Saravanan666/pypi-org huggingface dataset:
Package: evaldocsloader
MkDocs Plugin: Evaluation Function Documentation LoaderMkdocs plugin for fetching additional .md files registered in a db before render. Specifically from a web request which returns all the available evaluation functions endpoints.This plugin was specifially developped for theLambdaFeedbackplatform.NOTE: There is currently no safety checking to make sure downloaded markdown files are valid and able to be rendered, they are simply copied over directly from the evaluation function endpointConfigurationEnable plugin in themkdocs.ymlfile:plugins:-evaldocsloader:functions_announce_endpoint:"http://127.0.0.1:5050/testingfunctions"api_key:!ENV[API_KEY,"disable"]dev_section:["Developers","EvaluationFunctions"]user_section:["Teachers,"Evaluation Functions"]functions_announce_endpoint: Endpoint from which a list of evaluation functions be fetchedapi_keyKey to be passed onto the headers of the request ade to the functions announcing endpoint, used to authenticate the request.dev_sectionanduser_section: Paths under which the fetched documentation files should be included, for the developer and teacher-facing files respectively. Thes can be arbirarily long. In this example, developer documentation would be appended to content under the "Developers" section in the "Evaluation Functions" subsection.BehaviourThis plugin hooks into three events:on_config: After the config is loaded, a list of evaluation functions is fetched the endpoint specified infunctions_announce_endpoint. Documentation files are fetched from each of the urls returned, and saved to a temporary directory. Successfully downloaded files are then registered to thenavconfig, under the sections specified indev_sectionanduser_section.on_files: Downloaded files are appended onto the end of the mainmkdocs.structure.files.Filesobjecton_post_build: The created temporary directory is cleaned upFor all events, if a plugin-breaking error occurs, it will be caught and evaluation function documentation fetching is aborted.Dev NotesPackage can be installed locally usingpipinstall-e.I've included a small flask api for testing, it's not relevant to the actual plugin - just for development.Sources/ReferencesPlugin for loading external markdown files:https://github.com/fire1ce/mkdocs-embed-external-markdownTemplate for plugins:https://github.com/byrnereese/mkdocs-plugin-templateFile Selection:https://github.com/supcik/mkdocs-select-filesDealing with new files:https://github.com/oprypin/mkdocs-gen-files/blob/master/mkdocs_gen_files/plugin.py
Package: evaler
# Evaler
Python package that relies on MNE-python for quantifying resolution metrics for evaluating spatial fidelity of M/EEG source estimates.Example code in for_manuscript.py and some subject data in subj_data for regenerating data and the figures in the article
“Spatial Fidelity of MEG/EEG Source Estimates: A General Evaluation Approach”.Dependencies: The MNE-python environment and plyfile.Installation:
1. Install and activate the MNE-python environment (should work with version 0.18 and newer)
2. pip install plyfile
3. python setup.py install
Package: evaLEs
evaLEsEvaluate the Lyapunov Spectrum of a dynamical system described by ODEsDocumentationExamplesArticle ofSandriabout the algorithm of Benedettin et al.How to install:pip install evaLEsevaLEs v0.1:Create Package.evaLEs v0.1.1Added Discrete Dynamical Systems (maps) support.Added 1D system (Continuos and Maps) support.Code reorganized.evaLEs v0.1.2Update ReadmeBad semantic assigned to variables fixedevaLEs v0.1.3Bug FixedAdded history of the solution of the ODE in t
Package: eval-hj3415
eval-hj3415eval-hj3415 evaluate naver financial data.Quick startfrom eval_hj3415 import evalfor all eval dictionary..
eval.Eval(<code:str>).get_all()
or..
eval.Score(<code:str>).eval_dictfor get score..
s = eval.Score(<code:str>)
s.red()
s.mil()
s.blue()
Package: evalidator
Checks if an e-mail address exists or not.Installationpip install evalidatorThose who can’t wait for the next release:python setup.py installCommand-line Usage$ evalidate someone@email.com(where@email.comis one of below supported services)This will tell you if the given e-mail address exists on the internet or
not.Library Usage>>> import evalidator
>>> evalidator.validate('someone@email.com')(where@email.comis one of below supported services)Supported ServicesGoogle Mail (@gmail.com)Yahoo! Mail (@yahoo.com)Please open a new issue if you would like to validate e-mail
addresses for some other e-mail service.LicenseThe MIT License
Package: evalify
evalifyEvaluate Biometric Authentication Models Literally in Seconds.InstallationStable release:pipinstallevalifyBleeding edge:pipinstallgit+https://github.com/ma7555/evalify.gitUsed forEvaluating all biometric authentication models, where the model output is a high-level embeddings known as feature vectors for visual or behaviour biometrics or d-vectors for auditory biometrics.UsageimportnumpyasnpfromevalifyimportExperimentrng=np.random.default_rng()nphotos=500emb_size=32nclasses=10X=rng.random((self.nphotos,self.emb_size))y=rng.integers(self.nclasses,size=self.nphotos)experiment=Experiment()experiment.run(X,y)experiment.get_roc_auc()print(experiment.roc_auc)print(experiment.find_threshold_at_fpr(0.01))How it worksWhen you run an experiment, evalify tries all the possible combinations between individuals for authentication based on theXandyparameters and returns the results including FPR, TPR, FNR, TNR and ROC AUC.Xis an array of embeddings andyis an array of corresponding targets.Evalify can find the optimal threshold based on your agreed FPR and desired similarity or distance metric.Documentation:https://ma7555.github.io/evalify/FeaturesBlazing fast implementation for metrics calculation through optimized einstein sum and vectorized calculations.Many operations are dispatched to canonical BLAS, cuBLAS, or other specialized routines.Smart sampling options using direct indexing from pre-calculated arrays with total control over sampling strategy and sampling numbers.Supports most evaluation metrics:cosine_similaritypearson_similaritycosine_distanceeuclidean_distanceeuclidean_distance_l2minkowski_distancemanhattan_distancechebyshev_distanceComputation time for 4 metrics 4.2 million samples experiment is24 seconds vs 51 minutesif looping usingscipy.spatial.distanceimplemntations.TODOSafer memory allocation. I did not have issues but if you ran out of memory please manually set thebatch_sizeargument.ContributionContributions are welcomed, and they are greatly appreciated! Every little bit helps, and credit will always be given.Please checkCONTRIBUTING.mdfor guidelines.CitationIf you use this software, please cite it using the metadata fromCITATION.cff
Package: evalipy
EvaliPyEvaliPyis an evaluation framework for machine learning Models.The project was started in 2023. It's a package for evaluating different machine learning models and comparing them.It's currently maintained by me :)DependenciesPython (>= 3.5)NumPy (>= 1.17.3)joblib (>= 1.1.1)pandas (>= 1.5.0)matplotlib (>= 3.6.2)scikit-learn (>= 1.2.0)scipy (>= 1.10.0)seaborn (>= 0.12.2)Installationpip install evalipyUsageImportfromevalipyimport*Reportr=report.Report(model=model.Model(clf),actual_data=y,predicted_data=y_pred_1)*(optional)*print(r)Compare...tree_model.fit(X,y)linear_model.fit(X,y)...comparator=comparator.Comparator(models=[linear_model,tree_model],x=X,actual_data=y)print(comparator)AuthorsMR-EIGHT(Mehrdad Heshmat)
Package: evalit
eval-itEval-it 用于训练模型的评估。Featureslocal:本地使用的简单图片分拣GUIAD / 左右箭头移动, 自定义的快捷键放到对应目录UI长这样webuiapi:batch xyz plotUsageinstall:pipinstallevalitto launch a eval gui:PS C:\Users\Andre> eval-it local --help
Usage: eval-it local [OPTIONS]

  Launch the local evaluation GUI; can be used for sorting images or ranking
  them. Uses the default configs. :return:

Options:
  -i, --img_dir TEXT  Path to the root directory of the images.
  -o, --out_dir TEXT  Path to the root directory of the output.
  -m, --move BOOLEAN  Move the images instead of copying.
  --help              Show this message and exit.eval-itlocal-iE:\_benchmark\small-oE:\_benchmark_done-mFalseto generate images with webuiapi:eval-it webuiapi --config configs/webui_step_conf.yaml
Package: eval-jsonpath-elam
Get eval() method Jsonpath by json valueSearch for the (string, int, float, bool) value in a json object, and
return the jsonpath in a string formatted to use in eval() method.For example:
import json
from eval_jsonpath_elam.evalpath import get_jsonpath_by_keyjson_data = { 
    "objectKeyNameList": [ 
        {"someBoolKey": False },
        {"someFloatKey": 123456.34 },
        {"someIntKey": 7890 },
        {"someStrKey": "my_search_string" }
    ]
}

json_object = json.loads( json.dumps( json_data ) )
eval_string = get_jsonpath_by_key(json_object, "my_search_string") 
print( eval_string ) # "['objectKeyNameList'][3]['someStrKey']"
print( eval( "json_object" + eval_string ) ) # "my_search_string"Optional parametersProperty NameValuesDocdot_notationfalseortruedefault toFalse; this allows to be compatible to Python (ie: "['someObj']['Array'][3]['obj1']['key'][2]['prop']"). When set toTrue; allows string to be returned to be compatible to Java/Javascript (ie: "someObj.Array[3].obj1.key[2].prop")contains_stringfalseortruedefault toTrue; this searches for all occurances of the search string. When set toFalse; will only find the first occurance of the search string, int, float, or boolNote: bool, int, float, complex data types will be automatically converted to strings during search.
returned_json_path_string will return a string of the json path if found, otherwise theNoneobject will be returned.
Search string is case sensitive.LicenseAGPLv3AuthorsEric LamThe following is example code tested in Windows 11 with Python 3.11.4:importjson# either import this:fromeval_jsonpath_elamimportevalpath# or import this:fromeval_jsonpath_elam.evalpathimportget_jsonpath_by_keyjson_data={"alpha":["aaa","bbb","ccc"],"dimensions":{"length":"112","width":"103","height":"42","once":"thing1"},"meta_data":[{"id":11089769,"key1":"imported_gallery_files","value":["https://abc.com/unnamed-3.jpg","https://abc.com/unnamed-2.jpg","https://abc.com/unnamed-4.jpg",],},{"id":11089779,"key2":"imported_gallery_files2","value":["https://abc.com/unnamed-5.jpg","https://abc.com/unnamed-6.jpg","https://abc.com/unnamed-7.jpg",],"value2":{"branch1":["thing0"],"branch2":[{"branch2_0":"thing1"},{"branch2_1":"thing2"},{"branch2_2":"thing3"},],},},],}# used to just print the list of strings using eval(json_path_string)defprint_python_json(x):forvinx:print(f'data{v}={eval("data"+v)}')print()# load json into objectdata=json.loads(json.dumps(json_data))# find the key "thing" in json, and return the json path in eval string formatpath_str=get_jsonpath_by_key(data,"thing")print_python_json(path_str)path_str=evalpath.get_jsonpath_by_key(data,42)print_python_json(path_str1)path_str=get_jsonpath_by_key(data,42,dot_notation=True)print(path_str[0])path_str=get_jsonpath_by_key(data,11089769)print_python_json(path_str)path_str=get_jsonpath_by_key(data,"thing1",contains_string=False)print_python_json(path_str)path_str=get_jsonpath_by_key(data,"imported_gallery_files",contains_string=False)print_python_json(path_str)path_str=get_jsonpath_by_key(data,"imported_gallery_files",contains_string=True)print_python_json(path_str)
Package: evalmate
# EVALMATE[![PyPI](https://img.shields.io/pypi/v/evalmate.svg)](https://pypi.python.org/pypi/evalmate)
[![Run Status](https://api.shippable.com/projects/5b6c0016c767f20700d1d2e0/badge?branch=master)](https://app.shippable.com/github/ynop/evalmate)
[![Coverage Badge](https://api.shippable.com/projects/5b6c0016c767f20700d1d2e0/coverageBadge?branch=master)](https://app.shippable.com/github/ynop/evalmate)
[![Documentation Status](https://readthedocs.org/projects/evalmate/badge/?version=latest)](https://evalmate.readthedocs.io/en/latest/?badge=latest)Evalmate is a set of tools for evaluating audio related machine learning tasks.[Documentation](https://evalmate.readthedocs.io/)
[Examples](https://github.com/ynop/evalmate/tree/master/examples)
[Changelog](https://evalmate.readthedocs.io/en/latest/notes/changelog.html)## Installation`sh pip install evalmate `Install the latest development version:`sh pip installgit+https://github.com/ynop/evalmate.git`## Development### Prerequisites[A supported version of Python 3](https://docs.python.org/devguide/index.html#status-of-python-branches)It’s recommended to use a virtual environment when developing audiomate. To create one, execute the following command in the project’s root directory:` python-mvenv . `To install evalmate and all it’s dependencies, execute:` pip install-e. `### Running the test suite` pip install-e.[dev] python setup.py test `With PyCharm you might have to change the default test runner. Otherwise, it might only suggest to use nose. To do so, go to File > Settings > Tools > Python Integrated Tools (on the Mac it’s PyCharm > Preferences > Settings > Tools > Python Integrated Tools) and change the test runner to py.test.### Editing the DocumentationThe documentation is written in [reStructuredText](http://docutils.sourceforge.net/rst.html) and transformed into various output formats with the help of [Sphinx](http://www.sphinx-doc.org/).[Syntax reference reStructuredText](http://docutils.sourceforge.net/docs/user/rst/quickref.html)[Sphinx-specific additions to reStructuredText](http://www.sphinx-doc.org/en/stable/markup/index.html)To generate the documentation, execute:` pip install-e.[dev] cd docs make html `The generated files are written todocs/_build/html.### VersionsVersions is handled using [bump2version](https://github.com/c4urself/bump2version). To bump the version:` bump2version [major,minor,patch,release,num] `
Package: evalml
EvalML is an AutoML library which builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions.Key FunctionalityAutomation- Makes machine learning easier. Avoid training and tuning models by hand. Includes data quality checks, cross-validation and more.Data Checks- Catches and warns of problems with your data and problem setup before modeling.End-to-end- Constructs and optimizes pipelines that include state-of-the-art preprocessing, feature engineering, feature selection, and a variety of modeling techniques.Model Understanding- Provides tools to understand and introspect on models, to learn how they'll behave in your problem domain.Domain-specific- Includes repository of domain-specific objective functions and an interface to define your own.InstallationInstall fromPyPI:pipinstallevalmlor from the conda-forge channel onconda:condainstall-cconda-forgeevalmlAdd-onsUpdate checker- Receive automatic notifications of new Woodwork releasesPyPI:pipinstall"evalml[updater]"Conda:conda install -c conda-forge alteryx-open-src-update-checkerStartLoad and split example dataimportevalmlX,y=evalml.demos.load_breast_cancer()X_train,X_test,y_train,y_test=evalml.preprocessing.split_data(X,y,problem_type='binary')Run AutoMLfromevalml.automlimportAutoMLSearchautoml=AutoMLSearch(X_train=X_train,y_train=y_train,problem_type='binary')automl.search()View pipeline rankingsautoml.rankingsGet best pipeline and predict on new datapipeline=automl.best_pipelinepipeline.predict(X_test)Next StepsRead more about EvalML on ourdocumentation page:Installationandgetting started.Tutorialson how to use EvalML.User guidewhich describes EvalML's features.FullAPI referenceSupportThe EvalML community is happy to provide support to users of EvalML. Project support can be found in four places depending on the type of question:For usage questions, useStack Overflowwith theevalmltag.For bugs, issues, or feature requests start aGithub issue.For discussion regarding development on the core library, useSlack.For everything else, the core developers can be reached by email atopen_source_support@alteryx.comBuilt at AlteryxEvalMLis an open source project built byAlteryx. To see the other open source projects we’re working on visitAlteryx Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.
Package: evalo
Python code synthesisThis project is used to interpret Python code and generate Python code based on given values.This project is heavily influenced byBarliman, which implemented
this exact idea for Scheme. While Barliman works wonders, I don't think Scheme is a programming
language that is widely used in production environments. This is the reason I created this
project, so that the principle of Barliman could be applied to Python.ExamplesThis project can do both interpreting from Python code to values and vica versa. For example, AST
values will be converted to the correct interpreted values:> from evalo import *
> x = var()
> run(1, x, eval_expro(ast.BinOp(left=ast.Num(n=1), op=ast.Add(), right=ast.Num(n=1)), [], x))

(2,)Interpreting from values to Python code will generate AST objects, which can be directly translated
to Python code using external libraries. For example, if we want 5 different versions of Python code
that are interpreted to the value2, we can do the following:> from evalo import *
> x = var()
> run(5, x, eval_expro(x, [], 2))

(<_ast.Num at 0x7fdf44aabe48>,
 <_ast.BinOp at 0x7fdf44a87fd0>,
 <_ast.BinOp at 0x7fdf44a1d320>,
 <_ast.BinOp at 0x7fdf44a444e0>,
 <_ast.BinOp at 0x7fdf46ee36a0>)To translate this to human-readable values, we can useast_dump_if_possible:> [ast_dump_if_possible(y) for y in run(5, x, eval_expro(x, [], 2, maxdepth=3))]

['Num(n=2)',
 'BinOp(left=Num(n=0), op=Add(), right=Num(n=2))',
 'BinOp(left=Num(n=0), op=Sub(), right=BinOp(left=Num(n=0), op=Sub(), right=Num(n=2)))',
 'BinOp(left=Num(n=1), op=Mult(), right=Num(n=2))',
 'BinOp(left=BinOp(left=Num(n=0), op=Add(), right=Num(n=0)), op=Add(), right=Num(n=2))']Using theastunparselibrary we can directly translate this output to Python source code:> [astunparse.unparse(y).strip() for y in run(10, x, eval_expro(x, [], 2, maxdepth=3))]

['2',
 '(0 + 2)',
 '(0 - (0 - 2))',
 '(1 * 2)',
 '((0 + 0) + 2)',
 '((0 + 0) - (0 - 2))',
 '((1 + 0) * 2)',
 '((0 - 0) + 2)',
 '((0 - 0) - (0 - 2))',
 '((1 - 0) * 2)']DevelopmentIf you want to help develop on this project, you can install it like so using Python 3.7:mkvirtualenv -a $(pwd) $(basename $(pwd)) -p python3
pip install -r requirements/development.txt
pre-commit installThis project usestoxwithpytestfor testing, so just runtoxto run all tests.
Package: evalogger
EvaLoggerA simple alternative to the logging libraryEvaLogger is a simple logging library for Python. It is designed to be easy to use and to be easily integrated into existing projects.UsagefromevaloggerimportEvaLoggerimportrequestslog=EvaLogger()# create a EvaLogger instancelog.info("Hello World!")# log a info messagelog.warn("Hello World!")# log a warning messagelog.error("Hello World!")# log a error messagelog.exception_error(RuntimeError("Hello World!"))# log a exception error messagelog.success("Hello World!")# log a success messagelog.network(requests.get("https://google.com"))# log a network requestlog.network(requests.get("https://google.com"),text="Google")# log a network request with a custom nameConfigurationEvaLogger doesn't have alot of configuration options, but it does have some.You can configure EvaLogger by setting the following properties on the EvaLogger instance.fromevaloggerimportEvaLoggerlog=EvaLogger(save_to_file=True,filename="eva.log")EvaLogger has support for multiple loggers at once.fromevaloggerimportEvaLoggerlog_1=EvaLogger(name="eva1",save_to_file=True,filename="eva1.log")log_2=EvaLogger(name="eva2",save_to_file=False)log_1.info("Hello World!")# log a info message to eva1.loglog_2.info("Hello World!")# log an info message to stdoutExampleYou can find all examples in the examples folder in the repository.
Package: evalpy
evalpyA lightweight framework for experiment logging and automatic visualizationEvalpy aims at researchers that want a fast and efficient framework to log their experiment configurations alongside
the results. This includes an interface to both log the parameters and metrics of any single run as well as the progression during a run
in a time series manner.The second part of evalpy includes the GUI which is provided within the package.
To start the GUI simply activate the environment in which you installed evalpy in a console
and execute the following:evalpy runQuickstartThe intended usage involves the following steps:Declaring the project root, a file pathDeclaring the project name, the name of the project directoryStarting a run with an experiment nameIn the run one can log one time the parameters and metrics
and do a step logging for the run progressionA minimal usage outline is as followsimport evalpy


evalpy.set_project('my_first_project_path', 'my_project_folder_name')
with evalpy.start_run('experiment_name'):
    for log_step_stuff in model_training():
        evalpy.log_run_step(log_step_stuff, step_forward=True)  
    evalpy.log_run_entries(model_parameters_and_metrics)  # both methods expect a dict as input
Package: eval_scrubber
Eval/Base64 File Scrubber [![Build Status](https://travis-ci.org/michigan-com/eval_scrubber.svg?branch=master)](https://travis-ci.org/michigan-com/eval_scrubber)=========================This script will walk through all files in a directory, find, and removeany content that is suspected to be malicious.This scan uses a regular expression to seek out any potentially malicious content```infected_pattern = re.compile(r"<\?php\s*eval\((.+\()*base64_decode\(.+\)\).+\s*?>")```What it is matching is `<?php eval(base64_decode()) ?>` or `<?php eval(gzinflate(base64_decode())) ?>`It is strongly advized to check that this regular expression will match your needs.We have not covered all of the edge cases for this script so be warned running thisscript could have negative consequences.The script accepts two arguments: action and directoryActions:* Find - Scans directory recursively and lists all potentially infected files* Remove - Scans directory recursively and removes the regular express matchfrom all potentially infected files```python -m eval_scrubber find <dir>python -m eval_scrubber remove <dir>```Set the log level```DEBUG=1 python -m eval_scrubber find <dir>```Install via PIP---------------```pip install eval_scrubber```CHANGELOG=========0.0.3 2015-10-05----------------* Better unicode support0.0.1 2015-10-05----------------* Uploaded to pypi
Package: evalseg
Medical Image Segmentation EvaluationThis project is intended to evaluate Medical Segmentation approaches from multiple prespective.What is included on this repository?📃 Documentation🐋 A simpleContainerfileto build a container image for your project.Containerfileis a more open standard for building container images than Dockerfile, you can use buildah or docker with this file.🧪 Testing structure usingpytest✅ Code linting usingflake8📊 Code coverage reports usingcodecov🛳️ Automatic release toPyPIusingtwineand github actions.🎯 Entry points to execute your program usingpython -m <evalseg>or$ evalsegwith basic CLI argument parsing.🔄 Continuous integration usingGithub Actionswith jobs to lint, test and release your project on Linux, Mac and Windows environments.evalsegInstall it from PyPIpipinstallevalsegpipinstallgit+https://github.com/modaresimr/evalsegUsagecommingsoon$python-mevalseg#or$evalsegDevelopmentRead theCONTRIBUTING.mdfile.
Package: eval-type-backport
eval_type_backportThis is a tiny package providing a replacement fortyping._eval_typeto support newer typing features in older Python versions.Yes, that's very specific, and yes,typing._eval_typeis a protected function that you shouldn't normally be using. Really this package is specifically made forhttps://github.com/pydantic/pydantic/issues/7873.Specifically, this transformsX | Yintotyping.Union[X, Y]andlist[X]intotyping.List[X]etc. (for all the types made generic in PEP 585)
if the original syntax is not supported in the current Python version.