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Library of transfer learning and domain adaptation classifiers.

Project description

libTLDA: library of transfer learning and domain adaptation classifiers.

BuildStatus PyPI version Python version Matlab version DOI

This package contains the following classifiers:

Python-specific classifiers:

Matlab-specific classifiers:



Installation can be done through pip:

pip install libtlda

Environment management is generally a good idea. To create a conda environment, run the following commands:

conda env create -f environment.yml
source activate libtlda


Libtlda follows a similar logic as scikit-learn. Each type of adaptive classifier is a submodule, from which the classifiers can be imported:

from libtlda.iw import ImportanceWeightedClassifier
from libtlda.tca import TransferComponentClassifier
from libtlda.suba import SubspaceAlignedClassifier
from libtlda.scl import StructuralCorrespondenceClassifier
from libtlda.rba import RobustBiasAwareClassifier
from libtlda.flda import FeatureLevelDomainAdaptiveClassifier
from libtlda.tcpr import TargetContrastivePessimisticClassifier

From 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:

classifier = ImportanceWeightedClassifier().fit(X, y, Z)

Predictions can be made by calling the predict method:

y_pred = classifier.predict(Z)

Documentation will be improved soon. For now, have a look at the script. It shows a couple of options for training adaptive classifiers.



First clone the repository and change directory to matlab:

git clone
cd libTLDA/matlab/

In the matlab command window, call the installation script. It downloads all dependencies (minFunc, libsvm) and adds them - along with libtlda - to your path:



There is an example script that can be edited to test the different classifiers:



Questions, comments and bugs can be submitted in the issues tracker.

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