Saravanan666/pypi-org huggingface dataset:
Package: skdag
skdag - A more flexible alternative to scikit-learn Pipelinesscikit-dag (skdag) is an open-sourced, MIT-licenced library that provides advanced
workflow management to any machine learning operations that followscikit-learnconventions. Installation is simple:pipinstallskdagIt works by introducing Directed Acyclic Graphs as a drop-in replacement for traditional
scikit-learnPipeline. This gives you a simple interface for a range of use cases
including complex pre-processing, model stacking and benchmarking.fromskdagimportDAGBuilderdag=(DAGBuilder(infer_dataframe=True).add_step("impute",SimpleImputer()).add_step("vitals","passthrough",deps={"impute":["age","sex","bmi","bp"]}).add_step("blood",PCA(n_components=2,random_state=0),deps={"impute":["s1","s2","s3","s4","s5","s6"]}).add_step("rf",RandomForestRegressor(max_depth=5,random_state=0),deps=["blood","vitals"]).add_step("svm",SVR(C=0.7),deps=["blood","vitals"]).add_step("knn",KNeighborsRegressor(n_neighbors=5),deps=["blood","vitals"]).add_step("meta",LinearRegression(),deps=["rf","svm","knn"]).make_dag())dag.show(detailed=True)The above DAG imputes missing values, runs PCA on the columns relating to blood test
results and leaves the other columns as they are. Then they get passed to three
different regressors before being passed onto a final meta-estimator. Because DAGs
(unlike pipelines) allow predictors in the middle or a workflow, you can use them to
implement model stacking. We also chose to run the DAG steps in parallel wherever
possible.After building our DAG, we can treat it as any other estimator:fromsklearnimportdatasetsX,y=datasets.load_diabetes(return_X_y=True,as_frame=True)X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0)dag.fit(X_train,y_train)dag.predict(X_test)Just like a pipeline, you can optimise it with a gridsearch, pickle it etc.Note that this package does not deal with things like delayed dependencies and
distributed architectures - consider anestablishedsolutionfor such use cases.skdagis just for building and
executing local ensembles from estimators.Read onto learn more aboutskdag…
Package: skdd
A sample project that exists as an aid to thePython Packaging User Guide’sTutorial on Packaging and Distributing
Projects.This projects does not aim to cover best practices for Python project
development as a whole. For example, it does not provide guidance or tool
recommendations for version control, documentation, or testing.This is the README file for the project.The file should use UTF-8 encoding and be written usingreStructuredText. It
will be used to generate the project webpage on PyPI and will be displayed as
the project homepage on common code-hosting services, and should be written for
that purpose.Typical contents for this file would include an overview of the project, basic
usage examples, etc. Generally, including the project changelog in here is not
a good idea, although a simple “What’s New” section for the most recent version
may be appropriate.
Package: sk-dm
sk-dm📦 项目介绍 (for humans)这个第三方仓库是由深圳市名通科技股份有限公司AI团队提供的。团队致力于为Data Mining领域，提供一个稳定可靠， 功能完善的Data Mining常见操作。Installationcdyour_project
pipinstallsk-dmUsage本仓库主要包含了五个模块，分别为数据准备（data_prepare）、数据探索（data_explore）、特征工程（feature_engineering）、模型构建（data_model）和评估模型（model_evaluation）。接下来介绍一下每个模块的功能。[TOC]一.数据准备模块（data_prepare）sk_dm.data_prepare.file_reader模块sk_dm.data_prepare.db_helper模块两个模块主要是读取文件，分别读取不同格式的数据，其中包括读取txt、csv、excel、HDF5和json格式，数据挖掘一般会读取txt和csv格式居多。这也是数据挖掘的第一步。二.数据探索（data_explore）sk_dm.data_explore.data_process模块该模块是对数据进行处理的，比如常规的对数据进行归一化、标准化、二值化处理，使得数据离散度较大的情况下将数据聚拢到（0，1），同时去掉数据的量纲。同时当数据标签不均匀的时候，还提供了将数据标签降采样和欠采样的操作。本模块还提供了一些数据是否符合正态分布的画图操作，更加直观的观测数据分布情况。还有提供热力图方便观测特征与标签的关系。以下是数据探索的一些常规操作：importpandasaspdimportnumpyasnpcolname=''# 列名col1=''# 列名col2=''# 列名filepath=''# 文件路径x=''# 包含的字段y=''# 包含的字段df=pd.read_csv(filepath,encoding='utf-8')# 查看数据行数、列数df.shape[0]df.shape[1]# 查看数据前几行，后几行，默认为5行df.head()df.tail()# 查看数据汇总统计df.describe()# 查看数据概况df.info()# 查看数据列名df.columns# 查看数据类型df.dtypes# 各列平均值df.mean()# 按列名查看某列（两种方式）df[colname]df.colname# 查列名看多列df[[col1,col2]]# 按索引查看某行,第一行：df.iloc[0]# 按索引查看某行某列，三行四列那个元素df.iloc[2,3]# 选择某列大于1的行df[df[colname]>1]# 选择某列包含x或者y字段的行,pandas有很多字符串处理函数df[df[colname].str.contains(x|y)]# 替换字符，将'k'换成'000'df[df[colname].replace('k','000')]# 转换数据类型,转换成float型df.num=df.num.astype(float)# 查看某列的唯一值的个数df.colname.value_counts()# 按照某列排序（默认升序）df.sort_values(by=colname,ascending=True)# 应用函数，简单的可以用lambdadf.apply(lambdax:x.max()-x.min())# 也可以用numpy自带的，例如cumsum累加df.apply(np.cumsum)三.特征工程（feature_engineering）sk_dm.feature_engineering.feature_process模块此模块为特征工程的一些操作：
1.第一个模块是分箱的操作，根据一些设定将数字或特征分在一起。
2.第二个模块是特征降维操作，是对多特征数据进行操作，去掉无用或用处小的特征，提高模型准确率。
3.第三个模块是特征过滤，仍然是去除特征与标签相关性小的特征，提高模型准确率。四.模型构建（data_model）sk_dm.data_model模块该模块里是机器学习的一些模型，包括catboost、lightgbm、xgboost、decisiontree、DGBT、randomforest等，可以调用直接训练，并输出评估值。五.评估模型（model_evaluation）sk_dm.model_evaluation.data_split模块该模块是对数据进行切分评估模型，主要有交叉验证、留出法和自助法划分。More Resources[where is sklearn]https://scikit-learn.org/stable/[where is auto-sklearn]https://github.com/automl/auto-sklearnOfficial Python Packaging User GuideLicenseThis is free and unencumbered software released into the public domain.
Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: skduplo
scikit-duploVery simple reusable blocks for scikit-learn pipelines  (inspired by scikit-lego)InstallationInstallation from the source tree:python setup.py installOr via pip from PyPI:pip install scikit-duploContentsThe sci-kit duplo package contains multiple classes that you can use in a sci-kit
learn compatible pipeline. There are ensemble learning classes within themetasubdirectory.
These classes expect you to pass in multiple other Sci-kit learn compatible
machine learning classes. It will use these to build an ensemble of models to
predict the target variable.There are feature engineering classes inside thepreprocessingsubdirectory. These are
ColumnTransformer compatible classes that expect to receive a dataframe and set of column
names that it will transform for the downstream pipeline processes.
Package: ske19-api
SKE19 API for Python 3PrerequisitesPython 3.7 or laterpipInstallationOn Unix-like systems:python3 -m pip install ske19-apiOn Windows:py -m pip install ske19-apior if you've downloaded from Microsoft store:python3 -m pip install ske19-apiUsageIn your Python script, you can create a new client to login to SKE19 API by, and replacing the secret key with the server's key to access the API.session=SKE19(secret="secret-key",duration=3600)You can then access the API routes with two methods, which are#get_all_students()and#get_student(id: str)which will returnlist[Student]andStudentrespectively.session.get_all_students()# returns: [Student]session.get_student(id)# returns: Student(id, name, ...)TheStudentobjectAStudenthas properties that can be easily accessed but are protected it is recommended to use getter methods instead.Available methods:#get_id(),#get_name_english(),#get_name_thai(),#get_email()and#get_instagram().All methods were already documented, it is recommended to use IDE with code-completion or prediction to work with.
Package: sked
ConfiguresAWS Scheduled Scalingfor Amazon EC2 Auto Scaling based on Tags.You can Tag your EC2 instances and define in a config file the scaling profiles. The rest is magic…Installation$ pip install skedUsagesked create [-c=<config_file>]
sked delete [-c=<config_file>]
sked -h | --help
sked --versionExamplessked create -c my_schedule.yml
sked delete -c my_schedule.ymlIf you omit the -c parameter, the cli by default tries to use a file called sked.yml.Configuration file sample# sked.yml
profiles:
  small:
    desired_capacity: 5
    min_size: 1
    max_size: 10
    recurrence: 0 7 * * * # every day at 07:00
  overnight:
    desired_capacity: 0
    min_size: 0
    recurrence: 15 19 * * MON-FRI # 19:15 from Monday to Friday

auto_scaling_groups:
  - filter:
      tag:
        key: Name
        value: SomeTag
    schedule:
      - profile: small
        times:
          - start_time: 2018-03-01T00:00:00Z
          - start_time: 2018-04-01T00:00:00Z
  - filter:
      tags:
        - tag: {key: Name, value: MyInstanceA}
        - tag: {key: Name, value: 'My Instance B'}
    schedule:
      - profile: small
        times:
          - start_time: 2018-03-01T00:00:00Z
          - start_time: 2018-04-01T00:00:00ZAdditional NoteSked doesn’t store the state of the applied scheduled action. Everything is based upon the Yaml configuration file.If you want to use the delete command make sure you didn’t change the Yaml configuration file in the meantime.For instance, the following workflowwill produce an error:runsked createchange thesked.ymlfilerunsked delete
Package: skeddly-sdk
Skeddly SDK for PythonThe official Skeddly SDK for Python.RequirementsPython 3.6 or laterInstallationInstall the SDK usingpip.pip install skeddly-sdkUsageStep 1: In your.pyfile,importthe Skeddly namespace:importskeddlyStep 2: Create a configuration file in~/.skeddlyrc:[default]
accessKey=sk_12345678901234567890123456789012Thedefaultsection header is required.Step 3: Create your client object:client=skeddly.Client()Step 4: Call the Skeddly API:actions=client.list_actions(filter={"actionTypes":"amazon-start-ec2-instances"})Per-Client ConfigurationIf you want to set the Skeddly access key via code rather than in the~/skeddlyrcfile, you can do so by passing the access key into the client constructor:client=skeddly.Client(accessKey="sk_12345678901234567890123456789012")Data FormatsResponsesUnless otherwise mentioned, responses aredict.actions=client.list_actions()foractioninactions:print("Action ID: "+action["actionId"])print("Action Name: "+action["name"])Additional ResourcesSkeddly API DocumentationSkeddly Help CentreGetting HelpSDK BugsIf you think you found a bug in the code of this SDK, feel free tocreate an issue.Missing FunctionalityIf a method if missing from this library that is in the Skeddly API Documentation, feel free tocreate an issue.Pull RequestsWe can accept pull requests. Our review requirements are strict though :)All Other HelpPleasecontact us.
Package: skeetpy
skeetpyImplementation of the AT protocol in PythonAuthenticationatp = ATP(pds, identifier, password)
atp.authenticate()MethodsWe generated code for the entire at/proto protocol, see skeetpy.py. That said, for now only the following methods are known to work:atp.describe_server()      # show details about the current server
atp.list_app_passwords()   # list your app passwordsTest the library against bsky.socialexport PDS=bsky.social
export IDENTIFIER=yves.pt
export PASSWORD=<app password>

python3 skeetpy.py
Package: skelate
skelateA utility to create directory from skeletons, with templating support.This is a tool inspired by the functionality provided byansible-galaxy init.Requirementspython 3.8+InstallingInstall and update usingpip:pip install -U skelateAlternatively, you can package and runskelatein a docker container:make imageUsageBasic usage is to treat skelate likecp -r, with expansion ofj2files.skelate this/dir that/dirIfthis/dircontains no files with aj2extension, this will actexactlylikecp -r.Ifthis/dirdoes contain files with aj2extension, these will be expanded
intothat/dir, minus their.j2suffix.You can pass variables to templates in two ways:# Read variables from a JSON file
skelate --vars path/to/vars.json this/dir that/dir

# Read variables from command line
skelate --extra-vars "key=value" \
  --extra-vars 'key={"key": ["value", "value"]}' \
  this/dir that/dirFor more options:skelate --helpAuthorsiwaseatenbyagrue
Package: skele
A skeleton command line program in Python.PurposeThis is a skeleton application which demonstrates how to properly structure a
Python CLI application.I’ve done my best to structure this in a way that makes sense formostusers,
but if you have any feedback, please open a Github issue and I’ll take a look.The idea with this project is that you should be able to use this as a template
for building new CLI apps.You can fork this project and customize it to your liking, or just use it as a
reference.UsageIf you’ve cloned this project, and want to install the library (and all
development dependencies), the command you’ll want to run is:$ pip install -e .[test]If you’d like to run all tests for this project (assuming you’ve written
some), you would run the following command:$ python setup.py testThis will triggerpy.test, along with its popularcoverageplugin.Lastly, if you’d like to cut a new release of this CLI tool, and publish it to
the Python Package Index (PyPI), you can do so
by running:$ python setup.py sdist bdist_wheel
$ twine upload dist/*This will build both a source tarball of your CLI tool, as well as a newer wheel
build (and this will, by default, run on all platforms).Thetwine uploadcommand (which requires you to install thetwinetool) will then securely upload your
new package to PyPI so everyone in the world can use it!
Package: skele-test
A skeleton command line program in Python.PurposeThis is a skeleton application which demonstrates how to properly structure a
Python CLI application.I’ve done my best to structure this in a way that makes sense formostusers,
but if you have any feedback, please open a Github issue and I’ll take a look.The idea with this project is that you should be able to use this as a template
for building new CLI apps.You can fork this project and customize it to your liking, or just use it as a
reference.UsageIf you’ve cloned this project, and want to install the library (and all
development dependencies), the command you’ll want to run is:$ pip install -e .[test]If you’d like to run all tests for this project (assuming you’ve written
some), you would run the following command:$ python setup.py testThis will triggerpy.test, along with its popularcoverageplugin.Lastly, if you’d like to cut a new release of this CLI tool, and publish it to
the Python Package Index (PyPI), you can do so
by running:$ python setup.py sdist bdist_wheel
$ twine upload dist/*This will build both a source tarball of your CLI tool, as well as a newer wheel
build (and this will, by default, run on all platforms).Thetwine uploadcommand (which requires you to install thetwinetool) will then securely upload your
new package to PyPI so everyone in the world can use it!
Package: skeletonize
skeletonizeHandles skeletonization and deskeletonizaiton of programs. This allows for easily distributing assignments to students
that contain skeleton code, and determining which parts of the skeleton they filled in after the fact, extracting blanks.skeletonizationSkeletonization works by taking in a piece of code with "skeleton markers" as so:deffactorial(x):if<<<x==0>>>:return<<<1>>>else:return<<<x>>>*<<<factorial(x-1)>>>and converting it into either a skeletondeffactorial(x):if______:return______else:return______*______or a solutiondeffactorial(x):ifx==0:return1else:returnx*factorial(x-1)reskeletonizationReskeletonization works by taking a skeleton:deffactorial(x):if<<<x==0>>>:return<<<1>>>else:return<<<x>>>*<<<factorial(x-1)>>>and a student solution:deffactorial(x):ifnotx:return1else:returnx*factorial(x)and produces a reskeletonized programdeffactorial(x):if<<<notx>>>:return<<<1>>>else:return<<<x>>>*<<<factorial(x)>>>
Package: skeleton-says
A talking skeleton, just what everyone needs. It exists to illustrate the
principles ofdls-python3-skeletonin story form. Look through the git
commit history to see the evolution of the ideas.PyPIpip installskeleton-saysSource codehttps://github.com/thomascobb/skeleton-saysDocumentationhttps://thomascobb.github.io/skeleton-saysReleaseshttps://github.com/thomascobb/skeleton-says/releasesPrimarily useful from the commandline:$ skeleton-says Hello

 -------
( Hello )
 -------
  \
   \  .-.
    \(o.o)
      |=|
     __|__
   //.=|=.\\
  // .=|=. \\
  \\ .=|=. //
   \\(_=_)//
    (:| |:)
     || ||
     () ()
     || ||
     || ||
l42 ==' '==Although it can also be embedded into an application.fromskeleton_says.sayimportsayart=say("Hello")Seehttps://thomascobb.github.io/skeleton-saysfor more detailed documentation.
Package: skeletor
SkeletorUnlike itsnamesake, this Python 3
library does not (yet) seek to conquer Eternia but to turn meshes into skeletons.Heads-up: skeletor1.0.0introduced some breaking changes and major reorganizations.Please see thechangelogfor details.Installpip3installskeletorFor the dev version:pip3installgit+https://github.com/navis-org/skeletor@masterDependenciesAutomatically installed withpip:networkxnumpypandasscipyscikit-learntrimeshtqdmpython-igraphncollpydeOptional because not strictly required for the core functions but highly recommended:pygletis required by trimesh to preview meshes/skeletons in 3D:pip3 install pygletfastremapfor sizeable speed-ups with some methods:pip3 install fastremapDocumentationPlease see thedocumentationfor details.The change log can be foundhere.QuickstartFor the impatient a quick example:>>>importskeletorassk>>>mesh=sk.example_mesh()>>># To load and use your own mesh instead of the example mesh:>>># import trimesh as tm>>># mesh = tm.Trimesh(vertices, faces)  # or...>>># mesh = tm.load_mesh('mesh.obj')>>>fixed=sk.pre.fix_mesh(mesh,remove_disconnected=5,inplace=False)>>>skel=sk.skeletonize.by_wavefront(fixed,waves=1,step_size=1)>>>skel<Skeleton(vertices=(1258,3),edges=(1194,2),method=wavefront)>All skeletonization methods return aSkeletonobject. These are just
convenient objects to represent and inspect the results.>>># location of vertices (nodes)>>>skel.verticesarray([[16744,36720,26407],...,[22076,23217,24472]])>>># child -> parent edges>>>skel.edgesarray([[64,31],...,[1257,1252]])>>># Mapping for mesh to skeleton vertex indices>>>skel.mesh_maparray([157,158,1062,...,525,474,547])>>># SWC table>>>skel.swc.head()node_idparent_idxyzradius00-116744.00585936720.05859426407.9023440.00000011-15602.75195322266.75651015799.9912117.54258722-116442.66666714999.97851610887.9160165.333333>>># Save SWC file>>>skel.save_swc('skeleton.swc')If you installedpyglet(see above) you can also usetrimesh's plotting
capabilities to inspect the results:>>>skel.show(mesh=True)BenchmarksBenchmarkswere run on a 2018 MacBook Pro (2.2 GHz Core i7, 32Gb memory) with optionalfastremapdependency installed. Note some of these functions (e.g.
contraction and TEASAR/vertex cluster skeletonization) can vary a lot in
speed based on parameterization.ContributingPull requests are always welcome!References & AcknowledgmentsMesh contraction and the edge collapse approach are based on this paper:[1] Au OK, Tai CL, Chu HK, Cohen-Or D, Lee TY. Skeleton extraction by mesh contraction. ACM Transactions on Graphics (TOG). 2008 Aug 1;27(3):44.The abstract and the paper can be foundhere.
Also seethisYouTube video.Some of the code in skeletor was modified from thePy_BL_MeshSkeletonizationaddon for Blender 3D created by #0K Srinivasan Ramachandran and published under GPL3.The mesh TEASAR approach was adapted from the implementation inmeshpartyby Sven Dorkenwald, Casey
Schneider-Mizell and Forrest Collman.
Package: skeletorch
Skeleton Transformer Module in PytorchPytorch implementation of "skeleton transformer module", which is mentioned inSkeleton-based Action Recognition with Convolutional Neural Networks.InstallYou can install this module fromPyPI.pipinstallskeletorchHow to useModuleskeletorch.SkeletonTransformerParameters:All parameters are required.timesteps:Timesteps of input time-series data (equal to number of frames, mentioned as 'T' in the paper)kpts_dim:Dimentions of keypoints (usually 2 (x, y) or 3 (x, y, z))input_kpts_num:Number of joints in original keypoints (mentioned as 'N' in the paper)output_dim:Dimentions of output (mentioned as 'M' in the paper)Input:x:2-dimentional tensor of shape (timesteps, input_kpts_num*kpts_dim)ExampleimporttorchfromskeletorchimportSkeletonTransformer# parameterstimesteps=20kpts_dim=3input_kpts_num=17output_dim=10# input (the size is (20, 51) in this example)x=torch.Tensor(torch.randn(timesteps,kpts_dim*input_kpts_num))# make layerlayer=SkeletonTransformer(timesteps,kpts_dim,input_kpts_num,output_dim)layer(x)Of course you can use this module in your Pytorch network.Keras implementationWe also have Keras implementation of this module:https://github.com/Yutsuro/skeleton-transformer-Keras
Package: skelgen
skelA quick skeleton generator for projects.Fun fact: this project was generated using skel'spython-clitemplate.InstallationFrom PipRunpip3 install skelgen.From SourceClone this repo.Runpython3 setup.py installto install globally.
Runpython3 setup.py install --userto install for one user.UsageRunskel --helpfor help.Example usage:
Runskel python-cli --path testto generate a Python CLI application
in thetestdirectory.FeaturesAvailable templates:python, a basic Python package structurepython-cli, likepython, but includes logic for installing as a command line applicationcmake, a basic C++ CMake project structurezephyr, a basic Zephyr RTOS C application templateflask-api, a Flask CRUD API template with authentication based on SQLAlchemyAvailable "shortcut" templates (which just call a separate scaffolder):reactpreactvuenuxtPlanned templates:flask, a basic Flask template with GUIpyside-gui, a deployable PySide6 application with PyInstallerpyuavcan, a basic PyUAVCAN based applicationcaspar-graphics, a basic CasparCG HTML templates projectContributingGot an idea for a template? Put in a merge request!
It's very easy to create a custom project template.
All file/folder names and contents are templated using
the Jinja2 template engine. All you need to do is write the templates,
add a ___template.py for configuration (see the Python template for
examples), and rebuild!We also appreciate feature requests and bug reports.