Metadata-Version: 2.1
Name: adapt
Version: 0.2.1
Summary: Awesome Domain Adaptation Package Toolbox for Tensorflow and Scikit-learn
Home-page: https://github.com/adapt-python/adapt.git
Author: Antoine de Mathelin
Author-email: antoine.demat@gmail.com
License: BSD-2
Description: # ADAPT
        
        [![PyPI version](https://badge.fury.io/py/adaptation.svg)](https://pypi.org/project/adaptation)
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        **A**wesome **D**omain **A**daptation **P**ackage **T**oolbox
        
        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](https://adapt-python.github.io/adapt/)
        
        ## Installation
        
        This package is available on [Pypi](https://pypi.org/project/adaptation) 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:
        
        ```python
        import adapt
        ```
        
        ## Content
        
        ADAPT package is divided in three sub-modules containing the following domain adaptation methods:
        
        ### Feature-based methods
        
        - [FE](https://adapt-python.github.io/adapt/generated/adapt.feature_based.FE.html) (*Frustratingly Easy Domain Adaptation*)
        - [mSDA](https://adapt-python.github.io/adapt/generated/adapt.feature_based.mSDA.html) (*marginalized Stacked Denoising Autoencoder*)
        - [DANN](https://adapt-python.github.io/adapt/generated/adapt.feature_based.DANN.html) (*Discriminative Adversarial Neural Network*)
        - [ADDA](https://adapt-python.github.io/adapt/generated/adapt.feature_based.ADDA.html) (*Adversarial Discriminative Domain Adaptation*)
        - [CORAL](https://adapt-python.github.io/adapt/generated/adapt.feature_based.CORAL.html) (*CORrelation ALignment*)
        - [DeepCORAL](https://adapt-python.github.io/adapt/generated/adapt.feature_based.DeepCORAL.html) (*Deep CORrelation ALignment*)
        
        ### Instance-based methods
        
        - [KMM](https://adapt-python.github.io/adapt/generated/adapt.instance_based.KMM.html) (*Kernel Mean Matching*)
        - [KLIEP](https://adapt-python.github.io/adapt/generated/adapt.instance_based.KLIEP.html) (*Kullbackâ€“Leibler Importance Estimation Procedure*)
        - [TrAdaBoost](https://adapt-python.github.io/adapt/generated/adapt.instance_based.TrAdaBoost.html) (*Transfer AdaBoost*)
        - [TrAdaBoostR2](https://adapt-python.github.io/adapt/generated/adapt.instance_based.TrAdaBoostR2.html) (*Transfer AdaBoost for Regression*)
        - [TwoStageTrAdaBoostR2](https://adapt-python.github.io/adapt/generated/adapt.instance_based.TwoStageTrAdaBoostR2.html) (*Two Stage Transfer AdaBoost for Regression*)
        
        ### Parameter-based methods
        
        - [RegularTransferLR](https://adapt-python.github.io/adapt/generated/adapt.parameter_based.RegularTransferLR.html) (*Regular Transfer with Linear Regression*)
        - [RegularTransferLC](https://adapt-python.github.io/adapt/generated/adapt.parameter_based.RegularTransferLC.html) (*Regular Transfer with Linear Classification*)
        - [RegularTransferNN](https://adapt-python.github.io/adapt/generated/adapt.parameter_based.RegularTransferNN.html) (*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.
        
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