Saravanan666/pypi-org huggingface dataset:
Package: kage-cucounter
CuCounter: A Pythonkmer frequency counter object based on a massively parallel CUDA hash tableInstallationCuCounter requires NumPy and CuPy. It also currently only supports Nvidia GPUs with CUDA.Cucounter can be installed using pip:pipinstallkage-cucounter.. or via manual installation:clone the CuCounter repositoryuse pip to install all necessary dependencies as well as CuCounter from inside the cloned repositorygitclonehttps://github.com/jorgenwh/cucounter.gitcdcucounter
pipinstall-rrequirements.txt
pipinstall.UsageAll of CuCounter's methods (including its constructor) will accept either NumPy or CuPy arrays.
CuPy arrays are preferred as it circumvents having to copy memory back and fourth between the host and device.
NumPy is used in the example below, but the same code would work if NumPy had been replaced with CuPy.fromcucounterimportCounterimportnumpyasnp# Create a static set of 100 million unique 64-bit encoded kmers as keys for the counterunique_kmers=np.arange(100000000,dtype=np.uint64)# Create counter objectcounter=Counter(keys=unique_kmers)# Create a chunk of 200 million kmers to countkmers=np.random.randint(low=0,high=0xFFFFFFFFFFFFFFFF,size=(200000000,),dtype=np.uint64)# Count the observed kmer frequencies. Kmers not present in the original key set are ignoredcounter.count(kmers)# Fetch the observed frequencies for the original key setcounts=counter[unique_kmers]counts.dtype# np.uint32counts.shape# (100000000,)CuCounter also supports counting the reverse complements ofkmers aswell as the originalkmer.counter.count(kmers,count_revcomps=True)
Package: kaggle-autolgb
Authors@Aditta-daskaggle-autolgbkaggle autolgb is a combination of lightgbm and optuna. I tried to make kaggle monthly competition simple. Its only working with classification problem.Installationpip install kaggle-autolgbFeaturesautotuneautotrainauto submission file generateauto predictionDeploymentfrom autolgb import AutoLGB

model = AutoLGB(
    train_file = "trainning file path",
    test_file="testing file path",
    store_file= "store file path",
    storage_name = "store", # anything i.e: store/target/storage
    label="dataset label column name",
    num_folds=5, # num of num_folds
    direction="maximize", # maximize or minimize
    n_trials=2, # optuna trial
    gpu=False # gpu usage
)

best = model.train() # returns best params
model.predict(best)
model.submission_kaggle_format("kaggle submission file")LicenseApache 2.0
Package: kagglebuddy
kaggleBuddyThis is a machine learning libriary that i ofen used during competition and work, more features willed be added in the near future.How to install: pip install kaggleBuddy命名规则文件夹名：小子字母 + 下划线
函数名：下划线命名法, 参数等号左右不留空白cd folderpython3 setup.py sdist bdist_wheelpython3 -m twine upload dist/*pip install kaggleBuddy --upgrade"""Brief description.

Detailed description.

Parameters
----------
default_parameter: type, optional (default = 5)
	Detailed description.
required_parameter: type_1 or type_2
	Detailed description.
	- value_1: Detailed description.
	- value_2: Detailed description.

Returns
-------
parameter: type
	Detailed description.

Examples
--------
Your code here.
"""
Package: kaggle-cli-wrapper
IntroductionThe repository adds simple class for adding a wrapper on top of kaggle cli tools for ease of downloading competition datasets, listing competitions, downloading and un-zipping datasets.It aims to make it easy to use kaggle datasets directly from python scripts.
It's main usecase is to be used as a submodule in other repos/ or used as a standalone package.
If the library is used as a standalone package - the data is downloaded and stored in data folder.
In case of using the package as a submodule - the data is downloaded in a data folder created in the base folder of the calling script.InstallationThe repo relies on setuptools and kaggle library packages. To install the repo please use one of the following options:pipinstall-Ukagglesetuptools
pythonsetup.pyinstallIt also assumes that presence of Kaggle User ID and public API credentials atOn Windows: C:\Users\<Windows-username>\.kaggle\kaggle.json

Others: ~/. kaggle/kaggle.jsonAfter the above steps: the repo can be installed from git using:pip install git+https://github.com/sunny1401/kaggle_utils.git#egg=kaggle-cli-wrapperThe project can also be installed using pip:pip install kaggle-cli-wrapperCode detailsDataAPI classTo list all available datasetsfromkaggle_cli_wrapperimportKaggleDataApikda=KaggleDataApi(call_path=__file__)kda.list_all_kaggle_datasets(search_term="cityscapes")The call_path argument is required to decide the folder where downloaded files are stored.The function would return all available datasets with cityscape. This would also be saved as a .txt file.Currently the list is sorted by "votes".
To change this sorting please use the argumentsort_by. Allowed values of sort by are:'hottest','votes','updated','active','published'To list all available competition datasetsfromkaggle_cli_wrapperimportKaggleDataApikda=KaggleDataApi(call_path=__file__)kda.list_all_kaggle_competitions(search_term="cityscapes")Currently the list is sorted by "earliestDeadline".
To change this sorting please use the argumentsort_by. Allowed values of sort by are:'grouped','prize','earliestDeadline','latestDeadline','numberOfTeams','recentlyCreated'To download datasetsfromkaggle_cli_wrapperimportKaggleDataApikda=KaggleDataApi(call_path=__file__)kda.download_kaggle_dataset(dataset_name="cityscapes_train_val_test",is_competition_dataset=False)To download a competition dataset, is_competition_dataset needs to be set to TrueScoringAPIThe code currently supports minimum functionality for submission of files to a competition and getting scores for the competitionTo submit to a competitionfromkaggle_utils.kaggle_cli_wrapperimportKaggleScoringsApikaggle_scoring_api=KaggleScoringsApi(competition_name="facial-keypoints-detection")kaggle_scoring_api.submit_solution(submissions_file=submission_path,description="facial_keypoint_vanilla_cnn")kaggle_scoring_api.get_top_scores()Remaining Issuesimprove error handlingallow for saving of stdout
Package: kaggledatasets
kaggledatasetsCollection of Kaggle Datasets ready to use for EveryoneMore about Kaggle DatasetsInstallationBinariesFrom SourceGetting StartedCommunicationReleases and ContributingLicenseSystemPython 3.5Python 3.6Python 3.7LinuxmacOSWindowsMore About Kaggle Datasetsimportkaggledatasetsaskddataset=kd.structured.CreditCardFraudDetection(download=True)# Returns the split for train and test in Scikit and Tensorflowtrain,test=dataset.load()# Returns the train and test data loader for PyTorchtrain_dataloader,test_dataloader=dataset.dataloader()InstallationBinariesCommands to install from binaries via Conda or pip wheels are on our website:https://kaggledatasets.github.ioFrom SourceInstall Dependenciespip install -r requirements.txtGet the kaggledatasets Sourcegit clone --recursive https://github.com/kaggledatasets/kaggledatasets
cd kaggledatasetsInstall kaggledatasetspython setup.py installGetting StartedTutorials: Get started with understanding kaggledatasetsExamples: Easy to understand kaggledatasets codeAPI ReferenceCommunicationGitHub Issues: bug reports, feature requests, dataset requests, install issues, help wanted, thoughts, etc.Slack: TheKaggle Datasets Slackhosts a primary audience of moderate to experienced Kaggle Datasets users and developers for general chat, online discussions, collaboration etc. If you need a slack invite, please visit:http://bit.ly/kdslackReleases and Contributingkaggledatasets is expecting to have a 30 day release cycle (major releases). Please let us know if you encounter a bug byfiling an issue.We appreciate all contributions and make sure you go through ourContributing Guide. If you are planning to contribute back bug-fixes, please do so without any further discussion.If you plan to contribute new features, new datasets, utility functions or extensions to the core, please first open an issue and discuss the feature with us.
Sending a PR without discussion might end up resulting in a rejected PR, because we might be taking kaggledatasets in a different direction than you might be aware of.Licensekaggledatasets is Apache-2.0 licensed, as found in theLICENSEfile.
Package: kaggleDownloader
kaggleDownloaderHelps you download Kaggle Dataset to Google Colab Notebook or your own system in the current working directory (os.getcwd()) by using the Kaggle API Download Link and kaggle.json file obtained from the Kaggle account.SpecificationsProgramming Language :- Python 3Platforms Supported :- Google Colab Notebooks (Python 3) / LinuxFiles Required :- kaggle.json (to be obtained from kaggle account; kaggle_profile -> My Account -> API -> Create new API token)How to Use?Add the kaggle.json file to your current working directory (os.getcwd(), the directory where you wish to download your dataset to)Bythree lines of codeyou can download your kaggle dataset to your current working directory, either in Google Colab Notebooks or in your own systemThe lines of code are as follows:-1)if you are installing the packagea)on your own systempip install kaggleDownloaderb)on Google Colab Notebook!pip install kaggleDownloader2)Import the get_dataset function from kaggleDownloader modulefrom kaggleDownloader import get_dataset3)call the get_dataset() function; there are two waysa)get_dataset()or,b)or you can pass the Kaggle Dataset Download API link in the get_dataset argument in string format (""); eg. SpringLeaf Datasetget_dataset('kaggle competitions download -c springleaf-marketing-response')Follow along the instructions and you will be ready to work on your dataset.
Package: kaggle-downloader
kaggle-downloaderA wrapper around the Kaggle CLI to download multiple kernels at once.InstallationGrab the latest release ofPython.Install this tool withpip install kaggle-downloader.Follow the steps from theKaggle API documentationto create an API token. We never access this directly but let the official Kaggle API take care of authentication.Usageusage: kaggle-downloader [-h] {competition-refs,kernel-refs,kernels} ...

Download kernels from Kaggle.

positional arguments:
  {competition-refs,kernel-refs,kernels}
    competition-refs    Fetch competition references.
    kernel-refs         Fetch kernel references for a list of competition references.
    kernels             Fetch kernels for a list of kernel references.

optional arguments:
  -h, --help            show this help message and exitGetting to the actual kernels takes three steps:Get a list of references to competitions:usage: kaggle-downloader competition-refs [-h] -o OUT

optional arguments:
  -h, --help         show this help message and exit
  -o OUT, --out OUT  Output file.Get a list of references to kernels:usage: kaggle-downloader kernel-refs [-h] -c COMPETITIONS -e EXCLUDE -o OUT

optional arguments:
  -h, --help            show this help message and exit
  -c COMPETITIONS, --competitions COMPETITIONS
                        File with list of competitions references.
  -e EXCLUDE, --exclude EXCLUDE
                        File with list of competitions references to exclude. Gets updated with competitions as they are processed.
  -o OUT, --out OUT     Output directory.Get kernels themselves:usage: kaggle-downloader kernels [-h] -k KERNELS -e EXCLUDE -o OUT

optional arguments:
  -h, --help            show this help message and exit
  -k KERNELS, --kernels KERNELS
                        Directory with files containing a list of kernel references.
  -e EXCLUDE, --exclude EXCLUDE
                        File with list of kernel references to exclude. Gets updated with kernels as they are processed.
  -o OUT, --out OUT     Output directory.Example usage# Step 1:kaggle-downloadercompetition-refs-odata/competition-refs.txt# Step 2:kaggle-downloaderkernel-refs-cdata/competition-refs.txt-edata/excluded-competition-refs.txt-odata/kernel-refs# Step 3:kaggle-downloaderkernels-kdata/kernel-refs-edata/excluded-kernel-refs.txt-odata/kernelsFor developersGrab the latest release ofPython.Installpoetry.Runpoetry installin the root of the repository.Set the Python interpreter in your IDE to the virtual environment that was created.
Package: kaggle-driver
A package to support model development for Kaggle competitions.Free software: MIT licenseInstallationpip install kaggle-driverYou can also install the in-development version with:pip install https://github.com/christopherpriebe/kaggle-driver/archive/master.zipDocumentationhttps://kaggle-driver.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2023-06-09)Initial project skeleton added.
Package: kaggle-fastai-custom-metrics
kaggle fastai custom metricsCustom Metrics for fastai v1 for kaggle competitions.Disclaimer :Each Kaggle competition has a unique metrics suited to its, need this package lets you download those custom metrics to be used with fastai library.
Since Metrics are an important part to evaulate your models performance.Installationpipinstallkaggle-fastai-custom-metrics==1.0.2orgitclonehttps://github.com/shadab4150/kaggle_fastai_custom_metricscdkaggle_fastai_custom_metrics
pipinstall.Usage :fromkaggle_fastai_custom_metrics.kfcmimport*print_all_metrics()Competition URLMetrichttps://www.kaggle.com/c/plant-pathology-2020-fgvc7column_mean_loglosshttps://www.kaggle.com/c/jigsaw-toxic-comment-classification-challengecolumn_mean_aucrochttps://www.kaggle.com/c/trends-assessment-predictionweighted_maehttps://www.kaggle.com/c/alaska2-image-steganalysisalaska_weighted_auchttps://www.kaggle.com/c/google-quest-challengeAvgSpearmanhttps://www.kaggle.com/c/landmark-recognition-2020GAP_vectormetric = weighted_auc();
learn = Learner(data, arch, metrics= [metric] );
Package: kaggle-metrics
kaggle-metricsMetrics for Kaggle competitions.See wiki:Implemented metricsInstallationpython3.7-mpipinstallgit+https://github.com/krzjoa/kaggle-metrics.gitor:python3.7-mpipinstallkaggle_metricsUsagefromxgboostimportXGBRegressorimportkaggle_metricsaskmX_train,y_train,X_test,y_test=get_data()# Trainclf=XGBRegressor()clf.fit(X_train,y_train)# Get predictionsy_pred=clf.predict(X_test)# Evaluate with kaggle-metricskm.rmse(y_test,y_pred)DocumentationThe full documentation is athttp://kaggle-metrics.rtfd.org.
Package: kagglerecipes
Kaggle RecipesWhat's NewSept 2, 2021Release version 0.0.3Added support for multiprocessing to extract DICOM metadata for RSNA-MICCAI Brain Tumor Classification competition.Bug fixes.Aug 27, 2021Released the library on PyPI.Easily create voxel manipulated dataset for RSNA-MICCAI Brain Tumor Classification competition.Extract dicom metadata.Added utilities to log dataframe as tables and files/directory as artifacts.Added utiities to log basic W&B charts (line, bar, and scatter).Kaggle CompetitionsRSNA-MICCAI Brain Tumor Radiogenomic ClassificationCode & ScriptsRSNA-MICCAI Brain Tumor Radiogenomic ClassificationLINK TO NOTEBOOKInstallpip install kagglerecipesSample DatasetsWe have also logged smaller subsets of Kaggle commpeition datasets local development and fast prototyping.RSNA-MICCAI Brain Tumor Radiogenomic ClassificationDownload it manually fromhere.Or download it using this code snippet.import wandb

run = wandb.init()

artifact = run.use_artifact('wandb_fc/rsna-miccai-brain/sample:v0', type='dataset')

artifact_dir = artifact.download()CreditsThe code in this repository is a combination of many of the Kagglers' work that they have shared publicly as Kaggle kernels.Connecting voxel spacesbyMichael BeregovNormalized Voxels: Align Planes and Cropbyyu4uDICOM to 2D Resized Axial PNGs 256x256 [x36]bySofia Moschou
Package: kaggle-runner
Generated doc websitekaggle_runnerCheck main.py or test/test_coord.py for usage. It uses kaggle API to upload your script/notebook to kaggle servers and let the kernel run. And you will get running logs through message queue.AMQPAMQP is used for logging. Its license needs mention.ExampleUse thiskaggle competitionabout Pneumothorax Segmentation as an example. To run the example, you will need a kaggle account and set the kaggle command line tool up. And you issues this command to let it run:# install kaggle_runner, which will pull kaggle command line tool as the dependencypipinstallkaggle_runner# put your kaggle API token to the right placecat>~/.kaggle/kaggle.json<<EOF{"username": "YOUR_KAGGLE_USER_NAME","key": "YOUR_KAGGLE_API_ACCESS_TOKEN",}EOF# kaggle_runner will use kaggle API to push the template kernel codes to kaggle server and wait message backpython-mkaggle_runnerA demo:#0 Left panel: tcpserver listen for reverse shells#1 Upper panel: Logs from interactive session to our tcpserver which receive logs#2 Second upper panel: AMQP logs received#3 Main panel: vim window#4 Right bottom panel: logged in reverse shell for commit session
Package: kaggleupload
Kaggle uploadAn unofficial wrapper around kaggle-apiFeaturesupload a folder as dataset in one commandkaggleupload --dataname kaggledatasetname --folder .zip the folder then upload (this is much faster if you have many small files in the root folder)kaggleupload --dataname kaggledatasetname --folder . --zip_folderkeep the root folder (will add another empty file at the same level with the root folder)kaggleupload --dataname kaggledatasetname --folder . --keep_folderinstallationpip install kaggleuploadTODOsupport creating new versionfeel free to open an issueUsage$kaggleupload --help
usage: kaggleupload [<args>]

optional arguments:
  -h, --help            show this help message and exit
  -p FOLDER, --folder FOLDER
                        folder
  --dataname DATANAME   name for the dataset
  -d DIR_MODE, --dir_mode DIR_MODE
                        dir mode
  --convert_to_csv      Whether to convert to csv.
  -k, --keep_folder     Whether to keep the folder. (will be faster for folder with a lot of
                        small files).copyright noticepackage structure & setup.py modified fromhttps://github.com/huggingface/accelerate
Package: kagi
KagiKagi provides support for FIDO WebAuthn security keys and TOTP tokens in Django.Kagi is a relatively young project and has not yet been fully battle-tested.
Its use in a high-impact environment should be accompanied by a thorough
understanding of how it works before relying on it.Installationpython -m pip install kagiAddkagitoINSTALLED_APPSand includekagi.urlssomewhere in your
URL patterns. Set:LOGIN_URL = "kagi:login"Make sure that Django’s built-in login view does not have aurlpattern, because it will authenticate users without their second
factor. Kagi provides its own login view to handle that.DemoTo see a demo, use the test project included in this repository and perform the
following steps (creating and activating a virtual environment first is optional).First, installPoetry:curl -sSL https://install.python-poetry.org/ | python -Clone the Kagi source code and switch to its directory:git clone https://github.com/justinmayer/kagi.git && cd kagiInstall dependencies, run database migrations, create a user, and serve the demo:poetry install
poetry shell
invoke migrate
python testproj/manage.py createsuperuser
invoke serveYou should now be able to see the demo project login page in your browser at:http://localhost:8000/kagi/loginSupported browsers and versions can be found here:https://caniuse.com/webauthnFor domains other thanlocalhost, WebAuthn requires that the site is served
over a secure (HTTPS) connection.Since you haven’t added any security keys yet, you will be logged in with just a
username and password. Once logged in and on the multi-factor settings page,
choose “Manage WebAuthn keys” and then “Add another key” and follow the provided
instructions. Once WebAuthn and/or TOTP has been successfully configured, your
account will be protected by multi-factor authentication, and when you log in
the next time, your WebAuthn key or TOTP token will be required.You can manage the keys attached to your account on the key management page at:http://localhost:8000/kagi/keysUsing WebAuthn Keys on LinuxSome distros don’t come with udev rules to make USB HID /dev/
nodes accessible to normal users. If your key doesn’t light up
and start flashing when you expect it to, this might be what is
happening. Seehttps://github.com/Yubico/libu2f-host/issues/2andhttps://github.com/Yubico/libu2f-host/blob/master/70-u2f.rulesfor some
discussion of the rule to make it accessible. If you just want a quick
temporary fix, you can runsudo chmod 666 /dev/hidraw*every time
after you plug in your key (the files disappear after unplugging).GratitudeThis project would not exist without the significant contributions made byRémy HUBSCHER.Thanks to Gavin Wahl fordjango-u2f,
which served as useful initial scaffolding for this project.
Package: kaginawa
KaginawaAnunofficialclient to Kagi APIsInstallationpipinstallkaginawaUsagefromkaginawa.clientimportKaginawa# You can also set the KAGI_API_KEY environment variable.client=Kaginawa(token="YOUR_API_TOKEN")response:KaginawaFastGPTResponse=client.generate("Write a logstash pipeline file to send a heartbeat to a server ""https://example.com/heartbeat every 30 seconds")print(response.output)forreferenceinresponse.references:print(reference.title)print(reference.snippet)print(reference.url)response:KaginawaEnrichResponse=client.enrich_web(query="Best fermented hot sauce")# orresponse:KaginawaEnrichResponse=client.enrich_news(query="Is Oliver Tree okay?")forresultinresponse.results:print(result.rank)print(result.title)print(result.url)print(result.snippet)print(result.published)response:KaginawaSummarizationResponse=client.summarize(url="https://example.com",engine=KaginawaSummarizationEngine.AGNES,summary_type=KaginawaSummaryType.TAKEAWAY,target_language="FR")print(response.output)response:KaginawaSummarizationResponse=client.summarize(text="The rain in Spain…",engine=KaginawaSummarizationEngine.CECIL)print(response.output)Async!importasynciofromkaginawa.async_clientimportAsyncKaginawaasyncdefamain():kagi_client=AsyncKaginawa(...)res=awaitkagi_client.generate(...)print(res.output)# If you want to explicitly close the client.kagi_client.close()if__name__=="__main__":asyncio.run(amain())FAQDo you support the search API?I would love to but I don't have enterprise.Why the name?Because it's like the only word that starts with KagiAuthorsEstelle Poulindev@inspiredby.es
Package: kagsa
KAGSA is a new Interpreted programming language based on Python.it designed to be easy to learn and practical for use in various projects. It has a flexible syntax and allows some things that other languages prohibit, such as using certain symbols in variable names and starting variable names with numbers. KAGSA contains several main components, including a lexer, syntax checker, parser, and compiler. It supports object-oriented programming, iteration, and other features commonly used in programming languages.InstallationYou can download it via Python pip, launch the command prompt and run the following command:pip install kagsaKAGSA is also available forWindows, Linux and Termux, any version can be downloaded from theofficial websiteThere is a document explaining the installation method, you can read itHereGetting StartedOnce you have installed KAGSA, you can start writing and running KAGSA programs. Here are the basic steps:Create a new text file with a .kg extension.Open the file in a text editor and write your KAGSA code.Save the file.Open a terminal or command prompt and navigate to the directory where the file is saved.Type the following command and press Enter:kagsa filename.kgReplacefilenamewith the name of your file.Your KAGSA program should now run in the terminal.DocumentationFull documentation for KAGSA can be found in theKAGSA.ORG/DOCS.ExamplesKAGSA supports features such as object-oriented programming and iteration. Here are some example programs:Hello WorldObject-Oriented ProgrammingFactorialFizzBuzzPalindrome CheckerBinary SearchError HandlingAnd more you can find it atKAGSA/KAGSA/examples.