Neuraxle Pipelines

Code Machine Learning Pipelines - The Right Way.

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Neuraxle is a Machine Learning (ML) library for building machine learning pipelines.

• Component-Based: Build encapsulated steps, then compose them to build complex pipelines.
• Evolving State: Each pipeline step can fit, and evolve through the learning process
• Hyperparameter Tuning: Optimize your pipelines using AutoML, where each pipeline step has their own hyperparameter space.
• Compatible: Use your favorite machine learning libraries inside and outside Neuraxle pipelines.
• Production Ready: Pipeline steps can manage how they are saved by themselves, and the lifecycle of the objects allow for train, and test modes.
• Streaming Pipeline: Transform data in many pipeline steps at the same time in parallel using multiprocessing Queues.

Documentation

You can find the Neuraxle documentation

on the website 

The documentation is divided into several sections:

• Handler Methods 

  _

• Introduction to Automatic Hyperparameter Tuning 

  _

• Introduction to Random Distributions 

  _

• Step Saving And Lifecycle 

  _

• REST API Serving 

  _

• Complete API documentation 

  _

• Usage Examples 

  _

Installation

Simply do:

.. code:: bash

pip install neuraxle

Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

We have several

examples on the website 

For example, you can build a time series processing pipeline as such:

.. code:: python

p = Pipeline([
    TrainOnly(DataShuffler()),
    WindowTimeSeries(),
    MiniBatchSequentialPipeline([
        Tensorflow2ModelStep(
            create_model=create_model,
            create_optimizer=create_optimizer,
            create_loss=create_loss
        ).set_hyperparams(HyperparameterSpace({
            'hidden_dim': 12,
            'layers_stacked_count': 2,
            'lambda_loss_amount': 0.0003,
            'learning_rate': 0.001
            'window_size_future': sequence_length,
            'output_dim': output_dim,
            'input_dim': input_dim
        })).set_hyperparams_space(HyperparameterSpace({
            'hidden_dim': RandInt(6, 750),
            'layers_stacked_count': RandInt(1, 4),
            'lambda_loss_amount': Uniform(0.0003, 0.001),
            'learning_rate': Uniform(0.001, 0.01),
            'window_size_future': FixedHyperparameter(sequence_length),
            'output_dim': FixedHyperparameter(output_dim),
            'input_dim': FixedHyperparameter(input_dim)
        }))
    ])
])

Load data
X_train, y_train, X_test, y_test = generate_classification_data()
The pipeline will learn on the data and acquire state.
p = p.fit(X_train, y_train)
Once it learned, the pipeline can process new and
unseen data for making predictions.
y_test_predicted = p.predict(X_test)

You can also tune your hyperparameters using AutoML algorithms such as the TPE:

.. code:: python

auto_ml = AutoML(
    pipeline=pipeline,
    hyperparams_optimizer=TreeParzenEstimatorHyperparameterSelectionStrategy(
        number_of_initial_random_step=10,
        quantile_threshold=0.3,
        number_good_trials_max_cap=25,
        number_possible_hyperparams_candidates=100,
        prior_weight=0.,
        use_linear_forgetting_weights=False,
        number_recent_trial_at_full_weights=25
    ),
    validation_splitter=ValidationSplitter(test_size=0.20),
    scoring_callback=ScoringCallback(accuracy_score, higher_score_is_better=True),
    callbacks[
        MetricCallback(f1_score, higher_score_is_better=True),
        MetricCallback(precision, higher_score_is_better=True),
        MetricCallback(recall, higher_score_is_better=True)
    ],
    n_trials=7,
    epochs=10,
    hyperparams_repository=HyperparamsJSONRepository(cache_folder='cache'),
    refit_trial=True,
)

Load data, and launch AutoML loop !
X_train, y_train, X_test, y_test = generate_classification_data()
auto_ml = auto_ml.fit(X_train, y_train)
Get the model from the best trial, and make predictions using predict.
best_pipeline = auto_ml.get_best_model()
y_pred = best_pipeline.predict(X_test)

Why Neuraxle ?

Most research projects don't ever get to production. However, you want your project to be production-ready and already adaptable (clean) by the time you finish it. You also want things to be simple so that you can get started quickly. Read more about

the why of Neuraxle here. 

Community

For technical questions, please post them on

StackOverflow 

neuraxle

For suggestions, feature requests, and error reports, please open an

issue 

For contributors, we recommend using the PyCharm code editor and to let it manage the virtual environment, with the default code auto-formatter, and using pytest as a test runner. To contribute, first fork the project, then do your changes, and then open a pull request in the main repository. Please make your pull request(s) editable, such as for us to add you to the list of contributors if you didn't add the entry, for example. Ensure that all tests run before opening a pull request. You'll also agree that your contributions will be licensed under the

Apache 2.0
License 

Finally, you can as well join our

Slack
workspace 

Gitter 

License ~~~~~~~

Neuraxle is licensed under the

Apache License, Version
2.0 

Citation ~~~~~~~~~~~~

You may cite our

extended abstract 

.. code:: bibtex

@misc{neuraxle,
author = {Chevalier, Guillaume and Brillant, Alexandre and Hamel, Eric},
year = {2019},
month = {09},
pages = {},
title = {Neuraxle - A Python Framework for Neat Machine Learning Pipelines},
doi = {10.13140/RG.2.2.33135.59043}
}

Contributors ~~~~~~~~~~~~

Thanks to everyone who contributed to the project:

• Guillaume Chevalier: https://github.com/guillaume-chevalier
• Alexandre Brillant: https://github.com/alexbrillant
• Éric Hamel: https://github.com/Eric2Hamel
• Jérôme Blanchet: https://github.com/JeromeBlanchet
• Michaël Lévesque-Dion: https://github.com/mlevesquedion
• Philippe Racicot: https://github.com/Vaunorage
• Neurodata: https://github.com/NeuroData-ltd
• Klaimohelmi: https://github.com/Klaimohelmi
• Vincent Antaki: https://github.com/vincent-antaki

Supported By ~~~~~~~~~~~~

We thank these organisations for generously supporting the project:

• Neuraxio Inc.: https://github.com/Neuraxio

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• Umanéo Technologies Inc.: https://www.umaneo.com/

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• Solution Nexam Inc.: https://nexam.io/

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• La Cité, LP: https://www.lacitelp.com/

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• Kimoby: https://www.kimoby.com/

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