Saravanan666/pypi-org huggingface dataset:
Package: sklPCA
This package implements Supervised Kernel-based Longitudinal Principal Components Analysis (skl-PCA) for predictor dimension reduction in longitudinal models. The software was written by members of the Mindstrong Health Data Science team:Patrick Staples, PhDMin Ouyang, PhDBob Dougherty, PhDGreg Ryslik, PhD, FCAS, MAAAPaul Dagum, MD, PhDPlease contact us atdatascience@mindstronghealth.com.NOTE: If you use this software in your work, please cite the followingpaper:Patrick Staples, Min Ouyang, Robert F. Dougherty, Gregory A. Ryslik, and Paul Dagum (2018). Supervised Kernel PCA For Longitudinal Data.http://arxiv.org/abs/1808.06638.InstallationThe easiest way to install the package is viaeasy_installorpip:$ pip install sklPCAThis should also take care of the dependencies (numpy, scipy, pandas, and sklearn).UsageSee examples.py for examples of simulated data, predictor reduction, fitting, and cross-validated model performance.Copyright & LicenseCopyright (c) 2018,Mindstrong Health. GNU Affero General Public License.
Package: skmatter
A collection of scikit-learn compatible utilities that implement methods born out of the
materials science and chemistry communities.For details, tutorials, and examples, please have a look at ourdocumentation.InstallationYou can installscikit-mattereither via pip usingpipinstallskmatteror condacondainstall-cconda-forgeskmatterYou can thenimport skmatterand use scikit-matter in your projects!TestsWe are testing our code for Python 3.8 and 3.11 onWindows Server 2019,macOS 11andUbuntu LTS 22.04.Having problems or ideas?Having a problem with scikit-matter? Please let us know bysubmitting an issue.Submit new features or bug fixes through apull request.Call for ContributionsWe always welcome new contributors. If you want to help us take a look at ourcontribution guidelinesand afterwards you may start with an open issue marked asgood first issue.Writing code is not the only way to contribute to the project. You can also:reviewpull requestshelp us stay on top of new and oldissuesdevelopexamples and tutorialsmaintain andimprove our documentationcontributenew datasetsContributorsThanks goes to all people that make scikit-matter possible:
Package: skmfforever
skmf-foreverskmf-forever updates scikit-multiflow to work with the latest numpy version.Quick linksWebpageDocumentationCommunityFeaturesIncremental LearningStream learning models are created incrementally and are updated continuously. They are suitable
for big data applications where real-time response is vital.Adaptive learningChanges in data distribution harm learning. Adaptive methods are specifically designed to be
robust to concept drift changes in dynamic environments.Resource-wise efficientStreaming techniques efficiently handle resources such as memory and processing time given the
unbounded nature of data streams.Easy to usescikit-multiflow is designed for users with any experience level. Experiments are easy to design,
setup, and run. Existing methods are easy to modify and extend.Stream learning toolsIn its current state, scikit-multiflow contains data generators, multi-output/multi-target stream
learning methods, change detection methods, evaluation methods, and more.Open sourceDistributed under theBSD 3-Clause,scikit-multiflowis developed and maintained by an active, diverse and growingcommunity.Use casesThe following tasks are supported inscikit-multiflow:Supervised learningWhen working with labeled data. Depending on the target type can be either classification
(discrete values) or regression (continuous values)Single/multi outputSingle-output methods predict a single target-label (binary or multi-class) for classification or
a single target-value for regression. Multi-output methods simultaneously predict multiple
variables given an input.Concept drift detectionChanges in data distribution can harm learning. Drift detection methods are designed to rise an
alarm in the presence of drift and are used alongside learning methods to improve their robustness
against this phenomenon in evolving data streams.Unsupervised learningWhen working with unlabeled data. For example, anomaly detection where the goal is the
identification of rare events or samples which differ significantly from the majority of the data.Jupyter NotebooksIn order to display plots fromscikit-multiflowwithin aJupyter Notebookwe need to define
the proper mathplotlib backend to use. This is done by including the following magic command at the
beginning of the Notebook:%matplotlibnotebookJupyterLabis the next-generation user interface
for Jupyter, currently in beta, it can display interactive plots with some caveats. If you use
JupyterLab then the current solution is to use thejupyter-matplotlibextension:%matplotlibwidgetCitingscikit-multiflowIfscikit-multiflowhas been useful for your research and you would like to cite it in a academic
publication, please use the following Bibtex entry:@article{skmultiflow,author={Jacob Montiel and Jesse Read and Albert Bifet and Talel Abdessalem},title={Scikit-Multiflow: A Multi-output Streaming Framework },journal={Journal of Machine Learning Research},year={2018},volume={19},number={72},pages={1-5},url={http://jmlr.org/papers/v19/18-251.html}}
Package: sk_modelcurves
sk-modelcurvesA Python wrapper built for software engineers and researchers to facilitate
easy creation of learning and validation curve plots from scikit-learn.The module is meant to complement your workflow in scikit-learn and ease the
process of evaluating your models.The module includes many quality of life features that should save you precious
time whenever you want to plot a learning curve to check for bias/variance or
plot a validation curve to see the effect of tuning a hyperparameter.BackgroundFor those not familiar with learning curves, check out Andrew Ng’s excellent
discussion of their use athttp://cs229.stanford.edu/materials/ML-advice.pdfOver the process of writing many research papers and building many models, I
found myself using boilerplate code that I would copy paste for almost every
project whenever I wanted to plot a learning curve or validation curve to
evaluate models.Hopefully, this module will save you a few minutes each time you need to plot
a learning or validation curve so you can focus on other things.InstallPython’s pip is the recommended method of installation. From the terminal:$ pip install sk_modelcurvesExample UsageGenerate a learning curve using accuracy as a metric and 5-fold cross validation.Assumes a sklearn estimator called knn, training data matrix called X and
training labels called y:$ from sk_modelcurves.learning_curve import draw_learning_curve
$ draw_learning_curve(knn, X, y, scoring='accuracy', cv=5)
$ plt.show()Generate multiple learning curves for several estimators using F1 score as a
metric, 5-fold cross validation, and names for each of the estimators.Assumes 3 sklearn estimators called knn2, knn20, knn40, training data matrix
called X and training labels called y:$ from sk_modelcurves.learning_curve import draw_learning_curve
$ draw_learning_curve([knn2, knn20, knn40], X, y, scoring='f1', cv=5,
  estimator_titles=['2 Neighbors', '20 Neighbors', '40 Neighbors'])
$ plt.show()Many other options are available. Check out the source code docstrings or the
upcoming documentation.Important LinksOfficial source code repo:https://github.com/MasonGallo/sk-modelcurveHTML documentation: coming soon!Issue tracker:https://github.com/MasonGallo/sk-modelcurve/issuesDependenciessk-modelcurves is tested to work for Python 2.6 and Python 2.7. Python 3.3+ has
not been tested and is assumed to not work until tested.The required dependencies include scikit-learn (of course!), numpy >= 1.6.1,
and matplotlib >= 1.1.1.To run tests, you will need nose >= 1.1.2.ContributingAnyone is welcome!If you find a bug or would like to discuss a potential feature, please file an
issue first.TestingAfter installation, you can launch the test suite from outside the source
directory (you will need to have thenosepackage installed):$ nosetests -v sk_modelcurves
Package: skm-pyutils
PythonUtilsA module containing common scripts that I use in my research.
Documentation available athttps://seankmartin.github.io/PythonUtils/html/skm_pyutils/index.html.Installationpip install skm_pyutilsUpdate documentationpdoc --html -o "docs\html" skm_pyutils --forceCommand line runningpdf-merge-h
dir-list-hFor examplepdf-merge-d.-r-omerged.pdf
dir-list.-r-e.txt-otxt_file_list.txt
copy-filesinput_diroutput_dir-re.*results.*.pngModulesarray : Small numpy style functions.config : Config utils, e.g read a full .py python file as configuration using exec.log: Logging utils, e.g. logging exceptions to disk and stdout.merge : combine csv files together, or grab files of a particular type from recursive folders and merge into one folderpath: Path utils, e.g. finding all files in a directory with given extension recursively.pdf : PDF utils, e.g. merging all PDFs files in a directory, or pdfs with given pagesplot : Plotting utils, e.g. automatically handled gridding of plots, with color management.print : Printing utils, e.g. pretty printing a dictionary, or HDF5 file.profile : Code profiling, e.g. profiling code for runtime performance.save : Saving outputs, e.g. a full dictionary recursively.stats : Hyptothesis tests etc.tables : Pandas dataframe utils.
Package: skm-tea
Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) DatasetPaper|Dataset Download|Dataset Docs|Tutorial|AboutThe 2021 Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset pairs raw quantitative MRI (qMRI) data, image data, and dense labels of tissues and pathology for end-to-end exploration and evaluation of the MR imaging pipeline.This repository contains the building blocks for training and benchmarking models with the SKM-TEA dataset, such as PyTorch dataloaders, evaluation metrics, and baselines. It also contains tutorials for using the dataset and codebase. It utilizesMeddlrandPyTorch Lightningfor training, evaluation, and machine utilities.⚡ QuickStartpipinstallskm-teaInstalling locally: For local development, fork and clone the repo and runpip install -e ".[dev]"_Installing from main: For most up-to-date code without a local install, runpip install "skm-tea @ git+https://github.com/StanfordMIMI/skm-tea@main"Configure your paths and get going!importmeddlrasmrimportos# (Optional) Configure and save machine/cluster preferences.# This only has to be done once and will persist across sessions.cluster=mr.Cluster()cluster.set(results_dir="/path/to/save/results",data_dir="/path/to/datasets")cluster.save()# OR set these as environment variables.os.environ["MEDDLR_RESULTS_DIR"]="/path/to/save/results"os.environ["MEDDLR_DATASETS_DIR"]="/path/to/datasets"# TODO: Add how to easily fetch dataset.📝 DocumentationDocumentation for downloading and using the SKM-TEA dataset can be found inDATASET.md. Benchmarks are constantly evolving - check this repository for up-to-date baselines.🐘 Model ZooA list of pre-trained models can be foundhereand inGoogle Drive.To use them, pass the google drive urls for the config and weights (model) files tost.build_deployment_model:importskm_teaasst# Make sure to add "download://" before the url!model=st.get_model_from_zoo(cfg_or_file="download://https://drive.google.com/file/d/1DTSfmaGu2X9CpE5qW52ux63QrIs9L0oa/view?usp=sharing",weights_path="download://https://drive.google.com/file/d/1no9-COhdT2Ai3yuxXpSYMpE76hbqZTWn/view?usp=sharing",)✉️ AboutThis repository is being developed at the Stanford's MIMI Lab. Please reach out toarjundd [at] stanford [dot] eduif you would like to use or contribute to SKM-TEA.If you use the SKM-TEA dataset or code, please use the following BibTex:@inproceedings{desai2021skm,
  title={SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation},
  author={Desai, Arjun D and Schmidt, Andrew M and Rubin, Elka B and Sandino, Christopher Michael and Black, Marianne Susan and Mazzoli, Valentina and Stevens, Kathryn J and Boutin, Robert and Re, Christopher and Gold, Garry E and others},
  booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
  year={2021}
}
Package: skn-django-jazzmin
Django jazzmin (Jazzy Admin)Drop-in theme for django admin, that utilises AdminLTE 3 & Bootstrap 4 to make yo' admin look jazzy.
This is a fork from (django-jazzmin)[https://github.com/farridav/django-jazzmin] by (farridav)[https://github.com/farridav] and all of the credit goes to him.
I just had some issues with the original project in production environment with whitenoise but considering the changes that hasn't been released in 2.6.0,
I had to build and release my own version of the project.Installationpip install django-jazzminDocumentationSeeDocumentationorTest AppDemoLive demohttps://django-jazzmin.herokuapp.com/adminUsername:test@test.comPassword: testNote: Data resets nightlyFeaturesDrop-in admin skin, all configuration optionalCustomisable side menuCustomisable top menuCustomisable user menu4 different Change form templates (horizontal tabs, vertical tabs, carousel, collapsible)Bootstrap 4 modal (instead of the old popup window, optional)Search bar for any given model adminCustomisable UI (via Live UI changes, or custom CSS/JS)ResponsiveSelect2 drop-downsBootstrap 4 & AdminLTE UI componentsUsing the latestadminlte+bootstrapScreenshotsDashboardList viewChange form templatesCollapsed side menuExpanded side menuHorizontal tabsVertical tabsCollapsibleCarouselRelated modalHistory pageLogin viewUI CustomiserMobile layoutTablet layoutAdmin Docs (if installed)ThanksThis was initially a Fork ofhttps://github.com/wuyue92tree/django-adminlte-uithat we refactored so much we thought it
deserved its own package, big thanks to @wuyue92tree for all of his initial hard work, we are still patching into that
project were possible, but this project has taken a different direction.The javascript modal implementation uses some code fromdjango-admin-interface, so thanks to @fabiocaccamo for original workBased on AdminLTE 3:https://adminlte.io/Using Bootstrap 4:https://getbootstrap.com/Using Font Awesome 5:https://fontawesome.com/
Package: skn-drf-cli
Django Rest Framework CLIA usefull cli tool that creates and manages a Django Rest Framework project.ContentsInstallationCommandsLicenseInstallationYou can install this package by using pip:pip install skn-drf-cliCommandsInitYou can initialize a new starter project using:drf-cli init your-project-nameThis will create a starter project and you can build on top of that.LicenseEverything inside this repository isMIT licensed.
Package: sknni
SkNNISkNNI (pronounced "skinny") stands for spherical k-nearest neighbors
interpolation and is a geospatial interpolator.SetupSkNNI may simply be installed from PyPI usingpip.pip install sknniExampleHere's a short usage example.importnumpyasnpfromsknniimportSkNNIif__name__=='__main__':observations=np.array([[30,120,20],[30,-120,10],[-30,-120,20],[-30,120,0]])interpolator=SkNNI(observations)interp_coords=np.array([[30,0],[0,-120],[0,0],[0,120],[-30,0]])interpolation=interpolator(interp_coords)print(interpolation)# Output:# [[  30.          0.          9.312546]#  [   0.       -120.         14.684806]#  [   0.          0.         12.5     ]#  [   0.        120.         10.315192]#  [ -30.          0.         16.464548]]
Package: sknrf-core
scikit-nrf-coreBuild FlowBuilding and Deploying Python code with native shared libraries
a continuously involving ordeal, hence I have provided the equivalent
CMake/Makefile command in comments to be relatable to C++. Internally
Cmake and Make are invoked. The build-flow currently uses setuptools,
which is deprecated in Python 3.12.Install DependenciesSee requirements.txtEnvironment VariablesVariableDefault ValueDescriptionLLVM_INSTALL_DIR${HOME}/libclanglibclang directorySKNRF_DIRimport site ; site.getsitepackages()[0]Python sknrf-core Module DirectoryCONDA_PREFIX/usr/localC++ SysrootVISA_LIB@pyVisa Library LocationRelease Buildpython3setup.pyclean# sudo make cleanpython3setup.pyconfig`# cmake ..python3setup.pybuild# makepython3setup.pyinstall# sudo make installpython3 setup.py installwill take a while to execute because it builds the python documentation. As an alternativepython3 setup.py developcreates the same outcome by symbolicly linking the src directorysknrfto a file in thesite-packagesfolder. NOTE: This is a PYTHON-SPECIFIC
symbolic link that works cross-platform for PYTHON ONLY. You still need toinstallall other files as follows!python3setup.pyclean# sudo make cleanpython3setup.pyconfig`# cmake ..python3setup.pybuild# makepython3setup.pyinstall_clib# sudo make installpython3setup.pyinstall_data# sudo make installpython3setup.pydevelop# All changes in the source directory are now installed instantlypython3setup.pyinstall_doc# sudo make installExamplepython3${SKNRF_DIR}/sknrf/main.pyTestscd${SKNRF_DIR}/sknrf;nosetests--config=nose.cfgRuntime Configuration${SKNRF_DIR}/sknrf/sknrf.ymlcontains runtime configuration settings. Beware of Environment Variables set in this file.
Package: sko
Swarm Intelligence in Python(Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,Artificial Fish Swarm Algorithm in Python)Don't use sko, usescikit-optinsteadDocumentation:https://scikit-opt.github.io/scikit-opt/#/en/文档：https://scikit-opt.github.io/scikit-opt/#/zh/Source code:https://github.com/guofei9987/scikit-opt
Package: skoleintra
SkoleIntra APIBenyt ItsLearnings skoleintra kun ved brug af PythonInstallationpip install skoleintraBrugEn fuld dokumentation er ikke udgivet endnu men her er basis, der er også kun ugeplanen som man kan finde lige nu.fromskoleintraimportSkoleintrabaseUrl="https://minskole.m.skoleintra.dk"skoleintraClient=Skoleintra(brugernavn="brugernavn",adgangskode="adganskode",url=baseUrl)ugeplan=skoleintraClient.getWeeklyplans(week=10,year=2022)print(ugeplan)#Output: Ugeplanen for den ugeTo DoFlere funktionerUnderstøt mere end eleverDokumentationKommer snart!
Package: skolemizer
skolemizerA library with utils for performing Skolemization on blank nodes (RDF)W3C has a short intro to skolemization here:https://www.w3.org/2011/rdf-wg/wiki/SkolemisationDevelopmentRequirementspyenv(recommended)pipx(recommended)poetrynoxnox-poetry% pipx install poetry==1.1.6
% pipx install nox==2020.12.31
% pipx inject nox nox-poetryInstall% git clone https://github.com/Informasjonsforvaltning/skolemizer.git
% cd skolemizer
% pyenv install 3.9.4
% pyenv local 3.9.4
% poetry installGetting startedRemember before starting to always set the baseurl for skolemization, according to your organizations url.
This can be done by setting the environment variable skolemizer_baseurl on your operation system.
It can also e.g be done run time through Python's os package:os.environ[Skolemizer.baseurl_key] = "https://www.someorganiztion.somedomain/"If not set Skolemizer will use "http://example.com/" as base url.
In order to invoke the skolemizer for ensuring identifier of rdf-nodes simply addfrom skolemizer import Skolemizerand then before rdf-serialization of a class:if not getattr(self, "identifier", None):
            self.identifier = Skolemizer.add_skolemization()There should also be a skolemization check performed when serializing object properties.
When creating unit tests of skolemized rdf nodes a uuid will be applied as a substring of the complete skolemization.
Therefore, in order to create stable unit tests one can use the testsutils in order to mock a stable skolemization.E.g pytest-mock's MockFixture permits mocking of the skolemizer:from pytest_mock import MockFixture
    from skolemizer import skolemization

    catalog = Catalog()

    mocker.patch(
        "skolemizer.Skolemizer.add_skolemization",
        return_value=skolemization
    )which outputs e.g:@prefix dct: <http://purl.org/dc/terms/> .
        @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
        @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
        @prefix dcat: <http://www.w3.org/ns/dcat#> .

        <http://wwww.digdir.no/.well-known/skolem/284db4d2-80c2-11eb-82c3-83e80baa2f94>
         a dcat:Catalog  .Run all sessions% noxRun all tests with coverage reporting% nox -rs testsDebuggingYou can enter intoPdbby passing--pdbto pytest:nox -rs tests -- --pdbYou can set breakpoints directly in code by using the functionbreakpoint().
Package: skolmaten-python
skolmaten-pythonThis is a simple wrapper for theskolmaten.seservice.I wrote this becuase I needed a way to get the data for what I am going to eat in school for some stuff. So, why not publish it and help somebody else.FOR NON-SWEDISH PEPOLE:skolmaten.sehas data for what is going to be servered in schools all over Sweden.Usagefromskolmatenimportskolmaten# You can find the id of your school by going to skolmaten.se, selecting your school, and looking in the address bar.# For example this is what I see for polhemsskolan => https://skolmaten.se/polhemsskolan2/schoolFood=skolmaten("polhemsskolan2")weekly=schoolFood.getData()print(weekly)[{'date':datetime.datetime(2019,10,14,0,0),'food':[u'Falukorv med potatismos',u'Potatisfrestelse med salladsost',u'ängsbiffar']},...{'date':datetime.datetime(2019,10,21,0,0),'food':[u'Pastasås Arrabiata med linser och soltorkade tomater',u'Grönsakspaj']}]TodoBasic weekly data.100% Test coverage.Full documation.
Package: skolo
The API for SkoloCFDskolo is the SkoloCFD Software Development Kit (SDK) for PythonGetting StartedYou can install the library usingpip$ python -m pip install skoloOr, assuming that you have Python andvirtualenvinstalled, set up your environment and install the required dependencies like this:$ git clone https://github.com/skoloCFD/skolo
$ cd skolo
$ virtualenv venv
...
$ . venv/bin/activate
$ python -m pip install -r requirements.txt
$ python -m pip install -e .Configuring SkoloAfter installing skolo, you'll need to provide it credential access to the Skolo web server. Instructions for this can be found on the documentation page of the website.
Package: skoobpy
📘🐍 SkoobpyGives you methods to extract the users' data fromSkoob.⚙️ InstallationYou can install Skoobpy fromPyPI:pipinstallskoobpy💻 How to UseYou can run it as a module in a command line asskoobpyfollowed by anuser_id. It is going to generate a csv file namedbooks_user_id.csvwith all informations of user's desired books.$ python skoobpy <user_id>📑 Release History0.1.0First version - extract users' desired books in CSV file⚖️ LicenseDistributed under the MIT license. SeeLICENSEfor more information.🚧🚀 ContributingFeel free to contribute with anything.In case of any doubt, see this.To start with development, don't forget to install:$pipinstall-rrequirements.txt
Package: skoots
SKOOTSSKeletOn ObjecT Segmentation - 2D/3D instance segmentation of arbitrary biomedical images.To install, make sure pyTorch 2.1 is installed with CUDA support.pipinstallskootsDocumentation: skoots.readthedocs.ioBioRxiv:https://www.biorxiv.org/content/10.1101/2023.05.05.539611v1