adapt 0.2.1

Awesome Domain Adaptation Package Toolbox for Tensorflow and Scikit-learn

ADAPT

Awesome Domain Adaptation Package Toolbox

ADAPT is a python library which provides several domain adaptation methods usefull to improve machine learning models.

Documentation Website

Find the details of all implemented methods as well as illustrative examples here: ADAPT Documentation Website

Installation

This package is available on Pypi and can be installed with the following command line:

pip install adaptation

The following dependencies are required and will be installed with the library:

• numpy
• scipy
• tensorflow (>= 2.0)
• scikit-learn
• cvxopt

If for some reason, these packages failed to install, you can do it manually with:

pip install numpy scipy tensorflow scikit-learn cvxopt

Finally import the module in your python scripts with:

import adapt

Content

ADAPT package is divided in three sub-modules containing the following domain adaptation methods:

Feature-based methods

• FE (Frustratingly Easy Domain Adaptation)
• mSDA (marginalized Stacked Denoising Autoencoder)
• DANN (Discriminative Adversarial Neural Network)
• ADDA (Adversarial Discriminative Domain Adaptation)
• CORAL (CORrelation ALignment)
• DeepCORAL (Deep CORrelation ALignment)

Instance-based methods

• KMM (Kernel Mean Matching)
• KLIEP (Kullback–Leibler Importance Estimation Procedure)
• TrAdaBoost (Transfer AdaBoost)
• TrAdaBoostR2 (Transfer AdaBoost for Regression)
• TwoStageTrAdaBoostR2 (Two Stage Transfer AdaBoost for Regression)

Parameter-based methods

• RegularTransferLR (Regular Transfer with Linear Regression)
• RegularTransferLC (Regular Transfer with Linear Classification)
• RegularTransferNN (Regular Transfer with Neural Network)

Acknowledgement

Part of this work has been funded by the Industrial Data Analytics and Machine Learning chair from ENS Paris-Saclay, Borelli center.