libtlda 0.1.1

Library of transfer learning and domain adaptation classifiers.

[](https://travis-ci.org/wmkouw/libTLDA)
## libTLDA: library of transfer learning and domain adaptation classifiers.

This package contains the following classifiers:
- Importance-weighted classifier, with weight estimators:<br>
- Kernel density estimation <br>
- Ratio of Gaussians [(Shimodaira, 2000)](https://www.sciencedirect.com/science/article/pii/S0378375800001154) <br>
- Logistic discrimination [(Bickel et al., 2009)](http://www.jmlr.org/papers/v10/bickel09a.html) <br>
- Kernel Mean Matching [(Huang et al., 2006)](https://papers.nips.cc/paper/3075-correcting-sample-selection-bias-by-unlabeled-data) <br>
- Nearest-neighbour-based weighting [(Loog, 2015)](http://ieeexplore.ieee.org/document/6349714/) <br>
- Transfer Component Analysis [(Pan et al, 2009)](http://ieeexplore.ieee.org/document/5640675/) <br>
- Subspace Alignment [(Fernando et al., 2013)](https://dl.acm.org/citation.cfm?id=1610094) <br>
- Structural Correspondence Learning [(Blitzer et al., 2006)](https://dl.acm.org/citation.cfm?id=1610094) <br>
- Robust Bias-Aware [(Liu & Ziebart, 2014)](https://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias) <br>
- Feature-Level Domain Adaptation [(Kouw et al., 2016)](http://jmlr.org/papers/v17/15-206.html) <br>

## Python
Python versions 2.7, 3.4, 3.5 and 3.6.

### Installation
First clone and enter the repository:
```
sudo apt-get install git
git clone https://github.com/wmkouw/libTLDA
cd libTLDA/
```

Conda environments take care of all dependencies. If you don't have conda installed already, you can set it up through:
```
wget http://repo.continuum.io/miniconda/Miniconda2-latest-$(uname)-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda
export PATH="$HOME/miniconda/bin:$PATH"
```
Then, create and activate a new environment:
```
conda env create -f environment.yml
source activate libtlda
```

Afterwards, the package can be installed by running the following setup script:
```
python setup.py install
```

### Usage
The script in `example.py` shows a simple example of importing one of the adaptive classifiers and applying them to your data set.
```
cd python/
python example.py
```

<!-- ### Python-specific classifiers
- dann: Domain-Adversarial Neural Network (Ganin et al., 2015) (TODO) -->

## Matlab
Version: 9.2.0.556344 (R2017a) <br>

### Installation:
First clone the repository and change directory to matlab:
```
sudo apt-get install git
git clone https://github.com/wmkouw/libTLDA
cd libTLDA/matlab/
```

In the matlab command window, call the installation script. It downloads all dependencies ([minFunc](https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html), [libsvm](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)) and adds them - along with libTLDA - to your path:
```
install.m
```

### Usage
There is an example script that can be edited to test the different classifiers:
```
example.m
```

### Matlab-specific classifiers:
- Geodesic Flow Kernel [(Gong et al., 2012)](https://dl.acm.org/citation.cfm?id=1610094) <br>

## Contact:
Questions, comments and bugs can be submitted in the [issues tracker](https://github.com/wmkouw/libTLDA/issues).