[TO_REVIEW] ConditionalTransferableComponentsAdapter Implementation

README.md

@@ -25,7 +25,7 @@ The following algorithms are currently implemented.
 - Sample reweighting methods (Gaussian [1], Discriminant [2], KLIEPReweight [3],
   DensityRatio [4], TarS [21], KMMReweight [23])
 - Sample mapping methods (CORAL [5], Optimal Transport DA OTDA [6], LinearMonge [7], LS-ConS [21])
-- Subspace methods (SubspaceAlignment [8], TCA [9], Transfer Subspace Learning [27])
+- Subspace methods (SubspaceAlignment [8], TCA [9], Transfer Subspace Learning [27], CTC [29])
 - Other methods (JDOT [10], DASVM [11], OT Label Propagation [28])
 
 Any methods that can be cast as an adaptation of the input data can be used in one of two ways:
@@ -207,4 +207,6 @@ The library is distributed under the 3-Clause BSD license.
 
 [27] S. Si, D. Tao and B. Geng. In IEEE Transactions on Knowledge and Data Engineering, (2010) [Bregman Divergence-Based Regularization for Transfer Subspace Learning](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4118b4fc7d61068b9b448fd499876d139baeec81)
 
-[28] Solomon, J., Rustamov, R., Guibas, L., & Butscher, A. (2014, January). [Wasserstein propagation for semi-supervised learning](https://proceedings.mlr.press/v32/solomon14.pdf). In International Conference on Machine Learning (pp. 306-314). PMLR.
+[28] Solomon, J., Rustamov, R., Guibas, L., & Butscher, A. (2014, January). [Wasserstein propagation for semi-supervised learning](https://proceedings.mlr.press/v32/solomon14.pdf). In International Conference on Machine Learning (pp. 306-314). PMLR.
+
+[29] Gong, M., Zhang, K., Liu, T., Tao, D., Glymour, C., & Scholkopf, B. (2016). [Domain Adaptation with Conditional Transferable Components](https://proceedings.mlr.press/v48/gong16.pdf). JMLR workshop and conference proceedings, 48, 2839-2848.

docs/source/all.rst

@@ -65,6 +65,7 @@ DAEstimators with adapters (Pipeline):
       SubspaceAlignment
       TransferComponentAnalysis
       TransferJointMatching
+      ConditionalTransferableComponents
       CORAL
       OTMapping
       EntropicOTMapping
@@ -81,6 +82,7 @@ Adapters:
       TransferComponentAnalysisAdapter
       TransferJointMatchingAdapter
       TransferSubspaceLearning
+      ConditionalTransferableComponentsAdapter
       CORALAdapter
       OTMappingAdapter
       EntropicOTMappingAdapter

examples/methods/plot_subspace.py

@@ -9,6 +9,7 @@
 # Author:   Ruben Bueno <ruben.bueno@polytechnique.edu>
 #           Antoine Collas <contact@antoinecollas.fr>
 #           Oleksii Kachaiev <kachayev@gmail.com>
+#           Yanis Lalou <yanis.lalou@polytechnique.edu>
 #
 # License: BSD 3-Clause
 # sphinx_gallery_thumbnail_number = 4
@@ -21,6 +22,7 @@
 from sklearn.svm import SVC
 
 from skada import (
+    ConditionalTransferableComponents,
     SubspaceAlignment,
     TransferComponentAnalysis,
     TransferJointMatching,
@@ -46,6 +48,10 @@
 #   * :ref:`Transfer Component Analysis<Illustration of the Transfer Component
 #     Analysis method>`
 #   * :ref:`Transfer Joint Matching<Illustration of the Transfer Joint Matching method>`
+#   * :ref:`Transfer Subspace Learning
+#     <Illustration of the Transfer Subspace Learning method>`
+#   * :ref:`Conditional Transferable Components
+#     <Illustration of the Conditional Transferable Components method>`
 
 
 base_classifier = SVC()
@@ -374,6 +380,28 @@ def plot_subspace_and_classifier(
 clf.fit(X, y, sample_domain=sample_domain)
 plot_subspace_and_classifier(clf, "TransferSubspaceLearning")
 
+# %%
+#     Illustration of the Conditional Transferable Components method
+# ------------------------------------------
+#
+# The objective of Conditional Transferable Components (CTC) is to
+# learn domain-invariant representations by disentangling the data
+# representation into domain-specific and domain-invariant components.
+# By doing so, CTC enables the transfer of knowledge from the source domain
+# to the target domain while mitigating the effects of domain shift.
+#
+# See [29] for details:
+#
+# .. [29]  Gong, M., Zhang, K., Liu, T., Tao,
+#          D., Glymour, C., & Scholkopf, B. (2016).
+#          Domain Adaptation with Conditional Transferable Components.
+#          JMLR workshop and conference proceedings, 48, 2839-2848.
+#
+
+clf = ConditionalTransferableComponents(n_components=1)
+clf.fit(X, y, sample_domain=sample_domain)
+plot_subspace_and_classifier(clf, "ConditionalTransferableComponents")
+
 
 # %%
 #     Comparison of score between subspace methods:

examples/plot_method_comparison.py

@@ -25,6 +25,7 @@
 from skada import (
     CORAL,
     ClassRegularizerOTMapping,
+    ConditionalTransferableComponents,
     DensityReweight,
     DiscriminatorReweight,
     EntropicOTMapping,
@@ -58,6 +59,7 @@
     "Subspace Alignment",
     "TCA",
     "TSL",
+    "CTC",
     "OT mapping",
     "Entropic OT mapping",
     "Class Reg. OT mapping",
@@ -82,6 +84,7 @@
     SubspaceAlignment(base_estimator=SVC(), n_components=1),
     TransferComponentAnalysis(base_estimator=SVC(), n_components=1, mu=0.5),
     TransferSubspaceLearning(base_estimator=SVC(), n_components=1),
+    ConditionalTransferableComponents(base_estimator=SVC(), n_components=1),
     OTMapping(base_estimator=SVC()),
     EntropicOTMapping(base_estimator=SVC()),
     ClassRegularizerOTMapping(base_estimator=SVC()),

skada/__init__.py

@@ -49,6 +49,8 @@
     TransferJointMatchingAdapter,
     TransferSubspaceLearning,
     TransferSubspaceLearningAdapter,
+    ConditionalTransferableComponents,
+    ConditionalTransferableComponentsAdapter,
 )
 from ._ot import (
     solve_jdot_regression,
@@ -116,6 +118,8 @@
     "TransferJointMatching",
     "TransferSubspaceLearningAdapter",
     "TransferSubspaceLearning",
+    "ConditionalTransferableComponentsAdapter",
+    "ConditionalTransferableComponents",
 
     "DASVMClassifier",
     "solve_jdot_regression",