Saravanan666/pypi-org huggingface dataset:
Package: mlshed
mlshed####.. .. |PyPI-Status| |PyPI-Versions| |Build-Status| |Codecov| |LICENCE|Simple local/remote model store for Python... |mlshed_icon| image:: https://github.com/shaypal5/mlshed/blob/cc5595bbb78f784a3174a07157083f755fc93172/mlshed.png:height: 87:width: 40 px:scale: 50 %.. .. image:: https://github.com/shaypal5/mlshed/blob/b10a19a28cb1fc41d0c596df5bcd8390e7c22ee7/mlshed.png.. code-block:: pythonfrom mlshed import Model.. contents::.. section-numbering::Installation============.. code-block:: bashpip install mlshedFeatures========* Pure python.* Supports Python 3.5+.Use===TBAContributing============Package author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed.Installing for development----------------------------Clone:.. code-block:: bashgit clone git@github.com:shaypal5/mlshed.gitInstall in development mode, including test dependencies:.. code-block:: bashcd mlshedpip install -e '.[test]'Running the tests-----------------To run the tests use:.. code-block:: bashcd mlshedpytestAdding documentation--------------------The project is documented using the `numpy docstring conventions`_, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings. When documenting code you add to this project, follow `these conventions`_... _`numpy docstring conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt.. _`these conventions`: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txtAdditionally, if you update this ``README.rst`` file,  use ``python setup.py checkdocs`` to validate it compiles.Credits=======Created by `Shay Palachy <http://www.shaypalachy.com/>`_ (shay.palachy@gmail.com)... |PyPI-Status| image:: https://img.shields.io/pypi/v/mlshed.svg:target: https://pypi.python.org/pypi/mlshed.. |PyPI-Versions| image:: https://img.shields.io/pypi/pyversions/mlshed.svg:target: https://pypi.python.org/pypi/mlshed.. |Build-Status| image:: https://travis-ci.org/shaypal5/mlshed.svg?branch=master:target: https://travis-ci.org/shaypal5/mlshed.. |LICENCE| image:: https://img.shields.io/github/license/shaypal5/mlshed.svg:target: https://github.com/shaypal5/mlshed/blob/master/LICENSE.. |Codecov| image:: https://codecov.io/github/shaypal5/mlshed/coverage.svg?branch=master:target: https://codecov.io/github/shaypal5/mlshed?branch=master
Package: ml-simple-gui
ml_simple_guiПростой графический интерфейс для задач машинного обучения.Установка# pip install ml_simple_guiПример использования:from ml_simple_gui import Application
import os
from sklearn.svm import SVC


def model(**args):
    return SVC(**args)


app = Application(
    model=model,
    upload_path=os.path.join('upload'),
    csv_delimiter=',',
    model_params=[
        {
            'code': 'gamma',
            'name': 'gamma',
            'defaultValue': 'scale',
        }
    ],
    server_port=5000
)

app.run()Аргументmodel_paramsпринимает список параметров, которые будут отображаться в интерфейсе и пробрасываться в модель.Аргументupload_pathпринимает путь до папки, где будут хранится загруженные файлы. Поддерживаются тольо CSV-файлы.После инициализации приложения, оно будет доступно по адресуhttp://127.0.0.1:5000
Package: ml-simple-notify
一个简单可扩展的消息通知库安装pip install ml-simple-notify使用fromsrc.notifyimportuseNotify,channelsnotify=useNotify()notify.add(# 添加多个通知渠道channels.Bark({"token":"xxxxxx"}),channels.Ding({"token":"xxxxx","at_all":True}))notify.publish(title="消息标题",content="消息正文")支持的消息通知渠道列表WechatDingBarkEmailChanifyPushdeerPushover自己开发消息通知fromsrc.notify.channelsimportBaseChannelclassCustom(BaseChannel):"""自定义消息通知"""defsend(self,*args,**kwargs):...
Package: mlsl
# MlSL: Machine learning Student's Library_Technically_ pronounced "mill-sill"; conveniently pronounced "[^missile]".## EthosThis library is meant to to make machine learning _as understandable ashumanly possible_; to whiten the black boxes, to make the code as good to readas the documentation. Where it can, it holds your hand. And if it's saying toomuch, you can tell it to shut up.If there's somewhere it fails to achieve this goal, please open aGitHub [issue](https://github.com/jad-b/mlsl/issues).As such, it _should_ be out-performed by any of the "serious" ML packages:Theano, Autograd, TensorFlow, etc. If not, well, something's either wrong ontheir end, or I need to publish.[^missile]:https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&es_th=1&ie=UTF-8#q=+define+missile
Package: mlsocket
mlsocketMLSocket is a python package built on top of the python package of socket to easily send and receive numpy arrays and machine learning models through tcp connection.Free software: MIT licenseMLSocket class inherits a built-in socket package of python and supports all of the basic operations of the socket class.
It can not only send the byte data but also send numpy arrays and keras models.Installation$ pip install mlsocket --userFeaturesSystemLinuxmacOSWindowsStatusSupports Windows 10, Ubuntu, and macOs => Tested on github actionSupports data transfer of numpy arraysSupports data transfer of keras models as hdf5 filesSupports data transfer of scikit-learn classifiersSupport python 3.6, 3.6, 3.8 => Tested on github actionMLSocket inherits the socket object of the built-in python socket, but overrides 4 methods.MLSocket.send(data), MLSocket.sendall(data): Sends the data. Unlike socket.send, thses two functions always show blocking behavior that it waits for the reponse from the server that the server received all the data sent.MLSocket.recv(bufsize): Receives data of the given size, but the return value is not in the size of bufsize. It will receive all the data sent by the client, with the step size of a given bufsize.MLSocket.accept(): This function is just the same as socket.accept(), but returns the MLSocket object and the connected address.UsageThe basic usage of the MLSocket is demonstrated by the following scripts of server and client which sends and receives numpy array and machine learning models.Server-SidefrommlsocketimportMLSocketHOST='127.0.0.1'PORT=65432withMLSocket()ass:s.bind((HOST,PORT))s.listen()conn,address=s.accept()withconn:data=conn.recv(1024)# This will block until it receives all the data send by the client, with the step size of 1024 bytes.model=conn.recv(1024)# This will also block until it receives all the data.clf=conn.recv(1024)# SameClient-SidefrommlsocketimportMLSocketfromkeras.modelsimportSequentialfromkeras.layersimportDensefromsklearnimportsvmimportnumpyasnpHOST='127.0.0.1'PORT=65432# Make an ndarraydata=np.array([1,2,3,4])# Make a keras modelmodel=Sequential()model.add(Dense(8,input_shape=(None,4)))model.compile(optimizer='adam',loss='binary_crossentropy')# Make a scikit-learn classifierclf=svm.SVC(gamma=0.00314)# Send datawithMLSocket()ass:s.connect((HOST,PORT))# Connect to the port and hosts.send(data)# After sending the data, it will wait until it receives the reponse from the servers.send(model)# This will wait as wells.send(clf)# Same
Package: ml-solution
ML SolutionpypigithubmodelscopeThis package is used to quickly build a pipline for mechine learning.Quick StartInstallationEasy installation with pip:pipinstallml_solutionCode SampleThe code below shows how to use ml_solution to build flexible deep learning model frame quickly:importtorchfromtorchimportoptimimporttorch.nnasnnfromdatasetimportcreat_loaderimportmodelingfromml_solution.dl_toolsimportengine,engine_utils,train_utilsfromml_solutionimportdata_utilsfromtransformersimportXLMRobertaTokenizertrain_config=data_utils.json_load('./train_config.json')dataset_config=data_utils.json_load('./dataset_config.json')train_loader=creat_loader(dataset_config['train_json_path'],)valid_loader=creat_loader(dataset_config['valid_json_path'])dataloaders={'train':train_loader,'valid':valid_loader}model=modeling.get_model()optimizer=optim.Adam(model.parameters(),lr=train_config['lr'])criterion=train_utils.DictInputWarpper(nn.CrossEntropyLoss(),'logit','label')metric_grader=engine_utils.ConfusionMetrics(num_classes=4,metrics_list=train_config['metrics_list'])loss_grader=engine_utils.LossRecorder()computers={'conf_metrics':metric_grader,'loss':loss_grader}grader=engine_utils.Grader(computers)wandb_init_config=data_utils.json_manipulate_keys(train_config,['lr','batch_size',"architecture"],keep=True)wandb_init_config['criterion']=criterion.module.__class__.__name__wandb_init_config['optimizer']=optimizer.__class__.__name__logger=engine_utils.WandbLogger(config=wandb_init_config,project=train_config['project'])handler=engine.HandlerSaveModel(metric_name="ACC",log_root=train_config['log_root'],version=logger.version,ideal_th=5)trainer=engine.TorchTrainer(model,dataloaders,criterion,optimizer,device=device,mix_pre=train_config['mix_pre'])train_pipeline=engine.TrainPipeline(trainer,grader,logger,handler=handler)train_pipeline.train_epoches(train_config['epoches'])
Package: ml-solutions
Ml-SolutionsA Python Pacakage for Ready Made Machine Learning Solutions.InstallationTo install this package, you have to run this command on your terminalpip install ml-solutionsQuick Start Guidefromml_solutionsimportsolutions# Spam Message Detectorspam_message_detector=solutions.SpamMessageDetector()spam_message_detector.train()detected=spam_message_detector.detect_spam('Please subscribe my youtube channel for 100 years of good luck!')ifdetected==True:print("Message is Spam!")else:print("The message is not Spam!")# House Price Predictionhouse_price_predictor=solutions.HousePricePrediction()house_price_predictor.train()Price=house_price_predictor.get_price(rooms=2,bathrooms=1,landsize=230,lattidute=-346.876,longtitude=347.378)print(Price)# Image Classification# Training and Saving The Modelimage_classifier=solutions.ImageClassification()image_classifier.train()image_classifier.save_model('image_classifier')# After that you will see your training of the model has been started!# When training is complete, then remove all the code and write:# Loading the Modelimage_classifier=solutions.ImageClassification()image_classifier.load_model('image_classifier')# For image classification, you need a 32*32 px image, to convert your image into a 32*32 px image, go into this website: https://www.privatedaddy.com/resize-to-32x32# After you converted your image, give the path to the 32*32 imagerecognized=image_classifier.recognize_image('image.jpg')print(recognized)# You will see the name of the thing in the image :)# This supports with 'Plane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck'# And we have more Ml Solutions :)ML Algorithmsfromml_solutionsimportalgorithms# HLRegression (HyperLinearRegression)X_train=np.array(X_train)Y_train=np.array(Y_train)X_test=np.array(X_test)model=algorithms.HLRegression()model.train_model(X_train,Y_train)Prediction=model.predict(X_test)print(Prediction)# We will make more algorithms :)
Package: mlspeclib
ML Spec LibraryThe purpose of MLSpec Lib is to provide a library that can easily read and write schema-tized metadata related to the steps in an ML Pipeline.The original set of schema was drawn from here - - as a set of illustrative data for initial specs.If you had a sample pipeline that did the following:Read dataTransformed the data in some way and saved it to a new directoryTrained a model based on the dataWrote the results of the training to a new directoryConverted the model to a training formatCreated a serving endpoint for the modelAt each step, you would want to use first class python objects to drive the workflow, and, ideally, you would want to read/write
metadata about what you were doing to have a permenant record.This is what MLSpecLib is designed to do:Read the metadata inProvide a first class Python object for useAllow validation of the Python object according to a schemaWrite the object to disk in a readable YAML formatTo see it in action, go to the Sample Notebook.*** NOTE: The Sample Notebook is illustrative only! It does not actually execute any actual training and the schema are just for examples. ***Getting Started *Fork this repo to a directory.It is highly recommended you have a virtual environment set up - please see how to do that here:https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/26/python-virtual-env/# Go into the directory and install all packages
pip3 install -r requirements.txt

# Change into the notebook directory and install jupyter
pip3 install jupyter

# Start a notebook server
jupyter notebookNow go to the URL that jupyter gives you, and run through the Sample Notebook. It should provide examples of how to read and write full objects from disk using native feeling Python.TODO:Support better typing / flexibility in lists (including allowed)Real world examples of usage - more notebooksLibrary structure and tools are derived from the work of Kenneth Reitz:Learn more <http://www.kennethreitz.org/essays/repository-structure-and-python>_.If you want to learn more aboutsetup.pyfiles, check outthis repository <https://github.com/kennethreitz/setup.py>_.
Package: mlsploit-py
This package contains utilities for developing an MLsploit python module.SetupTo install this package on your system usingpip, run the following command:$pipinstallmlsploit-pyUsageThis package supports automatic loading of MLsploit jobs inside your module.# your_mlsploit_module.pyfrommlsploitimportJob# load input files and user defined optionsJob.initialize()function_name=Job.function_name# the function name the user wants to runinput_file_items=Job.input_file_items# you can load input_file_items[i].pathoptions=Job.options# can access options.option1, option.option2 for function_name# ...# do stuff based on the information above ...# ...# when you're done, you can reserve output files# that will be uploaded to MLsploitoutput_file_item=Job.reserve_output_file_item(output_file_name='output_file.txt',is_new_file=True)withopen(output_file_item.path,'w')asf:pass# ... write data to your output file here# you can also add tags to your output file# as defined in your module schemaoutput_file_item.add_tag(name='tagname',value='tagvalue')# you can add several output files in the above manner# when you're done done, don't forget to commit the output!Job.commit_output()# mlsploit-py will take care of the restYou can also use this package to programmatically create your MLsploit module configuration file (mlsploit_module.yaml)
instead of editing it manually. This will also validate your MLsploit module for errors.
For more information on the configuration file schema,see here.# make_dummy_module.pyfrommlsploitimportModulemodule=Module.build(display_name='Dummy Module',tagline='This is a dummy module!',doctxt="""Long documentation for this module will go here...""",icon_url='https://somedomain.org/icon.jpg')function=module.build_function(name='Test Function',doctxt="""Some long ducumentation of Test Function...""",creates_new_files=True,modifies_input_files=False,expected_filetype='txt',optional_filetypes=['rtf','ans'])function.add_option(name='option1',type='str',doctxt="""Some long ducumentation of option1...""",required=True)function.add_output_tag(name='tag1',type='str')function.add_output_tag(name='tag2',type='int')# you can add several functions and function options in the above manner# once you're done, the following command will save the mlsploit_module.yaml# in the same directory as this filemodule.save()
Package: mlsquare
[ML]² :  Machine Learning for Machine LearningML Square is a python library that utilises deep learning techniques toEnable interoperability between existing standard machine learning frameworks.Provide explainability as a first-class function.Make ML self learnable.Getting Started!Setting upmlsquareis simple and easyCreate a Virtual Environment(optional)virtualenv~/venvsource~/venv/bin/activateInstallmlsquarepackagepipinstallmlsquareImportdopefunction frommlsquareand pass thesklearnmodel object>>>frommlsquareimportdope>>>fromsklearn.linear_modelimportLinearRegression>>>fromsklearn.preprocessingimportStandardScaler>>>fromsklearn.model_selectionimporttrain_test_split>>>importpandasaspd>>>fromsklearn.datasetsimportload_diabetes>>>model=LinearRegression()>>>diabetes=load_diabetes()>>>X=diabetes.data>>>sc=StandardScaler()>>>X=sc.fit_transform(X)>>>Y=diabetes.target>>>x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.60,random_state=0)>>>m=dope(model)>>># All sklearn operations can be performed on m, except that the underlying implementation uses DNN>>>m.fit(x_train,y_train)>>>m.score(x_test,y_test)TutorialFor a comprehensive tutorial please do checkout thislinkContributeTo get started with contributing, refer our devoloper guidehereFor detailed documentation referdocumentationWe would love to hear your feedback. Drop us a mail atinfo@mlsquare.org
Package: mlstacking
## mlstackingmlstacking is a Python module for sklearn-API friendly multi-layer stacking built on top of scikit-learn and distributed under the 3-Clause BSD license.#### Installtion##### Dependencies##### User installationIf you already have a working installation of numpy and scipy, the easiest way to install mlstacking is usingpippip install mlstacking#### Help and Support##### DocumentationHTML documentation (stable release):https://mlstacking.readthedocs.io/
Package: mlstacks
MLStacks: Deploy your MLOps infrastructure in minutes🌰 In a nutshell: What is MLStacks?MLStacksis aPython packagethat allows you to quickly
spin up MLOps infrastructure using Terraform. It is designed to be used withZenML, but can be used with any MLOps tool or platform.Simply write stack and component YAML specification files and deploy them using
the MLStacks CLI. MLStacks will take care of the rest. We currently support
modular MLOps stacks on AWS, GCP and K3D (for local use).👷 Why We Built MLStacksWhen we first createdZenMLas an extensible MLOps framework
for creating portable, production-ready MLOps pipelines, we saw many of our
users having to deal with the pain of deploying infrastructure from scratch to
run these pipelines. The community consistently asked questions like:How do I deploy tool X with tool Y?Does a combination of tool X with Y make sense?Isn't there an easy way to just try these stacks out to make an informed
decision?To address these questions, the ZenML team presents you a series of
Terraform-based stacks to quickly provision popular combinations of MLOps tools.
These stacks will be useful for you if:You are at the start of your MLOps journey, and would like to explore
different tools.You are looking for guidelines for production-grade deployments.You would like to run your MLOps pipelines on your chosenZenML Stack.🔥Do you use these tools or do you want to add one to your MLOps stack?At
ZenML, we are looking for design partnerships and collaboration to implement and
develop these MLOps stacks in a real-world setting.If you'd like to learn more, pleasejoin our Slackand leave us a message!🤓 Learn MoreTry theQuickstart example in the documentationto get started with MLStacks.Discover what you can configure with the different stacks in theStacks documentation.Learn about our CLI commands in theCLI documentation.🙏🏻 AcknowledgementsThank you to the folks over atFuzzy Labsfor their
support and contributions to this repository. Also many thanks toAli Abbas Jaffrifor several stimulating
discussions around the architecture of this project.We'd also like to acknowledge some of the cool inspirations for this project:FuseMLCombinator.MLBuilding a ML Platform From Scratch
Package: mlstatpy
mlstatpyLe module contient essentiellement des digressions mathématiques
autour du machine learning. Parmi les choses intéressantes,
une courbeROCavec intervalle de confiance, détection
automatique de segment dans une image, un algorithme
d’autocomplétion, une distance d’édition entre graphes,
des petites choses pour les données de Wikipedia,
un algorithme de conversion d’un arbre de décision en
réseaux de neurones.The package mostly contains documentation. It also implements
some code rarely needed such as ROC curve with bandwidth,
automated segment detection in a image, some simple autocomplete
algorithm, a graph edit distance, some helpers on Wikipedia data,
an algorithm to convert decision trees into neural network.GitHub/mlstatpydocumentationBlog
Package: mlstclassifier-cd
MLSTclassifier_cdTable of ContentsOverviewInstallationUsageOutputOverviewEnhance your clade prediction process with MLSTclassifier_cd, a powerful machine learning tool that employs K-Nearest Neighbors (KNN) algorithm. Designed specifically for Multi-Locus Sequence Type (MLST) analysis ofC. difficilestrains, including cryptic variants, this tool streamlines and accelerates clade prediction. MLSTclassifier_cd achieves a prediction accuracy of approximately 92%.StatQuest methodology was used to build the model (https://www.youtube.com/watch?v=q90UDEgYqeI&t=3327s). Powered by the Scikit-learn library, MLSTclassifier_cd is a good tool to have a first classification of yourC. difficilestrains including cryptic ones.The model was trained using data from PubMLST (May 2023):https://pubmlst.org/bigsdb?db=pubmlst_cdifficile_seqdef&page=downloadProfiles&scheme_id=1. Cryptic strains for training were assessed manually using phylogenetic tree construction, fastbaps and popPUNK to refine clustering.GitHub repo:https://github.com/eliottBo/MLSTclassifier_cdInstallation:It is recommended to use a virtual environment.Install PyPI package:pip install mlstclassifier-cdhttps://pypi.org/project/mlstclassifier-cd/Usage:The query is a directory containing ".mlst" files like the ones retrieved on PubMLST or a path to a directory containing ".fastmlst" output files fromFastMLST.Basic Command:MLSTclassifier_cd [query directory path] [output file path]Example:MLSTclassifier_cd /Desktop/directory_name /Desktop/directory_name/example_output.csvOutput:After running MLSTclassifier_cd, the output file should contain an additional column named "predicted_clade".
It also creates the following files:"pie_chart.html" displays the proportions of the different classes found."count.csv" a csv file containing the raw value count of your predicted clades for you to generate your own graphs!
Package: mlstream
mlstreamMachine learning for streamflow prediction.PyPI:https://pypi.org/project/mlstream/Documentation:https://mlstream.readthedocs.io/UsageThis project is work in progress.
The idea is to create an easy way of training machine learning streamflow models:
Just provide your data, select a model (or provide your own), and get the predictions.Trainingexp=Experiment(data_path,is_train=True,run_dir=run_dir,start_date='01012000',end_date='31122015',basins=train_basin_ids,forcing_attributes=['precip','tmax','tmin'],static_attributes=['area','regulation'])exp.set_model(model)exp.train()Inferencerun_dir=Path('./experiments')exp=Experiment(data_path,is_train=False,run_dir=run_dir,basins=test_basin_ids,start_date='01012016',end_date='31122018')model.load(run_dir/'model.pkl')exp.set_model(model)results=exp.predict()