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"info": {
"author": "Wouter Kouw",
"author_email": "wmkouw@gmail.com",
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"classifiers": [
"Development Status :: 3 - Alpha",
"License :: OSI Approved :: MIT License",
"Operating System :: MacOS",
"Operating System :: POSIX :: Linux",
"Programming Language :: Python :: 2.7",
"Programming Language :: Python :: 3.4",
"Programming Language :: Python :: 3.5",
"Programming Language :: Python :: 3.6",
"Topic :: Scientific/Engineering :: Artificial Intelligence",
"Topic :: Software Development :: Libraries"
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"description": "## libTLDA: library of transfer learning and domain adaptation classifiers.\n\n[](https://travis-ci.org/wmkouw/libTLDA) [](https://badge.fury.io/py/libtlda)   [](https://zenodo.org/badge/latestdoi/41360294)\n\nThis package contains the following classifiers:\n- Importance-weighted classifier, with weight estimators:
\n\t- Kernel density estimation
\n\t- Ratio of Gaussians [(Shimodaira, 2000)](https://www.sciencedirect.com/science/article/pii/S0378375800001154)
\n\t- Logistic discrimination [(Bickel et al., 2009)](http://www.jmlr.org/papers/v10/bickel09a.html)
\n\t- Kernel Mean Matching [(Huang et al., 2006)](https://papers.nips.cc/paper/3075-correcting-sample-selection-bias-by-unlabeled-data)
\n\t- Nearest-neighbour-based weighting [(Loog, 2015)](http://ieeexplore.ieee.org/document/6349714/)
\n- Transfer Component Analysis [(Pan et al, 2009)](http://ieeexplore.ieee.org/document/5640675/)
\n- Subspace Alignment [(Fernando et al., 2013)](https://dl.acm.org/citation.cfm?id=1610094)
\n- Structural Correspondence Learning [(Blitzer et al., 2006)](https://dl.acm.org/citation.cfm?id=1610094)
\n- Robust Bias-Aware [(Liu & Ziebart, 2014)](https://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias)
\n- Feature-Level Domain Adaptation [(Kouw et al., 2016)](http://jmlr.org/papers/v17/15-206.html)
\n\n#### Python-specific classifiers:\n- Target Contrastive Pessimistic Risk [(Kouw et al., 2017)](https://arxiv.org/abs/1706.08082)\n\n#### Matlab-specific classifiers:\n- Geodesic Flow Kernel [(Gong et al., 2012)](https://dl.acm.org/citation.cfm?id=1610094)\n\n### Python\n\n#### Installation\nInstallation can be done through pip:\n```shell\npip install libtlda\n```\n\nEnvironment management is generally a good idea. To create a [conda](https://conda.io/docs/) environment, run the following commands:\n```\nconda env create -f environment.yml\nsource activate libtlda\n```\n\n#### Usage\nLibtlda follows a similar logic as [scikit-learn](http://scikit-learn.org/). Each type of adaptive classifier is a submodule, from which the classifiers can be imported:\n```python\nfrom libtlda.iw import ImportanceWeightedClassifier\nfrom libtlda.tca import TransferComponentClassifier\nfrom libtlda.suba import SubspaceAlignedClassifier\nfrom libtlda.scl import StructuralCorrespondenceClassifier\nfrom libtlda.rba import RobustBiasAwareClassifier\nfrom libtlda.flda import FeatureLevelDomainAdaptiveClassifier\nfrom libtlda.tcpr import TargetContrastivePessimisticClassifier\n```\nFrom there on, training is a matter of calling the `fit` method on your labeled source dataset `(X,y)` and unlabeled target dataset `Z`. For example:\n```python\nclassifier = ImportanceWeightedClassifier().fit(X, y, Z)\n```\n\nPredictions can be made by calling the `predict` method:\n```python\ny_pred = classifier.predict(Z)\n```\n\nDocumentation will be improved soon. For now, have a look at the `example.py` script. It shows a couple of options for training adaptive classifiers.\n\n\n\n### Matlab\n\n#### Installation:\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\nThere is an example script that can be edited to test the different classifiers:\n```MATLAB\nexample.m\n```\n\n### Contact:\nQuestions, comments and bugs can be submitted in the [issues tracker](https://github.com/wmkouw/libTLDA/issues).\n\n\n",
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