Saravanan666/pypi-org huggingface dataset:
Package: semopy
semopyVisit our website for a detailed introductionsemopyis a Python package that includes numerous Structural Equation Modelling (SEM) techniques.FeaturesWrite down a model description in a user-friendly syntaxEstimate model's parameters using a variety of objective functionsEstimate models with population structure via random effectsRestricted Maximum LikelihoodIntegration with gaussian processes/mixture models to tackle huge variety of phenomenaCalculate numerous statistics and fit indicesEstimate model's parameters in presence of ordinal variablesA vast number of settings to fit a researcher's needsFast and accurateAutomatically create fancy HTML reportsInstallationsemopyis available at PyPi and can be installed by typing the following line into terminal:pip install semopySyntaxTo specify SEM models, Thesemopyuses the syntax, which is natural to describe regression models in R. The syntax supports three operator symbols characterising relationships between variables:~ to specify structural part,=~ to specify measurement part,~~ to specify common variance between variables.For example, let a linear equation in the structural part of SEM model take the form:y = β1 x1 + β2 x2 + εThen, insemopysyntax it becomes:y ~ x1 + x2Parameters β1, β2 are to be estimated bysemopy. In some cases a user might want to fix some of parameters to particular value. For instance, let's assume that we want Î²1 to stay equal to 2.0 and we are only interested in estimating β2:y ~ 2*x1 + x2Likewise, if a latent variable Î· is explained by manifest variables y1, y2, y3, then insemopysyntax it can be written down this way:eta =~ y1 + y2 + y3QuickstartThe pipeline for working with SEM models insemopyconsists of three steps:Specifying a modelLoading a dataset.Estimating parameters of the model.Main object required for scpecifying and estimating an SEM model isModel.Modelis responsible for setting up a model from the proposed SEM syntax:# The first step
from semopy import Model
mod = """ x1 ~ x2 + x3
          x3 ~ x2 + eta1
          eta1 =~ y1 + y2 + y3
          eta1 ~ x1
      """
model = Model(mod)Then a dataset should be provided:# The second step
from pandas import read_csv
data = read_csv("my_data_file.csv", index_col=0)To estimate parameters of the model we run afitmethod with the dataset as an argument:# The third step
model.fit(data)The default objective function for estimating parameters is the likelihood function and the optimisation method is SLSQP (Sequential Least-Squares Quadratic Programming). However, thesemopysupports a wide range of other objective functions and optimisation schemes being specified as parameters in thefitmethod.Finally, user caninspectparameters' estimates:model.inspect()Would you like to know more?Tutorial and overview ofsemopyfeatures are available at theproject's website.Requirementsnumpy,pandas,scipy,sympy,sklearn,statmodelsAuthorsMescheryakov A. Georgy-Developer-georgy.m- student, SPbPUIgolkina A. Anna-Supervisor-iganna- Engineer, SPbPULicenseThis project is licensed under the MIT License - see the LICENSE.md file for details.
Package: semoss
semosssemossis a Python module for connecting toSEMOSS.Using this package you can:Pull data from an existing insight using REST API.Create Pandas DataFrame from responses generated by SEMOSS that have 'TASK_DATA' operation type.Run pixel and get the json response.Installpip install semossUsageTo interract with a semoss instance, import thesemoss_connectorand establish a session:Setup>>>fromsemoss.connectionimportsemoss_connector# create connection object by passing in the base url for api>>>semoss_connection=semoss_connector('http://localhost:8080/Monolith')# establish a session. This will prompt a username & password login. Alternatively, you can set 'SEMOSS_USERNAME' and 'SEMOSS_PASSWORD' in environement variables.>>>semoss_connection.establishSession()TrueUsing REST API to pull data from an Insight# define the Project ID>>>projectId='30991037-1e73-49f5-99d3-f28210e6b95c'# define the Insight ID>>>inishgtId='26b373b3-cd52-452c-a987-0adb8817bf73'# define the SQL for the SEMOSS frame you want to query within the insight>>>sql='select * FROM SEMOSS_FRAME123'# if you dont provide one of the following, it will ask you to provide it via prompt>>>diabetes_df=semoss_connection.importFrameFromSemossApi(project_id=projectId,insight_id=inishgtId,sql=sql)>>>diabetes_df.head()AGEPATIENTWEIGHT019482311911917790135220104115932027632744203750161Create a Pandas DataFrame from Task Data# define a pixel command>>>pixel='Database ( database = [ "995cf169-6b44-4a42-b75c-af12f9f45c36" ] ) | Select ( DIABETES__AGE , DIABETES__PATIENT , DIABETES__WEIGHT ) .as ( [ AGE , PATIENT , WEIGHT ] ) | Distinct ( false ) | Collect(-1)'# use getDataFrameFromTaskData method with the pixel command to create a Pandas DataFrame>>>df=semoss_connection.getDataFrameFromTaskData(industryPixel)>>>df.head()AGEPATIENTWEIGHT019482311911917790135220104115932027632744203750161Get the JSON response of any pixel# run the pixel>>>semoss_connection.runPixel('1+1'){'insightID':'8b419eaf-df7d-4a7f-869e-8d7d59bbfde8','sessionTimeRemaining':'7196','pixelReturn':[{'pixelId':'3','pixelExpression':'1 + 1 ;','isMeta':False,'output':2,'operationType':['OPERATION']}]}DevPlease email SEMOSS
Package: semp
semp (Selenium-Made-Simple)Semp is a markup language for selenium library which makes web manipulation easier and faster!Advantages of semp:Easy to understandFaster coding (no worries about syntax)Create web bots easierExternal .semp filesDirectly start from terminalHow To Use:browser / starts browser (browser used in line 1 is not in loop)goto *webaddress / go to websitefindelement (options: byname, byid, byxpath, bycssselector) *argument / find element by it's propertiessendkeys *keys / send keys to elementclearelement / clear elementclickelement / click elementsubmitelement / submit elementdoubleclickelement / Double-click elementscroll *xcoordinate *ycoordinate / scroll to specified coordinatescroll toelement / scroll to selected elementalert (options: accept, dismiss) / Accept or dismiss alert pop-upgetscreenshot *filesource / Save screenshot of full-pagequit / quit browserkillbrowser/ kill browserquitsemp / quit sempsleep *second / waiting time until next process startsbrowser / starts browser again after quiting (used other than 1st line)Example .semp files:Example 1Example 2Libraries used in sempSeleniumHQ-SeleniumBoni Garcia-WebDriverManager
Package: sempler
Sempler: generate synthetic and realistic semi-synthetic data with known ground truth for causal discovery[Documentation at https://sempler.readthedocs.io/en/latest/]Sempler allows you to generate synthetic data from SCMs and semi-synthetic data with known causal ground truth but distributions closely resembling those of a real data set of choice. It is one of the software contributions of the paper"Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions"by Juan L. Gamella, Armeen Taeb, Christina Heinze-Deml and Peter Bühlmann. You can find more details in Appendix E of the paper.If you find this code useful, please consider citing:@article{gamella2022characterization,
  title={Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions},
  author={Gamella, Juan L. and Taeb, Armeen and Heinze-Deml, Christina and B\"uhlmann, Peter},
  year={2022}
}OverviewThe semi-synthetic data generation procedure is implemented in the classsempler.DRFNet(seedocs). A detailed explanation of the procedure can be found in Appendix E of thepaper.Additionally, you can generate purely synthetic data from general additive-noise models. Two classes are defined for this purpose.sempler.ANMis for general (acyclic) additive noise SCMs. Any assignment function is possible, as are the distributions of the noise terms.sempler.LGANMis for linear Gaussian SCMs. While this is also possible withsempler.ANM, this class simplifies the interface and offers the additional functionality of sampling "in the population setting", i.e. by returning a symbolic gaussian distribution (seesempler.LGANM.sampleandsempler.NormalDistribution).To allow for random generation of SCMs and interventional distributions, the modulesempler.generatorscontains functions to sample random DAGs and intervention targets.InstallationYou can clone this repo or install using pip. To install sempler in its most basic form, i.e. to generate purely synthetic data withsempler.ANMandsempler.LGANM, simply runpip install semplerTo install the additional dependencies needed for the semi-synthetic data generation procedure, runpip install sempler[DRFNet]which will install sempler with the additionalrpy2dependency. You will also need:anRinstallation; you can find an installation guideheretheRpackagedrf, which you can install by typinginstall.packages("drf")in an R terminalVersioningSempler is still at its infancy and its API is subject to change. Non backward-compatible changes to the API are reflected by a change to the minor or major version number,e.g.code written using sempler==0.1.2 will run with sempler==0.1.3, but may not run with sempler==0.2.0.DocumentationYou can find the full documentation athttps://sempler.readthedocs.io/en/latest/.FeedbackFeedback is most welcome! You can add an issue  or send anemail.
Package: sempy
Note: This software is in a pre-alpha state.The main documentation for this package is located atwww.sempy.org, but this page offers a
minimal set of information to get started using Sempy.AboutSempy is a numerical software package designed to solve partial differential
equations arising from the analysis of fluid flow and heat transfer.
It is written in Python and carries the intuitive syntax of this language.
Furthermore, computational meshes can be imported from the Gmsh mesh generator,
making Sempy flexible towards handling complex geometries.Getting startedThe quickest way to get started using the Sempy package is to install it
witheasy_installThis can be achieved by e.g. typing:$ easy_install sempyon the command line. This requires an internet connection to work. Another
possibility is to download the latest version as aziportar.gzfile and then install it with:$ python setup.py installIt might be necessary to use super user privileges on the above
commands. For instance:$ sudo easy_install sempyAslo, it might be necessary to run:$ sudo easy_install --upgrade sempyon a regular basis to obtain updated versions.An example scriptSolve the Poisson problem with a simple script:import sempy

X = sempy.Space( filename = 'square', n = 4, dim = 2 )
A = sempy.operators.Laplacian( X ).matrix

f = sempy.Function( X, basis_coeff = 1.0 )
b = sempy.operators.Mass( X ).action_local( f.basis_coeff )
u = sempy.Function( X, basis_coeff = 0.0 )

[v, flag] = sempy.linsolvers.Krylov().solve( A, b, u.glob() )
u.basis_coeff = X.mapping_q( v )
u.plot()
Package: sempyver
Tool for teams that manage the creation and modification of theCHANGELOGbased on a specified set of changes.InstallationTo installSemPyVer, you can do it through pip:pip install sempyverPlease consider that it requiresPython >=3.8UsageHELP PAGE STILL ON WORK. Usesemver -helpfor more information.ContributingEveryone can contribute. Before contributing, please read ourcode of conduct.To contribute toSemPyVer, follow these steps:Fork this repository.Create a new branch.Make your changes and commit them.Push your changes to your fork.Create a pull request.LicenseProject Name is released under theMIT License.InspirationInspired byChangesets.
Package: semremover
Semantic Object RemovalUsing semantic segmentation and in-painting to remove objects based on labels. Inspired byInpaint Anythingby  Tao Yu et al. UsingMaskFormerfor semantic segmentation to select areas to remove usingLaMafor in-painting.InstallationInstall the package.python -m pip install semremoverUse theSemanticObjectRemoverin your code.fromsemremoverimportSemanticObjectRemoversem_obj_remover=SemanticObjectRemover()labels=['car','minibike','van']inpainted_image=sem_obj_remover.remove_objects_from_image("example.jpg",labels)DevelopmentInstallationInstall the Python requirements.python -m pip install -r requirements.txtUsageTo use the script you can call it with various options. The first positional argument is the input path, which can point to either an image or a directory of images. To remove objects from a picture add them to the labels option when running the script. The default labels can be found in./semremover/models/config/ade20k_labels.json.Exampleinputpython -m semremover example/paris.jpg --labels car minibike vanOutput
Package: semser
Semantic Scholar SearchSearches Semantic Scholar for papers or authors. Downloads the papers as pdfs if they are on arxive.Installationpip install semseror clone and installgit clone https://github.com/fergusfettes/semser
cd semser
poetry installUsageSearch for some terms. You will get a list of papers to choose from.$ semser search "retrieval augmented generation"
1. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, Bob et al., 15 Nov 2019
...
Papers to retrieve: 1
DownloadingSearch for an author. You can select a likely looking author and you will be shown a selection of their papers.$ semser author "Schmidhuber"
1. Jürgen Schmidhuber, 1 Jan 2015
...
Authors to retrieve: 1
1. Paper 1, 1 Jan 2015
2. Paper 2, 1 Jan 2015
Papers to retrieve: 1
DownloadingThe 'limit' applies to authors and to papers by the author:$ semser author "Schmidhuber" --limit 2Will only return two papers by them. But you can grab their id and use it instead:$ semser author 145341374 --limit 50You can also search for a paper by its Semantic Scholar ID, or by its DOI, or just use the url from semantic scholar.$ semser paper "10.1162/neco.1997.9.8.1735"
DownloadingSince it is made with typer, you can also just type$ semser --help
$ semser search --helpto get a list of commands and options.
Package: semsim
semsimCompare texts easily withsemsimPython package!FeaturesDozens ofparametersto be tuned by you for better performance!Default valuesof all the parameters validated on datasets for paraphrase detection task6 differentalgorithmsfor efficient syntax tree comparisonA small pack ofstandard "built-in" modelswhich can be easily downloaded viasemsimpackage itselfFlexibleclass taxonomywhich you can extend by simply inheriting from one of the model base classesPython librarysemsimwithcommand line interface(powered byclick)Dependenciesattrsclicknetworkxnumpypymorphy2scipysimple_elmotensorflowtensorrttextracttorchtorch-geometrictorch-scattertorch-sparsetorchwordembtqdmufal.udpipeQuick startTo installsemsimsimply run:pip install semsimNOTE: If you encounter problems when installingsemsimpackage,
consider first installing some prerequisites in advance:$ pip install torch tensorflow tensorrtThen proceed to installsemsim.Now you can usesemsimCLI tool as follows:$ semsim first_src.txt second_src.txt -o output.txtYou might want to download standard "built-in" (or we should say "add-on") models for better performance.
This can be done by executing the following line:$ semsim download cbowfor fetching pretrained CBOW embeddings or$ semsim download -afor downloadingallthe add-ons at once in parallel.More info can be found on thedocumentationpage.Codestyle linters and test frameworksThis library has been fully checked and tested with the following tools:flake8mypypydocstylepytestInterfaceCLI interface is described in theexamplessection ofdocumentation.
This is how you can usesemsimCLI tool:$ semsim compare first_src.txt second_src.txt -e cbow -k neural -o output.txt --max-out-pairs 200 -vAuthorsMathematician2000
Package: semsimian
semsimianSemsimian is a package to provide fast semantic similarity calculations for ontologies.
It is a Rust library with a Python interface.This includes implementation of Jaccard and Resnik similarity of terms in an ontology,
as well as a method to calculate the similarity of two sets of terms (so-called
termset similarity). Other methods will be added in the future.Semsimian is currently integrated intoOAKand
theMonarch appto provide fast semantic
similarity calculations.Rust Installationcargo add semsimianPython InstallationSet up your virtual environment of choice.cdsemsimian(home directory of this project)pip install maturinmaturin developpythonPython 3.9.16 (main, Jan 11 2023, 10:02:19) 
[Clang 14.0.6 ] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> from semsimian import Semsimian
>>> s = Semsimian([('banana', 'is_a', 'fruit'), ('cherry', 'is_a', 'fruit')])
>>> s.jaccard_similarity('banana', 'cherry')This should yield a value of 1.0.ReleasesAs of version 0.2.11, the semsimian source is released onGitHub, with a corresponding set of Python wheels released toPyPiand a corresponding release incrates.io.
Package: semsis
Semsisis a library for semantic similarity search.
It is designed to focus on the following goals:Simplicity: This library is not rich or complex and implements only the minimum necessary for semantic search.Maintainability: Unit tests, docstrings, and type hints are all available.Extensibility: Additional code can be implemented as needed easily.Efficiency: Billion-scale indexes can be constructed efficiently. Seedocs/technical_notes.rstfor details.REQUIREMENTSfaiss(seeINSTALL.md)The other requirements are defined inpyproject.tomland can be installed viapip install ./.INSTALLATIONgitclonehttps://github.com/de9uch1/semsis.gitcdsemsis/pipinstall./USAGECase 1: Use semsis as APIYou can see the example of text search inend2end_test.py.Note that this example is not optimized for billion-scale index construction.
If you find the efficient implementation, please seesemsis/cli/README.rst.Encode the sentences and store in a key–value store.fromsemsis.encoderimportSentenceEncoderfromsemsis.kvstoreimportKVStorefromsemsis.retrieverimportRetrieverFaissCPUimportmathimportnumpyasnpTEXT=["They listen to jazz and he likes jazz piano like Bud Powell.","I really like fruites, especially I love grapes.","I am interested in the k-nearest-neighbor search.","The numpy.squeeze() function is used to remove single-dimensional entries from the shape of an array.","This content is restricted.",]QUERYS=["I've implemented some k-nearest-neighbor search algorithms.","I often listen to jazz and I have many CDs which Bud Powell played.","I am interested in the k-nearest-neighbor search.",]KVSTORE_PATH="./kv.bin"INDEX_PATH="./index.bin"INDEX_CONFIG_PATH="./cfg.yaml"MODEL="bert-base-uncased"REPRESENTATION="avg"BATCH_SIZE=2encoder=SentenceEncoder.build(MODEL,REPRESENTATION)dim=encoder.get_embed_dim()num_sentences=len(TEXT)withKVStore.open(KVSTORE_PATH,mode="w")askvstore:# Initialize the kvstore.kvstore.new(dim)foriinrange(math.ceil(num_sentences/BATCH_SIZE)):b,e=i*BATCH_SIZE,min((i+1)*BATCH_SIZE,num_sentences)sentence_vectors=encoder.encode(TEXT[b:e]).numpy()kvstore.add(sentence_vectors)Next, read the key–value store and build the kNN index.withKVStore.open(KVSTORE_PATH,mode="r")askvstore:retriever=RetrieverFaissCPU.build(RetrieverFaissCPU.Config(dim))retriever.train(kvstore.key[:])retriever.add(kvstore.key[:],kvstore.value[:])retriever.save(INDEX_PATH,INDEX_CONFIG_PATH)Query.retriever=RetrieverFaissCPU.load(INDEX_PATH,INDEX_CONFIG_PATH)query_vectors=encoder.encode(QUERYS).numpy()distances,indices=retriever.search(query_vectors,k=1)assertindices.squeeze(1).tolist()==[2,0,2]assertnp.isclose(distances[2,0],0.0)Case 2: Use semsis as command line scriptsCommand line scripts are carefully designed to run efficiently for the billion-scale search.
Seesemsis/cli/README.rst.LICENSEThis library is published under the MIT-license.
Package: semspaces
Semantic spaces moduleThis is a python module that allows to compute semantic metrics based on
distributional semantics models.For example, to find words that are semantically similar to the word 'brain':fromsemspaces.spaceimportSemanticSpacespace=SemanticSpace.from_csv('space.w2v.gz')space.most_similar(['brain']){'brain':[(u'brain',0.0),(u'brains',0.34469844325620635),(u'cerebrum',0.4426992023455152),(u'cerebellum',0.4483798859566903),(u'cortical',0.469348588934828),(u'brainstem',0.4791188497952641),(u'cortex',0.479544888313173),(u'ganglion',0.49717579235842546),(u'thalamus',0.5030885466349713),(u'thalamic',0.5059524199702277)]}The module wraps dense and sparse matrix implementations to provide convenience
methods for computing semantic statistics as well as easy input and output of
the data.Installationpipinstall-rrequirements.txt
pythonsetup.pyinstallSemantic spacesYou can download a set of validated semantic spaces for English and Dutchhere(see Mandera, Keuleers, & Brysbaert,
in press).ContributeIssue Tracker:https://github.com/pmandera/semspaces/issuesSource Code:https://github.com/pmandera/semspacesAuthorsThe tool was developed at Center for Reading Research, Ghent University byPaweł Mandera.LicenseThe project is licensed under the Apache License 2.0.ReferencesMandera, P., Keuleers, E., & Brysbaert, M. (in press). Explaining human
performance in psycholinguistic tasks with models of semantic similarity based
on prediction and counting: A review and empirical validation.Journal of
Memory and Language.
Package: sems-portal-api
SEMS PortalDescriptionSEMS Portal is a simple wrapper designed to interact with various APIs exposed by a specific website. It simplifies the process of sending requests and handling responses, making it easier for developers to integrate and work with the available services.Getting StartedPrerequisitesBefore you begin, ensure you have met the following requirements:Python version >=3.5InstallationTo install SEMS Portal API, follow these steps:Clone the repository:gitclonehttps://github.com/andrewgierens/sems_portal_api.gitInstall the required packages:pipinstall-rrequirements.txtUsageTo use SEMS Portal, you need to have Python andaiohttpinstalled. Here’s a quick example to get you started:importaiohttpimportasynciofromsems_portal_apiimportlogin_to_sems,set_regionasyncdefmain():set_region('eu')asyncwithaiohttp.ClientSession()assession:account="your_account"password="your_password"data=awaitlogin_to_sems(session,account,password)print(data)if__name__=="__main__":asyncio.run(main())In this example, we import the required modules and define anasyncmain function. Inside this function, we create anaiohttp.ClientSession, which is used to send an HTTP request to the SEMS portal for logging in. Replace"your_account"and"your_password"with your actual login credentials.Thelogin_to_semsfunction takes three parameters: the session, account, and password, and it returns the data received from the SEMS portal. The returned data is then printed to the console.For testing the demo account credentials may be used:account="demo@goodwe.com"password="GoodweSems123!@#"ContributingContributions to SEMS Portal are welcome and appreciated. If you have any suggestions or bug reports, please open an issue in the repository.creating a pull request.LicenseThis project is licensed under the GNU GPLv3 License - see theLICENSE.mdfile for details.ContactIf you have any questions or want to reach out, you can contact me atapgierens@gmail.comContributorsAndrew GierensYannis Gerlach