Saravanan666/pypi-org huggingface dataset:
Package: mltable
# mltable: machine learning table data toolkit
MLTable is a Python package that provides fast, flexible data loading functions designed to make accessing “tabular” data easy and intuitive. MLTable will help you to abstract the schema definition for tabular data so that it is easier to materialize the table into a Pandas dataframe.
MlTable can be leveraged upon delimited text files, parquet files, delta lake, json-lines files from a cloud object store or local disk.## Main FeaturesHere are a few things that mltable does well:Flexiblesampling and filteringfunctionality on large dataRobust IO tools for loading data fromflatfiles (CSV and delimited),parquetfiles,delta lakeandjson-lines filesCapturing and definingschemacontained in flat filesFast materialization of data into Pandas DataFrame## Getting startedYou can install MLTable package via pip.`bash pip install mltable `Please note MLTable package is pre-installed on AzureML compute instances.## DocumentationThe official documentation is hosted on [working with tables](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli).MLTable artifact’s metadata file is called  MLTable which adheres to the [AzureML MLTable schema](https://learn.microsoft.com/en-us/azure/machine-learning/reference-yaml-mltable).# Release History## 1.6.1 (2024-01-24)
### Features Added
- added authrization support
-MLTable.save()bug fixes## 1.5.0 (2023-08-14)
### Features Added
-MLTable.save()supports cloud storage. Please find more details [here](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mltable?view=azureml-api-2&tabs=cli).
-from_delta_lakesupports pulling latest version by default### Bugs Fixed
- Fixsupport_multi_lineissue forMLTable.from_delimited_files## 1.4.1 (2023-06-19)
### Bugs Fixed
- Relaxing cryptography library dependency to allow versions greater than 41.*.*## 1.4.0 (2023-05-31)
### Features Added
- Updating runtime dependencies
- Improved error handling and argument validation## 1.3.0 (2023-04-07)
### Features Added
- bugfix (user error mapping, mltable save/load roundtrip)## 1.2.0 (2023-02-22)### Features Added
- bugfix (mltable save/load, validation schema)## 1.1.0 (2023-01-26)### Features Added
- bugfix (fix schema, flake8 errors)
- improve logging and exception message## 1.0.0 (2022-12-05)### Features Added
- factory apis(from_delta_lake)
- Authoring apis(convert_column_types, save, skip etc)## 0.1.0b4 (2022-10-05)### Features Added
- Factory apis(from_paths, from_delimited_files, from_parquet_files, from_json_lines_files).
- Authoring apis(keep_columns, drop_columns, take_random_sample, take etc).
- Support mltable load from data asset uri## 0.1.0b3 (2022-06-30)## 0.1.0b2 (2022-05-23)## 0.1.0b1 (2022-05-17)### Features Added
- Initial public preview release to load into pandas dataframe
Package: mltb2
Machine Learning Toolbox 2 - MLTB2📦 A box full of machine learning tools. 📦Documentation PageHow to contributeInstallationMLTB2 is available atthe Python Package Index (PyPI).
It can be installed with pip:pipinstallmltb2For many modules optional dependencies are necessary.
Those optional dependencies must be installed when you want to use the module.
To install those module specific dependencies seethe description of the individual modules.LicensingCopyright (c) 2023Philip MayCopyright (c) 2023Philip May,Deutsche Telekom AGLicensed under theMIT License(the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License by reviewing the fileLICENSEin the repository.
Package: mltemplate
ML TemplateML template is an easy-to-use tool to automate the boilerplate code for most machine learning projects.This tool creates a user-oriented project architecture for machine learning projects.Modify the code under#TODOcomments in the template project repository to easily adapt the template to your use-case.How to use it?Install the package as -pip install mltemplateThen, simply runmltempate <project-name>from your terminal to create your templateAnd Voila!This creates a project directory in your current folder similar to -template
├── Dockerfile.cpu
├── Dockerfile.gpu
├── LICENSE.md
├── Makefile
├── README.md
├── jupyter.sh
├── requirements.txt
└── template
    ├──__init__.py
    ├──__main__.py
    ├── cli
    │   ├──__init__.py
    │   ├── predict.py
    │   └── train.py
    ├── notebooks
    └── src
        ├──__init__.py
        ├── models.py
        ├── datasets.py
        └── transforms.pyAll you have to do next is -Update python frameworks and versions intemplate/requirements.txtas need for your projectHead totemplate/datasets.pyand modify create a new dataset that will work for your use caseNavigate totemplate/models.pyand create a new model class with your sota (or not) architectureIntemplate/transforms.pyadd transforms such as Normalizer, Denormalize etc.Follow theTODOsteps intemplate/cli/train.pyandtemplate/cli/predict.pyto make the necessary changesCheckout theREADME.mdin thetemplatedirectory for further instructions on how to train, predict and also monitor your loss plots using tensor board.Future WorkCurrently, this package only supports boilerplate creation for ML projects inpytorchWe plan to supportkerasin the future.DevelopmentLocal TestingRun the following command to generate the packaged librarypoetrybuildInstall the library from the generated whl file usingpip3installdist/mltemplate-<version>-py3-none-any.whl--force-reinstallYou can then test the functionality of the pypi package.Publish package to PyPiTo create a new version of the framework, update the version inpyproject.tomlfile
Merge your changes to main and then publish git tag to trigger the release cigittag<x.x.x>
gitpushorigin<x.x.x>LicenseCopyright © 2020 Sowmya YellapragadaDistributed under the MIT License (MIT).
Package: mltome
A collection of ML tools that builds on Python ML frameworks. The API of these tools arenotstable!InstallationYou can install mltome directly from pypi:pipinstallmltomemltome.pytorch- For installation instructions for PyTorch, visit thePyTorch website.DevelopmentThe development version can be installed by runningmake dev. Then we can lintmake lintand tests by runningpytest.
Package: mltool
mltool is a simpleLearning to Ranktool to build a regression tree based ranking model.
Currently mltool supports simple CART andRandom Forest. The implementation is strongly
inspired byrt-rank, but it lacks the support ofGradient Boosting Regression Trees.OverviewFeaturesDespite rt-rank, mltool provides:A parameter to set the seed for the random number generator to make the training
deterministicSerializable modelShow feature information gain statistics at the end of the trainingCan be used as an APISome highlights compared to other Random Forest implemented for Python:The implementation of the Random Forest makes use of numpy and it is quite optimizedParallel Random Forest trainingInstallationInstall from PyPI using pip:$ pip install mltoolNow you can run mltool:$ mltool -hDocumentationThe documentation is hosted byRead the Docsat the following url:http://readthedocs.org/docs/mltool/en/latest/Futureadd support for Stochastic Gradient Boosting Regression Treesadd support for simple regression and/or classification (i.e. not just focus on ranking)
Package: ml-tooling
Model Tooling libraryInstallationUse pip to install:pip install ml-toolingOr use condaconda install -c conda-forge ml_toolingTestWe usetoxfor managing build and test environments, to installtoxrun:pip install toxAnd to run tests:tox -e pyExample usageDefine a class using ModelData and implement the two required methods.
Here we simply implement a linear regression on the Boston dataset using sklearn.datasetsfromsklearn.datasetsimportfetch_california_housingfromsklearn.linear_modelimportLinearRegressionfromml_toolingimportModelfromml_tooling.dataimportDataset# Define a new data classclassCaliforniaData(Dataset):defload_prediction_data(self,idx):x,_=fetch_california_housing(return_X_y=True)returnx[idx]# Return given observationdefload_training_data(self):returnfetch_california_housing(return_X_y=True)# Instantiate a model with an estimatorlinear_california=Model(LinearRegression())# Instantiate the datadata=CaliforniaData()# Split training and test datadata.create_train_test()# Score the estimator yielding a Result objectresult=linear_california.score_estimator(data)# Visualize the resultresult.plot.prediction_error()print(result)<ResultLinearRegression:{'r2':0.68}>LinksDocumentation:https://ml-tooling.readthedocs.ioReleases:https://pypi.org/project/ml_tooling/Code:https://github.com/andersbogsnes/ml_toolingIssue Tracker:https://github.com/andersbogsnes/ml_tooling/issues
Package: mltoolkit
InstallationCreate your specific environment (with conda)An empty onecondacreate--namedatascpythonFrom a existing oneYou can check the list of current envs as followcondainfo--listYou can then clone the root env like thiscondacreate--namedatasc--clonerootActivate your environmentsourceactivatedatascInstall the mltoolkit libraryFrom Binstar (Anaconda.org)condainstall-chttps://conda.anaconda.org/tounnas-chttps://conda.anaconda.org/automltoolkitFrom sourcecondainstallconda-buildgitclonegit@github.com:tounnas/mltoolkit.gitcdmltoolkit/_buildcondabuild.condainstall--use-localmltoolkitUtilisationHave a look to the examples folderLicenseBSDDocumentationTheofficial documentation.
Package: ml-toolkit
nbdev templateUse this template to more easily create yournbdevproject.If you are using an older version of this template, and want to upgrade to the theme-based version, seethis helper script(more explanation of what this means is contained in the link to the script).Troubleshooting TipsMake sure you are using the latest version of nbdev withpip install -U nbdevIf you are using an older version of this template, see the instructions above on how to upgrade your template.It is important for you to spell the name of your user and repo correctly insettings.inior the website will not have the correct address from which to source assets like CSS for your site.  When in doubt, you can open your browser's developer console and see if there are any errors related to fetching assets for your website due to an incorrect URL generated by misspelled values fromsettings.ini.If you change the name of your repo, you have to make the appropriate changes insettings.iniAfter you make changes tosettings.ini, runnbdev_build_lib && nbdev_clean_nbs && nbdev_build_docsto make sure all changes are propagated appropriately.
Package: ml-toolkit-1
# ML-toolkit# Python package basicsCreate Source distribution:python setup.py sdistInstall package:pip install ml-toolkit-1-0.0.1.tar.gz(installs package into python site-packages)Create Wheel:python setup.py bdist_wheelUpload to pypi:Test server:twine upload –repository-url https://test.pypi.org/legacy/ -r ml-toolkit-prajit dist/ml-toolkit-1-0.0.1.tar.gzpypi.org:twine upload –repository-url https://upload.pypi.org/legacy/ -r ml-toolkit-prajit dist/ml-toolkit-1-0.0.1.tar.gz
Package: mltraq
Track and Collaborate on AI Experiments.The open-source Python library for AI developers to design, execute and share experiments.
Track anything, reproduce, collaborate, and resume the computation state anywhere.Documentation:https://www.mltraq.comSource code:https://github.com/elehcimd/mltraq(License:BSD 3-Clause)Discussions:Ask questions, share ideas, engageFunding: You cansponsor,cite,starthe project, andhire mefor DS/ML/AI work
Package: mltree
mltreeInstallpip install mltreeHow to useFirst, load the analytical base table:frommltree.trainimporttrain_tree_modelsimportpandasaspdfrompathlibimportPathpath=Path('..')datasets_path=path/'datasets'df=pd.read_csv(datasets_path/'churn_abt.csv')df.head()<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}</style>data_ref_safraseller_iduftot_orders_12mtot_items_12mtot_items_dist_12mreceita_12mrecencianao_revendeu_next_6m02018-01-010015a82c2db000af6aaaf3ae2ecb0532SP3312685.0074112018-01-01001cca7ae9ae17fb1caed9dfb1094831ES171207921275.232022018-01-01002100f778ceb8431b7a1020ff7ab48fSP384215781.802032018-01-01003554e2dce176b5555353e4f3555ac8GO111120.0016142018-01-01004c9cd9d87a3c30c522c48c4fc07416SP1301417516228.8880Split into train and test or out of time datasets:df_train=df.query('data_ref_safra < "2018-03-01"')df_oot=df.query('data_ref_safra == "2018-03-01"')Get features metadata and types:key_vars=['data_ref_safra','seller_id']target='nao_revendeu_next_6m'num_vars=[varforvarindf.select_dtypes(include='number').columns.tolist()ifvarnotin[target]]cat_vars=[varforvarindf.select_dtypes(exclude='number').columns.tolist()ifvarnotinkey_vars]Train based tree models:train_tree_models(df_train,df_oot,target=target,folds=5,cat_features=cat_vars,num_features=num_vars,seed=42){'dt': {'auc': {'train': 0.9139680595991275, 'test': 0.8968114296299949}},
 'rf': {'auc': {'train': 0.9072972070544887, 'test': 0.8964968670043654}}}
Package: mltreelib
mltreeThis package evovled from the attempt to makerightkind ofDecision Tresswhich was ideated by many people like Hastie,
Tibshirani, Friedman, Quilan, Loh, Chaudhari.InstallpipinstallmltreelibHow to useCreate a sample dataimportnumpyasnpimportpandasaspdfrommltreelib.dataimportDatasetfrommltreelib.treeimportTreen_size=1000rnd=np.random.RandomState(1234)dummy_data=pd.DataFrame({'numericfull':rnd.randint(1,500,size=n_size),'unitint':rnd.randint(1,25,size=n_size),'floatfull':rnd.random_sample(size=n_size),'floatsmall':np.round(rnd.random_sample(size=n_size)+rnd.randint(1,25,size=n_size),2),'categoryobj':rnd.choice(['a','b','c','d'],size=n_size),'stringobj':rnd.choice(["{:c}".format(k)forkinrange(97,123)],size=n_size)})dummy_data.head()<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}</style>numericfullunitintfloatfullfloatsmallcategoryobjstringobj0304180.9089598.56ac1212240.34858214.35ag2295150.39297721.98ay354200.7208565.33aq4205210.89758823.03ckCreate a Datasetdataset=Dataset(df=dummy_data)print(dataset)print('Pandas Data Frame        : ',np.round(dummy_data.memory_usage(deep=True).sum()*1e-6,2),'MB')print('Dataset Structured Array : ',np.round(dataset.data.nbytes*1e-6/1024*1024,2),'MB')dataset.data[:5]Dataset(df=Shape((1000, 6), reduce_datatype=True, encode_category=None, add_intercept=False, na_treatment=allow, copy=False, digits=None, n_category=None, split_ratio=None)
Pandas Data Frame        :  0.15 MB
Dataset Structured Array :  0.03 MB

array([(304, 18, 0.9089594 ,  8.56, 'a', 'c'),
       (212, 24, 0.34858167, 14.35, 'a', 'g'),
       (295, 15, 0.39297667, 21.98, 'a', 'y'),
       ( 54, 20, 0.7208556 ,  5.33, 'a', 'q'),
       (205, 21, 0.89758754, 23.03, 'c', 'k')],
      dtype=[('numericfull', '<u2'), ('unitint', 'u1'), ('floatfull', '<f4'), ('floatsmall', '<f4'), ('categoryobj', 'O'), ('stringobj', 'O')])
Package: mltrics
MltricsOfficial command line utility to use Mltrics API programmatically.We help businesses evaluate, compare, and monitor machine learning models in production. Therefore, identify failure cases and take action immediately.InstallationMltrics and its required dependencies can be installed using pip:pipinstallmltricsUsageOnce mltrics package is installed, check out the following usage documentation:Authenticate to mltrics platformfrommltrics.mltricsimportMltricsClientimportgetpassusername=input("Enter username: ")password=getpass.getpass(prompt='Enter password: ')client=MltricsClient(username=username,password=password,env="prod")Update user profileorganization,full_name="<your-organization-name>","<Your name>"client.update_user_profile(organization=organization,full_name=full_name)Create model and upload predictionsCreate a baseline modelbaseline_model_id,baseline_model_name='nn_iter_10k','Neural network (trained for 10K iters)'baseline_model=client.create_model(baseline_model_id,baseline_model_name)print(baseline_model)Update model detailsbaseline_model_new_name="Neural network (trained for 20K iters)"updated_model=client.update_model_details(baseline_model_id,baseline_model_new_name)print(updated_model)Get uploaded modelsmodels=client.get_models()modelsUpload predictions for modelbaseline_preds=[{'pred_class':'dog','label_class':None,'model_id':baseline_model_id,'image_id':'img1','image_url':'https://mltrics.s3.us-west-2.amazonaws.com/datasets/cats_vs_dogs/Cat/10896.jpg','pred_file':None,'predictions':{},},]response=client.upload_model_predictions(baseline_model_id,baseline_preds)print(response)See all uploaded predictionspredictions=client.get_model_predictions(baseline_model_id)predictionsCreate candidate model and upload predictions for candidate model### Create candidate model and upload predictionscandidate_model_id,candidate_model_name="nn_50k_iter","Neural Network (50K iter)"candidate_model=client.create_model(candidate_model_id,candidate_model_name)candidate_preds=[{'pred_class':'cat','label_class':None,'model_id':candidate_model_id,'image_id':'img1','image_url':'https://mltrics.s3.us-west-2.amazonaws.com/datasets/cats_vs_dogs/Cat/10896.jpg','pred_file':None,'predictions':{},},]response=client.upload_model_predictions(candidate_model_id,candidate_preds)print(response)Get model comparison between baseline and candidate modelcomparison_results=client.compare_model_predictions(baseline_model_id,candidate_model_id)print(comparison_results)
Package: mltrip-runner
runnerSimple DAG (Directed Acyclic Graph) implementation based on python concurrent.futuresInstallationRunner onlyDownloadand install PythonInstall runnerpipinstallmltrip-runnerDownloadJSONorYAMLDAG examplePlot DAGpython-mrunneraction.json--plotorpython-mrunneraction.yml--plot5. Run DAGpython-mrunneraction.jsonorpython-mrunneraction.ymlWithrunner-hubDownloadand install PythonDownloador clone runner-hubgitclonehttps://github.com/PC-Trip/runner-hub.gitInstall some requirements.txtcdrunner-hub/runner_hub/gmsh
pipinstall-rrequirements.txtPlot some DAGcdrunner-hub/runner_hub/gmsh/tenex/container_simple/dag
python-mrunneroptimize/optimize.json--plot5. Run some DAGcdrunner-hub/runner_hub/gmsh/tenex/container_simple/dag
python-mrunneroptimize/optimize.jsonSee resultscdrunner-hub/runner_hub/gmsh/tenex/container_simple/dag/work
Package: mltrons-sdk
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.Description: Automated Machine Learning PlatformPlatform: UNKNOWN
Package: mltu
MLTU - Machine Learning Training UtilitiesMachine Learning Training Utilities forTensorFlow 2.*andPyTorchwith Python 3Installation:To use MLTU in your own project, you can install it from PyPI:pipinstallmltuWhen running tutorials, it's necessary to install mltu for a specific tutorial, for example:pipinstallmltu==0.1.3Each tutorial has its own requirements.txt file for a specific mltu version. As this project progress, the newest versions may have breaking changes, so it's recommended to use the same version as in the tutorial.Tutorials and Examples can be found onPyLessons.comText Recognition With TensorFlow and CTC network, code inTutorials\01_image_to_wordfolder;TensorFlow OCR model for reading Captchas, code inTutorials\02_captcha_to_textfolder;Handwriting words recognition with TensorFlow, code inTutorials\03_handwriting_recognitionfolder;Handwritten sentence recognition with TensorFlow, code inTutorials\04_sentence_recognitionfolder;Introduction to speech recognition with TensorFlow, code inTutorials\05_speech_recognitionfolder;Introduction to PyTorch in a practical way, code inTutorials\06_pytorch_introductionfolder;Using custom wrapper to simplify PyTorch models training pipeline, code inTutorials\07_pytorch_wrapperfolder;Handwriting words recognition with PyTorch, code inTutorials\08_handwriting_recognition_torchfolder;Transformer training with TensorFlow for Translation task, code inTutorials\09_translation_transformerfolder;
Package: ml-tuning
ml_tuningml_tuning is a python module to tune some machine learning functions parametersInstallationDependenciesscikit-learn requires:

- Python (>= 3.6)
- NumPy (>= 1.13.3)**ml_tuning is 0.0.1 is the latest version, right now only support kNN algorithm.User installationpip install ml_tuningSource CodeYou can download the code fromhttps://github.com/Eajay/ml_tuning.gitTesting CaseAfter installation, if you want to try test cases, make sure install sklearn first
for input training data.How to use itfrom ml_tuning import kNN_tuning
knn = kNN_tuning.kNN_tuning
Package: mltz-base
mltz_baseBase Package for ML related devInstallation$pipinstallmltz_basefromPypi repoUsageComing soonContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licensemltz_basewas created by Tony Zoght. It is licensed under the terms of the MIT license.Creditsmltz_basewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.Code CoverageCodeCovRead The Docshttps://mltz-base.readthedocs.io/en/latest/?badge=latestGithub Pageshttps://tzoght.github.io/mltz-base/
Package: mlutil
mlutilUtilities for ML models.Whenever possibles, modules are made to be compatible withscikit-learn.Installationpip install mlutilModuleseval(cross-validation)model(ML models)transform(feature transformers)tune(TBD: ML model tuning)Evalimportnumpyasnpfromsklearn.linear_modelimportLinearRegressionfromsklearn.model_selectionimportcross_validatefrommlutil.evalimportTimeSeriesSplitX=np.vstack([np.random.normal(size=100),np.random.normal(size=100)]).Ty=np.random.normal(size=100)m=LinearRegression()cv=TimeSeriesSplit(test_size=50)scores=cross_validate(m,X,y,scoring=["neg_mean_squared_error"],cv=cv)Modelimportnumpyasnpfrommlutil.modelimportGAMm=GAM(n_splines=5)X=np.arange(20)[:,None]y=np.arange(20)+np.random.normal(scale=0.1,size=20)m.fit(X,y)X_test=np.arange(15,25)[:,None]y_test=np.arange(15,25)y_hat=m.predict(X_test)np.testing.assert_allclose(y_test,y_hat,atol=1.0)m.summary()Transformimportnumpyasnpimportpandasaspdfrommlutil.transformimportColumnSelector,SigmaClipperX=pd.DataFrame({"a":[np.nan,-1.0,2.0,1.0,1.0,302.0],"b":[-2.0,1.0,3.0,2.0,-201,np.nan],})t=ColumnSelector(SigmaClipper(low_sigma=3,high_sigma=3))X_new_=t.fit_transform(X)
Package: mlutils
mlutils package is collection of various utilities for machine learning and AI planning.-- N-Ary tree class supported various search algorithms: pre-order, post-order, breadth-firstheuristic (you should provide heuristic function) and random sampling.-- State space generation/search.-- Basic graph class. Implements generic directed graph.-- Finite automata graph. Implements discrete finite automata state machine on base of BasicGraph class.-- Processing graph. Implements network of asynchronius processing units, running in separate threads.Intended to be used as complex pipeline (pipenet) for machne learning or data processing.Dependency:Package does not have extra dependencies except python standard libraryInstallation:Standard installation for pure python modulesUsage examples:Each modute has test() function which implement brief self-testing and may serve as usage example