes strapped with most sci-kit learn models.Already comes with several plots to visualize the results.Easy to integrate with parameter tuning from GridSearch CV.Already gives you the average metrics from training, test, validation (train) and validation (test) sets.Allows you to define how many seeds to consider, so you can increase the significance of your results.Produces a Pandas DataFrame with the results (which can be exported to CSV and analyzed elsewhere).InstallationThe recommended way to install the library is throughpip install hundred_hammers. However, if you want to fiddle around with the repo yourself, you can clone this repository, and runpip install -e hundred_hammers/ExamplesFull examples can be found in theexamplesdirectory. As an appetizer, here's a simple one of how to use Hundred Hammers to run a
batch classification on Iris data:fromhundred_hammers.classifierimportHundredHammersClassifierfromhundred_hammers.plotsimportplot_batch_resultsfromsklearn.datasetsimportload_irisdata=load_iris()X,y=data.data,data.targethh=HundredHammersClassifier()df_results=hh.evaluate(X,y)plot_batch_results(df_results,metric_name="Accuracy",title="Iris Dataset")This already gives us a DataFrame with the results from several different models, and a nice plot of the results:Other plotsWe can also use Hundred Hammers to produce nice confusion matrices plots and regression predictions:fromhundred_hammers.plotsimportplot_confusion_matrixfromsklearn.datasetsimportload_irisfromsklearn.treeimportDecisionTreeClassifierdata=load_iris()X,y=data.data,data.targetplot_confusion_matrix(X,y,class_dict={0:"Setosa",1:"Versicolor",2:"Virginica"},model=DecisionTreeClassifier(),title="Iris Dataset")fromhundred_hammers.plotsimportplot_regression_predfromsklearn.datasetsimportload_diabetesfromsklearn.metricsimportmean_squared_errorfromsklearn.dummyimportDummyRegressordata=load_diabetes()X,y=data.data,data.targetplot_regression_pred(X,y,models=[DummyRegressor(strategy='median'),best_model],metric=mean_squared_error,title="Diabetes",y_label="Diabetes (Value)")Finally, it is also possible to compare different datasets and compare their results (each dot is a model).data=load_iris()X,y=data.data,data.targethh=HundredHammersClassifier()df=[]fori,feature_nameinenumerate(data.feature_names):X_i=X[:,[jforjinrange(X.shape[1])ifj!=i]]fordegreeinrange(8):df_i=hh.evaluate(X_i**degree,y,optim_hyper=False)df_i["Dataset"]=f"$X^{degree}$, w/out $x_{i}$"df.append(df_i)df_results=pd.concat(df,ignore_index=True)plot_multiple_datasets(df_results,metric_name="Avg ACC (Validation Test)",id_col="Dataset",title="Iris Dataset",display=True)
Package: hungarianalg
A Python 3 implementation of the Hungarian Algorithm for optimal matching in bipartite weighted graphs.Based on the graph theory implementation in [these notes](http://www.cse.ust.hk/~golin/COMP572/Notes/Matching.pdf) combined with the matrix interpretation in [these notes](https://montoya.econ.ubc.ca/Econ514/hungarian.pdf).For a detailed overview, see [this Jupyter notebook](https://github.com/jbrightuniverse/Hungarian-Algorithm-No.-5/blob/main/HungarianAlgorithm.ipynb).# UsageInstallation:pip3 install hungarianalgImport:from hungarianalg.alg import hungarianFunction call:result = hungarian(matrix)Properties:
- Optimal Matching:result.match- Revenues:result.revenues- Row Weights:result.row_weights- Col Weights:result.col_weights- Total Revenue:result.revenue_sumSeeexample.pyfor a comprehensive example.
Package: hungry
Python library for easily “eating” exceptions in functions. Eating can mean
either returning a default value or calling another function.IntroductionThis library is basically just a function, namelyhungry.eat. Initially, I
wrote it for a document parser in which there was a lot of exception handling
logic for stuff I didn’t really care particularly much about.Example usageEat all exceptions and returnNoneif one was raised:@hungry.eat()deffoo():...EatValueErrorexceptions and return 0 if one was raised:@hungry.eat(ValueError,error_value=0)deffoo():...EatIndexErrorandValueErrorand fall back to functionbarif one of them is raised:@hungry.eat(IndexError,ValueError,error_handler=bar)deffoo():...In the example above,barwill be passed the exception as the first
argument, followed by all the other arguments and keyword arguments in the
decorated function. This means thatbarwould have e.g. the following
signature:defbar(ex,*args,**kwargs):...NotesThis library willnotcatch exceptions and continue executing the
following code. Whenever it eats an exception, it immediately returns
error_value or calls error_handler.TODOComplete the test suite.
Package: hungry-hungry-hippos
Use casesGot a script that you want to be able to run multiple instances of in parallel?Want to write a program from scratch that's ready to scale up?Using someone else's code that has race conditions and want to singleton-ize it?IntroHungry Hungry Hippos, (HHH) is a python implementation of redlockhttps://redis.io/topics/distlockwith a set of examples that use docker, redis and docker compose.
The advantage of this approch, compared to multithreading, is that is can be applied in a single-threaded context (for example if you have to integrate existing code), or in a mutliple container instance context (think microservices)Featuresatomic locking guarantees single lock acquisitionworks across machines / containershandles dead lock recoverycan wrap existing code for cases when you cannot or do not want to do a rewrite.Key conceptsProcess isolationDestributed locks (seehttps://redis.io/topics/distlock)Command orchestrationToolchaindocker - images, containers, volumesdocker-compose - building, linking services, scaling servicesredis - in memory key-value databaseInstallationpip install hungry-hungry-hipposSee examples for use cases
Package: huni-utils
Python Package for Utility@created byhuni1023MotivationI've been managed various data like questionnaire, quiz response, system logs, etc.
Thery are sometimes well-organized but in many times, it is unstructured and unshaped.
Through out my experience, I've been established my data-handling-routines.
The packagehuni_utilsis my first python package, and it's a collection of python uility that I used frequently.
I hope this code might helpful for others too, and always welcome for advice or suggesion.InstallationThehuni_utilsis managed in PyPIpip install huni-utilsFunction1. load and save filesread_pklsave_pklread_yamlpandize_folder2. handle ospjoinvalidate structure of project directoryclear directorySuggestionany functions are needed, leave issues behind
Package: hunk
Helps client-side Web development by mocking JSON API server.Installationhunk can be installed via pip or easy_install:$pipinstallhunkOr:$easy_installhunkUsageSetup data directory and files.Directories under the root have a HTTP method nameDirectories and files have name to be resource pathFiles have conent to be response JSONFor example:$treedemo/simple/demo/simple/|--get||--members|||--100.json|||--200.json|||--300.json||...|+--sounds.json|--post|+--members.json...Move to the root directory and run hunk:$cddemo/simple/$hunk*Runningonhttp://localhost:8080/Now, you can access Web API with HTTP:$curlhttp://localhost:8080/members/100{"gender":"female","kind":"human","name":"Dorothy"}$curlhttp://localhost:8080/members[{"kind":"scarecrow","name":"Hunk"},...]$curlhttp://localhost:8080/sounds[{"title":"Over The Rainbow"},...]$curlhttp://localhost:8080/members-XPOST{"result":"success"}
Package: hunspell
[![Build Status](https://travis-ci.org/blatinier/pyhunspell.png)](https://travis-ci.org/blatinier/pyhunspell)# About PyHunSpellpyhunspellis a set of Python bindings for the Hunspell spellchecker
engine. It lets developers load Hunspell dictionaries, check words, get
suggestions, add new words, etc. It also provides some basic morphological
analysis related methods.See [ChangeLog](https://github.com/blatinier/pyhunspell/blob/master/CHANGELOG.md) for activity.See [Authors](https://github.com/blatinier/pyhunspell/blob/master/AUTHORS.md) for all contributors.# InstallationMake sure thatpython-devandlibhunspell-devare installed.You can install this package using pip:` pip install hunspell `Or from source using the following command as root:` python setup.py install `For OSX the version of the lib used to compile is hardcoded. If your installation fails, please try to clone the repo and changemain_module_kwargs[‘include_dirs’]insetup.pyto the correct one and/or open an issue stating the problem and the hunspell version with the.hlocation in your system.For windows7 [@tallforasmurf](https://github.com/tallforasmurf) compiled a python 3.4 compatible version of the pyhunspell==0.3.3 package available here:http://pyhunspell.latinier.fr(Warning: Those windows compiled packages are not reviewed or tested). It could work for other windows version or python versions.# Documentation[Go here for some documentation](https://github.com/blatinier/pyhunspell/wiki/Documentation)# Usage` >>> import hunspell >>> hobj =hunspell.HunSpell('/usr/share/hunspell/en_US.dic','/usr/share/hunspell/en_US.aff') >>>hobj.spell('spookie')False >>>hobj.suggest('spookie')['spookier', 'spookiness', 'spook', 'cookie', 'bookie', 'Spokane', 'spoken'] >>>hobj.spell('spooky')True >>>hobj.analyze('linked')[' st:linked', ' st:link fl:D'] >>>hobj.stem('linked')['linked', 'link'] >>>hobj.spell('spookie')False >>>hobj.add('spookie')>>>hobj.spell('spookie')True >>>hobj.remove('spookie')>>>hobj.spell('spookie')False `# TroubleshootingOn MacOS Sierra with python 3.5 you can come across this error:` fatal error:/Applications/XXX-bla-bla-bla-XXX/usr/bin/lipo:can't figure out the architecture type of:/var/folders/bla-bla-bla/XXX.out`It might be solved by doingbrew unlink apple-gcc42. See #31 and regebro/hovercraft#80.# MiscSaw a bug ? Wan’t a feature ? Go fill an issue [here](https://github.com/blatinier/pyhunspell/issues)For information, version naming (introduced for 0.4.0) is based on Hungarian cities ordered by population except Budapest.
The reason why is that hunspell stands for Hungarian Spell.# LicensePyHunspell itself is licensed under the LGPL version 3 or later, see
lgpl-3.0.txt and gpl-3.0.txt. The files in the debian/ directory and setup.py
are licensed under the GPL version 3.
Package: hunterk
ContentDataby Hunter KempfGet Content Data from Wikipedia and IMDbGet Data from Wikipedia Example:fromContentData.WikiScrapeimportCompanyurl='https://en.wikipedia.org/wiki/List_of_original_programs_distributed_by_Netflix'table_names=["Drama","Marvel series","Comedy","Animation","Anime","Children's programming - Animation","Children's programming - Musical shorts","Children's programming - Live action"]table_indexes=range(0,len(table_names))NFLX=Company("Netflix",url,table_names,table_indexes)NFLX.get_dataframe()Get Data from IMDb Example:fromContentData.IMDbScrapeimportimdb_dataIMDb=imdb_data()IMDb.get_list_mixed_data(["House of Cards","Hemlock Grove"])The WikiScrape section only handles tabular data similar to the netflix example. This can be further expanded to include List like data similar to the HBO wikipedia article.
Package: hunter-tdb
HunterSummaryThis is a simple project to explore the idea of simulating a hunter trying to survive in his/her environment, and the development of all the systems necessary to accomplish that.Currently, the gameplay is represented by ASCII characters rendered in a small console window. There is an H, the Hunter, that moves around and attempts to survive. The Hunter's vital stats are displayed on the left and the Hunter's action log is displayed at the bottom. When hungry, the Hunter will search for berries to eat or rabbits to hunt (the R's), and when tired, the Hunter will return to camp (C) to sleep.There are two player controls right now:F- toggle fog of warH- toggle user interfaceAnd that's it! This is not a game for playing so much as observing (like an ant farm). From this humble start, I hope to build a rich world that is genuinely enjoyable to observe. And eventually I do plan to add more ways for the player to interact with the world. But for now, you'll have to use your imagination. :)If you really want to tinker, you can modify the stats inthis file. For example, in the "rabbit" section try changing "spawn" to 0.2 and see what happens. User beware: there are tons of ways to break the game messing with this file!For more info, see blog entries in theblog directory. There, you can read about my progress, process, goals for the future, and general ruminations.This post, where I organized my thoughts on how to simulate creatures of varying complexity, may be a particularly interesting starting point.InstallationUsing Python EggsInstall pyenv. For MacOSX, run the following:> brew install pyenvNote: some other setup steps may be necessary.Using pyenv, install Python 3.8 or higher:> pyenv install 3.8.0Install the Hunter egg:> pip install hunter-tdb==0.8.0Run the game!> run hunterRunning from sourceInstall python 3.8 or higher:However you like. :)Clone source:git clone https://github.com/trevordbabcock/hunter.gitInstall requirements:cd hunter
pip install -r requirements.txtRun the game:./hunter_pkg/main.pyHave fun! Any feedback is welcome. :)
Package: huntflow-py
InfoRequirementsTo useHuntflow APIyou need to obtain personal token by asking support via email.This library also requires you to set proper (at least valid) email to let Huntflow staff contact you in case of emergency.UsageAfter you're ready, usage is quite simple:fromhuntflow_py.apiimportAPIv1asHuntflowhuntflow=Huntflow(token='your-token-here',email='your-email-here',)print(huntflow.me())For other methods seeapi.py.Right now I'm not sure how to generate available methods list automatically, feel free to contact and let me know.
Package: hunt-match-telemetry
Hunt Match TelemetryThis tool is intended to be used for automatically logging match data provided by the game.InstallationCreate a new Python 3.11virtual environmentand activate it,(optional)runpip install hunt-match-telemetryto install the package,download theSteamworks SDKand place it in./resources/steamassteamworks_sdk.zip.InstructionsRun the CLI version of the package by executinghunt-match-telemetry-cliin your preferred terminal,join a match from the game,finish the game (extract, die, etc.),return to the lobby screen (or any UI element that updates the last match information).ScreenshotsConsole log:Match log:Player log:Notice of Non-Affiliation and DisclaimerWe are not affiliated, associated, authorized, endorsed by, or in any way officially connected with Crytek GmbH, or any of its subsidiaries or its affiliates.The official Crytek GmbH website can be found athttps://www.crytek.com/.
The official Hunt: Showdown website can be found athttps://www.huntshowdown.com/.All product and company names are the registered trademarks of their original owners. The use of any trade name or trademark is for identification and reference purposes only and does not imply any association with the trademark holder of their product brand.
Package: huobi
# HuobiHuobi Python SDK## Requirements```bashPython>=3.6```## Installaton```bashpip install huobi```## Usage### Rest APIExample:```python>>> from huobi import HuobiRestClient>>> client = HuobiRestClient(access_key=..., secret_key=...)>>> trades = client.market_history_trade(symbol='ethusdt').data```To see all available methods and their arguments:```python>>> from huobi import HuobiRestClient>>> help(HuobiRestClient)>>> help(HuobiRestClient.symbols)```### Real Time API> Rudimentary websocket subscription supportPlease refer to Huobi's documentation for available subscribe channels.If callback is not a coroutine function, run_in_executor with defaultExecutor will be called.Example:```pythonfrom huobi import subscribe>>> def btc_callback(data):print(data)>>> async def eth_callback(data):print(data)>>> task = subscribe({'market.btcusdt.kline.1min': {'callback': btc_callback},'market.ethusdt.kline.1min': {'callback': eth_callback},})>>> asyncio.get_event_loop().run_until_complete(task)```
Package: huobiApi
HuobiApiHuobi REST API Package(sourcehttps://github.com/MJeremy2017/HuobiApi)Note: This is not an official package. For more info, please resort to official docsInstallationpip install huobiApiGet Accounts IDimport os
from huobiApi.service import HuobiSVC

ACCESS_KEY = os.getenv('ACCESS_KEY')
SECRET_KEY = os.getenv('SECRET_KEY')

svc = HuobiSVC(ACCESS_KEY, SECRET_KEY, url_type='normal')  # or aws

svc.get_accounts(){'status': 'ok',
 'data': [{'id': 01, 'type': 'spot', 'subtype': '', 'state': 'working'},
  {'id': 02, 'type': 'margin', 'subtype': 'btcusdt', 'state': 'working'},
  {'id': 03, 'type': 'margin', 'subtype': 'eosusdt', 'state': 'working'},
  {'id': 04, 'type': 'margin', 'subtype': 'ethusdt', 'state': 'working'},
  {'id': 05, 'type': 'margin', 'subtype': 'neousdt', 'state': 'working'},
  {'id': 06, 'type': 'otc', 'subtype': '', 'state': 'working'},
  {'id': 07, 'type': 'super-margin', 'subtype': '', 'state': 'working'}]}Get Kline Datasymbol = 'btcusdt'
res = svc.get_kline(symbol, '60min', size=50){'status': 'ok',
 'ch': 'market.btcusdt.kline.60min',
 'ts': 1580378774791,
 'data': [{'amount': 66.60260811521935,
   'open': 9364.89,
   'close': 9366.58,
   'high': 9376.21,
   'id': 1580378400,
   'count': 1080,
   'low': 9364.89,
   'vol': 624201.8357696668},
   ...Place an Orderdata = svc.send_order(
                acct_id='01',
                amount='0.001',
                price='7999',
                source='api',
                symbol='btcusdt',
                _type='sell-stop-limit',
                stop_price='8000',
                operator='lte')
Package: huoguoml
Note:HuoguoML is stil under development and can have some unknown issues.🍲 HuoguoMLWhen dealing with Machine Learning applications, there is a high management and coordination effort for data scientists,
as they have to collaborately analyze, evaluate and update many different models with different metadata on a regular
basis.HuoguoMLaims to simplify the management process by providing a platform for managing and serving machine
learning models. It enables:Individual Data Scientiststo track experiments locally on their machine and output models to ML engineers, who
then deploy them using HuoguoML's deployment tools.Data Science Teamsto set up a HuoguoML tracking server to log and compare the results of multiple data scientists
working on the same or a different problem. Then, by setting up a convention for naming their parameters and metrics,
they can try different algorithms to solve the same problem and then run the same algorithms again on new data to
compare models in the future. In addition, anyone can download and run a different model.ML Engineersto deploy models from different ML libraries in the same way by executing a simple command. Each
service is centrally maintained and supports OTA updates. On its own, a HuoguoML Service is based on FastAPI and can
be extended with standard FastAPI tools. It supports all use cases, be it storing predictions with middleware or
adding new endpoints e.g. for Prometheus.InstallationHuoguoML can be installed via PyPI. Install the stable version of HuoguoML via PyPI:pipinstallhuoguomlor get the development version, which is updated with every commit on the main branch:pipinstallhuoguoml-devExamplesJust starting out? Try out our examples which work out of the box:ExampleDescriptionTensorflow MNISTBuilding a MNIST classifier with Tensorflow and HuoguoMLDocumentationApart from learning from the examples, we highly recommended you go through
ourdocumentation, as it gives you a more detailed guide to HuoguoMLOur docs are built on every push to the main or docs branch.ContributingWe encourage you to contribute to HuoguoML! Please check out theContributing guidefor guidelines
about how to proceed.LicenseApache License Version 2.0, seeLICENSE
Package: huoguoml-dev
Note:HuoguoML is stil under development and can have some unknown issues.🍲 HuoguoMLWhen dealing with Machine Learning applications, there is a high management and coordination effort for data scientists,
as they have to collaborately analyze, evaluate and update many different models with different metadata on a regular
basis.HuoguoMLaims to simplify the management process by providing a platform for managing and serving machine
learning models. It enables:Individual Data Scientiststo track experiments locally on their machine and output models to ML engineers, who
then deploy them using HuoguoML's deployment tools.Data Science Teamsto set up a HuoguoML tracking server to log and compare the results of multiple data scientists
working on the same or a different problem. Then, by setting up a convention for naming their parameters and metrics,
they can try different algorithms to solve the same problem and then run the same algorithms again on new data to
compare models in the future. In addition, anyone can download and run a different model.ML Engineersto deploy models from different ML libraries in the same way by executing a simple command. Each
service is centrally maintained and supports OTA updates. On its own, a HuoguoML Service is based on FastAPI and can
be extended with standard FastAPI tools. It supports all use cases, be it storing predictions with middleware or
adding new endpoints e.g. for Prometheus.InstallationHuoguoML can be installed via PyPI. Install the stable version of HuoguoML via PyPI:pipinstallhuoguomlor get the development version, which is updated with every commit on the main branch:pipinstallhuoguoml-devExamplesJust starting out? Try out our examples which work out of the box:ExampleDescriptionTensorflow MNISTBuilding a MNIST classifier with Tensorflow and HuoguoMLDocumentationApart from learning from the examples, we highly recommended you go through
ourdocumentation, as it gives you a more detailed guide to HuoguoMLOur docs are built on every push to the main or docs branch.ContributingWe encourage you to contribute to HuoguoML! Please check out theContributing guidefor guidelines
about how to proceed.LicenseApache License Version 2.0, seeLICENSE
Package: hup
HupHupis a multi-purposePythonlibrary, which
primarily aims to supportFrootlabprojects by
serving as a common base library. It was originally created in 2019 for theLiquid Codeproject to separatePandoraandNemoa. The majority of the provided
modules, however, are kept generic to facilitate their application beyond
Frootlab projects as well.Hup is free and open source software, based on thePythonprogramming language and actively developed atFrootlab.Current Development StatusThe individual modules comprised within Hup are in
different development stages, ranging fromPlanningtoMature. In case of
doubt, the individual stages can be requested by the authors listed within the
__authors__ attribute of the respective modules.InstallationComprehensive information and installation support is provided within theonline manual. If you already have a
Python environment configured on your computer, you can install the latest
distributed version by using pip:$ pip install hupDocumentationThe documentation of the latest distributed version is available as anonline
manualand for download, given in the
formatsPDF,EPUBandHTML.ContributeContributors are very welcome! Feel free to report bugs and feature requests to
theissue trackerprovided by GitHub.
Currently, as the Frootlab Developers team still is growing, we do not provide
any Contribution Guide Lines to collaboration partners. However, if you are
interested to join the team, we would be glad, to receive an informalapplication.LicenseHup isopen sourceand available free for
any use under theGPLv3 license:Â© 2019 Frootlab Developers:
  Patrick Michl <patrick.michl@frootlab.org>