Saravanan666/pypi-org huggingface dataset:
Package: lightgbm-callbacks
LightGBM CallbacksA collection ofLightGBMcallbacks.
Provides implementations ofProgressBarCallback(#5867) andDartEarlyStoppingCallback(#4805), as well as anLGBMDartEarlyStoppingEstimatorthat automatically passes these callbacks. (#3313,#5808)InstallationInstall this via pip (or your favourite package manager):pipinstalllightgbm-callbacksUsageSciKit-Learn API, simplefromlightgbmimportLGBMRegressorfromlightgbm_callbacksimportLGBMDartEarlyStoppingEstimatorfromsklearn.datasetsimportload_diabetesfromsklearn.model_selectionimporttrain_test_splitX,y=load_diabetes(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y)LGBMDartEarlyStoppingEstimator(LGBMRegressor(boosting_type="dart"),# or "gbdt", ...stopping_rounds=10,# or n_iter_no_change=10test_size=0.2,# or validation_fraction=0.2shuffle=False,tqdm_cls="rich",# "auto", "autonotebook", ...).fit(X_train,y_train)Scikit-Learn API, manually passing callbacksfromlightgbmimportLGBMRegressorfromlightgbm_callbacksimportProgressBarCallback,DartEarlyStoppingCallbackfromsklearn.datasetsimportload_diabetesfromsklearn.model_selectionimporttrain_test_splitX,y=load_diabetes(return_X_y=True)X_train,X_test,y_train,y_test=train_test_split(X,y)X_train,X_val,y_train,y_val=train_test_split(X_train,y_train)early_stopping_callback=DartEarlyStoppingCallback(stopping_rounds=10)LGBMRegressor().fit(X_train,y_train,eval_set=[(X_train,y_train),(X_val,y_val)],callbacks=[early_stopping_callback,ProgressBarCallback(early_stopping_callback=early_stopping_callback),],)Details onDartEarlyStoppingCallbackBelow is a description of theDartEarlyStoppingCallbackmethodparameter andlgb.plot_metricfor eachlgb.LGBMRegressor(boosting_type="dart", n_estimators=1000)trained with entiresklearn_datasets.load_diabetes()dataset.MethodDescriptioniterationImageActual iteration(Baseline)If Early stopping is not used.n_estimators1000"none"Do nothing and return the original estimator.min(best_iteration + early_stopping_rounds, n_estimators)50"save"Save the best model by deepcopying the estimator and return the best model (usingpickle).min(best_iteration + 1, n_estimators)21"refit"Refit the estimator with the best iteration and return the refitted estimator.min(best_iteration, n_estimators)20Contributors ✨Thanks goes to these wonderful people (emoji key):34j💻🤔📖This project follows theall-contributorsspecification. Contributions of any kind welcome!
Package: lightgbm-embedding
LightGBM EmbeddingsFeature embeddings with LightGBMInstallationpip install lightgbm-embeddingExamplesimportpandasaspdfromsklearn.model_selectionimporttrain_test_splitfromlightgbm_embeddingimportLightgbmEmbeddingdf=pd.read_csv("https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/0e7a9b0a5d22642a06d3d5b9bcbad9890c8ee534/iris.csv")cols=df.columns[:-1]target=df.columns[-1]num_classes=df[target].nunique()X_train,X_test=train_test_split(df,test_size=0.2,stratify=df[target],random_state=42)n_dim=20emb=LightgbmEmbedding(n_dim=n_dim)emb.fit(X_train[cols],X_train[target])X_train_embed=emb.transform(X_train[cols])X_test_embed=emb.transform(X_test[cols])
Package: lightgbmlss
LightGBMLSS - An extension of LightGBM to probabilistic modellingWe introduce a comprehensive framework that models and predicts the full conditional distribution of a univariate target as a function of covariates. Choosing from a wide range of continuous, discrete, and mixed discrete-continuous distributions, modelling and predicting the entire conditional distribution greatly enhances the flexibility of LightGBM, as it allows to create probabilistic forecasts from which prediction intervals and quantiles of interest can be derived.FeaturesEstimation of all distributional parameters.Normalizing Flows allow modelling of complex and multi-modal distributions.Mixture-Densities can model a diverse range of data characteristics.Zero-Adjusted and Zero-Inflated Distributions for modelling excess of zeros in the data.Automatic derivation of Gradients and Hessian of all distributional parameters usingPyTorch.Automated hyper-parameter search, including pruning, is done viaOptuna.The output of LightGBMLSS is explained usingSHapley Additive exPlanations.LightGBMLSS provides full compatibility with all the features and functionality of LightGBM.LightGBMLSS is available in Python.News2023-08-28: Release of v0.4.0 introduces Mixture-Densities. See therelease notesfor an overview.2023-07-20: Release of v0.3.0 introduces Normalizing Flows. See therelease notesfor an overview.2023-06-22: Release of v0.2.2. See therelease notesfor an overview.2023-06-15: LightGBMLSS now supports Zero-Inflated and Zero-Adjusted Distributions.2023-05-26: Release of v0.2.1. See therelease notesfor an overview.2023-05-23: Release of v0.2.0. See therelease notesfor an overview.2022-01-05: LightGBMLSS now supports estimating the full predictive distribution viaExpectile Regression.2022-01-05: LightGBMLSS now supports automatic derivation of Gradients and Hessians.2022-01-04: LightGBMLSS is initialized with suitable starting values to improve convergence of estimation.2022-01-04: LightGBMLSS v0.1.0 is released!InstallationTo install LightGBMLSS please usepipinstalllightgbmlssAvailable DistributionsOur framework is built upon PyTorch and Pyro, enabling users to harness a diverse set of distributional families. LightGBMLSS currently supports thefollowing distributions.How to UsePlease visit theexample sectionfor guidance on how to use the framework.DocumentationFor more information and context, please visit thedocumentation.FeedbackWe encourage you to provide feedback on how to enhance LightGBMLSS or request the implementation of additional distributions by opening anew discussion.How to CiteIf you use LightGBMLSS in your research, please cite it as:@misc{Maerz2023,author={Alexander M\"arz},title={{LightGBMLSS: An Extension of LightGBM to Probabilistic Modelling}},year={2023},howpublished={\url{https://github.com/StatMixedML/LightGBMLSS}}}Reference Paper
Package: lightgbm-transform
InstallationPreparation32-bit Python is not supported. Please install 64-bit version. If you have a strong need to install with 32-bit Python, refer toBuild 32-bit Version with 32-bit Python section.setuptoolsis needed.Install fromPyPIpipinstalllightgbm-transformYou may need to installwheelviapip install wheelfirst.Compiled library that is included in the wheel file supportsLightGBM MPIversions out of the box.TroubleshootingIn case you are facing any errors during the installation process, you can examine$HOME/LightGBM_compilation.logfile, in which all operations are logged, to get more details about occurred problem. Also, please attach this file to the issue on GitHub to help faster indicate the cause of the error.Refer toFAQ.ExamplesRefer to the walk through examples inTransform tutorial.Development GuideThe code style of Python-package followsPEP 8. If you would like to make a contribution and not familiar with PEP 8, please check the PEP 8 style guide first. Otherwise, the check won’t pass. Only E501 (line too long) and W503 (line break occurred before a binary operator) can be ignored.Documentation strings (docstrings) are written in the NumPy style.
Package: lighthouse
Lighthouse is a service node discovery system written in python, built with
resilience, flexibility and ease-of-use in mind and inspired by Airbnb’sSmartStacksolution.  Out of the box it supports discovery viaZookeeperwith
cluster load balancing handled by an automatically configuredHAProxy.DocumentationMore detailed documentation can be found onRead the Docs.OverviewA lighthouse setup consists of three parts running locally on each node: a load
balancer, thelighthouse-writerscript and (usually) thelighthouse-reporterscript.In a Lighthouse setup, no node’s application code is aware of the existence of
other nodes, they talk to a local port handled by an instance of the load
balancer which in turn routes traffice among the various known other nodes.This local load balancer is automatically updated when nodes come and go
via thelighthouse-writerscript, which talks to the discovery method (e.g.
Zookeeper) to keep track of which nodes on which clusters are up.Thelighthouse-reporterscript likewise talks to the discovery method, it
it is responsible for running health checks on any services on the local
node and reports to the discovery method that the healthy services are up
and the unhealthy ones are down.DevelopmentThe code is hosted onGitHubTo file a bug or possible enhancement see theIssue Tracker, also found
on GitHub.License(c) 2014-2016 William GlassLighthouse is licensed under the terms of the Apache license (2.0).  See theLICENSEfile for more details.
Package: lighthousedataextract
LightHouse Data ExtractThis tool  parses the google lighthouse json data, accepts a csv file for categories of the URLs and returns 4  pandas DataFrames for metrics, opportunities, diagnostics and resources.InstallpipinstalllighthousedataextractImportfromlighthousedataextractimportLightHouseDataExtractCreate a report variableIf json files are in directory ./repprt/lighthouse/ and you don't want to give an input file for categories of URLsreport=LightHouseDataExtract()If your json files are in another directoryreport=LightHouseDataExtract(path_to_json="./data/lighthouse/report/lighthouse/")If you want to seperate URLs in categoriesYour CSV of URLs should have two columns, without headers. Below you can see  an example:https://www.example.com/Home Pagehttps://www.example.com/categories/category-1Middle Tailhttps://www.example.com/products/product-1234Long Tailreport=LightHouseDataExtract(url_category_file="./data/lighthouse/category.csv")Create a lighthouse metrics DataFramefromlighthousedataextractimportLightHouseDataExtractreport=LightHouseDataExtract(path_to_json="./data/lighthouse/report/lighthouse/",url_category_file="./data/lighthouse/category.csv",)df_report=report.df_report()df_report.set_index("url").TCreate other DataFramesdf_opportunities=report.df_opportunities()display(df_opportunities)df_diagnostics=report.df_diagnostics()display(df_diagnostics)df_resources=report.df_resources()display(df_resources)If json files are obtained by gooogle pagespeed insights api thenapi_report=LightHouseDataExtract(from_api=True)
Package: lighthouse-garden-kmi
Lighthouse GardenAggregate a performance overview for various target pages using theGoogle Lighthouseservice.FeaturesTrack multiple target pagesLighthouse audits for performance, accessibility, best practices and SEOAverage performance dataHistory performance graph with stored lighthouse reportsInformational badges:InstallationPrerequisiteThe script needspython3.5or higher. It is necessary for some additional functionalities to havepipinstalled on your local machine.pipThe library can be installed fromPyPI:$pip3installlighthouse-garden-kmiConfigurationThe configuration file contains important information about the script processing and target pages.{"export_path":"path/to/export/","keep_history":10,"targets":[{"title":"Google","identifier":"google","url":"https://www.google.com"}]}UsageRun the python script via command line.$python3lighthouse_gardenusage:lighthouse_garden[-h][-v][-cCONFIG][--clear]Monitoringperformancedatabylighthouse.

optionalarguments:-h,--helpshowthishelpmessageandexit-v,--verboseEnableextendedconsoleoutput-cCONFIG,--configCONFIGPathtoconfigfile--clearClearallperformancedataandresettheapplicationCronjobRegister optionally a cronjob for regularly updates05***python3lighthouse_garden-c/var/www/html/config.jsonTestRun the script within theDDEVcontainer:$ddevstart
$ddevssh
/var/www/html$pip3install-rrequirements.txt
/var/www/html$python3lighthouse_garden-ctests/config.jsonOpenhttp://lighthouse-garden.ddev.site/BuildThe packaging process of the python module is described onpython.org.CreditsPerformance analysis bylighthouseGraph visualization byplot.lyIcon made byfreepik
Package: lighthouse-python-plus
Lighthouse runner for pythonAbout This RepositoryThis repository is based on the original work ofAdam Cupiałand was forked from hislighthouse-pythonrepository. While the original repository had not been updated in five years, I found it to be a valuable resource and decided to update it (I have made some changes, fixed some bugs) and turn it into a package for PyPI. I want to acknowledge the original author for creating the foundation of this project and making it available to the community.DescriptionThis library is a simple wrapper around lighthouse-cli runner that runs the audit and parses a result in friendly manner.Installationif lighthouse is not installed:npminstall-glighthousepipinstalllighthouse-python-plusUsagefromlighthouseimportLighthouseRunnerTIMINGS=['speed-index']report=LighthouseRunner('https://github.com/adamcupial',form_factor='desktop',quiet=False,timings=TIMINGS).reportassertreport.score['performance']>0.5print(report.audits(0.5)['performance'].failed)report has 3 properties:score: returns dict where keys are categories and values are scores (0 to 1)timings: returns dict where keys are timings and values are timedelta objectsaudits: dict where keys are categories and values are objects with passed and failed lists attachedTo see all the usage examples of this library, please check out thedemofolder.Dependenciespython 3.6+node package lighthouse>=10.3.0 installedChangesYou can find all changes in CHANGELOG!
Package: lighthouse-scoring-calculator
Lighthouse Scoring CalculatorPython package to calculate the Lighthouse scoring for a website (same aspagespeed.web.dev)Authors@fabiomanzAcknowledgementslh-scorecalcInspired by this website:https://googlechrome.github.io/lighthouse/scorecalc/Awesome READMEFeaturesPython module to directly use the calculator in Python projectsCLI to use the calculator directly from the command lineMultiple Lighthouse version supportCross platformSupport for Lighthouse mobile and desktop testUsage/Examplesfromlighthouse_scoring_calculatorimportLighthouseScoringCalculatortestData={"FCP":2130,"SI":5800,"FMP":4000,"TTI":7300,"FCI":6500,"LCP":4000,"TBT":600,"CLS":0.25}calculator=LighthouseScoringCalculator(testData,"mobile","v9")print("Score:",calculator.calc_score())print("Rating of FCP:",calculator.get_rating("FCP"))CLI Referenceusage: lighthouse_scoring_calculator.py [-h] [-d DEVICE] [-v VERSION] [--fcp FCP] [--si SI] [--fmp FMP] [--tti TTI] [--fci FCI] [--lcp LCP] [--tbt TBT] [--cls CLS]

Calculate the score and rating of each metric
Example: $ python3 -m lighthouse_scoring_calculator -d mobile -v v9 --fcp 2130 --si 5800 --fmp 4000 --tti 7300 --fci 6500 --lcp 4000 --tbt 600 --cls 0.25

options:
  -h, --help            show this help message and exit
  -d DEVICE, --device DEVICE
                        The device type
  -v VERSION, --version VERSION
                        The lighthouse version
  --fcp FCP             The First Contentful Paint metric
  --si SI               The Speed Index metric
  --fmp FMP             The First Meaningful Paint metric
  --tti TTI             The Time to Interactive metric
  --fci FCI             The First CPU Idle metric
  --lcp LCP             The Largest Contentful Paint metric
  --tbt TBT             The Total Blocking Time metric
  --cls CLS             The Cumulative Layout Shift metricInstallationYou can install the Lighthouse Scoring Calculator fromPyPI:python -m pip install lighthouse-scoring-calculatorFeedbackIf you have any feedback, please reach out to us via GitHub
Package: lighthouseweb3
Lighthouse Python SDKLighthouse is a permanent decentralized file storage protocol that allows the ability to pay once and store forever. While traditionally, users need to repeatedly keep track and pay for their storage after every fixed amount of time, Lighthouse manages this for them and makes sure that user files are stored forever. The aim is to move users from a rent-based cost model where they are renting their own files on cloud storage to a permanent ownership model. It is built on top of IPFS, Filecoin, and Polygon. It uses the existing miner network and storage capacity of the filecoin network.Installationpip install lighthouseweb3UsageInstantiate the clientfromlighthouseweb3importLighthouse# use token from env variable LIGHTHOUSE_TOKENlh=Lighthouse()# or you can pass token as parameterlh=Lighthouse(token="your_token")Uploading a filefromlighthouseweb3importLighthouselh=Lighthouse()response=lh.upload("path/to/file")print(response)# prints a dict containing the cid of the fileUploading a directoryfromlighthouseweb3importLighthouselh=Lighthouse("my-lightouse-token")response=lh.upload("path/to/directory")print(response)# prints a dict containing the root cid of the directoryTestingThe tests are written with inheritance from the unittest module. To run the tests, run the following command:pip install -r requirements.txt && python -m unittest discoveror using nose2pip install -r requirements.txt && python -m nose2
Package: lightkde
lightkdeA lightning fast, lightweight, and reliable kernel density estimation.Easy to use, e.g.density_vec, x_vec = kde_1d(sample_vec=sample).Works with 1d and 2d samples.Works with weighted samples as well.Based on the MATLAB implementations of Botev:kde,kde2d.InstallpipinstalllightkdeUsageimportnumpyasnpfromlightkdeimportkde_1dsample=np.random.randn(1000)density_vec,x_vec=kde_1d(sample_vec=sample)For further examples see thedocumentation.Other kde packagesOther python packages for kernel density estimation:scipy.stats.gaussian_kdeKDEpy
Package: lightkit
LightKitLightKit provides simple utilities for working with PyTorch and PyTorch Lightning. At the moment,
it provides three simple features:A data loader for tabular data that is orders of magnitude faster than PyTorch's builtin data
loader for medium-sized datasets and larger ones.A mixin for modules that allows to save not only weights, but also the configuration to the file
system such that it is easier to retrieve trained models.A typed base class for estimators that enables users to easily create estimators with PyTorch and
PyTorch Lightning which are fully compatible with Scikit-learn.For more details, consult thedocumentation.InstallationLightKit is available viapip:pipinstalllightkitIf you are usingPoetry:poetryaddlightkit
Package: light-koreanbots
light-koreanbots(비공식 모듈)공식 SDK가 너무 무거운가요? 그냥 서버수 업데이트만 하면 되나요? 그러면 이 모듈을 사용해보세요!기능서버수 자동 업데이트이 외의 기능은 추가할 생각이 없습니다설치pip install light-koreanbots예제importlight_koreanbotsaslkbimportdiscordclient=discord.Client()kbot_token="koreanbots_token"lkb_client=lkb.LKBClient(bot=client,token=kbot_token)client.run("discord_token")
Package: lightkurve
A friendly package for Kepler & TESS time series analysis in Python.Documentation: https://docs.lightkurve.orgLightkurveis a community-developed, open-source Python package which offers a beautiful and user-friendly way
to analyze astronomical flux time series data,
in particular the pixels and lightcurves obtained byNASA’s Kepler and TESS exoplanet missions.This package aims to lower the barrier for students, astronomers,
and citizen scientists interested in analyzing Kepler and TESS space telescope data.
It does this by providinghigh-quality building blocks and tutorialswhich enable both hand-tailored data analyses and advanced automated pipelines.DocumentationRead the documentation athttps://docs.lightkurve.org.Quickstart and InstallationPlease visit our quickstart guide athttps://docs.lightkurve.org/quickstart.html.The easiest way to installLightkurveand all of its dependencies is to use thepipcommand,
which is a standard part of all Python distributions.
To installLightkurve, run the following command in a terminal window:$ python -m pip install lightkurve --upgradeThe--upgradeflag is optional, but recommended if you already
haveLightkurveinstalled and want to upgrade to the latest version.Depending on the specific Python environment, you may need to replacepythonwith the correct Python interpreter, e.g.,python3.If you want to experiment with the latest development version ofLightkurve, you can install it straight from the main branch on GitHub:$gitclonehttps://github.com/lightkurve/lightkurve.git$cdlightkurve$python-mpipinstall.If you want to have a so-called editable install which enables the installed
version to immediately reflect changes made in the source tree, you can use:$python-mpipinstallpoetry$poetryinstallPlease see our guide onhttps://docs.lightkurve.org/development/index.htmlfor additional instructions.ContributingWe welcome community contributions!
Please read the  guidelines athttps://docs.lightkurve.org/development/contributing.html.CitingIf you find Lightkurve useful in your research, please cite it and give us a GitHub star!
Please read the citation instructions athttps://docs.lightkurve.org/about/citing.html.ContactLightkurve is an open source community project created bythe authors.
The best way to contact us is toopen an issueor to e-mailtesshelp@bigbang.gsfc.nasa.gov.
Please include a self-contained example that fully demonstrates your problem or question.
Package: lightlab
A python library for remote laboratory control. Laboratory remoting facilitates experimental research:access from anywherelarge dataset gathering, storage, and analysisrepeatabilityreal-time status and progress monitoring,intra- and inter-group collaborationThis package includes:A shared library of instrument driversRepresentation of state for labs of multiple usersDriver-independent abstractions of instrumentsUtilities for data structures, advanced sweeping, search procedures, spectrum analysis, and characterizationConstructs of laboratory virtualization for repeatability and rapid user code developmentDetailed documentation of tips, tricks, and instructions for prepping your lab for remotingHere is ourphilosophyof how a modern lab can look.InstallationFrom PyPI:pip install lightlabFrom source (developer mode):git clone https://github.com/lightwave-lab/lightlab.git
cd lightlab
pip install -e .  # install in editable mode.Getting startedConfigure GPIB/ethernet communication on apersonal computer or instrumentation serverInitialize the representation of yourlab stateWrite a driveror use an existing oneRead about advanced features andtutorialsGet going! Need more help? Have suggestions? File anissueReadthedocs:lightlab.readthedocs.ioGithub:https://github.com/lightwave-lab/lightlabSupported platformsServer: Mac OS and Linux running ≥python3.6; not tested on Windows (to do).Auxiliary hosts: Mac OS, Linux, WindowsAcknowledgementsThis  material  is  based  in part upon  work  supported  by  the  National Science Foundation under Grant Number E2CDA-1740262. Any  opinions,  findings,  and  conclusions  or  recommendations expressed  in  this  material  are  those  of  the  author(s)  and  do  not necessarily reflect the views of the National Science Foundation.