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libTLDA\n\n[![Coverage](https://scrutinizer-ci.com/g/wmkouw/libTLDA/badges/coverage.png?b=master)](https://scrutinizer-ci.com/g/wmkouw/libTLDA/statistics/) [![BuildStatus](https://travis-ci.org/wmkouw/libTLDA.svg?branch=master)](https://travis-ci.org/wmkouw/libTLDA) [![docs](https://readthedocs.org/projects/libtlda/badge/?version=latest)](https://libtlda.readthedocs.io/en/latest/) [![DOI](https://zenodo.org/badge/41360294.svg)](https://zenodo.org/badge/latestdoi/41360294)\n\n## Library of transfer learners and domain-adaptive classifiers.\nThis package contains the following classifiers:\n- Importance-weighting with ratios of Gaussians [(Shimodaira, 2000)](https://www.sciencedirect.com/science/article/pii/S0378375800001154) \u003cbr\u003e\n- Importance-weighting with kernel density estimation [(Sugiyama \\\u0026 Müller, 2005)](https://www.degruyter.com/dg/viewarticle/j$002fstnd.2005.23.issue-4_2005$002fstnd.2005.23.4.249$002fstnd.2005.23.4.249.xml)\u003cbr\u003e\n- Importance-weighting with logistic discrimination [(Bickel et al., 2009)](http://www.jmlr.org/papers/v10/bickel09a.html) \u003cbr\u003e\n- Kernel Mean Matching [(Huang et al., 2006)](https://papers.nips.cc/paper/3075-correcting-sample-selection-bias-by-unlabeled-data) \u003cbr\u003e\n- Nearest-neighbour-based weighting [(Loog, 2015)](http://ieeexplore.ieee.org/document/6349714/) \u003cbr\u003e\n- Transfer Component Analysis [(Pan et al, 2009)](http://ieeexplore.ieee.org/document/5640675/) \u003cbr\u003e\n- Geodesic Flow Kernel [(Gong et al., 2012)](https://dl.acm.org/citation.cfm?id=1610094) (matlab-only)\n- Subspace Alignment [(Fernando et al., 2013)](https://dl.acm.org/citation.cfm?id=1610094) \u003cbr\u003e\n- Semi-supervised Subspace Alignment [(Yao et al., 2015)](https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Yao_Semi-Supervised_Domain_Adaptation_2015_CVPR_paper.html) (python-only) \u003cbr\u003e\n- Structural Correspondence Learning [(Blitzer et al., 2006)](https://dl.acm.org/citation.cfm?id=1610094) \u003cbr\u003e\n- Robust Bias-Aware Classification [(Liu \u0026 Ziebart, 2014)](https://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias) \u003cbr\u003e\n- Feature-Level Domain Adaptation [(Kouw et al., 2016)](http://jmlr.org/papers/v17/15-206.html) \u003cbr\u003e\n- Target Contrastive Pessimistic Risk [(Kouw et al., 2017)](https://arxiv.org/abs/1706.08082) (python-only)\n\n## Python\n![Python version](https://img.shields.io/badge/python-2.7%2C%203.5%2C%203.6-blue.svg)\n\n#### Installation\n\nInstallation can be done through pip:\n```shell\npip install libtlda\n```\n\nThe pip package installs all dependencies. To ensure that these dependencies that don't mess up your current python environment, you should set up a virtual environment. If you're using [conda](https://conda.io/docs/), this can be taken care of by running:\n```\nconda env create -f environment.yml\nsource activate libtlda\n```\n\n#### Usage\n\nLibTLDA follows a similar structure as [scikit-learn](http://scikit-learn.org/). There are several classes of classifiers that can be imported through for instance:\n\n```python\nfrom libtlda.iw import ImportanceWeightedClassifier\n```\n\nWith a data set of labeled source samples `(X,y)` and unlabeled target samples `Z`, the classifier can be called and trained using:\n\n```python\nclf = ImportanceWeightedClassifier(iwe='kmm')\nclf.fit(X, y, Z)\n```\n\nGiven a trained classifier, predictions can be made as follows:\n```python\npredictions = clf.predict(Z)\n```\n\nCheck the [documentation](https://libtlda.readthedocs.io/en/latest/) for more information on specific classes, methods and functions.\n\n## Matlab\n![Matlab version](https://img.shields.io/badge/matlab-R2017a-blue.svg)\n\n#### Installation:\n\nFirst clone the repository and change directory to matlab:\n```shell\ngit clone https://github.com/wmkouw/libTLDA\ncd libTLDA/matlab/\n```\n\nIn 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:\n```MATLAB\ninstall.m\n```\n\n#### Usage\n\nThere is an example script that can be edited to test the different classifiers:\n```MATLAB\nexample.m\n```\n\n## Contact:\n\nQuestions, comments and bugs can be submitted in the [issues tracker](https://github.com/wmkouw/libTLDA/issues). Any particular method / algorithm / technique that you feel should be included, can be submitted as an issue as well.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Flibtlda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwmkouw%2Flibtlda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Flibtlda/lists"}