├── .gitattributes ├── .github ├── labeler.yml ├── release-drafter.yml ├── semantic.yml └── workflows │ ├── check-docs.yml │ ├── pr-labeler.yml │ ├── publish-doc-to-remote.yml │ ├── release-drafter.yml │ ├── run-test-coverage.yml │ ├── run-test.yml │ └── upload-to-pypi.yml ├── .gitignore ├── .pylintrc ├── LICENSE.txt ├── Makefile ├── README.md ├── adapt ├── __init__.py ├── _tree_utils.py ├── base.py ├── feature_based │ ├── __init__.py │ ├── _adda.py │ ├── _ccsa.py │ ├── _cdan.py │ ├── _coral.py │ ├── _dann.py │ ├── _deepcoral.py │ ├── _fa.py │ ├── _fmmd.py │ ├── _mcd.py │ ├── _mdd.py │ ├── _pred.py │ ├── _sa.py │ ├── _tca.py │ └── _wdgrl.py ├── instance_based │ ├── __init__.py │ ├── _balancedweighting.py │ ├── _iwc.py │ ├── _iwn.py │ ├── _kliep.py │ ├── _kmm.py │ ├── _ldm.py │ ├── _nearestneighborsweighting.py │ ├── _rulsif.py │ ├── _tradaboost.py │ ├── _ulsif.py │ └── _wann.py ├── metrics.py ├── parameter_based │ ├── __init__.py │ ├── _finetuning.py │ ├── _linint.py │ ├── _regular.py │ └── _transfer_tree.py └── utils.py ├── codecov.yml ├── docs ├── .nojekyll ├── _images │ ├── adda.png │ ├── all.gif │ ├── cdan.png │ ├── colab_logo_32px.png │ ├── contribute.png │ ├── coral.gif │ ├── dann.gif │ ├── dann.png │ ├── deep_da.png │ ├── deepcoral.png │ ├── examples_Classification_9_0.png │ ├── examples_Developer_Guide_17_0.png │ ├── examples_Developer_Guide_29_0.png │ ├── examples_Flowers_example_14_0.png │ ├── examples_Flowers_example_23_0.png │ ├── examples_Flowers_example_29_0.png │ ├── examples_Flowers_example_5_0.png │ ├── examples_Heart_Failure_13_0.png │ ├── examples_Heart_Failure_14_0.png │ ├── examples_Heart_Failure_17_0.png │ ├── examples_Heart_Failure_18_0.png │ ├── examples_Heart_Failure_21_0.png │ ├── examples_Heart_Failure_22_0.png │ ├── examples_Heart_Failure_25_0.png │ ├── examples_Heart_Failure_26_0.png │ ├── examples_Heart_Failure_29_1.png │ ├── examples_Heart_Failure_30_0.png │ ├── examples_Heart_Failure_33_0.png │ ├── examples_Heart_Failure_34_0.png │ ├── examples_Heart_Failure_37_0.png │ ├── examples_Heart_Failure_38_0.png │ ├── examples_Heart_Failure_41_0.png │ ├── examples_Heart_Failure_42_0.png │ ├── examples_Heart_Failure_45_0.png │ ├── examples_Heart_Failure_46_0.png │ ├── examples_Heart_Failure_6_0.png │ ├── examples_Multi_fidelity_11_0.png │ ├── examples_Multi_fidelity_14_0.png │ ├── examples_Multi_fidelity_17_0.png │ ├── examples_Multi_fidelity_6_0.png │ ├── examples_Office_example_24_0.png │ ├── examples_Office_example_26_0.png │ ├── examples_Office_example_34_0.png │ ├── examples_Office_example_36_0.png │ ├── examples_Office_example_5_0.png │ ├── examples_Office_example_5_1.png │ ├── examples_Quick_start_3_0.png │ ├── examples_Regression_9_0.png │ ├── examples_Rotation_11_0.png │ ├── examples_Rotation_12_0.png │ ├── examples_Rotation_14_0.png │ ├── examples_Rotation_15_0.png │ ├── examples_Rotation_16_1.png │ ├── examples_Rotation_17_0.png │ ├── examples_Rotation_4_0.png │ ├── examples_Rotation_6_0.png │ ├── examples_Rotation_8_1.png │ ├── examples_Sample_bias_example_13_0.png │ ├── examples_Sample_bias_example_16_0.png │ ├── examples_Sample_bias_example_19_0.png │ ├── examples_Sample_bias_example_27_0.png │ ├── examples_Sample_bias_example_31_0.png │ ├── examples_Sample_bias_example_39_0.png │ ├── examples_Sample_bias_example_50_0.png │ ├── examples_Two_moons_12_0.png │ ├── examples_Two_moons_14_0.png │ ├── examples_Two_moons_17_0.png │ ├── examples_Two_moons_19_0.png │ ├── examples_Two_moons_22_0.png │ ├── examples_Two_moons_24_0.png │ ├── examples_Two_moons_27_0.png │ ├── examples_Two_moons_29_0.png │ ├── examples_Two_moons_32_1.png │ ├── examples_Two_moons_34_0.png │ ├── examples_Two_moons_37_0.png │ ├── examples_Two_moons_39_0.png │ ├── examples_Two_moons_42_0.png │ ├── examples_Two_moons_44_0.png │ ├── examples_Two_moons_47_0.png │ ├── examples_Two_moons_49_0.png │ ├── examples_Two_moons_53_0.png │ ├── examples_Two_moons_55_0.png │ ├── examples_Two_moons_6_0.png │ ├── examples_sample_bias_12_0.png │ ├── examples_sample_bias_15_0.png │ ├── examples_sample_bias_2d_11_0.png │ ├── examples_sample_bias_2d_14_0.png │ ├── examples_sample_bias_2d_17_0.png │ ├── examples_sample_bias_2d_6_0.png │ ├── examples_sample_bias_6_0.png │ ├── examples_sample_bias_9_0.png │ ├── feature_based.png │ ├── flowers.jpg │ ├── fork.png │ ├── github_logo_32px.png │ ├── instance_based.png │ ├── kmm.gif │ ├── parameter_based.png │ ├── regular.gif │ ├── regulartransfer.png │ ├── sample_bias_img.png │ ├── srcOnlyCla.gif │ ├── srcOnlyReg.gif │ ├── tgtOnly.gif │ ├── tradaboost.gif │ └── wdgrl.png ├── _static │ ├── basic.css │ ├── binder_badge_logo.svg │ ├── broken_example.png │ ├── css │ │ ├── badge_only.css │ │ ├── custom.css │ │ ├── custom_index.css │ │ ├── fonts │ │ │ ├── Roboto-Slab-Bold.woff │ │ │ ├── Roboto-Slab-Bold.woff2 │ │ │ ├── Roboto-Slab-Regular.woff │ │ │ ├── Roboto-Slab-Regular.woff2 │ │ │ ├── fontawesome-webfont.eot │ │ │ ├── fontawesome-webfont.svg │ │ │ ├── fontawesome-webfont.ttf │ │ │ ├── fontawesome-webfont.woff │ │ │ ├── fontawesome-webfont.woff2 │ │ │ ├── lato-bold-italic.woff │ │ │ ├── lato-bold-italic.woff2 │ │ │ ├── lato-bold.woff │ │ │ ├── lato-bold.woff2 │ │ │ ├── lato-normal-italic.woff │ │ │ ├── lato-normal-italic.woff2 │ │ │ ├── lato-normal.woff │ │ │ └── lato-normal.woff2 │ │ └── theme.css │ ├── doctools.js │ ├── documentation_options.js │ ├── file.png │ ├── images │ │ ├── adda.png │ │ ├── borelli.jpg │ │ ├── carto4.png │ │ ├── cdan.png │ │ ├── classification_setup.png │ │ ├── colab_logo_32px.png │ │ ├── contribute.png │ │ ├── dann.png │ │ ├── deep_da.png │ │ ├── deepcoral.png │ │ ├── download_logo_32px.png │ │ ├── feature_based.png │ │ ├── finetuned.png │ │ ├── flowers.jpg │ │ ├── fork.png │ │ ├── github_logo_32px.png │ │ ├── idaml.jpg │ │ ├── instance_based.png │ │ ├── michelin.png │ │ ├── multifidelity_setup.png │ │ ├── office_item.png │ │ ├── parameter_based.png │ │ ├── regression_setup.png │ │ ├── regulartransfer.png │ │ ├── results_qs.png │ │ ├── rotation_setup.png │ │ ├── sample_bias_2d_setup.png │ │ ├── sample_bias_corr_img.png │ │ ├── sample_bias_img.png │ │ ├── sample_bias_setup.png │ │ ├── thumbnai_flowchart.PNG │ │ ├── two_moons_setup.png │ │ └── wdgrl.png │ ├── jquery-3.5.1.js │ ├── jquery.js │ ├── js │ │ ├── badge_only.js │ │ ├── custom.js │ │ ├── html5shiv-printshiv.min.js │ │ ├── html5shiv.min.js │ │ └── theme.js │ ├── language_data.js │ ├── minus.png │ ├── no_image.png │ ├── plus.png │ ├── pygments.css │ ├── searchtools.js │ ├── sg_gallery-binder.css │ ├── sg_gallery-dataframe.css │ ├── sg_gallery-rendered-html.css │ ├── sg_gallery.css │ ├── underscore-1.13.1.js │ └── underscore.js ├── contents.html ├── examples │ ├── Classification.html │ ├── Developer_Guide.html │ ├── Flowers_example.html │ ├── Heart_Failure.html │ ├── Multi_fidelity.html │ ├── Office_example.html │ ├── Quick_start.html │ ├── Regression.html │ ├── Rotation.html │ ├── Sample_bias_example.html │ ├── Two_moons.html │ ├── sample_bias.html │ ├── sample_bias_2d.html │ └── tradaboost_experiments.html ├── gallery │ ├── ADDA.html │ ├── BalancedWeighting.html │ ├── CCSA.html │ ├── CDAN.html │ ├── CORAL.html │ ├── DANN.html │ ├── DeepCORAL.html │ ├── FA.html │ ├── FineTuning.html │ ├── IWC.html │ ├── IWN.html │ ├── KLIEP.html │ ├── KMM.html │ ├── LDM.html │ ├── LinInt.html │ ├── MCD.html │ ├── MDD.html │ ├── NearestNeighborsWeighting.html │ ├── PRED.html │ ├── RULSIF.html │ ├── RegularTransferLC.html │ ├── RegularTransferLR.html │ ├── RegularTransferNN.html │ ├── SA.html │ ├── TCA.html │ ├── TrAdaBoost.html │ ├── TrAdaBoostR2.html │ ├── TransferForest.html │ ├── TransferTreeClassifier.html │ ├── TwoStageTrAdaBoostR2.html │ ├── ULSIF.html │ ├── WANN.html │ ├── WDGRL.html │ └── fMMD.html ├── generated │ ├── adapt.feature_based.ADDA.html │ ├── adapt.feature_based.CCSA.html │ ├── adapt.feature_based.CDAN.html │ ├── adapt.feature_based.CORAL.html │ ├── adapt.feature_based.DANN.html │ ├── adapt.feature_based.DeepCORAL.html │ ├── adapt.feature_based.FA.html │ ├── adapt.feature_based.FADA.html │ ├── adapt.feature_based.FSDA.html │ ├── adapt.feature_based.FSSP.html │ ├── adapt.feature_based.JDA.html │ ├── adapt.feature_based.MCD.html │ ├── adapt.feature_based.MDD.html │ ├── adapt.feature_based.MME.html │ ├── adapt.feature_based.PRED.html │ ├── adapt.feature_based.SA.html │ ├── adapt.feature_based.SSDANN.html │ ├── adapt.feature_based.TCA.html │ ├── adapt.feature_based.WDGRL.html │ ├── adapt.feature_based.fMMD.html │ ├── adapt.instance_based.BalancedWeighting.html │ ├── adapt.instance_based.GDM.html │ ├── adapt.instance_based.IWC.html │ ├── adapt.instance_based.IWN.html │ ├── adapt.instance_based.KLIEP.html │ ├── adapt.instance_based.KMM.html │ ├── adapt.instance_based.LDM.html │ ├── adapt.instance_based.NearestNeighborsWeighting.html │ ├── adapt.instance_based.RULSIF.html │ ├── adapt.instance_based.TrAdaBoost.html │ ├── adapt.instance_based.TrAdaBoostR2.html │ ├── adapt.instance_based.TwoStageTrAdaBoostR2.html │ ├── adapt.instance_based.ULSIF.html │ ├── adapt.instance_based.WANN.html │ ├── adapt.metrics.cov_distance.html │ ├── adapt.metrics.domain_classifier.html │ ├── adapt.metrics.frechet_distance.html │ ├── adapt.metrics.linear_discrepancy.html │ ├── adapt.metrics.make_uda_scorer.html │ ├── adapt.metrics.neg_j_score.html │ ├── adapt.metrics.normalized_frechet_distance.html │ ├── adapt.metrics.normalized_linear_discrepancy.html │ ├── adapt.metrics.reverse_validation.html │ ├── adapt.parameter_based.FineTuning.html │ ├── adapt.parameter_based.LinInt.html │ ├── adapt.parameter_based.NRC.html │ ├── adapt.parameter_based.PRED.html │ ├── adapt.parameter_based.RegularTransferGP.html │ ├── adapt.parameter_based.RegularTransferLC.html │ ├── adapt.parameter_based.RegularTransferLR.html │ ├── adapt.parameter_based.RegularTransferNN.html │ ├── adapt.parameter_based.SHOT.html │ ├── adapt.parameter_based.TransferForestClassifier.html │ ├── adapt.parameter_based.TransferForestSelector.html │ ├── adapt.parameter_based.TransferTreeClassifier.html │ ├── adapt.parameter_based.TransferTreeSelector.html │ ├── adapt.utils.GradientHandler.html │ ├── adapt.utils.UpdateLambda.html │ ├── adapt.utils.accuracy.html │ ├── adapt.utils.check_arrays.html │ ├── adapt.utils.check_estimator.html │ ├── adapt.utils.check_fitted_estimator.html │ ├── adapt.utils.check_fitted_network.html │ ├── adapt.utils.check_network.html │ ├── adapt.utils.check_sample_weight.html │ ├── adapt.utils.get_default_discriminator.html │ ├── adapt.utils.get_default_encoder.html │ ├── adapt.utils.get_default_task.html │ ├── adapt.utils.make_classification_da.html │ ├── adapt.utils.make_regression_da.html │ └── adapt.utils.set_random_seed.html ├── genindex.html ├── index.html ├── install.html ├── map.html ├── modules │ ├── feature_based.html │ ├── instance_based.html │ ├── parameter_based.html │ └── utils.html ├── objects.inv ├── real_examples.html ├── search.html ├── searchindex.js └── synthetic_examples.html ├── examples └── transfertree.py ├── make.bat ├── requirements.txt ├── setup.cfg ├── setup.py ├── src_docs ├── _static │ ├── css │ │ ├── custom.css │ │ └── custom_index.css │ ├── images │ │ ├── adda.png │ │ ├── borelli.jpg │ │ ├── carto4.png │ │ ├── cdan.png │ │ ├── classification_setup.png │ │ ├── colab_logo_32px.png │ │ ├── contribute.png │ │ ├── dann.png │ │ ├── deep_da.png │ │ ├── deepcoral.png │ │ ├── download_logo_32px.png │ │ ├── feature_based.png │ │ ├── finetuned.png │ │ ├── flowers.jpg │ │ ├── fork.png │ │ ├── github_logo_32px.png │ │ ├── idaml.jpg │ │ ├── instance_based.png │ │ ├── michelin.png │ │ ├── multifidelity_setup.png │ │ ├── office_item.png │ │ ├── parameter_based.png │ │ ├── regression_setup.png │ │ ├── regulartransfer.png │ │ ├── results_qs.png │ │ ├── rotation_setup.png │ │ ├── sample_bias_2d_setup.png │ │ ├── sample_bias_corr_img.png │ │ ├── sample_bias_img.png │ │ ├── sample_bias_setup.png │ │ ├── thumbnai_flowchart.PNG │ │ ├── two_moons_setup.png │ │ └── wdgrl.png │ └── js │ │ └── custom.js ├── _templates │ ├── class.rst │ ├── function.rst │ ├── index.html │ └── layout.html ├── carto.html ├── conf.py ├── contents.rst ├── docutils.conf ├── examples │ ├── Classification.ipynb │ ├── Developer_Guide.ipynb │ ├── Flowers_example.ipynb │ ├── Heart_Failure.ipynb │ ├── Multi_fidelity.ipynb │ ├── Office_example.ipynb │ ├── Quick_start.ipynb │ ├── Regression.ipynb │ ├── Rotation.ipynb │ ├── Sample_bias_example.ipynb │ ├── Two_moons.ipynb │ ├── sample_bias.ipynb │ ├── sample_bias_2d.ipynb │ └── tradaboost_experiments.ipynb ├── gallery │ ├── ADDA.rst │ ├── BalancedWeighting.rst │ ├── CCSA.rst │ ├── CDAN.rst │ ├── CORAL.rst │ ├── DANN.rst │ ├── DeepCORAL.rst │ ├── FA.rst │ ├── FineTuning.rst │ ├── IWC.rst │ ├── IWN.rst │ ├── KLIEP.rst │ ├── KMM.rst │ ├── LDM.rst │ ├── LinInt.rst │ ├── MCD.rst │ ├── MDD.rst │ ├── NearestNeighborsWeighting.rst │ ├── PRED.rst │ ├── RULSIF.rst │ ├── RegularTransferLC.rst │ ├── RegularTransferLR.rst │ ├── RegularTransferNN.rst │ ├── SA.rst │ ├── TCA.rst │ ├── TrAdaBoost.rst │ ├── TrAdaBoostR2.rst │ ├── TransferForest.rst │ ├── TransferTreeClassifier.rst │ ├── TwoStageTrAdaBoostR2.rst │ ├── ULSIF.rst │ ├── WANN.rst │ ├── WDGRL.rst │ └── fMMD.rst ├── generated │ ├── adapt.feature_based.ADDA.rst │ ├── adapt.feature_based.CCSA.rst │ ├── adapt.feature_based.CDAN.rst │ ├── adapt.feature_based.CORAL.rst │ ├── adapt.feature_based.DANN.rst │ ├── adapt.feature_based.DeepCORAL.rst │ ├── adapt.feature_based.FA.rst │ ├── adapt.feature_based.FADA.rst │ ├── adapt.feature_based.FSDA.rst │ ├── adapt.feature_based.FSSP.rst │ ├── adapt.feature_based.JDA.rst │ ├── adapt.feature_based.MCD.rst │ ├── adapt.feature_based.MDD.rst │ ├── adapt.feature_based.MME.rst │ ├── adapt.feature_based.PRED.rst │ ├── adapt.feature_based.SA.rst │ ├── adapt.feature_based.SSDANN.rst │ ├── adapt.feature_based.TCA.rst │ ├── adapt.feature_based.WDGRL.rst │ ├── adapt.feature_based.fMMD.rst │ ├── adapt.instance_based.BalancedWeighting.rst │ ├── adapt.instance_based.GDM.rst │ ├── adapt.instance_based.IWC.rst │ ├── adapt.instance_based.IWN.rst │ ├── adapt.instance_based.KLIEP.rst │ ├── adapt.instance_based.KMM.rst │ ├── adapt.instance_based.LDM.rst │ ├── adapt.instance_based.NearestNeighborsWeighting.rst │ ├── adapt.instance_based.RULSIF.rst │ ├── adapt.instance_based.TrAdaBoost.rst │ ├── adapt.instance_based.TrAdaBoostR2.rst │ ├── adapt.instance_based.TwoStageTrAdaBoostR2.rst │ ├── adapt.instance_based.ULSIF.rst │ ├── adapt.instance_based.WANN.rst │ ├── adapt.metrics.cov_distance.rst │ ├── adapt.metrics.domain_classifier.rst │ ├── adapt.metrics.frechet_distance.rst │ ├── adapt.metrics.linear_discrepancy.rst │ ├── adapt.metrics.make_uda_scorer.rst │ ├── adapt.metrics.neg_j_score.rst │ ├── adapt.metrics.normalized_frechet_distance.rst │ ├── adapt.metrics.normalized_linear_discrepancy.rst │ ├── adapt.metrics.reverse_validation.rst │ ├── adapt.parameter_based.FineTuning.rst │ ├── adapt.parameter_based.LinInt.rst │ ├── adapt.parameter_based.NRC.rst │ ├── adapt.parameter_based.PRED.rst │ ├── adapt.parameter_based.RegularTransferGP.rst │ ├── adapt.parameter_based.RegularTransferLC.rst │ ├── adapt.parameter_based.RegularTransferLR.rst │ ├── adapt.parameter_based.RegularTransferNN.rst │ ├── adapt.parameter_based.SHOT.rst │ ├── adapt.parameter_based.TransferForestClassifier.rst │ ├── adapt.parameter_based.TransferForestSelector.rst │ ├── adapt.parameter_based.TransferTreeClassifier.rst │ ├── adapt.parameter_based.TransferTreeSelector.rst │ ├── adapt.utils.GradientHandler.rst │ ├── adapt.utils.UpdateLambda.rst │ ├── adapt.utils.accuracy.rst │ ├── adapt.utils.check_arrays.rst │ ├── adapt.utils.check_estimator.rst │ ├── adapt.utils.check_fitted_estimator.rst │ ├── adapt.utils.check_fitted_network.rst │ ├── adapt.utils.check_network.rst │ ├── adapt.utils.check_sample_weight.rst │ ├── adapt.utils.get_default_discriminator.rst │ ├── adapt.utils.get_default_encoder.rst │ ├── adapt.utils.get_default_task.rst │ ├── adapt.utils.make_classification_da.rst │ ├── adapt.utils.make_regression_da.rst │ └── adapt.utils.set_random_seed.rst ├── images │ ├── all.gif │ ├── coral.gif │ ├── dann.gif │ ├── kmm.gif │ ├── regular.gif │ ├── srcOnlyCla.gif │ ├── srcOnlyReg.gif │ ├── tgtOnly.gif │ └── tradaboost.gif ├── index.rst ├── install.rst ├── map.rst ├── modules │ ├── feature_based.rst │ ├── instance_based.rst │ ├── parameter_based.rst │ └── utils.rst ├── real_examples.rst └── synthetic_examples.rst └── tests ├── __init__.py ├── test_adda.py ├── test_balancedweighting.py ├── test_base.py ├── test_ccsa.py ├── test_cdan.py ├── test_coral.py ├── test_dann.py ├── test_fa.py ├── test_finetuning.py ├── test_fmmd.py ├── test_iwc.py ├── test_iwn.py ├── test_kliep.py ├── test_kmm.py ├── test_ldm.py ├── test_linint.py ├── test_mcd.py ├── test_mdd.py ├── test_metrics.py ├── test_nnw.py ├── test_pred.py ├── test_regular.py ├── test_sa.py ├── test_tca.py ├── test_tradaboost.py ├── test_transfertree.py ├── test_treeutils.py ├── test_ulsif.py ├── test_utils.py ├── test_wann.py └── test_wdgrl.py /.gitattributes: -------------------------------------------------------------------------------- 1 | *.ipynb linguist-documentation 2 | -------------------------------------------------------------------------------- /.github/labeler.yml: -------------------------------------------------------------------------------- 1 | # Config file for the labeler Github Action 2 | # https://hub.docker.com/r/jimschubert/labeler-action 3 | # labeler "full" schema 4 | 5 | # enable labeler on issues, prs, or both. 6 | enable: 7 | issues: true 8 | prs: true 9 | 10 | # comments object allows you to specify a different message for issues and prs 11 | 12 | # comments: 13 | # issues: | 14 | # Thanks for opening this issue! 15 | # I have applied any labels matching special text in your title and description. 16 | 17 | # Please review the labels and 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'^docs(\(.*\))?:(.*)' 47 | exclude: [] 48 | 'Type: Refactoring': 49 | include: 50 | - '^(refactor|style)(\(.*\))?:(.*)' 51 | exclude: [] 52 | 'Type: Testing': 53 | include: 54 | - '^test(\(.*\))?:(.*)' 55 | exclude: [] 56 | 'Type: Maintenance': 57 | include: 58 | - '^(chore|mnt)(\(.*\))?:(.*)' 59 | exclude: [] 60 | 'Type: CI': 61 | include: 62 | - '^ci(\(.*\))?:(.*)' 63 | exclude: [] 64 | 'Type: Performance': 65 | include: 66 | - '^perf(\(.*\))?:(.*)' 67 | exclude: [] 68 | 'Type: Revert': 69 | include: 70 | - '^revert(\(.*\))?:(.*)' 71 | exclude: [] 72 | 'skip-changelog': 73 | include: 74 | - '^(chore: pre-commit autoupdate)' 75 | exclude: [] 76 | -------------------------------------------------------------------------------- /.github/release-drafter.yml: -------------------------------------------------------------------------------- 1 | name-template: '$RESOLVED_VERSION' 2 | tag-template: '$RESOLVED_VERSION' 3 | categories: 4 | - title: '🚀 Features' 5 | labels: 6 | - 'Type: Feature' 7 | - 'Type: Performance' 8 | - title: '🐛 Bug Fixes' 9 | labels: 10 | - 'Type: Fix' 11 | - title: '📚 Documentation' 12 | label: 'Type: Documentation' 13 | - title: '🧰 Maintenance' 14 | label: 15 | - 'Type: Maintenance' 16 | - 'Type: Build' 17 | - 'Type: Refactoring' 18 | - 'Type: CI' 19 | change-template: '- $TITLE @$AUTHOR (#$NUMBER)' 20 | change-title-escapes: '\<*_&' # You can add # and @ to disable mentions, and add ` to disable code blocks. 21 | version-resolver: 22 | major: 23 | labels: 24 | - 'major' 25 | minor: 26 | labels: 27 | - 'minor' 28 | patch: 29 | labels: 30 | - 'patch' 31 | default: patch 32 | exclude-labels: 33 | - 'skip-changelog' 34 | template: | 35 | ## Changes 36 | 37 | $CHANGES 38 | -------------------------------------------------------------------------------- /.github/semantic.yml: -------------------------------------------------------------------------------- 1 | # Always validate the PR title, and ignore the commits 2 | titleOnly: true 3 | 4 | # By default types specified in commitizen/conventional-commit-types is used. 5 | # See: https://github.com/commitizen/conventional-commit-types/blob/v3.0.0/index.json 6 | # You can override the valid types 7 | types: 8 | - feat 9 | - fix 10 | - docs 11 | - style 12 | - refactor 13 | - perf 14 | - test 15 | - build 16 | - ci 17 | - chore 18 | - revert 19 | -------------------------------------------------------------------------------- /.github/workflows/check-docs.yml: -------------------------------------------------------------------------------- 1 | name: "Check docs" 2 | on: 3 | push: 4 | branches: [ master ] 5 | pull_request: 6 | branches: [ master ] 7 | 8 | jobs: 9 | docs: 10 | runs-on: ubuntu-latest 11 | steps: 12 | - uses: actions/checkout@v2 13 | - name: Set up Python 3.8 14 | uses: actions/setup-python@v2 15 | with: 16 | python-version: '3.8' 17 | - name: Install doc dependencies 18 | run: | 19 | sudo apt install pandoc 20 | python -m pip install --upgrade pip 21 | pip install jinja2==3.0.3 sphinx==4.4.0 numpydoc==1.2 nbsphinx==0.8.8 sphinx_gallery==0.10.1 sphinx_rtd_theme==1.0.0 ipython==8.0.1 22 | - name: Install adapt dependencies 23 | run: | 24 | python -m pip install --upgrade pip 25 | pip install -r requirements.txt 26 | - name: Install adapt 27 | run: | 28 | pip install -e . 29 | - name: Build documentation 30 | run: | 31 | sudo rm -r -f docs/* 32 | make html 33 | sudo rm -r -f docs/doctrees 34 | sudo rm -r -f docs/html/_sources 35 | sudo rm -r -f docs/html/examples/*.ipynb 36 | mv -v docs/html/* docs/ 37 | sudo rm -r -f docs/html 38 | touch .nojekyll 39 | - uses: actions/upload-artifact@v3 40 | with: 41 | name: DocumentationHTML 42 | path: docs/ 43 | -------------------------------------------------------------------------------- /.github/workflows/pr-labeler.yml: -------------------------------------------------------------------------------- 1 | name: Label PRs and issues 2 | on: 3 | issues: 4 | types: [opened, edited, milestoned] 5 | pull_request_target: 6 | types: [opened, edited] 7 | 8 | jobs: 9 | 10 | labeler: 11 | runs-on: ubuntu-latest 12 | 13 | steps: 14 | - name: Check Labels 15 | id: labeler 16 | uses: jimschubert/labeler-action@v2 17 | with: 18 | GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} 19 | -------------------------------------------------------------------------------- /.github/workflows/publish-doc-to-remote.yml: -------------------------------------------------------------------------------- 1 | name: "Publish docs online" 2 | 3 | on: 4 | release: 5 | types: [created] 6 | workflow_dispatch: 7 | 8 | jobs: 9 | docs: 10 | runs-on: ubuntu-latest 11 | steps: 12 | - uses: actions/checkout@v2 13 | - name: Set up Python 3.8 14 | uses: actions/setup-python@v2 15 | with: 16 | python-version: '3.8' 17 | - name: Install doc dependencies 18 | run: | 19 | sudo apt install pandoc 20 | python -m pip install --upgrade pip 21 | pip install jinja2==3.0.3 sphinx==4.4.0 numpydoc==1.2 nbsphinx==0.8.8 sphinx_gallery==0.10.1 sphinx_rtd_theme==1.0.0 ipython==8.0.1 22 | - name: Install adapt dependencies 23 | run: | 24 | python -m pip install --upgrade pip 25 | pip install -r requirements.txt 26 | - name: Install adapt 27 | run: | 28 | pip install -e . 29 | - name: Build documentation 30 | run: | 31 | sudo rm -r -f docs/* 32 | make html 33 | sudo rm -r -f docs/doctrees 34 | sudo rm -r -f docs/html/_sources 35 | sudo rm -r -f docs/html/examples/*.ipynb 36 | mv -v docs/html/* docs/ 37 | sudo rm -r -f docs/html 38 | touch .nojekyll 39 | - name: Push changes to remote 40 | run: | 41 | git config --global user.name "github-actions[bot]" 42 | git config --global user.email "41898282+github-actions[bot]@users.noreply.github.com" 43 | git add docs 44 | git commit -m "Update docs" 45 | git push 46 | -------------------------------------------------------------------------------- /.github/workflows/release-drafter.yml: -------------------------------------------------------------------------------- 1 | name: Release drafter 2 | 3 | on: 4 | push: 5 | # branches to consider in the event; optional, defaults to all 6 | branches: 7 | - master 8 | 9 | jobs: 10 | update_release_draft: 11 | runs-on: ubuntu-latest 12 | steps: 13 | # Drafts your next Release notes as Pull Requests are merged into "master" 14 | - uses: release-drafter/release-drafter@v5 15 | # with: 16 | # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml 17 | # config-name: my-config.yml 18 | env: 19 | GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} -------------------------------------------------------------------------------- /.github/workflows/run-test-coverage.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests, generate coverage 2 | # report and upload it to codecov. 3 | # For more information, see: 4 | # https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions 5 | 6 | name: Run tests with coverage 7 | 8 | on: 9 | push: 10 | branches: [ master ] 11 | pull_request: 12 | branches: [ master ] 13 | 14 | jobs: 15 | coverage: 16 | runs-on: ubuntu-latest 17 | steps: 18 | - uses: actions/checkout@v2 19 | - name: Set up Python 3.11 20 | uses: actions/setup-python@v2 21 | with: 22 | python-version: '3.11' 23 | - name: Install dependencies 24 | run: | 25 | python -m pip install --upgrade pip 26 | pip install pytest pytest-cov codecov 27 | pip install -r requirements.txt 28 | - name: Install adapt 29 | run: | 30 | pip install -e . 31 | - name: Test with pytest 32 | run: | 33 | pytest -vv --cov=adapt 34 | - name: Upload codecov 35 | run: | 36 | codecov 37 | -------------------------------------------------------------------------------- /.github/workflows/run-test.yml: -------------------------------------------------------------------------------- 1 | # This workflow will install Python dependencies, run tests and lint with a variety of Python versions 2 | # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions 3 | 4 | name: Run tests 5 | 6 | on: 7 | push: 8 | branches: [ master ] 9 | pull_request: 10 | branches: [ master ] 11 | 12 | jobs: 13 | build: 14 | strategy: 15 | matrix: 16 | python-version: ['3.9', '3.10', '3.11', '3.12'] 17 | os: [ubuntu-latest, windows-latest, macos-latest] 18 | runs-on: ${{ matrix.os }} 19 | steps: 20 | - uses: actions/checkout@v2 21 | - name: Set up Python ${{ matrix.python-version }} 22 | uses: actions/setup-python@v2 23 | with: 24 | python-version: ${{ matrix.python-version }} 25 | - name: Install dependencies 26 | run: | 27 | python -m pip install --upgrade pip 28 | pip install pytest pytest-cov codecov 29 | pip install -r requirements.txt 30 | - name: Install adapt 31 | run: | 32 | pip install -e . 33 | - name: Test with pytest 34 | run: | 35 | python -m pytest --no-cov -------------------------------------------------------------------------------- /.github/workflows/upload-to-pypi.yml: -------------------------------------------------------------------------------- 1 | name: Upload package to Pypi 2 | 3 | on: 4 | workflow_dispatch: 5 | inputs: 6 | overrideVersion: 7 | description: Manually force a version 8 | 9 | env: 10 | CIBW_BUILD_VERBOSITY: 1 11 | SETUPTOOLS_SCM_PRETEND_VERSION: ${{ github.event.inputs.overrideVersion }} 12 | 13 | jobs: 14 | make_sdist: 15 | name: Make SDist 16 | runs-on: ubuntu-latest 17 | steps: 18 | - uses: actions/checkout@v1 19 | - name: Setup Python 20 | uses: actions/setup-python@v2 21 | with: 22 | python-version: '3.8' 23 | - name: Install deps 24 | run: python -m pip install build twine 25 | - name: Build SDist 26 | run: python -m build --sdist 27 | - uses: actions/upload-artifact@v2 28 | with: 29 | path: dist/*.tar.gz 30 | - name: Check metadata 31 | run: twine check dist/* -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.ipynb_checkpoints 2 | __pycache__ 3 | 4 | # Distribution / packaging 5 | .Python 6 | env/ 7 | develop-eggs/ 8 | dist/ 9 | downloads/ 10 | eggs/ 11 | .eggs/ 12 | lib/ 13 | lib64/ 14 | parts/ 15 | sdist/ 16 | var/ 17 | *.egg-info/ 18 | .installed.cfg 19 | *.egg 20 | docs_build/ 21 | docs/html/ 22 | docs/doctrees/ 23 | adapt/datasets.py 24 | datasets/ 25 | debug.ipynb -------------------------------------------------------------------------------- /.pylintrc: -------------------------------------------------------------------------------- 1 | [REPORTS] 2 | output-format=colorized 3 | 4 | [FORMAT] 5 | good-names=X,Xs,Xt,y,ys,yt,i,j,k 6 | 7 | [MESSAGES CONTROL] 8 | disable= 9 | bad-continuation, 10 | logging-fstring-interpolation, 11 | too-few-public-methods, 12 | too-many-ancestors, 13 | too-many-arguments, 14 | too-many-instance-attributes, 15 | too-many-locals, 16 | too-many-public-methods, 17 | W0201, 18 | C0116 19 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright (c) 2020, Antoine de Mathelin 2 | All rights reserved. 3 | 4 | Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 5 | 6 | 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 7 | 8 | 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 9 | 10 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 11 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = src_docs 9 | BUILDDIR = docs 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | 22 | 23 | #html-noplot: 24 | # $(SPHINXBUILD) -D plot_gallery=0 -b html $(ALLSPHINXOPTS) $(SOURCEDIR) $(BUILDDIR)/html 25 | # @echo 26 | # @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." -------------------------------------------------------------------------------- /adapt/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | ADAPT: Awesome Domain Adaptation Package Toolbox 3 | """ 4 | 5 | from adapt import feature_based 6 | from adapt import instance_based 7 | from adapt import parameter_based 8 | from adapt import utils 9 | from adapt import metrics 10 | from adapt import base 11 | 12 | __all__ = ["feature_based", 13 | "instance_based", 14 | "parameter_based", 15 | "utils", 16 | "metrics", 17 | "base"] 18 | -------------------------------------------------------------------------------- /adapt/feature_based/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Feature-Based Methods Module 3 | """ 4 | 5 | from ._fa import FA 6 | from ._coral import CORAL 7 | from ._dann import DANN 8 | from ._adda import ADDA 9 | from ._deepcoral import DeepCORAL 10 | from ._mcd import MCD 11 | from ._mdd import MDD 12 | from ._wdgrl import WDGRL 13 | from ._cdan import CDAN 14 | from ._sa import SA 15 | from ._fmmd import fMMD 16 | from ._ccsa import CCSA 17 | from ._tca import TCA 18 | from ._pred import PRED 19 | 20 | __all__ = ["FA", "CORAL", "DeepCORAL", "ADDA", "DANN", 21 | "MCD", "MDD", "WDGRL", "CDAN", "SA", "fMMD", "CCSA", "TCA", "PRED"] 22 | -------------------------------------------------------------------------------- /adapt/instance_based/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Instance-Based Methods Module 3 | """ 4 | 5 | from ._kliep import KLIEP 6 | from ._kmm import KMM 7 | from ._tradaboost import TrAdaBoost, TrAdaBoostR2, TwoStageTrAdaBoostR2 8 | from ._wann import WANN 9 | from ._ldm import LDM 10 | from ._nearestneighborsweighting import NearestNeighborsWeighting 11 | from ._balancedweighting import BalancedWeighting 12 | from ._iwn import IWN 13 | from ._ulsif import ULSIF 14 | from ._rulsif import RULSIF 15 | from ._iwc import IWC 16 | 17 | __all__ = ["LDM", "KLIEP", "KMM", "TrAdaBoost", "TrAdaBoostR2", 18 | "TwoStageTrAdaBoostR2", "WANN", "NearestNeighborsWeighting", 19 | "BalancedWeighting", "IWN", "ULSIF", "RULSIF", "IWC"] -------------------------------------------------------------------------------- /adapt/parameter_based/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Parameter-Based Methods Module 3 | """ 4 | 5 | from ._regular import RegularTransferLR, RegularTransferLC, RegularTransferNN, RegularTransferGP 6 | from ._finetuning import FineTuning 7 | from ._transfer_tree import TransferTreeClassifier 8 | from ._transfer_tree import TransferForestClassifier 9 | from ._transfer_tree import TransferTreeSelector 10 | from ._transfer_tree import TransferForestSelector 11 | from ._linint import LinInt 12 | 13 | __all__ = ["RegularTransferLR", 14 | "RegularTransferLC", 15 | "RegularTransferNN", 16 | "RegularTransferGP", 17 | "FineTuning", 18 | "TransferTreeClassifier", 19 | "TransferForestClassifier", 20 | "TransferTreeSelector", 21 | "TransferForestSelector", 22 | "LinInt"] 23 | -------------------------------------------------------------------------------- /codecov.yml: -------------------------------------------------------------------------------- 1 | # Docs ref: https://docs.codecov.io/docs/codecovyml-reference 2 | # Validation check: $ curl --data-binary @codecov.yml https://codecov.io/validate 3 | 4 | codecov: 5 | token: ad499a8d-3f1a-44b1-9672-27164a1281ce 6 | bot: "codecov-io" 7 | strict_yaml_branch: "yaml-config" 8 | require_ci_to_pass: yes 9 | notify: 10 | wait_for_ci: yes 11 | 12 | coverage: 13 | precision: 2 14 | round: down 15 | range: "70...100" 16 | -------------------------------------------------------------------------------- /docs/.nojekyll: 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https://raw.githubusercontent.com/adapt-python/adapt/c7c8dccbcd41ad0674ae28f263685af8dbb94f27/docs/objects.inv -------------------------------------------------------------------------------- /make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=src_docs 11 | set BUILDDIR=docs 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | scipy 3 | tensorflow 4 | scikit-learn 5 | cvxopt 6 | scikeras 7 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | # Inside of setup.cfg 2 | [metadata] 3 | description-file = README.md -------------------------------------------------------------------------------- /setup.py: 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//var tables = document.getElementsByClassName("longtable docutils align-default"); 9 | //var className = document.getElementsByClassName("py class"); 10 | 11 | 12 | function otherParams() { 13 | 14 | if (docs.length == 0) { 15 | docs = document.getElementsByClassName("field-list"); 16 | setTimeout(attributes, 50); 17 | return; 18 | } else { 19 | for (var i = 0; i < docs[0].children.length; i++) { 20 | console.log(docs[0].children[i].innerHTML); 21 | if (docs[0].children[i].innerHTML == "Yields") { 22 | docs[0].children[i].innerHTML = "Other Parameters"; 23 | }; 24 | }; 25 | }; 26 | }; 27 | 28 | 29 | function attributes() { 30 | 31 | if (docs.length == 0) { 32 | docs = document.getElementsByClassName("field-list"); 33 | setTimeout(attributes, 50); 34 | return; 35 | } else { 36 | docs[0].appendChild(docs[1].children[0]); 37 | docs[0].appendChild(docs[1].children[0]); 38 | if (docs[0].children.length>2) { 39 | if (docs[0].children[2].innerHTML == "Yields") { 40 | docs[0].children[2].innerHTML = "Other Parameters"; 41 | }; 42 | }; 43 | }; 44 | }; 45 | 46 | 47 | function changeLinks() { 48 | 49 | if (links.length == 0) { 50 | links = document.getElementsByClassName("wy-menu wy-menu-vertical"); 51 | setTimeout(changeLinks, 50); 52 | return; 53 | } else { 54 | links[0].innerHTML = links[0].innerHTML.replace(/##/g, "#") 55 | }; 56 | }; 57 | 58 | function addLinks() { 59 | 60 | if (tables.length == 0 || className.length ==0) { 61 | tables = document.getElementsByClassName("longtable docutils align-default"); 62 | className = document.getElementsByClassName("py class"); 63 | setTimeout(addLinks, 50); 64 | return; 65 | } else { 66 | var tbody = tables[0].children[1]; 67 | for (var i = 0; i < tbody.children.length; i++) { 68 | var splits = tbody.children[i].innerHTML.split(""); 69 | splits[1] = splits[1].split(""); 70 | console.log(splits); 71 | newInner = splits[0].concat("").concat(splits[1][0]).concat("").concat(splits[1][1]); 72 | tbody.children[i].innerHTML = newInner; 73 | console.log(newInner); 74 | }; 75 | window.scrollTo(0,0); 76 | }; 77 | }; 78 | 79 | function changeColor() { 80 | 81 | if (code.length == 0) { 82 | code = document.getElementsByClassName("highlight"); 83 | setTimeout(changeColor, 50); 84 | return; 85 | } 86 | else { 87 | for (var k = 0; k < code.length; k++) { 88 | var elements = code[k].children[0].children; 89 | var isPoint = false 90 | for (var i = 0; i < elements.length; i++) { 91 | var elem = elements[i] 92 | 93 | if (isPoint == true) { 94 | elem.style.color = "#0e84b5"; 95 | } 96 | 97 | isPoint = false 98 | 99 | if (elem.className == "o" && ponctList.includes(elem.innerHTML)) { 100 | elem.style.color = "#212529"; 101 | elem.style.fontWeight = "normal"; 102 | 103 | if (elem.innerHTML == ".") { 104 | isPoint = true; 105 | } 106 | 107 | } 108 | } 109 | } 110 | } 111 | } 112 | 113 | changeColor(); 114 | attributes(); 115 | //otherParams(); 116 | //changeLinks(); 117 | //addLinks(); 118 | 119 | -------------------------------------------------------------------------------- /src_docs/_templates/class.rst: -------------------------------------------------------------------------------- 1 | :ref:`{{module}} <{{module}}>`.{{objname}} 2 | {{ underline }}==================================== 3 | 4 | .. currentmodule:: {{module}} 5 | 6 | .. autoclass:: {{objname}} 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | {% block methods %} 13 | 14 | {%- set excludedmethods = [ 15 | 'compute_metrics', 16 | 'compute_loss', 17 | 'add_loss', 18 | 'add_metric', 19 | 'add_update', 20 | 'add_variable', 21 | 'add_weight', 22 | 'apply', 23 | 'build', 24 | 'build_from_config', 25 | 'call', 26 | 'compile_from_config', 27 | 'compute_mask', 28 | 'compute_output_shape', 29 | 'compute_output_signature', 30 | 'count_params', 31 | 'evaluate', 32 | 'evaluate_generator', 33 | 'export', 34 | 'finalize_state', 35 | 'fit_generator', 36 | 'from_config', 37 | 'get_build_config', 38 | 'get_compile_config', 39 | 'get_config', 40 | 'get_input_at', 41 | 'get_input_mask_at', 42 | 'get_input_shape_at', 43 | 'get_layer', 44 | 'get_losses_for', 45 | 'get_metrics_result', 46 | 'get_output_at', 47 | 'get_output_mask_at', 48 | 'get_output_shape_at', 49 | 'get_updates_for', 50 | 'get_weights', 51 | 'get_weight_paths', 52 | 'load_own_variables', 53 | 'make_predict_function', 54 | 'make_test_function', 55 | 'make_train_function', 56 | 'predict_generator', 57 | 'predict_on_batch', 58 | 'predict_step', 59 | 'reset_metrics', 60 | 'reset_states', 61 | 'save', 62 | 'save_spec', 63 | 'save_own_variables', 64 | 'set_weights', 65 | 'summary', 66 | 'test_on_batch', 67 | 'test_step', 68 | 'to_json', 69 | 'to_yaml', 70 | 'train_on_batch', 71 | 'train_step', 72 | 'with_name_scope'] 73 | %} 74 | 75 | .. rubric:: Methods 76 | 77 | .. autosummary:: 78 | {% for item in methods %} 79 | {%- if item not in excludedmethods %} 80 | ~{{objname}}.{{ item }} 81 | {%- endif %} 82 | {%- endfor %} 83 | 84 | {% for item in methods %} 85 | {%- if item not in excludedmethods %} 86 | .. automethod:: {{ item }} 87 | {%- endif %} 88 | {%- endfor %} 89 | 90 | {% endblock %} 91 | 92 | 93 | .. raw:: html 94 | 95 |

Examples

96 | 97 | .. include:: ../gallery/{{objname}}.rst 98 | 99 | -------------------------------------------------------------------------------- /src_docs/_templates/function.rst: -------------------------------------------------------------------------------- 1 | :ref:`{{module}} <{{module}}>`.{{objname}} 2 | {{ underline }}==================================== 3 | 4 | .. currentmodule:: {{ module }} 5 | 6 | .. autofunction:: {{ objname }} 7 | 8 | 9 | -------------------------------------------------------------------------------- /src_docs/docutils.conf: -------------------------------------------------------------------------------- 1 | [parsers] 2 | tab_width: 4 -------------------------------------------------------------------------------- /src_docs/gallery/ADDA.rst: -------------------------------------------------------------------------------- 1 | .. nbgallery:: 2 | :maxdepth: 1 3 | 4 | ../examples/Two_moons 5 | ../examples/Rotation 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/src_docs/gallery/CORAL.rst: -------------------------------------------------------------------------------- 1 | .. nbgallery:: 2 | :maxdepth: 1 3 | 4 | ../examples/Two_moons 5 | ../examples/Rotation 6 | ../examples/Regression -------------------------------------------------------------------------------- /src_docs/gallery/DANN.rst: -------------------------------------------------------------------------------- 1 | .. nbgallery:: 2 | :maxdepth: 1 3 | 4 | ../examples/Two_moons 5 | ../examples/Classification 6 | ../examples/Rotation -------------------------------------------------------------------------------- /src_docs/gallery/DeepCORAL.rst: -------------------------------------------------------------------------------- 1 | .. nbgallery:: 2 | :maxdepth: 1 3 | 4 | ../examples/Two_moons -------------------------------------------------------------------------------- /src_docs/gallery/FA.rst: -------------------------------------------------------------------------------- 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~ADDA.__init__ 19 | ~ADDA.compile 20 | ~ADDA.fit 21 | ~ADDA.get_params 22 | ~ADDA.predict 23 | ~ADDA.predict_disc 24 | ~ADDA.predict_task 25 | ~ADDA.pretrain_step 26 | ~ADDA.score 27 | ~ADDA.set_params 28 | ~ADDA.transform 29 | ~ADDA.unsupervised_score 30 | 31 | 32 | .. automethod:: __init__ 33 | .. automethod:: compile 34 | .. automethod:: fit 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_disc 38 | .. automethod:: predict_task 39 | .. automethod:: pretrain_step 40 | .. automethod:: score 41 | .. automethod:: set_params 42 | .. automethod:: transform 43 | .. automethod:: unsupervised_score 44 | 45 | 46 | 47 | 48 | .. raw:: html 49 | 50 |

Examples

51 | 52 | .. include:: ../gallery/ADDA.rst 53 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.CCSA.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.CCSA 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: CCSA 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~CCSA.__init__ 19 | ~CCSA.compile 20 | ~CCSA.fit 21 | ~CCSA.get_params 22 | ~CCSA.predict 23 | ~CCSA.predict_disc 24 | ~CCSA.predict_task 25 | ~CCSA.score 26 | ~CCSA.set_params 27 | ~CCSA.transform 28 | ~CCSA.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/CCSA.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.CDAN.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.CDAN 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: CDAN 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~CDAN.__init__ 19 | ~CDAN.compile 20 | ~CDAN.fit 21 | ~CDAN.get_params 22 | ~CDAN.predict 23 | ~CDAN.predict_disc 24 | ~CDAN.predict_task 25 | ~CDAN.score 26 | ~CDAN.set_params 27 | ~CDAN.transform 28 | ~CDAN.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/CDAN.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.CORAL.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.CORAL 2 | ============================================================= 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: CORAL 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~CORAL.__init__ 19 | ~CORAL.fit 20 | ~CORAL.fit_estimator 21 | ~CORAL.fit_transform 22 | ~CORAL.get_params 23 | ~CORAL.predict 24 | ~CORAL.predict_estimator 25 | ~CORAL.score 26 | ~CORAL.set_params 27 | ~CORAL.transform 28 | ~CORAL.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/CORAL.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.DANN.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.DANN 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: DANN 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~DANN.__init__ 19 | ~DANN.compile 20 | ~DANN.fit 21 | ~DANN.get_params 22 | ~DANN.predict 23 | ~DANN.predict_disc 24 | ~DANN.predict_task 25 | ~DANN.score 26 | ~DANN.set_params 27 | ~DANN.transform 28 | ~DANN.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/DANN.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.DeepCORAL.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.DeepCORAL 2 | ================================================================= 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: DeepCORAL 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~DeepCORAL.__init__ 19 | ~DeepCORAL.compile 20 | ~DeepCORAL.fit 21 | ~DeepCORAL.get_params 22 | ~DeepCORAL.predict 23 | ~DeepCORAL.predict_disc 24 | ~DeepCORAL.predict_task 25 | ~DeepCORAL.score 26 | ~DeepCORAL.set_params 27 | ~DeepCORAL.transform 28 | ~DeepCORAL.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/DeepCORAL.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.FA.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.FA 2 | ========================================================== 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: FA 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~FA.__init__ 19 | ~FA.fit 20 | ~FA.fit_estimator 21 | ~FA.fit_transform 22 | ~FA.get_params 23 | ~FA.predict 24 | ~FA.predict_estimator 25 | ~FA.score 26 | ~FA.set_params 27 | ~FA.transform 28 | ~FA.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/FA.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.FADA.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.FSDA.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.FSSP.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.JDA.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.MCD.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.MCD 2 | =========================================================== 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: MCD 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~MCD.__init__ 19 | ~MCD.compile 20 | ~MCD.fit 21 | ~MCD.get_params 22 | ~MCD.predict 23 | ~MCD.predict_avg 24 | ~MCD.predict_disc 25 | ~MCD.predict_task 26 | ~MCD.pretrain_step 27 | ~MCD.score 28 | ~MCD.set_params 29 | ~MCD.transform 30 | ~MCD.unsupervised_score 31 | 32 | 33 | .. automethod:: __init__ 34 | .. automethod:: compile 35 | .. automethod:: fit 36 | .. automethod:: get_params 37 | .. automethod:: predict 38 | .. automethod:: predict_avg 39 | .. automethod:: predict_disc 40 | .. automethod:: predict_task 41 | .. automethod:: pretrain_step 42 | .. automethod:: score 43 | .. automethod:: set_params 44 | .. automethod:: transform 45 | .. automethod:: unsupervised_score 46 | 47 | 48 | 49 | 50 | .. raw:: html 51 | 52 |

Examples

53 | 54 | .. include:: ../gallery/MCD.rst 55 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.MDD.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.MDD 2 | =========================================================== 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: MDD 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~MDD.__init__ 19 | ~MDD.compile 20 | ~MDD.fit 21 | ~MDD.get_params 22 | ~MDD.predict 23 | ~MDD.predict_disc 24 | ~MDD.predict_task 25 | ~MDD.score 26 | ~MDD.set_params 27 | ~MDD.transform 28 | ~MDD.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/MDD.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.MME.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.PRED.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.PRED 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: PRED 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~PRED.__init__ 19 | ~PRED.fit 20 | ~PRED.fit_estimator 21 | ~PRED.fit_transform 22 | ~PRED.get_params 23 | ~PRED.predict 24 | ~PRED.predict_estimator 25 | ~PRED.score 26 | ~PRED.set_params 27 | ~PRED.transform 28 | ~PRED.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/PRED.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.SA.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.SA 2 | ========================================================== 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: SA 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~SA.__init__ 19 | ~SA.fit 20 | ~SA.fit_estimator 21 | ~SA.fit_transform 22 | ~SA.get_params 23 | ~SA.predict 24 | ~SA.predict_estimator 25 | ~SA.score 26 | ~SA.set_params 27 | ~SA.transform 28 | ~SA.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/SA.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.SSDANN.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.TCA.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.TCA 2 | =========================================================== 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: TCA 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TCA.__init__ 19 | ~TCA.fit 20 | ~TCA.fit_estimator 21 | ~TCA.fit_transform 22 | ~TCA.get_params 23 | ~TCA.predict 24 | ~TCA.predict_estimator 25 | ~TCA.score 26 | ~TCA.set_params 27 | ~TCA.transform 28 | ~TCA.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/TCA.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.WDGRL.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.WDGRL 2 | ============================================================= 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: WDGRL 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~WDGRL.__init__ 19 | ~WDGRL.compile 20 | ~WDGRL.fit 21 | ~WDGRL.get_params 22 | ~WDGRL.predict 23 | ~WDGRL.predict_disc 24 | ~WDGRL.predict_task 25 | ~WDGRL.score 26 | ~WDGRL.set_params 27 | ~WDGRL.transform 28 | ~WDGRL.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/WDGRL.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.feature_based.fMMD.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.feature_based `.fMMD 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.feature_based 5 | 6 | .. autoclass:: fMMD 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~fMMD.__init__ 19 | ~fMMD.fit 20 | ~fMMD.fit_estimator 21 | ~fMMD.fit_transform 22 | ~fMMD.get_params 23 | ~fMMD.predict 24 | ~fMMD.predict_estimator 25 | ~fMMD.score 26 | ~fMMD.set_params 27 | ~fMMD.transform 28 | ~fMMD.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_transform 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/fMMD.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.BalancedWeighting.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.BalancedWeighting 2 | ========================================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: BalancedWeighting 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~BalancedWeighting.__init__ 19 | ~BalancedWeighting.fit 20 | ~BalancedWeighting.fit_estimator 21 | ~BalancedWeighting.fit_weights 22 | ~BalancedWeighting.get_params 23 | ~BalancedWeighting.predict 24 | ~BalancedWeighting.predict_estimator 25 | ~BalancedWeighting.predict_weights 26 | ~BalancedWeighting.score 27 | ~BalancedWeighting.set_params 28 | ~BalancedWeighting.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/BalancedWeighting.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.GDM.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.IWC.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.IWC 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: IWC 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~IWC.__init__ 19 | ~IWC.fit 20 | ~IWC.fit_estimator 21 | ~IWC.fit_weights 22 | ~IWC.get_params 23 | ~IWC.predict 24 | ~IWC.predict_estimator 25 | ~IWC.predict_weights 26 | ~IWC.score 27 | ~IWC.set_params 28 | ~IWC.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/IWC.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.IWN.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.IWN 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: IWN 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~IWN.__init__ 19 | ~IWN.compile 20 | ~IWN.fit 21 | ~IWN.fit_estimator 22 | ~IWN.fit_weights 23 | ~IWN.get_params 24 | ~IWN.predict 25 | ~IWN.predict_disc 26 | ~IWN.predict_task 27 | ~IWN.predict_weights 28 | ~IWN.pretrain_step 29 | ~IWN.score 30 | ~IWN.set_params 31 | ~IWN.transform 32 | ~IWN.unsupervised_score 33 | 34 | 35 | .. automethod:: __init__ 36 | .. automethod:: compile 37 | .. automethod:: fit 38 | .. automethod:: fit_estimator 39 | .. automethod:: fit_weights 40 | .. automethod:: get_params 41 | .. automethod:: predict 42 | .. automethod:: predict_disc 43 | .. automethod:: predict_task 44 | .. automethod:: predict_weights 45 | .. automethod:: pretrain_step 46 | .. automethod:: score 47 | .. automethod:: set_params 48 | .. automethod:: transform 49 | .. automethod:: unsupervised_score 50 | 51 | 52 | 53 | 54 | .. raw:: html 55 | 56 |

Examples

57 | 58 | .. include:: ../gallery/IWN.rst 59 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.KLIEP.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.KLIEP 2 | ============================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: KLIEP 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~KLIEP.__init__ 19 | ~KLIEP.fit 20 | ~KLIEP.fit_estimator 21 | ~KLIEP.fit_weights 22 | ~KLIEP.get_params 23 | ~KLIEP.predict 24 | ~KLIEP.predict_estimator 25 | ~KLIEP.predict_weights 26 | ~KLIEP.score 27 | ~KLIEP.set_params 28 | ~KLIEP.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/KLIEP.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.KMM.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.KMM 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: KMM 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~KMM.__init__ 19 | ~KMM.fit 20 | ~KMM.fit_estimator 21 | ~KMM.fit_weights 22 | ~KMM.get_params 23 | ~KMM.predict 24 | ~KMM.predict_estimator 25 | ~KMM.predict_weights 26 | ~KMM.score 27 | ~KMM.set_params 28 | ~KMM.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/KMM.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.LDM.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.LDM 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: LDM 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~LDM.__init__ 19 | ~LDM.fit 20 | ~LDM.fit_estimator 21 | ~LDM.fit_weights 22 | ~LDM.get_params 23 | ~LDM.predict 24 | ~LDM.predict_estimator 25 | ~LDM.predict_weights 26 | ~LDM.score 27 | ~LDM.set_params 28 | ~LDM.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/LDM.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.NearestNeighborsWeighting.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.NearestNeighborsWeighting 2 | ================================================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: NearestNeighborsWeighting 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~NearestNeighborsWeighting.__init__ 19 | ~NearestNeighborsWeighting.fit 20 | ~NearestNeighborsWeighting.fit_estimator 21 | ~NearestNeighborsWeighting.fit_weights 22 | ~NearestNeighborsWeighting.get_params 23 | ~NearestNeighborsWeighting.predict 24 | ~NearestNeighborsWeighting.predict_estimator 25 | ~NearestNeighborsWeighting.predict_weights 26 | ~NearestNeighborsWeighting.score 27 | ~NearestNeighborsWeighting.set_params 28 | ~NearestNeighborsWeighting.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/NearestNeighborsWeighting.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.RULSIF.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.RULSIF 2 | =============================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: RULSIF 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~RULSIF.__init__ 19 | ~RULSIF.fit 20 | ~RULSIF.fit_estimator 21 | ~RULSIF.fit_weights 22 | ~RULSIF.get_params 23 | ~RULSIF.predict 24 | ~RULSIF.predict_estimator 25 | ~RULSIF.predict_weights 26 | ~RULSIF.score 27 | ~RULSIF.set_params 28 | ~RULSIF.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/RULSIF.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.TrAdaBoost.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.TrAdaBoost 2 | =================================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: TrAdaBoost 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TrAdaBoost.__init__ 19 | ~TrAdaBoost.fit 20 | ~TrAdaBoost.fit_estimator 21 | ~TrAdaBoost.get_params 22 | ~TrAdaBoost.predict 23 | ~TrAdaBoost.predict_estimator 24 | ~TrAdaBoost.predict_weights 25 | ~TrAdaBoost.score 26 | ~TrAdaBoost.set_params 27 | ~TrAdaBoost.unsupervised_score 28 | 29 | 30 | .. automethod:: __init__ 31 | .. automethod:: fit 32 | .. automethod:: fit_estimator 33 | .. automethod:: get_params 34 | .. automethod:: predict 35 | .. automethod:: predict_estimator 36 | .. automethod:: predict_weights 37 | .. automethod:: score 38 | .. automethod:: set_params 39 | .. automethod:: unsupervised_score 40 | 41 | 42 | 43 | 44 | .. raw:: html 45 | 46 |

Examples

47 | 48 | .. include:: ../gallery/TrAdaBoost.rst 49 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.TrAdaBoostR2.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.TrAdaBoostR2 2 | ===================================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: TrAdaBoostR2 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TrAdaBoostR2.__init__ 19 | ~TrAdaBoostR2.fit 20 | ~TrAdaBoostR2.fit_estimator 21 | ~TrAdaBoostR2.get_params 22 | ~TrAdaBoostR2.predict 23 | ~TrAdaBoostR2.predict_estimator 24 | ~TrAdaBoostR2.predict_weights 25 | ~TrAdaBoostR2.score 26 | ~TrAdaBoostR2.set_params 27 | ~TrAdaBoostR2.unsupervised_score 28 | 29 | 30 | .. automethod:: __init__ 31 | .. automethod:: fit 32 | .. automethod:: fit_estimator 33 | .. automethod:: get_params 34 | .. automethod:: predict 35 | .. automethod:: predict_estimator 36 | .. automethod:: predict_weights 37 | .. automethod:: score 38 | .. automethod:: set_params 39 | .. automethod:: unsupervised_score 40 | 41 | 42 | 43 | 44 | .. raw:: html 45 | 46 |

Examples

47 | 48 | .. include:: ../gallery/TrAdaBoostR2.rst 49 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.TwoStageTrAdaBoostR2.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.TwoStageTrAdaBoostR2 2 | ============================================================================= 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: TwoStageTrAdaBoostR2 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TwoStageTrAdaBoostR2.__init__ 19 | ~TwoStageTrAdaBoostR2.fit 20 | ~TwoStageTrAdaBoostR2.fit_estimator 21 | ~TwoStageTrAdaBoostR2.get_params 22 | ~TwoStageTrAdaBoostR2.predict 23 | ~TwoStageTrAdaBoostR2.predict_estimator 24 | ~TwoStageTrAdaBoostR2.predict_weights 25 | ~TwoStageTrAdaBoostR2.score 26 | ~TwoStageTrAdaBoostR2.set_params 27 | ~TwoStageTrAdaBoostR2.unsupervised_score 28 | 29 | 30 | .. automethod:: __init__ 31 | .. automethod:: fit 32 | .. automethod:: fit_estimator 33 | .. automethod:: get_params 34 | .. automethod:: predict 35 | .. automethod:: predict_estimator 36 | .. automethod:: predict_weights 37 | .. automethod:: score 38 | .. automethod:: set_params 39 | .. automethod:: unsupervised_score 40 | 41 | 42 | 43 | 44 | .. raw:: html 45 | 46 |

Examples

47 | 48 | .. include:: ../gallery/TwoStageTrAdaBoostR2.rst 49 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.ULSIF.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.ULSIF 2 | ============================================================== 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: ULSIF 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~ULSIF.__init__ 19 | ~ULSIF.fit 20 | ~ULSIF.fit_estimator 21 | ~ULSIF.fit_weights 22 | ~ULSIF.get_params 23 | ~ULSIF.predict 24 | ~ULSIF.predict_estimator 25 | ~ULSIF.predict_weights 26 | ~ULSIF.score 27 | ~ULSIF.set_params 28 | ~ULSIF.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: fit 33 | .. automethod:: fit_estimator 34 | .. automethod:: fit_weights 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_estimator 38 | .. automethod:: predict_weights 39 | .. automethod:: score 40 | .. automethod:: set_params 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/ULSIF.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.instance_based.WANN.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.instance_based `.WANN 2 | ============================================================= 3 | 4 | .. currentmodule:: adapt.instance_based 5 | 6 | .. autoclass:: WANN 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~WANN.__init__ 19 | ~WANN.compile 20 | ~WANN.fit 21 | ~WANN.get_params 22 | ~WANN.predict 23 | ~WANN.predict_disc 24 | ~WANN.predict_task 25 | ~WANN.predict_weights 26 | ~WANN.pretrain_step 27 | ~WANN.score 28 | ~WANN.set_params 29 | ~WANN.transform 30 | ~WANN.unsupervised_score 31 | 32 | 33 | .. automethod:: __init__ 34 | .. automethod:: compile 35 | .. automethod:: fit 36 | .. automethod:: get_params 37 | .. automethod:: predict 38 | .. automethod:: predict_disc 39 | .. automethod:: predict_task 40 | .. automethod:: predict_weights 41 | .. automethod:: pretrain_step 42 | .. automethod:: score 43 | .. automethod:: set_params 44 | .. automethod:: transform 45 | .. automethod:: unsupervised_score 46 | 47 | 48 | 49 | 50 | .. raw:: html 51 | 52 |

Examples

53 | 54 | .. include:: ../gallery/WANN.rst 55 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.cov_distance.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.cov_distance 2 | ============================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: cov_distance 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.domain_classifier.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.domain_classifier 2 | =================================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: domain_classifier 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.frechet_distance.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.frechet_distance 2 | ================================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: frechet_distance 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.linear_discrepancy.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.linear_discrepancy 2 | ==================================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: linear_discrepancy 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.make_uda_scorer.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.make_uda_scorer 2 | ================================================================= 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: make_uda_scorer 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.neg_j_score.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.neg_j_score 2 | ============================================================= 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: neg_j_score 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.normalized_frechet_distance.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.normalized_frechet_distance 2 | ============================================================================= 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: normalized_frechet_distance 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.normalized_linear_discrepancy.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.normalized_linear_discrepancy 2 | =============================================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: normalized_linear_discrepancy 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.metrics.reverse_validation.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.metrics `.reverse_validation 2 | ==================================================================== 3 | 4 | .. currentmodule:: adapt.metrics 5 | 6 | .. autofunction:: reverse_validation 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.FineTuning.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.FineTuning 2 | ==================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: FineTuning 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~FineTuning.__init__ 19 | ~FineTuning.compile 20 | ~FineTuning.fit 21 | ~FineTuning.get_params 22 | ~FineTuning.predict 23 | ~FineTuning.predict_disc 24 | ~FineTuning.predict_task 25 | ~FineTuning.pretrain_step 26 | ~FineTuning.score 27 | ~FineTuning.set_params 28 | ~FineTuning.transform 29 | ~FineTuning.unsupervised_score 30 | 31 | 32 | .. automethod:: __init__ 33 | .. automethod:: compile 34 | .. automethod:: fit 35 | .. automethod:: get_params 36 | .. automethod:: predict 37 | .. automethod:: predict_disc 38 | .. automethod:: predict_task 39 | .. automethod:: pretrain_step 40 | .. automethod:: score 41 | .. automethod:: set_params 42 | .. automethod:: transform 43 | .. automethod:: unsupervised_score 44 | 45 | 46 | 47 | 48 | .. raw:: html 49 | 50 |

Examples

51 | 52 | .. include:: ../gallery/FineTuning.rst 53 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.LinInt.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.LinInt 2 | ================================================================ 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: LinInt 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~LinInt.__init__ 19 | ~LinInt.fit 20 | ~LinInt.fit_estimator 21 | ~LinInt.get_params 22 | ~LinInt.predict 23 | ~LinInt.predict_estimator 24 | ~LinInt.score 25 | ~LinInt.set_params 26 | ~LinInt.unsupervised_score 27 | 28 | 29 | .. automethod:: __init__ 30 | .. automethod:: fit 31 | .. automethod:: fit_estimator 32 | .. automethod:: get_params 33 | .. automethod:: predict 34 | .. automethod:: predict_estimator 35 | .. automethod:: score 36 | .. automethod:: set_params 37 | .. automethod:: unsupervised_score 38 | 39 | 40 | 41 | 42 | .. raw:: html 43 | 44 |

Examples

45 | 46 | .. include:: ../gallery/LinInt.rst 47 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.NRC.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.PRED.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.RegularTransferGP.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.RegularTransferGP 2 | =========================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: RegularTransferGP 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~RegularTransferGP.__init__ 19 | ~RegularTransferGP.fit 20 | ~RegularTransferGP.fit_estimator 21 | ~RegularTransferGP.get_params 22 | ~RegularTransferGP.predict 23 | ~RegularTransferGP.predict_estimator 24 | ~RegularTransferGP.score 25 | ~RegularTransferGP.set_params 26 | ~RegularTransferGP.unsupervised_score 27 | 28 | 29 | .. automethod:: __init__ 30 | .. automethod:: fit 31 | .. automethod:: fit_estimator 32 | .. automethod:: get_params 33 | .. automethod:: predict 34 | .. automethod:: predict_estimator 35 | .. automethod:: score 36 | .. automethod:: set_params 37 | .. automethod:: unsupervised_score 38 | 39 | 40 | 41 | 42 | .. raw:: html 43 | 44 |

Examples

45 | 46 | .. include:: ../gallery/RegularTransferGP.rst 47 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.RegularTransferLC.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.RegularTransferLC 2 | =========================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: RegularTransferLC 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~RegularTransferLC.__init__ 19 | ~RegularTransferLC.fit 20 | ~RegularTransferLC.fit_estimator 21 | ~RegularTransferLC.get_params 22 | ~RegularTransferLC.predict 23 | ~RegularTransferLC.predict_estimator 24 | ~RegularTransferLC.score 25 | ~RegularTransferLC.set_params 26 | ~RegularTransferLC.unsupervised_score 27 | 28 | 29 | .. automethod:: __init__ 30 | .. automethod:: fit 31 | .. automethod:: fit_estimator 32 | .. automethod:: get_params 33 | .. automethod:: predict 34 | .. automethod:: predict_estimator 35 | .. automethod:: score 36 | .. automethod:: set_params 37 | .. automethod:: unsupervised_score 38 | 39 | 40 | 41 | 42 | .. raw:: html 43 | 44 |

Examples

45 | 46 | .. include:: ../gallery/RegularTransferLC.rst 47 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.RegularTransferLR.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.RegularTransferLR 2 | =========================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: RegularTransferLR 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~RegularTransferLR.__init__ 19 | ~RegularTransferLR.fit 20 | ~RegularTransferLR.fit_estimator 21 | ~RegularTransferLR.get_params 22 | ~RegularTransferLR.predict 23 | ~RegularTransferLR.predict_estimator 24 | ~RegularTransferLR.score 25 | ~RegularTransferLR.set_params 26 | ~RegularTransferLR.unsupervised_score 27 | 28 | 29 | .. automethod:: __init__ 30 | .. automethod:: fit 31 | .. automethod:: fit_estimator 32 | .. automethod:: get_params 33 | .. automethod:: predict 34 | .. automethod:: predict_estimator 35 | .. automethod:: score 36 | .. automethod:: set_params 37 | .. automethod:: unsupervised_score 38 | 39 | 40 | 41 | 42 | .. raw:: html 43 | 44 |

Examples

45 | 46 | .. include:: ../gallery/RegularTransferLR.rst 47 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.RegularTransferNN.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.RegularTransferNN 2 | =========================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: RegularTransferNN 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~RegularTransferNN.__init__ 19 | ~RegularTransferNN.compile 20 | ~RegularTransferNN.fit 21 | ~RegularTransferNN.get_params 22 | ~RegularTransferNN.predict 23 | ~RegularTransferNN.predict_disc 24 | ~RegularTransferNN.predict_task 25 | ~RegularTransferNN.score 26 | ~RegularTransferNN.set_params 27 | ~RegularTransferNN.transform 28 | ~RegularTransferNN.unsupervised_score 29 | 30 | 31 | .. automethod:: __init__ 32 | .. automethod:: compile 33 | .. automethod:: fit 34 | .. automethod:: get_params 35 | .. automethod:: predict 36 | .. automethod:: predict_disc 37 | .. automethod:: predict_task 38 | .. automethod:: score 39 | .. automethod:: set_params 40 | .. automethod:: transform 41 | .. automethod:: unsupervised_score 42 | 43 | 44 | 45 | 46 | .. raw:: html 47 | 48 |

Examples

49 | 50 | .. include:: ../gallery/RegularTransferNN.rst 51 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.SHOT.rst: -------------------------------------------------------------------------------- 1 | Not Implemented 2 | =============== 3 | 4 | This algorithm is not implemented yet. If you are interested by its implementation, please open an Issue on GitHub: https://github.com/adapt-python/adapt/issues 5 | 6 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.TransferForestClassifier.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.TransferForestClassifier 2 | ================================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: TransferForestClassifier 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TransferForestClassifier.__init__ 19 | ~TransferForestClassifier.fit 20 | ~TransferForestClassifier.fit_estimator 21 | ~TransferForestClassifier.get_params 22 | ~TransferForestClassifier.predict 23 | ~TransferForestClassifier.predict_estimator 24 | ~TransferForestClassifier.score 25 | ~TransferForestClassifier.set_params 26 | ~TransferForestClassifier.unsupervised_score 27 | 28 | 29 | .. automethod:: __init__ 30 | .. automethod:: fit 31 | .. automethod:: fit_estimator 32 | .. automethod:: get_params 33 | .. automethod:: predict 34 | .. automethod:: predict_estimator 35 | .. automethod:: score 36 | .. automethod:: set_params 37 | .. automethod:: unsupervised_score 38 | 39 | 40 | 41 | 42 | .. raw:: html 43 | 44 |

Examples

45 | 46 | .. include:: ../gallery/TransferForestClassifier.rst 47 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.TransferForestSelector.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.TransferForestSelector 2 | ================================================================================ 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: TransferForestSelector 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TransferForestSelector.__init__ 19 | ~TransferForestSelector.fit 20 | ~TransferForestSelector.fit_estimator 21 | ~TransferForestSelector.get_params 22 | ~TransferForestSelector.model_selection 23 | ~TransferForestSelector.predict 24 | ~TransferForestSelector.predict_estimator 25 | ~TransferForestSelector.score 26 | ~TransferForestSelector.set_params 27 | ~TransferForestSelector.unsupervised_score 28 | 29 | 30 | .. automethod:: __init__ 31 | .. automethod:: fit 32 | .. automethod:: fit_estimator 33 | .. automethod:: get_params 34 | .. automethod:: model_selection 35 | .. automethod:: predict 36 | .. automethod:: predict_estimator 37 | .. automethod:: score 38 | .. automethod:: set_params 39 | .. automethod:: unsupervised_score 40 | 41 | 42 | 43 | 44 | .. raw:: html 45 | 46 |

Examples

47 | 48 | .. include:: ../gallery/TransferForestSelector.rst 49 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.TransferTreeClassifier.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.TransferTreeClassifier 2 | ================================================================================ 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: TransferTreeClassifier 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TransferTreeClassifier.__init__ 19 | ~TransferTreeClassifier.extend 20 | ~TransferTreeClassifier.fit 21 | ~TransferTreeClassifier.fit_estimator 22 | ~TransferTreeClassifier.get_params 23 | ~TransferTreeClassifier.predict 24 | ~TransferTreeClassifier.predict_estimator 25 | ~TransferTreeClassifier.prune 26 | ~TransferTreeClassifier.score 27 | ~TransferTreeClassifier.set_params 28 | ~TransferTreeClassifier.swap_subtrees 29 | ~TransferTreeClassifier.unsupervised_score 30 | ~TransferTreeClassifier.updateSplit 31 | ~TransferTreeClassifier.updateValue 32 | 33 | 34 | .. automethod:: __init__ 35 | .. automethod:: extend 36 | .. automethod:: fit 37 | .. automethod:: fit_estimator 38 | .. automethod:: get_params 39 | .. automethod:: predict 40 | .. automethod:: predict_estimator 41 | .. automethod:: prune 42 | .. automethod:: score 43 | .. automethod:: set_params 44 | .. automethod:: swap_subtrees 45 | .. automethod:: unsupervised_score 46 | .. automethod:: updateSplit 47 | .. automethod:: updateValue 48 | 49 | 50 | 51 | 52 | .. raw:: html 53 | 54 |

Examples

55 | 56 | .. include:: ../gallery/TransferTreeClassifier.rst 57 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.parameter_based.TransferTreeSelector.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.parameter_based `.TransferTreeSelector 2 | ============================================================================== 3 | 4 | .. currentmodule:: adapt.parameter_based 5 | 6 | .. autoclass:: TransferTreeSelector 7 | :no-members: 8 | :no-inherited-members: 9 | :no-special-members: 10 | 11 | 12 | 13 | 14 | .. rubric:: Methods 15 | 16 | .. autosummary:: 17 | 18 | ~TransferTreeSelector.__init__ 19 | ~TransferTreeSelector.fit 20 | ~TransferTreeSelector.fit_estimator 21 | ~TransferTreeSelector.get_params 22 | ~TransferTreeSelector.predict 23 | ~TransferTreeSelector.predict_estimator 24 | ~TransferTreeSelector.score 25 | ~TransferTreeSelector.select 26 | ~TransferTreeSelector.set_params 27 | ~TransferTreeSelector.unsupervised_score 28 | 29 | 30 | .. automethod:: __init__ 31 | .. automethod:: fit 32 | .. automethod:: fit_estimator 33 | .. automethod:: get_params 34 | .. automethod:: predict 35 | .. automethod:: predict_estimator 36 | .. automethod:: score 37 | .. automethod:: select 38 | .. automethod:: set_params 39 | .. automethod:: unsupervised_score 40 | 41 | 42 | 43 | 44 | .. raw:: html 45 | 46 |

Examples

47 | 48 | .. include:: ../gallery/TransferTreeSelector.rst 49 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.GradientHandler.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.GradientHandler 2 | =============================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: GradientHandler 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.UpdateLambda.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.UpdateLambda 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: UpdateLambda 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.accuracy.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.accuracy 2 | ======================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: accuracy 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_arrays.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_arrays 2 | ============================================================ 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_arrays 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_estimator.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_estimator 2 | =============================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_estimator 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_fitted_estimator.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_fitted_estimator 2 | ====================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_fitted_estimator 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_fitted_network.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_fitted_network 2 | ==================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_fitted_network 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_network.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_network 2 | ============================================================= 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_network 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.check_sample_weight.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.check_sample_weight 2 | =================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: check_sample_weight 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.get_default_discriminator.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.get_default_discriminator 2 | ========================================================================= 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: get_default_discriminator 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.get_default_encoder.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.get_default_encoder 2 | =================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: get_default_encoder 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.get_default_task.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.get_default_task 2 | ================================================================ 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: get_default_task 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.make_classification_da.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.make_classification_da 2 | ====================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: make_classification_da 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.make_regression_da.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.make_regression_da 2 | ================================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: make_regression_da 7 | 8 | -------------------------------------------------------------------------------- /src_docs/generated/adapt.utils.set_random_seed.rst: -------------------------------------------------------------------------------- 1 | :ref:`adapt.utils `.set_random_seed 2 | =============================================================== 3 | 4 | .. currentmodule:: adapt.utils 5 | 6 | .. autofunction:: set_random_seed 7 | 8 | -------------------------------------------------------------------------------- /src_docs/images/all.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/adapt-python/adapt/c7c8dccbcd41ad0674ae28f263685af8dbb94f27/src_docs/images/all.gif -------------------------------------------------------------------------------- /src_docs/images/coral.gif: 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If you use this library in your research, please cite ADAPT using the following reference: 10 | 11 | .. code-block:: 12 | 13 | @article{de2021adapt, 14 | title={ADAPT: Awesome Domain Adaptation Python Toolbox}, 15 | author={de Mathelin, Antoine and Deheeger, Fran{\c{c}}ois and Richard, Guillaume and Mougeot, Mathilde and Vayatis, Nicolas}, 16 | journal={arXiv preprint arXiv:2107.03049}, 17 | year={2021} 18 | } 19 | 20 | Pypi Installation 21 | ----------------- 22 | 23 | This package is available on `Pypi `_. It has been tested on Linux, MacOSX and Windows 24 | for Python versions: 3.6, 3.7, 3.8 and 3.9. It can be installed with the following command line: 25 | 26 | .. code-block:: python 27 | 28 | pip install adapt 29 | 30 | The following dependencies are required and will be installed with the library: 31 | 32 | - numpy 33 | - scipy 34 | - tensorflow (>= 2.0) 35 | - scikit-learn 36 | - cvxopt 37 | 38 | If for some reason, these packages failed to install, you can do it manually with: 39 | 40 | .. code-block:: python 41 | 42 | pip install numpy scipy tensorflow scikit-learn cvxopt 43 | 44 | Finally import the module in your python scripts with: 45 | 46 | .. code-block:: python 47 | 48 | import adapt -------------------------------------------------------------------------------- /src_docs/map.rst: -------------------------------------------------------------------------------- 1 | Selecting the right domain adaptation model 2 | =========================================== 3 | 4 | When facing a new domain adaptation problem, it can be particularly difficult to choose the appropriate transfer learning algorithm. 5 | 6 | The flowchart below has been designed to help the user to quickly identify which type of algorithm 7 | could be used in a specific case. The choice of transfer method is driven by practical characteristics 8 | derived from the available datasets. 9 | 10 | Click on any algorithm in the diagram below to see its documentation. 11 | 12 | 13 | .. raw:: html 14 | :file: carto.html 15 | -------------------------------------------------------------------------------- /src_docs/modules/feature_based.rst: -------------------------------------------------------------------------------- 1 | Feature-based 2 | --------------- 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | ../generated/adapt.feature_based.FE 8 | ../generated/adapt.feature_based.CORAL 9 | ../generated/adapt.feature_based.DeepCORAL 10 | ../generated/adapt.feature_based.DANN 11 | ../generated/adapt.feature_based.ADDA 12 | ../generated/adapt.feature_based.WDGRL 13 | ../generated/adapt.feature_based.CDAN 14 | ../generated/adapt.feature_based.MCD 15 | ../generated/adapt.feature_based.MDD -------------------------------------------------------------------------------- /src_docs/modules/instance_based.rst: -------------------------------------------------------------------------------- 1 | Instance-based 2 | --------------- 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | ../generated/adapt.instance_based.KLIEP 8 | ../generated/adapt.instance_based.KMM 9 | ../generated/adapt.instance_based.TrAdaBoost 10 | ../generated/adapt.instance_based.TrAdaBoostR2 11 | ../generated/adapt.instance_based.TwoStageTrAdaBoostR2 12 | ../generated/adapt.instance_based.WANN -------------------------------------------------------------------------------- /src_docs/modules/parameter_based.rst: -------------------------------------------------------------------------------- 1 | Parameter-based 2 | --------------- 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | ../generated/adapt.parameter_based.RegularTransferLR 8 | ../generated/adapt.parameter_based.RegularTransferLC 9 | ../generated/adapt.parameter_based.RegularTransferNN -------------------------------------------------------------------------------- /src_docs/modules/utils.rst: -------------------------------------------------------------------------------- 1 | Utility Functions 2 | ----------------- 3 | 4 | .. toctree:: 5 | :maxdepth: 2 6 | 7 | ../generated/adapt.utils.check_arrays 8 | ../generated/adapt.utils.check_estimator 9 | ../generated/adapt.utils.check_network 10 | ../generated/adapt.utils.get_default_encoder 11 | ../generated/adapt.utils.get_default_task 12 | ../generated/adapt.utils.get_default_discriminator 13 | ../generated/adapt.utils.GradientHandler 14 | ../generated/adapt.utils.make_classification_da 15 | ../generated/adapt.utils.make_regression_da 16 | ../generated/adapt.utils.check_sample_weight 17 | ../generated/adapt.utils.set_random_seed -------------------------------------------------------------------------------- /src_docs/real_examples.rst: -------------------------------------------------------------------------------- 1 | Real Examples 2 | ============= 3 | 4 | .. nbgallery:: 5 | :maxdepth: 1 6 | 7 | examples/Sample_bias_example 8 | examples/Flowers_example 9 | examples/Office_example 10 | examples/tradaboost_experiments 11 | examples/Heart_Failure -------------------------------------------------------------------------------- /src_docs/synthetic_examples.rst: -------------------------------------------------------------------------------- 1 | .. _gallery: 2 | 3 | Synthetic Examples 4 | ================== 5 | 6 | .. nbgallery:: 7 | :maxdepth: 1 8 | 9 | examples/Quick_start 10 | examples/Classification 11 | examples/Two_moons 12 | examples/Rotation 13 | examples/Regression 14 | examples/sample_bias 15 | examples/sample_bias_2d 16 | examples/Multi_fidelity -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Tests for adapt package. 3 | """ 4 | -------------------------------------------------------------------------------- /tests/test_adda.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for adda module. 3 | """ 4 | 5 | 6 | import numpy as np 7 | import tensorflow as tf 8 | from tensorflow.keras import Sequential, Model 9 | from tensorflow.keras.layers import Dense, Input 10 | from tensorflow.keras.initializers import GlorotUniform 11 | from tensorflow.keras.optimizers import Adam 12 | 13 | from adapt.feature_based import ADDA 14 | 15 | Xs = np.concatenate(( 16 | np.linspace(0, 1, 100).reshape(-1, 1), 17 | np.zeros((100, 1)) 18 | ), axis=1) 19 | Xt = np.concatenate(( 20 | np.linspace(0, 1, 100).reshape(-1, 1), 21 | np.ones((100, 1)) 22 | ), axis=1) 23 | ys = 0.2 * Xs[:, 0].ravel() 24 | yt = 0.2 * Xt[:, 0].ravel() 25 | 26 | 27 | def _get_encoder(input_shape=Xs.shape[1:]): 28 | model = Sequential() 29 | model.add(Input(shape=input_shape)) 30 | model.add(Dense(1, 31 | kernel_initializer="ones", 32 | use_bias=False)) 33 | model.compile(loss="mse", optimizer="adam") 34 | return model 35 | 36 | 37 | def _get_discriminator(input_shape=(1,)): 38 | model = Sequential() 39 | model.add(Input(shape=input_shape)) 40 | model.add(Dense(10, 41 | kernel_initializer=GlorotUniform(seed=0), 42 | activation="elu")) 43 | model.add(Dense(1, 44 | kernel_initializer=GlorotUniform(seed=0), 45 | activation="sigmoid")) 46 | model.compile(loss="mse", optimizer="adam") 47 | return model 48 | 49 | 50 | def _get_task(input_shape=(1,), output_shape=(1,)): 51 | model = Sequential() 52 | model.add(Input(shape=input_shape)) 53 | model.add(Dense(np.prod(output_shape), 54 | use_bias=False, 55 | kernel_initializer=GlorotUniform(seed=0))) 56 | model.compile(loss="mse", optimizer=Adam(0.1)) 57 | return model 58 | 59 | 60 | def test_fit(): 61 | model = ADDA(_get_encoder(), 62 | _get_task(), _get_discriminator(), pretrain__epochs=100, 63 | loss="mse", optimizer_enc=Adam(0.005), optimizer_disc=Adam(0.01), 64 | metrics=["mae"], random_state=0) 65 | model.fit(Xs, ys, Xt, yt, 66 | epochs=100, batch_size=34, verbose=0) 67 | assert isinstance(model, Model) 68 | assert np.abs(model.encoder_.get_weights()[0][1][0]) < 0.2 69 | assert np.sum(np.abs(np.ravel(model.predict_task(Xs, domain="src")) - ys)) < 13 70 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 25 71 | 72 | 73 | def test_nopretrain(): 74 | tf.random.set_seed(0) 75 | np.random.seed(0) 76 | encoder = _get_encoder() 77 | task = _get_task() 78 | 79 | src_model = Sequential() 80 | src_model.add(encoder) 81 | src_model.add(task) 82 | src_model.compile(loss="mse", optimizer=Adam(0.01)) 83 | 84 | src_model.fit(Xs, ys, epochs=100, batch_size=34, verbose=0) 85 | 86 | Xs_enc = src_model.predict(Xs, verbose=0) 87 | 88 | model = ADDA(encoder, task, _get_discriminator(), pretrain=False, 89 | loss="mse", optimizer_enc=Adam(0.005), optimizer_disc=Adam(0.01), 90 | metrics=["mae"], random_state=0) 91 | model.fit(Xs_enc, ys, Xt, epochs=100, batch_size=34, verbose=0) 92 | assert np.abs(model.encoder_.get_weights()[0][1][0]) < 0.2 93 | assert np.sum(np.abs(np.ravel(model.predict(Xs)) - ys)) < 25 94 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 25 -------------------------------------------------------------------------------- /tests/test_balancedweighting.py: -------------------------------------------------------------------------------- 1 | from sklearn.linear_model import RidgeClassifier 2 | from adapt.utils import make_classification_da 3 | from adapt.instance_based import BalancedWeighting 4 | 5 | Xs, ys, Xt, yt = make_classification_da() 6 | 7 | def test_good_ratio(): 8 | model = BalancedWeighting(RidgeClassifier(), gamma=0.5, Xt=Xt[:3], yt=yt[:3], 9 | verbose=0, random_state=0) 10 | model.fit(Xs, ys) 11 | model.predict(Xt) 12 | assert model.score(Xt, yt) > 0.9 13 | 14 | 15 | def test_bad_ratio(): 16 | model = BalancedWeighting(RidgeClassifier(), gamma=0.99, Xt=Xt[:3], yt=yt[:3], 17 | verbose=0, random_state=0) 18 | model.fit(Xs, ys) 19 | assert model.score(Xt, yt) < 0.7 20 | 21 | model = BalancedWeighting(RidgeClassifier(), gamma=0.01, Xt=Xt[:3], yt=yt[:3], 22 | verbose=0, random_state=0) 23 | model.fit(Xs, ys) 24 | assert model.score(Xt, yt) < 0.9 -------------------------------------------------------------------------------- /tests/test_ccsa.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import tensorflow as tf 3 | 4 | from adapt.utils import make_classification_da 5 | from adapt.feature_based import CCSA 6 | from tensorflow.keras.initializers import GlorotUniform 7 | from tensorflow.keras.optimizers import Adam 8 | 9 | np.random.seed(0) 10 | tf.random.set_seed(0) 11 | 12 | task = tf.keras.Sequential() 13 | task.add(tf.keras.layers.Dense(50, activation="relu", kernel_initializer=GlorotUniform(seed=0))) 14 | task.add(tf.keras.layers.Dense(2, activation="softmax", kernel_initializer=GlorotUniform(seed=0))) 15 | 16 | ind = np.random.choice(100, 10) 17 | Xs, ys, Xt, yt = make_classification_da() 18 | 19 | 20 | def test_ccsa(): 21 | ccsa = CCSA(task=task, loss="categorical_crossentropy", 22 | optimizer=Adam(), metrics=["acc"], gamma=0.1, random_state=0) 23 | ccsa.fit(Xs, tf.one_hot(ys, 2).numpy(), Xt=Xt[ind], 24 | yt=tf.one_hot(yt, 2).numpy()[ind], epochs=100, verbose=0) 25 | assert np.mean(ccsa.predict(Xt).argmax(1) == yt) > 0.8 26 | 27 | ccsa = CCSA(task=task, loss="categorical_crossentropy", 28 | optimizer=Adam(), metrics=["acc"], gamma=1., random_state=0) 29 | ccsa.fit(Xs, tf.one_hot(ys, 2).numpy(), Xt=Xt[ind], 30 | yt=tf.one_hot(yt, 2).numpy()[ind], epochs=100, verbose=0) 31 | 32 | assert np.mean(ccsa.predict(Xt).argmax(1) == yt) < 0.9 -------------------------------------------------------------------------------- /tests/test_coral.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for coral module. 3 | """ 4 | 5 | import numpy as np 6 | from sklearn.linear_model import LogisticRegression 7 | from scipy import linalg 8 | import tensorflow as tf 9 | from tensorflow.keras import Sequential, Model 10 | from tensorflow.keras.layers import Dense 11 | from tensorflow.keras.initializers import GlorotUniform 12 | 13 | from adapt.feature_based import CORAL, DeepCORAL 14 | 15 | 16 | np.random.seed(0) 17 | Xs = np.random.multivariate_normal( 18 | np.array([0, 0]), 19 | np.array([[0.001, 0], [0, 1]]), 20 | 1000) 21 | Xt = np.random.multivariate_normal( 22 | np.array([0, 0]), 23 | np.array([[0.1, 0.2], [0.2, 0.5]]), 24 | 1000) 25 | ys = np.zeros(1000) 26 | yt = np.zeros(1000) 27 | 28 | ys[Xs[:, 1]>0] = 1 29 | yt[(Xt[:, 1]-0.5*Xt[:, 0])>0] = 1 30 | 31 | 32 | def _get_encoder(input_shape=Xs.shape[1:]): 33 | model = Sequential() 34 | model.add(Dense(2, input_shape=input_shape, 35 | kernel_initializer=GlorotUniform(seed=0), 36 | use_bias=False)) 37 | model.compile(loss="mse", optimizer="adam") 38 | return model 39 | 40 | 41 | def _get_task(input_shape=(2,), output_shape=(1,)): 42 | model = Sequential() 43 | model.add(Dense(np.prod(output_shape), 44 | kernel_initializer=GlorotUniform(seed=0), 45 | input_shape=input_shape, 46 | use_bias=False, 47 | activation="sigmoid")) 48 | model.compile(loss="mse", optimizer="adam") 49 | return model 50 | 51 | 52 | def test_setup(): 53 | model = LogisticRegression() 54 | model.fit(Xs, ys) 55 | assert model.coef_[0][0] < 0.1 * model.coef_[0][1] 56 | assert (model.predict(Xs) == ys).sum() / len(Xs) >= 0.99 57 | assert (model.predict(Xt) == yt).sum() / len(Xt) < 0.97 58 | 59 | 60 | def test_fit_coral(): 61 | np.random.seed(0) 62 | model = CORAL(LogisticRegression(), lambda_=0.) 63 | model.fit(Xs, ys, Xt=Xt) 64 | assert isinstance(model.estimator_, LogisticRegression) 65 | assert len(model.estimator_.coef_[0]) == Xs.shape[1] 66 | assert (model.predict(Xt) == yt).sum() / len(Xt) >= 0.99 67 | 68 | 69 | def test_fit_coral_complex(): 70 | np.random.seed(0) 71 | model = CORAL(LogisticRegression(), lambda_=0.) 72 | Xs_ = np.random.randn(10, 100) 73 | Xt_ = np.random.randn(10, 100) 74 | model.fit(Xs_, ys[:10], Xt=Xt_) 75 | assert np.iscomplexobj(linalg.inv(linalg.sqrtm(model.Cs_))) 76 | assert np.iscomplexobj(linalg.sqrtm(model.Ct_)) 77 | model.predict(Xs_, domain="src") 78 | 79 | 80 | def test_fit_deepcoral(): 81 | tf.random.set_seed(0) 82 | np.random.seed(0) 83 | model = DeepCORAL(_get_encoder(), _get_task(), metrics=["mse"]) 84 | model.fit(Xs, ys, Xt, 85 | epochs=100, batch_size=64, verbose=0) 86 | assert isinstance(model.encoder_, Model) 87 | assert isinstance(model.task_, Model) 88 | assert len(model.encoder_.get_weights()[0]) == Xs.shape[1] 89 | assert np.abs(np.cov(Xs, rowvar=False) - 90 | np.cov(Xt, rowvar=False)).sum() > 0.5 91 | assert np.abs(np.cov(model.encoder_.predict(Xs), rowvar=False) - 92 | np.cov(model.encoder_.predict(Xt), rowvar=False)).sum() < 0.3 93 | assert (np.abs(model.predict(Xt) - yt) < 0.5).sum() / len(Xt) >= 0.99 -------------------------------------------------------------------------------- /tests/test_fa.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for fe module. 3 | """ 4 | 5 | 6 | import numpy as np 7 | import pytest 8 | from sklearn.linear_model import Ridge, LinearRegression 9 | 10 | from adapt.feature_based import FA 11 | 12 | 13 | Xs = np.ones((75, 10)) 14 | Xt = np.ones((25, 10)) 15 | ys = np.ones(75) 16 | yt = np.zeros(25) 17 | domains = np.concatenate((np.ones(25)*0, np.ones(25), np.ones(25)*2)) 18 | 19 | 20 | def test_fit(): 21 | model = FA(Ridge(fit_intercept=False)) 22 | model.fit(Xs, ys, Xt, yt) 23 | assert isinstance(model.estimator_, Ridge) 24 | assert len(model.estimator_.coef_) == 30 25 | assert np.abs(model.estimator_.coef_[20:].sum() + 26 | model.estimator_.coef_[:10].sum() - 1) < 0.01 27 | assert np.abs(model.estimator_.coef_[20:].sum() + 28 | model.estimator_.coef_[10:20].sum()) < 0.01 29 | 30 | 31 | def test_fit_default(): 32 | model = FA() 33 | model.fit(Xs, ys, Xt, yt) 34 | assert isinstance(model.estimator_, LinearRegression) 35 | 36 | 37 | def test_fit_params(): 38 | model = FA(Ridge(alpha=123)) 39 | model.fit(Xs, ys, Xt, yt) 40 | assert model.estimator_.alpha == 123 41 | 42 | 43 | def test_predict(): 44 | model = FA(Ridge()) 45 | model.fit(Xs, ys, Xt, yt) 46 | y_pred = model.predict(Xt) 47 | assert np.all(y_pred < 0.01) 48 | y_pred = model.predict(Xt, domain="target") 49 | assert np.all(y_pred < 0.01) 50 | y_pred = model.predict(Xs, domain="source") 51 | assert np.all(y_pred - 1 < 0.01) 52 | with pytest.raises(ValueError): 53 | model.predict(Xs, domain="tirelipimpon") 54 | 55 | 56 | def test_multisource(): 57 | model = FA(Ridge(fit_intercept=False)) 58 | model.fit(Xs, ys, Xt, yt, domains=domains) 59 | assert isinstance(model.estimator_, Ridge) 60 | assert len(model.estimator_.coef_) == 50 61 | y_pred = model.predict(Xt) 62 | assert np.all(y_pred < 0.01) 63 | y_pred = model.predict(Xs, domain="src_1") 64 | assert np.all(y_pred - 1 < 0.01) 65 | -------------------------------------------------------------------------------- /tests/test_finetuning.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import tensorflow as tf 3 | from sklearn.base import clone 4 | 5 | from adapt.utils import make_classification_da 6 | from adapt.parameter_based import FineTuning 7 | from tensorflow.keras.initializers import GlorotUniform 8 | from tensorflow.keras.optimizers import Adam 9 | 10 | np.random.seed(0) 11 | tf.random.set_seed(0) 12 | 13 | encoder = tf.keras.Sequential() 14 | encoder.add(tf.keras.layers.Dense(50, activation="relu", kernel_initializer=GlorotUniform(seed=0))) 15 | encoder.add(tf.keras.layers.Dense(50, activation="relu", kernel_initializer=GlorotUniform(seed=0))) 16 | 17 | task = tf.keras.Sequential() 18 | task.add(tf.keras.layers.Dense(1, activation="sigmoid", kernel_initializer=GlorotUniform(seed=0))) 19 | 20 | ind = np.random.choice(100, 10) 21 | Xs, ys, Xt, yt = make_classification_da() 22 | 23 | 24 | def test_finetune(): 25 | model = FineTuning(encoder=encoder, task=task, loss="bce", optimizer=Adam(), random_state=0) 26 | model.fit(Xs, ys, epochs=100, verbose=0) 27 | 28 | assert np.mean((model.predict(Xt).ravel()>0.5) == yt) < 0.7 29 | 30 | fine_tuned = FineTuning(encoder=model.encoder_, task=model.task_, 31 | training=False, 32 | loss="bce", optimizer=Adam(), random_state=0) 33 | fine_tuned.fit(Xt[ind], yt[ind], epochs=100, verbose=0) 34 | 35 | assert np.abs(fine_tuned.encoder_.get_weights()[0] - model.encoder_.get_weights()[0]).sum() == 0. 36 | assert np.mean((fine_tuned.predict(Xt).ravel()>0.5) == yt) > 0.6 37 | assert np.mean((fine_tuned.predict(Xt).ravel()>0.5) == yt) < 0.8 38 | 39 | fine_tuned = FineTuning(encoder=model.encoder_, task=model.task_, 40 | training=True, 41 | loss="bce", optimizer=Adam(), random_state=0) 42 | fine_tuned.fit(Xt[ind], yt[ind], epochs=100, verbose=0) 43 | 44 | assert np.abs(fine_tuned.encoder_.get_weights()[0] - model.encoder_.get_weights()[0]).sum() > 0.5 45 | assert np.mean((fine_tuned.predict(Xt).ravel()>0.5) == yt) > 0.9 46 | 47 | fine_tuned = FineTuning(encoder=model.encoder_, task=model.task_, 48 | training=[True, False], 49 | loss="bce", optimizer=Adam(), random_state=0) 50 | fine_tuned.fit(Xt[ind], yt[ind], epochs=100, verbose=0) 51 | 52 | assert np.abs(fine_tuned.encoder_.get_weights()[0] - model.encoder_.get_weights()[0]).sum() == 0. 53 | assert np.abs(fine_tuned.encoder_.get_weights()[-1] - model.encoder_.get_weights()[-1]).sum() > .5 54 | 55 | fine_tuned = FineTuning(encoder=model.encoder_, task=model.task_, 56 | training=[False], 57 | loss="bce", optimizer=Adam(), random_state=0) 58 | fine_tuned.fit(Xt[ind], yt[ind], epochs=100, verbose=0) 59 | 60 | assert np.abs(fine_tuned.encoder_.get_weights()[0] - model.encoder_.get_weights()[0]).sum() == 0. 61 | assert np.abs(fine_tuned.encoder_.get_weights()[-1] - model.encoder_.get_weights()[-1]).sum() == 0 62 | 63 | 64 | def test_finetune_pretrain(): 65 | model = FineTuning(encoder=encoder, task=task, pretrain=True, pretrain__epochs=2, 66 | loss="bce", optimizer=Adam(), random_state=0) 67 | model.fit(Xs, ys, epochs=1, verbose=0) 68 | 69 | 70 | def test_clone(): 71 | model = FineTuning(encoder=encoder, task=task, 72 | loss="bce", optimizer=Adam(), random_state=0) 73 | model.fit(Xs, ys, epochs=1, verbose=0) 74 | 75 | new_model = clone(model) 76 | new_model.fit(Xs, ys, epochs=1, verbose=0) 77 | new_model.predict(Xs); 78 | assert model is not new_model 79 | -------------------------------------------------------------------------------- /tests/test_fmmd.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import numpy as np 3 | import tensorflow as tf 4 | 5 | from adapt.feature_based import fMMD 6 | from adapt.feature_based._fmmd import _get_optim_function 7 | 8 | np.random.seed(0) 9 | n = 50 10 | m = 50 11 | p = 6 12 | 13 | Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.]) 14 | Xt = np.random.randn(n, p)*0.1 15 | 16 | 17 | def test_fmmd(): 18 | fmmd = fMMD() 19 | fmmd.fit_transform(Xs, Xt); 20 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 21 | assert np.all(fmmd.selected_features_ == [True]*4 + [False]*2) 22 | assert np.abs(fmmd.transform(Xs) - Xs[:, :4]).sum() == 0. 23 | 24 | fmmd.set_params(kernel="rbf") 25 | fmmd.fit_transform(Xs, Xt); 26 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 27 | 28 | fmmd.set_params(kernel="poly", degree=2, gamma=0.1) 29 | fmmd.fit_transform(Xs, Xt); 30 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 31 | 32 | 33 | def test_fmmd_diff_size(): 34 | fmmd = fMMD() 35 | fmmd.fit_transform(Xs, Xt[:40]); 36 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 37 | assert np.all(fmmd.selected_features_ == [True]*4 + [False]*2) 38 | assert np.abs(fmmd.transform(Xs) - Xs[:, :4]).sum() == 0. 39 | 40 | fmmd.set_params(kernel="rbf") 41 | fmmd.fit_transform(Xs, Xt[:40]); 42 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 43 | 44 | fmmd.set_params(kernel="poly", degree=2, gamma=0.1) 45 | fmmd.fit_transform(Xs, Xt[:40]); 46 | assert fmmd.features_scores_[-2:].sum() > 10 * fmmd.features_scores_[:-2].sum() 47 | 48 | 49 | def test_kernel_fct(): 50 | tf.config.experimental_run_functions_eagerly(True) 51 | fct = _get_optim_function(Xs, Xt, kernel="linear") 52 | with pytest.raises(Exception) as excinfo: 53 | fct(tf.identity(np.ones(6))) 54 | 55 | fct = _get_optim_function(Xs, Xt, kernel="rbf") 56 | with pytest.raises(Exception) as excinfo: 57 | fct(tf.identity(np.ones(6))) 58 | 59 | fct = _get_optim_function(Xs, Xt, kernel="poly") 60 | with pytest.raises(Exception) as excinfo: 61 | fct(tf.identity(np.ones(6))) 62 | 63 | -------------------------------------------------------------------------------- /tests/test_iwc.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for iwn module. 3 | """ 4 | 5 | import numpy as np 6 | from sklearn.linear_model import RidgeClassifier 7 | from adapt.utils import make_classification_da 8 | from adapt.instance_based import IWC 9 | from adapt.utils import get_default_discriminator 10 | from tensorflow.keras.optimizers import Adam 11 | 12 | Xs, ys, Xt, yt = make_classification_da() 13 | 14 | def test_iwn(): 15 | model = IWC(RidgeClassifier(0.), classifier=RidgeClassifier(0.), 16 | Xt=Xt, random_state=0) 17 | model.fit(Xs, ys); 18 | model.predict(Xt) 19 | model.score(Xt, yt) 20 | w1 = model.predict_weights() 21 | w2 = model.predict_weights(Xs) 22 | assert np.abs(w1-w2).sum() < 10**-5 23 | 24 | 25 | def test_default_classif(): 26 | model = IWC(RidgeClassifier(0.), classifier=None, 27 | Xt=Xt, random_state=0) 28 | model.fit(Xs, ys); 29 | model.predict(Xt) 30 | model.score(Xt, yt) 31 | w1 = model.predict_weights() 32 | w2 = model.predict_weights(Xs) 33 | assert np.abs(w1-w2).sum() < 10**-5 34 | 35 | 36 | def test_nn_classif(): 37 | model = IWC(RidgeClassifier(0.), classifier=get_default_discriminator(), 38 | cl_params=dict(epochs=10, optimizer=Adam(), loss="bce", verbose=0), 39 | Xt=Xt, random_state=0) 40 | model.fit(Xs, ys); 41 | model.predict(Xt) 42 | model.score(Xt, yt) 43 | w1 = model.predict_weights() 44 | w2 = model.predict_weights(Xs) 45 | assert np.abs(w1-w2).sum() < 10**-5 46 | 47 | -------------------------------------------------------------------------------- /tests/test_iwn.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for iwn module. 3 | """ 4 | 5 | from sklearn.linear_model import RidgeClassifier 6 | from adapt.utils import make_classification_da 7 | from adapt.instance_based import IWN 8 | from adapt.utils import get_default_task 9 | from sklearn.neighbors import KNeighborsClassifier 10 | from tensorflow.keras.optimizers import Adam 11 | 12 | Xs, ys, Xt, yt = make_classification_da() 13 | 14 | def test_iwn(): 15 | model = IWN(RidgeClassifier(0.), Xt=Xt, sigma_init=0.1, random_state=0, 16 | pretrain=True, pretrain__epochs=100, pretrain__verbose=0) 17 | model.fit(Xs, ys, epochs=100, batch_size=256, verbose=0) 18 | model.score(Xt, yt) 19 | model.predict(Xs) 20 | model.predict_weights(Xs) 21 | 22 | 23 | def test_iwn_fit_estim(): 24 | task = get_default_task() 25 | task.compile(optimizer=Adam(), loss="mse", metrics=["mae"]) 26 | model = IWN(task, Xt=Xt, sigma_init=0.1, random_state=0, 27 | pretrain=True, pretrain__epochs=100, pretrain__verbose=0) 28 | model.fit(Xs, ys) 29 | model.score(Xt, yt) 30 | model.predict(Xs) 31 | model.predict_weights(Xs) 32 | 33 | model = IWN(KNeighborsClassifier(), Xt=Xt, sigma_init=0.1, random_state=0, 34 | pretrain=True, pretrain__epochs=100, pretrain__verbose=0) 35 | model.fit(Xs, ys) 36 | model.score(Xt, yt) 37 | model.predict(Xs) 38 | model.predict_weights(Xs) 39 | -------------------------------------------------------------------------------- /tests/test_kmm.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for kmm module. 3 | """ 4 | 5 | import os 6 | import numpy as np 7 | from sklearn.linear_model import LinearRegression 8 | 9 | from adapt.instance_based import KMM 10 | 11 | np.random.seed(0) 12 | Xs = np.concatenate(( 13 | np.random.randn(50)*0.1, 14 | np.random.randn(50)*0.1 + 1., 15 | )).reshape(-1, 1) 16 | Xt = (np.random.randn(100) * 0.1).reshape(-1, 1) 17 | ys = np.array([0.2 * x if x<0.5 18 | else 10 for x in Xs.ravel()]).reshape(-1, 1) 19 | yt = np.array([0.2 * x if x<0.5 20 | else 10 for x in Xt.ravel()]).reshape(-1, 1) 21 | 22 | 23 | def test_setup(): 24 | lr = LinearRegression(fit_intercept=False) 25 | lr.fit(Xs, ys) 26 | assert np.abs(lr.coef_[0][0] - 10) < 1 27 | 28 | # Bug with windows latest 29 | def test_fit(): 30 | if os.name != 'nt': 31 | np.random.seed(0) 32 | model = KMM(LinearRegression(fit_intercept=False), gamma=1.) 33 | model.fit(Xs, ys, Xt=Xt) 34 | assert np.abs(model.estimator_.coef_[0][0] - 0.2) < 1 35 | assert model.weights_[:50].sum() > 50 36 | assert model.weights_[50:].sum() < 0.1 37 | assert np.abs(model.predict(Xt) - yt).sum() < 10 38 | assert np.all(model.weights_ == model.predict_weights()) 39 | 40 | 41 | def test_tol(): 42 | if os.name != 'nt': 43 | np.random.seed(0) 44 | model = KMM(LinearRegression(fit_intercept=False), gamma=1., tol=0.1) 45 | model.fit(Xs, ys, Xt=Xt) 46 | assert np.abs(model.estimator_.coef_[0][0] - 0.2) > 5 47 | 48 | 49 | def test_batch(): 50 | np.random.seed(0) 51 | model = KMM(LinearRegression(fit_intercept=False), gamma=1., 52 | max_size=35) 53 | model.fit(Xs, ys, Xt=Xt) 54 | assert np.abs(model.estimator_.coef_[0][0] - 0.2) < 1 55 | assert model.weights_[:50].sum() > 50 56 | assert model.weights_[50:].sum() < 0.1 57 | assert np.abs(model.predict(Xt) - yt).sum() < 10 58 | assert np.all(model.weights_ == model.predict_weights()) 59 | 60 | 61 | def test_kernel_param(): 62 | if os.name != 'nt': 63 | model = KMM(LinearRegression(fit_intercept=False), 64 | kernel="poly", 65 | coef0=2, 66 | gamma=0.1, 67 | degree=3) 68 | model.fit(Xs, ys, Xt=Xt) 69 | 70 | model = KMM(LinearRegression(fit_intercept=False), 71 | kernel="sigmoid", 72 | coef0=2., 73 | gamma=1.) 74 | model.fit(Xs, ys, Xt=Xt) -------------------------------------------------------------------------------- /tests/test_ldm.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import os 3 | 4 | from adapt.instance_based import LDM 5 | 6 | np.random.seed(0) 7 | n = 50 8 | m = 50 9 | p = 6 10 | 11 | Xs = np.concatenate((np.random.randn(int(m/2), p)*0.1, 12 | 2+np.random.randn(int(m/2), p)*0.1)) 13 | Xt = np.random.randn(n, p)*0.1 14 | ys = Xs[:,0] 15 | ys[Xs[:,0]>1] = 2. 16 | yt = Xt[:,0] 17 | 18 | 19 | def test_ldm(): 20 | if os.name != 'nt': 21 | ldm = LDM() 22 | weights = ldm.fit_weights(Xs, Xt) 23 | ldm.fit(Xs, ys, Xt) 24 | yp = ldm.predict(Xt) 25 | assert ldm.score(Xt, yt) > 0.9 26 | assert weights[:25].mean() > 10 * weights[25:].mean() 27 | 28 | 29 | def test_ldm_diff_size(): 30 | if os.name != 'nt': 31 | ldm = LDM() 32 | weights = ldm.fit_weights(Xs, Xt[:40]) 33 | assert weights[:25].mean() > 10 * weights[25:].mean() -------------------------------------------------------------------------------- /tests/test_linint.py: -------------------------------------------------------------------------------- 1 | from sklearn.linear_model import Ridge 2 | from adapt.utils import make_regression_da 3 | from adapt.parameter_based import LinInt 4 | 5 | Xs, ys, Xt, yt = make_regression_da() 6 | 7 | def test_linint(): 8 | model = LinInt(Ridge(), Xt=Xt[:6], yt=yt[:6], 9 | verbose=0, random_state=0) 10 | model.fit(Xs, ys) 11 | model.fit(Xs, ys, Xt[:6], yt[:6]) 12 | model.predict(Xt) 13 | model.score(Xt, yt) -------------------------------------------------------------------------------- /tests/test_mcd.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for dann module. 3 | """ 4 | 5 | import numpy as np 6 | import tensorflow as tf 7 | from tensorflow.keras import Sequential, Model 8 | from tensorflow.keras.layers import Dense, Input 9 | from tensorflow.keras.optimizers import Adam 10 | from tensorflow.keras.initializers import GlorotUniform 11 | 12 | from adapt.feature_based import MCD 13 | 14 | Xs = np.concatenate(( 15 | np.linspace(0, 1, 100).reshape(-1, 1), 16 | np.zeros((100, 1)) 17 | ), axis=1) 18 | Xt = np.concatenate(( 19 | np.linspace(0, 1, 100).reshape(-1, 1), 20 | np.ones((100, 1)) 21 | ), axis=1) 22 | ys = 0.2 * Xs[:, 0].ravel() 23 | yt = 0.2 * Xt[:, 0].ravel() 24 | 25 | 26 | def _get_encoder(input_shape=Xs.shape[1:]): 27 | model = Sequential() 28 | model.add(Input(shape=input_shape)) 29 | model.add(Dense(1, 30 | kernel_initializer="ones", 31 | use_bias=False)) 32 | model.compile(loss="mse", optimizer="adam") 33 | return model 34 | 35 | 36 | def _get_discriminator(input_shape=(1,)): 37 | model = Sequential() 38 | model.add(Input(shape=input_shape)) 39 | model.add(Dense(10, 40 | kernel_initializer=GlorotUniform(seed=0), 41 | activation="relu")) 42 | model.add(Dense(1, 43 | kernel_initializer=GlorotUniform(seed=0), 44 | activation="sigmoid")) 45 | model.compile(loss="mse", optimizer="adam") 46 | return model 47 | 48 | 49 | def _get_task(input_shape=(1,), output_shape=(1,)): 50 | model = Sequential() 51 | model.add(Input(shape=input_shape)) 52 | model.add(Dense(np.prod(output_shape), 53 | kernel_initializer=GlorotUniform(seed=0), 54 | use_bias=False)) 55 | model.compile(loss="mse", optimizer=Adam(0.1)) 56 | return model 57 | 58 | 59 | def test_fit(): 60 | tf.random.set_seed(0) 61 | np.random.seed(0) 62 | model = MCD(_get_encoder(), _get_task(), 63 | loss="mse", optimizer=Adam(0.01), metrics=["mse"]) 64 | model.fit(Xs, ys, Xt, yt, 65 | epochs=50, batch_size=34, verbose=0) 66 | assert isinstance(model, Model) 67 | assert np.abs(model.encoder_.get_weights()[0][1][0]) < 0.2 68 | assert np.sum(np.abs(model.predict(Xs).ravel() - ys)) > 5 69 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 11 70 | 71 | yp_avg = model.predict_avg(Xt) 72 | ypt = model.predict(Xt) 73 | ypd = model.predict_disc(Xt) 74 | assert np.all(yp_avg == 0.5 * (ypt+ypd)) 75 | 76 | 77 | def test_n_steps(): 78 | tf.random.set_seed(0) 79 | np.random.seed(0) 80 | model = MCD(_get_encoder(), _get_task(), n_steps=4, 81 | loss="mse", optimizer=Adam(0.01), metrics=["mse"]) 82 | model.fit(Xs, ys, Xt, yt, 83 | epochs=50, batch_size=34, verbose=0) 84 | assert isinstance(model, Model) 85 | assert np.abs(model.encoder_.get_weights()[0][1][0]) < 0.1 86 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 11 87 | -------------------------------------------------------------------------------- /tests/test_metrics.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test base 3 | """ 4 | 5 | import os 6 | from sklearn.linear_model import LogisticRegression, LinearRegression 7 | from sklearn.model_selection import GridSearchCV 8 | from adapt.base import BaseAdaptDeep, BaseAdaptEstimator 9 | from adapt.metrics import * 10 | from adapt.instance_based import KMM 11 | from adapt.feature_based import CORAL 12 | 13 | Xs = np.random.randn(100, 2) 14 | Xt = np.random.randn(100, 2)+1. 15 | ys = np.random.randn(100) 16 | 17 | base_est = BaseAdaptEstimator(Xt=Xt) 18 | base_deep = BaseAdaptDeep(Xt=Xt) 19 | 20 | base_est.fit(Xs, ys) 21 | base_deep.fit(Xs, ys) 22 | 23 | 24 | def test_all_metrics(): 25 | cov_distance(Xs, Xt) 26 | frechet_distance(Xs, Xt) 27 | linear_discrepancy(Xs, Xt) 28 | normalized_linear_discrepancy(Xs, Xt) 29 | normalized_frechet_distance(Xs, Xt) 30 | neg_j_score(Xs, Xt) 31 | domain_classifier(Xs, Xt) 32 | domain_classifier(Xs, Xt, LogisticRegression()) 33 | reverse_validation(base_est, Xs, ys, Xt) 34 | reverse_validation(base_deep, Xs, ys, Xt) 35 | 36 | 37 | def test_adapt_scorer(): 38 | if os.name != 'nt': 39 | scorer = make_uda_scorer(neg_j_score, Xs, Xt) 40 | adapt_model = KMM(LinearRegression(), Xt=Xt, kernel="rbf", gamma=0.) 41 | gs = GridSearchCV(adapt_model, {"gamma": [1000, 1e-5]}, 42 | scoring=scorer, return_train_score=True, 43 | cv=3, verbose=0, refit=False) 44 | gs.fit(Xs, ys) 45 | assert gs.cv_results_['mean_train_score'].argmax() == 0 46 | 47 | scorer = make_uda_scorer(cov_distance, Xs, Xt) 48 | adapt_model = CORAL(LinearRegression(), Xt=Xt, lambda_=1.) 49 | gs = GridSearchCV(adapt_model, {"lambda_": [1e-5, 10000.]}, 50 | scoring=scorer, return_train_score=True, 51 | cv=3, verbose=0, refit=False) 52 | gs.fit(Xs, ys) 53 | assert gs.cv_results_['mean_train_score'].argmax() == 0 54 | assert gs.cv_results_['mean_test_score'].argmax() == 0 -------------------------------------------------------------------------------- /tests/test_nnw.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from adapt.instance_based import NearestNeighborsWeighting 4 | 5 | np.random.seed(0) 6 | n = 50 7 | m = 50 8 | p = 6 9 | 10 | Xs = np.concatenate((np.random.randn(int(m/2), p)*0.1, 11 | 2+np.random.randn(int(m/2), p)*0.1)) 12 | Xt = np.random.randn(n, p)*0.1 13 | 14 | 15 | def test_nnw(): 16 | nnw = NearestNeighborsWeighting(n_neighbors=5) 17 | weights = nnw.fit_weights(Xs, Xt) 18 | assert weights[:25].mean() > 10 * weights[25:].mean() 19 | 20 | nnw = NearestNeighborsWeighting(n_neighbors=45) 21 | weights = nnw.fit_weights(Xs, Xt) 22 | assert np.abs(weights[:25].mean() / weights[25:].mean()) < 1.5 -------------------------------------------------------------------------------- /tests/test_pred.py: -------------------------------------------------------------------------------- 1 | from sklearn.linear_model import RidgeClassifier 2 | from adapt.utils import make_classification_da 3 | from adapt.feature_based import PRED 4 | 5 | Xs, ys, Xt, yt = make_classification_da() 6 | 7 | def test_pred(): 8 | model = PRED(RidgeClassifier(), pretrain=True, Xt=Xt[:3], yt=yt[:3], 9 | verbose=0, random_state=0) 10 | model.fit(Xs, ys) 11 | model.predict(Xt) 12 | model.predict(Xt, "src") 13 | model.score(Xt, yt, domain="src") 14 | model.score(Xt, yt, domain="tgt") 15 | 16 | model = PRED(RidgeClassifier().fit(Xs, ys), 17 | pretrain=False, Xt=Xt[:3], yt=yt[:3], 18 | verbose=0, random_state=0) 19 | model.fit(Xs, ys) 20 | model.predict(Xt) 21 | model.predict(Xt, "src") 22 | model.score(Xt, yt, domain="src") 23 | model.score(Xt, yt, domain="tgt") -------------------------------------------------------------------------------- /tests/test_sa.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from adapt.metrics import normalized_linear_discrepancy 4 | from adapt.feature_based import SA 5 | 6 | np.random.seed(0) 7 | n = 50 8 | m = 50 9 | p = 6 10 | 11 | Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.]) 12 | Xt = np.random.randn(n, p)*0.1 13 | 14 | 15 | def test_sa(): 16 | sa = SA(n_components=2) 17 | Xst = sa.fit_transform(Xs, Xt) 18 | assert np.abs(Xst - sa.transform(Xs, "src")).sum() == 0. 19 | assert Xst.shape[1] == 2 20 | assert (normalized_linear_discrepancy(Xs, Xt) > 21 | 2 * normalized_linear_discrepancy(Xst, sa.transform(Xt))) -------------------------------------------------------------------------------- /tests/test_tca.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from adapt.metrics import normalized_linear_discrepancy 4 | from adapt.feature_based import TCA 5 | 6 | np.random.seed(0) 7 | n = 50 8 | m = 50 9 | p = 6 10 | 11 | Xs = np.random.randn(m, p)*0.1 + np.array([0.]*(p-2) + [2., 2.]) 12 | Xt = np.random.randn(n, p)*0.1 13 | 14 | 15 | def test_tca(): 16 | tca = TCA(n_components=2, kernel="rbf", gamma=0.01, random_state=0) 17 | Xst = tca.fit_transform(Xs, Xt) 18 | assert np.abs(Xst - tca.transform(Xs, "src")).sum() < 10**-8 19 | assert Xst.shape[1] == 2 20 | assert (normalized_linear_discrepancy(Xs, Xt) > 21 | 2 * normalized_linear_discrepancy(Xst, tca.transform(Xt))) -------------------------------------------------------------------------------- /tests/test_ulsif.py: -------------------------------------------------------------------------------- 1 | from sklearn.linear_model import RidgeClassifier 2 | from adapt.utils import make_classification_da 3 | from adapt.instance_based import ULSIF, RULSIF 4 | 5 | Xs, ys, Xt, yt = make_classification_da() 6 | 7 | 8 | def test_ulsif(): 9 | model = ULSIF(RidgeClassifier(0.), Xt=Xt[:73], kernel="rbf", 10 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 11 | model.fit(Xs, ys); 12 | model.predict(Xs) 13 | model.score(Xt, yt) 14 | model.predict_weights() 15 | model.predict_weights(Xs) 16 | 17 | model = ULSIF(RidgeClassifier(0.), Xt=Xt, kernel="rbf", 18 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 19 | model.fit(Xs, ys); 20 | 21 | model = ULSIF(RidgeClassifier(0.), Xt=Xt, kernel="rbf", 22 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 23 | model.fit(Xs[:73], ys[:73]); 24 | 25 | 26 | def test_rulsif(): 27 | model = RULSIF(RidgeClassifier(0.), Xt=Xt, kernel="rbf", alpha=0.1, 28 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 29 | model.fit(Xs[:73], ys[:73]); 30 | model.predict(Xs) 31 | model.score(Xt, yt) 32 | model.predict_weights() 33 | model.predict_weights(Xs) 34 | 35 | model = RULSIF(RidgeClassifier(0.), Xt=Xt, kernel="rbf", alpha=0.1, 36 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 37 | model.fit(Xs, ys); 38 | 39 | model = RULSIF(RidgeClassifier(0.), Xt=Xt[:73], kernel="rbf", alpha=0.1, 40 | lambdas=[0.1, 1., 10.], gamma=[0.1, 1., 10.], random_state=0) 41 | model.fit(Xs, ys); -------------------------------------------------------------------------------- /tests/test_wann.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for wann module. 3 | """ 4 | 5 | import numpy as np 6 | from sklearn.linear_model import LinearRegression 7 | from tensorflow.keras.optimizers import Adam 8 | import tensorflow as tf 9 | from adapt.instance_based import WANN 10 | 11 | np.random.seed(0) 12 | Xs = np.concatenate(( 13 | np.random.randn(50)*0.1, 14 | np.random.randn(50)*0.1 + 1., 15 | )).reshape(-1, 1) 16 | Xt = (np.random.randn(100) * 0.1).reshape(-1, 1) 17 | ys = np.array([0.2 * x if x<0.5 18 | else 10 for x in Xs.ravel()]).reshape(-1, 1) 19 | yt = np.array([0.2 * x if x<0.5 20 | else 10 for x in Xt.ravel()]).reshape(-1, 1) 21 | 22 | def test_fit(): 23 | np.random.seed(0) 24 | tf.random.set_seed(0) 25 | model = WANN(random_state=0, optimizer=Adam(0.01)) 26 | model.fit(Xs, ys, Xt, yt, epochs=200, verbose=0) 27 | assert np.abs(model.predict(Xt) - yt).sum() < 10 28 | -------------------------------------------------------------------------------- /tests/test_wdgrl.py: -------------------------------------------------------------------------------- 1 | """ 2 | Test functions for wdgrl module. 3 | """ 4 | 5 | import numpy as np 6 | import tensorflow as tf 7 | from tensorflow.keras import Sequential, Model 8 | from tensorflow.keras.layers import Dense 9 | from tensorflow.keras.optimizers import Adam 10 | from tensorflow.keras.initializers import GlorotUniform 11 | 12 | from adapt.feature_based import WDGRL 13 | 14 | Xs = np.concatenate(( 15 | np.linspace(0, 1, 100).reshape(-1, 1), 16 | np.zeros((100, 1)) 17 | ), axis=1) 18 | Xt = np.concatenate(( 19 | np.linspace(0, 1, 100).reshape(-1, 1), 20 | np.ones((100, 1)) 21 | ), axis=1) 22 | ys = 0.2 * Xs[:, 0].ravel() 23 | yt = 0.2 * Xt[:, 0].ravel() 24 | 25 | 26 | def _get_encoder(input_shape=Xs.shape[1:]): 27 | model = Sequential() 28 | model.add(Dense(1, input_shape=input_shape, 29 | kernel_initializer="ones", 30 | use_bias=False)) 31 | model.compile(loss="mse", optimizer="adam") 32 | return model 33 | 34 | 35 | def _get_discriminator(input_shape=(1,)): 36 | model = Sequential() 37 | model.add(Dense(10, 38 | input_shape=input_shape, 39 | kernel_initializer=GlorotUniform(seed=0), 40 | activation="elu")) 41 | model.add(Dense(1, 42 | kernel_initializer=GlorotUniform(seed=0), 43 | activation=None)) 44 | model.compile(loss="mse", optimizer="adam") 45 | return model 46 | 47 | 48 | def _get_task(input_shape=(1,), output_shape=(1,)): 49 | model = Sequential() 50 | model.add(Dense(np.prod(output_shape), 51 | kernel_initializer=GlorotUniform(seed=0), 52 | use_bias=False, 53 | input_shape=input_shape)) 54 | model.compile(loss="mse", optimizer=Adam(0.1)) 55 | return model 56 | 57 | 58 | def test_fit_lambda_zero(): 59 | tf.random.set_seed(1) 60 | np.random.seed(1) 61 | model = WDGRL(_get_encoder(), _get_task(), _get_discriminator(), 62 | lambda_=0, loss="mse", optimizer=Adam(0.01), metrics=["mse"], 63 | random_state=0) 64 | model.fit(Xs, ys, Xt, yt, 65 | epochs=300, verbose=0) 66 | assert isinstance(model, Model) 67 | assert model.encoder_.get_weights()[0][1][0] == 1.0 68 | assert np.sum(np.abs(model.predict(Xs).ravel() - ys)) < 0.01 69 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) > 10 70 | 71 | 72 | def test_fit_lambda_one(): 73 | tf.random.set_seed(1) 74 | np.random.seed(1) 75 | model = WDGRL(_get_encoder(), _get_task(), _get_discriminator(), 76 | lambda_=1, gamma=0, loss="mse", optimizer=Adam(0.01), 77 | metrics=["mse"], random_state=0) 78 | model.fit(Xs, ys, Xt, yt, 79 | epochs=300, verbose=0) 80 | assert isinstance(model, Model) 81 | assert np.abs(model.encoder_.get_weights()[0][1][0] / 82 | model.encoder_.get_weights()[0][0][0]) < 0.3 83 | assert np.sum(np.abs(model.predict(Xs).ravel() - ys)) < 2 84 | assert np.sum(np.abs(model.predict(Xt).ravel() - yt)) < 5 85 | --------------------------------------------------------------------------------