Saravanan666/pypi-org huggingface dataset:
Package: mlqa
MLQAA Package to perform QA on data flows for Machine Learning.IntroductionMLQA is a Python package that is created to help data scientists, analysts and developers to perform quality assurance (i.e. QA) onpandas dataframesand 1d arrays, especially for machine learning modeling data flows. It's designed to work withlogginglibrary to log and notify QA steps in a descriptive way. It includes stand alone functions (i.e.checkers) for different QA activities andDiffCheckerclass for integrated QA capabilities on data.InstallationYou can install MLQA with pip.pip install mlqaMLQA depends on Pandas and Numpy and works in Python 3.6+.QuickstartDiffCheckeris designed to perform QA on data flows for ML. You can easily save statistics from the origin data such as missing value rate, mean, min/max, percentile, outliers, etc., then to compare against the new data. This is especially important if you want to keep the prediction data under the same assumptions with the training data.Below is a quick example on how it works, just initiate and save statistics from the input data.>>>frommlqa.identifiersimportDiffChecker>>>importpandasaspd>>>dc=DiffChecker()>>>dc.fit(pd.DataFrame({'mean_col':[1,2]*50,'na_col':[None]*50+[1]*50}))Then, you can check on new data if it's okay for given criteria. Below, you can see some data that is very similar in columnmean_colbut increased NA count in columnna_col. The default threshold is 0.5 which means it should be okay if NA rate is 50% more than the origin data. NA rate is 50% in the origin data so up to 75% (i.e. 50*(1+0.5)) should be okay. NA rate is 70% in the new data and, as expected, the QA passes.>>>dc.check(pd.DataFrame({'mean_col':[.99,2.1]*50,'na_col':[None]*70+[1]*30}))TrueSee more examples atDocumentation/Quickstart. You can also read the full documentationhere.TestsTests are written withunittestand can be located in thetestsfolder. There are also some tests in docstring to be run bydoctest.LicenseMIT
Package: ml-random-utils
ml_random_utilsRandom utilities I use in ML projects.Installation$pipinstallml_random_utilsUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseml_random_utilswas created by Marco Podda. It is licensed under the terms of the MIT license.Creditsml_random_utilswas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: ml-rapids
ml-rapids: Incremental learning written in C++ exposed in Pythonml-rapidsimplements incremental learning methods in C++ and exposes them via SWIG in Python. Installation can be achieved simply withpip install ml_rapids. You can test your installation with running Python:# testing ml-rapidsimportml_rapidsml_rapids.test()Further documentation is available here:Users' ManualImplemented incremental learning methodsClassificationMajority ClassNaive BayesLogistic RegressionPerceptronVFDT (Very Fast Decision Trees) aka Hoeffding TreesHAT (Hoeffding Adaptive Trees)BaggingRegression/All the methods implementsklearnincremantal learner interface (includesfit,partial_fitandpredictmethods).Future plansStreaming random forest on top of Hoeffding trees will be implemented.
The library will be exposed via also vianpmpackages.DevelopmentDevelopment notes can be readhere.Python deployment notes can be readhere.Acknowledgementsml-rapidsis developed by AILab at Jozef Stefan Institute.This repository is based strongly onstreamDM-cpp.Project has received funding from European Union's Horizon 2020 Research and Innovation Programme under the Grant Agreement776115(PerceptiveSentinel).
Package: mlrd
mlrdA command line tool for downloading MyCourses lectures.Responsible use & copyright warning:warning: Before using this tool, read theuniversity's responsible IT use document.:warning: Do not share downloaded lectures without prior permission from the owner. Sharing without permission is a potential breach of the owner's copyright.PrerequisitesPython 3.8+ffmpegUsageAfter installation, run the following command in your terminal:mlrd <course_id> <output_dir> <auth_token>This will download all lectures for the given course to your directory of choice. The<course_id>and<auth_token>can be found by:navigating to desired course's lecture recording page on MyCourses.right click > inspect.ctrl/cmd + f and search forHF_CourseIdandHF_JWT. The values of these elements correspond to the<course_id>and<auth_token>.
Package: mlre
MLRE: Machine Learning and Research Environment.MLRE is a collection of tools that may help machine learning researchers and developers.RadarRadar is a networked event reporting library. Its main feature is the freeze frame support, where with each event we record observations from the environment to aid in debugging. This type of tracing is inspired by the automotive industry.Starting the Radar API server:FLASK_APP=mlre.radar.radar_app:create_default_appflaskrun
Package: ml-recipe
PyTorch Lightning Applications以 PyTorch Lightning 實作的機器學習與深度學習應用。本地開發載下 repo：exportREPO=pytorch_lightning_applications
gitclonehttps://github.com/leemengtaiwan/$REPO.gitcd$REPO建立新的Anaconda環境並安裝函式庫：condacreate-n$REPOpython=3.6-y
condaactivate$REPOpipinstall-rrequirements.txt設置nbdev環境並以可修改的方式安裝此 package：nbdev_install_git_hooks
nbdev_build_lib
pipinstall-e.
nbdev_build_docs
Package: mlreflect
mlreflectmlreflectis a Python package for training and using artificial neural networks to analyze specular X-ray and
neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow.InstallationThe mlreflect package can be installed directly from the command line using the python package manager pip:pip install mlreflectIn case the newest version is not available on PyPI, the package can also be installed locally. Download the package, unzip it and navigate to the folder containing the downloaded mlreflect folder. Then use:pip install mlreflect/Online documentationDocumentation and API reference can be found online onhttps://mlreflect.readthedocs.io/en/latest/Usage exampleThe package can then be imported in python usingimport mlreflectorfrom mlreflect import <module>An example of how to generate training data, train the model and test it on experimental data is shown in theexample/notebooks/training_example.ipynbJupyter notebook.An example of how to use the default pre-trained model for single layers on Si/SiOx substrates to fit data directly
from a SPEC file is shown inexamples/notebooks/spec_usage_example.ipynbJupyter notebook.A detailed explanation as well as API info can be found in the documentation.AuthorsMain authorAlessandro Grecoalessandro.greco@uni-tuebingen.de(Institut für Angewandte Physik, University of Tübingen)ContributorsVladimir Starostin (Institut für Angewandte Physik, University of Tübingen)Christos Karapanagiotis (Institut für Physik, Humboldt Universität zu Berlin)Alexander Hinderhofer (Institut für Angewandte Physik, University of Tübingen)Alexander Gerlach (Institut für Angewandte Physik, University of Tübingen)Linus Pithan (ESRF The European Synchrotron)Sascha Liehr (Bundesanstalt für Materialforschung und -prüfung (BAM))Frank Schreiber (Institut für Angewandte Physik, University of Tübingen)Stefan Kowarik (Bundesanstalt für Materialforschung und -prüfung (BAM) and Institut für Physik, Humboldt Universität zu Berlin)
Package: ml-research
PyPIAnacondaML-Researchis an open source library for machine learning research.  It
contains the software implementation of most algorithms used or developed in
my research. Specifically, it containsscikit-learncompatible
implementations for Active Learning and Oversampling, as well as Datasets and
various utilities to assist in experiment design and results reporting.The LaTeX and Python code for generating the paper, experiments' results and
visualizations shown in each paper is available (whenever possible) in thepublications repo.Contributions at the algorithm level are available in the
packagemlresearch.InstallationA Python distribution of version >= 3.8 is required to run this
project. Earlier Python versions might work in most cases, but they are not
tested.ML-Researchrequires:numpy (>= 1.14.6)pandas (>= 1.3.5)sklearn (>= 1.0.0)imblearn (>= 0.8.0)rich (>= 10.16.1)Some functions in themlresearch.utilssubmodule (the ones in the script_visualization.py) require Matplotlib >= 2.2.3.User InstallationThe easiest way to install ml-research is usingpip:pip install -U ml-researchOrconda:conda install -c conda-forge ml-researchThe documentation includes more detailedinstallation
instructions.Installing from sourceThe following commands should allow you to setup the development version of the
project with minimal effort:# Clone the project.
git clone https://github.com/joaopfonseca/ml-research.git
cd ml-research

# Create and activate an environment 
make environment 
conda activate mlresearch # Adapt this line accordingly if you're not running conda

# Install project requirements and the research package. Dependecy group
# "all" will also install the dependency groups shown below.
pip install .[optional,tests,docs]Citing ML-ResearchIf you use ML-Research in a scientific publication, we would appreciate
citations to the following paper:@article{fonseca2023improving,
  title={Improving Active Learning Performance through the Use of Data Augmentation},
  author={Fonseca, Joao and Bacao, Fernando and others},
  journal={International Journal of Intelligent Systems},
  volume={2023},
  year={2023},
  publisher={Hindawi}
}
Package: ml-research-toolkit
Basic informationThis package provides some utilities for training machine learning models. In particular, it provides a package for sending messages to popular instant messengers. Secondly, it provides a package to automatically interact with datasets from theUCIrepository.NotificationsIt provides a package for simple message sending to messengers during model training.The following messengers are currently supported:TelegramDatasetsIt provides a package for automatically interact with datasets.The following datasets repository are currently supported:UCI.Requirements and InstallationA simple instruction of installation using pip is provided near thesource code.More information about installation can be found in documentationinstallationpage.Example of useA simple examples of module usage can be found in documentationexamplepage.
Package: mlrl-testbed
"MLRL-Testbed": Utilities for Evaluating Multi-label Rule Learning AlgorithmsThis software package providesutilities for training and evaluating single- and multi-label rule learning algorithmsthat have been implemented using the "MLRL-Common" library, including the following ones:BOOMER (Gradient Boosted Multi-label Classification Rules): A state-of-the art algorithm that usesgradient boostingto learn an ensemble of rules that is built with respect to a given multivariate loss function.FunctionalitiesMost notably, the package includes command line APIs that allow configuring the algorithms mentioned above, applying them to different datasets, and evaluating their predictive performance in terms of commonly used measures (provided by thescikit-learnframework). In summary, it provides the following functionalities:Sinle- and multi-label datasets in theMulanandMeka formatare supported.Datasets can automatically be split into training and test data, including the possibility to use cross validation.Alternatively, predefined splits can be used by supplying the data as separate files.One-hot-encodingcan be applied to nominal or binary features.Binary predictions, regression scores, or probability estimatescan be obtained from a model. Evaluation measures that are suited for the respective type of predictions are picked automatically.Evaluation scores can be savedto output files and printed on the console.Rule models can be evaluated incrementally, i.e., they can be evaluated repeatedly using a subset of the rules with increasing size.Textual representations of rule models can be savedto output files and printed on the console. In addition, thecharacteristics of models can also be savedand printed.Characteristics of datasets can be savedto output files and printed on the console.Unique label vectors contained in a dataset can be savedto output files and printed on the console.Predictions can be savedto output files and printed on the console. In addition,characteristics of binary predictions can also be savedand printed.Models for the calibration of probabilities can be savedto output files and printed on the console.Models can be saved on diskin order to be reused by future experiments.Algorithmic parameters can be read from configuration filesinstead of providing them via command line arguments. When providing parameters via the command line, corresponding configuration files can automatically be saved on disk.LicenseThis project is open source software licensed under the terms of theMIT license. We welcome contributions to the project to enhance its functionality and make it more accessible to a broader audience. A frequently updated list of contributors is availablehere.
Package: mlrose
mlrose: Machine Learning, Randomized Optimization and SEarchmlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.Project Backgroundmlrose was initially developed to support students of Georgia Tech's OMSCS/OMSA offering of CS 7641: Machine Learning.It includes implementations of all randomized optimization algorithms taught in this course, as well as functionality to apply these algorithms to integer-string optimization problems, such as N-Queens and the Knapsack problem; continuous-valued optimization problems, such as the neural network weight problem; and tour optimization problems, such as the Travelling Salesperson problem. It also has the flexibility to solve user-defined optimization problems.At the time of development, there did not exist a single Python package that collected all of this functionality together in the one location.Main FeaturesRandomized Optimization AlgorithmsImplementations of: hill climbing, randomized hill climbing, simulated annealing, genetic algorithm and (discrete) MIMIC;Solve both maximization and minimization problems;Define the algorithm's initial state or start from a random state;Define your own simulated annealing decay schedule or use one of three pre-defined, customizable decay schedules: geometric decay, arithmetic decay or exponential decay.Problem TypesSolve discrete-value (bit-string and integer-string), continuous-value and tour optimization (travelling salesperson) problems;Define your own fitness function for optimization or use a pre-defined function.Pre-defined fitness functions exist for solving the: One Max, Flip Flop, Four Peaks, Six Peaks, Continuous Peaks, Knapsack, Travelling Salesperson, N-Queens and Max-K Color optimization problems.Machine Learning Weight OptimizationOptimize the weights of neural networks, linear regression models and logistic regression models using randomized hill climbing, simulated annealing, the genetic algorithm or gradient descent;Supports classification and regression neural networks.Installationmlrose was written in Python 3 and requires NumPy, SciPy and Scikit-Learn (sklearn).The latest released version is available at thePython package indexand can be installed usingpip:pip install mlroseDocumentationThe official mlrose documentation can be foundhere.A Jupyter notebook containing the examples used in the documentation is also availablehere.Licensing, Authors, Acknowledgementsmlrose was written by Genevieve Hayes and is distributed under the3-Clause BSD license.You can cite mlrose in research publications and reports as follows:Hayes, G. (2019).mlrose: Machine Learning, Randomized Optimization and SEarch package for Python.https://github.com/gkhayes/mlrose. Accessed:day month year.BibTeX entry:@misc{Hayes19,
 author = {Hayes, G},
 title 	= {{mlrose: Machine Learning, Randomized Optimization and SEarch package for Python}},
 year 	= 2019,
 howpublished = {\url{https://github.com/gkhayes/mlrose}},
 note 	= {Accessed: day month year}
}
Package: mlrun-connect
mlrun_connectA collection of tools to simplify integration between MLRun and services from cloud providers.QuickstartThe package can be installed using:pip install mlrun_connectMLRun is an open-source MLOps orchestration framework.  It enables end-to-end development of
machine learning models, from exploratory data analysis to prototyping to operationalization.A common use case would be to install MLRun on-premise or with a cloud provider, and connect
to data sources for exploratory analysis.  While the Nuclio library offers a HTTP-based approach
to integration with external services, there are a variety of other approaches that may be prefered
(i.e. messaging systems).mlrun_connect will provide tools to ease integration with these services.Azure Service Bus QueueThe AzureSBTMLRun class can be in conjunction with a Nuclio function to initiate the execution of a mlrun pipeline
based on an incoming message.  The AzureSBToMLRun object becomes the parent to a new class that is instantiated
within the Nuclio init_context function, as follows:from mlrun_connect.azure import AzureSBToMlrun

def init_context(context):
    pipeline = load_project(<PATH_TO_MLRUN_PROJECT>)
    class SBHandler(AzureSBToMLRun):
        def run_pipeline(self, event):
            arguments = {"incoming_data": event["key"]}
            workflow_id = pipeline.run(arguments = arguments)
            return workflow_id
Package: mlrunner
Maintaining many machine learning experiments requires much manual effort. This lightweight tool helps you currently run aLOTof experiments with simple commands and configurations. You can easily aggregate custom metrics for each experiment with a single line of code.Install$ pip install mlrunnerUsageDownload and editparams.yaml, then simply$ runWhen all experiments finish, start a jupyter notebook and analyze results usingexamine.Examiner.See thegithub repofor example use cases.
Package: mls
This simple module implements simple unicode-like object, which can contain
multiple language mutation of one string. Actually it’s subclass of unicode
type (for python 2k) / string type (for py3k) with few helping methods,
that allow you to translate your string to another languages.Here, I’ll show you some usage examples:fromlocaleimportsetlocale,LC_ALLfrommlsimportmlssetlocale(LC_ALL,"en_US.UTF-8")# Our system locale will be US englishempty=mls()# same as unicode, butprint(empty.language)# with .language attribute containing "en"s=mls("Hello, world!")t=s.translate_to("ru_RU",u"Здравствуй, мир!")# generate new ru-mutationprint(t.language)# => "ru"print(t)# => "Здравствуй, мир!"x=mls("Ahoj",language="cs")# czech mutation of "Hi"x<<="Nazdar"# Change translation to anotherprint(repr(x))# => "cs'Nazdar'"a=mls({"en":"Bye","ru":u"Пока","cs":u"Čau"})print(a)# => "Bye"b=a.translate_to("ru")print(b)# => "Пока"c=a>>"cs"print(c)# => "Čau"v=mls(en="Vodka",pl=u"Wódka",ru=u"Водка")print(repr(v>>"pl"))# => "pl'W\xf3dka'"print(repr(v>>"fr"))# => "fr'Vodka'" (no translation to french)# Also you can use any unicode/str methodsprint("world"ins)# => Trueprint(s[:5])# => "Hello"
Package: mls-api
Python library for integrating withMLS APIfunctionality.RequirementsPython 2.7 and later.SetupYou can install this package by using the pip tool and installing:$pipinstallmls-apiOr:$easy_installmls-apiUsage Exampleimportmls_apifromdotenvimportload_dotenvimportos## Loads environment variables from .envload_dotenv('.env')username=os.getenv('_USERNAME')password=os.getenv('_PASSWORD')## Authenticationmls_api.login(username,password)## Retrieve MLS Real-Time Datamls_rtd=mls_api.get_rtd()print(mls_rtd)## Retrieve MLS Historical Datamls_historical=mls_api.get_historical_data()print(mls_historical)## Retrieve MLS Players Datalimit=5offset=5mls_players_l=mls_api.get_players(limit=limit)print(mls_players_l)mls_players=mls_api.get_players(limit=limit,offset=offset)print(mls_players)## Retrieve MLS Assist Datamls_assists=mls_api.get_assists()print(mls_assists)## Retrieve MLS Offence Datamls_offence=mls_api.get_offence()print(mls_offence)## Retrieve MLS Top Scorers Datamls_top_scorer=mls_api.get_top_scorer()print(mls_top_scorer)## Retrieve MLS Teams Datamls_teams=mls_api.get_teams()print(mls_teams)## Retrieve MLS Fixtures Datamls_fixtures=mls_api.get_fixtures()print(mls_fixtures)## Retrieve MLS Standings Datamls_standings=mls_api.get_standings()print(mls_standings)## Retrieve MLS Latest News Datamls_latest_news=mls_api.get_latest_news()print(mls_latest_news)Setting up an MLS API AccountSign up for a self-serviceuser account.Using the MLS APIYou can read theAPI documentationto understand what’s possible with the MLS API. If you need further assistance, don’t hesitate tocontact us.LicenseThis project is licensed under theMIT License.CopyrightCopyright © 2020 - 2024Hori Systems Limited. All Rights Reserved.
Package: mls.apiclient
mls.apiclient is a Python client for the RESTful API of thePropertyshelf MLS.InstallationUsepiporeasy_installto install the package:$ pip install mls.apiclientOr install from the sources:$ tar xzf mls.apiclient-0.1.tgz
$ cd mls.apiclient-0.1
$ python setup.py installMostly HarmlessChangelog1.5 (2017-04-24)Allow additional search params for getting a single resource.1.4.1 (2017-04-10)Move responses requirement to test section.1.4 (2017-04-10)Update tests.Don’t break on SSL verification. Deactivate SSL verification, if failed in first attempt, and try again.1.3 (2016-01-26)Added connection timeout for MLS requests.Check for JSON return value.1.2 (2015-05-13)Restructured API request and response handling and generation of resources for the new RESTful API.1.1.1 (2013-06-26)First public release.1.1 (2012-08-20)Return list of tuples instead list of lists.Raise MLSError if ValueError occurs during connection buildup.1.0 (2012-02-02)Initial release.mls.apiclient InstallationWhen you’re reading this you have probably already runeasy_install mls.apiclient. Find out how to install setuptools (and EasyInstall) here:http://peak.telecommunity.com/DevCenter/EasyInstallAlternatively, if you are using zc.buildout and the plone.recipe.zope2instance
recipe to manage your project, you can do this:Addmls.apiclientto the list of eggs to install, e.g.:[buildout]
...
eggs =
    ...
    mls.apiclientRe-run buildout, e.g. with:$ ./bin/buildoutmls.apiclient is copyright Propertyshelf, Inc.This program is free software; you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation; either version 2 of the License, or (at your
option) any later version.This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.You should have received a copy of the GNU General Public License along
with this program; if not, write to the Free Software Foundation, Inc.,
59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.