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scikit-adaptation
Domain adaptation toolbox compatible with scikit-learn and pytorch
In this example: ```python X, y, sample_domain = da_dataset.pack_train(as_sources=['s'], as_targets=['t']) estimator = make_da_pipeline( ReweightDensityAdapter(), LogisticRegression().set_score_request(sample_weight=True), ) cv = ShuffleSplit(n_splits=3, test_size=0.3, random_state=0) _, target_labels, _ = da_dataset.pack(as_sources=['s'], as_targets=['t'], train=False) scoring =...
For example, the one with personal/business flights I've been experimenting with. It would be nice to have more than CV provided out-of-the box.
As of now we have to classes defined for `skada.model_selection` module: * `SourceTargetShuffleSplit` * `LeaveOneDomainOut` To make sure those splitter work as expected we need to cover them up with...
I guess the change happened somewhere in the process of moving from older version of the API, unintentionally.
This check is currently not implemented.
Good example is the test suite for Office31, as it first downloads the dataset from the external source. We need to mark those tests as "slow", so we can run...
* review methods and naming convention, make sure all methods are necessary * write docstrings * test cases, specifically to cover the functionality of adding new domains, getting domain and...
Before when doing a model.predict_features(X) we were passing all the input in a single batch. This created CUDA out of memory issues when working with big datasets. Thus here I...
Subspace methods can have cubic complexity with the number of samples and we believe that subsampling can be an efficient way to learn an approximate subspace
This PR allows the user to put several layer names to adapt to different network levels, as done in several papers. This PR also forces the features of each layer...
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Domain adaptation toolbox compatible with scikit-learn and pytorch