Saravanan666/pypi-org huggingface dataset:
Package: mlbands
ML Band Gaps (Materials)Ideal candidate: skilled ML data scientist with solid knowledge of materials science.OverviewThe aim of this task is to create a python package that implements automatic prediction of electronic band gaps for a set of materials based on training data.User storyAs a user of this software I can predict the value of an electronic band gap after passing training data and structural information about the target material.Requirementssuggest the bandgap values for a set of materials designated by their crystallographic and stoichiometric propertiesthe code shall be written in a way that can facilitate easy addition of other characteristics extracted from simulations (forces, pressures, phonon frequencies etc)Expectationsthe code shall be able to suggest realistic values for slightly modified geometry sets - eg. trained on Si and Ge it should suggest the value of bandgap for Si49Ge51 to be between those of Si and Gemodular and object-oriented implementationcommit early and often - at least once per 24 hoursTimelineWe leave exact timing to the candidate. Must fit Within 5 days total.Notesuse a designated github repository for version controlsuggested source of training data: materialsproject.org
Package: ml-base
ml-baseml-baseis a package that provides base classes and utilities that are useful for deploying machine learning models.InstallationThe easiest way to install ml-base is using pippipinstallml-baseUsageThere are several examples of how to use the ml-base framework in thedocumentation.DevelopmentFirst, download the source code with this command:gitclonehttps://github.com/schmidtbri/ml-baseThen create a virtual environment and activate it:# go into the project directorycdml-base

makevenvsourcevenv/bin/activateInstall the dependencies:makedependenciesTestingTo run the unit test suite execute these commands:# first install the test dependenciesmaketest-dependencies# run the test suitemaketest# clean up the unit testsmakeclean-test
Package: mlbcal
mlbcalDownload the schedule for a given team using the MLB Stats API.installationpip install mlbcalNo external dependencies to run. Need pytest to run the tests.usageusage: mlbcal [-h] [--year YEAR] [--full] [--nopre] [--csv] team

CLI to download MLB calendar for a given team. Default output is json

positional arguments:
  team         Team name, abbreviation, or city

options:
  -h, --help   show this help message and exit
  --year YEAR  Change schedule year (default: current year)
  --full       Return unparsed response (cannot use --csv and --nopre)
  --nopre      Filter out preseason spring training games
  --csv        Format results as csv (default: json)examplesjson defaultAll outputs are to stdout so you need to redirect to save files. The default format is json with two space indents:$ mlbcal mariners > mariners.jsoncsv$ mlbcal yankees --csv > yankees.csvflagsThe default year is the current year but you can change it. I don't know how far back the MLB API will respond with data:$ mlbcal "San Diego" --csv --year 2021 > padres_last_year.csvYou can also filter out preseason games with a flag:$ mlbcal LAA --csv --nopre > angels.csvYou can also get the unfurled but otherwise unparsed response this way:$ mlbcal mets --full > mets.jsonnote on the team name lookupdata/teams.jsoncontains a lookup of team ids and different variations of the team names including abbreviations and city names to allow for flexible user input for the team names. The lookup data shipped in this repo was pulled in March 2022 which means it may not align with historical team names if you lookup past schedules.You can download the team lookup for previous seasons by providing a year argument to theutils/download_teams.pyhelper script.
Package: mlbee
mlbeemlbee cli: a multilingual aligner based on a machine learning modelInstall itpipinstallmlbee# pip install git+https://github.com/ffreemt/mlbee-cli# poetry add git+https://github.com/ffreemt/mlbee-cli# git clone https://github.com/ffreemt/mlbee-cli && cd mlbee-cliUse itpython-mmlbee--help# or mlbee --help
Package: mlbee-cli
mlbee-climlbee-cli descrInstall itpipinstallgit+https://github.com/ffreemt/mlbee-cli# poetry add git+https://github.com/ffreemt/mlbee-cli# git clone https://github.com/ffreemt/mlbee-cli && cd mlbee-cliUse itfrommlbee_cliimportmlbee_cli
Package: mlb-fantasy
mlb-fantasyA MLB fantasy league tracker setup using MLB scoresHow To UseThis is a python package and can be installed viapip install mlb-fantasyAfter installing you will want to make adraft_picks.txtfile with formatting as such:Each player needs to be on a new line with the full team name spelled out in the pick, if you have questions on the team names the package in use ispybaseballPlayer 1: Houston Astros,Philadelphia Phillies,Seattle Mariners,San Francisco Giants,Boston Red Sox,Oakland Athletics
Player 2: Tampa Bay Rays,Milwaukee Brewers,Cleveland Guardians,Arizona Diamondbacks,Pittsburgh Pirates,Oakland AthleticsNow you can run the program viamlb-fantasy draft_picks.txtand it should generate a .xlsx file for us.  This file will have 3 tabs along the bottom for the Draft Picks, the actual MLB Standings from the live data, and the actual Player Standings data based on the MLB Standings and their picks.
Package: mlbgame
mlbgame is a Python API to retrieve and read MLB GameDay data.
mlbgame works with real time data, getting information as games are being played.mlbgame uses the same data that MLB GameDay uses,
and therefore is updated as soon as something happens in a game.mlbgamedocumentationmlbgame onGithub(Source Code)If you have a question or need help, the quickest way to get a response
is to file an issue on theGithub issue trackermlbgame’s submodules should not really be used other than as
used by the main functions of the package (in__init__.py).Use of mlbgame must follow the terms stated in thelicenseand onmlb.comInstallationmlbgame is in thePython Package Index (PyPI).
Installing withpipis recommended for all systems.mlbgame can be installed by running:pip install mlbgameAlternatively, the latest release of mlbgame can be downloaded as aziportarball.
If you do not install withpip, you must also installlmxlas specified insetup.py.If you want to help develop mlbgame, you must also install the dev dependencies, which can be done by runningpip install-e.[dev]from within the directory.
Package: ml-botting-core
Welcome to ML_Botting_Core!Solving Complex UI Challenges w/ MLpip install ml-botting-corePublic Eve Online ModelsThis package will auto download these models fromHereat runtime and maintain a copy on your device. (and auto update)Sample configHerewith all models.TrainingTrain your own modelsHereSort your training images into folders (images shape must be the same shape) and target the root for training. The classifer will train those image samples to the name of the folder they are in.Image names do not matter.PNGs only.Number of samples per folder do not matter, however you want enough, 80% for training, 20% for validation.├── training_data│   ├── char_select│   │   ├── image_1.png│   │   ├── image_2.png│   │   ├── image_3.png│   ├── connection_lost│   │   ├── image_1.png│   │   ├── image_2.png│   │   ├── image_3.png│   ├── in_flight│   │   ├── image_1.png│   │   ├── image_2.png│   │   ├── image_3.png│   ├── in_hanger│   │   ├── image_1.png│   │   ├── image_2.png│   │   ├── image_3.pngUsage:Check out the samplesHere.ml_botting_core_config.json{"public_models":[{"game":"eve_online","model_name":"game_state","download_latest":1,"download_latest_from":"https://storage.googleapis.com/eve_online_models/","model_root_directory":"O:\\eve_live_models\\game_state","model_log_directory":"O:\\eve_live_logs\\game_state","save_images":0}]}Implementationfromml_botting_coreimportuniversal_predictorup_config=json.load(open(r'ml_botting_core_config.json'))up=universal_predictor(config=up_config)img=Image.open('some_image.png')state_result=up.predict(img,'game_state')state_result{"epoc_time":"1682138565007.508","argmax_index":2,"value_at_argmax":"1.0","class":"jump_though_first","classes":["dock_now","invalid","jump_though_first","jump_through_second","no_action","warp_to_dock_3","warp_to_dock_4"],"scores":[1.2750345662162804e-15,1.4581948495906438e-11,1.0,5.21881417175057e-17,1.4712418422554443e-18,1.2777047215389858e-12,6.730089694497203e-17],"id":"98ad373b-e0a6-11ed-9b27-2cf05d9fe8eb","image_saved":0,"model_name":"nav_options"}
Package: mlbox
MLBox is a powerful Automated Machine Learning python library.It provides the following features:Fast reading and distributed data preprocessing/cleaning/formattingHighly robust feature selection and leak detectionAccurate hyper-parameter optimization in high-dimensional spaceState-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…)Prediction with models interpretationFor more details, please refer to theofficial documentationHow to ContributeMLBox has been developed and used by many active community members. Your help is very valuable to make it better for everyone.Check outcall for contributionsto see what can be improved, or open an issue if you want something.Contribute to theteststo make it more reliable.Contribute to thedocumentsto make it clearer for everyone.Contribute to theexamplesto share your experience with other users.Openissueif you met problems during development.For more details, please refer toCONTRIBUTING.
Package: ml-bricks
ML BricksA package for reusing functions that repeat during machine learning and data science related worksInstall from Pypipip install ml_bricksLocal installgit clone https://github.com/jerinka/ml_brickspip3 install -e ml_bricksTest and Coveragecoverage run --source=ml_bricks/ -m pytest -v test/ && coverage report -mcoverage html -d coverage_htmlbuildpip install wheelpython setup.py sdist bdist_wheeltestpypitwine upload --repository testpypi dist/*pip install -i https://test.pypi.org/simple/ ml_brickspypitwine upload dist/*pip install -U ml_bricksQuick usageimport ml_bricks as pk1pk1.subpackage1.moduleA.fun_a()Referencehttps://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
Package: mlb-sdk
MLB SDKThis library is intended to make it easier to use the MLB APIs (Stats API, DSP, etc). It does not provide access to the underlying APIs, you need to come with your own credentials provided by the league.Copyright NoticeThis package and its author are not affiliated with MLB or any MLB team. This API wrapper interfaces with MLB's Stats API. Use of MLB data is subject to the notice posted athttp://gdx.mlb.com/components/copyright.txt.
Package: mlbugdetection
MLBugDetectionMachine learning explainability and unexpectated behaviors detectionOverviewMost machine learning explainability packages requires both trained models and the training data to create Explainer objects that explain the model's behavior. This package allows ceteris paribus analysis of features using only the trained model and one or more input samples.Documentationhttps://jpgianfaldoni.github.io/MLBugDetection/Installation:pipinstallmlbugdetectionHow to useFunctionsMonotonic:frommlbugdetection.monotonicimportcheck_monotonicity_single_sample,check_monotonicity_multiple_samplesUsage:
For 1 samplecheck_monotonicity_single_sample(model,sample,feature,start,stop,step=1)For more than 1 sample:check_monotonicity_multiple_samples(model,samples,feature,start,stop,step=1)Critical Values:frommlbugdetection.critical_valuesimportfind_critical_values,find_several_critical_valuesUsage:
For 1 samplefind_critical_values(model,sample,feature,start,stop,step=1)For more than 1 sample:find_several_critical_values(model,samples,feature,start,stop,step=1,bins=15,keep_n=5,log=False)Calibration:frommlbugdetection.calibrationimportcalibration_checkUsage:calibration_check(model,samples,target,pos_label=1)Sanity:frommlbugdetection.sanityimportsanity_check,sanity_check_with_indexesUsage:sanity_check(model,samples,target)If test not pass, check the indexessanity_check_with_indexes(model,samples,target)Virtual Environment with Jupyter Notebookpython3-mvirtualenvvenvsourcevenv/bin/activate
python-mpipinstall--upgradepip
pipinstall-rrequirements.txt
Package: ml-caboodle
ML CaboodleAuthor: Marius Helf
(helfsmarius@gmail.com)All the stuff that doesn't have another homeChangelog0.2.2chore: bump scikit-learn dependency to 1.0.00.2.1fix: speculative rounds not removed when out of candidates0.2.0feature: BackwardFeatureEliminationvarious bugfixes0.1.1fix: ForwardFeatureSelection: return correct features when stopping
after speculative rounds.improve logging output0.1.0Initial releaseLicenseMIT
Package: mlcalcdriver
Machine Learning Calculations Driver (ML_Calc_Driver)ML_Calc_Driver is a driver to emulate DFT calculations using machine learned predictive models.
To use this package, one needs an already trained model, that can predict energy or forces from an input atomic geometry.
Supported systems depends on the model, this package should not be a limitation.This package is tested with python 3.10.Credit tommorinierefor theMyBigDFT packagewhich served as a foundation for this work. Some classes have been directly adapted from MyBigDFT.Dependencies scanned byPyUp.ioDocumentationThe documentation is availablehere.InstallationFrom PyPiTo install the latest version available on PyPi:pip install mlcalcdriverTo upgrade to last version:pip install -U mlcalcdriverFrom sourcesgit clone git@github.com:OMalenfantThuot/ML_Calc_Driver.git
cd ML_Calc_Driver/
pip install -r requirements_dev.txt
pip install .To modify the sources, instead ofpip install ., usepip install -e .
Package: mlc-auth
UsageInstallpip install mlc_authInitializeBefore initializing MLC_Auth, the app.config['SECRET_KEY'] must be set.frommlc_authimportMLC_Authmlc_auth=MLC_Auth(app=app)Use on routesThe accessable_by parameter is optional. When accessable_by is None, all logged in users have access.@app.route('/edit',methods=['GET'])@mlc_auth.auth_required(accessable_by=['guest','user','manager','administrator'])defedit_page():returnrender_template('edit.html')Current user modelUser model properties:idemailnameroleorganisation_idorganisation_nameusage:fromflask_loginimportcurrent_userprint(current_user.id)Fetch data from the APIfrommlc_authimportMLC_Authorganisation_info=MLC_Auth.api.get(endpoint='/api/organisation/1',params={'key':'value'})response=MLC_Auth.api.put(endpoint='/api/user/1/settings',body={'key':'value'},params={'key':'value'})Development environmentThe following line needs to be added to your Hosts file:127.0.0.1 local-<Your app name here>.mlc-services.comStart the application development server.Go tohttp://local-<Your app name here>.mlc-services.com:5000
Package: mlchemad
MLChemADApplicability domain definitions for cheminformatics modelling.Getting StartedInstallpip install mlchemadExample UsageWith molecular fingerprints, prefer the use of theKNNApplicabilityDomainwithk=1,scaling=None,hard_threshold=0.3, anddist='jaccard'.Otherwise, the use of theTopKatApplicabilityDomainis recommended.frommlchemadimportTopKatApplicabilityDomain,KNNApplicabilityDomain,data# Create the applicability domain using TopKat's definitionapp_domain=TopKatApplicabilityDomain()# Fit it to the training setapp_domain.fit(data.mekenyan1993.training)# Determine outliers from multiple samples (rows) ...print(app_domain.contains(data.mekenyan1993.test))# ... or a unique samplesample=data.mekenyan1993.test.iloc[5]# Obtain the 5th row as a pandas.Series objectprint(app_domain.contains(sample))# Now with Morgan fingerprintsapp_domain=KNNApplicabilityDomain(k=1,scaling=None,hard_threshold=0.3,dist='jaccard')app_domain.fit(data.broccatelli2011.training.drop(columns='Activity'))print(app_domain.contains(data.broccatelli2011.test.drop(columns='Activity')))Depending on the definition of the applicability domain, some samples of the training set might be outliers themselves.Applicability domainsThe applicability domain defined by MLChemAD as the following:Bounding BoxPCA Bounding BoxConvex Hull(does not scale well)TOPKAT's Optimum Prediction Space(recommended with molecular descriptors)LeverageHotelling T²Distance to Centroidsk-Nearest Neighbors(recommended with molecular fingerprints with the use ofdist='rogerstanimoto',scaling=Noneandhard_threshold=0.75for ECFP fingerprints)Isolation ForestsNon-parametric Kernel Densities
Package: ml-clerk
What is ml-clerk?ML-clerk, at its core, is a lightweight logging library that helps you keep track of performance, model parameters, timing (or whatever) throughout your data science experiment.I'm a data scientist. Why should I care?As a data scientist you're constantly running new experiments. As you run new experiments, you are recording your results (if not, you should be recording your results). ML clerk does exactly that. It reduces the inertia by automatically recording the results of your experiment, so you don't have to worry about keeping track.Ok now that I'm convinced, where does model-clerk log stuff?ML clerk has two places where it tracks things - excel or google sheets. Excel setup is easier, so start with that. For google sheets usage, see the link below.Train a model - for demo purposes only# Importsfromsklearn.datasetsimportload_irisfromsklearn.linear_modelimportLogisticRegressiondataset=load_iris()# Define dataX=dataset['data']y=dataset['target']# Train the modellogistic_regression_classifier=LogisticRegression()logistic_regression_classifier.fit(X,y)predictions=logistic_regression_classifier.predict(X)probabilities=logistic_regression_classifier.predict_proba(X)Excel Usage# Record artifacts to excelfromml_clerkimportClerk# Set up the clerk with excel modeclerk=Clerk(excel_mode=True)# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheetclerk.set_up(filepath='hello_world.xlsx',sheet_name='Sheet1')# Record all the artifacts in one goclerk.record(predictions=predictions,probabilities=probabilities,model_parameters=logistic_regression_classifier.get_params())Figure out google sheets permissions prior to using the google sheets mode. Follow directions below if you need any help.Google Sheets Usage# Record artifacts to excelfromml_clerkimportClerk# Set up the clerk with google sheets modeclerk=Clerk(google_sheets_mode=True)# file_path refers to the excel workbook you want to record in, and the sheet name referes to the sheetclerk.set_up(file_path='#your google sheets url',sheet_name='#your sheet name')# Record all the artifacts in one goclerk.record(predictions=predictions,probabilities=probabilities,model_parameters=logistic_regression_classifier.get_params())Google sheets permissionsFollow this link for how to permission your Google account -https://erikrood.com/Posts/py_gsheets.htmlMake sure you enable the Google sheets API under the project in Google console.Update the.envwith the path to token.json credentials file.
Package: mlclient
ML ClientML Client is a python library providing a python API to manage a MarkLogic instance.Low-levelMLClient:>>>frommlclientimportMLClient>>>config={..."host":"localhost",..."port":8002,..."username":"admin",..."password":"admin",...}>>>withMLClient(**config)asclient:...resp=client.post(endpoint="/v1/eval",...body={"xquery":"xdmp:database() => xdmp:database-name()"})...print(resp.text)...--6a5df7d535c71968Content-Type:text/plainX-Primitive:stringApp-Services--6a5df7d535c71968--Medium-levelMLResourcesClient:>>>frommlclientimportMLResourcesClient>>>config={..."host":"localhost",..."port":8002,..."username":"admin",..."password":"admin",...}>>>withMLResourcesClient(**config)asclient:...resp=client.eval(xquery="xdmp:database() => xdmp:database-name()")...print(resp.text)...--6a5df7d535c71968Content-Type:text/plainX-Primitive:stringApp-Services--6a5df7d535c71968--Parsed response :>>>frommlclientimportMLResourcesClient,MLResponseParser>>>config={..."host":"localhost",..."port":8002,..."username":"admin",..."password":"admin",...}>>>withMLResourcesClient(**config)asclient:...resp=client.eval(xquery="xdmp:database() => xdmp:database-name()")...parsed_resp=MLResponseParser.parse(resp)...print(parsed_resp)...App-ServicesInstallationInstall MLClient with pippipinstallmlclient
Package: ml-cloud-tools
ML-Cloud-Tools -Code Doc☁️ Machine Learning Tools for the Cloud. Currently very much focused on AWS and S3. ☁️Table of ContentsInstallationSupport and FeedbackReporting Security VulnerabilitiesContributionCode of ConductLicensingInstallationML-Cloud-Tools is available atthe Python Package Index (PyPI).
It can be installed with pip:$pipinstallml-cloud-toolsSupport and FeedbackThe following channels are available for discussions, feedback, and support requests:open an issue in our GitHub repositorysend an e-mail to our open source teamReporting Security VulnerabilitiesThis project is built with security and data privacy in mind to ensure your data is safe.
We are grateful for security researchers and users reporting a vulnerability to us, first.
To ensure that your request is handled in a timely manner and non-disclosure of vulnerabilities
can be assured, please follow the below guideline.Please do not report security vulnerabilities directly on GitHub.
GitHub Issues can be publicly seen and therefore would result in a direct disclosure.Please address questions about data privacy, security concepts,
and other media requests to theopensource@telekom.demailbox.ContributionOur commitment to open source means that we are enabling - in fact encouraging - all interested
parties to contribute and become part of our developer community.Contribution and feedback is encouraged and always welcome. For more information about how to
contribute, as well as additional contribution information, see ourContribution Guidelines.Code of ConductThis project has adopted theContributor Covenantas our code of conduct. Please see the details in ourContributor Covenant Code of Conduct.
All contributors must abide by the code of conduct.LicensingCopyright (c) 2021 Timothy Wolff-Piggott, Deutsche Telekom AGCopyright (c) 2021-2023 Philip May, Deutsche Telekom AGCopyright (c) 2021-2023 Philip MayLicensed under theMIT License(the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License by reviewing the fileLICENSEin the repository.
Package: mlcm
MLCM creates a 2D Multi-Label Confusion MatrixPlease read the following paper for more information:M. Heydarian, T. Doyle, and R. Samavi, MLCM: Multi-Label Confusion Matrix,IEEE Access, Feb. 2022, DOI: 10.1109/ACCESS.2022.3151048For other projects please seehttps://biomedic.ai/Please cite the paper if you are using the MLCM.This work is licensed under a Creative Commons Attribution 4.0 License.For more information, seehttps://creativecommons.org/licenses/by/4.0/An example on how to use MLCM package:% Importing librariesfrom mlcm import mlcmimport numpy as np% Creating random input (multi-label data)number_of_samples = 1000number_of_classes = 5label_true = np.random.randint(2, size=(number_of_samples, number_of_classes))label_pred = np.random.randint(2, size=(number_of_samples, number_of_classes))% Calling mlcm and illustrating the resultsconf_mat,normal_conf_mat = mlcm.cm(label_true,label_pred)print('\nRaw confusion Matrix:')print(conf_mat)print('\nNormalized confusion Matrix (%):')print(normal_conf_mat)one_vs_rest = mlcm.stats(conf_mat)