Saravanan666/pypi-org huggingface dataset:
Package: mlcolvar
Machine Learning Collective Variables for Enhanced SamplingCODEPAPERmlcolvaris a Python library aimed to help design data-driven collective-variables (CVs) for enhanced sampling simulations. The key features are:A unified framework to help test and use (some) of the CVs proposed in the literature.A modular interface to simplify the development of new approaches and the contamination between them.A streamlined distribution of CVs in the context of advanced sampling.The library is built upon thePyTorchML library as well as theLightninghigh-level framework.Some of theCVswhich are implemented, organized by learning setting:Unsupervised: PCA, (Variational) AutoEncoders [1,2]Supervised: LDA [3], DeepLDA [4], DeepTDA [5]Time-informed: TICA [6], DeepTICA/SRVs [7,8], VDE [9]And many others can be implemented based on the building blocks or with simple modifications. Check out the documentation and the examples section!Install withpipThe library is available onPyPiand can be installed withpip. This is the preferred choice forusersas it automatically installs the package requirements.pipinstallmlcolvarClone from GitHubThe library can also be installed cloning the repository from GitHub. This is the preferred choice fordevelopersas it provides more flexibility and allows editable installation.gitclonehttps://github.com/luigibonati/mlcolvar.gitcdmlcolvar
pip-einstall.PLUMED interface: the resulting CVs can be deployed for enhancing sampling with thePLUMEDpackage via thepytorchinterface, available since version 2.9.Notes: in early versions (v<=0.2.*) the library was calledmlcvs. This is still accessible for compatibility with PLUMED masterclasses in thereleasesor by cloning thepre-lightningbranch.Copyright (c) 2023 Luigi Bonati, Enrico Trizio, Andrea Rizzi and Michele Parrinello.
Structure of the project is based onComputational Molecular Science Python Cookiecutter.
Package: mlconfound
mlconfoundTools for analyzing and quantifying effects of counfounder variables on machine learning model predictions.Installpip install mlconfoundUsage# y   : prediction target
# yhat: prediction
# c   : confounder

from mlconfound.stats import partial_confound_test

partial_confound_test(y, yhat, c)Run the quickstart notebook in Binder:Read the docs for more details.Documentationhttps://mlconfound.readthedocs.ioCitationT. Spisak, Statistical quantification of confounding bias in predictive modelling, preprint onarXiv:2111.00814, 2021.
Package: mlcrypto
ML-CryptoThis library ispycrypto/pycryptodomewrapper that standardises the MissingLink.ai-s encryption and allows easy extentions to support additional encryption schemes.OverviewThis library contains a set ofCiphers.  EachCipherhasencrpytanddecryptfunctions thatMUSTreturn/accept byte array andencrpyt_string/decrypt_stringthat work with string objects that will be abase64representation of the encrypted byte output of the underlying[en|de]crypt functionsIf your cipher returns structured data (such asIVand other data), you SHOULD usenamedtuplesand extend them withMsgPackNamedTuplemixin. This will allow you to easly dump the named tuple into optimised byterarray, in order to be flexable with the object, currently we are converting the named tuple intodictin order to be able to add fields in the featureYou SHOULD provide key generation function as part of the cipher that will generate key (the function may perform external calls in case of cloud kms and etc.)Current CiphersSymmetric -AES-CTRencryption with256bits (32 bytes) key lengthAsymmetric -PKCS1_OAEPencryption withSHA512hashAlgoEnvelope - Envelope encryption where the body is encrypted with theSymmetriccipher. theDEK(data encryption key) is encrypted using non-specific cipher provided during initPyCryptoDome vs PyCrypto vs NoneWhen installing, no cryptography package is provided by default. Usemlcrypto[pycryptodome]ormlcrypto[pycrypto]to ensure one is installed or use the default if you know you have one installedAspycryptois dead, prefer usingpycryptodomeand expect some issues (The main one is encrypted ssh private keys) not to work
Package: ml-crypto
ML-CryptoThis library ispycrypto/pycryptodomewrapper that standardises the MissingLink.ai-s encryption and allows easy extentions to support additional encryption schemes.OverviewThis library contains a set ofCiphers.  EachCipherhasencrpytanddecryptfunctions thatMUSTreturn/accept byte array andencrpyt_string/decrypt_stringthat work with string objects that will be abase64representation of the encrypted byte output of the underlying[en|de]crypt functionsIf your cipher returns structured data (such asIVand other data), you SHOULD usenamedtuplesand extend them withMsgPackNamedTuplemixin. This will allow you to easly dump the named tuple into optimised byterarray, in order to be flexable with the object, currently we are converting the named tuple intodictin order to be able to add fields in the featureYou SHOULD provide key generation function as part of the cipher that will generate key (the function may perform external calls in case of cloud kms and etc.)Current CiphersSymmetric -AES-CTRencryption with256bits (32 bytes) key lengthAsymmetric -PKCS1_OAEPencryption withSHA512hashAlgoEnvelope - Envelope encryption where the body is encrypted with theSymmetriccipher. theDEK(data encryption key) is encrypted using non-specific cipher provided during initPyCryptoDome vs PyCrypto vs NoneWhen installing, no cryptography package is provided by default. Useml-crypto[pycryptodome]orml-crypto[pycrypto]to ensure one is installed or use the default if you know you have one installedAspycryptois dead, prefer usingpycryptodomeand expect some issues (The main one is encrypted ssh private keys) not to work
Package: mlctl
mlctlmlctlis the Command Line Interface (CLI)/Software Development Kit (SDK) for MLOps. It allows for all ML Lifecycle operations, such asTraining,Deploymentetc. to be controlled via a simple-to-use command line interface. Additionally,mlctlprovides a SDK for use in a notebook environment and employs an extensible mechanism for plugging in various back-end providers, such as SageMaker.The following ML Lifecycle operations are currently supported viamlctltrain- operations related to model traininghost- operations related to hosting a model for online inferencebatch inference- operations for running model inference in a batch methodGetting StartedInstallation(Optional) Create a new virtual environment formlctlpip install virtualenv
virtualenv ~/envs/mlctl
source ~/envs/mlctl/bin/activateInstallmlctl:pip install mlctlUpgrade an existing version:pip install --upgrade mlctlUsageOptional Setupmlctlrequires users to specify the plugin and a profile/credentials file for authenticating operations. These values can either be stored as environment variables as shown below OR they can be passed as command line options. Use--helpfor more details.```
export PLUGIN=
export PROFILE=
```CommandsmlctlCLI commands have the following structure:mlctl <command> <subcommand> [OPTIONS]To view help documentation, run the following:mlctl --help
mlctl <command> --help
mlctl <command> <subcommand> --helpInitialize ML Modelmlctl init [OPTIONS]OptionsDescriptiontemplate or -t(optional) Location of the project template github location.Training Commandsmlctl train <subcommand> [OPTIONS]SubcommandDescriptionstarttrain a modelstopstop an ongoing training jobinfoget training job informationHosting Commandsmlctl hosting <subcommand> [OPTIONS]SubcommandDescriptioncreatecreate a model from trained model artifactdeploydeploy a model to create an endpoint for inferenceundeployundeploy a modelinfoget endpoint informationBatch Inference Commandsmlctl batch <subcommand> [OPTIONS]SubcommandDescriptionstartperform batch inferencestopstop an ongoing batch inferenceinfoget batch inference informationExamplesSagemakerContributingFor information on how to contribute tomlctl, please read through thecontributing guidelines.
Package: mlc-tools
ciStatusTravisTrello BoardSimple using:cd src
python main.pyConfigured launch:Parameters:-h, --help      show this help message and exit
    -i              Path to classes configs. Default = ./config/
    -o              Out Path for classes. Default = ./out/
    -l              Used language. Supported cpp, py, php. Default = cpp
    -f              Used serialized format. Supported xml, json. Default = xml
    -side           To different side generation, used both, server, client. Default = both
    -data           Path to static data configs. Default = empty, conversion is not used
    -data_out       Out path for static data config. Default = empty
    -only_data      Flag for build only data xml. Default = no
    -protocols      Path to file with serialization protocols. Default = empty, default protocol is used
    -php_validate   Check PHP features on generate other languages. Default = yes
    -test_script    The path to the script to run the tests
    -use_colors     Using colors on outputting to the consoleSyntax:class core/Request<RequestBase>
{
    Action action
    int int_data
    float float_data = 0
    function void some_function(){}
}class- keywordcore/- The module. Used only for grouping classes into folders for c++ generationRequest- Name of the classRequestBase- The inherited class. Multiple inheritance is not supported.{ ... }- Body of the class or methodint int_data- Field of classint_dataof integer type.int float_data- Field of classfloat_dataof float type with initialized.function void some_function(){}- Method of the class without body
Package: mlcvzoo-base
MLCVZoo BaseThe MLCVZoo is an SDK for simplifying the usage of various (machine learning driven)
computer vision algorithms. The packagemlcvzoo_baseprovides the base modules
that are defining the MLCVZoo API. Furthermore, it includes modules that allow to handle
and process the data structures of the MLCVZoo, as well as providing modules for
running evaluations / calculation of metrics.Further information about the MLCVZoo can be foundhere.Installpip install mlcvzoo-baseTechnology stackPython
Package: mlcvzoo-mmdetection
MLCVZoo MMDetectionThe MLCVZoo is an SDK for simplifying the usage of various (machine learning driven)
computer vision algorithms. The packagemlcvzoo_mmdetectionis the wrapper module for
themmdetection framework.Install for developerClone repositorycdYOUR_PATH
gitclonegit@git.openlogisticsfoundation.org:silicon-economy/base/ml-toolbox/mlcvzoo-models/mlcvzoo-mmdetection.gitcdmlcvzoo-mmdetectionCreate python environmentvirtualenv.venvsource.venv/bin/activatesourcebuild.shinstall--no-rootFor VSCode IDE add the relevant environment variables in .venv/bin/activate.exportPYTHONPATH=PARENT_OF_YOUR_REPOSITORY# The MMDETECTION_DIR is needed in our unit tests for defining the# path to the configuration files of mmdetection. Since mmdet v2.15.0# the configuration are stored in the mmdet/.mim/ folder of the installed# mmdet package in your python environment. For older versions a specific checkout# is needed.exportMMDETECTION_DIR="$VIRTUAL_ENV/lib/python3.10/site-packages/mmdet/.mim/"Install for package usersThe following command installs the mlcvzoo-mmdetection package, however we
recommend to have a look at the ./build.sh script. It is gathering all fixes
that are needed to setup a running python environment.pipinstallmlcvzoo-mmdetectionTechnology stackPython
Package: mlcvzoo-mmrotate
MLCVZoo MMRotateThe MLCVZoo is an SDK for simplifying the usage of various (machine learning driven)
computer vision algorithms. The packagemlcvzoo_mmrotateis the wrapper module for
themmrotate framework.Install for developerClone repositorycdYOUR_PATH
gitclonegit@gitlab.cc-asp.fraunhofer.de:silicon-economy/base/ml-toolbox/mlcvzoo-models/mlcvzoo-mmrotate.gitcdmlcvzoo-mmrotateCreate python environmentvirtualenv.venvsource.venv/bin/activatesourcebuild.shinstall--no-rootFor VSCode IDE add the relevant environment variables in .venv/bin/activate.exportPYTHONPATH=PARENT_OF_YOUR_REPOSITORY# The MMDETECTION_DIR is needed in our unit tests for defining the# path to the configuration files of mmdetection. Since mmdet v2.15.0# the configuration are stored in the mmdet/.mim/ folder of the installed# mmdet package in your python environment. For older versions a specific checkout# is needed.exportMMROTATE_DIR="$VIRTUAL_ENV/lib/python3.10/site-packages/mmrotate/.mim"Install for package usersThe following command installs the mlcvzoo-mmrotate package, however we
recommend to have a look at the ./build.sh script. It is gathering all fixes
that are needed to setup a running python environment.pipinstallmlcvzoo-mmrotateTechnology stackPython
Package: mlcvzoo-yolox
MLCVZoo YOLOXThe MLCVZoo is an SDK for simplifying the usage of various (machine learning driven)
computer vision algorithms. The packagemlcvzoo_yoloxis the wrapper module for
theyolox Object Detector.Further information about the MLCVZoo can be foundhere.Installpip install mlcvzoo-yoloxTechnology stackPython
Package: mldas
MLDAS is a Python package providing tools for studying Distributed Acoustis Sensing (DAS) data and train machine learning algorithms on them. The documentation can be accessed via the following link:https://ml4science.gitlab.io/mldasInstallationTo install, you can use the Python package managerpipas follows:sudo pip install mldasOnce installed on your system, the package can be loaded into any Python script as follows:from mldas import *python mldas/train.py configs/multilabel.yaml -v --depth 2 --lr 0.1 --epochs  2 --sample-size 1 --batch-size 128 --output-dir output_testModified BSD License AgreementMLDAS is released under a modified BSD license. A full description of the license agreement can be found in theLICENSE.txtfile.AboutMachine Learning for Distributed Acoustic Sensing data (MLDAS)
Copyright (c) 2020, The Regents of the University of California,
through Lawrence Berkeley National Laboratory (subject to receipt of
any required approvals from the U.S. Dept. of Energy). All rights reserved.If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab's Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit others to do so.
Package: ml-dash
For detailed codumentation,
seeml-dash-tutorialML-dash replaces visdom and tensorboard. It allows you to see real-time
updates, review 1000+ of experiments quickly, and dive in-depth into
individual experiments with minimum mental effort.Parallel CoordinatesAggregating Over Multiple Runs (with different seeds)Preview Videos, ``matplotlib`` figures, and images.UsageTo make sure youinstallthe newest version ofml_dash:condainstallpycurlpipinstallml-loggerml-dash--upgrade--no-cacheJust doing this would not work. The landscape of python modules is a lot
messier than that of javascript. The most up-to-date graphene requires
the following versioned dependencies:yes|pipinstallgraphene==2.1.3yes|pipinstallgraphql-core==2.1yes|pipinstallgraphql-relay==0.4.5yes|pipinstallgraphql-server-core==1.1.1There are two servers:a server that serves the static web-application filesml_dash.appThis is just a static server that serves the web application client.To run this:python-mml_dash.appthe visualization backendml_dash.serverThis server usually lives on your logging server. It offers agraphQLAPI backend for the dashboard client.python-mml_dash.server--logdir=my/folderNote: the server accepts requests from ``localhost`` only by
default for safety reasons.To overwrite this, see the
documentation here:ml-dash-tutorialImplementation NotesSeehttps://github.com/episodeyang/ml_logger/tree/master/ml-dash-server/notes/README.md
Package: mldatalib
mldatalibmldatalib (Machine Learning Data Library) provides a Python library which simplifies processing and extracting features
(for machine learning) from files.
Stores features in a SQLite database, has label transformation options, functions which convert features to NumPy arrays,
etc.
Original idea and feature list by Viktor Evstratov (viktor.evst@gmail.com).
Originally designed for use with the Galaxy Zoo challenge on Kaggle.
Requires numpy and SQLAlchemy.Why?This is an attempt to minimize the amount of effort needed to extract, save and retrieve new features and allow
a user to spend more time on more ‘scientific’ work. If several users are working on the same project,
they can each extract independent sets of features and then share the database files and copy the features they are
missing.FunctionalityBasic functionality includes:
Extracting features and storing them in a database (the user provides the extractor function), retrieving features
by name, extracting and transforming labels from a file and storing them in a database, copying features
from one database to another, returning features as a numpy array.RoadmapAdd a pure SQL way of storing and retrieving features (by converting them to JSON format and creating columns via ALTER
TABLE statements), thus allowing easy use from other languages. At least add this functionality for numpy arrays
and lists.Add a dataset class for CSV files (where all data is stored in a single CSV file).
Package: mlderes.dstoolkit
DSToolkit - utilities for better analytics projectsA library of tools that I use to manage files, clean datasets and do exploratory data analysisTable of ContentsGeneral infoTechnologiesUsageContributionsTodoLicenseGeneral InfoThis library is a set of tools for managing files, cleaning data and doing exploratory data analysis.This all started because I found myself creating lots and lots of versions of data files in various states of completeness.  I would scrape some data, write it a file (in the data/raw folder) then work on it some and save it to the data/processed folder.  After a few iterations, I couldn't remember if it was data/raw/scraped_page1.csv or data/raw/scraped_page101.csv that was the latest.  So I started to name the files with a timestamp appendage scraped_page_01011850.csv (for a file that was created on Jan 1 at 6:50pm).  So I needed a utility to create the timestamps and then get the lastest version of the file.  I copied this code so much that I decided to use it as a way to learn about creating real Python projects, GitHub hooks, Visual Studio Code, Docker Containers and more.TechnologiesPython 3.7PandasUsagepip install -U mlderes.dstoolkitIn your module:from mlderes.dstoolkit import get_latest_data_filename, DataFolder, make_ts_filename, write_data

data_folder = DataFolder('./data') # root data folder
DATA_RAW = data_folder.RAW
DATA_EXTERNAL = data_folder/'external'

# Get the filename (path) of the file like foo* in the ./data/raw directory
fp = get_latest_data_filename(DATA_RAW, 'foo')ContributionsThis project was developed usingVisual Studio Codeand leverages the support the platform has for developing in containers, so if you haveDocker Desktopinstalled, you should be able to fork this repo, download a copy to locally and open the folder in a container.  All the dependencies are there, nothing to install, no need to worry about specific versions of libraries, creating venvs on your machine.  Heck you don't even need Python installed!Contributions to documentation, utilities and issues are welcome.  All pull requests must include unittests and all existing tests must pass before being considered.TodoMake documentation as part of buildAdd more samples to documentationLicenseThis work is licensed under theGPL, which guarentees end users the freedom to study, share, and modify the software for your own use.
Package: mldev
MLDev softwareThis repository contains the MLDev software, that facilitates running data science experiments,
help in results presentation and eases paper preparation for students, data scientists and researchers.The MLDev software provides the following features to help with automating machine learning experiments:Configure stages and parameters of a data science experiment separately from your codeConduct a repeatable experiment in Google Colab or PaperSpaceKeep versions of your code, results and intermediate files on Google Drive (other repos coming soon)Use a variety of pre-built templates to get startedMLDev also provides some services that run alongside your experiment code:
You can have the notifications via Telegram about the exeptions while training your modelKeep updated on current experiment state using TensorBoard (even on Colab)Deploy and demo your model with a model controller (feature in progress)Install mldevMLDev comes with a couple of extensions (extras), see the homepage for details.basedvctensorboardcontrollerbotjupytercollabInstall usinginstall_mldev.shscriptGet the latest version of our install file to your local machine and run it.$ curlhttps://gitlab.com/mlrep/mldev/-/raw/develop/install_mldev.sh-oinstall_mldev.sh$ chmod +x ./install_mldev.sh$ ./install_mldev.shYou may be asked forrootprivileges if there are system packages to be installed.Wait a couple of minutes until installation will complete, congrats!Install from PyPIUsepipto install MLDev package from PyPI. This will not install any system dependencies.Useful for including MLDev intorequirements.txtin your experiments.$ python3-mpip install mldev[base]ContactsYou can reach developers at theTelegram user groupHomepagehttps://gitlab.com/mlrep/mldevSee the project page at the Open Data Science websitehttps://ods.ai/projects/mldevLicenseThe software is licensed under Apache 2.0 license
Package: mldictionary
MLDictionaryMLDictionaryis word's dictionary for several language>>>frommldictionaryimportEnglish>>>english_dictionary=English()>>>snake_means=english_dictionary.get_meanings('snake')>>>len(snake_means)4>>>snake_means['a reptile with a long body and no legs: '...]...Installing MLDictionary$pipinstallmldictionaryMLDictionary officially supports 3.9+.Some examples>>>frommldictionaryimportPortuguese>>>portuguese_dictionary=Portuguese()>>>vida_means=portuguese_dictionary.get_meanings('vida')>>>vida_means['Conjunto dos hábitos e costumes de alguém; maneira de viver: tinha uma vida de milionário.'...]>>>frommldictionaryimportSpanish>>>spanish_dictionary=Spanish()>>>coche_means=spanish_dictionary.get_meanings('coche')>>>coche_means['Automóvil destinado al transporte de personas y con capacidad no superior a siete plazas.'...]Make your own dictionaryfrommldictionaryimportDictionaryclassMyOwnDictionary(Dictionary):url='somedictionary.com'language='language name'target_tag='tag_where_means_is'target_attr={'attr':'attr_value'}replaces={'something','another thing'}>>>myowndictionary=MyOwnDictionary()>>>myowndictionary.get_meanings('other language word')To more details, see thewikiAlso, it has a insightfularticle on linkedin
Package: ml-digits-recognition
Convolutional Neural Network using MNIST dataset for Digit RecognitionOfficial repository:https://github.com/MartinBraquet/ml-digits-recognition.Test online:https://martinbraquet.com/index.php/solo_page_digits_recognition.Installation from PyPIpip install ml-digits-recognitionUsagefrom ml_digits_recognition import drawing
drawing.run()Installation from Sourcepip install -r requirements.txtDocumentationConvolutional neural network visualization.nn_visualization.ipynbTrainingTrain the model and save it asmodel.pt.ml_digits_recognition_training.ipynbAccuracy vs epochs.Loss vs epochs.TestTest in Jupiter Notebook. The model can be loaded from the training above inmodel.ptor from the
default precise model inmodel_precise.pt.ml_digits_recognition_test.ipynbTest in Python.python drawing.pyToolsDraw a digit and save it as a PNG file.user_input_drawing.ipynb