pymilo 0.6

Transportation of ML models

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Table of contents

• Overview
• Installation
• Usage
• Issues & Bug Reports
• Contribution
• Authors
• License
• Show Your Support
• Changelog
• Code of Conduct

Overview

PyMilo is an open source Python package that provides a simple, efficient, and safe way for users to export pre-trained machine learning models in a transparent way. By this, the exported model can be used in other environments, transferred across different platforms, and shared with others. PyMilo allows the users to export the models that are trained using popular Python libraries like scikit-learn, and then use them in deployment environments, or share them without exposing the underlying code or dependencies. The transparency of the exported models ensures reliability and safety for the end users, as it eliminates the risks of binary or pickle formats.

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Code Quality

Installation

PyPI

• Check Python Packaging User Guide
• Run pip install pymilo==0.6

Source code

• Download Version 0.6 or Latest Source
• Run pip install .

Usage

Model preparation

>>> from sklearn import datasets
>>> from pymilo import Export, Import
>>> from sklearn.linear_model import LinearRegression
>>> import os
>>> X, Y = datasets.load_diabetes(return_X_y=True)
>>> threshold = 20
>>> X_train, X_test = X[:-threshold], X[-threshold:]
>>> Y_train, Y_test = Y[:-threshold], Y[-threshold:]
>>> model = LinearRegression()
>>> #### Train the model using the training sets
>>> model.fit(X_train, Y_train)

Save model

>>> #### Export the fitted model to a transparent json file
>>> exported_model = Export(model)
>>> PATH_TO_JSON_FILE = os.path.join(os.getcwd(),"test.json")
>>> exported_model.save(PATH_TO_JSON_FILE)

Load model

>>> #### Import the pymilo-exported model and get a real scikit model
>>> imported_model = Import(PATH_TO_JSON_FILE)

Get the associated model

>>> imported_sklearn_model = imported_model.to_model()

Note: imported_sklearn_model has the exact same functionality as the model object earlier.

Supported ML models

scikit-learn	PyTorch
Linear Models ✅	-
Neural networks ✅	-
Trees ✅	-
Clustering ✅	-
Naïve Bayes ✅	-
Support vector machines (SVMs) ✅	-
Nearest Neighbors ❌	-
Ensemble Models ❌	-
Details are available in Supported Models.

Issues & bug reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [e-mail].

• Please complete the issue template

You can also join our discord server

Show your support

Star this repo

Give a ⭐️ if this project helped you!

Donate to our project

If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

Unreleased

Added

Changed

0.6 - 2024-03-27

Added

• deserialize_primitive_type function in GeneralDataStructureTransporter
• is_deserialized_ndarray function in GeneralDataStructureTransporter
• deep_deserialize_ndarray function in GeneralDataStructureTransporter
• deep_serialize_ndarray function in GeneralDataStructureTransporter
• SVR model
• SVC model
• One Class SVM model
• NuSVR model
• NuSVC model
• Linear SVR model
• Linear SVC model
• SVM models test runner
• SVM chain

Changed

• pymilo_param.py updated
• pymilo_obj.py updated to use predefined strings
• TreeTransporter updated
• get_homogeneous_type function in util.py updated
• GeneralDataStructureTransporter updated to use deep ndarray serializer & deserializer
• check_str_in_iterable updated
• Label Binarizer Transporter updated
• Function Transporter updated
• CFNode Transporter updated
• Bisecting Tree Transporter updated
• Tests config modified
• SVM params initialized in pymilo_param
• SVM support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.5 - 2024-01-31

Added

• reset function in the Transport interface
• reset function implementation in AbstractTransporter
• Gaussian Naive Bayes declared as GaussianNB model
• Multinomial Naive Bayes model declared as MultinomialNB model
• Complement Naive Bayes model declared as ComplementNB model
• Bernoulli Naive Bayes model declared as BernoulliNB model
• Categorical Naive Bayes model declared as CategoricalNB model
• Naive Bayes models test runner
• Naive Bayes chain

Changed

• Transport function of AbstractTransporter updated
• fix the order of CFNode fields serialization in CFNodeTransporter
• GeneralDataStructureTransporter support list of ndarray with different shapes
• Tests config modified
• Naive Bayes params initialized in pymilo_param
• Naive Bayes support added to pymilo_func.py
• SUPPORTED_MODELS.md updated
• README.md updated

0.4 - 2024-01-22

Added

• has_named_parameter method in util.py
• CFSubcluster Transporter(inside CFNode Transporter)
• CFNode Transporter
• Birch model
• SpectralBiclustering model
• SpectralCoclustering model
• MiniBatchKMeans model
• feature_request.yml template
• config.yml for issue template
• BayesianGaussianMixture model
• serialize_tuple method in GeneralDataStructureTransporter
• import_function method in util.py
• Function Transporter
• FeatureAgglomeration model
• HDBSCAN model
• GaussianMixture model
• OPTICS model
• DBSCAN model
• AgglomerativeClustering model
• SpectralClustering model
• MeanShift model
• AffinityPropagation model
• Kmeans model
• Clustering models test runner
• Clustering chain

Changed

• LossFunctionTransporter enhanced to handle scikit 1.4.0 _loss_function_ field
• Codacy Static Code Analyzer's suggestions applied
• Spectral Clustering test folder refactored
• Bug report template modified
• GeneralDataStructureTransporter updated
• Tests config modified
• Clustering data set preparation added to data_exporter.py
• Clustering params initialized in pymilo_param
• Clustering support added to pymilo_func.py
• Python 3.12 added to test.yml
• dev-requirements.txt updated
• Code quality badges added to README.md
• SUPPORTED_MODELS.md updated
• README.md updated

0.3 - 2023-09-27

Added

• scikit-learn decision tree models
• ExtraTreeClassifier model
• ExtraTreeRegressor model
• DecisionTreeClassifier model
• DecisionTreeRegressor model
• Tree Transporter
• Decision Tree chain

Changed

• Tests config modified
• DecisionTree params initialized in pymilo_param
• Decision Tree support added to pymilo_func.py

0.2 - 2023-08-02

Added

• scikit-learn neural network models
• MLP Regressor model
• MLP Classifier model
• BernoulliRBN model
• SGDOptimizer transporter
• RandomState(MT19937) transporter
• Adamoptimizer transporter
• Neural Network chain
• Neural Network exceptions
• ndarray_to_list method in GeneralDataStructureTransporter
• list_to_ndarray method in GeneralDataStructureTransporter
• neural_network_chain.py chain

Changed

• GeneralDataStructure Transporter updated
• LabelBinerizer Transporter updated
• linear model chain updated
• GeneralDataStructure transporter enhanced
• LabelBinerizer transporter updated
• transporters' chain router added to pymilo func
• NeuralNetwork params initialized in pymilo_param
• pymilo_test updated to support multiple models
• linear_model_chain refactored

0.1 - 2023-06-29

Added

• scikit-learn linear models support
• Export class
• Import class