pymilo 0.2

Transportation of ML models

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Table of contents

• Overview
• Installation
• Usage
• Issues & Bug Reports
• Todo
• 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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Installation

PyPI

• Check Python Packaging User Guide
• Run pip install pymilo==0.2

Source code

• Download Version 0.2 or Latest Source
• Run pip install .

Usage

Simple Linear 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 Scikit 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 ✅	-
Clustering ❌	-
Trees ❌	-
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

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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

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