{ "info": { "author": "Neuraxio Inc.", "author_email": "guillaume.chevalier@neuraxio.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Financial and Insurance Industry", "Intended Audience :: Healthcare Industry", "Intended Audience :: Information Technology", "Intended Audience :: Manufacturing", "Intended Audience :: Science/Research", "Intended Audience :: System Administrators", "Intended Audience :: Telecommunications Industry", "License :: OSI Approved :: Apache Software License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Topic :: Adaptive Technologies", "Topic :: Office/Business", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Artificial Life", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Image Recognition", "Topic :: Scientific/Engineering :: Information Analysis", "Topic :: Scientific/Engineering :: Interface Engine/Protocol Translator", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Medical Science Apps.", "Topic :: Scientific/Engineering :: Physics", "Topic :: Software Development", "Topic :: Software Development :: Assemblers", "Topic :: Software Development :: Build Tools", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Software Development :: Object Brokering", "Topic :: Software Development :: Pre-processors", "Topic :: Software Development :: Quality Assurance", "Topic :: Software Development :: Testing", "Topic :: System", "Topic :: System :: Clustering", "Topic :: System :: Distributed Computing", "Topic :: System :: Networking", "Topic :: Text Processing", "Topic :: Text Processing :: Filters", "Topic :: Text Processing :: Linguistic", "Topic :: Utilities", "Typing :: Typed" ], "description": "Neuraxle Pipelines\n==================\n\nCode Machine Learning Pipelines - The Right Way.\n\n.. image:: https://img.shields.io/github/workflow/status/Neuraxio/Neuraxle/Test%20Python%20Package/master? :alt: Build\n :target: https://github.com/Neuraxio/Neuraxle\n\n.. image:: https://img.shields.io/gitter/room/Neuraxio/Neuraxle? :alt: Gitter\n :target: https://gitter.im/Neuraxle/community\n\n.. image:: https://img.shields.io/pypi/l/neuraxle? :alt: PyPI - License\n :target: https://www.neuraxle.org/stable/Neuraxle/README.html#license\n\n.. image:: https://img.shields.io/pypi/dm/neuraxle? :alt: PyPI - Downloads\n :target: https://pypi.org/project/neuraxle/\n\n.. image:: https://img.shields.io/github/v/release/neuraxio/neuraxle? :alt: GitHub release (latest by date)\n :target: https://pypi.org/project/neuraxle/\n\n.. image:: https://img.shields.io/codacy/grade/d56d39746e29468bac700ee055694192? :alt: Codacy\n :target: https://www.codacy.com/gh/Neuraxio/Neuraxle/dashboard\n\n.. image:: assets/images/neuraxle_logo.png\n :alt: Neuraxle Logo\n :align: center\n\nNeuraxle is a Machine Learning (ML) library for building machine learning pipelines.\n\n- **Component-Based**: Build encapsulated steps, then compose them to build complex pipelines.\n- **Evolving State**: Each pipeline step can fit, and evolve through the learning process\n- **Hyperparameter Tuning**: Optimize your pipelines using AutoML, where each pipeline step has their own hyperparameter space.\n- **Compatible**: Use your favorite machine learning libraries inside and outside Neuraxle pipelines.\n- **Production Ready**: Pipeline steps can manage how they are saved by themselves, and the lifecycle of the objects allow for train, and test modes.\n- **Streaming Pipeline**: Transform data in many pipeline steps at the same time in parallel using multiprocessing Queues.\n\nDocumentation\n-------------\n\nYou can find the Neuraxle documentation `on the website `_. It also contains multiple examples demonstrating some of its features.\n\n\nInstallation\n------------\n\nSimply do:\n\n.. code:: bash\n\n pip install neuraxle\n\n\nExamples\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWe have several `examples on the website `__.\n\nFor example, you can build a time series processing pipeline as such:\n\n.. code:: python\n\n p = Pipeline([\n TrainOnlyWrapper(DataShuffler()),\n WindowTimeSeries(),\n MiniBatchSequentialPipeline([\n Tensorflow2ModelStep(\n create_model=create_model,\n create_optimizer=create_optimizer,\n create_loss=create_loss\n ).set_hyperparams(HyperparameterSpace({\n 'hidden_dim': 12,\n 'layers_stacked_count': 2,\n 'lambda_loss_amount': 0.0003,\n 'learning_rate': 0.001\n 'window_size_future': sequence_length,\n 'output_dim': output_dim,\n 'input_dim': input_dim\n })).set_hyperparams_space(HyperparameterSpace({\n 'hidden_dim': RandInt(6, 750),\n 'layers_stacked_count': RandInt(1, 4),\n 'lambda_loss_amount': Uniform(0.0003, 0.001),\n 'learning_rate': Uniform(0.001, 0.01),\n 'window_size_future': FixedHyperparameter(sequence_length),\n 'output_dim': FixedHyperparameter(output_dim),\n 'input_dim': FixedHyperparameter(input_dim)\n }))\n ])\n ])\n\n # Load data\n X_train, y_train, X_test, y_test = generate_classification_data()\n\n # The pipeline will learn on the data and acquire state.\n p = p.fit(X_train, y_train)\n\n # Once it learned, the pipeline can process new and\n # unseen data for making predictions.\n y_test_predicted = p.predict(X_test)\n\nYou can also tune your hyperparameters using AutoML algorithms such as the TPE:\n\n.. code:: python\n\n auto_ml = AutoML(\n pipeline=pipeline,\n hyperparams_optimizer=TreeParzenEstimatorHyperparameterSelectionStrategy(\n number_of_initial_random_step=10,\n quantile_threshold=0.3,\n number_good_trials_max_cap=25,\n number_possible_hyperparams_candidates=100,\n prior_weight=0.,\n use_linear_forgetting_weights=False,\n number_recent_trial_at_full_weights=25\n ),\n validation_splitter=ValidationSplitter(validation_size=0.20),\n scoring_callback=ScoringCallback(accuracy_score, higher_score_is_better=True),\n callbacks[\n MetricCallback(f1_score, higher_score_is_better=True),\n MetricCallback(precision, higher_score_is_better=True),\n MetricCallback(recall, higher_score_is_better=True)\n ],\n n_trials=7,\n epochs=10,\n refit_best_trial=True,\n )\n\n # Load data, and launch AutoML loop !\n X_train, y_train, X_test, y_test = generate_classification_data()\n auto_ml = auto_ml.fit(X_train, y_train)\n\n # Get the model from the best trial, and make predictions using predict.\n y_pred = auto_ml.predict(X_test)\n\n\n--------------\nWhy Neuraxle ?\n--------------\n\nMost research projects don't ever get to production. However, you want\nyour project to be production-ready and already adaptable (clean) by the\ntime you finish it. You also want things to be simple so that you can\nget started quickly. Read more about `the why of Neuraxle here. `_\n\n---------\nCommunity\n---------\n\nFor **technical questions**, please post them on\n`StackOverflow `__\nusing the ``neuraxle`` tag. The StackOverflow question will automatically\nbe posted in `Neuraxio's Slack\nworkspace `__ and our `Gitter `__ in the #Neuraxle channel. \n\nFor **suggestions, feature requests, and error reports**, please\nopen an `issue `__.\n\nFor **contributors**, we recommend using the PyCharm code editor and to\nlet it manage the virtual environment, with the default code\nauto-formatter, and using pytest as a test runner. To contribute, first\nfork the project, then do your changes, and then open a pull request in\nthe main repository. Please make your pull request(s) editable, such as\nfor us to add you to the list of contributors if you didn't add the\nentry, for example. Ensure that all tests run before opening a pull\nrequest. You'll also agree that your contributions will be licensed\nunder the `Apache 2.0\nLicense `__,\nwhich is required for everyone to be able to use your open-source\ncontributions.\n\nFinally, you can as well join our `Slack\nworkspace `__ and our `Gitter `__ to collaborate with us. We <3 collaborators. You can also subscribe to our mailing list where we will post some `updates and news `__. \n\n\nLicense\n~~~~~~~\n\nNeuraxle is licensed under the `Apache License, Version\n2.0 `__.\n\nCitation\n~~~~~~~~~~~~\n\nYou may cite our `extended abstract `__ that was presented at the Montreal Artificial Intelligence Symposium (MAIS) 2019. Here is the bibtex code to cite:\n\n.. code:: bibtex\n\n @misc{neuraxle,\n author = {Chevalier, Guillaume and Brillant, Alexandre and Hamel, Eric},\n year = {2019},\n month = {09},\n pages = {},\n title = {Neuraxle - A Python Framework for Neat Machine Learning Pipelines},\n doi = {10.13140/RG.2.2.33135.59043}\n }\n\nContributors\n~~~~~~~~~~~~\n\nThanks to everyone who contributed to the project:\n\n- Guillaume Chevalier: https://github.com/guillaume-chevalier\n- Alexandre Brillant: https://github.com/alexbrillant\n- \u00c9ric Hamel: https://github.com/Eric2Hamel\n- J\u00e9r\u00f4me Blanchet: https://github.com/JeromeBlanchet\n- Micha\u00ebl L\u00e9vesque-Dion: https://github.com/mlevesquedion\n- Philippe Racicot: https://github.com/Vaunorage\n- Neurodata: https://github.com/NeuroData-ltd\n- Klaimohelmi: https://github.com/Klaimohelmi\n- Vincent Antaki: https://github.com/vincent-antaki\n\nSupported By\n~~~~~~~~~~~~\n\nWe thank these organisations for generously supporting the 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