Saravanan666/pypi-org huggingface dataset:
Package: ai-katie
ai-katieThe utility for Data Science and AI using common libraries.
Extended by myself when I’m running to problem that might happen more than once.Github:https://github.com/danpeczek/ai-katiemainSetupTo setup to library you need at least Python 3.8 and pip installed.Also as the package uses PyTorch I strongly suggest to install the PyTorch
in the distribution of your choice from:https://pytorch.org/get-started/locally/before installing the library.When all dependencies are installed you can install the library by:$ pip install ai-katieLicenseMIT LICENSE
Package: aika-time
Introductionaika time is part of the aika project for working with time series data, you can find more details
on theproject webpage:aika-timeUtility methods for working with timestamps and time series in the context of pandas. Principal components are:Time stamp class that enforces olsen timestamps, i.e. all timestamps must have a time zone.TimeOfDay class that represents a time of day in a given timezone, and given a
set of dates produce the correct timestamp.TimeRange class that represents a timerange.causal.py - utilities for aligning one series on another in a causally correct way.Installpython -m pip install aika-time
Package: aiken
This project is a Python parser for the Aiken question format used in Moodle.
Aiken is a very simple format to represent multiple choice questions (https://docs.moodle.org/24/en/Aiken_format)
It accept two very similar syntaxes:What is the correct answer to this question?
A. Is it this one?
B. Maybe this answer?
C. Possibly this one?
D. Must be this one!
ANSWER: DAnd this:Which LMS has the most quiz import formats?
A) Moodle
B) ATutor
C) Claroline
D) Blackboard
E) WebCT
F) Ilias
ANSWER: AUsageThe aiken module simply expose theloadanddumpfunctions that respectively
parse a question file and convert a parsed question object back to code. Let us
parse a question string:>>> import aiken
>>> question = aiken.load("""
... Is this a valid Aiken Question?
... A. Yes
... B. No
... ANSWER: A
... """)
>>> question.options
['Yes', 'No']Now, we make some changes and convert it back to a string of code:>>> question.options.append('Who knows?')
>>> print(aiken.dump(question))
Is this a valid Aiken Question?
A. Yes
B. No
C. Who knows?
ANSWER: A
Package: aikit
aikitAutomatic Tool Kit for Machine Learning and Datascience.The objective is to provide tools to ease the repetitive part of the DataScientist job and so that he/she can focus on modelization. This package is still in alpha and more features will be added.
Its mains features are:improved and new "scikit-learn like" transformers ;GraphPipeline : an extension of sklearn Pipeline that handles more generic chains of tranformations ;an AutoML to automatically search throught several transformers and models.Full documentation is available here:https://aikit.readthedocs.io/en/latest/You can run exampleshere, thanks toBinder.GraphPipelineThe GraphPipeline object is an extension ofsklearn.pipeline.Pipelinebut the transformers/models can be chained with any directed graph.The objects takes as input two arguments:models: dictionary of models (each key is the name of a given node, and each corresponding value is the transformer corresponding to that node)edges: list of tuples that links the nodes to each otherExample:gpipeline=GraphPipeline(models={"vect":CountVectorizerWrapper(analyzer="char",ngram_range=(1,4),columns_to_use=["text1","text2"]),"cat":NumericalEncoder(columns_to_use=["cat1","cat2"]),"rf":RandomForestClassifier(n_estimators=100)},edges=[("vect","rf"),("cat","rf")])AutoMLAikit contains an AutoML part which will test several models and transformers for a given dataset.For example, you can create the following python scriptrun_automl_titanic.py:fromaikit.datasetsimportload_dataset,DatasetEnumfromaikit.ml_machineimportMlMachineLauncherdefloader():dfX,y,*_=load_dataset(DatasetEnum.titanic)returndfX,ydefset_configs(launcher):""" modify that function to change launcher configuration """launcher.job_config.score_base_line=0.75launcher.job_config.allow_approx_cv=Truereturnlauncherif__name__=="__main__":launcher=MlMachineLauncher(base_folder="~/automl/titanic",name="titanic",loader=loader,set_configs=set_configs)launcher.execute_processed_command_argument()And then run the command:python run_automl_titanic.py run -n 4To run the automl using 4 workers, the results will be stored in the specified folder
You can aggregate those result using:python run_automl_titanic.py result
Package: aiko
aiko is a base asyncio’s lightweight web application framework.
It is designed to makekoaapi.InstallingInstall by code$ git clone https://github.com/zeromake/aiko
$ cd aiko
$ python setup.py installA Simple ExampleimportasynciofromaikoimportApploop=asyncio.get_event_loop()app=App(loop)defhello(ctx,next_call):return"Hello, World!"app.use(hello)if__name__=="__main__":app.run(host="0.0.0.0",port=5000)$ curl http://127.0.0.1:5000
Hello, World!LinksTodo[ ] request api like koa[ ] method[ ]accepts[ ]acceptsEncodings->accepts_encodings[ ]acceptsCharsets->accepts_charsets[ ]acceptsLanguages->accepts_languages[ ]is[x]get[ ] getter, setter[x]header[x] getter[ ] setter[x]headers[x] getter[ ] setter[x]url[x]origin[x]href[x]method[x]path[x]query[x]querystring[x]search[ ] getter[x]host[x]hostname[ ]URL[x]fresh[x]stale[x]idempotent[x]socket[x]charset[x]length[x]protocol[x]secure[x]ips[ ]subdomains[x]type[x]originalUrl->original_url[x]ip[ ] response api like koa[x] proxy class property attr and method[x] likefreshmethod
Package: ailab
AI LabAI Lab is a gui and a server backend.
The GUI provides an overview over experiments and their status, based on logfiles on the disk.
The server provides functionalities for the gui such as parsing the logfiles, getting the usage of the system and providing the files to view in the gui.
Furthermore, the server also provides a rempy host, where jobs can be submitted for exection.InstallationSimply pip install this package from git:pipinstallailabRunning: AI Lab ServerAI Lab Visualization consists of a ui and a server.
Since the ui is a static website that works on your local webbrowser no installation is needed. The static website is hostedhere.Running is as simple as running the module in python providing a path to a config file.ailabmy_config.jsonA config file must contain a host or * for any interface, a port, a list of users as a map and a path to your checkpoints.
(Typically the checkpoint path is on a network share, where all computers add their checkpoints and this pc reads them.)
The list ofgpusgives you the opportunity to limit the gpus ailab will assign for scheduled tasks.
The gpu ids are equivalent to the numbers used forCUDA_VISIBLE_DEVICES.{"host":"*","port":12345,"users":{"admin":"CHANGE_THIS"},"workspace":"/home/$USER/Git","results":"/home/$USER/Results","queue":"/tmp/ailab_queue","auto_detect_experiments":false,"projects":{},"gpus":[0]}PrivacyAll connection data is stored locally in your webbrowser and nothing is transmitted to the host of ailab ui.
There is only direct communication between your webbroser and the server you add via the "Add Server" Dialog.The servers you add are not controlled by us and therefore can do whatever they want with your data.
However, when the servers are owned/run by you and use the official ailab-server software, they will not track activities or report back information to a third party.Even though this sounds pretty safe, there is yet no ssl implementation for the connection to your servers, keep that in mind.
(If you know how to implement an easy to use ssl on the client and the server, I will be happy to receive your pull request.)
Package: ailabs-asr
AILabs ASR Python software development kitDevelopment EnvironmentPython 3.9# install portaudio first if you develop on MAC OS Xbrewinstallportaudio

pipinstall--global-option='build_ext'--global-option='-I/usr/local/include'--global-option='-L/usr/local/lib'-rrequirements_dev.txt# please check PyAudio site: https://people.csail.mit.edu/hubert/pyaudio/# if you encouter some issues while installing PyAudioInstallationpipinstallailabs-asrSamples# init the streaming clientasr_client=StreamingClient('api-key-applied-from-devconsole')# start streaming with wav fileasr_client.start_streaming_wav(pipeline='asr-zh-en-std',file='voice.wav'verbose=False,# enable verbose to show detailed recognition resulton_processing_sentence=on_processing_sentence,on_final_sentence=on_final_sentence)# without file to start streaming with the computer's microphoneasr_client.start_streaming_wav(pipeline='asr-zh-en-std',on_processing_sentence=on_processing_sentence,on_final_sentence=on_final_sentence):bulb:start_streaming_wav()method allow users to provide callback function to handle the recognition result see the result formatbelow:bulb: lookup the available pipelines inthe next section:bulb: see more samples in thesample respositorySupport Language(pipeline)pipelineInfolanguageasr-zh-en-stdUse it when speakers speak Chinese more than EnglishMandarin and Englishasr-zh-tw-stdUse it when speakers speak Chinese and Taiwanese.Mandarin and Taiwaneseasr-en-stdEnglishEnglishasr-jp-stdJapaneseJapaneseMessage FormatThere are 2 kinds of recognized result:The Processing Sentence(Segment){"asr_sentence":"範例句子"}The Final Sentence(Complete Sentence){"asr_final":true,"asr_begin_time":9.314,"asr_end_time":11.314,"asr_sentence":"完整的範例句子","asr_confidence":0.5263263653207881,"asr_word_time_stamp":[{"word":"完整的","begin_time":9.74021875,"end_time":10.100875},{"word":"範例句子","begin_time":10.100875,"end_time":10.1664375}],"text_segmented":"完整的 範例句子"}LimitationAudio Data:warning: Send audio data withbinary framewith following spec:Audio data format16kHz, mono16 bitsper samplePCMSample rate per secs: 16K(16000)Sample sizes per sec: 16000(samples) x 1(sec) x 16/8(2 bytes) = 32000 bytes ~= 32 KB(/sec)Each chunk size: 2000 bytes, 1/16 secs
Package: ailabtools
Zalo AI Lab toolsPip package tools for Deep learning taskInstallationpip install ailabtoolsDevelop instructionInstallation for developingClone project to folderailabtoolsGo toailabtoolsfolder, install usingpip:pip install .for editable package (for development process), run:pip install --editable .Check if package installed succesfully by running this python code:from ailabtools import common
common.check()right output:>>> from ailabtools import common
>>> common.check()
AILab Server Check OKmake sure that the output isAILab Server Check OKwithout any problems.Optional, check information of package:pip show ailabtoolsContribution processDeployment branch:master.Development branch:develop.Contribution process steps:Checkout new branch.Add own module.Create pull request to branchdevelop.Waiting for pull request review.Pull request merged, ready for beta deployment.Stable, ready formastermerge for offical deployment.Package modifiedCheckoutsetup.pyfor package information.Add moduleAdd module inailabtoolsfolder.DocumentsCheck wiki pages
Package: ailearn
此为ailearn人工智能算法包。包含了Swarm、RL、nn、utils四个模块。Swarm模块当中，实现了粒子群算法、人工鱼群算法、萤火虫算法和进化策略，以及一些对智能算法进行评估的常用待优化的函数。RL模块包括两部分，TabularRL部分和Environment部分。TabularRL部分集成了一些经典的强化学习算法，包括Q学习、Q(Lambda)、Sarsa、Sarsa(lambda)、Dyna-Q等。Environment部分集成了一些强化学习经典的测试环境，如FrozenLake问题、CliffWalking问题、GridWorld问题等。nn模块包括一些常用的激活函数及损失函数。utils模块包括一些常用的功能，包括距离度量、评估函数、PCA算法、标签值与one-hot编码的相互转换、Friedman检测等等。安装方式（在终端中输入）：` pip install ailearn `更新方式（在终端中输入）：` pip install ailearn--upgrade`更新历史：2018.4.10   0.1.3   第一个版本，首次实现了粒子群算法和人工鱼群算法，首次集成到pip当中。2018.4.16   0.1.4   加入了进化策略的实现，添加了Evaluation模块。2018.4.18   0.1.5   添加了TabularRL模块和Environment模块。2018.4.19   0.1.8   将TabularRL模块和Environment模块整合为RL模块，添加了项目的相关描述,更新了相关协议2018.4.25   0.1.9   输出信息由中文改为英文，并更新了一些已知错误2019.1.15   0.2.0   添加了utils模块，加入了一些常用的功能，包括距离度量、评估函数、PCA算法、标签值与one-hot编码的相互转换、Friedman检测等等；添加了nn模块，加入了一些常用的激活函数及损失函数；更新了Swarm模块的算法，使它们更新得更快。2021.4.6    0.2.1   添加了爬虫工具，增加了RL模块与Swarm模块的示例；添加强化学习经典环境Windy GridWorld环境。其他更新：更新了绘制决策边界的方法更新了绘制数据集的方法优化了Friedman检测的方法项目网址：https://pypi.org/project/ailearn/https://github.com/axi345/ailearn/
Package: ailib
Python Artificial Intelligence FrameworkCopyright (c) 2014-2019 Jeremie DECOCK (http://www.jdhp.org)Web site:http://www.ailib.ioOnline documentation:http://www.ailib.io/docs/Examples:http://www.ailib.io/docs/gallery/Notebooks:https://github.com/jeremiedecock/ailib-notebooksSource code:https://github.com/jeremiedecock/ailibIssue tracker:https://github.com/jeremiedecock/ailib/issuesAILib on PyPI:https://pypi.org/project/ailib/AILib on Anaconda Cloud:https://anaconda.org/jdhp/ailibDescriptionAILib is a set of open source frameworks for Artificial Intelligence
(mostly machine learning and optimization).This contains (among others):a blackbox non linear noisy optimization framework;a machine learning framework;a multistage optimization and Markov Decision Process framework
(Markov Decision Processes).WarningThis project is in beta stage.DependenciesPython >= 3.0NumpyMatplotlibInstallationGnu/LinuxYou can install, upgrade, uninstall AILib with these commands (in a
terminal):pip install --pre ailib
pip install --upgrade ailib
pip uninstall ailibOr, if you have downloaded the AILib source code:python3 setup.py installWindowsYou can install, upgrade, uninstall AILib with these commands (in acommand prompt):py -m pip install --pre ailib
py -m pip install --upgrade ailib
py -m pip uninstall ailibOr, if you have downloaded the AILib source code:py setup.py installMacOSXYou can install, upgrade, uninstall AILib with these commands (in a
terminal):pip install --pre ailib
pip install --upgrade ailib
pip uninstall ailibOr, if you have downloaded the AILib source code:python3 setup.py installDocumentationOnline documentation:http://ailib.readthedocs.orgAPI documentation:http://ailib.readthedocs.org/en/latest/api.htmlExample usageTODOBug reportsTo search for bugs or report them, please use the AILib Bug Tracker at:https://github.com/jeremiedecock/ailib/issuesLicenseThis project is provided under the terms and conditions of theMIT License.
Package: ailiga
AILigaGoalsMonthly releases of session/tournament resultsUser foldersStrict versioning for reproducibility (ocne a version is pushed, gitignore it)InstallationgitcloneTHIS_PROJECT_URL
poertyinstall
poetryshellTesting and TrainingCurrently, training/testing fighters works through the fighter tests.pythontests/test_dqn_fighter.pyTensorboardtensorboard--logdirlog/--load_fast=falseLimitationsCurrently, the implementation throughtianshou.BasePolicyseems to only support DQNPolicy and also notDiscrete()observation spaces.ReferencesFrameworkshttps://github.com/Farama-Foundation/PettingZoohttps://github.com/vwxyzjn/cleanrlhttps://github.com/Farama-Foundation/Gymnasiumhttps://github.com/deepmind/open_spielhttps://github.com/datamllab/rlcardhttps://tianshou.readthedocs.io/en/master/Bookshttp://incompleteideas.net/book/the-book-2nd.htmlDevelopmentWe use black throughpackage/python structure:https://mathspp.com/blog/how-to-create-a-python-package-in-2022https://www.brainsorting.com/posts/publish-a-package-on-pypi-using-poetry/
Package: ai-logicplum
ChangelogAll notable changes to this project will be documented in this file.[1.0.0] - 2023-12-08Released.[1.0.1] - 2023-12-08code optimized.[1.0.2] - 2023-12-08Passing individual params instead of passing as dictionary except training.[1.0.3] - 2023-12-081.0.2 fixes.[1.0.4] - 2023-12-08config file changes.[1.0.5] - 2023-12-081.0.4 fixes[1.0.6] - 2023-12-08Code Fixes[1.0.7] - 2023-12-08Code Fixes[1.0.8] - 2023-12-08Code Fixes[1.0.9] - 2023-12-08Training module new updates[1.1.0] - 2023-12-08Report module new updates[1.1.1] - 2023-12-08graph module new updates[1.1.2] - 2023-12-08graph module new updates[1.1.3] - 2023-12-08graph module new updates[1.1.4] - 2023-12-08training new updates[1.1.5] - 2023-12-08training new updates[1.1.6] - 2023-12-08graph displaying feature added in plot.[1.1.7] - 2023-12-08Fixes.[1.1.8] - 2023-12-08JWT Authentication implemented[1.1.9] - 2023-12-08Fixes[1.2.0] - 2023-12-08Fixes[1.2.1] - 2023-12-08Newplot blueprint and auto model selection in deployment added .[1.2.2] - 2023-12-08Retrain model feature added.[1.2.3] - 2023-12-08Deployment auto model selection fixes.[1.2.4] - 2023-12-08Removed auto model selection from deployment .[1.2.5] - 2023-12-08Content type changed in deployment.[1.2.6] - 2023-12-08Fixes in deployment.[1.2.7] - 2023-12-08Fixes in deployment.[1.2.8] - 2023-12-08Manual prediction changes.
Package: aim3
AppImages-ManagerA little project i made for myself to sort my AppImages collection.
I made it really just for myself to be able to sort and categorize my AppImages in an automated method, but I thought i'd share it incase anyone else finds it useful.Run with eitheraimfor the command line, oraimguifor the GUI.Open the Config button in the app to choose where your Downloads folder is (where your browser will download appimages to), and the Storage path (Where this app stores the appimages).
Press Install button to move all apps from Downloads in to Storage.
Refresh reloads the list box with the files that are in Storage.
Run launches the selected app. If the app doesn't have permissions to execute, it'll handle that automatically.
Delete removes the selected appimage file from the system.
Group button allows you to sort the selected image in to categories or groups. Either enter a name in the box for a new group, or select a button for an existing group.
Edit button lets you edit the groups. (For now, just used for removing images from a group.CLI VersionThere is also a command-line version available. Run the aim-cli.py script for a non-gui way of quickly managing appimages.
If run on its own, enters an interactive mode where it'll ask for commands as you go. If you run it with command arguments, it'll run the command then exit.
aim-cli.py help shows all the commands
Currently supported operations: finding apps in directory, running apps, installing apps from Downloads, automatically handling execution permissions on appimages.RequiresJust tkinter. Everything else should be stock python3 libraries.
Only tested in python3.
Package: aima
IntroductionCode for Artificial Intelligence: A Modern Approach (AIMA) 4th edition by Peter Norvig and Stuart Russel.Shameless reuse of Norvig's official repository athttps://github.com/aimacode/aima-python/The code should work in Python 3.7+.How to Browse the CodeYou can get some use out of the code here just by browsing, starting at the root of the source tree or by clicking on the links in the index on the project home page. The source code is in the .py files; the .txt files give examples of how to use the code.How to Install the CodeIf you like what you see, install the code using either one of these methods:From a command shell on your computer, execute the svn checkout command given on the source tab of the project. This assumes you have previously installed the version control system Subversion (svn).
Download and unzip the zip file listed as a "Featured download"on the right hand side of the project home page. This is currently (Oct 2011) long out of date; we mean to make a new .zip when the svn checkout settles down.You'll also need to install the data files from the aima-data project. These are text files that are used by the tests in the aima-python project, and may be useful for yout own work.You can put the code anywhere you want on your computer, but it should be in one directory (you might call it aima but you are free to use whatever name you want) with aima-python as a subdirectory that contains all the files from this project, and data as a parallel subdirectory that contains all the files from the aima-data project.How to Test the CodeFirst, you need to install Python (version 2.5 through 2.7; parts of the code may work in other versions, but don't expect it to). Python comes preinstalled on most versions of Linux and Mac OS. Versions are also available for Windows, Solaris, and other operating systems. If your system does not have Python installed, you can download and install it for free.In the aima-python directory, execute the commandpython doctests.py -v *.pyThe "-v" is optional; it means "verbose". Various output is printed, but if all goes well there should be no instances of the word "Failure", nor of a long line of "". If you do use the "-v" option, the last line printed should be "Test passed."How to Run the CodeYou're on your own -- experiment! Create a new python file, import the modules you need, and call the functions you want.AcknowledgementsMany thanks for the bug reports, corrected code, and other support from Phil Ruggera, Peng Shao, Amit Patil, Ted Nienstedt, Jim Martin, Ben Catanzariti, and others.
Package: ai-maintainer-git-util
AI Maintainer Git UtilA git util for working with the AI Maintainer go git repo in google cloudFree software: MIT licenseDocumentation:https://ai-maintainer-git-util.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2023-08-31)First release on PyPI.
Package: ai-management
Artificial Intelligence ManagementThis is a toolbox to help AI & ML teams to have a better management of their metrics and processes.Our desire is to enable the company with data related to AI solution, in a easy way to read and use. Some new goals are going to be included laterConfluence Documentation LinkTangram LinkTable of ContentsProject StructureFeaturesInstallation/UsageContactProject StructureDescribe the structure of theprojectfolder, including the organization of modules, directories, and any important files.ai_management/
├── __init__.py
├── model_evaluation.py
├── config.yamlExplain the purpose of each module or significant files.ModelEvaluationHistorize the technical model evaluation results at a Google Big Query table at a Google Cloud Platform project.Installationpipinstallai-managementUsageBinary classificationy_true=[1,0,0,1,1]y_pred=[1,0,0,0,1]y_test_a_lst=y_truey_pred_a_lst=y_predy_test_a_arr=np.array(y_true)y_pred_a_arr=np.array(y_pred)Multi class classificationy_true=[0,1,2,1,2]y_pred=[[0.9,0.1,0.0],[0.3,0.2,0.5],[0.2,0.3,0.5],[0.1,0.8,0.1],[0.1,0.2,0.7]]y_test_b_lst=y_truey_pred_b_lst=y_predy_test_b_arr=np.array(y_true)y_pred_b_arr=np.array(y_pred)Multi label classificationy_test=[[0,1,2],[3,4,5],[6,7,8]]y_pred=[[0,1,2],[3,4,5],[6,7,9]]y_test_c_lst=y_testy_pred_c_lst=y_predy_test_c_arr=np.array(y_true)y_pred_c_arr=np.array(y_pred)Regressiony_true=[2.5,3.0,4.0,5.5,6.0]y_pred=[2.0,3.5,3.8,5.0,6.5]y_test_d_lst=y_truey_pred_d_lst=y_predy_test_d_arr=np.array(y_true)y_pred_d_arr=np.array(y_pred)Assossiation Rulesimportpandasaspdimportnumpyasnp# Create a dataframe with random valuesdf_assossiation=pd.DataFrame({'ID_PRNCPAL':np.random.randint(1,50000,size=103846),'CONFIDENCE':np.random.uniform(0.01,0.03,size=103846)})df_assossiation.sort_values('ID_PRNCPAL')Solution Evaluationimportai_managementasaimclient_bq=bigquery.Client(project='project')me=aim.ModelEvaluation(client_bq=client_bq,destination='project.dataset.table')# Historizing standard metricsme.historize_model_evaluation(soltn_nm='Solution X',lst_mdls=[{'mdl_nm':'Model A','algrthm_typ':'binary_classification','data':[y_test_a_lst,y_pred_a_lst]},{'mdl_nm':'Model B','algrthm_typ':'multi_class_classification','data':[y_test_b_lst,y_pred_b_lst]},{'mdl_nm':'Model C','algrthm_typ':'multi_label_classification','data':[y_test_c_lst,y_pred_c_lst]},{'mdl_nm':'Model D','algrthm_typ':'assossiation','data':['confidence',df_assossiation]},])# Historizing custom metricsme.historize_custom_metric(soltn_nm="Solution Y",lst_mdls=[{'mdl_nm':'Model E','algrthm_typ':'regression','data':[["Lin's Concordance Correlation Coefficient",0.85,None],["Huber's error",123,{"delta":0.75}],]},])ContactLeroy Merlin Brazil AI scientists and developers:chapter_inteligencia_artificia@leroymerlin.com.br