Saravanan666/pypi-org huggingface dataset:
Package: mljar
|Build Status| |PyPI version| |Coverage Status| |PyPI pyversions|mljar-api-python================A simple python wrapper over mljar API. It allows MLJAR users to createMachine Learning models with few lines of code:.. code:: pythonfrom mljar import Mljarmodel = Mljar(project='My awesome project', experiment='First experiment')model.fit(X,y)model.predict(X)That's all folks! Yeah, I know, this makes Machine Learning super easy!You can use this code for following Machine Learning tasks: \* Binaryclassification (your target has only two unique values) \* Regression(your target value is continuous) \* More is coming soon!How to install--------------You can install mljar with **pip**:::pip install -U mljaror from source code:::python setup.py installHow to use it-------------1. Create an account at mljar.com and login.2. Please go to your users settings (top, right corner).3. Get your token, for example 'exampleexampleexample'.4. Set environment variable ``MLJAR_TOKEN`` with your token value:::export MLJAR_TOKEN=exampleexampleexample5. That's all, you are ready to use MLJAR in your python code!What's going on?-----------------  This wrapper allows you to search through different Machine Learningalgorithms and tune each of the algorithm.-  By searching and tuning ML algorithm to your data you will get veryaccurate model.-  By calling method ``fit`` from ``Mljar class`` you create new projectand start experiment with models training. All your results will beaccessible from your mljar.com account - this makes Machine Learningsuper easy and keeps all your models and results in beautiful order.So, you will never miss anything.-  All computations are done in MLJAR Cloud, they are executed inparallel. So after calling ``fit`` method you can switch yourcomputer off and MLJAR will do the job for you!-  I think this is really amazing! What do you think? Please let us knowat ``contact@mljar.com``.Examples--------The examples are `here! <https://github.com/mljar/mljar-examples>`__.Testing-------To run tests with command:::python -m tests.run.. |Build Status| image:: https://travis-ci.org/mljar/mljar-api-python.svg?branch=master:target: https://travis-ci.org/mljar/mljar-api-python.. |PyPI version| image:: https://badge.fury.io/py/mljar.svg:target: https://badge.fury.io/py/mljar.. |Coverage Status| image:: https://coveralls.io/repos/github/mljar/mljar-api-python/badge.svg?branch=master:target: https://coveralls.io/github/mljar/mljar-api-python?branch=master
Package: ml-jdp4
ML_JDP4 PackageAn integrated quantum mechanics-machine learning approach for ultra-fast NMR structural elucidationAuthors: Ariel M. Sarotti & María M. ZanardiUsage:ml_jdp4Installation RequirementsML_JDP4.pyneeds python 3.8 or later to work. You can install the module from the command line console using:pip3 install ml-jdp4User GuideYou need to create a folder containing the following files:1. The gaussian outputs of the NMR and NBO calculations (all conformers for all isomers).
  
  2. The excel file containing the experimental data and the labels of each nucleus associated with each experimental value.See theproject repositoryfor more details.
Package: mljet
mljetIf you have been working on ML models, then you have probably faced the task of deploying these models.
Perhaps you are participating in a hackathon or want to show your work to management.According to our survey, more than60%of the data-scientists surveyed faced this task and more than60%of the respondents spent more than half an hour creating such a service.The most common solution is to wrap it in some kind of web framework (like Flask).Our team believes that it can be made even easier!Our tool automatically collects all the necessary files and dependencies, creates a docker container, and launches it! And all this in one line of source code.PipelineFirst, we initialize the project directory for the next steps;Next, we serialize your machine learning models (for example, with Joblib or Pickle);Next, we create a final.pyfile based on the templates that contains the endpoint handlers. Handlers are chosen based on models, and templates based on your preferences (templates are also.pyfiles using, for example, Sanic or Flask);Copy or additionally generate the necessary files (e.g. Dockerfile);The next step is to compile the API documentation for your project;After these steps, we build a Docker container, or a Python package, or we just leave the final directory and then we can deploy your project in Kubernetes, or in Heroku.PrerequisitesOn your PC with local run you must have Docker & Python >= 3.8InstallationInstallmljetwith pip:pipinstallmljetor with your favorite package manager.Examplefromsklearn.datasetsimportload_irisfromsklearn.ensembleimportRandomForestClassifierfrommljetimportcookX,y=load_iris(return_X_y=True,as_frame=True)clf=RandomForestClassifier()clf.fit(X,y)cook(strategy="docker",model=clf,port=5010)After running script you can see new Docker container.
To interact with service simply open URL, logged after script running.On this page you can see Swagger UI, test simple requests (examples included).
For direct post-requests you can use Curl:curl-XPOST"http://127.0.0.1:5001/predict"-H"accept: application/json"-H"Content-Type: application/json"-d"{\"data\":[{\"sepal length (cm)\":5.8,\"sepal width (cm)\":2.7,\"petal length (cm)\":3.9,\"petal width (cm)\":1.2}]}"Models supportCurrently, we support the following models:sklearnxgboostcatboostlightgbmRoadMapDeploy to Heroku & clustersModel's basic vizualizationTighter integration withLightAutoMLSupport many popular ML-frameworks, such asXGBoost,TensorFlow,CatBoost, etc.Your ideas!ContributionWe are always open to your contributions!
Please check our issue's and make PR.
Package: mlkatas
# mlkatasUtility functions for Machine Learning, used by the [Machine Learning Handbook](https://github.com/bpesquet/machine-learning-handbook) and [Machine Learning Katas](https://github.com/bpesquet/machine-learning-katas).Mostly compiled from the article series [Applied Deep Learning](https://towardsdatascience.com/applied-deep-learning-part-1-artificial-neural-networks-d7834f67a4f6) and examples from the book [Deep Learning with Python](https://github.com/fchollet/deep-learning-with-python-notebooks).
Package: mll
MLL-language-designPoC of creating a new language focused on ML and DL.In this repo, you will find only ideas/theories but not implementation, you will find also examples of how it should work made by hand. Implementation will be added in another repo when it will be done.Feel free to contact me atmachine.learning.language@gmail.comto leave ideas or issues.This is a part of my bachelor's degree thesis at Ca' Foscari University, Dept of Cs, Venice.
My supervisors are Lucchese Claudio and Spanò Alvise.You can download this from Pypi:pip install mll
Package: mlLang
Unified Machine Learning Language (mlLang)==========================================**mlLang** takes hold of the Unified Machine Learning Language (mlLang) inside R. We have developed **mlLang** as an XML-based, unified language for machine learning. It standardizes all relevant steps to train superior models: preprocessing operations, model specification, and the tuning process. It thereby makes model tuning reproducible and documents the underlying process.**mlLang** ships the converters for different programming language. For this purpose, it implements converters in two directions.(1) It automatically reads files in the unified machine learning language from custom XML files and then constructs a corresponding machine learning model in a given programming language.(2) It also supports the other direction and automatically converts machine learning models into XML files according to the unified machine learning language.Supported Languages-------Currently, the following languages are supported:* [**R**](https://github.com/sfeuerriegel/mllang/tree/master/R):  Simply load [**mlLang for R**](https://github.com/sfeuerriegel/mllang/tree/master/R) when starting your programming session. Afterwards, the machine learning operations from caret are recorded and written to the disk in an open XML format. This file can be later loaded to reproduce models and training processes from machine learning.* **Python**: A Python variant for scikit-learn is currently in development.License-------**mlLang** is released under the [MIT License](https://opensource.org/licenses/MIT)Copyright (c) 2017 Stefan Feuerriegeln
Package: mllb
mllbMachine Learning algorithms.pip install mllbOverviewThis repository contains Machine Learning algorithms for Linear Regression, Logistic Regression, and Convolutional Neural Networks.
It also helps to easily and efficiently port, edit and select rows in csv files directly for training.This project was inpired by the author's quest to understand the basic concepts and mathematics of Machine Learning.It uses NumPyheavilyfor its computations.BasicsHere, the basic a few basic things that can be done with the package are discussed. A more detailed documentation can be found in thedocsdirectory.Linear RegressionTo train a simple Linear Regression algorithm from x, and y.import mllb as ml

x = [1, 2, 3, 4, 5, 6]
y = [3, 5, 7, 9, 11, 13]

# Create a linear regression model
model = ml.linear_regression(x, y)

# Choose a learning rate
model.learning_rate.initial(0.1)

# Choose the number of epochs and train, the default optimizer is Stochastic Gradient Descent
model.train(10)

# Test the model by predicting a value
pr = model.predict([10])

# Print the prediction
print(pr)Convolutional Neural NetworkTo train a simple Convolutional Neural Neural network, four simple 'pictures' whose resolutions are 4 x 4 pixel pictures will be used as an example here.image 1 = [white, white]
          [black, black]

image 2 = [black, white]
          [white, black]

image 3 = [black, black]
          [white, white]

image 4 = [white, black]
          [black, white]import mllb as ml

# Using white = 1 and black = 0, the four images can be represented as x
x = [[1, 1, 0, 0], [0, 1, 1, 0], [0, 0, 1, 1], [1, 0, 0, 1]]

# Choose a corresponding value y that represents the classifier. Here, we choose:
# image 1 as [0, 0]; image 2 as [0, 1]; image 3 as [1, 0] and image 4 as [1, 1]
y = [[0, 0], [0, 1], [1, 0], [1, 1]]

# Create a cnn (Convolutional Neural Network model)
model = ml.cnn(x, y)

# Create the layers for the network using the activation function names
# Here two layers will be created; a ReLU layer with 5 nodes, and a sigmoid layer with 2 nodes
model.layers.create(ml.relu(5), ml.sigmoid(2))

# Intialize the layer weights with xavier method for faster convergence
model.layers.xavier_initialization()

# Set an intial learning rate
model.learning_rate.initial(0.01)

# If you wish, the learning rate can be multiplied by a value after every epoch for faster convergence.
# This part optional. Here we use 1.01 (increase learning rate by 1% after every epoch)
model.learning_rate.multiply_after_epoch(1.01)

# Train the model and choose the number of epochs, an epoch of 800 is chosen here. It defaults to Stochastic Gradient Descent.
model.train(800)

# Test your model by predicting an image
img1 = model.predict([1, 1, 0, 0])
img2 = model.predict([0, 1, 1, 0])
img3 = model.predict([0, 0, 1, 1])
img4 = model.predict([1, 0, 0, 1])

# Print the predictions
print(img1)
print(img2)
print(img3)
print(img4)# Print the predictions
print(img1)
print(img2)
print(img3)
print(img4)
Package: mllearn
# multi-label-learnmlleran is a python library for multi-label classification bulti on scikit-learn and numpy.## ImplementationThe implementation is based on the paper [A Review on Multi-Label Learning Algorithms](https://ieeexplore.ieee.org/document/6471714/), and the implementated algorithms include:**Problem Transformation**- [x] Binary Relevance- [x] Classifier Chains- [x] Calibrated Label Ranking- [x] Random k-Labelsets**Algorithm Adaptation**- [x] Multi-Label k-Nearest Neighbor- [x] Multi-Label Decision Tree- [ ] Ranking Support Vector Machine- [ ] Collective Multi-Label Classifier## Installation```bashpip install mllearn```**Note: Support Python3 only.**## Data FormatAll data type should be `ndarray`, especially y should be the binary format. For example, if your dataset totally have 5 labels and one of your samples has only first and last labels, then the corresponding output should be `[1, 0, 0, 0, 1]`.```pythonsamples, features = X_train.shapesamples, labels = y_train.shapesamples_test, features = X_test.shapesamples_test, labels = y_test.shape```You can also find multi-label dataset provided by Mulan [here](http://mulan.sourceforge.net/datasets-mlc.html).## Example UsageThis library includes 2 parts, algorithms and metrics.```pythonfrom mllearn.problem_transform import BinaryRelevanceclassif = BinaryRelevance()classif.fit(X_train, y_train)predictions = classif.predict(X_test)``````pythonfrom mllearn.metrics import subset_accacc = subset_acc(y_test, predictions)```
Package: mllesson
简介本python包用于机器学习课程使用，方便大家调用一些通用功能，如下载课程演示用数据集等。将会根据课程内容和需要进行更新。主要功能查询可下载数据集名称函数名: mllesson.datasets.getnames无参数调取数据集名称与文件名字典函数名: mllesson.datasets.getdict无参数下载数据集时会被mllesson.datasets.getdataset调用下载数据集函数名: mllesson.datasets.getdataset参数name: 可使用mllesson.datasets.getnames函数查询参数filedir: 数据集下载路径，默认为'./'调用serverless服务器iris数据集预测结果函数名: mllesson.api.iris参数k1: 花萼长度参数k2: 花萼宽度参数k3: 花瓣长度参数k4: 花瓣宽度
Package: mllibs
About mllibsSome key points about the library:mllibsis a Machine Learning (ML) library which utilises natural language processing (NLP)Development of such helper modules are motivated by the fact that everyones understanding of coding & subject matter (ML in this case) may be differentOften we see people create functions and classes to simplify the process of code automation (which is good practice)Likewise, NLP based interpreters follow this trend as well, except, in this case our only inputs for activating certain code isnatural languageUsing python, we can interpretnatural languagein the form of string type data, using natural langauge interpretersmllibsaims to provide an automated way to do machine learning using natural languageCode AutomationTypes of ApproachesThere are different ways we can automate code execution:The first two (function,class) should be familiar, such approaches presume we have coding knowledge.Another approach is to utilisenatural languageto automate code automation, this method doesn't require any coding knowledge.FunctionFunction based code automation should be very familiar to people who code, we define a function & then simply call the function, entering any relevant input arguments which it requires, in this casendeffib_list(n):result=[]a,b=0,1whilea<n:result.append(a)a,b=b,a+breturnresultfib_list(5)ClassAnother common approach to automate code is using a class based approach. UtilisingOOPconcepts we can initialise & then call classmethodsin order to automate code:classfib_list:def__init__(self,n):self.n=ndefget_list(self):result=[]a,b=0,1whilea<self.n:result.append(a)a,b=b,a+breturnresultfib=fib_list(5)fib.get_list()Natural LanguageAnother approach, whichmllibsuses in natural language based code automation:input='calculate the fibonacci'sequenceforthevalueof5'nlp_interpreter(input)All these methods will give the following result:[0, 1, 1, 2, 3]Library Contentsmllibsis constantly evolving and has a repository documentation and development page which you can visit,mllibs documentationHow to ContributePredefined TasksI'm constantly looking for people to contribute to the development of the library. I'vecreated a pagewhere I set different tasks that you can do and join themllibs group, if you are interested, please get in touch me on telegramshtrauss2or viashtrausslearningOur own ideas and contributionsHere's how you can get started:Fork the repositoryCreate a new branch for your changesMake your changes and commit themSubmit a pull request
Package: mllogger
ml-loggerLogging utility for ML experimentsWhyPeople use different tools for logging experimental results -Tensorboard,Wandbetc to name a few. Working with different collaborators, I will have to switch my logging tool with each new project. So I made this simple tool that provides a common interface to logging results to different loggers.Installationpip install "mllogger[all]"If you want to use only the filesystem logger, usepip install "mllogger"Install from sourcegit clone git@github.com:shagunsodhani/ml-logger.gitcd ml-loggerpip install ".[all]"Alternatively,pip install "git+https://git@github.com/shagunsodhani/ml-logger.git@master#egg=ml_logger[all]"If you want to use only the filesystem logger, usepip install .orpip install "git+https://git@github.com/shagunsodhani/ml-logger.git@master#egg=ml_logger".Documentationhttps://shagunsodhani.github.io/ml-loggerUseMake alogbook_config:from ml_logger import logbook as ml_logbook
logbook_config = ml_logbook.make_config(
    logger_dir = <path to write logs>,
    wandb_config = <wandb config or None>,
    tensorboard_config = <tensorboard config or None>,
    mlflow_config = <mlflow config or None>)The API formake_configcan be accessedhere.Make aLogBookinstance:logbook = ml_logbook.LogBook(config = logbook_config)Use thelogbookinstance:log = {
    "epoch": 1,
    "loss": 0.1,
    "accuracy": 0.2
}
logbook.write_metric(log)The API forwrite_metriccan be accessedhere.NoteIf you are writing to wandb, thelogmust have a key calledstep. If yourlogalready captures thestepbut as a different key (sayepoch), you can pass thewandb_key_mapargument (set as{epoch: step}). For more details, refer the documentationhere.If you are writing to mlflow, thelogmust have a key calledstep. If yourlogalready captures thestepbut as a different key (sayepoch), you can pass themlflow_key_mapargument (set as{epoch: step}). For more details, refer the documentationhere.If you are writing to tensorboard, thelogmust have a key calledmain_tagortagwhich acts as the data Identifier and another key calledglobal_step. These keys are describedhere. If yourlogalready captures these values but as different key (saymodeformain_tagandepochforglobal_step), you can pass thetensorboard_key_mapargument (set as{mode: main_tag, epoch: global_step}). For more details, refer the documentationhere.Dev Setuppip install -e ".[dev]"Install pre-commit hookspre-commit installThe code is linted using:blackflake8mypyisortTests can be run locally usingnoxAcknowledgementsConfig forcircleci,pre-commit,mypyetc are borrowed/modified fromHydra
Package: mllopenaimanager
OpenAiManagerChatManagerAutomatically store current chat history.Simple Chatting.Manually handle chat history.init(openai_key)Resets chat historyTries to login using OpenAI API keysetup(system_message, model)Adds system message to the chat historySets the AI modelsay(message)Formats the message to work with OpenAIGets the response using the model and chat historyReturns the responseshow_history()Returns chat historyreset_history()Clears the history listadd_memory(memory, role)Adds the string message as a string to the chat historyremove_memory(memory, role)Checks if the string memory is in the chat history listRemoves it from the chat historyImageManagerGenerate Images with prefered modelinit(openai_key)Tries to login using OpenAI API keysetup(model)Sets the AI modelgen(message, size, quality, amount)Generates Images based off input (message)Specifies the image size (size)Specifies the image quality (quality)Generates 1-10 images (amount)SpeechManagerConvert Text to SpeechConvert Speech to Textinit(openai_key)Tries to login using OpenAI API keysetup(model)Sets the AI modelTTS(voice, message, file_path)Converts Text (message) to Speech (voice)Creates audio file (file_path)STT(file_path)Opens file (file_path)Converts Speech to Text (model from setup)Returns Speech to Text
Package: mllpa
ML-LPAMachine Learning-assisted Lipid Phase AnalysisLINK TO THE PROJECT WEBSITEGeneral informationsDate:21/09/2020Author(s), Contact & Affiliation(s):Vivien WALTER (walter.vivien@gmail.com)(1)Olivier BENZERARA(2)Celine RUSCHER(2)Fabrice THALMANN(2)(1)Department of Chemistry, King's College London (UK)(2)Institut Charles Sadron, CNRS, Universite de Strasbourg (FR)DescriptionGeneral DescriptionML-LPA is a Python 3 module made to analyse simulation files and run Machine Learning analysis but also Voronoi tessellations on the molecules contained inside.The module has been specifically designed to analyse the thermodynamic phases of individual lipid molecules inside cell membranes (e.g. DPPC, DSPC);
and optimised to work on all-atom representation (e.g. Charmm36). However, the module has been written to analyse unified-atom or coarse grain
(e.g. Martini) representations as well, and can be used to analyse the states of any molecule selected.ReferencesPackage(s) usedThe module is based on other Python modules that have also been published, namely:MDAnalysis, to open and read the simulation files (Michaud-Agrawal et al.MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations.J. Comput. Chem. 32 (2011), 2319-2327).Scikit-Learn, to perform the Machine Learning training and predictions on the systems (Pedregosa et al.,Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research 12 (2011), 2825-2830).Tess, which is based on the C++ libraryvoro++, to perform the tessellation on the systems (Chris H. Rycroft.Voro++: A three-dimensional voronoi cell library in c++. Chaos (2008), 19(041111)).LiteratureCite usIf you use this package for your research, please cite the following publication:Walter et al. A machine learning study of the two states model for lipid bilayer phase transitions, PCCP (2020), 22, 19147-19154Published example(s)Applications of the module can be found in the literature:Walter et al. A machine learning study of the two states model for lipid bilayer phase transitions, PCCP (2020), 22, 19147-19154DocumentationInstallationUsing PyPiTo install ML-LPA using a terminal and PyPi, simply use the following command>pipinstallmllpaThe PyPi repo can be found on thislink.Using the GitHub repoML-LPA can be installed directly from the source files available on our GitHub repo. The detailed process to install from these files is described below:Get the files, by clicking onCode > Download ZIP.Unzip the folder, open it in the Terminal and navigate inside thesource/.Run the installation>python3setup.pyinstallDetailed instructions can be found on thewebsite of the project.Tutorials and APIAll the documentation on ML-LPA can be found on thewebsite of the project.