Saravanan666/pypi-org huggingface dataset:
Package: modelbaker
modelbaker - the libraryThe modelbaker library is a package containing two main areas:Wrapper for ili2dbiliwrapperModules to generate a QGIS project from it's database source and interacting with this sourceThis Library is the backend of the QGIS PluginQGIS Model Baker.Installationpip install modelbakerStructure.
├── __init__.py
├── iliwrapper
│   ├── __init__.py
│   ├── globals.py
│   ├── ili2dbargs.py
│   ├── ili2dbconfig.py
│   ├── ili2dbtools.py
│   ├── ili2dbutils.py
│   ├── ilicache.py
│   ├── iliexecutable.py
│   ├── iliexporter.py
│   ├── iliimporter.py
│   ├── iliupdater.py
│   └── ilivalidator.py
├── dataobjects
│   ├── __init__.py
│   ├── fields.py
│   ├── form.py
│   ├── layers.py
│   ├── legend.py
│   ├── project.py
│   └── relations.py
├── dbconnector
│   ├── __init__.py
│   ├── config.py
│   ├── db_connector.py
│   ├── gpkg_connector.py
│   ├── mssql_connector.py
│   └── pg_connector.py
├── db_factory
│   ├── __init__.py
│   ├── db_command_config_manager.py
│   ├── db_factory.py
│   ├── db_simple_factory.py
│   ├── gpkg_command_config_manager.py
│   ├── gpkg_factory.py
│   ├── gpkg_layer_uri.py
│   ├── layer_uri.py
│   ├── mssql_command_config_manager.py
│   ├── mssql_factory.py
│   ├── mssql_layer_uri.py
│   ├── pg_command_config_manager.py
│   ├── pg_factory.py
│   └── pg_layer_uri.py
├── generator
│   ├── __init__.py
│   ├── config.py
│   ├── domain_relations_generator.py
│   └── generator.py
├── ilitoppingmaker
│   ├── __init__.py
│   ├── ili2dbsettings.py
│   ├── ilidata.py
│   ├── iliprojecttopping.py
│   ├── ilitarget.py
│   ├── metaconfig.py
└── utils
    ├── __init__.py
    ├── db_utils.py
    ├── globals.py
    ├── qgis_utils.py
    └── qt_utils.pyili2dbIn the current release we use ili2db version 4.10Infos for DevsCode styleIs enforced with pre-commit. To use, make:pip install pre-commit
pre-commit installAnd to run it over all the files (with infile changes):pre-commit run --color=always --all-fileNeeded packages from PyPINeeded packages from PyPI are downloaded and packaged on deployment to the plugin's libs folder.Run the script to download and unpack them or install them to your system.Script:./scripts/package_pip_packages.sh
Package: model-bakery
Model Bakery: Smart fixtures for better testsModel Bakeryoffers you a smart way to create fixtures for testing in
Django.
With a simple and powerful API you can create many objects with a single
line of code.Model Bakery is a rename of the legacyModel Mommy project.Installpipinstallmodel-bakeryUsage and InfoBasic usage# models.pyfromdjango.dbimportmodelsclassCustomer(models.Model):enjoy_jards_macale=models.BooleanField()name=models.CharField(max_length=30)email=models.EmailField()age=models.IntegerField()bio=models.TextField()days_since_last_login=models.BigIntegerField()birthday=models.DateField()last_shopping=models.DateTimeField()# test_models.pyfromdjango.testimportTestCasefrommodel_bakeryimportbakerfrompprintimportpprintclassTestCustomerModel(TestCase):defsetUp(self):self.customer=baker.make('shop.Customer')pprint(self.customer.__dict__)"""{'_state': <django.db.models.base.ModelState object at 0x1129a3240>,'age': 3841,'bio': 'vUFzMUMyKzlnTyiCxfgODIhrnkjzgQwHtzIbtnVDKflqevczfnaOACkDNqvCHwvtWdLwoiKrCqfppAlogSLECtMmfleeveyqefkGyTGnpbkVQTtviQVDESpXascHAluGHYEotSypSiHvHzFteKIcUebrzUVigiOacfnGdvijEPrZdSCIIBjuXZMaWLrMXyrsUCdKPLRBRYklRdtZhgtxuASXdhNGhDsrnPHrYRClhrSJSVFojMkUHBvSZhoXoCrTfHsAjenCEHvcLeCecsXwXgWJcnJPSFdOmOpiHRnhSgRF','birthday': datetime.date(2019, 12, 3),'enjoy_jards_macale': True,'id': 1,'last_shopping': datetime.datetime(2019, 12, 3, 21, 42, 34, 77019),'name': 'qiayYnESvqcYLLBzxpFOcGBIfnQEPx','days_since_last_login': 6016}"""Check outdocumentationfor more complete examples.ContributingDetailed infohere.MaintainersBernardo FontesRustem SaiargalievTim KleinCreatorVanderson Mota
Package: modelbase
modelbasemodelbase is a python package to help you build and analyze dynamic mathematical models of biological systems. It has originally been designed for the simulation of metabolic systems, but can be used for virtually any processes, in which some substances get converted into others.modelbase incorporates an easy construction method to define 'reactions'. A rate law and the stoichiometry need to be specified, and the system of differential equations is assembled automatically.modelbase allows 'algebraic modules', which are useful to implement rapid equilibrium or quasi steady-state approximations. In the simplest instance, they allow easy incorporation of conserved quantities.modelbase also allows a simple construction of isotope-specific models. This class contains a constructor method that automatically construct all isotope specific versions of a particular reaction. Very cool - check it out!Release notesVersions 1.0 and 0.4.0 introduced changes not compatible with the previous official
release 0.2.5. API changes are summarised inthis notebookVersion 0.4.5was the prior stable versionVersion 0.2.5is the official release for the submission of the
mansucript "Building mathematical models of biological systems
with modelbase, a Python package for semi-automatic ODE assembly
and construction of isotope-specific models" to the Journal of Open
Research Software.See changelog.md for details on changes of earlier versions.InstallationIf you quickly want to test out modelbase, or do not require assimulo support, install modelbase viapipinstallmodelbaseTo enable assimulo support, the easiest way is to install modelbase via conda. We also recommend using the conda-forge channels.# recommended to avoid package clashescondaconfig--addchannelsconda-forge# Create a new environment (not necessary, but recommended)condacreate-nmb39python=3.9
condainstall-cconda-forgemodelbaseLicenseGPL 3DocumentationThe official documentation is hostedhere on gitlab.Issues and supportIf you experience issues using the software please contact us through ourissuespage.Contributing to modelbaseAll contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. See ourcontribution guidefor more information.How to citeIf you use this software in your scientific work, please citethis article:van Aalst, M., Ebenhöh, O. & Matuszyńska, A. Constructing and analysing dynamic models with modelbase v1.2.3: a software update. BMC Bioinformatics 22, 203 (2021)doibibtex file
Package: modelbox
ModelBox Python APIThis package contains the client library for the ModelBox API for managing Deep Learning models, checkpoints from experiments, and other model operations related services.Concepts and Understanding the ModelBox APINamespaceA Namespace is a mechanism to organize related models or models published by a team. They are also use for access control and such to the metadata of uploaded models, invoking benchmarks or other model transformation work. Namespaces are automatically created when a new model or experieemnt specifies the namespace it wants to be associated with.ModelA model is an object to track common metadata, and to apply policies on models created by experiments to solve a machine learning task. For ex. datasets to evaluate all trained models of a task can be tracked using this object. Users can also add rules around retention policies of trained versions, setup policies for labelling a trained model if it has better metrics on a dataset, and meets all other criterion.Model VersionA model version is a trained model, it includes the model binary, related files that a user wants to track - dataset file handles, any other metadata, model metrics, etc. Model versions are always related to a Model and all the policies created for a Model are applied to Model Versions.Experiment and CheckpointsExperiments are used to injest model checkpoints created during a training run. ModelBox is not an experiment metadata tracker so there is no support for rich experiment management which are available on experiment trackers such as Weights and Biases, the experiment abstraction here exists so that we can track and injest model checkpoints which eventually become model versions if they have good metrics and does well in benchmarks.Examplefrom modelbox import ModelBoxClient, MLFramework

client = ModelBoxClient(SERVER_ADDR)

model = self._client.create_model(
            "yolo",
            "owner@email.com",
            "ai/vision/",
            "object_detection",
            "yolo_des",
            {"meta": "foo"},
        )
model_version = self._client.create_model_version(
            model.id, 
            "yolo4_v1",
            "v1",
            "A Yolo v4 trained with custom dataset", 
            ["s3://path/to/bucket/model.pt],
            {"model_hyperparam_1": "value"},
            MLFramework.PYTORCH,
        )

client.close()Local Development and InstallationThe modelbox client library can be installed locally in the following way -cd <project-root>/client-py/
pip install .This installs the version of the client checked out with the repo.Build the client and create distribution packagescd <project-root>/client-py/
python -m build .Run Testscd <project-root>/client-py/
python tests/test_modelbox_api.py
Package: model-card
Model Card ToolkitThe Model Card Toolkit (MCT) streamlines and automates generation ofModel Cards[1], machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with researchers, developers, reporters, and more.Some use cases of model cards include:Facilitating the exchange of information between model builders and product developers.Informing users of ML models to make better-informed decisions about how to use them (or how not to use them).Providing model information required for effective public oversight and accountability.InstallationThe Model Card Toolkit is hosted onPyPI, and can be installed withpip install model-card-toolkit(orpip install model-card-toolkit --use-deprecated=legacy-resolverfor pip20.3). Seethe installation guidefor more details.Getting Startedimport model_card_toolkit

# Initialize the Model Card Toolkit with a path to store generate assets
model_card_output_path = ...
mct = model_card_toolkit.ModelCardToolkit(model_card_output_path)

# Initialize the model_card_toolkit.ModelCard, which can be freely populated
model_card = mct.scaffold_assets()
model_card.model_details.name = 'My Model'

# Write the model card data to a JSON file
mct.update_model_card_json(model_card)

# Return the model card document as an HTML page
html = mct.export_format()Automatic Model Card GenerationIf your machine learning pipeline uses theTensorFlow Extended (TFX)platform orML Metadata, you can automate model card generation. Seethis demo notebookfor a demonstration of how to integrate the MCT into your pipeline.SchemaModel cards are stored in JSON as an intermediate format. You can see the model card JSON schema in theschemadirectory. Note that this is not a finalized path and may be hosted elsewhere in the future.References[1]https://arxiv.org/abs/1810.03993
Package: model-card-toolkit
Model Card ToolkitThe Model Card Toolkit (MCT) streamlines and automates generation ofModel Cards[1], machine learning documents that provide context and transparency into a model's development and performance. Integrating the MCT into your ML pipeline enables the sharing model metadata and metrics with researchers, developers, reporters, and more.Some use cases of model cards include:Facilitating the exchange of information between model builders and product developers.Informing users of ML models to make better-informed decisions about how to use them (or how not to use them).Providing model information required for effective public oversight and accountability.InstallationThe Model Card Toolkit is hosted onPyPI, and can be installed withpip install model-card-toolkit(orpip install model-card-toolkit --use-deprecated=legacy-resolverfor versions of pip starting with 20.3). Seethe installation guidefor more details.Getting Startedimport model_card_toolkit

# Initialize the Model Card Toolkit with a path to store generate assets
model_card_output_path = ...
mct = model_card_toolkit.ModelCardToolkit(model_card_output_path)

# Initialize the model_card_toolkit.ModelCard, which can be freely populated
model_card = mct.scaffold_assets()
model_card.model_details.name = 'My Model'

# Write the model card data to a proto file
mct.update_model_card(model_card)

# Return the model card document as an HTML page
html = mct.export_format()Model Card Generation on TFXIf you are usingTensorFlow Extended (TFX), you can
incorporate model card generation into your TFX pipeline via theModelCardGeneratorcomponent.TheModelCardGeneratorcomponent is moving to theTFX Addonslibrary and will no longer
be packaged in Model Card Toolkit from version 2.0.0. Before you can use the
component, you will need to install thetfx-addonspackage:pipinstalltfx-addons[model_card_generator]This page will be updated to include the new links for the Model Cards in TFX
guide and the end-to-end demo when the migration is completed.SchemaModel cards are stored in proto as an intermediate format. You can see the model card JSON schema in theschemadirectory.References[1]https://arxiv.org/abs/1810.03993
Package: modelChooserTransfer
Projeto de IC em autoML e transfer learningBases de dados originais utilizadas:https://www.kaggle.com/datasets/die9origephit/children-vs-adults-imageshttps://www.kaggle.com/datasets/saurabhshahane/barkvn50https://www.kaggle.com/datasets/paultimothymooney/blood-cellshttps://www.kaggle.com/datasets/tolgadincer/labeled-chest-xray-imageshttps://www.kaggle.com/datasets/itsahmad/indoor-scenes-cvpr-2019?ref=hackernoon.comhttps://www.kaggle.com/datasets/die9origephit/nike-adidas-and-converse-imagedhttps://www.kaggle.com/datasets/puneet6060/intel-image-classification?ref=hackernoon.comhttps://www.kaggle.com/datasets/muratkokludataset/pistachio-image-datasethttps://www.kaggle.com/datasets/lantian773030/pokemonclassificationhttps://www.kaggle.com/datasets/projectshs/pizza-classification-data
Package: modelcif
This is a Python package to assist in handlingmmCIFandBinaryCIFfiles compliant with theModelCIFextension. It works with Python 2.7 or Python 3.Pleasesee the documentationor someworked examplesfor more details.Installation with conda or pipIf you are usingAnaconda Python, install withconda install -c conda-forge modelcifOn a Fedora or RedHat Enterprise Linux box, install withdnf copr enable salilab/salilab; dnf install python3-modelcifAlternatively, install with pip:pip install modelcifInstallation from source codeTo build and install from a clone of the GitHub repository,
first build and install version 0.34 or later of thepython-ihmmodule. Then run:python setup.py build
python setup.py installIf you want to read or writeBinaryCIFfiles, you will also need the
Pythonmsgpackpackage.TestingThere are a number of testcases in thetestdirectory. Each one can be run
like a normal Python script to test the library. They can also be all run at
once usingnoseorpytest.
Package: modelcomp
modelcomp: comparison tool for machine learning modelsmodelcomp is a python package that helps you compare between machine learning models' performance on your dataset. It was originally developed for a microbiome research atBorenstein Lab, Tel Aviv University.Thanks toAlpha programfor making this happen.Dependenciesmodelcomp currently supports Python 3.6+.Check out thereqs filefor additional package requirements.InstallationThe latest stable release (and required dependencies) can be installed fromPyPI:pip install modelcompThe current version (0.0.1a1) is the version used in the research, and thus all pre-0.0.1 versions are very unstable and may be subject to backwards incompatible changes.Anaconda support coming soon!ContributingFeel free toreport an issuein the package repository.ResearchAn appendix with the dataset used, results & module is available atmodelcomp-appendix.
Package: model-container
The Model Container is used as an abstraction layer on top of a simulator and deals with biological entities and end-user concepts instead of mathematical equations. It provides a solver independent internal storage format for models that allows user independent optimizations of the numerical core. By containing the biological model, the Model Container makes the implementation of the numerical core independent of software implementation.
The Model Container API abstracts away all the mathematical and computational details of the simulator. Optimized to store large models in little memory it stores neuronal models in a fraction of the memory that would be used by conventional simulators and provides automatic partitioning of the model such that simulations can be run in parallel. From the modeler’s perspective, the Model Container will be able to import and export NeuroML files to facilitate model exchange and ideas.
Package: model-contrast
Models ContrastA simple package for compare the performance of two ML models in sklearn, python.InstallationUse the package managerpipto install model-contrast.pipinstallmodel-contrastUsageCompare 2 Binary Classifiersfromsklearn.datasetsimportmake_classificationfromsklearn.ensembleimportRandomForestClassifierfromsklearn.linear_modelimportLogisticRegressionfromsklearn.model_selectionimporttrain_test_split# create two demo modelsX,y=make_classification(n_samples=700,random_state=42)X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)model1=RandomForestClassifier(n_estimators=10,random_state=42)model2=LogisticRegression()#train the modelsmodel1.fit(X_train,y_train)model2.fit(X_train,y_train)Now let's compare them with our package:frommodel_contrastimportclassificator_contrastclassificator_contrast(model1,model2,X_test,y_test)and it return:Compare Multi-Class Classifiersfromsklearn.datasetsimportmake_classificationfromsklearn.ensembleimportRandomForestClassifierfromsklearn.linear_modelimportLogisticRegressionfromsklearn.model_selectionimporttrain_test_split# create two demo modelsX,y=make_classification(n_samples=700,random_state=42,n_classes=4,n_informative=4)X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)model1=RandomForestClassifier(n_estimators=10,random_state=42)model2=LogisticRegression()#train the modelsmodel1.fit(X_train,y_train)model2.fit(X_train,y_train)Compare them:frommodel_contrastimportclassificator_contrastclassificator_contrast(model1,model2,X_test,y_test)and it returns:Compare 2 Regressorsfromsklearn.datasetsimportmake_regressionfromsklearn.model_selectionimporttrain_test_splitfromsklearn.linear_modelimportLinearRegressionfromsklearn.ensembleimportRandomForestRegressor#create the regressorX,y=make_regression(n_samples=700,random_state=42)X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=42)model1=RandomForestRegressor(n_estimators=10,random_state=42)model2=LinearRegression()#train the regressorsmodel1.fit(X_train,y_train)model2.fit(X_train,y_train)Compare them:frommodel_contrastimportregressor_contrastregressor_contrast(model1,model2,X_test,y_test)and it returns:ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: model-creator-bird-sing-v2
IA singing birdThis is my final project of my engineering school. It has been made in one month with an other student
who works in cloud computing. The idea was to generate birds songs with an Artificial Intelligence. We
started with a tutorial from Valerio Velardo on sound VAE. We changed a bit the model to make it more
more complex. We trained the model on Deep Learning machins because we needed GPU. Moreover, we tried
different achitectures to find the best model.The modelHow to use the modelThis work is made to be test and used again by professionals of birds. You can install this part of the work on Pypi :pip install model-creator-bird-sing-v2
Package: modeldb
A python client for working withModelDB machine learning management system.This library makes it easy for users of theModelDB ML management systemto automatically catalog models built withscikit-learn.ModelDB is an end-to-end system for managing machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB runs on Python 2.X and 3.X and can be used with any ML environment via the ModelDB Light API.Quick startInstallYou can install it usingpipdirectly from PyPI:pip install modeldbCustom ConfigurationOnce installed, you can create a custom syncing scheme setup by typing:python -m modeldb create_configUnless an alternative syncing scheme is specialized, modeldb will use the packagedsyncer.jsondefaults.UseThis library requires a connection to a ModelDB server to work. You can see thegetting started docs here.Additional documentation on thelight_apiandscikit-learn clientis also available.
Package: modeldb-basic
A basic python client for working withModelDB machine learning management system.Visit the original project at :https://github.com/mitdbg/modeldbThis project goal is to try isolate a basic client to operate within model database, where main features will be:Sync projects/models from file (json, yaml)Sync projects/models by using basic mechanism (without sklearn dependencies)Python 2.7 and 3.{5,6} compatibility.Quick startInstallRemotelyThe most widely known way to install a python package is bypipcommand.
The python package is available at [pypi repository](https://pypi.org/project/modeldb-basic/) (legacy repo [here](https://pypi.python.org/pypi/modeldb-basic)).Just type thispipcommand to install it from pypi package repository:pip install modeldb-basicAlternatively it’s possible to install it by using any of these URLs:pip installgit+https://github.com/engapa/modeldb-basic[@<git_ref>]#egg=modeldb-basicpip install <release_file>Where [@<git_ref>] is an optional reference to a git reference (i.e: @master, @v0.1.6) and
<release_file> is the URL of one release file athttps://github.com/engapa/modeldb-basic/releasesLocallyPreviously downloaded in your host, somebody may install the package by typing:pip install .or:python setup.py install
Package: modeldb-community
A python client has xgboost support for working withModelDB machine learning management system.This library makes it easy for users of theModelDB ML management systemto automatically catalog models built withxgboost pyspark tensorflow scikit-learn.Extend by mullerhttps://github.com/mullerhai/tsxgb.gitModelDB is an end-to-end system for managing machine learning models. It ingests models and associated metadata as models are being trained, stores model data in a structured format, and surfaces it through a web-frontend for rich querying. ModelDB runs on Python 2.X and 3.X and can be used with any ML environment via the ModelDB Light API.Quick startInstallYou can install it usingpip3directly from PyPI:pip3 install modeldb-community  #suggest python 3.6Custom ConfigurationOnce installed, you can create a custom syncing scheme setup by typing:python3 -m modeldb create_configUnless an alternative syncing scheme is specialized, modeldb will use the packagedsyncer.jsondefaults.UseThis library requires a connection to a ModelDB server to work. You can see thegetting started docs here.Additional documentation on thelight_apiandscikit-learn clientis also available.