Saravanan666/pypi-org huggingface dataset:
Package: pykkachu
PykkachuA convenience library on top of Pykka and PySM to help with common Actor Model patterns.GithubRoadmapv0.1Expand to work with all Actor types instead of just ThreadingActorsBetter encapsulate Pykka and PySM so they are not imported by usersMake Fleet abstract and take a subactor selection algorithm as paramUnit testsBeyondCreate a config loader for wiring up simple ActorsAdd simple patterns like "Retry" and "When"Changelogv0.0.1Made it exist by pulling it fromgabFactored it into classesPulled a Message class in fromgabDeploymentrm -rf dist
pipenv shell
python3 setup.py sdist bdist_wheel
python3 -m twine upload dist/*
exit
Package: pykka-injector
pykka-injector is a module unitingInjector(Dependency Injection framework) andPykka(Python implementation of the actor model). It’s only purpose is to allow you to inject dependencies into Pykka Actors.Works with:CPython 2.x >= 2.6, 3.x >= 3.2PyPy >= 1.9Platform independent.Usage examplefrom injector import inject, Injector, InstanceProvider, Key, singleton
from pykka import ThreadingActor
from pykka_injector import ActorStarter

Config = Key('Config')

class MyActor(ThreadingActor):
    @inject(config=Config)
    def __init__(self, config, user):
        self.config = config
        self.user = user

def configure(binder):
    binder.bind(
        Config,
        to=InstanceProvider(dict(environment='dev')),
        scope=singleton,
    )

if __name__ == '__main__':
    injector = Injector(configure)
    starter = injector.get(ActorStarter)
    actor_ref = starter.start(MyActor, kwargs=dict(user='root'))

    actor_proxy = actor_ref.proxy()
    print(actor_proxy.config.get(), actor_proxy.user.get())

    actor_ref.stop()CopyrightCopyright (C) 2013 Jakub StasiakThis source code is licensed under MIT license, see LICENSE file for details.
Package: pykl
PYKL is the kltool for python, toolset for web, http, cache, dht, xml, json and so on.Install it typing in your console:pipinstallpyklUsageYou can either dopykl--testto start a test server for testing
queries orpyklQUERY_FILEThis command will write in the standard output (or other output if
specified via--output) the resulting JSON.YourQUERY_FILEcould look similar to this:{
  page(url:"http://news.ycombinator.com") {
    items: query(selector:"tr.athing") {
      rank: text(selector:"td span.rank")
      title: text(selector:"td.title a")
      sitebit: text(selector:"span.comhead a")
      url: attr(selector:"td.title a", name:"href")
      attrs: next {
         score: text(selector:"span.score")
         user: text(selector:"a:eq(0)")
         comments: text(selector:"a:eq(2)")
      }
    }
  }
}Advanced usageIf you want to generalize your pykl query to any page, just rewrite your
query file adding the$pagevar. So should look to something like
this:query ($page: String) {
  page(url:$page) {
    # ...
  }
}And then, query it like:pyklQUERY_FILEhttp://news.ycombinator.com
Package: pykleio
Versioned Storage Python SDK|JAVA SDK IS HEREProposed solution:To enable block-based data versioning for nd data, a mix is created of:Version block index usingZarr+GitA key value store: usingN5for nowHow to:index_store = ZarrIndexStore(INDEX_PATH)
store = VersionedFSStore(index_store, RAW_PATH)

z = zarr.open(store, mode="a")

# Commit
store.vc.commit()

# Push
store.vc.push()

# Create new branch
store.vc.create_new_branch(BRANCH_NAME)

# Checkout new branch
store.vc.create_new_branch(BRANCH_NAME)Features:Multiple branchesMultiple collaboratorsCan jump anytime to any historical pointData is not replicated and no extra reading writing costEvery change is a now commit:
Package: pyklip
pyKLIPA python library for PSF subtraction for both exoplanet and disk imaging. It uses a parallelized and optimzied implmentation ofKLIPthat supports ADI, SDI, and RDI with a variety of tunable parameters. For characterization, forward modelling tools include a suite of tools built offKLIP-FMfor astrometry, spectroscopy, planet detection, and disk modelling. pyKLIP is modular and supports data from the Gemini Planet Imager, P1640, Keck/NIRC2, MagAO/VisAO, and SPHERE, with more on the way.Want to get started? Check out thequick GPI KLIP tutorialfor the basics of pyKLIP.Development led by Jason Wang. Contributions made by Jonathan Aguilar, JB Ruffio, Rob de Rosa, Schuyler Wolff, Abhijith Rajan, Zack Briesemeister, Kate Follette, Maxwell Millar-Blanchaer, Alexandra Greenbaum, Simon Ko, Tom Esposito, Elijah Spiro, Pauline Arriaga, Bin Ren, Alan Rainot, Arthur Vigan, Graça Rocha, Jacob Golomb, Johan Mazoyer, Jea Adams, Aarynn Carter, Minghan Chen, Robert Thompson, Jens Kammerer, and Laurent Pueyo (see contributors.txt for a details).If you use pyKLIP in your research, please cite the Astrophysical Source Code Library record of it:ASCLorADS.Wang, J. J., Ruffio, J.-B., De Rosa, R. J., et al. 2015, Astrophysics Source Code Library, ascl:1506.001For setup instructions, example code, and API details,read the documentationonline!
Package: pykma-grid
pykma_gridpykma_grid는 위도와 경도를 Lambert Conformal Conic Projection을 사용하여 격자 좌표로 변환하는 파이썬 패키지입니다. 이 패키지는기상청 API에서 격자X, Y를 사용할 때 유용합니다.기상청 API 문서 내에 C언어로 된 격자 변환 코드를 자바스크립트로 변환한 코드입니다.설치 방법패키지는 pip을 사용하여 쉽게 설치할 수 있습니다:pipinstallpykma_grid기본 사용 방법pykma_grid패키지를 사용하기 위해서는, 먼저Coordinate클래스를 임포트하고 위도 및 경도 값을 인자로 전달하여 인스턴스를 생성합니다. 그 후,gridX와gridY속성을 사용하여 격자 좌표를 얻을 수 있습니다.frompykma_gridimportCoordinatecoord=Coordinate(37.565,126.9780)print("Grid X:",coord.gridX)print("Grid Y:",coord.gridY)API 문서Coordinate(latitude, longitude)latitude(float): 위도 (degrees)longitude(float): 경도 (degrees)속성gridX: 격자 X 좌표를 반환합니다 (float).gridY: 격자 Y 좌표를 반환합니다 (float).라이선스이 프로젝트는 MIT 라이선스를 따릅니다. 라이선스에 대한 자세한 정보는 LICENSE 파일을 참고해주세요.기여기여를 원하시면 프로젝트의 GitHub 저장소에 pull request를 보내주시거나 이슈를 등록해주세요.문의질문이나 제안 사항이 있으시면 이슈로 연락 주시기 바랍니다.
Package: pykmaze
AbstractGeonaute Keymaze 500/700 GPS watch devices are shipped with a Windows-only
application software.Pykmaze alleviates this issue and allows to transfer data from/to the device
with any host.FeaturesPykmaze is a pure Python command-line application that does not offer a
graphical interface as the original application software does, but nevertheless
allows to download recorded tracks and upload waypoints.Its produces and reads Google Earth .KML file, so that tracks and waypoints may
be observed and edited with the Google Earth application.Important noticeThe Keymaze 500/700 protocol has been reverse-engineered from scratch. Pykmaze
code is not based on published information and may fail to comply with the
communication protocol the Keymaze device expects.Use it at your own risk.Supported devicesAlthough it seems that Keymaze 500 and 700 share the same communication
protocol, no test has been run with Keymaze 700 GPS devices yet. It seems that
the Geonaute device is a rebranded GlobalSat GH-615B device, so pykmaze might
also be able to communicate with these devices.Pykmaze is not compatible with earlier devices such as Keymaze 300 that may
use a different communication protocol. Check out thegpsd4project
if you’re looking for Keymaze 300 device support. Both projects are fully
unrelated.For now, pykmaze has been successfully run on an Intel Mac OS X Snow Leopard
64-bit host. It should run fine on any Linux host.
Package: pyknackhq
pyknackhqallows developer to manipulate knackhq backend database with minimum works in Python. So you can easily integrate the convenience of the knackhq with powerful database, web application, etc…Knackhqis an awesome developer tools. It allows developer to build a user friendly, database backed web application in few clicks. So the non-developer can enjoy the power and convenience of non-sql database with no Pain. However, the developer may still want to program it, that’s how is API for.Quick Link:Githubpyknackhq PyPI HomepageOnline DocumentationQuick StartProgramming referenceInstallpyknackhqis released on PyPI, so all you need is:$pipinstallpyknackhqTo upgrade to latest version:$pipinstall--upgradepyknackhq
Package: pykn-mysqltools
pykn_mysqltoolsHandy utility tools for interacting with a MySQL database with Python. This
project was created with a need to interact with MySQL utilizing lots of CSVs,
but the complexity ofpandaswas a bit overkill. Perhaps as my needs grow,
I'll see the light and move over topandas.Typical UsageYou'll primarily want to interact with the DBConnection class. Example:from pykn_mysqltools import utilities as pyknmt

my_conn = pyknmt.DBConnection(
    db_hostname="hostname",
    db_username="username",
    db_password="password",
    db_schema="schema",
    db_port="port"
)From here you can interact with your database throughmy_conn, and access
helpful tools like theimport_csvmethod:my_conn.import_csv("./path/to/your.csv", "tablename")If your use case calls for inserting data directly, theinsertmethod is your
friend.
Package: pyknock
pyknockUDP port knocking suite with HMAC-PSK authentication. Once it receives valid packet signed with valid pre-shared key, it runs command to open or close access. Commands should be specified by user via server command line. Server allows use of substitution placeholders in configured commands:$ip- IP address mentioned in signed packet$af- IP address type:inetorinet6$cmd- requested action:openorcloseSee Usage for examples.:heart: :heart: :heart:You can say thanks to the author by donations to these wallets:ETH:0xB71250010e8beC90C5f9ddF408251eBA9dD7320eBTC:Legacy:1N89PRvG1CSsUk9sxKwBwudN6TjTPQ1N8aSegwit:bc1qc0hcyxc000qf0ketv4r44ld7dlgmmu73rtlntwMain IdeaIn this application UDP datagrams are choosen for a reason. Typical configuration of firewalled machine allows only packets to some public ports and drops packets to all other ports. With UDP external observer can't distinguish between accepted packet and packet dropped by firewall. Therefore, if firewall configuration drops all UDP packets except packets to pyknock port, external observer can't even detect there is something awaiting for magic packet. So, it may be used to hide machine completely from network for unauthenticated peers. Also, it may be used as classical port-knocking solution, adding another protection layer to sensitive network application.FeaturesUses cryptographically authenticated messages.Resistant to replay attacks.Post-quantum ready cryptography (HMAC-PSK with SHA-256).Works completely in user-space. May run even as unprivileged user.RequirementsOnly Python 2.6+ required. Python 3 is also supported.InstallationPlace file anywhere you want and run. Or usepip install pyknockto install it into your system as python package. Scripts shall become available at standard binary paths.UsageServer example:pyknock-serverMySecretPSK'ipset add -exist myallowedset $ip timeout 3600''ipset del -exist myallowedset $ip'Client example:pyknock-clientopenmy-protected-host.comMySecretPSKClient behind NAT example:pyknock-client-S$(curl-shttps://canihazip.com/s)openmy-protected-host.comMySecretPSKSee help for more options.
Package: pyknos
DescriptionPython package for conditional density estimation. It either wraps or
implements diverse conditional density estimators.Density estimation with normalizing flowsThis package provides pass-through access to all the
functionalities ofnflows.SetupClone the repo and install all the dependencies using theenvironment.ymlfile to create a conda environment:conda env create -f environment.yml. If you already have apyknosenvironment
and want to refresh dependencies, just runconda env update -f environment.yml --prune.Alternatively, you can install viasetup.pyusingpip install -e ".[dev]"(the dev flag installs development and testing
dependencies).ExamplesExamples are collected in notebooks inexamples/.Binary files and Jupyter notebooksUsingWe use git lfs to store large binary files. Those files are not
downloaded by cloning the repository, but you have to pull them
separately. To do so follow installation instructions herehttps://git-lfs.github.com/. In
particular, in a freshly cloned repository on a new machine, you will
need to run bothgit-lfs installandgit-lfs pull.ContributingWe use a filename filter to identify large binary files. Once you
installed and pulled git lfs you can add a file to git lfs by
appending_gitlfsto the basename, e.g.,oldbase_gitlfs.npy. Then
add the file to the index, commit, and it will be tracked by git lfs.Additionally, to avoid large diffs due to Jupyter notebook outputs we
are usingnbstripoutto remove output from notebooks before every
commit. Thenbstripoutpackage is downloaded automatically during
installation ofpyknos. However,please make sure to set up the
filter yourself, e.g., throughnbstriout --installor with
different options as describedhere.Namepyknós (πυκνός) is the transliterated Greek root for density
(pyknótita) and also meanssagacious.Copyright noticeThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.You should have received a copy of the GNU Affero General Public License
along with this program.  If not, seehttps://www.gnu.org/licenses/.AcknowledgementsThanks to Artur Bekasov, Conor Durkan and George Papamarkarios for
their work onnflows.The MDN implementation in this package is by Conor M. Durkan.
Package: pyknotid
Python (and optional Cython) modules for detecting and measuring
knotting and linking. pyknotid can analyse space-curves, i.e. sets of
points in three-dimensions, or can parse standard topological
representations of knot diagrams.A graphical interface to some of these tools is available online atKnot ID.pyknotid was developed as part of the Leverhulme Trust Research
Programme Grant RP2013-K-009: Scientific Properties of Complex Knots
(SPOCK), a collaboration between the University of Bristol and Durham
University in the UK. For more information, see theSPOCK homepage.If you use pyknotid in your research, pleasecite us.Questions or comments are welcome, please emailalexander.taylor@bristol.ac.uk.Documentationpyknotid is documented online atreadthedocs.
Package: pyknp
pyknp: Python Module for JUMAN++/KNP形態素解析器JUMAN++(JUMAN)と構文解析器KNPのPythonバインディング (Python2系と3系の両方に対応)。RequirementsPythonVerified Versions: 2.7.15,  3.7.11形態素解析器JUMAN++[EN]
(orJUMAN[EN])JUMAN++ はJUMANの後継にあたる形態素解析器構文解析器KNP[EN]Installation$ pip install pyknpDocumentshttps://pyknp.readthedocs.io/en/latest/Authors/Contact京都大学 黒橋・河原研究室 (contact@nlp.ist.i.kyoto-u.ac.jp)John Richardson, Tomohide Shibata, Yuta Hayashibe, Tomohiro Sakaguchi
Package: pyko
pykopyko[파이코]는 한국어 텍스트 처리를 위한 파이썬 라이브러리입니다. 자연어 처리에서 한국어가 갖는 독자적인 특징을
반영해 처리합니다.설치PyPI에 등록된 패키지를 다음과 같이 설치할 수 있습니다.pip install pyko세종말뭉치세종말뭉치를 NLTK CorpusReader를 활용하는 것과 유사하게 활용할 수 있습니다. 세종말뭉치는국립국어원 언어정보나눔터에서 획득할 수 있습니다.사용예시:frompyko.readerimportSejongCorpusReader세종말뭉치=SejongCorpusReader(root,fileids)파일목록=세종말뭉치.fileids()형태분석목록=세종말뭉치.words(tagged=True)print(형태분석목록)"""[('뭐', (('뭐', 'NP'),)), ('타고', (('타', 'VV'), ('고', 'EC'))), ('가?', (('가', 'VV'), ('ㅏ', 'EF'), ('?', 'SF'))), ('지하철.', (('지하철', 'NNG'), ('.', 'SF'))), ('기차?', (('기차', 'NNG'), ('?', 'SF'))), ('아침에', (('아침', 'NNG'), ('에', 'JKB'))), ...]"""형태분석문장목록=세종말뭉치.sents(tagged=True)print(형태분석문장목록[0])"""[('뭐', (('뭐', 'NP'),)),('타고', (('타', 'VV'), ('고', 'EC'))),('가?', (('가', 'VV'), ('ㅏ', 'EF'), ('?', 'SF')))]"""형태소 분리 및 품사 예측v0.4.0+형태소 분석기는 딥러닝 기반의 카카오 형태소 분석기,kakao/khaiii를 내부적으로 활용합니다. 해당 패키지가 시스템에 설치된 것을 가정합니다.모든 환경이 미리 설정된 도커(docker) 이미지를 활용하면 편리합니다.pyko 도커 이미지:codebasic/pyko도커 이미지 사용 예시$ docker run -it codebasic/pyko사용예시:frompykoimporttokenizeras형태소_분석기예문='한국어를 잘 처리하는지 궁금합니다.'형태소목록=형태소_분석기.tokenize(예문)print(형태소목록)"""['한국어', '를', '잘', '처리', '하', '는지', '궁금', '하', 'ㅂ니다', '.']"""형태분석결과=형태소_분석기.tokenize(예문,tagged=True)print(형태분석결과)"""[('한국어', 'NNP'),('를', 'JKO'),('잘', 'MAG'),('처리', 'NNG'),('하', 'XSV'),('는지', 'EC'),('궁금', 'XR'),('하', 'XSA'),('ㅂ니다', 'EF'),('.', 'SF')]"""NLTK 연동말뭉치 관리를 위해 NLTK CourpusReader와 연동해서 사용할 수 있습니다.사용예시:frompykoimporttokenizeras형태소_분석기fromnltk.corpusimportPlaintextCorpusReaderreader=PlaintextCorpusReader(root,fileids,word_tokenizer=형태소_분석기)형태분석결과=reader.words()print(형태분석결과)"""['세종', '(', '世宗', ',', '1397', '년', '5', '월', '7', '일', '(', '음력', '4', '월', ...]"""
Package: pykoa
Version 1 of PyKOA offers access to public raw science and calibration files
acquired with the HIRES echelle-spectrograph, and for Keck Observatory PIs,
secure access to their protected data with the KOA credentials already assigned
to them. The remaining Keck instruments are expected to be available before the
end of 2020. Queries made through PyKOA are underpinned by nexsciTAP, a NExScI
Python-based server that implements the IVOA Table Access Protocol (TAP).
This design enables a rich variety of searches, including cone, box, polygon,
or all-sky spatial searches; temporal searches; searches on program
infortmation; and complex searches on multiple attributes.The Keck Observatory Archive (KOA) is a collaboration between the NASA
Exoplanet Science Institute (NExScI) and the W. M. Keck Observatory (WMKO).
NExScI is sponsored by NASA’s Exoplanet Exploration Program, and operated by
the California Institute of Technology in coordination with the Jet Propulsion
Laboratory (JPL).If you used PyKOA in your research, please acknowledge KOA as follows:“This research has made use of the Keck Observatory Archive (KOA), which
is operated by the W. M. Keck Observatory and the NASA Exoplanet Science
Institute (NExScI), under contract with the National Aeronautics and
Space Administration.”Please also acknowledge the PI(s) of datasets that have been accesses
through PyKOA.
Package: pyKoalaRemote
pyKoalaRemotePython wrapper for dotNet Koala Remote Client provided by LynceeTec to control Digital Holographic Microscope using proprietary Koala softwareThe wrapper uses the same methods names and arguments as the dotNet library distributed by LynceeTec SA LynceeTec.KoalaRemote.Client for Koala version 8.6.25517.0-beta.LynceeTec.KoalaRemote.Client.dll documentationMethods that return a dotNet Array are modified to return a Numpy Array, modified methods are described bellow.Installation using pippip install pyKoalaRemoteTestFrom Ipython consolefrom pyKoalaRemote import client
run clientUsageImport the class client from the pyKoalaRemote and create an instancefrom  pyKoalaRemote import client
remote = client.pyKoalaRemoteClient()Run standard methodsremote.Connect("localhost")
remote.Login("password")
remote.OpenConfig(137)New methodsTwo new methods enable a dialog in the console.ConnectAndLoginDialogArguments : void(nothing)Return : true if the connection and login was successfulComments :Connect and Login to Koala within a single function.IP address and password are defined in the consoleIf the user leave empty field, the default value is usedOpenConfigDialogArguments : void(nothing)Return : true if the connection and login was successfulComments :Config number is defined in the consoleGetPhaseProfileAxisDescription : return calibrated axis in [um] of a profile cutArguments : void(nothing)Return : 1D Numpy array of np.double of profile lengthModified methodsThe methods that returns dotNet Array are modified to return Numpy ArrayConnect(hostname, quiet=True)Arguments :hostName (String): The IP of the computer where Koala is running. Use localhost if running on the same computerquiet (Boolean): Deprecated parameter, will be removed in a later version. Set either to true or falseReturn : true if the connection was successfulComments : the instance variable username is set with usernameGetAxesPosMuArguments : void (nothing)Return : 1D Numpy array of np.double of size 4, where the data for the X, Y, Z and Theta axis respectivelyGetHoloImageArguments : void (nothing)Return : 2D Numpy array of np.ubyte of hologram sizeGetIntensityImageArguments : void (nothing)Return : 2D Numpy array of np.ubyte of ROI sizeGetIntensity32fImageArguments : void (nothing)Return : 2D Numpy array of np.single of ROI sizeGetPhaseImageArguments : void (nothing)Return : 2D Numpy array of np.ubyte of ROI sizeGetPhase32fImageArguments : void (nothing)Return : 2D Numpy array of np.single of ROI sizeGetPhaseProfileArguments : void (nothing)Return : 1D Numpy array of np.double of profile lengthWritten withStackEdit.
Package: pykodi
PyKodiAn async python interface forKodiover JSON-RPC.
This is mostly designed to integrate with HomeAssistant. If you have other needs, there might be better packages available.InstallationYou can install PyKodi fromPyPI:pip3 install pykodiPython 3.7 and above are supported.How to usefrompykodiimportget_kodi_connection,Kodikc=get_kodi_connection(<host>,<port>,<ws_port>,<username>,<password>,<ssl>,<timeout>,<session>)# if ws_port is None the connection will be over HTTP, otherwise over WebSocket.# ssl defaults to False (only relevant if you have a proxy), timeout to 5 (seconds)# session is generated if not passed in# you can also pass in your own sessionawaitkc.connect()kodi=Kodi(kc)awaitkodi.ping()properties=awaitkodi.get_application_properties(["name","version"])awaitkodi.play()awaitkodi.volume_up()awaitkodi.pause()...
Package: pykoges
PyKoges 데이터 분석 패키지PyKoges는 Python 기반의 코호트 데이터 분석 도구로, 특히Koges(한국인유전체역학조사사업) 데이터를 처리, 분석, 모델링하는데 특화되어 있습니다. 다음은 PyKoges 패키지를 사용하여 데이터를 읽고, 전처리하며, 분석 및 예측 모델을 구축하는 예시입니다.:exclamation: 데이터가 포함되지 않은 분석 도구입니다. :exclamation:pip install pykoges시작하기 전에PyKoges 패키지를 사용하기 전에 필요한 모듈을 임포트합니다.frompykogesimportcodingbook,koges,stats,model1. 코딩북 읽기데이터 분석을 위해 먼저 코딩북을 읽어와야 합니다.q=codingbook.read(folder_name='./data_fixed/')# q.summary() # 코딩북의 요약 정보를 확인하고 싶다면 이 코드를 실행합니다.2. 변수 설정분석에 사용할 변수를 설정합니다.x_list={# 여기에 입력 변수를 설정합니다...}y_list={# 여기에 출력 변수를 설정합니다...}patientinfo_list={# 환자를 구분할 수 있는 정보를 넣습니다...}# 변수 설정을 PyKoges에 적용합니다.var=koges.Variables(q=q,x_list=x_list,y_list=y_list,patientinfo_list=patientinfo_list,)var.summary(display_datainfo=True,display_userinfo=True,)3. 데이터 읽기설정된 변수를 바탕으로 데이터를 읽어옵니다.kg=koges.read(variables=var,folder_name='./data_fixed/',filter_patient=True,)4. 데이터 전처리데이터를 전처리합니다. 여기에서는 몇 가지 전처리 옵션을 선택할 수 있습니다.custom_functions=[# 여기에 원하는 커스텀 함수를 설정할 수 있습니다.#  ([x, y], f)의 형태로 설정합니다.]kg_converted=koges.convert(koges=kg,muscle_weight_ratio=False,# muscle/weightmuscle_height_ratio=False,# muscle/heightmuscle_bmi_ratio=False,# muscle/bmiwaist_hip_ratio=False,# waist/hipfev_fvc_ratio=False,# fev1/fvcgrip_of_grwhich=True,# 주 사용손 악력weight_height_bmi=False,# bmicustom_functions=custom_functions,)5. 결측치 제거결측치 처리 및 이상치 제거 등을 진행합니다.kg_dropped=koges.drop(koges=kg_converted,drop_threshold=0.3,# drop_threshold 이상이 결측치인 변수 제거filter_alpha=3,# 3SD를 벗어나느 데이터 제거data_impute=False,# KNN알고리즘으로 결측치 채우기display_result=True,display_count=True,)6. 통계 분석데이터 분리kg_splitted=stats.split(koges=kg_dropped,n_class=4,# 그룹 개수p_threshold=0.05,with_normality=False,with_homogenity=False,isdisplay=True)통계 분석 수행ifkg_splitted.n_class==2:kg_stats=stats.t_test(koges=kg_splitted,p_threshold=.05)else:kg_stats=stats.anova(koges=kg_splitted,p_threshold=.05)분석 결과 요약stats.summary(koges=kg_stats,isdisplay=True)분석 결과 boxplotstats.boxplot(koges=kg_stats,isdisplay=True)상관관계, 산점도 분석stats.correlation(koges=kg_stats,isdisplay=True)stats.scatter(koges=kg_stats,isdisplay=True)7. 머신러닝머신러닝 모델을 선택하여 훈련시키고 예측 진행을 합니다.여/부는 로지스틱, 이산변수는 softmax, 연속변수는 선형 회귀를 진행합니다.ml=model(koges=kg_stats,scalers=["minmax","robust","standard","maxabs"],)ifkg_stats.type=="binary":ml.logistic(isdisplay=True)elifkg_stats.type=="discrete":ml.softmax(display_roc_curve=True,display_confusion_matrix=True,)elifkg_stats.type=="continuous":ml.linear(isdisplay=True)# regression의 결과가 안좋은 경우 quantile classification도 진행합니다.ifml.r2<0.8:ml.softmax(display_roc_curve=True,display_confusion_matrix=True,)예시이미지8. 결과 저장모든 작업의 결과물과 모델을/result폴더에 저장합니다.ml.equation(isdisplay=True)kg_stats.save(isdisplay=True)이 문서는 PyKoges 패키지의 핵심 기능과 사용 방법을 제공하는 가이드라인을 제시합니다. 실제 데이터 분석 시 이 문서를 참고하여 PyKoges를 활용해보세요.
Package: pykoko
kokoKOKO is an easy-to-use entity extraction toolFree software: Apache Software License 2.0Documentation:https://pykoko.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-09-12)First release on PyPI.